Table of contents
  1. Story
  2. Slides
    1. Slide 1 Title Health Datapalooza IV: Child and Adolesent Health Data App
    2. Slide 2 Making data accessible to all. It's your data...your story!
    3. Slide 3 Semantic Community: All Content As Data
    4. Slide 4 Data Resource Center for Child and Adolesent Health Site Map
    5. Slide 5 Health Datapalooza IV Knowledge Base for Semantic Search Index
    6. Slide 6 National, HSRA, and State Indicator Profiles in Web Table
    7. Slide 7 National. HRSA, and State Indicator Profiles in Spreadsheet
    8. Slide 8 National Indicator Spreadsheet Profiles in Spotfire
    9. Slide 9 HSRA Region Indicator Spreadsheet Profiles in Spotfire
    10. Slide 10 State Indicator Spreadsheet Profiles in Spotfire
    11. Slide 11 Semantic Search of Knowledge Base
    12. Slide 12 Conclusions and Recommendations
    13. Slide 13 Additional Slides 1
    14. Slide 14 DRC 2003 and 2007 NSCH Merged
    15. Slide 15 DRC 2009-2010 NS-CSHCN Datasets
    16. Slide 16 Additional Slide 2
  3. Spotfire Dashboard
  4. Research Notes
  5. Harness Data to Address Diabetes in the US
    1. Diabetes Statistics
      1. Total prevalence of diabetes
      2. Race and ethnic differences in prevalence of diagnosed diabetes
      3. Morbidity and Mortality
      4. Complications
      5. Cost of Diabetes
      6. For Additional Information
    2. 2011 National Diabetes Fact Sheet
    3. Fast Facts
    4. Diabetes Interactive Atlases
      1. Maps & Data Tables—County Data
      2. Maps & Data Tables—County Rankings
      3. Maps & Motion Charts—All States
      4. Maps & Motion Charts—Select a State
      5. How to use Interactive Atlases
      6. Methods for Calculating Estimates
      7. Contact Us
        1. County Data
        2. County Rankings
        3. Maps & Motion Charts-All States
        4. Maps & Motion Charts-County
        5. CDC Diabetes Interactive Atlas Example
  6. Slides
    1. Slide 1 Harnessing Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 Harness Data to Address Diabetes in the US Knowledge Base
    4. Slide 4 Diabetes Data Ecosystem Spreadsheet
    5. Slide 5 Diabetes Data Ecosystem Spotifre 1
    6. Slide 6 Diabetes Data Ecosystem Spotfire 2
    7. Slide 7 IHME Diabetes State 2009: Spotfire
    8. Slide 8 IHME Diabetes County 2009: Spotfire
  7. Spotfire Dashboard
  8. Research Notes
  9. Health.Data.gov
    1. All-State Data Tables for All Measures
    2. 2009 State Snapshot Methods
      1. All-State Snapshot Measures
      2. State Snapshot Summary Measures
        1. 1. Defining the Content
        2. 2. Classifying State Performance
        3. 3. Scoring State Performance (Meter Score)
      3. Best Performing and All Other States Tables
      4. State Snapshot Strongest (Weakest) Measures
      5. State Snapshot Focus on Diabetes
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Diabetes Costs
          1. Step 1: Determine Number of Covered Lives With Diabetes
          2. Step 2: Estimate Health Care Expenditures Associated With Diabetes Care
          3. Step 3: Estimate Excess Costs Associated With Poor Control of Blood Glucose
        4. 4. Disparities in Treatment
      6. State Snapshot Focus on Asthma
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Quality Improvement
      7. State Snapshot Focus on Healthy People 2010
      8. State Snapshot Focus on Disparities
      9. State Snapshot Focus on Payer
      10. State Snapshot Focus on Variation Over Time
      11. State Snapshot Ranking Table
      12. State Snapshot Contextual Factors
      13. Appendix I: 2009 NHQR Measures, by 2009 State Snapshot Summary Measure Assignment
        1. Types of Care: Preventive Care Measures
        2. Types of Care: Acute Care Measures
        3. Types of Care: Chronic Care Measures
        4. Settings of Care: Hospital Care Measures
        5. Settings of Care: Ambulatory Care Measures
        6. Settings of Care: Nursing Home Care Measures
        7. Settings of Care: Home Health Care Measures
        8. Care by Clinical Area: Cancer Care Measures
        9. Care by Clinical Area: Diabetes Care Measures
        10. Care by Clinical Area: Heart Disease Care Measures
        11. Care by Clinical Area: Maternal and Child Health Care Measures
        12. Care by Clinical Area: Respiratory Diseases Care Measures
        13. Clinical Preventive Services
      14. Appendix II: U.S. Census Region and Division Definitions Used in the 2009 State Snapshots
      15. Acknowledgments
      16. Endnotes
      17. Internet Citation
    3. Focus on Diabetes
      1. Alabama
        1. Diabetes Cost Calculator for Employers
        2. Prevalence
        3. Quality of Care: Processes of Care
        4. Quality of Care: Outcomes of Care
          1. Table
        5. Quality Improvement: Lives and Expenses
        6. Quality Improvement: Excess Costs of Diabetes
        7. Disparities in Treatment: By Income
        8. Disparities in Treatment: By Race/Ethnicity
    4. CDC WONDER Natality Information Live Births
      1. Data Query Interface
      2. Data Query Result
      3. Data Summary
        1. Summary
        2. Natality Data Request
          1. Step 1. Organize table layout:
          2. Step 2. Select maternal residence:
          3. Step 3. Select other maternal characteristics:
          4. Step 4. Select birth characteristics:
          5. Step 5. Select maternal risk factors:
          6. Step 6. Other options:
        3. Data Source Information
        4. Additional Information
          1. Suggested Citation
          2. Contact
          3. Notes
        5. Frequently Asked Questions about Natality
          1. Why are the data results sometimes very slow in returning?
        6. Locations - About FIPS State and County Codes
          1. Modifications of FIPS State and County Codes
  10. Slides
    1. Slide 1 Harnessing Health.Data.gov Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 HealthData.gov and Health Datapalooza III Knowledge Base
    4. Slide 4 HealthData.gov and Health Datapalooza III Spotfire Data Ecosystem
    5. Slide 5 Health Datapalooza IV Technology Development Track
    6. Slide 6 Vocab.Data.gov: Government Data Vocabulary
    7. Slide 7 Health Data Platform Metadata Challenge
    8. Slide 8 IBM Watson at RPI
    9. Slide 9 Health.Data.gov
    10. Slide 10 Health.Data.gov: Search for Diabetes
    11. Slide 11 HealthData.gov Catalog Hub
    12. Slide 12 HealthData.gov Catalog Hub: CDC WONDER Births
    13. Slide 13 HealthData.tw.rpi.edu Catalog Hub: CDC WONDER Births
    14. Slide 14 CDC WONDER: Natality Information Live Births
    15. Slide 15 CDC WONDER: Natality Data Live Births - Diabetes
    16. Slide 16 CDC WONDER: Natality Data Live Births - Diabetes
    17. Slide 17 Harness Health.Data.gov Data to Address Diabetes in the US Knowledge Base
    18. Slide 18 Diabetes Data Ecosystem Spreadsheet
    19. Slide 19 NHQR State Snapshots 2009
    20. Slide 20 AHRQ State Snapshots Conclusion
    21. Slide 21 AHRQ Quality of Care for Diabetes by Region and State for 2005-2006 by Conditions
    22. Slide 22 CDC WONDER Births Natality Diabetes
    23. Slide 23 Diabetes Data Ecosystem Spotfire
    24. Slide 24 Conclusions and Recommendations
  11. Spotfire Dashboard
  12. First Lady Michelle Obama on Exercise and Dr. Amen on Natural Supplements Data in Preventing and Treating Diabetes
  13. Email Invitation
  14. Request For Data Resource Center Indicator Data Set
  15. Present at Health Datapalooza
  16. Submission
  17. PROVE IT! Win $100,000
  18. Redesigning Data
    1. Prove It! 2013 Data Design Diabetes Innovation Challenge is Open for Submissions
  19. Site Map
    1. Home
      1. Sign up for E-Updates
      2. About CAHMI
      3. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
      4. Accessibility
        1. Site Design Guidelines
        2. Accessibility Assistance
        3. Non-compliant documents
      5. Legal and Privacy Policy
    2. About the Data Resource Center
      1. About the Data Resource Center
        1. Mission
        2. Available Data on the DRC Website
        3. What You Can Do on the DRC Website
        4. Sponsors of the Data Resource Center
        5. Our Partners
      2. Data Resource Center Staff
      3. DRC News
        1. Newsletters & Announcements from the DRC
        2. DRC in the News
      4. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
    3. Learn About the Surveys
      1. National Survey of Children’s Health (NSCH)
        1. Links
        2. Data at a Glance
        3. Connect with the DRC
        4. The National Survey of Children's Health
        5. NSCH Highlights
        6. Most Popular Topics
        7. Learn about the NSCH
      2. National Survey of Children with Special Health Care Needs (NS-CSHCN)
      3. Guide to Topics and Questions
        1. National Survey of Children's Health
        2. National Survey of Children with Special Health Care Needs
      4. Survey FAQs
      5. Fast Facts about the Survey
        1. National Survey of Children’s Health
        2. National Survey of Children with Special Health Care Needs
      6. Survey Methods and Documentation
        1. Guide to Changes across Survey Years
          1. National Survey of Children with Special Health Care Needs - 2001, 2005/06  and 2009/10
          2. National Survey of Children's Health - 2003 and 2007
        2. Survey Sampling & Administration Process
          1. National Survey of Children with Special Health Care Needs
          2. National Survey of Children's Health
        3. Full Length Survey Instruments
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
        4. Survey Design and Operations Manuals
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
          3. Methods for analyzing complex sample survey data
        5. SAS & SPSS Codebooks
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
    4. Browse the Data
      1. Browse by Survey & Topic
      2. Get State Snapshots
        1. 1. Click on your state, HRSA Region, or Nationwide to view your snapshot.
          1. HRSA Regions
        2. 2. Select a Snapshot from the Categories below. Customizable profiles, where you can choose your own indicators, are marked with an asterisk*. 
          1. Nationwide
      3. Browse Data Trends
        1. Which measures can be compared across survey years in the NS-CSHCN?
        2. Which measures can be compared across survey years in the NSCH?
        3. What determines whether a question can be compared across survey years?
        4. Use the interactive feature below to browse trends across survey years.
        5. Browse Measures for CSHCN using the NS-CSHCN
          1. CSHCN Prevalence and Demographics
          2. MCHB Core Outcomes and Key Indicators
          3. Subcomponents and Details for Core Outcomes
        6. Browse Measures for All Children using the NSCH
          1. Overall Child Health
          2. Health Insurance, Health Care Access, and Quality of Care
          3. Community, Family, and Neighborhood
      4. Get US Data Maps
        1. 2009/10 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
        2. 2007 NSCH State Ranking Maps
          1. Health Status
          2. Health Care
          3. School and Activities
          4. Child's Family
          5. Child & Family's Neighborhood
        3. 2005/06 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
      5. Browse Healthy People 2020 Topics
        1. Use the interactive feature below to browse data by Healthy People Focus Areas
          1. Access to Health Services
          2. Maternal, Infant and Child Health
          3. Early and Middle Childhood
          4. Adolescent Health
          5. Disability and Health & Repiratory Diseases
          6. Oral Health
          7. Mental Health and Mental Disorders & Substance Abuse
          8. Nutrition and Weight Status & Physical Activity
          9. Health Communication and Health Information Technology
          10. Other Healthy People Focus Areas
      6. Medical Home Data Portal
        1. Search for More Medical Home Data
        2. Learn About Medical Home
        3. Medical Home Measurement in States
        4. Medical Home Measurement in Practices
        5. Medical Home: New Opportunities for Implementation Through Health Care Reform
        6. Medical Home: Data in Action!
        7. Medical Home Measurement for Families
      7. Quality Measurement Portal
        1. Using the DRC to Monitor National Quality Priorities
        2. Maternal and Child Health Bureau Core Outcomes for CSHCN
        3. Data on Health Care Quality and System Performance
        4. Quality Improvement Data Briefs
        5. Select Publications and Presentations
        6. Additional Pediatric Quality Measures, Data and Resources
        7. Select Publications on Parent- and Self-Reported Data
      8. Browse Title V Topics
    5. Put Data into Action
      1. How to Use Data Effectively
        1. Communication of Data Results
        2. Tools for Effective Data Use
      2. Examples of Data Use
        1. Featured Stories
        2. Family Leaders
        3. State Users
        4. Report Briefs
      3. Data Briefs
        1. MCHB Core Quality Outcomes for CSHCN
        2. Children with Special Health Care Needs
        3. Specific Conditions
        4. Complementary and Alternative Medicine (CAM)
        5. Methods for Using Data
        6. Title V & Healthy People 2020
        7. Related Resources
      4. Articles
        1. 2007 NSCH Selected Publications
        2. 2005/06 NS-CSHCN Selected Publications
        3. 2003 NSCH Selected Publications
        4. 2001 NS-CSHCN Selected Publications
      5. Presentations
        1. Selected Presentations from the DRC
        2. Presentations from Data Users
        3. Audioconferences
        4. MCHB DataSpeak
      6. Chartbooks
        1. Maternal and Child Health Bureau Chartbooks
          1. 2007 National Survey of Children's Health
          2. 2003 National Survey of Children's Health
          3. 2005/06 National Survey of Children with Special Health Care Needs
          4. 2001 National Survey of Children with Special Health Care Needs
        2. Other Chartbooks & Reports
          1. Includes Data from the 2007 NSCH
          2. Includes Data from the 2003 NSCH
          3. Includes Data from the 2005/06 NS-CSHCN
      7. Family-to-Family Profiles
      8. Child Health Data Resources
        1. CDC Data, Surveys and Reports
        2. Other Sources of Data by Topic
          1. General Child Health/All Topics
          2. Adolescent Health and Transition to Adulthood
          3. Adoption
          4. Autism
          5. Breastfeeding, Maternal Health, and Perinatal Health
          6. Cancer and Chronic Diseases
          7. Homelessness
          8. Medical Home
          9. Mental and Emotional Health
          10. Obesity, Overweight, and Physical Activity
          11. All Other Topics
        3. Using, understanding, and working with data and the surveys
    6. Get Help
      1. How to Use this Site
        1. Take a tour
          1. Introduction: How to Navigate the New Look 
          2. DRC Tutorial: Getting Started with Your Data Search
          3. Tutorial: Interpreting Your Data
        2. Want to learn more about the DRC? 
        3. Quick Guides to Searching the DRC
        4. Related Resources
      2. Ask Us a Question
      3. Survey FAQs
        1. About the DRC
          1. What is the Data Resource Center (DRC)?
          2. Who Sponsors the DRC?
        2. About the Surveys
          1. What is the National Survey of Children's Health (NSCH)?
          2. What is the National Survey of Children with Special Health Care Needs (NS-CSHCN)?
          3. Who sponsors the NSCH and the NS-CSHCN?
          4. How are data for the surveys collected?
          5. Where can I find information about the sampling and administration methods used for the NSCH and the NS-CSHCN?
          6. Why are the surveys sometimes called “SLAITS”?
          7. What topics are asked about in the NSCH and NS-CSHCN?
          8. Will the NSCH and NS-CSHCN be collected again?
          9. Are the NSCH and NS-CSHCN data files available to the public?
          10. Is county or city-level data available in either the NSCH or the NS-CSHCN?
          11. What is new in the 2009/2010 NS-CSHCN survey?
          12. What will be new in the 2011 NSCH survey?
        3. Definitions and Measure Development
          1. How can I find out which questions were used to develop a specific child health indicator?
          2. Why aren’t there any data on this website for Asian, American Indian, Alaska Native, or Native Hawaiian/Pacific Islander children?
          3. What criteria are used to create the race/ethnicity categories on this website?
          4. What is a HRSA region?
          5. What is a "medical home"?
          6. How are unknown or missing values handled in the DRC interactive data query results?
          7. Why are there sometimes different estimates of CSHCN in different surveys?
          8. What is the CSHCN Screener?
        4. Using the DRC and the Surveys
          1. Is it possible to get a list of publications that have used the NSCH or the NS-CSHCN?
          2. Is there a standard format for citing information from the DRC website in an academic paper?
          3. How do I receive assistance if I am having a hard time interpreting output from the NSCH or NS-CSHCN?
          4. Is it possible to look at two subgroups at the same time?
          5. What browsers does the DRC support?
          6. How can I link to your site?
      4. Request a Dataset
        1. Frequenty Asked Questions about DRC Indicator Data Sets for the NSCH & NS-CSHCN
          1. What is included in the NSCH data sets?
          2. What is included in the NS-CSHCN Indicator data sets?
          3. Do the data sets include local-level data?
          4. How do I cite an indicator dataset from the Data Resource Center?
          5. How are DRC Indicator data sets different from National Center for Health Statistics data files?
          6. Where can I find out how derived variables in DRC data sets were conceptualized and constructed in SAS or SPSS?
          7. Is there a charge for data sets provided by the DRC?
          8. What can I expect when I request a DRC Indicator dataset?
          9. What if I have additional questions?
      5. Glossary
        1. AAP
        2. ADA
        3. Agency for Healthcare Research and Quality
        4. AHRQ
        5. American Academy of Pediatrics
        6. Americans with Disabilities Act
        7. BMI
        8. BMI-for-age
        9. body mass index
        10. CAHMI
        11. CAHPS-CCC
        12. CAHPS®
        13. care coordination
        14. CATCH
        15. CATI
        16. CDC
        17. Center for Disease Control and Prevention
        18. Center for Health Care Strategies
        19. Center for Medical Home Improvement
        20. Centers for Medicare & Medicaid Services
        21. Champions for Progress
        22. chance
        23. CHCS
        24. Child and Adolescent Health Measurement Initiative
        25. Children with Special Health Care Needs (CSHCN, CYSHCN)
        26. CI
        27. CMHI
        28. CMS
        29. Community Access to Child Health
        30. Computer-Assisted Telephone Interviewing
        31. confidence interval
        32. Consumer Assessment of Health Plans Survey®
        33. CSHCN
        34. cultural competency
        35. CYSHCN
        36. data
        37. dependent variable
        38. direct health services
        39. distribution
        40. Early & Periodic Screening, Diagnosis, & Treatment
        41. Early Intervention Research Institute
        42. EPSDT
        43. EREI
        44. Family Voices
        45. Family Voices State Coordinators
        46. family-centered care
        47. Federal Poverty Level
        48. FPL
        49. frequency table
        50. FV
        51. Health Plan Employer Data and Information Set
        52. Health Resources and Services Administration
        53. Healthy People 2010
        54. HEDIS
        55. HP 2010
        56. HRSA
        57. ICHP
        58. IDEA
        59. independent variable
        60. indicator
        61. Individuals with Disabilities Education Act
        62. Institute for Child Health Policy
        63. Maternal and Child Health Bureau
        64. MCHB
        65. mean
        66. median
        67. medical home
        68. Metropolitan Statistical Areas
        69. MSA
        70. n
        71. NASHP
        72. National Academy for State Health Policy
        73. National Center for Cultural Competence
        74. National Center for Health Statistics
        75. National Committee for Quality Assurance
        76. National Health Interview Survey
        77. National Initiative for Children's Healthcare Quality
        78. National Survey of Children with Special Health Care Needs
        79. National Survey of Children's Health
        80. NCCC
        81. NCHS
        82. NCQA
        83. needs assessment
        84. NHIS
        85. NICHQ
        86. NS-CSHCN
        87. NSCH
        88. p-value
        89. performance measures
        90. population
        91. population estimate
        92. population weights
        93. prevalence
        94. preventive services
        95. primary care
        96. probability
        97. QI
        98. quality improvement
        99. random sampling
        100. regression equation
        101. reliability
        102. RUCA or RUCA Code
        103. Rural Urban Commuting Area
        104. sample
        105. sample size
        106. significance
        107. SLAITS
        108. standard deviation
        109. standard error
        110. standardization
        111. State and Local Area Integrated Telephone Survey
        112. summary score
        113. Synthetic Estimate
        114. TA
        115. technical assistance
        116. Title V
        117. Title V Block Grants
        118. Title V Information System
        119. TVIS
        120. validity
        121. variable
        122. weighted estimate
      6. Additional Resources
        1. Non-Profit Organizations
        2. Governmental Organizations
  20. NEXT

Health Datapalooza IV

Last modified
Table of contents
  1. Story
  2. Slides
    1. Slide 1 Title Health Datapalooza IV: Child and Adolesent Health Data App
    2. Slide 2 Making data accessible to all. It's your data...your story!
    3. Slide 3 Semantic Community: All Content As Data
    4. Slide 4 Data Resource Center for Child and Adolesent Health Site Map
    5. Slide 5 Health Datapalooza IV Knowledge Base for Semantic Search Index
    6. Slide 6 National, HSRA, and State Indicator Profiles in Web Table
    7. Slide 7 National. HRSA, and State Indicator Profiles in Spreadsheet
    8. Slide 8 National Indicator Spreadsheet Profiles in Spotfire
    9. Slide 9 HSRA Region Indicator Spreadsheet Profiles in Spotfire
    10. Slide 10 State Indicator Spreadsheet Profiles in Spotfire
    11. Slide 11 Semantic Search of Knowledge Base
    12. Slide 12 Conclusions and Recommendations
    13. Slide 13 Additional Slides 1
    14. Slide 14 DRC 2003 and 2007 NSCH Merged
    15. Slide 15 DRC 2009-2010 NS-CSHCN Datasets
    16. Slide 16 Additional Slide 2
  3. Spotfire Dashboard
  4. Research Notes
  5. Harness Data to Address Diabetes in the US
    1. Diabetes Statistics
      1. Total prevalence of diabetes
      2. Race and ethnic differences in prevalence of diagnosed diabetes
      3. Morbidity and Mortality
      4. Complications
      5. Cost of Diabetes
      6. For Additional Information
    2. 2011 National Diabetes Fact Sheet
    3. Fast Facts
    4. Diabetes Interactive Atlases
      1. Maps & Data Tables—County Data
      2. Maps & Data Tables—County Rankings
      3. Maps & Motion Charts—All States
      4. Maps & Motion Charts—Select a State
      5. How to use Interactive Atlases
      6. Methods for Calculating Estimates
      7. Contact Us
        1. County Data
        2. County Rankings
        3. Maps & Motion Charts-All States
        4. Maps & Motion Charts-County
        5. CDC Diabetes Interactive Atlas Example
  6. Slides
    1. Slide 1 Harnessing Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 Harness Data to Address Diabetes in the US Knowledge Base
    4. Slide 4 Diabetes Data Ecosystem Spreadsheet
    5. Slide 5 Diabetes Data Ecosystem Spotifre 1
    6. Slide 6 Diabetes Data Ecosystem Spotfire 2
    7. Slide 7 IHME Diabetes State 2009: Spotfire
    8. Slide 8 IHME Diabetes County 2009: Spotfire
  7. Spotfire Dashboard
  8. Research Notes
  9. Health.Data.gov
    1. All-State Data Tables for All Measures
    2. 2009 State Snapshot Methods
      1. All-State Snapshot Measures
      2. State Snapshot Summary Measures
        1. 1. Defining the Content
        2. 2. Classifying State Performance
        3. 3. Scoring State Performance (Meter Score)
      3. Best Performing and All Other States Tables
      4. State Snapshot Strongest (Weakest) Measures
      5. State Snapshot Focus on Diabetes
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Diabetes Costs
          1. Step 1: Determine Number of Covered Lives With Diabetes
          2. Step 2: Estimate Health Care Expenditures Associated With Diabetes Care
          3. Step 3: Estimate Excess Costs Associated With Poor Control of Blood Glucose
        4. 4. Disparities in Treatment
      6. State Snapshot Focus on Asthma
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Quality Improvement
      7. State Snapshot Focus on Healthy People 2010
      8. State Snapshot Focus on Disparities
      9. State Snapshot Focus on Payer
      10. State Snapshot Focus on Variation Over Time
      11. State Snapshot Ranking Table
      12. State Snapshot Contextual Factors
      13. Appendix I: 2009 NHQR Measures, by 2009 State Snapshot Summary Measure Assignment
        1. Types of Care: Preventive Care Measures
        2. Types of Care: Acute Care Measures
        3. Types of Care: Chronic Care Measures
        4. Settings of Care: Hospital Care Measures
        5. Settings of Care: Ambulatory Care Measures
        6. Settings of Care: Nursing Home Care Measures
        7. Settings of Care: Home Health Care Measures
        8. Care by Clinical Area: Cancer Care Measures
        9. Care by Clinical Area: Diabetes Care Measures
        10. Care by Clinical Area: Heart Disease Care Measures
        11. Care by Clinical Area: Maternal and Child Health Care Measures
        12. Care by Clinical Area: Respiratory Diseases Care Measures
        13. Clinical Preventive Services
      14. Appendix II: U.S. Census Region and Division Definitions Used in the 2009 State Snapshots
      15. Acknowledgments
      16. Endnotes
      17. Internet Citation
    3. Focus on Diabetes
      1. Alabama
        1. Diabetes Cost Calculator for Employers
        2. Prevalence
        3. Quality of Care: Processes of Care
        4. Quality of Care: Outcomes of Care
          1. Table
        5. Quality Improvement: Lives and Expenses
        6. Quality Improvement: Excess Costs of Diabetes
        7. Disparities in Treatment: By Income
        8. Disparities in Treatment: By Race/Ethnicity
    4. CDC WONDER Natality Information Live Births
      1. Data Query Interface
      2. Data Query Result
      3. Data Summary
        1. Summary
        2. Natality Data Request
          1. Step 1. Organize table layout:
          2. Step 2. Select maternal residence:
          3. Step 3. Select other maternal characteristics:
          4. Step 4. Select birth characteristics:
          5. Step 5. Select maternal risk factors:
          6. Step 6. Other options:
        3. Data Source Information
        4. Additional Information
          1. Suggested Citation
          2. Contact
          3. Notes
        5. Frequently Asked Questions about Natality
          1. Why are the data results sometimes very slow in returning?
        6. Locations - About FIPS State and County Codes
          1. Modifications of FIPS State and County Codes
  10. Slides
    1. Slide 1 Harnessing Health.Data.gov Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 HealthData.gov and Health Datapalooza III Knowledge Base
    4. Slide 4 HealthData.gov and Health Datapalooza III Spotfire Data Ecosystem
    5. Slide 5 Health Datapalooza IV Technology Development Track
    6. Slide 6 Vocab.Data.gov: Government Data Vocabulary
    7. Slide 7 Health Data Platform Metadata Challenge
    8. Slide 8 IBM Watson at RPI
    9. Slide 9 Health.Data.gov
    10. Slide 10 Health.Data.gov: Search for Diabetes
    11. Slide 11 HealthData.gov Catalog Hub
    12. Slide 12 HealthData.gov Catalog Hub: CDC WONDER Births
    13. Slide 13 HealthData.tw.rpi.edu Catalog Hub: CDC WONDER Births
    14. Slide 14 CDC WONDER: Natality Information Live Births
    15. Slide 15 CDC WONDER: Natality Data Live Births - Diabetes
    16. Slide 16 CDC WONDER: Natality Data Live Births - Diabetes
    17. Slide 17 Harness Health.Data.gov Data to Address Diabetes in the US Knowledge Base
    18. Slide 18 Diabetes Data Ecosystem Spreadsheet
    19. Slide 19 NHQR State Snapshots 2009
    20. Slide 20 AHRQ State Snapshots Conclusion
    21. Slide 21 AHRQ Quality of Care for Diabetes by Region and State for 2005-2006 by Conditions
    22. Slide 22 CDC WONDER Births Natality Diabetes
    23. Slide 23 Diabetes Data Ecosystem Spotfire
    24. Slide 24 Conclusions and Recommendations
  11. Spotfire Dashboard
  12. First Lady Michelle Obama on Exercise and Dr. Amen on Natural Supplements Data in Preventing and Treating Diabetes
  13. Email Invitation
  14. Request For Data Resource Center Indicator Data Set
  15. Present at Health Datapalooza
  16. Submission
  17. PROVE IT! Win $100,000
  18. Redesigning Data
    1. Prove It! 2013 Data Design Diabetes Innovation Challenge is Open for Submissions
  19. Site Map
    1. Home
      1. Sign up for E-Updates
      2. About CAHMI
      3. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
      4. Accessibility
        1. Site Design Guidelines
        2. Accessibility Assistance
        3. Non-compliant documents
      5. Legal and Privacy Policy
    2. About the Data Resource Center
      1. About the Data Resource Center
        1. Mission
        2. Available Data on the DRC Website
        3. What You Can Do on the DRC Website
        4. Sponsors of the Data Resource Center
        5. Our Partners
      2. Data Resource Center Staff
      3. DRC News
        1. Newsletters & Announcements from the DRC
        2. DRC in the News
      4. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
    3. Learn About the Surveys
      1. National Survey of Children’s Health (NSCH)
        1. Links
        2. Data at a Glance
        3. Connect with the DRC
        4. The National Survey of Children's Health
        5. NSCH Highlights
        6. Most Popular Topics
        7. Learn about the NSCH
      2. National Survey of Children with Special Health Care Needs (NS-CSHCN)
      3. Guide to Topics and Questions
        1. National Survey of Children's Health
        2. National Survey of Children with Special Health Care Needs
      4. Survey FAQs
      5. Fast Facts about the Survey
        1. National Survey of Children’s Health
        2. National Survey of Children with Special Health Care Needs
      6. Survey Methods and Documentation
        1. Guide to Changes across Survey Years
          1. National Survey of Children with Special Health Care Needs - 2001, 2005/06  and 2009/10
          2. National Survey of Children's Health - 2003 and 2007
        2. Survey Sampling & Administration Process
          1. National Survey of Children with Special Health Care Needs
          2. National Survey of Children's Health
        3. Full Length Survey Instruments
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
        4. Survey Design and Operations Manuals
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
          3. Methods for analyzing complex sample survey data
        5. SAS & SPSS Codebooks
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
    4. Browse the Data
      1. Browse by Survey & Topic
      2. Get State Snapshots
        1. 1. Click on your state, HRSA Region, or Nationwide to view your snapshot.
          1. HRSA Regions
        2. 2. Select a Snapshot from the Categories below. Customizable profiles, where you can choose your own indicators, are marked with an asterisk*. 
          1. Nationwide
      3. Browse Data Trends
        1. Which measures can be compared across survey years in the NS-CSHCN?
        2. Which measures can be compared across survey years in the NSCH?
        3. What determines whether a question can be compared across survey years?
        4. Use the interactive feature below to browse trends across survey years.
        5. Browse Measures for CSHCN using the NS-CSHCN
          1. CSHCN Prevalence and Demographics
          2. MCHB Core Outcomes and Key Indicators
          3. Subcomponents and Details for Core Outcomes
        6. Browse Measures for All Children using the NSCH
          1. Overall Child Health
          2. Health Insurance, Health Care Access, and Quality of Care
          3. Community, Family, and Neighborhood
      4. Get US Data Maps
        1. 2009/10 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
        2. 2007 NSCH State Ranking Maps
          1. Health Status
          2. Health Care
          3. School and Activities
          4. Child's Family
          5. Child & Family's Neighborhood
        3. 2005/06 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
      5. Browse Healthy People 2020 Topics
        1. Use the interactive feature below to browse data by Healthy People Focus Areas
          1. Access to Health Services
          2. Maternal, Infant and Child Health
          3. Early and Middle Childhood
          4. Adolescent Health
          5. Disability and Health & Repiratory Diseases
          6. Oral Health
          7. Mental Health and Mental Disorders & Substance Abuse
          8. Nutrition and Weight Status & Physical Activity
          9. Health Communication and Health Information Technology
          10. Other Healthy People Focus Areas
      6. Medical Home Data Portal
        1. Search for More Medical Home Data
        2. Learn About Medical Home
        3. Medical Home Measurement in States
        4. Medical Home Measurement in Practices
        5. Medical Home: New Opportunities for Implementation Through Health Care Reform
        6. Medical Home: Data in Action!
        7. Medical Home Measurement for Families
      7. Quality Measurement Portal
        1. Using the DRC to Monitor National Quality Priorities
        2. Maternal and Child Health Bureau Core Outcomes for CSHCN
        3. Data on Health Care Quality and System Performance
        4. Quality Improvement Data Briefs
        5. Select Publications and Presentations
        6. Additional Pediatric Quality Measures, Data and Resources
        7. Select Publications on Parent- and Self-Reported Data
      8. Browse Title V Topics
    5. Put Data into Action
      1. How to Use Data Effectively
        1. Communication of Data Results
        2. Tools for Effective Data Use
      2. Examples of Data Use
        1. Featured Stories
        2. Family Leaders
        3. State Users
        4. Report Briefs
      3. Data Briefs
        1. MCHB Core Quality Outcomes for CSHCN
        2. Children with Special Health Care Needs
        3. Specific Conditions
        4. Complementary and Alternative Medicine (CAM)
        5. Methods for Using Data
        6. Title V & Healthy People 2020
        7. Related Resources
      4. Articles
        1. 2007 NSCH Selected Publications
        2. 2005/06 NS-CSHCN Selected Publications
        3. 2003 NSCH Selected Publications
        4. 2001 NS-CSHCN Selected Publications
      5. Presentations
        1. Selected Presentations from the DRC
        2. Presentations from Data Users
        3. Audioconferences
        4. MCHB DataSpeak
      6. Chartbooks
        1. Maternal and Child Health Bureau Chartbooks
          1. 2007 National Survey of Children's Health
          2. 2003 National Survey of Children's Health
          3. 2005/06 National Survey of Children with Special Health Care Needs
          4. 2001 National Survey of Children with Special Health Care Needs
        2. Other Chartbooks & Reports
          1. Includes Data from the 2007 NSCH
          2. Includes Data from the 2003 NSCH
          3. Includes Data from the 2005/06 NS-CSHCN
      7. Family-to-Family Profiles
      8. Child Health Data Resources
        1. CDC Data, Surveys and Reports
        2. Other Sources of Data by Topic
          1. General Child Health/All Topics
          2. Adolescent Health and Transition to Adulthood
          3. Adoption
          4. Autism
          5. Breastfeeding, Maternal Health, and Perinatal Health
          6. Cancer and Chronic Diseases
          7. Homelessness
          8. Medical Home
          9. Mental and Emotional Health
          10. Obesity, Overweight, and Physical Activity
          11. All Other Topics
        3. Using, understanding, and working with data and the surveys
    6. Get Help
      1. How to Use this Site
        1. Take a tour
          1. Introduction: How to Navigate the New Look 
          2. DRC Tutorial: Getting Started with Your Data Search
          3. Tutorial: Interpreting Your Data
        2. Want to learn more about the DRC? 
        3. Quick Guides to Searching the DRC
        4. Related Resources
      2. Ask Us a Question
      3. Survey FAQs
        1. About the DRC
          1. What is the Data Resource Center (DRC)?
          2. Who Sponsors the DRC?
        2. About the Surveys
          1. What is the National Survey of Children's Health (NSCH)?
          2. What is the National Survey of Children with Special Health Care Needs (NS-CSHCN)?
          3. Who sponsors the NSCH and the NS-CSHCN?
          4. How are data for the surveys collected?
          5. Where can I find information about the sampling and administration methods used for the NSCH and the NS-CSHCN?
          6. Why are the surveys sometimes called “SLAITS”?
          7. What topics are asked about in the NSCH and NS-CSHCN?
          8. Will the NSCH and NS-CSHCN be collected again?
          9. Are the NSCH and NS-CSHCN data files available to the public?
          10. Is county or city-level data available in either the NSCH or the NS-CSHCN?
          11. What is new in the 2009/2010 NS-CSHCN survey?
          12. What will be new in the 2011 NSCH survey?
        3. Definitions and Measure Development
          1. How can I find out which questions were used to develop a specific child health indicator?
          2. Why aren’t there any data on this website for Asian, American Indian, Alaska Native, or Native Hawaiian/Pacific Islander children?
          3. What criteria are used to create the race/ethnicity categories on this website?
          4. What is a HRSA region?
          5. What is a "medical home"?
          6. How are unknown or missing values handled in the DRC interactive data query results?
          7. Why are there sometimes different estimates of CSHCN in different surveys?
          8. What is the CSHCN Screener?
        4. Using the DRC and the Surveys
          1. Is it possible to get a list of publications that have used the NSCH or the NS-CSHCN?
          2. Is there a standard format for citing information from the DRC website in an academic paper?
          3. How do I receive assistance if I am having a hard time interpreting output from the NSCH or NS-CSHCN?
          4. Is it possible to look at two subgroups at the same time?
          5. What browsers does the DRC support?
          6. How can I link to your site?
      4. Request a Dataset
        1. Frequenty Asked Questions about DRC Indicator Data Sets for the NSCH & NS-CSHCN
          1. What is included in the NSCH data sets?
          2. What is included in the NS-CSHCN Indicator data sets?
          3. Do the data sets include local-level data?
          4. How do I cite an indicator dataset from the Data Resource Center?
          5. How are DRC Indicator data sets different from National Center for Health Statistics data files?
          6. Where can I find out how derived variables in DRC data sets were conceptualized and constructed in SAS or SPSS?
          7. Is there a charge for data sets provided by the DRC?
          8. What can I expect when I request a DRC Indicator dataset?
          9. What if I have additional questions?
      5. Glossary
        1. AAP
        2. ADA
        3. Agency for Healthcare Research and Quality
        4. AHRQ
        5. American Academy of Pediatrics
        6. Americans with Disabilities Act
        7. BMI
        8. BMI-for-age
        9. body mass index
        10. CAHMI
        11. CAHPS-CCC
        12. CAHPS®
        13. care coordination
        14. CATCH
        15. CATI
        16. CDC
        17. Center for Disease Control and Prevention
        18. Center for Health Care Strategies
        19. Center for Medical Home Improvement
        20. Centers for Medicare & Medicaid Services
        21. Champions for Progress
        22. chance
        23. CHCS
        24. Child and Adolescent Health Measurement Initiative
        25. Children with Special Health Care Needs (CSHCN, CYSHCN)
        26. CI
        27. CMHI
        28. CMS
        29. Community Access to Child Health
        30. Computer-Assisted Telephone Interviewing
        31. confidence interval
        32. Consumer Assessment of Health Plans Survey®
        33. CSHCN
        34. cultural competency
        35. CYSHCN
        36. data
        37. dependent variable
        38. direct health services
        39. distribution
        40. Early & Periodic Screening, Diagnosis, & Treatment
        41. Early Intervention Research Institute
        42. EPSDT
        43. EREI
        44. Family Voices
        45. Family Voices State Coordinators
        46. family-centered care
        47. Federal Poverty Level
        48. FPL
        49. frequency table
        50. FV
        51. Health Plan Employer Data and Information Set
        52. Health Resources and Services Administration
        53. Healthy People 2010
        54. HEDIS
        55. HP 2010
        56. HRSA
        57. ICHP
        58. IDEA
        59. independent variable
        60. indicator
        61. Individuals with Disabilities Education Act
        62. Institute for Child Health Policy
        63. Maternal and Child Health Bureau
        64. MCHB
        65. mean
        66. median
        67. medical home
        68. Metropolitan Statistical Areas
        69. MSA
        70. n
        71. NASHP
        72. National Academy for State Health Policy
        73. National Center for Cultural Competence
        74. National Center for Health Statistics
        75. National Committee for Quality Assurance
        76. National Health Interview Survey
        77. National Initiative for Children's Healthcare Quality
        78. National Survey of Children with Special Health Care Needs
        79. National Survey of Children's Health
        80. NCCC
        81. NCHS
        82. NCQA
        83. needs assessment
        84. NHIS
        85. NICHQ
        86. NS-CSHCN
        87. NSCH
        88. p-value
        89. performance measures
        90. population
        91. population estimate
        92. population weights
        93. prevalence
        94. preventive services
        95. primary care
        96. probability
        97. QI
        98. quality improvement
        99. random sampling
        100. regression equation
        101. reliability
        102. RUCA or RUCA Code
        103. Rural Urban Commuting Area
        104. sample
        105. sample size
        106. significance
        107. SLAITS
        108. standard deviation
        109. standard error
        110. standardization
        111. State and Local Area Integrated Telephone Survey
        112. summary score
        113. Synthetic Estimate
        114. TA
        115. technical assistance
        116. Title V
        117. Title V Block Grants
        118. Title V Information System
        119. TVIS
        120. validity
        121. variable
        122. weighted estimate
      6. Additional Resources
        1. Non-Profit Organizations
        2. Governmental Organizations
  20. NEXT

  1. Story
  2. Slides
    1. Slide 1 Title Health Datapalooza IV: Child and Adolesent Health Data App
    2. Slide 2 Making data accessible to all. It's your data...your story!
    3. Slide 3 Semantic Community: All Content As Data
    4. Slide 4 Data Resource Center for Child and Adolesent Health Site Map
    5. Slide 5 Health Datapalooza IV Knowledge Base for Semantic Search Index
    6. Slide 6 National, HSRA, and State Indicator Profiles in Web Table
    7. Slide 7 National. HRSA, and State Indicator Profiles in Spreadsheet
    8. Slide 8 National Indicator Spreadsheet Profiles in Spotfire
    9. Slide 9 HSRA Region Indicator Spreadsheet Profiles in Spotfire
    10. Slide 10 State Indicator Spreadsheet Profiles in Spotfire
    11. Slide 11 Semantic Search of Knowledge Base
    12. Slide 12 Conclusions and Recommendations
    13. Slide 13 Additional Slides 1
    14. Slide 14 DRC 2003 and 2007 NSCH Merged
    15. Slide 15 DRC 2009-2010 NS-CSHCN Datasets
    16. Slide 16 Additional Slide 2
  3. Spotfire Dashboard
  4. Research Notes
  5. Harness Data to Address Diabetes in the US
    1. Diabetes Statistics
      1. Total prevalence of diabetes
      2. Race and ethnic differences in prevalence of diagnosed diabetes
      3. Morbidity and Mortality
      4. Complications
      5. Cost of Diabetes
      6. For Additional Information
    2. 2011 National Diabetes Fact Sheet
    3. Fast Facts
    4. Diabetes Interactive Atlases
      1. Maps & Data Tables—County Data
      2. Maps & Data Tables—County Rankings
      3. Maps & Motion Charts—All States
      4. Maps & Motion Charts—Select a State
      5. How to use Interactive Atlases
      6. Methods for Calculating Estimates
      7. Contact Us
        1. County Data
        2. County Rankings
        3. Maps & Motion Charts-All States
        4. Maps & Motion Charts-County
        5. CDC Diabetes Interactive Atlas Example
  6. Slides
    1. Slide 1 Harnessing Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 Harness Data to Address Diabetes in the US Knowledge Base
    4. Slide 4 Diabetes Data Ecosystem Spreadsheet
    5. Slide 5 Diabetes Data Ecosystem Spotifre 1
    6. Slide 6 Diabetes Data Ecosystem Spotfire 2
    7. Slide 7 IHME Diabetes State 2009: Spotfire
    8. Slide 8 IHME Diabetes County 2009: Spotfire
  7. Spotfire Dashboard
  8. Research Notes
  9. Health.Data.gov
    1. All-State Data Tables for All Measures
    2. 2009 State Snapshot Methods
      1. All-State Snapshot Measures
      2. State Snapshot Summary Measures
        1. 1. Defining the Content
        2. 2. Classifying State Performance
        3. 3. Scoring State Performance (Meter Score)
      3. Best Performing and All Other States Tables
      4. State Snapshot Strongest (Weakest) Measures
      5. State Snapshot Focus on Diabetes
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Diabetes Costs
          1. Step 1: Determine Number of Covered Lives With Diabetes
          2. Step 2: Estimate Health Care Expenditures Associated With Diabetes Care
          3. Step 3: Estimate Excess Costs Associated With Poor Control of Blood Glucose
        4. 4. Disparities in Treatment
      6. State Snapshot Focus on Asthma
        1. 1. Prevalence
        2. 2. Quality-of-Care Performance Measures
        3. 3. Quality Improvement
      7. State Snapshot Focus on Healthy People 2010
      8. State Snapshot Focus on Disparities
      9. State Snapshot Focus on Payer
      10. State Snapshot Focus on Variation Over Time
      11. State Snapshot Ranking Table
      12. State Snapshot Contextual Factors
      13. Appendix I: 2009 NHQR Measures, by 2009 State Snapshot Summary Measure Assignment
        1. Types of Care: Preventive Care Measures
        2. Types of Care: Acute Care Measures
        3. Types of Care: Chronic Care Measures
        4. Settings of Care: Hospital Care Measures
        5. Settings of Care: Ambulatory Care Measures
        6. Settings of Care: Nursing Home Care Measures
        7. Settings of Care: Home Health Care Measures
        8. Care by Clinical Area: Cancer Care Measures
        9. Care by Clinical Area: Diabetes Care Measures
        10. Care by Clinical Area: Heart Disease Care Measures
        11. Care by Clinical Area: Maternal and Child Health Care Measures
        12. Care by Clinical Area: Respiratory Diseases Care Measures
        13. Clinical Preventive Services
      14. Appendix II: U.S. Census Region and Division Definitions Used in the 2009 State Snapshots
      15. Acknowledgments
      16. Endnotes
      17. Internet Citation
    3. Focus on Diabetes
      1. Alabama
        1. Diabetes Cost Calculator for Employers
        2. Prevalence
        3. Quality of Care: Processes of Care
        4. Quality of Care: Outcomes of Care
          1. Table
        5. Quality Improvement: Lives and Expenses
        6. Quality Improvement: Excess Costs of Diabetes
        7. Disparities in Treatment: By Income
        8. Disparities in Treatment: By Race/Ethnicity
    4. CDC WONDER Natality Information Live Births
      1. Data Query Interface
      2. Data Query Result
      3. Data Summary
        1. Summary
        2. Natality Data Request
          1. Step 1. Organize table layout:
          2. Step 2. Select maternal residence:
          3. Step 3. Select other maternal characteristics:
          4. Step 4. Select birth characteristics:
          5. Step 5. Select maternal risk factors:
          6. Step 6. Other options:
        3. Data Source Information
        4. Additional Information
          1. Suggested Citation
          2. Contact
          3. Notes
        5. Frequently Asked Questions about Natality
          1. Why are the data results sometimes very slow in returning?
        6. Locations - About FIPS State and County Codes
          1. Modifications of FIPS State and County Codes
  10. Slides
    1. Slide 1 Harnessing Health.Data.gov Data to Address Diabetes in the US
    2. Slide 2 Background
    3. Slide 3 HealthData.gov and Health Datapalooza III Knowledge Base
    4. Slide 4 HealthData.gov and Health Datapalooza III Spotfire Data Ecosystem
    5. Slide 5 Health Datapalooza IV Technology Development Track
    6. Slide 6 Vocab.Data.gov: Government Data Vocabulary
    7. Slide 7 Health Data Platform Metadata Challenge
    8. Slide 8 IBM Watson at RPI
    9. Slide 9 Health.Data.gov
    10. Slide 10 Health.Data.gov: Search for Diabetes
    11. Slide 11 HealthData.gov Catalog Hub
    12. Slide 12 HealthData.gov Catalog Hub: CDC WONDER Births
    13. Slide 13 HealthData.tw.rpi.edu Catalog Hub: CDC WONDER Births
    14. Slide 14 CDC WONDER: Natality Information Live Births
    15. Slide 15 CDC WONDER: Natality Data Live Births - Diabetes
    16. Slide 16 CDC WONDER: Natality Data Live Births - Diabetes
    17. Slide 17 Harness Health.Data.gov Data to Address Diabetes in the US Knowledge Base
    18. Slide 18 Diabetes Data Ecosystem Spreadsheet
    19. Slide 19 NHQR State Snapshots 2009
    20. Slide 20 AHRQ State Snapshots Conclusion
    21. Slide 21 AHRQ Quality of Care for Diabetes by Region and State for 2005-2006 by Conditions
    22. Slide 22 CDC WONDER Births Natality Diabetes
    23. Slide 23 Diabetes Data Ecosystem Spotfire
    24. Slide 24 Conclusions and Recommendations
  11. Spotfire Dashboard
  12. First Lady Michelle Obama on Exercise and Dr. Amen on Natural Supplements Data in Preventing and Treating Diabetes
  13. Email Invitation
  14. Request For Data Resource Center Indicator Data Set
  15. Present at Health Datapalooza
  16. Submission
  17. PROVE IT! Win $100,000
  18. Redesigning Data
    1. Prove It! 2013 Data Design Diabetes Innovation Challenge is Open for Submissions
  19. Site Map
    1. Home
      1. Sign up for E-Updates
      2. About CAHMI
      3. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
      4. Accessibility
        1. Site Design Guidelines
        2. Accessibility Assistance
        3. Non-compliant documents
      5. Legal and Privacy Policy
    2. About the Data Resource Center
      1. About the Data Resource Center
        1. Mission
        2. Available Data on the DRC Website
        3. What You Can Do on the DRC Website
        4. Sponsors of the Data Resource Center
        5. Our Partners
      2. Data Resource Center Staff
      3. DRC News
        1. Newsletters & Announcements from the DRC
        2. DRC in the News
      4. Contact Us
        1. The Child and Adolescent Health Measurement Initiative (CAHMI)
        2. Have a Question?
        3. Stay in Touch!
    3. Learn About the Surveys
      1. National Survey of Children’s Health (NSCH)
        1. Links
        2. Data at a Glance
        3. Connect with the DRC
        4. The National Survey of Children's Health
        5. NSCH Highlights
        6. Most Popular Topics
        7. Learn about the NSCH
      2. National Survey of Children with Special Health Care Needs (NS-CSHCN)
      3. Guide to Topics and Questions
        1. National Survey of Children's Health
        2. National Survey of Children with Special Health Care Needs
      4. Survey FAQs
      5. Fast Facts about the Survey
        1. National Survey of Children’s Health
        2. National Survey of Children with Special Health Care Needs
      6. Survey Methods and Documentation
        1. Guide to Changes across Survey Years
          1. National Survey of Children with Special Health Care Needs - 2001, 2005/06  and 2009/10
          2. National Survey of Children's Health - 2003 and 2007
        2. Survey Sampling & Administration Process
          1. National Survey of Children with Special Health Care Needs
          2. National Survey of Children's Health
        3. Full Length Survey Instruments
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
        4. Survey Design and Operations Manuals
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
          3. Methods for analyzing complex sample survey data
        5. SAS & SPSS Codebooks
          1. National Survey of Children's Health
          2. National Survey of Children with Special Health Care Needs
    4. Browse the Data
      1. Browse by Survey & Topic
      2. Get State Snapshots
        1. 1. Click on your state, HRSA Region, or Nationwide to view your snapshot.
          1. HRSA Regions
        2. 2. Select a Snapshot from the Categories below. Customizable profiles, where you can choose your own indicators, are marked with an asterisk*. 
          1. Nationwide
      3. Browse Data Trends
        1. Which measures can be compared across survey years in the NS-CSHCN?
        2. Which measures can be compared across survey years in the NSCH?
        3. What determines whether a question can be compared across survey years?
        4. Use the interactive feature below to browse trends across survey years.
        5. Browse Measures for CSHCN using the NS-CSHCN
          1. CSHCN Prevalence and Demographics
          2. MCHB Core Outcomes and Key Indicators
          3. Subcomponents and Details for Core Outcomes
        6. Browse Measures for All Children using the NSCH
          1. Overall Child Health
          2. Health Insurance, Health Care Access, and Quality of Care
          3. Community, Family, and Neighborhood
      4. Get US Data Maps
        1. 2009/10 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
        2. 2007 NSCH State Ranking Maps
          1. Health Status
          2. Health Care
          3. School and Activities
          4. Child's Family
          5. Child & Family's Neighborhood
        3. 2005/06 NS-CSHCN State Ranking Maps
          1. CORE OUTCOMES/PERFORMANCE MEASURES
          2. NATIONAL CHARTBOOK INDICATORS
      5. Browse Healthy People 2020 Topics
        1. Use the interactive feature below to browse data by Healthy People Focus Areas
          1. Access to Health Services
          2. Maternal, Infant and Child Health
          3. Early and Middle Childhood
          4. Adolescent Health
          5. Disability and Health & Repiratory Diseases
          6. Oral Health
          7. Mental Health and Mental Disorders & Substance Abuse
          8. Nutrition and Weight Status & Physical Activity
          9. Health Communication and Health Information Technology
          10. Other Healthy People Focus Areas
      6. Medical Home Data Portal
        1. Search for More Medical Home Data
        2. Learn About Medical Home
        3. Medical Home Measurement in States
        4. Medical Home Measurement in Practices
        5. Medical Home: New Opportunities for Implementation Through Health Care Reform
        6. Medical Home: Data in Action!
        7. Medical Home Measurement for Families
      7. Quality Measurement Portal
        1. Using the DRC to Monitor National Quality Priorities
        2. Maternal and Child Health Bureau Core Outcomes for CSHCN
        3. Data on Health Care Quality and System Performance
        4. Quality Improvement Data Briefs
        5. Select Publications and Presentations
        6. Additional Pediatric Quality Measures, Data and Resources
        7. Select Publications on Parent- and Self-Reported Data
      8. Browse Title V Topics
    5. Put Data into Action
      1. How to Use Data Effectively
        1. Communication of Data Results
        2. Tools for Effective Data Use
      2. Examples of Data Use
        1. Featured Stories
        2. Family Leaders
        3. State Users
        4. Report Briefs
      3. Data Briefs
        1. MCHB Core Quality Outcomes for CSHCN
        2. Children with Special Health Care Needs
        3. Specific Conditions
        4. Complementary and Alternative Medicine (CAM)
        5. Methods for Using Data
        6. Title V & Healthy People 2020
        7. Related Resources
      4. Articles
        1. 2007 NSCH Selected Publications
        2. 2005/06 NS-CSHCN Selected Publications
        3. 2003 NSCH Selected Publications
        4. 2001 NS-CSHCN Selected Publications
      5. Presentations
        1. Selected Presentations from the DRC
        2. Presentations from Data Users
        3. Audioconferences
        4. MCHB DataSpeak
      6. Chartbooks
        1. Maternal and Child Health Bureau Chartbooks
          1. 2007 National Survey of Children's Health
          2. 2003 National Survey of Children's Health
          3. 2005/06 National Survey of Children with Special Health Care Needs
          4. 2001 National Survey of Children with Special Health Care Needs
        2. Other Chartbooks & Reports
          1. Includes Data from the 2007 NSCH
          2. Includes Data from the 2003 NSCH
          3. Includes Data from the 2005/06 NS-CSHCN
      7. Family-to-Family Profiles
      8. Child Health Data Resources
        1. CDC Data, Surveys and Reports
        2. Other Sources of Data by Topic
          1. General Child Health/All Topics
          2. Adolescent Health and Transition to Adulthood
          3. Adoption
          4. Autism
          5. Breastfeeding, Maternal Health, and Perinatal Health
          6. Cancer and Chronic Diseases
          7. Homelessness
          8. Medical Home
          9. Mental and Emotional Health
          10. Obesity, Overweight, and Physical Activity
          11. All Other Topics
        3. Using, understanding, and working with data and the surveys
    6. Get Help
      1. How to Use this Site
        1. Take a tour
          1. Introduction: How to Navigate the New Look 
          2. DRC Tutorial: Getting Started with Your Data Search
          3. Tutorial: Interpreting Your Data
        2. Want to learn more about the DRC? 
        3. Quick Guides to Searching the DRC
        4. Related Resources
      2. Ask Us a Question
      3. Survey FAQs
        1. About the DRC
          1. What is the Data Resource Center (DRC)?
          2. Who Sponsors the DRC?
        2. About the Surveys
          1. What is the National Survey of Children's Health (NSCH)?
          2. What is the National Survey of Children with Special Health Care Needs (NS-CSHCN)?
          3. Who sponsors the NSCH and the NS-CSHCN?
          4. How are data for the surveys collected?
          5. Where can I find information about the sampling and administration methods used for the NSCH and the NS-CSHCN?
          6. Why are the surveys sometimes called “SLAITS”?
          7. What topics are asked about in the NSCH and NS-CSHCN?
          8. Will the NSCH and NS-CSHCN be collected again?
          9. Are the NSCH and NS-CSHCN data files available to the public?
          10. Is county or city-level data available in either the NSCH or the NS-CSHCN?
          11. What is new in the 2009/2010 NS-CSHCN survey?
          12. What will be new in the 2011 NSCH survey?
        3. Definitions and Measure Development
          1. How can I find out which questions were used to develop a specific child health indicator?
          2. Why aren’t there any data on this website for Asian, American Indian, Alaska Native, or Native Hawaiian/Pacific Islander children?
          3. What criteria are used to create the race/ethnicity categories on this website?
          4. What is a HRSA region?
          5. What is a "medical home"?
          6. How are unknown or missing values handled in the DRC interactive data query results?
          7. Why are there sometimes different estimates of CSHCN in different surveys?
          8. What is the CSHCN Screener?
        4. Using the DRC and the Surveys
          1. Is it possible to get a list of publications that have used the NSCH or the NS-CSHCN?
          2. Is there a standard format for citing information from the DRC website in an academic paper?
          3. How do I receive assistance if I am having a hard time interpreting output from the NSCH or NS-CSHCN?
          4. Is it possible to look at two subgroups at the same time?
          5. What browsers does the DRC support?
          6. How can I link to your site?
      4. Request a Dataset
        1. Frequenty Asked Questions about DRC Indicator Data Sets for the NSCH & NS-CSHCN
          1. What is included in the NSCH data sets?
          2. What is included in the NS-CSHCN Indicator data sets?
          3. Do the data sets include local-level data?
          4. How do I cite an indicator dataset from the Data Resource Center?
          5. How are DRC Indicator data sets different from National Center for Health Statistics data files?
          6. Where can I find out how derived variables in DRC data sets were conceptualized and constructed in SAS or SPSS?
          7. Is there a charge for data sets provided by the DRC?
          8. What can I expect when I request a DRC Indicator dataset?
          9. What if I have additional questions?
      5. Glossary
        1. AAP
        2. ADA
        3. Agency for Healthcare Research and Quality
        4. AHRQ
        5. American Academy of Pediatrics
        6. Americans with Disabilities Act
        7. BMI
        8. BMI-for-age
        9. body mass index
        10. CAHMI
        11. CAHPS-CCC
        12. CAHPS®
        13. care coordination
        14. CATCH
        15. CATI
        16. CDC
        17. Center for Disease Control and Prevention
        18. Center for Health Care Strategies
        19. Center for Medical Home Improvement
        20. Centers for Medicare & Medicaid Services
        21. Champions for Progress
        22. chance
        23. CHCS
        24. Child and Adolescent Health Measurement Initiative
        25. Children with Special Health Care Needs (CSHCN, CYSHCN)
        26. CI
        27. CMHI
        28. CMS
        29. Community Access to Child Health
        30. Computer-Assisted Telephone Interviewing
        31. confidence interval
        32. Consumer Assessment of Health Plans Survey®
        33. CSHCN
        34. cultural competency
        35. CYSHCN
        36. data
        37. dependent variable
        38. direct health services
        39. distribution
        40. Early & Periodic Screening, Diagnosis, & Treatment
        41. Early Intervention Research Institute
        42. EPSDT
        43. EREI
        44. Family Voices
        45. Family Voices State Coordinators
        46. family-centered care
        47. Federal Poverty Level
        48. FPL
        49. frequency table
        50. FV
        51. Health Plan Employer Data and Information Set
        52. Health Resources and Services Administration
        53. Healthy People 2010
        54. HEDIS
        55. HP 2010
        56. HRSA
        57. ICHP
        58. IDEA
        59. independent variable
        60. indicator
        61. Individuals with Disabilities Education Act
        62. Institute for Child Health Policy
        63. Maternal and Child Health Bureau
        64. MCHB
        65. mean
        66. median
        67. medical home
        68. Metropolitan Statistical Areas
        69. MSA
        70. n
        71. NASHP
        72. National Academy for State Health Policy
        73. National Center for Cultural Competence
        74. National Center for Health Statistics
        75. National Committee for Quality Assurance
        76. National Health Interview Survey
        77. National Initiative for Children's Healthcare Quality
        78. National Survey of Children with Special Health Care Needs
        79. National Survey of Children's Health
        80. NCCC
        81. NCHS
        82. NCQA
        83. needs assessment
        84. NHIS
        85. NICHQ
        86. NS-CSHCN
        87. NSCH
        88. p-value
        89. performance measures
        90. population
        91. population estimate
        92. population weights
        93. prevalence
        94. preventive services
        95. primary care
        96. probability
        97. QI
        98. quality improvement
        99. random sampling
        100. regression equation
        101. reliability
        102. RUCA or RUCA Code
        103. Rural Urban Commuting Area
        104. sample
        105. sample size
        106. significance
        107. SLAITS
        108. standard deviation
        109. standard error
        110. standardization
        111. State and Local Area Integrated Telephone Survey
        112. summary score
        113. Synthetic Estimate
        114. TA
        115. technical assistance
        116. Title V
        117. Title V Block Grants
        118. Title V Information System
        119. TVIS
        120. validity
        121. variable
        122. weighted estimate
      6. Additional Resources
        1. Non-Profit Organizations
        2. Governmental Organizations
  20. NEXT

Story

Slides Report

Making data accessible to all. It’s your data…your story!

As Steve Covey said in his book "The 7 Habits of Highly Effective People Habit 2: Begin with the End in Mind. So my end is to present at the Health Datapalooza IV and enter the Redesigning Data competition using the the National Survey of Children’s Health and National Survey of Children with Special Health Care Needs provided by the Data Resource Center for Child and Adolescent Health

The Health Datapalooza IV presentation criteria are (and my answers):

  • The extent to which the application or activity uses health data (does not have to be publicly available data) (Yes, uses CAHMI indicator data)
  • How the application addresses an explicit problem or health issue (Knowledge Base and Dashboard of Child and Adolescent Healthdata)
  • Whether the application is newly created (developed within the past 12 months) or, if exhibited during last year’s Datapalooza, features enhanced capabilities (Yes, newly created)
  • Whether the application has demonstrated utility (improved health outcomes, reduction in health care costs, etc.) (The CAHMI program has and improved use of their data should help their success)
  • Whether the application has a sustainability plan or future plan of use (Yes, part of a new Data Science Company)

The Sanofi US 2013 Data Design Diabetes Innovation Challenge – Prove It! kicks off the Series, inviting innovators to develop solutions that use or produce data for decision-making to help improve health outcomes for people living with diabetes. Through baseline knowledge models, evidence-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the US.

Their Concept Criteria are:

  • EVIDENCE-BASED HEALTH OUTCOMES: Ability to demonstrate in an evidence-based way how the concept can improve the outcomes and/or experience of people living with diabetes in the US.
  • TARGET AUDIENCE: Ability to support one or more members of the healthcare ecosystem and provide them with data-driven tools or evidence-based insight that can help them make better contributions to staving the diabetes epidemic in the US.
  • DECISION-MAKING: Ability to illustrate how the concept can enable better data-driven decision-making at a particular stage across the spectrum of type 1 or type 2 diabetes, from lifestyle and environmental factors to diagnosis, treatment, maintenance, and beyond.
  • DATA SCIENCE: Utilize new or traditional data methodology — such as baseline knowledge models, evidence-based practice, and predictive analysis — to create a tool that may change the landscape of diabetes management through richer insight, more timely information, or better sets of decisions.

The Data Resource Center Indicator data sets contain constructed measures that were developed by CAHMI (Child and Adolescent Health Measurement Initiative) in collaboration with a national technical advisory panel for the Data Resource Center for Child and Adolescent Health. The purpose of this project is to provide support and technical assistance to states for interpretation and utilization of results of the National Survey of Children’s Health and National Survey of Children with Special Health Care Needs.

My concept is that childhood and adolescent factors in the CAHMI like:

  • percent of children age 4 months to 5 years determined to be at moderate or high risk based on parents' specific concerns
  • percent of children age 10-17 years who are overweight or obese (BMI-for-age at or above 85th percentile)

are indicators of potential diabetes and precursors of adult diabetes.

The process to develop the concept is to treat all content as data and to make all content data in a knowledge base, spreadsheet and dashboard as summarized in the matrix below.

 

Four Vs Concept Method Goal Result
Volume and Velocity Big Data=All Content Make all content as data. Federal Digital Government Strategy Knowledge Base
Veracity Web-Linked Data Semantic Web Data Strong Relationships Spreadsheets
Value Unified Data Architecture Data Integration Data Ecosystem Network Visualizations

 

I both formally requested the CAHMI data and extratced it it from the CAHMI web site. So far my knowledge base contains Words: 39532, Chars: 270473, and can be semantically searched. My spreadsheets contain 14 tabs of data sets and the Web Player contains 11 tabs of visualizations. It should be emphasized that the author is focusing on creating web-linked data with strong relationships (see Semantic Medline and our Semantic Web Strategy for Data).

My Conclusions and Recommendations are:

  • The New Digital Government Strategy of treating all content as data has been applied to the CAHMI Web content
  • The CAHMI has been turned into data in spreadsheets and statistical visualizations in Spotfire 5.
  • This simplifies the complex CAHMI interface which requires lots of extra mouse clicks and provides no faceted semantic search.
  • The CAHMI Data Use Agreement for the Data Resource Center Indicator Data Sets provides for additional data access that will be used to supplement this work.
  • This process provides the beginning of a Unified Data Architecture and Ecosystem for Data Integration using the View Data function in Spotfire 5.

The CAHMI Data Sets and others will be used to supplement this work for the Health Datapalooza presentation and Redesigning Data  concept.

 
My additional Conclusions and Recommendations are:
  • Spotfire 5 allows one to filter the Web Site data set by Category and Indicator:
    • Demo Example: Child Weight Status (e.g. Obesity-Low) and Medical Home (Homecare-High) which is useful to decision makers.
  • The CAHMI Data Use Agreement provided additional data access to two large data sets (and a third in the near future):
  • Treemaps are ideal for displaying large amounts of hierarchically structured (tree-structured) data. The space in the visualization is split up into rectangles that are sized and ordered by a quantitative variable.
  • The American Diabetes Association and others are working on improved diagnosis and treatment of child (and adult) diabetes using data:
  • My Concept for the Redesigning Data 2013 Data Design Diabetes Innovation Challenge – Prove It!
    • Through baseline knowledge models, evidence-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the US.
 
My Note: The author attended the The World Congress Leadership Summit on Building the Data Infrastructure to Drive Evidence-Based Improvements in Health Care where he was impressed with a presentation on "Use of diabetes data to invent and operationalize a “Diabetes Tune-Up” program for “Extreme Diabetics” that provided three examples of Using Data to Drive Better Care at Medstar Health. The author also demonstrated the Medicare Zombie Hunter application at Health Datapalooza II in 2011.

Slides

Slides

Slide 2 Making data accessible to all. It's your data...your story!

BrandNIemann03252013Slide2.PNG

Slide 3 Semantic Community: All Content As Data

BrandNIemann03252013Slide3.PNG

Slide 4 Data Resource Center for Child and Adolesent Health Site Map

http://childhealthdata.org/home/site-map

BrandNIemann03252013Slide4.PNG

Slide 5 Health Datapalooza IV Knowledge Base for Semantic Search Index

http://semanticommunity.info/Health_Datapalooza_IV

BrandNIemann03252013Slide5.PNG

Slide 6 National, HSRA, and State Indicator Profiles in Web Table

http://childhealthdata.org/browse/sn...s?rpt=16&geo=1

BrandNIemann03252013Slide6.PNG

Slide 7 National. HRSA, and State Indicator Profiles in Spreadsheet

http://semanticommunity.info/@api/deki/files/23601/CAHMI.xlsx

BrandNIemann03252013Slide7.PNG

Slide 8 National Indicator Spreadsheet Profiles in Spotfire

Web Player

BrandNIemann03252013Slide8.PNG

Slide 9 HSRA Region Indicator Spreadsheet Profiles in Spotfire

Web PlayerBrandNIemann03252013Slide9.PNG

Slide 10 State Indicator Spreadsheet Profiles in Spotfire

Web Player

BrandNIemann03252013Slide10.PNG

Slide 11 Semantic Search of Knowledge Base

http://semanticommunity.info/Health_Datapalooza_IV

BrandNIemann03252013Slide11.PNG

Slide 12 Conclusions and Recommendations

BrandNIemann03252013Slide12.PNG

Slide 14 DRC 2003 and 2007 NSCH Merged

Web Player

BrandNIemann03252013Slide14.png

 

Slide 15 DRC 2009-2010 NS-CSHCN Datasets

Web Player

BrandNIemann03252013Slide15.png

 

Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Error: Embedded data could not be displayed. Use Google Chrome

Harness Data to Address Diabetes in the US

This is more work since submitting the Health Datapalooza IV presentation and The Sanofi US 2013 Data Design Diabetes Innovation Challenge – Prove It!, which kicks off the Series, inviting innovators to develop solutions that use or produce data for decision-making to help improve health outcomes for people living with diabetes. Through baseline knowledge models, evidence-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the US. (Bolding by me-see below)

Their Concept Criteria are:

  • EVIDENCE-BASED HEALTH OUTCOMES: Ability to demonstrate in an evidence-based way how the concept can improve the outcomes and/or experience of people living with diabetes in the US.
  • TARGET AUDIENCE: Ability to support one or more members of the healthcare ecosystem and provide them with data-driven tools or evidence-based insight that can help them make better contributions to staving the diabetes epidemic in the US.
  • DECISION-MAKING: Ability to illustrate how the concept can enable better data-driven decision-making at a particular stage across the spectrum of type 1 or type 2 diabetes, from lifestyle and environmental factors to diagnosis, treatment, maintenance, and beyond.
  • DATA SCIENCE: Utilize new or traditional data methodology — such as baseline knowledge models, evidence-based practice, and predictive analysis — to create a tool that may change the landscape of diabetes management through richer insight, more timely information, or better sets of decisions.

April 9, 2013 Email Received: Now Available-More Data from the 2011/12 National Survey of Children's Health (NSCH)

Source: http://campaign.r20.constantcontact.com/render?llr=zfbhvfcab&v=001EjBomfbIyfjq2VhT3VxI9ShD5_I93TXuSXyFxgeVMIVFks84XL3sEk1TLG2ZQwJfmWgY1tHIQ4jEWPA0LtJyPptXqY4HBi6Z6PtMShkrQlM%3D

This is what I used before, but I do not see diabetes data in the update.

So how would one search, select, and organize an ecosystem of diabetes data to harness it?

The use of the word "harness" here must be as a verb meaning "to capture, control or put to use" and that is what I am trying to do

To capture, control, or put to use, diabetes data, one can: Google Search, Subject Matter Knowledge Search, Ask Experts, etc.

From an initial Google Search I found: A compilation and assessment of epidemiologic, public health, and clinical data on diabetes and its complications in the United at: http://www2.niddk.nih.gov/Research/Resources/DiabetesResources.htm and http://www2.niddk.nih.gov/Research/Resources/AllDatabases.htm

I could index and explore this amazing compilation which would involve considerable time and effort.

I decided to first get an example of these three types of data: epidemiologic, public health, and clinical data.

I also found that the American Diabetes Assocaition has a research database of 377 projects that could be mined for databases:

http://www.diabetes.org/news-research/research/research-database/

They also have diabetes statistics: http://www.diabetes.org/diabetes-basics/diabetes-statistics/

that are based on data from:

http://www.cdc.gov/diabetes/pubs/factsheet11.htm?loc=diabetes-statistics

and from: http://professional.diabetes.org/ResourcesForProfessionals.aspx?cid=91777&loc=dorg-statistics

which are the product of a joint collaboration of the CDC, NIDDK, the American Diabetes Association, and other government and nonprofit agencies.

A good example was: The prevalence of diagnosed diabetes in the U.S. increased by 128% from 1988 to 2008 - Calculated from NIHS data

This led to tables and graphs that could be readily reused!

This in turn lead to: http://www.cdc.gov/diabetes/atlas/ which has county and state data that can be mapped: http://www.cdc.gov/diabetes/atlas/in...tive_atlas.htm from data downloaded at: http://www.cdc.gov/diabetes/atlas/interactive_atlas.htm

So this is a good start of finding epidemiologic , public health, and clinical data for a diabetes data ecosystem.

Diabetes Statistics

Source: http://www.diabetes.org/diabetes-basics/diabetes-statistics/

    

Statistics about diabetes

Data from the 2011 National Diabetes Fact Sheet (released Jan. 26, 2011)

Total prevalence of diabetes

Total: 25.8 million children and adults in the United States—8.3% of the population—have diabetes.

Diagnosed: 18.8 million people

Undiagnosed: 7.0 million people

Prediabetes: 79 million people*

New Cases: 1.9 million new cases of diabetes are diagnosed in people aged 20 years and older in 2010.

* In contrast to the 2007 National Diabetes Fact Sheet, which used fasting glucose data to estimate undiagnosed diabetes and prediabetes, the 2011 National Diabetes Fact Sheet uses both fasting glucose and A1C levels to derive estimates for undiagnosed diabetes and prediabetes. These tests were chosen because they are most frequently used in clinical practice.
 

Under 20 years of age

  • 215,000, or 0.26% of all people in this age group have diabetes
  • About 1 in every 400 children and adolescents has diabetes

Age 20 years or older

  • 25.6 million, or 11.3% of all people in this age group have diabetes

Age 65 years or older

  • 10.9 million, or 26.9% of all people in this age group have diabetes

Men

  • 13.0 million, or 11.8% of all men aged 20 years or older have diabetes

Women

  • 12.6 million, or 10.8% of all women aged 20 years or older have diabetes

Race and ethnic differences in prevalence of diagnosed diabetes

After adjusting for population age differences, 2007-2009 national survey data for people diagnosed with diabetes, aged 20 years or older include the following prevalence by race/ethnicity:

  • 7.1% of non-Hispanic whites
  • 8.4% of Asian Americans
  • 12.6% of non-Hispanic blacks
  • 11.8% of Hispanics

Among Hispanics rates were:

  • 7.6% for Cubans
  • 13.3% for Mexican Americans
  • 13.8% for Puerto Ricans.

Morbidity and Mortality

  • In 2007, diabetes was listed as the underlying cause on 71,382 death certificates and was listed as a contributing factor on an additional 160,022 death certificates. This means that diabetes contributed to a total of 231,404 deaths.

Complications

Heart disease and stroke

  • In 2004, heart disease was noted on 68% of diabetes-related death certificates among people aged 65 years or older.
  • In 2004, stroke was noted on 16% of diabetes-related death certificates among people aged 65 years or older.
  • Adults with diabetes have heart disease death rates about 2 to 4 times higher than adults without diabetes.
  • The risk for stroke is 2 to 4 times higher among people with diabetes.

High blood pressure

  • In 2005-2008, of adults aged 20 years or older with self-reported diabetes, 67% had blood pressure greater than or equal to 140/90 mmHg or used prescription medications for hypertension.

Blindness

  • Diabetes is the leading cause of new cases of blindness among adults aged 20–74 years.
  • In 2005-2008, 4.2 million (28.5%) people with diabetes aged 40 years or older had diabetic retinopathy, and of these, almost 0.7 million (4.4% of those with diabetes) had advanced diabetic retinopathy that could lead to severe vision loss.

Kidney disease

  • Diabetes is the leading cause of kidney failure, accounting for 44% of new cases in 2008.
  • In 2008, 48,374 people with diabetes began treatment for end-stage kidney disease in the United States.
  • In 2008, a total of 202,290 people with end-stage kidney disease due to diabetes were living on chronic dialysis or with a kidney transplant in the United States.

Nervous system disease (Neuropathy)

  • About 60% to 70% of people with diabetes have mild to severe forms of nervous system damage.

Amputation

  • More than 60% of nontraumatic lower-limb amputations occur in people with diabetes.
  • In 2006, about 65,700 nontraumatic lower-limb amputations were performed in people with diabetes.

Cost of Diabetes

Updated March 6, 2013

  • $245 billion: Total costs of diagnosed diabetes in the United States in 2012
  • $176 billion for direct medical costs
  • $69 billion in reduced productivity

After adjusting for population age and sex differences, average medical expenditures among people with diagnosed diabetes were 2.3 times higher than what expenditures would be in the absence of diabetes.

Factoring in the additional costs of undiagnosed diabetes, prediabetes, and gestational diabetes brings the total cost of diabetes in the United States in 2007 to $218 billion.

  • $18 billion for people with undiagnosed diabetes
  • $25 billion for American adults with prediabetes
  • $623 million for gestational diabetes

For Additional Information

MY NOTE: See these two links below.

These statistics and additional information can be found in the National Diabetes Fact Sheet, 2011, the most recent comprehensive assessment of the impact of diabetes in the United States, jointly produced by the CDC, NIH, ADA, and other organizations.

You can download the Association's two-page summary in PDF format from DiabetesPro.

2011 National Diabetes Fact Sheet

Source: http://www.cdc.gov/diabetes/pubs/factsheet11.htm?loc=diabetes-statistics

National estimates and general information on diabetes and prediabetes in the United States

2011 National Diabetes Fact Sheet Adobe PDF file [PDF–2.7MB]

Hoja informativa nacional sobre la diabetes, 2011 Adobe PDF file [PDF–2.8MB]

Data sources, references, and methods 
HTML  Adobe PDF file [PDF–220KB]
 

National Diabetes Fact Sheet Figures
 Microsoft PowerPoint file [PPT–311 KB]   Adobe PDF file [PDF–75KB]
 

Downloadable buttons, badges, and thumbnails

Fast Facts

Source: http://professional.diabetes.org/ResourcesForProfessionals.aspx?cid=91777&loc=dorg-statistics

 
Unless otherwise noted, all references in Fast Facts are from the National Diabetes Fact Sheet, 2011. The Fact Sheet is the product of a joint collaboration of the CDC, NIDDK, the American Diabetes Association, and other government and nonprofit agencies.
 
Sources of data for Fast Facts that do not come from the Fact Sheet:

Diabetes Interactive Atlases

MY NOTE:   This illustrates filtering big data to get the gist for integrattion! Excel

Contact Us

  • CDC Diabetes Public Inquiries
  • Mail
  • 800-CDC-INFO
    (800-232-4636)
    TTY: (888) 232-6348
    8am-8pm ET
    Monday-Friday
    Closed Holidays
  • Contact CDC-INFO
 
Page last reviewed: November 12, 2012

Page last updated: November 12, 2012

Content source: National Center for Chronic Disease Prevention and Health Promotion,
Division of Diabetes Translation

CDC Diabetes Interactive Atlas Example

Source: http://www.cdc.gov/diabetes/atlas/co...ata/atlas.html

MY NOTE: Can do this with Spotfire!

CDCDiabetesInteractive Atlas.png

 

Slides

Slides

Slide 1 Harnessing Data to Address Diabetes in the US

http://semanticommunity.info/
http://gov.aol.com/bloggers/brand-niemann/
http://semanticommunity.info/Health_Datapalooza_IV#Harness_Data_to_Address_Diabetes_in_the_US

BrandNiemann04122013Slide1.PNG

Slide 3 Harness Data to Address Diabetes in the US Knowledge Base

http://semanticommunity.info/Health_Datapalooza_IV#Harness_Data_to_Address_Diabetes_in_the_US

BrandNiemann04122013Slide3.PNG

Slide 4 Diabetes Data Ecosystem Spreadsheet

http://semanticommunity.info/@api/deki/files/23811/Diabetes.xlsx

BrandNiemann04122013Slide4.PNG

Slide 5 Diabetes Data Ecosystem Spotifre 1

Web Player

BrandNiemann04122013Slide5.PNG

Slide 6 Diabetes Data Ecosystem Spotfire 2

Web Player

BrandNiemann04122013Slide6.PNG

Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPa d App

Error: Embedded data could not be displayed. Use Google Chrome

 

Research Notes

MY NOTE: I prefer to both human-readable and machine-readable metadata instead of just the later which I find at the URLs below.

http://healthdatapalooza.org/agenda/...lopment-track/

http://www.healthdata.gov/

http://challenge.gov/HHS/372-my-air-...alth-challenge

See Data Repository

http://vocab.data.gov/

https://vocab.data.gov/user/3

http://reference.data.gov/def/govdata/

http://reference.data.gov/cqld/about.html

http://www.healthdata.gov/blog/domai...nge-1-metadata

http://www.health2con.com/devchallen...tform-metadata-challenge/

http://watson.rpi.edu/

http://statesnapshots.ahrq.gov/snaps...jsp?menuId=67&state=AL#conclusion

Getting started on quality improvement is not an easy task. One strategy a State may find helpful is to identify other States with populations similar to those targeted for a quality improvement effort. For example, a State seeking to improve rates of pneumonia vaccination for people discharged from hospitals may want to model its efforts on those of a State that has previously implemented an improvement program in this area and demonstrated success. In many cases, the greatest value in comparison may lie in identifying States that have started from relatively low performance and made incremental improvements. The State with the greatest improvements may have the most to contribute in demonstrating to other States how to encourage delivery system change that improves quality of care.

Health.Data.gov

Source: http://www.healthdata.gov/

Seach for CAHMI: Did not find it!

Search for Diabetes: Found http://healthdata.gov/dataset/search/diabetes

The State Snapshots provide graphical representations of State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The goal is to help State officials and their public- and private-sector partners better understand health care quality and disparities in their State. State-level information used to create the State Snapshots is based on data collected for the National Healthcare Quality Report (NHQR). The State Snapshots include summary measures of quality of care and States' performances relative to all States, the region, and best performing States by overall health care quality, types of care (preventive, acute, and chronic), settings of care (hospitals, ambulatory care, nursing home, and home health), and clinical conditions (cancer, diabetes, heart disease, maternal and child health, and respiratory diseases). Special focus areas on diabetes, asthma, Healthy People 2010, clinical preventive services, disparities, payer, and variation over time are also featured. The Agency for Healthcare Research and Quality (AHRQ) has released the State Snapshots each year in conjunction with the 2004 NHQR through the 2009 NHQR.

Sub-Agency: Agency for Healthcare Research and Quality

Subject: Quality Measurement
Date Updated: Jul 22, 2010
Geographic Granularity: State
The Same Paragraph As Above
Download the Data: Query Tool

Which Leads to: http://statesnapshots.ahrq.gov/snaps09/allStatesallMeasures.jsp?menuId=63&state= See Below

 
Agency and Program Information
Agency: Department of Health & Human Services
Sub-Agency: Agency for Healthcare Research and Quality
Subject: Quality Measurement
Date Updated: Jul 22, 2010
 
Data Collection and Frequency
Collection Frequency: Annually
Unit of Analysis: The unit of analysis is the state, region, or national rate
Geographic Scope: State, region, and nation
Geographic Granularity: State
 
For Developers

All-State Data Tables for All Measures

Source: http://statesnapshots.ahrq.gov/snaps09/allStatesallMeasures.jsp?menuId=63&state=

Please note that the downloadable data table has more than 5,000 entries and the amount of time needed to download it will depend on your computer, browser, and Internet connection.

A data table including all measures by all States in the NHQR is available for download in Excel or XML by selecting one of the following choices:

Download the Excel version (3.5 mb)* MY NOTE: I downloaded this Excel

Download the data in XML format (6.5 mb)

A data table including contextual factors related to demographics, health status, and resources for all States is also available for download in Excel or XML by selecting one of the following choices:

Download the Excel version (425.5 kb)* MY NOTE: I downloaded this Excel

Download the data in XML format (532.1 kb)

*Download Excel Viewer. Use this free download to access the data table if you do not have Excel installed.

2009 State Snapshot Methods

Source: http://statesnapshots.ahrq.gov/snaps09/Methods.jsp?menuId=68&state=AL

All-State Snapshot Measures

The State Snapshot primarily include State-level estimates selected from the 2009 National Healthcare Quality Report (NHQR). A few Snapshot sections include supplemental State-level analyses. Additional data sources are noted in the appropriate methodology section.

State Snapshot Summary Measures

The following methods were used to develop summary measures from the 2009 NHQR for the following sections of the State Snapshots: state dashboard, overall health care quality, types of care (preventive, acute, chronic), settings of care (hospital, ambulatory, nursing home, home health), care by clinical area (cancer, diabetes, heart disease, maternal and child health, respiratory diseases), and clinical preventive services.

For each section the summary measure represented by a multi-colored meter combines multiple NHQR measures in a way that accounts for how a State performed on each measure within a year: better than average, average, or worse than average. The method encompasses three sequential decisions:

  1. Defining the Content of the summary measure, that is, deciding which NHQR measures to include.
  2. Classifying State performance into better than average, average, or worse than average on each NHQR measure in the summary measure.
  3. Scoring State performance (meter score) on each NHQR measure and on multiple NHQR measures into a summary measure. Data were available for 2 years in the NHQR: baseline and most recent data year. Both years were used to create performance scores.

Each of these decision points is discussed separately below.

1. Defining the Content

All NHQR measures that had State-level estimates available within the 2009 NHQR Data Tables Appendix were grouped into summary measures. These included an overall health care quality measure and 12 other summary measures within three broad areas: type of care (three summary measures), setting of care (four summary measures), and care by clinical area (five summary measures). In addition, one summary measure was defined to track clinical preventive services. NHQR measures were assigned to each of these areas on the following basis:

  • Type-of-care summary measures track consumer aims (staying healthy, getting better, living with illness) with provider roles (preventing sickness, treating acute disease, and managing chronic illness) in maintaining health. The three summary measures are:
    • Preventive care: Measures that assess whether health care providers deliver specific services that prevent disease and detect it early.
    • Acute care: Measures that assess how well health care providers deliver specific services known to cure disease or speed recovery.
    • Chronic care: Measures that assess how well health care providers monitor and manage patients with incurable conditions so that the patients can live better lives.
  • Setting-of-care summary measures track the quality of care delivered in different care settings. They are:
    • Hospital care: Measures that assess the quality of care provided to patients with specific health problems when they are treated in the hospital.
    • Ambulatory care: Measures that assess the quality of care provided to patients with specific conditions when they are treated in doctors' offices, clinics, and other sites of walk-in care.
    • Nursing home care: Measures that assess the quality of care provided to residents of nursing homes.
    • Home health care: Measures that assess the quality of care that is given by home health agencies to clients who receive care at home from a health care professional.
  • Care-by-clinical-area summary measures track the quality of care delivered for specific types of conditions. These measures include prevention, process, and outcome measures covered under care types and settings referenced above but reorganized by clinical area. They are:
    • Cancer care: Measures that assess the quality of care provided to patients with cancer. These measures address cancer screening rates (seven measures) and cancer mortality rates (seven measures).
    • Diabetes care: Measures that assess the quality of care provided to patients with diabetes. These measures address prevention (one measure), processes of care (four measures), and outcomes of care (three avoidable hospitalizations).
    • Heart disease care: Measures that assess the quality of care provided to patients with heart disease, including heart attack (also called acute myocardial infarction, or AMI) and heart failure. These measures address prevention (three measures), processes of hospital inpatient care (nine measures), and outcomes of ambulatory care (one avoidable hospitalization).
    • Maternal and child health care: Measures that assess the quality of care provided to pregnant women and to children. These measures address prevention (two measures) and outcomes of care (seven measures).
    • Respiratory disease care: Measures that assess the quality of care provided to patients with asthma or pneumonia and to those at risk of influenza. These measures address prevention (six measures), processes of care (four measures), and outcomes of care (four measures).
  • The Clinical Preventive Services summary measure represents compliance with selected recommendations of the U.S. Preventive Services Task Force and the CDC's Advisory Committee on Immunization Practice. These two expert bodies use the best research evidence available to make recommendations on preventive services for people without symptoms of disease. Such services include immunizations, tests to screen for the presence of diseases, and behavioral counseling (such as programs that encourage smokers to quit). Most preventive services are provided in primary care ambulatory clinical settings.

A complete list of the NHQR measures considered, and the summary measures to which they were assigned, is included in Appendix I.

2. Classifying State Performance

Each NHQR measure in a State for which data were available in a year was classified twice: once reflecting its regional performance and once reflecting its national performance. The States assigned to each of the nine regions are listed in Appendix II; they are based on the nine U.S. Census Divisions.

The same approach was used below to classify each State's NHQR measures across all States (national performance) and across just those States within its region (regional performance).

Calculating the all-State and regional averages. For the all-State (national) and regional averages, we used estimates from all States that had available data for the measure. A State was excluded from the all-State or regional average if any of the following three conditions existed:

  • The State estimate was unavailable.
  • The standard error of the State estimate was unavailable.
  • The relative standard error (RSE) of the estimate was greater than or equal to 30 percent (RSE ≥ 30%).

The RSE is calculated by dividing the standard error by the estimate. Thus, to be included in the all-State average, the standard error of a State estimate had to be less than 30 percent of the State estimate.

Instead of a typical State average from estimates weighted by the number of observations available for a State, the all-State and regional averages are from estimates weighted by the inverse of their variances, which approximates the count of observations. The differences between averages using these two methods are very small. We use the average weighted by the inverse of the variance (or a precision-weighted average) because the NHQR data tables do not include the number of observations for many of the NHQR measures.

Assigning categories. For each NHQR measure within a State, three categories were created. These categories distinguished better-than-averageaverage, and worse-than-average results for each NHQR measure for each State compared to the Nation and the State's region, by data year. All measures were translated into a worst-to-best metric so that measures for which "higher" represents a better result could be combined accurately with measures for which "lower" represents a better result.

To determine where each State estimate fits within the better-than-average, average, and worse-than-average categories, we applied statistical tests to each State's NHQR measures. To ensure that statistical tests gave reasonable results, we carried out the test for a State estimate only when the estimate for an NHQR measure had an RSE below 30 percent. This criterion was not applied in the tables of the NHQR. We applied it here because we were explicitly comparing States and needed more stringent criteria for statistical reliability across the items of comparison (States).

The statistical criteria used are noted in the table below.

Category Statistical Criteria
Better-than-average The State rate on an NHQR measure is better than the all-State/regional average and is statistically different from the all-State/regional average.
Average The State rate on an NHQR measure is not statistically different from the all-State/regional average.
Worse-than-average The State rate on an NHQR measure is worse than the all-State/regional average and is statistically different from the all-State/regional average.
N/A An estimate or standard error was not available for a State measure or the relative standard error is greater than or equal to 30 percent.

 

3. Scoring State Performance (Meter Score)

For each of the summary measures, each State received two sets of performance meter scores per data year—one set for national performance (n) and one set for regional performance(r), as follows:

  • 1 point for each NHQR measure that was better than average.
  • 0.5 point for each NHQR measure that was average.
  • 0 points for each NHQR measure that was worse than average.
Let A = number of better-than-average NHQR measures in the summary.
B = number of average NHQR measures in the summary.
C = number of worse-than-average NHQR measures in the summary.

Depending on the comparison (national or regional), the meter score was calculated using NHQR measures taken either from all States (for comparisons to the entire Nation) or from States within the region (for comparisons to the State's region). The total number of points assigned in either comparison was divided by the total number of NHQR measures available within the respective State (A + B + C). Thus, the two equations were:

National meter score = ((An*1) + (Bn*0.5) + (Cn*0)) * 100 
                           A + B + C

Regional meter score = ((Ar*1) + (Br*0.5) + (Cr*0)) * 100
                           A + B + C

where An, Bn, and Cn indicate the comparisons to the Nation and Ar, Br, and Cr indicate the comparisons to the region.

The result of these equations will always be: 0 < meter score < 100, equal to 0 if all NHQR measures are worse than average and equal to 100 if all NHQR measures are better than average. Scores between 0 and 100 will represent the mix of measures that are worse than average, average, and better than average. Higher scores represent better performance because the score increases with the number of measures that are average and increases more rapidly with the number of measures that are better than average. These scores are the basis for the performance meter "needles," which represent the score from 0 to 100 on a 180-degree semicircle for visual presentation. The two needles represent two different years—the most recent year of data available (a solid needle) and a baseline year (a dashed needle).

After the meter score is calculated for a summary measure, the score is assigned to one of five categories as follows for visual discrimination on the 180-degree semicircle:

  • Very Weak: 0 ≤ score < 20
  • Weak: 20 ≤ score < 40
  • Average: 40 ≤ score < 60
  • Strong: 60 ≤ score < 80
  • Very Strong: 80 ≤ score ≤ 100

All meters show a solid needle for the most recent year of available data if there are a minimum number of measures reported for the composite. The minimum is set to five measures for national comparisons and set to three for the regional comparisons. The baseline year is represented as a dashed needle when the baseline has more than two-thirds of the measures available in the most recent year. This formula is applied to ensure similar comparisons between the baseline and most recent year. The text below the meter will indicate when there is insufficient data.

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Best Performing and All Other States Tables

Because comparison to the average does not represent the best that a State can achieve, a table has been added to the 2009 NHQR State Snapshots for each summary measure to allow States to compare their score on each summary measure to the top five States in the Nation, plus ties.

This score is the same as the meter score described above. Each table simply lists the meter score for the State of interest and the meter scores individually for the five States with the highest scores. No statistical test is applied to this comparison. It simply shows how far from the best performers the State of interest is in the context of the meter scoring.

In addition, these tables include selected percentiles (75th, 50th, 25th) for the meter scores for all States available. Each represents the meter score cutoff for that percentage of States. For example, the score for the 75th percentile is the score for which 25 percent of the States were higher and 75 percent of the States were lower. This gives a view of the spread of scores for each summary measure.

A table listing the meter score of all States alphabetically accompanies the Best Performing States Table. This all States table is included as another tool for comparing States' scores on each summary measure, and similar to the Best Performing States Table, it simply lists the meter score for each State. No statistical test of differences is applied.

State Snapshot Strongest (Weakest) Measures

The strongest (weakest) measures for a State are based on two criteria related to all States:

  • First, the measures were selected that were better (worse) than average compared to all States with data.
  • Second, among those better-than-average (worse-than-average) measures for each State, the measure that ranked the highest (lowest) among the States with data were selected in turn until at least five measures were identified.

The ranking for this purpose was the ordinal rank from 1 to 51 across the States and the District of Columbia, when all jurisdictions collected the measure. When fewer jurisdictions collected a measure, the ordinal rank was inflated to a relative position as if all 51 had collected the data. For example, if 25 States collected percent of women receiving mammograms, then their ordinal rank would range from 1 to 25. To get a rank comparable to all 51 jurisdictions, the ordinal rank would be multiplied by 2.04 (2.04 = 51/25) to obtain an adjusted relative rank from 2.04 to 51.

When the fifth strongest (weakest) measure was tied in rank with additional measures beyond it, all of those measures were included in the strongest (weakest) list. For example, if the fifth measure was ranked 2 and the next three measures on the list were also ranked 2, then eight strong measures would be listed for the State.

State Snapshot Focus on Diabetes

The Focus on Diabetes section of the NHQR State Snapshots provides information on prevalence, quality, disparities, costs, and potential savings from quality improvement for diabetes care. Diabetes increasingly affects residents of every State, and State health policymakers should understand these issues more completely. The measures and methods used to develop the Focus on Diabetes estimates for the 2009 NHQR State Snapshots are described below.

1. Prevalence

The maps visually summarize the prevalence of diabetes for each State in 2008 by categorizing the state-level information into four quartiles. Prevalence is highest in the first quartile (greater than 9.5 percent) and lowest in last quartile (less than 7.2 percent). The data for the maps are self-reported by adults and come from the Behavioral Risk Factor Surveillance System (BRFSS).

2. Quality-of-Care Performance Measures

The summary measure for the quality of diabetes processes of care is created and scored in the same manner as the summary measures described in Scoring State Performance (Meter Score) above. The summary measure for diabetes outcomes of care is created differently to show the actual number of hospitalizations for diabetes. This was done because it is useful for States to be able to ascertain the number of hospitalizations related to diabetes to determine the potential for cost savings.

The four diabetes process measures are from the Behavioral Risk Factor Surveillance System (BRFSS), which collects data on health behaviors in most States. These include measures of appropriate care for people with diabetes: hemoglobin A1c (HbA1c) testing, eye exams, foot exams, and flu shots. When data are not available for all of these measures for a State, that State is not reported. When data are available for only 1 year, that year of data is reported for that State.

The four diabetes outcome measures are from AHRQ's Healthcare Cost and Utilization Project (HCUP). These are measures of avoidable hospital admissions for long-term diabetes complications, short-term diabetes complications, uncontrolled diabetes without complications, and amputations related to diabetes. More information on HCUP, the participating statewide data sources, and the use of HCUP data in the NHQR can be found on the HCUP User Support Web site under Methods Series Reports (http://www.hcup-us.ahrq.gov/reports/methods.jsp).

These four diabetes outcome measures report the number of hospital admissions for different levels of diabetes severity, with each measure defined per 100,000 people in the State. Because the denominators of these outcomes are the same, the numerators can be added to determine the total number of diabetes admissions per 100,000 people in the State. The diabetes outcomes bar chart shows the total number of diabetes admissions per 100,000 people in the State, in the region, and in the U.S. The national estimate is labeled "U.S." rather than "All States" because it is a weighted national estimate that accounts for missing States. ("All-State" estimates are estimates that include States with available data.) The regional estimate is based on the four U.S. Census Regions instead of the nine U.S. Census Divisions due to the lack of sufficient State estimates within each U.S. Census Division. States included within each U.S. Census Region are listed in Appendix II.

3. Diabetes Costs

The Focus on Diabetes section provides information about the potential impact on State government health care costs of implementing diabetes interventions. This section presents estimates of the burden to State governments of diabetes among State employees and their dependents. It also presents the excess costs incurred by State governments if their employees are not in a disease management program or intensive intervention for improving their diabetes care. States are significant purchasers of health care, providing health care not only to State government employees but also to poor people and people with disabilities. The focus on State government employees is possible because AHRQ has sponsored work to synthesize and translate research findings into information that can aid the decisions of employers, including State government employers.

These estimates were developed with the Employers' Diabetes Costs Calculator, a tool developed from research on diabetes care, its costs, and the effectiveness of disease management. The tool was developed by The Lewin Group for AHRQ to aid employers' decisions on quality improvement related to diabetes care for their employees. The calculator provides public and private employers a rough estimate of their health care costs associated with diabetes and of the excess costs associated with poor control of blood glucose. The result reflects the potential savings that might be realized from a carefully designed disease management program or other type of quality improvement program for diabetes care. AHRQ staff and external experts have reviewed the calculator, but additional reviews and further refinements may occur. The estimates presented in the 2009 NHQR State Snapshots are in 2008 dollars.

Three steps are needed to estimate diabetes costs:

  1. Determine the number of covered lives of State government employees and their dependents and the number of covered lives with diabetes.
  2. Estimate health care expenditures associated with diabetes care.
  3. Estimate excess costs associated with poor control of blood glucose.

Calculations for each of these steps are detailed in the following paragraphs.

Step 1: Determine Number of Covered Lives With Diabetes

This step involves calculating the following:

  1. Number of covered lives of State government employees and their dependents by age, gender, and race/ethnicity estimated by State based on multiple data sources.
  2. Diabetes prevalence by age, gender, and race/ethnicity based on national diabetes prevalence rates for these subgroups.

State government employees and their dependents. Several data sources were used to estimate the number of State government employees by race/ethnicity, gender, and age because this information is not readily available from one source. First, the number of State government employees was taken from the Bureau of Labor Statistics, 2004 Quarterly Census of Employment and Wages (QCEW).1 To determine the number of State government employees by age, the age distribution of the employed population in the State was estimated from the Bureau of Labor Statistics Current Population Survey (CPS) averaged over 3 years, 2003-2005, and then applied to the QCEW data.2 Then, in order to determine race/ethnicity and gender distribution, two main sources were used: the U.S. Census Equal Employment Opportunity (EEO) Data Tool and U.S. Census State population estimates.

The EEO database provided race/ethnicity data for State government employees in cities with a minimum population of 100,000.3 The distribution of these employees by race/ethnicity was applied to all State government workers to obtain statewide counts of employees by race/ethnicity. When EEO data were missing for the State, the race/ethnicity distribution was taken from the Census data for the State's entire population.4 This was done for Alabama, Alaska, Florida, Illinois, Indiana, Kansas, Kentucky, Maryland, Michigan, Missouri, Nevada, New Jersey, New Mexico, New York, Tennessee, and Washington. For Hawaii, approximately 20 percent of the State's population was missing when Census race categories in the EEO data tool were used because the tool did not include the mixed race category. To account for people of mixed race, Claritas race data were used.5 The race/ethnicity distribution was assumed to be the same for males and females.

The race/ethnicity and gender distributions were then applied to the estimated number of State government employees by age to produce the number of State government employees by age, gender, and race/ethnicity for each State.

To estimate the number of State government employees who have dependents covered by their health insurance, the model estimates employees who select family coverage and have children. Estimates of the percentage of employees who select family coverage were based on AHRQ's Medical Expenditure Panel Survey (MEPS) data.6 The number of children per employee who selects family coverage was based on State averages from the U.S. Census Bureau.7

Number of covered lives with diabetes. To estimate covered lives with diabetes, the national diabetes prevalence rate was applied to the number of covered lives by State, described above. These prevalence rates were calculated using the 2005 files of the National Health Interview Survey (NHIS), with prevalence rates stratified by age, gender, and race/ethnicity.8 Because NHIS data are based on self-reported diabetes prevalence, another step is needed to account for the number of people with undiagnosed diabetes. The total prevalence estimate is multiplied by 1.42, the factor suggested by the 2005 CDC statistic that for every 100 people diagnosed with diabetes, approximately 42 people with diabetes have not yet been diagnosed (see the National Diabetes Fact Sheet)

Step 2: Estimate Health Care Expenditures Associated With Diabetes Care

Estimates of average, per capita health care expenditures for privately insured people with and without diabetes were calculated by combining information from the following:

  • The Lewin Group's Health Benefits Simulation Model (HBSM).9
  • Medical Expenditure Panel Survey data and information from a major insurance company to produce diabetes-attributed costs by age group.
  • Estimates of the prevalence of diabetes for different age groups based on an analysis of the 2005 National Health Interview Survey.
  • Medical Care Component of the Consumer Price Index to update the estimates to current year dollars.
  • The Council for Community and Economic Research's10 Cost of Living Index that provides a cross-State comparison of health care costs.
  • Wage data for 2005 from the Bureau of Labor Statistics to estimate indirect costs of lost productivity.

A review of the literature identified no current estimates of health care costs for people with and without diabetes for the privately insured population. Consequently, cost estimates in the Employers' Diabetes Cost Calculator were calculated using the following steps:

  1. Annual medical expenditures (excluding nursing home expenses) in the 2004 MEPS were calculated for each privately insured person.
  2. For each person, diagnosis codes were used to identify whether the person had diabetes during the year and whether he or she had a health encounter for each of the major comorbidities associated with diabetes (see list of diagnosis codes in Hogan et al., 2003).
  3. For each age group, two regression models were estimated, with annual medical expenditures as the dependent variable. These models attempt to define a boundary around the cost of diabetes observed in individuals who have other health problems that may or may not be associated with diabetes. In the first model, the only explanatory variable was the indicator of diabetes. The result, which does not control for other conditions, is an upper bound on the annual additional cost of diabetes compared to people without diabetes. In the second model, the explanatory variables also included the indicator variables for each comorbidity group to isolate the cost of diabetes while holding constant the cost associated with other serious comorbidities. This result, which overcontrols for the comorbidities associated with diabetes, was considered a lower bound on the annual additional cost of diabetes.
  4. The annual costs estimated from the two models were averaged to produce an estimate of diabetes-attributed annual cost.

Estimates of the share of employee health care expenditures that was spent on diabetes care were made by comparing diabetes cost estimates for State employees and dependents to the State budget spent on all health care for State employees and dependents. The budget figures were obtained from the National Association of State Budget Officers.11 For three jurisdictions (Alaska, District of Columbia, and New Mexico), the share estimates looked unreasonable and were omitted. The budget estimates for those three jurisdictions may not be as complete as for other States.

Step 3: Estimate Excess Costs Associated With Poor Control of Blood Glucose

The distribution of HbA1c levels for diabetic employees and their dependents was estimated by fitting the employer population to the distribution of HbA1c levels in the CDC National Health and Nutrition Examination Survey data for 2001-2002.12 The distribution is based on the reported HbA1c levels of respondents who either (1) have been told by their physician that they have diabetes and had HbA1c levels greater than 6, or (2) have not been told by their physician that they have diabetes or have been told that they are borderline diabetic and had HbA1c levels greater than 7.

Costs were estimated by assessing the impact of two hypothetical interventions. One assumes that a population's HbA1c levels can be reduced by 0.48 percentage point, on average. Another assumes that the reduction can reach 1.09 percentage points, on average. Evidence suggests that carefully designed diabetes care quality improvement programs can achieve a 0.48-point average reduction. Intensive disease management programs can achieve a 1.09-point average reduction. Both reductions imply improved glycemic control of the population.13 Improved glycemic control results in fewer complications for people with diabetes over time.14  15

Differences in cost associated with an assumed improvement in HbA1c levels were based on the findings of another study. That study observed inpatient and outpatient health care charges of patients with diabetes in a large commercial health plan for 3 years. The researchers analyzed the difference in health care costs for patients who started the study period at different HbA1c levels.16 That study did not reevaluate the HbA1c levels of the study subjects at the end of the 3 years. Thus, the estimates of lower costs associated with better glycemic control assume that changes in HbA1c levels lead to fewer complications, which result in lower costs.

These assumptions were applied to State employee and dependent populations so that estimates of the cost impact of reducing their HbA1c levels by either 0.48 percentage point or 1.09 percentage points represent the difference in health care costs for States' employee populations. Their distribution of HbA1c levels was based on national HbA1c distributions for people with diabetes and a distribution of HbA1c levels in which everyone has shifted down either by 0.48 percentage point or 1.09 percentage points. The State Snapshots Web site rounds the percentage point reductions to 0.5 and 1.0, respectively, and rounds all dollar estimates to the nearest $100,000 to denote the precision that the estimates are likely to provide.

In addition to estimates of health care cost savings, estimates were made of the cost impact of gains in productivity resulting from a population's reduced HbA1c levels. The findings of a study examining the impact of HbA1c levels on rates of absenteeism and productive capacity17 were used to estimate the change in these rates. The estimates were based on downward shifts of either 0.48 percentage point or 1.09 percentage points in the distribution of State employee and dependent populations' HbA1c levels. The changes were then applied to median hourly wage data from the BLS to produce estimates of cost savings from gains in productivity under both conditions. The State Snapshots Web siterounds the percentage point reductions (to 0.5 and 1.0, respectively) and rounds all dollar estimates to the nearest $100,000 to denote the precision that the estimates are likely to provide.

For more detail on these calculations, go to Employers' Diabetes Costs Calculator.

4. Disparities in Treatment

Data. The Disparities in Treatment section presents the percentage of adults with diabetes who had an HbA1c measurement in the past year based on data from the Diabetes Supplement to the Behavioral Risk Factor Surveillance System (BRFSS).18 The BRFSS is a household survey that, as noted above, collects data on health behaviors, including diabetes care, in most States. BRFSS data are limited in several ways:

  • They are self-reported and reflect the perceptions of respondents. For example, respondents may not know about HbA1c testing or may have difficulty recalling whether they had an HbA1c test.
  • A few States did not collect the Diabetes Supplement to BRFSS; thus, disparities data for them cannot be reported.
  • Some jurisdictions did not report data for one or more data years.
  • Small samples, which are typical in BRFSS, result in higher variance and poorer reliability of estimates. To improve the estimates, BRFSS data were pooled together for 3 years for this analysis.
  • Some States do not have sufficient sample sizes for comparisons of subpopulations, such as by race/ethnicity. Estimates based on a cell size of less than 30 or with relative standard errors greater than 30 percent of the estimate were not used.

Racial/ethnic comparisons. In the Disparities in Treatment section, three racial/ethnic categories from BRFSS are presented:

  • Non-Hispanic Black
  • Hispanic
  • Non-Hispanic White

Other racial/ethnic categories are not included due to small sample sizes. That is, some States either do not have many people with specific racial/ethnic heritage or did not collect large enough samples of minority groups to support analyses.

Maps of gaps in HbA1c testing. The maps visually summarize the comparison of two subpopulations across two geographic areas in terms of relative rates of HbA1c testing. That is, non-Hispanic Blacks are compared to non-Hispanic Whites, Hispanics are compared to non-Hispanic Whites, and low-income groups are compared to high-income groups within a State. Then those relative rates are compared to the same relative rates across all States with data. The result is a ratio of a ratio that represents the gap within the State in treatment between two subpopulations relative to the gap for all States with data.

For the maps, the State's gap is presented in terms of three groups and an unknown category. Assignment to the groups depends on the relative size of the gap and the relative direction of the gap between the group of interest (e.g., non-Hispanic Blacks versus non-Hispanic Whites in the State) and the comparison group (e.g., non-Hispanic Blacks versus non-Hispanic Whites in all States). To capture size and directional effects, the ratio of ratios can be assessed for whether it is substantially below, near, or substantially above 1.0. The cutoff used was below and above 5 percent. Thus, the three categories are defined as follows:

  • Worse than the all-State gap: a State's relative rate is more than 5 percentage points lower than the all-State relative rate.
  • Similar to the all-State gap: a State's relative rate is equal to or within 5 percentage points of the all-State relative rate (that is, within 5 points above 1.0 or 5 points below 1.0).
  • Better than the all-State gap: a State's relative rate is more than 5 percentage points higher than the all-State relative rate.

Bar chart of racial/ethnic HbA1c testing rates. The bar chart shows the percentage of adults with diabetes who had a hemoglobin A1c measurement in the past year for three geographic areas. The bars represent the percentages for the State, for the region (i.e., one of nine Census Divisions) in which the State is located, and for all States with data. The data are from BRFSS. (Go to Data for a description of the BRFSS and the jurisdictions missing this information.)

Region (Census Division) or State peer-group average. The calculation of the average for each State's peer group uses the individual responses for all people in the reporting States in the relevant Census Division. (Select Appendix II for list of Census Divisions.)

All-State average. The calculation of the all-State average uses the individual responses for all the people in all reporting States.

State Snapshot Focus on Asthma

The Focus on Asthma section of the NHQR State Snapshots provides information on prevalence, quality, and estimates of the potential financial returns from quality improvement programs for populations diagnosed with asthma. Such information is important to State health policymakers, health plan administrators, and others considering or planning these programs.

1. Prevalence

The maps visually summarize the prevalence of asthma for each State in 2008 by categorizing the state-level information into four quartiles. Prevalence is highest in the first quartile (greater than 9.6 percent) and lowest in last quartile (less than 8.1 percent). The data for the maps are self-reported by adults and come from the Behavioral Risk Factor Surveillance System (BRFSS).

2. Quality-of-Care Performance Measures

The three asthma outcome measures are from AHRQ's Healthcare Cost and Utilization Project (HCUP). These are measures of avoidable hospital admissions for asthma for children ages 2-17, adults ages 18-64, and adults ages 65 and over. More information on HCUP, the participating statewide data sources, and the use of HCUP data in the NHQR can be found on the HCUP User Support Web site under Methods Series Reports (http://www.hcup-us.ahrq.gov/reports/methods.jsp).

These three asthma outcome measures report the number of hospital admissions for different age groups, with each measure defined per 100,000 people of that age group in the State. The quality of asthma care bar chart shows the total number of asthma admissions per 100,000 people per age group in the State, in the region, and in the U.S. The national estimate is labeled "U.S" rather than "All States" because it is a weighted national estimate that accounts for missing States. ("All-State" estimates are estimates that include States with available data.) The regional estimate is based on the four U.S. Census Regions instead of the nine U.S. Census Divisions due to the lack of sufficient State estimates within each U.S. Census Division. States included within each U.S. Census Region are listed in Appendix II.

3. Quality Improvement

Asthma care improvement programs typically follow the guidelines of the National Asthma Education and Prevention Program (NAEPP). The NAEPP calls for education of patients and providers to better manage the disease. The NAEPP activities for patients focus on self-management to avoid triggers, anticipate problems, and use medications appropriately. The activities for providers focus on accurate diagnosis, appropriate medication prescribing, patient monitoring, and patient education on how to maintain control and avoid attacks. Often education leads to reductions in the need for hospitalization, emergency department visits, urgent office visits, and missed work or school days due to asthma attacks, although it may result in greater pharmaceutical use. Ultimately, net reductions save health care dollars and improve productivity.

The estimates of the health care savings and productivity gains provided in the Focus on Asthma section were developed using the Asthma Return-on-Investment Calculator. The calculator, developed for AHRQ by Thomson Reuters with AHRQ funding, is a tool which uses findings from the literature to estimate potential financial returns of quality improvement programs.

The calculator combines specific characteristics of the targeted population and of the planned asthma program with current research findings (as of March 2007), to generate results which include:

  1. Health care savings per participant
  2. Productivity gains per participant
  3. Return on investment (the number of dollars saved for each dollar invested in the program)

The estimates presented in the focus on asthma section were developed based on a hypothetical asthma care quality improvement intervention program which 1) targets patients with persistent asthma with at least one acute care visit in a one year period, 2) assumes 25 percent of eligible patients will participate, 3) costs $395 per patient per year, and 4) runs for four years. This hypothetical intervention targets the sickest asthma patients, and, thus, shows the best estimated returns. Not all hypothetical intervention programs will result in a favorable ROI. For the purpose of this modeling, State employees are the same as privately insured. For a detailed description of the calculator and the default settings used to generate the estimates, refer to the model description section of the Asthma Return-on-Investment Calculator. Note that results presented in the focus on asthma section are in 2008 dollars.

State Snapshot Focus on Healthy People 2010

The Focus on Healthy People 2010 section compiles a table of 20 measures that reflect U.S. health goals and that are reported by States. These health goals are intended to increase life expectancy, improve quality of life, and eliminate health disparities throughout the Nation. Launched by the Department of Health and Human Services in 2000, the goals provide Federal, State, and local government agencies and nongovernmental organizations with a framework for assessing progress in a comprehensive set of seven focus areas:

  • Access to Quality Health Services
  • Cancer
  • Chronic Kidney Disease
  • Heart Disease and Stroke
  • HIV
  • Immunization and Infectious Diseases
  • Mental Health and Mental Illness

The table, sorted by focus area, displays the Healthy People 2010 target rate, the most recent State rate and data year, and the baseline State rate and data year. Measure definitions are also provided.

State Snapshot Focus on Disparities

The Focus on Disparities section presents data on disparities in the quality of care among racial, ethnic and socioeconomic groups. For ethnicity, State-level quality of care measures for non-Hispanic Blacks, non-Hispanic Asian/Pacific Islanders and Hispanics are compared against the same measures for non-Hispanic Whites. For socioeconomic groups, State-level quality of care measures for low-income communities are compared against the same measures for high-income communities. A performance meter displays how the disparities in quality of care for the State relates to the disparity in the Nation—whether the State level is about average, below average or above average when compared to the National level. The performance meter score is based on up to 29 measures of quality of care and is reported only if at least five measures are available.

The quality of care measures used to rate a State’s performance on the Performance Meter focus on potentially preventable hospitalizations, in addition to hospital mortality, and safety and birth/obstetrics. Potentially preventable hospitalizations include the following clinical areas:

  1. Respiratory Care
  2. Heart Disease
  3. Diabetes

Disparities are presented by these areas in tables which list the measures related to each area, and which indicate, for each measure, how the quality of care or outcomes of the minority group compare to the quality of care or outcomes for non-Hispanic Whites in that State and how the State performance compares to U.S. performance. These comparisons are determined by calculating the ratio of the minority group measure to the non-Hispanic white reference group measure at the State and National levels.

Data for this focus area are from AHRQ's Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) and use version 3.1 of the AHRQ Quality Indicator (QI) software. Data are only available from selected States that participate in HCUP. More information on HCUP can be found on the HCUP User Support Web site under Method Series Reports (http://www.hcup-us.ahrq.gov/reports/methods.jsp).

Relative rates are calculated by dividing the measure estimate for the minority group (or low-income community) by the measure estimate for non-Hispanic Whites (or high-income community). A relative rate above 1.0 indicates the minority group (or low-income community) has worse outcomes or receives poorer quality of care than non-Hispanic Whites (or high-income communities). The higher the relative rate, the greater the disparity. A relative rate less than 1.0 indicates the minority group (or low-income community) has better outcomes or receives better quality of care than non-Hispanic Whites (or high-income communities). The up and down arrow symbols displayed with the relative rates are based on statistical significant differences of the numerator and denominator (p-value < 0.05) and a differential of at least 10 percent.

Comparative rates are calculated by dividing the State-specific relative rate by the U.S. relative rate. A value above 1.0 indicates that the State is doing worse than the U.S. in terms of the disparity in quality of care of the minority group. A value below 1.0 indicates that the State is doing better than the U.S. in terms of the disparity in quality of care of the minority group. A relative rate of 1.1 or above is reported as "worse" and a relative rate of 0.9 or below is reported as "better" in the graphics. The up and down triangle symbols displayed with the comparative rates are based on a differential of at least 10 percent.

State Snapshot Focus on Payer

The Focus on Payer section includes information on hospital care measures that refer to inpatient mortality and potentially avoidable complications by expected primary payer (privately insured, Medicare, Medicaid, and the uninsured).

The pie chart on the opening page of Focus on Payer shows the distribution of all inpatient hospitalizations for 2006 by expected primary payer in the State and serves as contextual information when reviewing how each insurance type performs on different measures of hospital care. Data for this focus area are from AHRQ's Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID). Data are only available from selected States that participate in HCUP. More information on HCUP can be found on the HCUP User Support Web site (http://www.hcup-us.ahrq.gov).

In the all-payer section, a comparison of Medicare, Medicaid, and the uninsured to the privately insured is featured. Data are from the 2006 HCUP SID and Nationwide Inpatient Sample (NIS) and use version 3.1 of the AHRQ Quality Indicator (QI) software. The performance meters display how the disparity in quality of care for the State relates to the disparity in the U.S.—whether the State level is about average, below average, or above average when compared to the U.S. level. The performance meter scores are based on up to 15 measures of quality of care and are reported only if at least five measures are available.

In each payer-specific section, the State-level payer-specific rates for 15 different measures are compared to the U.S. rates. Data are also from the 2006 HCUP SID and NIS and use version 3.1 of the AHRQ QIs. The performance meter score summarizes the State's performance over the 15 QIs and is reported only if at least five measures are available. The data table displays the State and U.S. rates with an indication of a differential of at least 10 percent.

State Snapshot Focus on Variation Over Time

The Focus on Variation Over Time section shows the high degree of variation across States and over time in potentially avoidable adult and pediatric hospital admissions for composites of acute and chronic conditions. Graphs present States’ performance compared to the U.S. using the AHRQ Prevention Quality Indicators (PQIs) and Pediatric Quality Indicators (PDIs) composite measures. These measures refer to hospital admissions that evidence suggests could have been avoided, at least in part, through high-quality outpatient care. Rates for three years (2000, 2004, and 2006) are reported per 100,000 population. Rates for interim years are not available. For adults, the PQI composites are defined as follows:

  • PQI Acute Composite – admissions for dehydration, bacterial pneumonia, and urinary infections.
  • PQI Chronic Composite – admissions for diabetes (short-term complications, long-term complications, uncontrolled, and lower extremity amputations), hypertension, congestive heart failure, angina without procedure, and asthma. This measure has been modified to exclude chronic obstructive pulmonary disease because of inconsistencies across data years 2000 to 2006 for the ICD-9-CM diagnosis codes needed to identify the condition.
  • PQI Combined Composite – admissions for all of the acute and chronic conditions listed above.

For children ages 6 to 17 years old, the PDI composites are defined as follows:

  • PDI Acute Composite – admissions for gastroenteritis and urinary infections.
  • PDI Chronic Composite – admissions for asthma and diabetes with short-term complications.
  • PDI Combined Composite – admissions for all of the acute and chronic conditions listed above.

Data for this focus area are from AHRQ's Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) and use version 3.1 of the AHRQ Quality Indicator (QI) software. Data are only available from selected States that participate in HCUP. More information on HCUP can be found on the HCUP User Support Web site under Method Series Reports (http://www.hcup-us.ahrq.gov/reports/methods.jsp).

State Snapshot Ranking Table

To enable simple direct comparisons of States on some health care quality measures underlying the summary measures, States were ranked from 1 to 51 on a select set of 18 measures from the NHQR for which all States reported. These measures include core measures for the most common diseases reported in the NHQR. Core measures represent the most important and scientifically credible measures of health care quality for the Nation. They were selected by the Department of Health and Human Services Interagency Workgroup for the NHQR. Many of the core measures selected by that workgroup did not have State-level data. The other measures in the core areas were selected to round out the group of 18 reported in the State Snapshots.

State Snapshot Contextual Factors

The context of the State's environment is shown in a series of dials. Seven dials relate to State demographics, three relate to health status, and three relate to health care resources. These factors provide a backdrop to the State's health care quality and may aid in interpreting the State's performance meters. These contextual factors might have a cause, effect, or other indirect association with the results shown in the performance meter. For example, if a high percentage of the State's population is without health insurance, a high percentage of the State's population might not use preventive services.

The dials show the State's rate for the factor and the range of rates across all reporting States. An orange wedge on each dial shows the spread of values for all reporting States (or reporting States in the region), ranging from the State with the lowest to the State with the highest value. The arrow (on top of the orange portion) represents the State's percent or rate of the factor.

Data sources for the contextual factor dials follow:

  • The Kaiser Family Foundation (KFF) (see http://www.statehealthfacts.org). The KFF compiles data from:
    • U.S. Census Bureau's Current Population Survey (CPS: Annual Social and Economic Supplements)
    • Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System Survey Data (BRFSS)
    • Healthleaders, Inc.
    • AHA Annual Surveys
  • Morbidity and Mortality Weekly Report, Centers for Disease Control and Prevention, February 2005
  • U.S. Census Bureau
  • Area Resource Data File

Appendix I: 2009 NHQR Measures, by 2009 State Snapshot Summary Measure Assignment

This appendix lists the NHQR measures included in the summary measures, excluding the overall summary measure. The overall summary includes all measures in the tables below (except for those in the excluded table) reported by a State. Individual measures may appear in multiple groupings. The list of measures is organized by:

Types of Care: Preventive Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 11_1_11.3 Nursing home long-stay residents - given flu vaccine Percentage of long-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_12.3 Nursing home short-stay residents - given flu vaccine Percentage of short-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_13.3 Nursing home long-stay residents - given pneumococcal vaccine Percentage of long-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 11_1_14.3 Nursing home short-stay residents - given pneumococcal vaccine Percentage of short-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 12_1_2.2 Inpatient surgery - appropriate antibiotic timing Percentage of adult surgery patients who received appropriate timing of antibiotics
Table 12_1_3.2 Inpatient surgery - antibiotics within 1 hour Percentage of adult surgery patients who received prophylactic antibiotics within 1 hour prior to surgical incision
Table 12_1_4.2 Inpatient surgery - antibiotics stopped within 24 hours Percentage of adult surgery patients who had prophylactic antibiotics discontinued within 24 hours after surgery end time
Table 1_1_1.3 Mammograms Percentage of women age 40 and over who received a mammogram in the last 2 years
Table 1_1_2.1 Breast cancer diagnosed at advanced stage Breast cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 women age 40 and over
Table 1_1_5.3 Breast cancer deaths Breast cancer deaths per 100,000 female population per year
Table 1_2_1.3 Pap tests Percentage of women age 18 and over who received a Pap smear within the last 3 years
Table 1_2_2.1 Cervical cancer diagnosed at advanced stage Cervical cancer diagnosed at advanced stage (all invasive tumors) per 100,000 women age 20 and over
Table 1_3_2.3 Colonoscopy, sigmoidoscopy, or proctoscopy Percentage of adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
Table 1_3_3.3 Fecal occult blood tests Percentage of adults age 50 and over who received a fecal occult blood test in the last 2 years
Table 1_3_4.1 Colorectal cancer diagnosed at advanced stage Colorectal cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 population age 50 and over
Table 1_3_6.3 Colorectal cancer deaths Colorectal cancer deaths per 100,000 population per year
Table 1_4_1.3 All cancer deaths All cancer deaths per 100,000 population per year
Table 1_4_2.3 Prostate cancer deaths Prostate cancer deaths per 100,000 male population per year
Table 1_4_3.3 Lung cancer deaths Lung cancer deaths per 100,000 population per year
Table 4_1_3.3 Blood cholesterol testing Percentage of adults age 18 and over who had their blood cholesterol checked within the preceding 5 years
Table 6_3_1.3 Children fully vaccinated Percentage of children ages 19-35 months who received all recommended vaccines (4:3:1:3:3)
Table 7_1_7.3 Suicide deaths Suicide deaths per 100,000 population
Table 8_1_1.3 Flu vaccine in past 12 months - high-risk, age 18-64 Percentage of adults ages 18-64 at high risk (e.g., COPD) who received an influenza vaccination in the last 12 months
Table 8_1_2.3 Flu vaccine in past 12 months - age 65 and over Percentage of adults age 65 and over who received an influenza vaccination in the last 12 months
Table 8_1_3.3 Avoidable hospitalizations - influenza Immunization-preventable influenza admissions ages 65 and over (excluding transfers from other institutions) per 100,000 population
Table 8_1_4.3 Pneumonia vaccine ever - high-risk, age 18-64 Percentage of high-risk people ages 18-64 who ever received a pneumococcal vaccination
Table 8_1_5.3 Pneumonia vaccine ever - age 65 plus Percentage of adults age 65 and over who ever received a pneumococcal vaccination
Table 9_1_1.3 Smoking cessation advice Percentage of adult current smokers who received advice to quit smoking
Types of Care: Acute Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 12_1_5.3 Postoperative sepsis per 1,000 elective-surgery discharges Postoperative sepsis per 1,000 adult elective-surgery discharges with an operating room procedure (excluding patients admitted for infection; patients with cancer or immunocompromised states; obstetric conditions; stays under 4 days; and admissions specifically for sepsis)
Table 12_1_6.4 Selected infections due to medical care per 1,000 discharges Selected infections due to medical care per 1,000 adult medical and surgical discharges (excluding immunocompromised and cancer patients, stays under 2 days, and admissions specifically for such infections)
Table 12_2_9.4 Reclosure of postoperative abdominal wound separation per 1,000 discharges Reclosure of postoperative abdominal wound separation per 1,000 adult abdominopelvic-surgery discharges (excluding immunocompromised patients, stays under 2 days, and obstetric conditions)
Table 12_3_5.4 Iatrogenic pneumothorax per 1,000 discharges Iatrogenic pneumothorax per 1,000 adult discharges (excluding obstetrical admissions and patients with chest trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery)
Table 12_3_9.3 Deaths per 1,000 admissions in low-mortality DRGs Deaths per 1,000 adult admissions in low-mortality diagnosis-related groups (DRGs)
Table 13_1_1.7 Got routine appointments - adults on Medicare managed care Percentage of adults who had an appointment for routine health care in the last 12 months who got appointments for routine care as soon as wanted, Medicare managed care
Table 13_1_1.8 Got routine appointments - adults on Medicare fee for service Percentage of adults who had an appointment for routine health care in the last 12 months who got appointments for routine care as soon as wanted, Medicare fee for service
Table 13_2_3.2 Heart attack - PCI in 90 minutes Percentage of hospital patients with heart attack who received percutaneous coronary intervention (PCI) within 90 minutes of arrival
Table 13_2_4.2 Heart attack - fibrinolytic medication within 30 minutes Percentage of hospital patients with heart attack who received fibrinolytic medication within 30 minutes of arrival
Table 14_4_3.2 Heart failure - complete instructions at discharge Percentage of hospital patients with heart failure who were given complete written discharge instructions
Table 2_3_1.4 Avoidable hospitalizations - diabetes, uncomplicated Adult admissions for uncontrolled diabetes without complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.5 Avoidable adult hospitalizations - diabetes, short-term complications Adult admissions for diabetes with short-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.6 Avoidable pediatric hospitalizations - diabetes, short-term complications Pediatric admissions ages 6-17 for diabetes with short-term complications (excluding transfers from other institutions) per 100,000 population
Table 2_3_3.3 Avoidable hospitalizations - diabetes, long-term complications Adult admissions for diabetes with long-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 4_2_1.2 Heart attack - ACEI or ARB at discharge Percentage of hospital patients with heart attack and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_2_2.3 Heart attack deaths in hospital Deaths per 1,000 adult admissions with acute myocardial infarction (AMI) as principal diagnosis (excluding transfers to another hospital)
Table 4_3_1.2 Heart failure - recommended hospital care received Percentage of hospital patients with heart failure who received recommended hospital care (evaluation of left ventricular ejection fraction and ACE inhibitor or ARB prescription at discharge, if indicated, for left ventricular systolic dysfunction)
Table 4_3_2.2 Heart failure - evaluation of ejection fraction test in hospital Percentage of hospital patients with heart failure who received an evaluation of left ventricular ejection fraction
Table 4_3_3.2 Heart failure - ACEI/ARB at discharge Percentage of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_3_4.1 Avoidable hospitalizations - heart failure Adult admissions for congestive heart failure (excluding patients with cardiac procedures, obstetric conditions, and transfers from other institutions) per 100,000 population
Table 4_3_5.3 Congestive heart failure deaths in hospital Deaths per 1,000 adult hospital admissions with congestive heart failure as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Table 4_4_1.3 Abdominal aortic aneurysm repair deaths in hospital Deaths per 1,000 adult admissions with abdominal aortic aneurysm (AAA) repair (excluding obstetric admissions and transfers to another hospital)
Table 4_4_2.3 Coronary artery bypass graft deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with coronary artery bypass graft (excluding obstetric admissions and transfers to another hospital)
Table 4_4_3.3 Angioplasty deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with percutaneous transluminal coronary angioplasties (excluding obstetric admissions and transfers to another hospital)
Table 6_2_1.3 Birth trauma injury to neonate per 1,000 live births Birth trauma - injury to neonate per 1,000 live births (excluding preterm and osteogenesis imperfecta births)
Table 6_2_2.3 Obstetric trauma per 1,000 vaginal deliveries without instrument assistance Obstetric trauma with 3rd or 4th degree lacerations per 1,000 vaginal deliveries without instrument assistance
Table 6_2_3.3 Obstetric trauma per 1,000 instrument-assisted deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 instrument-assisted vaginal deliveries
Table 6_2_4.3 Obstetric trauma per 1,000 cesarean deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 cesarean deliveries
Table 8_2_1.2 Pneumonia - recommended hospital care received Percentage of hospital patients with pneumonia who received recommended hospital care
Table 8_2_2.2 Pneumonia - blood cultures before antibiotics in hospital Percentage of hospital patients with pneumonia who had blood cultures collected before antibiotics were administered
Table 8_2_3.2 Pneumonia - antibiotics within 6 hours in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic dose within 6 hours of hospital arrival
Table 8_2_4.2 Pneumonia - recommended initial antibiotics in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic consistent with current recommendations
Table 8_2_5.2 Pneumonia - flu vaccination screening in hospital Percentage of hospital patients age 50 and over with pneumonia discharged during October-February who received influenza screening or vaccination
Table 8_2_6.2 Pneumonia - pneumococcal vaccination screening in hospital Percentage of hospital patients age 65 and over with pneumonia who received pneumococcal screening or vaccination
Table 8_2_7.3 Pneumonia deaths in hospital Deaths per 1,000 adult admissions with pneumonia as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Types of Care: Chronic Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 10_1_2.3 Home health care - improved mobility Percentage of home health care patients who get better at walking or moving around
Table 10_1_3.3 Nursing home long-stay residents - with declining mobility Percentage of long-stay nursing home residents whose ability to move about in and around their room got worse
Table 10_1_4.3 Home health care - improved transferring Percentage of home health care patients who get better at getting in and out of bed
Table 10_1_5.3 Nursing home long-stay residents - with increased need for help Percentage of long-stay nursing home residents whose need for help with daily activities has increased
Table 10_1_6.3 Nursing home long-stay residents - bed/chair bound Percentage of long-stay nursing home residents who spend most of their time in bed or in a chair
Table 10_1_7.3 Home health care - improved bathing Percentage of home health care patients who get better at bathing
Table 10_1_8.3 Home health care - improved oral drug management Percentage of home health care patients who get better at taking their medicines correctly (by mouth)
Table 11_1_1.3 Nursing home long-stay residents - with moderate to severe pain Percentage of long-stay nursing home residents who have moderate to severe pain
Table 11_1_10.3 Home health care - home after home health care Percentage of home health care patients who stay at home after an episode of home health care ends
Table 11_1_15.3 Nursing home short-stay residents - with moderate to severe pain Percentage of short-stay nursing home residents who had moderate to severe pain
Table 11_1_16.3 Home health care - improved pain management when mobile Percentage of home health care patients who have less pain when moving around
Table 11_1_17.3 Nursing home long-stay residents - physically restrained Percentage of long-stay nursing home residents who were physically restrained
Table 11_1_18.3 Nursing home long-stay residents - high-risk with pressure sores Percentage of high-risk long-stay nursing home residents who have pressure sores
Table 11_1_19.3 Nursing home long-stay residents - low-risk with pressure sores Percentage of low-risk long-stay nursing home residents who have pressure sores
Table 11_1_2.3 Nursing home long-stay residents - with urinary tract infections Percentage of long-stay nursing home residents with a urinary tract infection
Table 11_1_20.3 Nursing home short-stay residents - with pressure sores Percentage of short-stay nursing home residents with pressure sores
Table 11_1_21.3 Nursing home short-stay residents - with delirium Percentage of short-stay nursing home residents with delirium
Table 11_1_22.3 Home health care - hospitalization Percentage of home health care patients who had to be admitted to the hospital
Table 11_1_23.2 Hospice care - timely referral to hospice Percentage of hospice patient caregivers who perceived patient was referred to hospice at the right time
Table 11_1_24.2 Hospice care - appropriate medication for pain management Percentage of hospice patients who received the right amount of medicine for pain management
Table 11_1_25.2 Hospice care - patients' wishes followed Percentage of hospice patients who received care consistent with their stated end-of-life wishes
Table 11_1_3.3 Nursing home long-stay residents - more depressed or anxious Percentage of long-stay nursing home residents who are more depressed or anxious
Table 11_1_4.3 Nursing home long-stay residents - low-risk with less control of bowels or bladder Percentage of low-risk long-stay nursing home residents who lose control of their bowels or bladder
Table 11_1_5.3 Nursing home long-stay residents - low-risk with urinary catheter left in Percentage of low-risk long-stay nursing home residents with a catheter inserted and left in their bladder
Table 11_1_6.3 Nursing home long-stay residents - with too much weight loss Percentage of long-stay nursing home residents who lose too much weight
Table 11_1_7.3 Home health care - improved breathing Percentage of home health care patients who are short of breath less often
Table 11_1_8.3 Home health care - improved bladder control Percentage of home health care patients whose bladder control improves
Table 11_1_9.3 Home health care - plus urgent care Percentage of home health care patients who need urgent, unplanned medical care
Table 2_1_2.3 Diabetes hemoglobin A1c tests Percentage of adults age 40 and over with diagnosed diabetes who received a hemoglobin A1c measurement in the calendar year
Table 2_1_3.3 Diabetes eye exams Percentage of adults age 40 and over with diagnosed diabetes who received a dilated eye examination in the calendar year
Table 2_1_4.3 Diabetes foot exams Percentage of adults age 40 and over with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
Table 2_1_5.3 Diabetes flu shots Percentage of noninstitutionalized high-risk adults ages 18-64 with diabetes who had a flu shot in the last 12 months
Table 3_1_1.3 Dialysis and good urea reduction - Medicare Percentage of adult hemodialysis patients with adequate dialysis (urea reduction ratio 65% or greater), Medicare
Table 3_1_2.3 Dialysis and on kidney transplant list Percentage of dialysis patients under age 70 who were registered on a waiting list for transplantation
Table 3_1_3.3 Renal failure and kidney transplant Patients with treated chronic kidney failure who received a transplant within 3 years of date of renal failure
Table 3_1_4.1 Dialysis and survival - Medicare Ratio of observed deaths to expected deaths among Medicare hemodialysis patients
Table 5_2_4.3 HIV deaths HIV infection deaths per 100,000 population
Table 8_3_2.7 Avoidable adult hospitalizations - asthma Adult asthma admissions ages 18 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions, and transfers from other institutions) per 100,000 population
Table 8_3_2.8 Avoidable pediatric hospitalizations - asthma Pediatric asthma admissions ages 2-17 (excluding patients with cystic fibrosis or anomalies of the respiratory system and transfers from other institutions) per 100,000 population
Table 8_3_2.9 Avoidable hospitalizations for seniors - asthma Adult asthma admissions age 65 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions and transfers from other institutions) per 100,000 population
Settings of Care: Hospital Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 12_1_2.2 Inpatient surgery - appropriate antibiotic timing Percentage of adult surgery patients who received appropriate timing of antibiotics
Table 12_1_3.2 Inpatient surgery - antibiotics within 1 hour Percentage of adult surgery patients who received prophylactic antibiotics within 1 hour prior to surgical incision
Table 12_1_4.2 Inpatient surgery - antibiotics stopped within 24 hours Percentage of adult surgery patients who had prophylactic antibiotics discontinued within 24 hours after surgery end time
Table 12_1_5.3 Postoperative sepsis per 1,000 elective-surgery discharges Postoperative sepsis per 1,000 adult elective-surgery discharges with an operating room procedure (excluding patients admitted for infection; patients with cancer or immunocompromised states; obstetric conditions; stays under 4 days; and admissions specifically for sepsis)
Table 12_1_6.4 Selected infections due to medical care per 1,000 discharges Selected infections due to medical care per 1,000 adult medical and surgical discharges (excluding immunocompromised and cancer patients, stays under 2 days, and admissions specifically for such infections)
Table 12_2_9.4 Reclosure of postoperative abdominal wound separation per 1,000 discharges Reclosure of postoperative abdominal wound separation per 1,000 adult abdominopelvic-surgery discharges (excluding immunocompromised patients, stays under 2 days, and obstetric conditions)
Table 12_3_5.4 Iatrogenic pneumothorax per 1,000 discharges Iatrogenic pneumothorax per 1,000 adult discharges (excluding obstetrical admissions and patients with chest trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery)
Table 12_3_9.3 Deaths per 1,000 admissions in low-mortality DRGs Deaths per 1,000 adult admissions in low-mortality diagnosis-related groups (DRGs)
Table 13_2_3.2 Heart attack - PCI in 90 minutes Percentage of hospital patients with heart attack who received percutaneous coronary intervention (PCI) within 90 minutes of arrival
Table 13_2_4.2 Heart attack - fibrinolytic medication within 30 minutes Percentage of hospital patients with heart attack who received fibrinolytic medication within 30 minutes of arrival
Table 14_4_3.2 Heart failure - complete instructions at discharge Percentage of hospital patients with heart failure who were given complete written discharge instructions
Table 4_2_1.2 Heart attack - ACEI or ARB at discharge Percentage of hospital patients with heart attack and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_2_2.3 Heart attack deaths in hospital Deaths per 1,000 adult admissions with acute myocardial infarction (AMI) as principal diagnosis (excluding transfers to another hospital)
Table 4_3_1.2 Heart failure - recommended hospital care received Percentage of hospital patients with heart failure who received recommended hospital care (evaluation of left ventricular ejection fraction and ACE inhibitor or ARB prescription at discharge, if indicated, for left ventricular systolic dysfunction)
Table 4_3_2.2 Heart failure - evaluation of ejection fraction test in hospital Percentage of hospital patients with heart failure who received an evaluation of left ventricular ejection fraction
Table 4_3_3.2 Heart failure - ACEI/ARB at discharge Percentage of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_3_5.3 Congestive heart failure deaths in hospital Deaths per 1,000 adult hospital admissions with congestive heart failure as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Table 4_4_1.3 Abdominal aortic aneurysm repair deaths in hospital Deaths per 1,000 adult admissions with abdominal aortic aneurysm (AAA) repair (excluding obstetric admissions and transfers to another hospital)
Table 4_4_2.3 Coronary artery bypass graft deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with coronary artery bypass graft (excluding obstetric admissions and transfers to another hospital)
Table 4_4_3.3 Angioplasty deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with percutaneous transluminal coronary angioplasties (excluding obstetric admissions and transfers to another hospital)
Table 6_2_1.3 Birth trauma injury to neonate per 1,000 live births Birth trauma - injury to neonate per 1,000 live births (excluding preterm and osteogenesis imperfecta births)
Table 6_2_2.3 Obstetric trauma per 1,000 vaginal deliveries without instrument assistance Obstetric trauma with 3rd or 4th degree lacerations per 1,000 vaginal deliveries without instrument assistance
Table 6_2_3.3 Obstetric trauma per 1,000 instrument-assisted deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 instrument-assisted vaginal deliveries
Table 6_2_4.3 Obstetric trauma per 1,000 cesarean deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 cesarean deliveries
Table 8_2_1.2 Pneumonia - recommended hospital care received Percentage of hospital patients with pneumonia who received recommended hospital care
Table 8_2_2.2 Pneumonia - blood cultures before antibiotics in hospital Percentage of hospital patients with pneumonia who had blood cultures collected before antibiotics were administered
Table 8_2_3.2 Pneumonia - antibiotics within 6 hours in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic dose within 6 hours of hospital arrival
Table 8_2_4.2 Pneumonia - recommended initial antibiotics in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic consistent with current recommendations
Table 8_2_5.2 Pneumonia - flu vaccination screening in hospital Percentage of hospital patients age 50 and over with pneumonia discharged during October-February who received influenza screening or vaccination
Table 8_2_6.2 Pneumonia - pneumococcal vaccination screening in hospital Percentage of hospital patients age 65 and over with pneumonia who received pneumococcal screening or vaccination
Table 8_2_7.3 Pneumonia deaths in hospital Deaths per 1,000 adult admissions with pneumonia as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Settings of Care: Ambulatory Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 13_1_1.7 Got routine appointments - adults on Medicare managed care Percentage of adults who had an appointment for routine health care in the last 12 months who got appointments for routine care as soon as wanted, Medicare managed care
Table 13_1_1.8 Got routine appointments - adults on Medicare fee for service Percentage of adults who had an appointment for routine health care in the last 12 months who got appointments for routine care as soon as wanted, Medicare fee for service
Table 13_1_3.7 Got appointment for illness/injury/condition - adults on Medicare managed care Percentage of adults who needed care right away for an illness, injury, or condition in the last 12 months who got care as soon as wanted, Medicare managed care
Table 13_1_3.8 Got appointment for illness/injury/condition - adults on Medicare fee for service Percentage of adults who needed care right away for an illness, injury, or condition in the last 12 months who got care as soon as wanted, Medicare fee for service
Table 14_1_1.7 Had good communication with providers - adults on Medicare managed care Percentage of adults who had a doctor's office or clinic visit in the last 12 months whose health providers listened carefully, explained things clearly, respected what they had to say, and spent enough time with them, Medicare managed care
Table 14_1_1.8 Had good communication with providers - adults on Medicare fee for service Percentage of adults who had a doctor's office or clinic visit in the last 12 months whose health providers listened carefully, explained things clearly, respected what they had to say, and spent enough time with them, Medicare fee for service
Table 14_1_11.7 Best rating for care - adults on Medicare managed care Percentage of adults who had a doctor's office or clinic visit in the last 12 months who gave a best rating for health care they received, Medicare managed care
Table 14_1_11.8 Best rating for care - adults on Medicare fee for service Percentage of adults who had a doctor's office or clinic visit in the last 12 months who gave a best rating for health care they received, Medicare fee for service
Table 1_1_1.3 Mammograms Percentage of women age 40 and over who received a mammogram in the last 2 years
Table 1_1_2.1 Breast cancer diagnosed at advanced stage Breast cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 women age 40 and over
Table 1_1_5.3 Breast cancer deaths Breast cancer deaths per 100,000 female population per year
Table 1_2_1.3 Pap tests Percentage of women age 18 and over who received a Pap smear within the last 3 years
Table 1_2_2.1 Cervical cancer diagnosed at advanced stage Cervical cancer diagnosed at advanced stage (all invasive tumors) per 100,000 women age 20 and over
Table 1_3_2.3 Colonoscopy, sigmoidoscopy, or proctoscopy Percentage of adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
Table 1_3_3.3 Fecal occult blood tests Percentage of adults age 50 and over who received a fecal occult blood test in the last 2 years
Table 1_3_4.1 Colorectal cancer diagnosed at advanced stage Colorectal cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 population age 50 and over
Table 1_3_6.3 Colorectal cancer deaths Colorectal cancer deaths per 100,000 population per year
Table 1_4_1.3 All cancer deaths All cancer deaths per 100,000 population per year
Table 1_4_2.3 Prostate cancer deaths Prostate cancer deaths per 100,000 male population per year
Table 1_4_3.3 Lung cancer deaths Lung cancer deaths per 100,000 population per year
Table 2_1_2.3 Diabetes hemoglobin A1c tests Percentage of adults age 40 and over with diagnosed diabetes who received a hemoglobin A1c measurement in the calendar year
Table 2_1_3.3 Diabetes eye exams Percentage of adults age 40 and over with diagnosed diabetes who received a dilated eye examination in the calendar year
Table 2_1_4.3 Diabetes foot exams Percentage of adults age 40 and over with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
Table 2_1_5.3 Diabetes flu shots Percentage of noninstitutionalized high-risk adults ages 18-64 with diabetes who had a flu shot in the last 12 months
Table 2_3_1.4 Avoidable hospitalizations - diabetes, uncomplicated Adult admissions for uncontrolled diabetes without complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.5 Avoidable adult hospitalizations - diabetes, short-term complications Adult admissions for diabetes with short-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.6 Avoidable pediatric hospitalizations - diabetes, short-term complications Pediatric admissions ages 6-17 for diabetes with short-term complications (excluding transfers from other institutions) per 100,000 population
Table 2_3_3.3 Avoidable hospitalizations - diabetes, long-term complications Adult admissions for diabetes with long-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 3_1_1.3 Dialysis and good urea reduction - Medicare Percentage of adult hemodialysis patients with adequate dialysis (urea reduction ratio 65% or greater), Medicare
Table 3_1_2.3 Dialysis and on kidney transplant list Percentage of dialysis patients under age 70 who were registered on a waiting list for transplantation
Table 3_1_3.3 Renal failure and kidney transplant Patients with treated chronic kidney failure who received a transplant within 3 years of date of renal failure
Table 3_1_4.1 Dialysis and survival - Medicare Ratio of observed deaths to expected deaths among Medicare hemodialysis patients
Table 4_1_3.3 Blood cholesterol testing Percentage of adults age 18 and over who had their blood cholesterol checked within the preceding 5 years
Table 4_3_4.1 Avoidable hospitalizations - heart failure Adult admissions for congestive heart failure (excluding patients with cardiac procedures, obstetric conditions, and transfers from other institutions) per 100,000 population
Table 5_2_4.3 HIV deaths HIV infection deaths per 100,000 population
Table 6_3_1.3 Children fully vaccinated Percentage of children ages 19-35 months who received all recommended vaccines (4:3:1:3:3)
Table 7_1_7.3 Suicide deaths Suicide deaths per 100,000 population
Table 8_1_1.3 Flu vaccine in past 12 months - high-risk, age 18-64 Percentage of adults ages 18-64 at high risk (e.g., COPD) who received an influenza vaccination in the last 12 months
Table 8_1_2.3 Flu vaccine in past 12 months - age 65 and over Percentage of adults age 65 and over who received an influenza vaccination in the last 12 months
Table 8_1_3.3 Avoidable hospitalizations - influenza Immunization-preventable influenza admissions ages 65 and over (excluding transfers from other institutions) per 100,000 population
Table 8_1_4.3 Pneumonia vaccine ever - high-risk, age 18-64 Percentage of high-risk people ages 18-64 who ever received a pneumococcal vaccination
Table 8_1_5.3 Pneumonia vaccine ever - age 65 plus Percentage of adults age 65 and over who ever received a pneumococcal vaccination
Table 8_3_2.7 Avoidable adult hospitalizations - asthma Adult asthma admissions ages 18 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions, and transfers from other institutions) per 100,000 population
Table 8_3_2.8 Avoidable pediatric hospitalizations - asthma Pediatric asthma admissions ages 2-17 (excluding patients with cystic fibrosis or anomalies of the respiratory system and transfers from other institutions) per 100,000 population
Table 8_3_2.9 Avoidable hospitalizations for seniors - asthma Adult asthma admissions age 65 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions and transfers from other institutions) per 100,000 population
Table 9_1_1.3 Smoking cessation advice Percentage of adult current smokers who received advice to quit smoking
Settings of Care: Nursing Home Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 10_1_3.3 Nursing home long-stay residents - with declining mobility Percentage of long-stay nursing home residents whose ability to move about in and around their room got worse
Table 10_1_5.3 Nursing home long-stay residents - with increased need for help Percentage of long-stay nursing home residents whose need for help with daily activities has increased
Table 10_1_6.3 Nursing home long-stay residents - bed/chair bound Percentage of long-stay nursing home residents who spend most of their time in bed or in a chair
Table 11_1_1.3 Nursing home long-stay residents - with moderate to severe pain Percentage of long-stay nursing home residents who have moderate to severe pain
Table 11_1_11.3 Nursing home long-stay residents - given flu vaccine Percentage of long-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_12.3 Nursing home short-stay residents - given flu vaccine Percentage of short-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_13.3 Nursing home long-stay residents - given pneumococcal vaccine Percentage of long-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 11_1_14.3 Nursing home short-stay residents - given pneumococcal vaccine Percentage of short-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 11_1_15.3 Nursing home short-stay residents - with moderate to severe pain Percentage of short-stay nursing home residents who had moderate to severe pain
Table 11_1_17.3 Nursing home long-stay residents - physically restrained Percentage of long-stay nursing home residents who were physically restrained
Table 11_1_18.3 Nursing home long-stay residents - high-risk with pressure sores Percentage of high-risk long-stay nursing home residents who have pressure sores
Table 11_1_19.3 Nursing home long-stay residents - low-risk with pressure sores Percentage of low-risk long-stay nursing home residents who have pressure sores
Table 11_1_2.3 Nursing home long-stay residents - with urinary tract infections Percentage of long-stay nursing home residents with a urinary tract infection
Table 11_1_20.3 Nursing home short-stay residents - with pressure sores Percentage of short-stay nursing home residents with pressure sores
Table 11_1_21.3 Nursing home short-stay residents - with delirium Percentage of short-stay nursing home residents with delirium
Table 11_1_3.3 Nursing home long-stay residents - more depressed or anxious Percentage of long-stay nursing home residents who are more depressed or anxious
Table 11_1_4.3 Nursing home long-stay residents - low-risk with less control of bowels or bladder Percentage of low-risk long-stay nursing home residents who lose control of their bowels or bladder
Table 11_1_5.3 Nursing home long-stay residents - low-risk with urinary catheter left in Percentage of low-risk long-stay nursing home residents with a catheter inserted and left in their bladder
Table 11_1_6.3 Nursing home long-stay residents - with too much weight loss Percentage of long-stay nursing home residents who lose too much weight
Settings of Care: Home Health Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 10_1_2.3 Home health care - improved mobility Percentage of home health care patients who get better at walking or moving around
Table 10_1_4.3 Home health care - improved transferring Percentage of home health care patients who get better at getting in and out of bed
Table 10_1_7.3 Home health care - improved bathing Percentage of home health care patients who get better at bathing
Table 10_1_8.3 Home health care - improved oral drug management Percentage of home health care patients who get better at taking their medicines correctly (by mouth)
Table 11_1_10.3 Home health care - home after home health care Percentage of home health care patients who stay at home after an episode of home health care ends
Table 11_1_16.3 Home health care - improved pain management when mobile Percentage of home health care patients who have less pain when moving around
Table 11_1_22.3 Home health care - hospitalization Percentage of home health care patients who had to be admitted to the hospital
Table 11_1_7.3 Home health care - improved breathing Percentage of home health care patients who are short of breath less often
Table 11_1_8.3 Home health care - improved bladder control Percentage of home health care patients whose bladder control improves
Table 11_1_9.3 Home health care - plus urgent care Percentage of home health care patients who need urgent, unplanned medical care
Care by Clinical Area: Cancer Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 1_1_1.3 Mammograms Percentage of women age 40 and over who received a mammogram in the last 2 years
Table 1_1_2.1 Breast cancer diagnosed at advanced stage Breast cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 women age 40 and over
Table 1_1_5.3 Breast cancer deaths Breast cancer deaths per 100,000 female population per year
Table 1_2_1.3 Pap tests Percentage of women age 18 and over who received a Pap smear within the last 3 years
Table 1_2_2.1 Cervical cancer diagnosed at advanced stage Cervical cancer diagnosed at advanced stage (all invasive tumors) per 100,000 women age 20 and over
Table 1_3_2.3 Colonoscopy, sigmoidoscopy, or proctoscopy Percentage of adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
Table 1_3_3.3 Fecal occult blood tests Percentage of adults age 50 and over who received a fecal occult blood test in the last 2 years
Table 1_3_4.1 Colorectal cancer diagnosed at advanced stage Colorectal cancer diagnosed at advanced stage (regional and distant SEER summary stage) per 100,000 population age 50 and over
Table 1_3_6.3 Colorectal cancer deaths Colorectal cancer deaths per 100,000 population per year
Table 1_4_1.3 All cancer deaths All cancer deaths per 100,000 population per year
Table 1_4_2.3 Prostate cancer deaths Prostate cancer deaths per 100,000 male population per year
Table 1_4_3.3 Lung cancer deaths Lung cancer deaths per 100,000 population per year
Care by Clinical Area: Diabetes Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 2_1_2.3 Diabetes hemoglobin A1c tests Percentage of adults age 40 and over with diagnosed diabetes who received a hemoglobin A1c measurement in the calendar year
Table 2_1_3.3 Diabetes eye exams Percentage of adults age 40 and over with diagnosed diabetes who received a dilated eye examination in the calendar year
Table 2_1_4.3 Diabetes foot exams Percentage of adults age 40 and over with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
Table 2_1_5.3 Diabetes flu shots Percentage of noninstitutionalized high-risk adults ages 18-64 with diabetes who had a flu shot in the last 12 months
Table 2_3_1.4 Avoidable hospitalizations - diabetes, uncomplicated Adult admissions for uncontrolled diabetes without complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.5 Avoidable adult hospitalizations - diabetes, short-term complications Adult admissions for diabetes with short-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Table 2_3_2.6 Avoidable pediatric hospitalizations - diabetes, short-term complications Pediatric admissions ages 6-17 for diabetes with short-term complications (excluding transfers from other institutions) per 100,000 population
Table 2_3_3.3 Avoidable hospitalizations - diabetes, long-term complications Adult admissions for diabetes with long-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population
Care by Clinical Area: Heart Disease Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 4_1_3.3 Blood cholesterol testing Percentage of adults age 18 and over who had their blood cholesterol checked within the preceding 5 years
Table 4_2_1.2 Heart attack - ACEI or ARB at discharge Percentage of hospital patients with heart attack and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_2_2.3 Heart attack deaths in hospital Deaths per 1,000 adult admissions with acute myocardial infarction (AMI) as principal diagnosis (excluding transfers to another hospital)
Table 4_3_1.2 Heart failure - recommended hospital care received Percentage of hospital patients with heart failure who received recommended hospital care (evaluation of left ventricular ejection fraction and ACE inhibitor or ARB prescription at discharge, if indicated, for left ventricular systolic dysfunction)
Table 4_3_2.2 Heart failure - evaluation of ejection fraction test in hospital Percentage of hospital patients with heart failure who received an evaluation of left ventricular ejection fraction
Table 4_3_3.2 Heart failure - ACEI/ARB at discharge Percentage of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
Table 4_3_4.1 Avoidable hospitalizations - heart failure Adult admissions for congestive heart failure (excluding patients with cardiac procedures, obstetric conditions, and transfers from other institutions) per 100,000 population
Table 4_3_5.3 Congestive heart failure deaths in hospital Deaths per 1,000 adult hospital admissions with congestive heart failure as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Table 4_4_1.3 Abdominal aortic aneurysm repair deaths in hospital Deaths per 1,000 adult admissions with abdominal aortic aneurysm (AAA) repair (excluding obstetric admissions and transfers to another hospital)
Table 4_4_2.3 Coronary artery bypass graft deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with coronary artery bypass graft (excluding obstetric admissions and transfers to another hospital)
Table 4_4_3.3 Angioplasty deaths in hospital Deaths per 1,000 adult admissions ages 40 and over with percutaneous transluminal coronary angioplasties (excluding obstetric admissions and transfers to another hospital)
Care by Clinical Area: Maternal and Child Health Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 6_2_1.3 Birth trauma injury to neonate per 1,000 live births Birth trauma - injury to neonate per 1,000 live births (excluding preterm and osteogenesis imperfecta births)
Table 6_2_2.3 Obstetric trauma per 1,000 vaginal deliveries without instrument assistance Obstetric trauma with 3rd or 4th degree lacerations per 1,000 vaginal deliveries without instrument assistance
Table 6_2_3.3 Obstetric trauma per 1,000 instrument-assisted deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 instrument-assisted vaginal deliveries
Table 6_2_4.3 Obstetric trauma per 1,000 cesarean deliveries Obstetric trauma with 3rd or 4th degree lacerations per 1,000 cesarean deliveries
Table 6_3_1.3 Children fully vaccinated Percentage of children ages 19-35 months who received all recommended vaccines (4:3:1:3:3)
Care by Clinical Area: Respiratory Diseases Care Measures
NHQR Table Short Measure Title Full Measure Title
Table 8_1_1.3 Flu vaccine in past 12 months - high-risk, age 18-64 Percentage of adults ages 18-64 at high risk (e.g., COPD) who received an influenza vaccination in the last 12 months
Table 8_1_2.3 Flu vaccine in past 12 months - age 65 and over Percentage of adults age 65 and over who received an influenza vaccination in the last 12 months
Table 8_1_3.3 Avoidable hospitalizations - influenza Immunization-preventable influenza admissions ages 65 and over (excluding transfers from other institutions) per 100,000 population
Table 8_1_4.3 Pneumonia vaccine ever - high-risk, age 18-64 Percentage of high-risk people ages 18-64 who ever received a pneumococcal vaccination
Table 8_1_5.3 Pneumonia vaccine ever - age 65 plus Percentage of adults age 65 and over who ever received a pneumococcal vaccination
Table 8_2_1.2 Pneumonia - recommended hospital care received Percentage of hospital patients with pneumonia who received recommended hospital care
Table 8_2_2.2 Pneumonia - blood cultures before antibiotics in hospital Percentage of hospital patients with pneumonia who had blood cultures collected before antibiotics were administered
Table 8_2_3.2 Pneumonia - antibiotics within 6 hours in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic dose within 6 hours of hospital arrival
Table 8_2_4.2 Pneumonia - recommended initial antibiotics in hospital Percentage of hospital patients with pneumonia who received the initial antibiotic consistent with current recommendations
Table 8_2_5.2 Pneumonia - flu vaccination screening in hospital Percentage of hospital patients age 50 and over with pneumonia discharged during October-February who received influenza screening or vaccination
Table 8_2_6.2 Pneumonia - pneumococcal vaccination screening in hospital Percentage of hospital patients age 65 and over with pneumonia who received pneumococcal screening or vaccination
Table 8_2_7.3 Pneumonia deaths in hospital Deaths per 1,000 adult admissions with pneumonia as principal diagnosis (excluding obstetric admissions and transfers to another hospital)
Table 8_3_2.7 Avoidable adult hospitalizations - asthma Adult asthma admissions ages 18 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions, and transfers from other institutions) per 100,000 population
Table 8_3_2.8 Avoidable pediatric hospitalizations - asthma Pediatric asthma admissions ages 2-17 (excluding patients with cystic fibrosis or anomalies of the respiratory system and transfers from other institutions) per 100,000 population
Table 8_3_2.9 Avoidable hospitalizations for seniors - asthma Adult asthma admissions age 65 and over (excluding patients with cystic fibrosis or anomalies of the respiratory system, obstetric admissions and transfers from other institutions) per 100,000 population
Clinical Preventive Services
NHQR Table Short Measure Title Full Measure Title
Table 11_1_11.3 Nursing home long-stay residents - given flu vaccine Percentage of long-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_12.3 Nursing home short-stay residents - given flu vaccine Percentage of short-stay nursing home residents given influenza vaccination during the flu season
Table 11_1_13.3 Nursing home long-stay residents - given pneumococcal vaccine Percentage of long-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 11_1_14.3 Nursing home short-stay residents - given pneumococcal vaccine Percentage of short-stay nursing home residents who were assessed and given pneumococcal vaccination
Table 1_1_1.3 Mammograms Percentage of women age 40 and over who received a mammogram in the last 2 years
Table 1_2_1.3 Pap tests Percentage of women age 18 and over who received a Pap smear within the last 3 years
Table 1_3_2.3 Colonoscopy, sigmoidoscopy, or proctoscopy Percentage of adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
Table 1_3_3.3 Fecal occult blood tests Percentage of adults age 50 and over who received a fecal occult blood test in the last 2 years
Table 4_1_3.3 Blood cholesterol testing Percentage of adults age 18 and over who had their blood cholesterol checked within the preceding 5 years
Table 8_1_1.3 Flu vaccine in past 12 months - high-risk, age 18-64 Percentage of adults ages 18-64 at high risk (e.g., COPD) who received an influenza vaccination in the last 12 months
Table 8_1_2.3 Flu vaccine in past 12 months - age 65 and over Percentage of adults age 65 and over who received an influenza vaccination in the last 12 months
Table 8_1_4.3 Pneumonia vaccine ever - high-risk, age 18-64 Percentage of high-risk people ages 18-64 who ever received a pneumococcal vaccination
Table 8_1_5.3 Pneumonia vaccine ever - age 65 plus Percentage of adults age 65 and over who ever received a pneumococcal vaccination
Table 9_1_1.3 Smoking cessation advice Percentage of adult current smokers who received advice to quit smoking

Appendix II: U.S. Census Region and Division Definitions Used in the 2009 State Snapshots

Region I: Northeast
(includes Divisions 1-2)
Region II: Midwest
(includes Divisions 3-4)
Region III: South
(includes Divisions 5-7)
Region IV: West
(includes Divisions 8-9)
Division 1 Division 2 Division 3 Division 4 Division 5 Division 6 Division 7 Division 8 Division 9
New England Middle Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific
6 States 3 States 5 States 7 States 9 States 4 States 4 States 8 States 5 States
Connecticut New Jersey Illinois Iowa Delaware Alabama Arkansas Arizona Alaska
Maine New York Indiana Kansas Washington, D.C. Kentucky Louisiana Colorado California
Massachusetts Pennsylvania Michigan Minnesota Florida Mississippi Oklahoma Idaho Hawaii
New Hampshire   Ohio Missouri Georgia Tennessee Texas Montana Oregon
Rhode Island   Wisconsin Nebraska Maryland     Nevada Washington
Vermont     North Dakota North Carolina     New Mexico  
      South Dakota South Carolina     Utah  
        Virginia     Wyoming  
        West Virginia        

Acknowledgments

The 2009 State Snapshots were developed from the 2009 National Healthcare Quality Report through a team effort including the Agency for Healthcare Research and Quality(Ernest Moy, Karen Ho, Karen Migdail, Morgan Liscinsky, Biff LeVee, Doreen Bonnett, Roxanne Andrews), Thomson Reuters (Healthcare) Inc. (Rosanna Coffey, Jillian Dudek, Elizabeth Stranges, Minya Sheng, Susan Raetzman), ML Barrett, Inc. (Marguerite Barrett), Social & Scientific Systems (Paul Gorrell, Laurie MacCallum, Anil Koninty, Jeffrey Schinckle, Nathalie Fike, Janette Walters).

These State Snapshots have built on work of earlier years and contributions of the above individuals and others: Agency for Healthcare Research and Quality (Edward Kelley, Dwight McNeill, Jeffrey Brady, Marybeth Farquhar, DonnaRae Castillo, David Atkins, Christine Williams, Sandi Isaacson, Mary Nix, Kathy Crosson, Gerri Michael-Dyer, Marjorie Shofer), Thomson Reuters (Healthcare) Inc. (Craig Hunter, Julia Nisbet, Jim Blakley, Mirjana Milenkovic, Kathy Hickey, Angela Fulmer), Social & Scientific Systems (Dale Byington, Mikki Hall, Debbie Machen), ECRI (Vivian Coates, Steve Rhoads, Evan LeGault, and Pamela Nash),Kenney IS Consulting (Tim Kenney), the National Governors Association, the National Conference of State Legislators, the Council of State Governments, the Association of State and Territorial Health Officials, and the Federal Interagency Workgroup for the National Healthcare Quality Report. Additional support was provided by AcademyHealth (Enrique Martinez-Vidal, Amanda Brodt), the Lewin Group (Tim Dall, Sarah Stout), and the Madison Design Group (Russ Surles, Anne Kerns, Darin Ruchirek).

Endnotes

1 Bureau of Labor Statistics. Quarterly Census of Employment and Wages, 2004. Available at: http://www.bls.gov/cew/home.htm

2 Bureau of Labor Statistics and U.S. Census Bureau. Current Population Survey, Annual Social and Economic Supplement, 2003, 2004, 2005. Available at: Bureau of Labor Statistics and U.S. Census Bureau

3 U.S. Census Bureau. Census 2000 EEO Data Tool. Available at: http://www.census.gov/eeo2000/index.html

4 U.S. Census Bureau. Population estimates by State, 2004. Available at: http://www.census.gov/popest/states/asrh/SC-EST2004-03.html

5 Claritas, Inc. 2001 Demographic Data and the Claritas Update: Demographics Methodology. San Diego, Claritas: 2001. 

6 More information on data from the Medical Expenditure Panel Survey is available at: http://www.meps.ahrq.gov/mepsweb/data_stats/data_overview.jsp

7 U.S. Census Bureau. Table ST-F1-2000: Average number of children per family and per family with children, by State: 2000 Census. Available at:http://www.census.gov/population/socdemo/hh-fam/tabST-F1-2000.pdf

8 Centers for Disease Control and Prevention, National Center for Health Statistics. National Health Interview Survey data, 1998, 1999, 2000. Available at:http://www.cdc.gov/nchs/nhis.htm

9 The Health Benefits Simulation Model (HBSM) is a microsimulation model of the U.S. health care system. HBSM is based upon a representative sample of households in the United States, which includes information on the economic and demographic characteristics of these individuals as well as their utilization and expenditures for health care. The HBSM household data are based on AHRQ's 1999 through 2001 MEPS, which were used together with the March 2004 Current Population Survey. The data were adjusted to show the amount of health spending by type of service and source of payment as estimated by the Office of the Actuary of the Centers for Medicare & Medicaid Services (CMS) and various agencies. More information on the HBSM data and methods are available in Sheils J, Haught R. Covering America: cost and coverage analysis of ten proposals to expand health insurance coverage. Appendix A: Health Benefits Simulation Model (HBSM): uniform methodology and assumptions. October 1, 2003. Available at:http://www.rwjf.org/files/research/costCoverageMethodology.pdf

10 ACCRA Cost of Living Index. Arlington, VA: Council for Community and Economic Research, 2005. Available at: http://www.coli.org/

11 National Association of State Budget Officers. Table 19: Total State employee health expenditures, fiscal 2002 and 2003. In: 2002-2003 State Health Expenditure Report. New York, NY: Milbank Memorial Fund, 2005. Available at: http://www.milbank.org/reports/05NASBO/nasbotable19.pdf

12 Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey data. Available at: http://www.cdc.gov/nchs/nhanes.htm

13 Shojania KG, Ranji SR, Shaw LK, et al. Diabetes mellitus care. In: Shojania KG, McDonald KM, Wachter RM, et al. Closing the quality gap: a critical analysis of quality improvement strategies. Vol. 2. Rockville, MD: Agency for Healthcare Research and Quality; 2004. Technical Review 9. AHRQ Publication No. 04-0051-2. Available at:http://www.ahrq.gov/downloads/pub/evidence/pdf/qualgap2/qualgap2.pdf

14 The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. New Engl J Med 1993;329(14):977-986. 

15 Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes. UKPDS 38. UK Prospective Diabetes Study Group. Br Med J1998;317(7160):703-13. 

16 Gilmer TP, O'Conner PJ, Rush WA, et al. Predictors of health care costs in adults with diabetes. Diabetes Care 2005;28:59-64. 

17 Testa MA, Simonson, DC. Health economic benefits and quality of life during improved glycemic control in patients with type 2 diabetes mellitus: a randomized, controlled, double-blind trial. JAMA 1998;280:1490-96. 

18 Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System, 2004-2006. Available at: http://www.cdc.gov/brfss.

Internet Citation

2009 State Snapshots: Methods. Derived from 2009 National Healthcare Quality Report. March 2010. Rockville, MD: Agency for Healthcare Research and Quality.http://statesnapshots.ahrq.gov/.

Focus on Diabetes

Source: http://statesnapshots.ahrq.gov/snaps09/diabetes.jsp?menuId=22&state=

and selecting a State: Alabama See Below

Alabama

Source: http://statesnapshots.ahrq.gov/snaps09/diabetes.jsp?menuId=22&state=AL

Focus on Diabetes

Diabetes is a serious chronic illness that affects more than 18 million Americans, is growing, and is linked to the obesity epidemic. This section includes data on prevalence, care quality, costs, potential savings from diabetes care quality improvement efforts, and disparities in treatment.

Take a closer look at Alabama's performance in the treatment of patients with diabetes across these areas: MY NOTE: See these links below.

Explore diabetes quality improvement resources and innovations: MY NOTE: The two links below are broken, but the third link is amazing!  See Below.

Diabetes Cost Calculator for Employers

Source: http://archive.ahrq.gov/populations/diabcostcalc/

This information is for reference purposes only. It was current when produced and may now be outdated. Archive material is no longer maintained, and some links may not work. Persons with disabilities having difficulty accessing this information should contact us at: https://info.ahrq.gov. Let us know the nature of the problem, the Web address of what you want, and your contact information.

Please go to http://www.ahrq.gov for current information.


The Agency for Healthcare Research and Quality (AHRQ) has created an evidence-based tool that employers can use to estimate how much diabetes costs them and the potential savings that would result from better management of diabetes. 

The calculator was developed at the request of members of the Mid-Atlantic Business Group on Health and in partnership with the National Business Coalition on Health (NBCH).


Screen shot of diabetes calculator showing sample results charts.

Based on an employer's industry, location, and firm size, the calculator estimates:

  • Number of people with diabetes.
  • Annual diabetes-related medical costs.
  • Annual diabetes-related productivity costs.
  • Potential savings associated with better management of diabetes.

Cost savings estimates are based on evidence that better management of diabetes (e.g., improved blood glucose control) is linked to lower health care costs. The calculator draws on the best available evidence from peer-reviewed medical journals and trusted data sources such as the National Health and Nutrition Examination Survey, National Health Interview Survey, U.S. Census Bureau, U.S. Bureau of Labor Statistics, and AHRQ Medical Expenditure Panel Survey.

The calculator is:

  • Evidence based. It combines findings from literature, analysis by The Lewin Group, and expert opinion.
  • Easy to use. It requires minimal information from the user.
  • Individualized. For most user inputs, the calculator provides default data based on State, industry, and firm size.
  • Adaptable. Users can choose to model projected savings based on several interventional scenarios.
  • Scalable. Users can adjust potential savings based on the percentage of covered lives likely to receive an intervention or achieve a target.
  • Transparent. Assumptions and sources are stated.
Screen shot of diabetes calculator with information about sample Company A.

Select to access the Calculator (Excel® file, 2.4 MB; Plugin Software Help). MY NOTE: I download this Excel and was amazed!

Select for instructions for downloading and using the calculator (PDF file, 280 KB; Plugin Software Help). MY NOTE: I download this PDF and was amazed!

 

 

Prevalence

Diabetes is a disease in which the body does not produce or properly use insulin. Persons with diabetes are at risk of serious complications and premature death resulting from high levels of blood glucose. The map below shows how the prevalence of diabetes varies by State. This information was collected by the Behavioral Risk Factor Surveillance System (BRFSS).

Adult Self-Reported Lifetime Diabetes Prevalence for Each State in 2008.

Adult Self-Reported Lifetime Diabetes Prevalence for Each State in 2008. Prevalence is indicated by four quartiles: red is the first quartile (highest prevalence), yellow is the second quartile (second highest prevalence), blue is the third quartile (second lowest prevalence), and green is the last quartile (lowest prevalence).
Adult Self-Reported Lifetime Diabetes Prevalence for Each State in 2008. The prevalence is highest (defined by the first quartile) in the following States: Alabama, Georgia, Indiana, Kentucky, Louisiana, Mississippi, Ohio, Oklahoma, South Carolina, Tennessee, Texas, and West Virginia. The prevalence is second highest (defined by the second quartile) in the following States: Arkansas, California, Delaware, Florida, Illinois, Maine, Maryland, Michigan, Missouri, Nevada, New Jersey, New York, North Carolina, and Pennsylvania. The prevalence is second lowest (defined by the third quartile) in the following States: Arizona, District of Columbia, Hawaii, Kansas, Massachusetts, Nebraska, New Hampshire, New Mexico, North Dakota, Rhode Island, Virginia, Wisconsin, and Wyoming. The prevalence is lowest (defined by the last quartile) in the following States: Alaska, Colorado, Connecticut, Idaho, Iowa, Minnesota, Montana, Oregon, South Dakota, Utah, Vermont, and Washington.
Quality of Care: Processes of Care

What Is the Diabetes Process-of-Care Quality Performance Compared to All States?

How Has That Performance Changed?

Compared to all States, for the most recent data year, the performance for Alabama for diabetes care process measures is in the very weak range. For the baseline year, performance is in the average range.
The position of the solid arrow represents the State's performance meter score for the most recent data year, while the dashed arrow represents the same for the baseline year. The most recent data year and baseline years are defined in the All-State Data Table for All Measures.

 

 

 


The meter represents Alabama's balance of below average, average, and above average measures compared to all States. The performance meter has five categories: very weak, weak, average, strong, and very strong. An arrow pointing to "very weak" means all or nearly all included measures for a State are below average within a given data year. An arrow pointing to "very strong" indicates that all or nearly all available measures for a State are above average within a given data year. A solid arrow describes results for the most recent data year; a dashed arrow describes the baseline year. A missing arrow means there were insufficient data to create the summary measure for this State. Compared to all States, for the most recent data year, the performance for Alabama for diabetes care process measures is in the very weak range. For the baseline year, performance is in the average range.

Quality of Care: Outcomes of Care

How Do Diabetes Care Outcomes in Alabama Compare to East South Central States and All States?

The State's performance on diabetes care outcomes is assessed through inpatient admissions, some portion of which might be avoidable with better access to excellent ambulatory care in the State. When the State's number of admissions is higher than the Nation's, reductions in avoidable hospitalizations should be feasible. These measures are from the most recent two data years of the Healthcare Cost and Utilization Project (HCUP).

MY NOTE: This graphic links to the table below.

Hospitalizations for Complications Related to Diabetes per 100,000 People in Alabama, 2005 and 2006.1

Hospitalizations for Complications Related to Diabetes per 100,000 People in Alabama, 2005 and 2006.
Hospitalizations for Complications Related to Diabetes per 100,000 People in Alabama, 2005 and 2006. Bar chart. Year 2005: No information for Alabama; South 277; U.S. 234. Year 2006: No information for Alabama; South 273; U.S. 244.

1 Four diabetes complications-related admission measures are combined: Lower Extremity AmputationsUncontrolledLong-Term Complications, and Short-Term Complications.

Table

Source: http://statesnapshots.ahrq.gov/snaps09/diab_outcome_table.jsp?menuId=26&state=AL

MY NOTE: This table can be copied to a spreadsheet per the Federal Digital Government Strategy I set the Table Properties to Fxed from Flexible and had to work with the column labels for use in Spotfire.

Specific Quality Measures and Metrics that Constitute the Hospitalizations for Complications Related to Diabetes per 100,000 People, 2006 and 2005

State Diabetes hospitalizations per 100,000 population1 Lower extremity amputations among patients with diabetes per 100,000 population, age 18 years and older2 Admissions for uncontrolled diabetes without complication per 100,000 population, age 18 years and older3 Admissions for diabetes with long-term complications per 100,000 population, age 18 years and older4 Admissions for diabetes with short-term complications per 100,000 population, age 18 years and older4
Adjusted Rate Adjusted Rate Adjusted Rate Adjusted Rate Adjusted Rate
2006 Estimate 2005 Estimate 2006 Estimate 2005 Estimate 2006 Estimate 2005 Estimate 2006 Estimate 2005 Estimate 2006 Estimate 2005 Estimate
Total U.S. 243.6 234.0 35.4 34.9 21.6 21.6 126.9 122.4 59.6 56.3
Northeast 285.6 239.3 40.2 35.1 29.3 29.3 156.7 131.5 59.4 50.7
Midwest 219.0 216.0 31.7 31.1 19.4 19.4 111.6 110.5 56.3 53.8
South 272.5 277.0 41.1 42.1 25.5 25.5 136.4 140.9 69.5 68.1
West 184.1 177.2 25.5 26.9 10.7 10.7 100.3 95.7 47.6 44.8
Arizona 205.9 213.9 27.1 29.6 13.8 13.8 109.4 115.5 55.6 55.8
Arkansas 265.9 265.0 31.8 30.5 32.0 32.0 124.9 121.8 77.2 78.7
California 198.0 198.5 30.3 31.4 11.4 11.4 111.2 110.2 45.2 44.6
Colorado 147.6 143.1 21.7 22.5 6.0 6.0 72.8 71.5 47.1 44.1
Connecticut 212.5 197.6 37.8 36.6 8.4 8.4 117.1 104.0 49.2 50.9
Florida 233.2 236.8 32.3 32.4 28.0 28.0 115.6 119.8 57.4 57.2
Georgia 263.2 270.1 41.8 43.4 23.2 23.2 125.4 131.8 72.8 70.9
Hawaii 158.4 173.3 36.7 39.0 4.4 4.4 80.6 89.3 36.8 37.0
Illinois 235.9 240.0 29.8 31.8 29.9 29.9 124.4 125.0 51.8 51.3
Indiana 238.9 233.4 35.5 36.8 22.0 22.0 120.6 116.1 60.7 58.7
Iowa 158.4 148.5 24.3 23.6 12.5 12.5 75.7 71.4 45.9 40.9
Kansas 221.9 211.5 26.8 24.7 28.7 28.7 111.0 106.8 55.4 50.1
Kentucky 250.8 270.9 33.2 36.8 28.4 28.4 122.4 132.4 66.7 72.3
Maine 139.1 No Data 21.0 No Data 7.3 7.3 72.1 No Data 38.7 No Data
Maryland 255.7 232.1 35.5 36.7 18.4 18.4 136.7 120.5 65.1 57.7
Massachusetts 201.7 194.7 32.8 32.2 8.6 8.6 115.6 109.6 44.7 45.1
Michigan 213.8 215.9 31.0 32.4 12.0 12.0 109.5 110.2 61.3 61.5
Minnesota 156.6 159.7 20.8 22.0 9.5 9.5 86.9 89.1 39.3 38.6
Missouri 240.6 247.2 37.3 39.6 19.5 19.5 121.7 123.2 62.1 61.2
Nebraska 143.7 152.4 21.0 26.7 10.1 10.1 77.6 80.1 35.0 36.2
Nevada 198.4 206.8 21.3 24.4 13.3 13.3 97.0 98.9 66.8 70.3
New Hampshire 150.0 145.7 25.6 23.1 7.1 7.1 78.0 76.9 39.2 40.6
New Jersey 259.3 260.5 34.4 34.7 30.1 30.1 142.0 143.0 52.8 54.0
New York 278.7 276.8 34.8 36.9 38.0 38.0 152.4 148.7 53.5 53.1
North Carolina 266.3 263.7 42.2 43.8 21.9 21.9 133.4 129.6 68.7 68.4
Ohio 254.1 247.2 34.8 36.8 20.1 20.1 128.6 123.7 70.5 66.3
Oklahoma 239.0 242.5 30.3 31.4 25.4 25.4 115.2 116.0 68.2 70.6
Oregon 144.5 141.7 24.0 22.7 5.3 5.3 70.0 68.2 45.2 45.1
Rhode Island 195.4 197.9 32.6 30.0 10.7 10.7 102.8 112.1 49.3 44.7
South Carolina 289.7 305.5 46.1 48.7 22.6 22.6 145.9 156.1 75.1 74.9
Tennessee 274.6 284.4 37.9 39.0 27.6 27.6 129.8 135.1 79.4 79.4
Texas 277.5 278.0 47.5 49.5 23.0 23.0 147.8 149.6 59.1 55.9
Utah 130.6 126.7 18.4 17.2 2.8 2.8 71.0 66.0 38.4 39.6
Vermont 115.7 107.6 21.9 19.3 2.6 2.6 62.7 56.0 28.4 28.4
Virginia 225.3 No Data 34.8 No Data 13.5 13.5 120.6 No Data 56.5 No Data
Washington 145.7 136.8 22.5 21.0 6.3 6.3 70.2 67.0 46.7 41.5
West Virginia 267.9 284.9 30.7 32.9 23.3 23.3 128.8 142.1 85.1 83.2
Wisconsin 173.0 175.5 30.9 30.7 8.7 8.7 87.3 92.1 46.1 43.8


1 Sum of four measures. 

2 Excluding trauma, obstetric admissions, and transfers from other institutions. 

3 Excluding obstetric and neonatal admissions and transfers from other institutions. 

4 Excluding obstetric admissions and transfers from other institutions.

Quality Improvement: Lives and Expenses

Lives and Expenses

2008 Estimated Share (%) of Health Expenditures on State Government Employees That Relates to Diabetes Care, Compared to East South Central States and All States.

Alabama's Estimated Share of Health Expenditures on State Government Employees that Relates to Diabetes Care, 2008.
States are significant purchasers of health care. Based on an estimated 7,200 Alabama government employees and their dependents who likely had diabetes (diagnosed and undiagnosed), Alabama is estimated in 2008 to have spent $28,240,000, or 7.4%, of State government employee health dollars on care for people diagnosed with diabetes.

 

Alabama's Estimated Share of Health Expenditures on State Government Employees that Relates to Diabetes Care, 2008. Bar chart. Alabama 7.4; East South Central States 5.9; All States 3.7.

These percentages:

  • Are rough estimates of the share of expenses attributed to diabetes care and are based on the Diabetes Cost Calculator and State health care expenditures on State government employees and dependents.
  • Are missing for the State when data were unreliable.
Quality Improvement: Excess Costs of Diabetes

2008 Excess Costs Associated With Diabetes for State Government Employees

HbA1c is a marker of blood glucose levels and is used as an indicator of the quality of diabetes care. Diabetes quality improvement programs have produced reductions in HbA1c an average of 0.5% across a population of participants. The best resultsreductions of 1.0%, occur when intensive disease management programs coordinate assessment, treatment, and referral with primary care.

Average Results
If Alabama's employees' and dependents' HbA1c levels were reduced by 0.5%, then spending on diabetes care of State government employees might be reduced by about $700,000 per year. In addition, excess costs due to lost productivity among employees with diabetes could be reduced by $5,700,000 a year.

Best Results
If Alabama's employees' and dependents' HbA1c levels were reduced by 1.0%, then spending on diabetes care of State government employees might be reduced by about $1,200,000 per year. In addition, excess costs due to lost productivity among employees with diabetes could be reduced by $10,500,000 a year.

Note—These savings:

  • May not be realized for years.
  • Do not include the cost of quality improvement programs that would be needed to achieve a 0.5% or 1.0% reduction, respectively. Depending on intensity, a diabetes disease management program costs between $20 and $60 per participant per month.
  • Are most likely for a State that has not yet instituted a quality improvement or disease management program for its State government employees.

Other things to consider:

  • While a quality improvement or disease management program should reduce the use of the most expensive services (e.g., emergency rooms and inpatient stays), doctor visits and prescription drug costs would probably increase. The calculation above does account for such changes.
  • Serious consequences of diabetes—risk of heart attack, stroke, and amputations—can be reduced with excellent blood glucose control. The calculation above may not fully account for long-term savings associated with avoiding these serious complications.
  • States with higher rates of emergency room use and inpatient stays are more likely to reduce diabetes care costs with a quality improvement or disease management program. Other factors to consider include patient education on how to maintain blood glucose control, patient adherence, and access to care.
  • Quality improvement programs should be designed to deal with all problems associated with diabetes (including potential heart attack and stroke):
    • Test and control HbA1c levels
    • Conduct physical exams for retina and feet
    • Test and control blood pressure
    • Test and control cholesterol
    • Vaccinate for influenza
  • For more information on diabetes quality of care and how States can establish and lead a quality improvement program on diabetes care statewide, go to Diabetes Care Quality Improvement: A Resource Guide for State Action.

Methods—The calculations above are based on:

  • A review of the clinical literature demonstrating the effects of diabetes quality improvement programs on average HbA1c levels (Shojania et al., 2004).
  • A review of health services research showing that lower HbA1c levels are associated with lower costs of diabetes care (Gilmer et al., 2005).
  • A calculator developed for AHRQ that incorporates those potential outcomes, possible cost savings, national HbA1c levels, and characteristics of Alabama's government employees (For information on the calculator, select Methods).
Disparities in Treatment: By Income

Disparities in Treatment: By Income

The map below shows whether the gap in the rate of HbA1c testing among people with diabetes with low income compared to high income within a State is worse than, similar to, or better than the gap that exists across all States with data. The bar chart shows the actual percentage of people with diabetes by income who receive HbA1c monitoring in the State (if available), in the region, and in all States.

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes and Low-Income (under $15,000) Compared to High-Income ($50,000 or more).

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes and Low-Income (under $15,000) Compared to High-Income ($50,000 or more).The gap between low- and high-income groups is worse (red), similar (yellow), or better (green) than the all-State gap. Unknown or insufficient data is represented by no color.

 

 

HbA1c monitoring uses a blood test that indicates to a health care provider how well a patient's diabetes has been controlled. It is an important test that helps providers monitor and guide patients to minimize and avoid serious complications. In the map above:

  • Worse than the all-State gap means the gap in HbA1c testing between people with diabetes at low-income levels and people with diabetes at high-income levels is worse than the gap between these groups across all States with data.
  • Similar to the all-State gap means the gap in HbA1c testing between people with diabetes at low-income levels and people with diabetes at high-income levels is similar to the gap between these groups across all States with data.
  • Better than the all-State gap means the gap in HbA1c testing between people with diabetes at low-income levels and people with diabetes at high-income levels is better than the gap between these groups across all States with data.
  • Unknown/data insufficient means a measure for the State could not be made.
 
For 2006-2008, The Gap in HbA1c Testing for People with Diabetes and Low-Income (under $15,000) Compared to High-Income ($50,000 or more). The gap is worse than the all-State gap in the following States: Arizona, District of Columbia, Mississippi, New Jersey, Texas, and Wyoming. The gap is similar to the all-State gap in the following States: Alabama, Alaska, Arkansas, California, Colorado, Connecticut, Florida, Hawaii, Idaho, Iowa, Kentucky, Louisiana, Missouri, Montana, Nevada, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, Tennessee, Utah, Vermont, Virginia, and Washington. The gap is better than the all-State gap in the following States: Delaware, Georgia, Indiana, Maine, Michigan, Minnesota, South Dakota, West Virginia, and Wisconsin. There is insufficient data for these States: Illinois, Kansas, Maryland, Massachusetts, Nebraska, and Rhode Island.

These categories are based on comparisons of the relative rates of HbA1c testing for people with diabetes within the two income groups in each State relative to the all-State rates for those income groups, for the period 2006–2008. Data are from the Behavioral Risk Factor Surveillance System. For more information, select Methods.

The chart below shows the rate at which HbA1c monitoring was done for people who are in low- or high-income groups within the State, the region, and all States.

Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Income, 2006 to 2008.

Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Income, 2006 to 2008.
Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Income, 2006 to 2008. Bar chart. For low-income (under $15,000), the percentage in Alabama is 88.5; the percentage in the East South Central States is 87.6; and the percentage in all States is 86.0. For high-income ($50,000 or more), the percentage in Alabama is 94.4; the percentage in the East South Central States is 94.8; and the percentage in all States is 93.1.

The bar chart represents the percent of people with diabetes who had an HbA1c test in the previous 12 months for the period 2006–2008. Data are from the Behavioral Risk Factor Surveillance System. For more information, select Methods.

Disparities in Treatment: By Race/Ethnicity

Disparities in Treatment: HbA1c Testing for Blacks, Hispanics, and Whites

For a few States, racial/ethnic groups also can be evaluated for HbA1c monitoring. When sufficient data are available, the maps below show whether the gap in the rate of HbA1c testing among different racial groups and Whites within a State is worse than, similar to, or better than the gap that exists across all States with data. The bar chart shows the actual percentage of people with diabetes in racial and ethnic groups who receive HbA1c monitoring in the State (if available), in the region, and in all States.

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Non-Hispanic Blacks Compared to Non-Hispanic Whites.

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Non-Hispanic Blacks Compared to Non-Hispanic Whites.The gap between Blacks and Whites is worse (red), similar (yellow), or better (green) than the all-State gap. Unknown or insufficient data is represented by no color.

 

 

HbA1c monitoring uses a blood test that indicates to a health care provider how well a patient's diabetes has been controlled. It is an important test that helps providers monitor and guide patients to minimize and avoid serious complications. In the map above:

  • Worse than the all-State gap means the gap in HbA1c testing between non-Hispanic Black people with diabetes and non-Hispanic White people with diabetes is worse than the gap between these groups across all States with data.
  • Similar to the all-State gap means the gap in HbA1c testing between non-Hispanic Black people with diabetes and non-Hispanic White people with diabetes is similar to the gap between these groups across all States with data.
  • Better than the all-State gap means the gap in HbA1c testing between non-Hispanic Black people with diabetes and non-Hispanic White people with diabetes is better than the gap between these groups across all States with data.
  • Unknown/data insufficient means a measure for the State could not be made.
 
For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Non-Hispanic Blacks Compared to Non-Hispanic Whites. The gap is worse than the all-State gap in the following States: Ohio, and Oklahoma. The gap is similar to the all-State gap in the following States: Alabama, Arkansas, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Nevada, New Jersey, New York, North Carolina, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, Washington, and Wisconsin. The gap is better than the all-State gap in the following States: California, and Indiana. There is insufficient data for these States: Alaska, Arizona, Hawaii, Idaho, Illinois, Iowa, Kansas, Maine, Maryland, Massachusetts, Minnesota, Montana, Nebraska, New Hampshire, New Mexico, North Dakota, Oregon, Rhode Island, South Dakota, Utah, Vermont, West Virginia, and Wyoming.

These categories are based on comparisons of the relative rates of HbA1c testing for people with diabetes within the two race/ethnicity groups in each State relative to the all-State rates for those race/ethnicity groups, for the period 2006–2008. Data are from the Behavioral Risk Factor Surveillance System. For more information, select Methods.

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Hispanics Compared to Non-Hispanic Whites.

For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Hispanics Compared to Non-Hispanic Whites.The gap between Hispanics and Whites is worse (red), similar (yellow), or better (green) than the all-State gap. Unknown or insufficient data is represented by no color.

 

HbA1c monitoring uses a blood test that indicates to a health care provider how well a patient's diabetes has been controlled. It is an important test that helps providers monitor and guide patients to minimize and avoid serious complications. In the map above:

  • Worse than the all-State gap means the gap in HbA1c testing between Hispanic people with diabetes and non-Hispanic White people with diabetes is worse than the gap between these groups across all States with data.
  • Similar to the all-State gap means the gap in HbA1c testing between Hispanic people with diabetes and non-Hispanic White people with diabetes is similar to the gap between these groups across all States with data.
  • Better than the all-State gap means the gap in HbA1c testing between Hispanic people with diabetes and non-Hispanic White people with diabetes is better than the gap between these groups across all States with data.
  • Unknown/data insufficient means a measure for the State could not be made.
 
For 2006-2008, The Gap in HbA1c Testing for People with Diabetes for Hispanics Compared to Non-Hispanic Whites. The gap is worse than the all-State gap in the following States: Arizona, Florida, Idaho, New Jersey, South Carolina, Texas, Utah, and Virginia. The gap is similar to the all-State gap in the following States: Colorado, Connecticut, North Carolina, Oklahoma, Washington, and Wyoming. The gap is better than the all-State gap in the following States: California, Georgia, Hawaii, Indiana, Kentucky, Nevada, New Mexico, Oregon, and Pennsylvania. There is insufficient data for these States: Alabama, Alaska, Arkansas, Delaware, District of Columbia, Illinois, Iowa, Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New York, North Dakota, Ohio, Rhode Island, South Dakota, Tennessee, Vermont, West Virginia, and Wisconsin.

These categories are based on comparisons of the relative rates of HbA1c testing for people with diabetes within the two race/ethnicity groups in each State relative to the all-State rates for those race/ethnicity groups, for the period 2006–2008. Data are from the Behavioral Risk Factor Surveillance System. For more information, select Methods.

The chart below shows the actual rates of HbA1c monitoring for people who are non-Hispanic Black, Hispanic, or non-Hispanic White within the State, the region, and all States. Any missing bars reflect insufficient data for a group within the State.

Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Race/Ethnicity, 2006 to 2008.

Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Race/Ethnicity, 2006 to 2008.
Percent of People in Alabama With Diabetes Who Had an HbA1c Test, by Race/Ethnicity, 2006 to 2008. Bar chart. For Non-Hispanic Blacks, the percentage in Alabama is 88.6; the percentage in the East South Central States is 89.2; and the percentage in all States is 89.8. For Hispanics, the percentage in Alabama is not available; the percentage in the East South Central States is 84.3; and the percentage in all States is 83.1. For Non-Hispanic Whites, the percentage in Alabama is 91.6; the percentage in the East South Central States is 90.1; and the percentage in all States is 90.8.

The bar chart represents the percent of people with diabetes who had an HbA1c test in the previous 12 months for the period 2006–2008. Data are from the Behavioral Risk Factor Surveillance System. For more information, select Methods.

CDC WONDER Natality Information Live Births

Source: http://wonder.cdc.gov/natality.html

The Natality online databases report counts of live births occurring within the United States to U.S. residents and non-residents. Counts can be obtained by state, county, child's gender and weight, mother's race, mother's age, mother's education, gestation period, prenatal care, birth plurality, and mother's medical and tobacco use risk factors. The data are derived from birth certificates. For more information, refer to Natality data description.

Select from following:

Natality for 2007 - 2010  My Note: Selected this. See Below.
Natality for 2003 - 2006

Natality for 1995 - 2002

The Natality data are offered in three separate online databases because of changes in data reporting standards beginning in 2003. The race group categories changed from 8 categories for the years 1995-2002 to 4 "bridged-race" categories for the years 2003-2006. Beginning in 2003, county-level data are available for 66 additional counties, because the 2003-2006 data reference the year 2000 census to determine suppression for counties with populations less than 100,000 persons. The 1995-2002 data reference the 1990 census to determine county-level data suppression. Beginning in 2007, data are reported from the the 2003 U.S. standard Certificate of Live Birth. With the implementation of the 2003 U.S. standard Certificate of Live Birth by the states, some data items are not comparable with the previous 1989 revision, resulting in changes to the data items available here. Beginning with year 2007, data for five new birth anomalies are available, and data for five maternal risk factors are no longer available.


This page last reviewed: Friday, December 28, 2012

Data Summary

Source: http://wonder.cdc.gov/wonder/help/natality.html

Summary
Summary This dataset has counts and rates of births occurring within the United States to U.S. residents and non-residents. State and county are defined by the mother's place of residence recorded on the birth certificate. Data elements include demographics, and maternal risk factors.

 

Privacy policy: As of May 23, 2011, all sub-national data representing zero to nine (0-9) births are suppressed. See Assurance of Confidentiality for more information.

Population Live births in the United States, 1995-2010.

Source

United States Department of Health and Human Services (US DHHS), 
Centers for Disease Control and Prevention (CDC), 
National Center for Health Statistics (NCHS), 
Division of Vital Statistics (DVS), 
Natality public-use data on CDC WONDER Online Database, 
for years 1995-2002 published November 2005, 
for years 2003-2006 published March 2009, and 
for years 2007-2010 published December 2012.

In WONDER You can produce tablesmapscharts, and data extracts. Request national, state and county summary counts of live births for the years 1995-2008. Calculate birth rates (normalized to the total population) and fertility rates (normalized to females age 15 - 44 years old). Limit and index your data by any and all of these variables:

Natality Data Items

  1. Mother's residence:
  2. Location - National, Regional, Division, State and County (total population of 100,000 or more; no territories) of mother's legal residence at the time of birth.

  3. Mother's Characteristics:
    Available for All Years:
    • Maternal Hispanic Origin - Central or South American; Cuban; Mexican; Non-Hispanic Black; Non - Hispanic other races; Non-Hispanic White; Origin unknown or not stated; Other and Unknown Hispanic; Puerto Rican;
    • Marital Status - Married; Unmarried; Unknown or not stated.
    • Age of Mother - under 15 years; 5 year age groups through age 54.
    Available Years 1995-2002:
    • Maternal Race - American Indian or Alaska Native; Black; Chinese; Filipino; Guamanian; Hawaiian (includes part-Hawaiian); Japanese; Other Asian; White.
    Available Years 2003-2010:
    • Maternal Race - American Indian or Alaska Native; Asian or Pacific Islander; Black or African American; White.
    Available Years 1995-2006:
    • Maternal Education 0 - 8 years; 9 - 11 years;12 years; 13 - 15 years; 16 years and over; Not stated/Not on Certificate; Excluded.
    Available Years 2007-2010:
    • Maternal Education 8th grade or less; 9th through 12th grade with no diploma; High school graduate or GED completed; Some college credit, but not a degree; Associate degree (AA, AS); Bachelor's degree (BA, AB, BS); Master's degree (MA, MS); Doctorate (PHD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD); Unknown/Not on certificate; Excluded.

    • Birth Characteristics:
    Available for All Years:
    • Year - 1995-2010. Three online databases are available: 1995-2002, 2003-2006, and 2007-2010.
    • Gender(Sex) of Child - Female; Male.
    • Birth Weight - 499 grams or less; 500 - 999 grams; 1000 - 1499 grams; 1500 - 1999 grams; 2000 - 2499 grams; 2500 - 2999 grams; 3000 - 3499 grams; 3500 - 3999 grams; 4000 - 4499 grams; 4500 - 4999 grams; 5000 - 8165 grams; not stated.
    • Plurality or Multiple Birth - Single; Twin; Triplet; Quadruplet; Quintuplet or more.
    Available only for Years 1999-2002:
    • Gestational Age at Birth - Under 20 weeks; 20 - 27 weeks; 28 - 31 weeks; 32 - 35 weeks; 36 weeks; 37 - 39 weeks; 40 weeks; 41 weeks; 42 weeks; Not stated.
    Available only for Years 2003 and later:
    • Month - February-December.
    • Weekday - Sunday-Saturday.
    • Gestational Age Group1: Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-33 weeks; 34-36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 or more weeks; Unknown.
    • Gestational Age Group2: Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-35 weeks; 36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 weeks; Not stated.
    • Gestational Age Weekly: 17 weeks; 18 weeks; 19 weeks; 20 weeks; 21 weeks; 22 weeks; 23 weeks; 24 weeks; 25 weeks; 26 weeks; 27 weeks; 28 weeks; 29 weeks; 30 weeks; 31 weeks; 32 weeks; 33 weeks; 34 weeks; 35 weeks; 36 weeks; 37 weeks; 38 weeks; 39 weeks; 40 weeks; 41 weeks; 42 weeks; 43 weeks; 44 weeks; 45 weeks; 46 weeks; 47 weeks; Unknown.
    • Month When Maternal Prenatal Care Began - No prenatal care; 1st month through 9th month of pregnancy; Unknown or not stated; Not on Certificate.
    • Live Birth Order - One child born alive to mother through six live births, and Not Stated.
    • Birthplace - In Hospital; Not in Hospital; Unknown or Not Stated.
    • Delivery Method - Cesarean, Not Stated, and Vaginal.
    • Medical Attendant - Doctor of Medicine (MD); Doctor of Osteopathy (DO); Certified Nurse Midwife (CNM); Other Midwife; Other; Unknown or Not Stated.
    Only available year 2007 and later:
    Available for All Years :
    Available only for Years 1999-2006:

 

Contents: Natality Data Request 
Data Source Information 
Additional Information
Natality Data Items
Frequently Asked Questions

Natality Data Request
Output You can produce tablesmapscharts, and data extracts. The following measures are available:
- Summary counts of births
Birth rates (normalized to the total population)
Fertility rates (normalized to females age 15-44) 
Variables You can limit and index your data by any and all of the data variables.
How? The Request screen has sections to guide you through the making a data request as step-by-step process. However, first time users might want to simply press any Send button to get the default table. The data results for your query appear on the Table screen. Click the Request tab above the table to return to the Request screen and form another query. After you get your data results, click the Chart or Map tabs to go to those screens and makes charts or maps. Click the export button above the data table to download your data table as a tab-delimited line listed file.

For more information, see the following:

Quick Start Guide;
Step 1, Organize your request;
Step 2, Select maternal residence;
Step 3, Select other maternal characteristics;
Step 4, Select birth characteristics;
Step 5, Select maternal risk factors;
Step 6, Other options.

 

'By-Variables' Select variables that serve as keys (indexes) for organizing your data. See How do I organize my data? for more information. 
Note:    To map your data, you must select at least one geographical location as a "By-Variable" for grouping your data, such as State.
Help Click on any button labeled "Help", located to the right hand side of the screen at the top of each section. Each Control's label, such as the "Location" label next to the Location entry box, is linked to the online help for that item.
Send Sends your data request to be processed on the CDC WONDER databases. The Send buttons are located on the bottom of the Request page, and also in the upper right corner of each section, for easy access.


Step 1. Organize table layout:
Group Results By   Select up to five variables to group (summarize, stratify, index) your data. For example, you could select to group your data by Year, State, Race and Gestational Age. See How do I organize my data? for more information.
Optional Measures:    Birth rates and fertility rates are available for the Natality data beginning in year 2003. Select the checkbox to report the desired rates.
Title:   Enter any desired description to display as a title with your results.


Group Results By

Select up to five variables that serve as keys for grouping your data. For example, you could select to group (summarize, stratify, index) your data by Year, State, Race and Gestational Age.

How?    See How do I organize my data? for more information.

Hints:   

  1. To make a map, you must request data with a geographic location variable, such as State, as a "By-Variable." Then click the Map tab.
  2. You cannot make charts when your data has more than two By-Variables.

 



Birth Rates

Birth rates are calculated as the number of births divided by total population in the given year(s).

When the numerator is sub-set by mother's race, location, or year of birth, then the same sub-set for race, location and year applies to the denominator population. If the data are sub-set by any other variable, then birth rates and denominator data are not available.

See Denominator Population Sources below for more information.

Notes:
  • Birth rates are available for the Natality data beginning in year 2003.
  • Birth rates are only available for the total population, or for mother's race, mother's place of residence and the year of birth. If data are grouped by any other variable, or limited for any other variable, then birth rates are not calculated. Comparable denominator data are not available for the other variables.


Fertility Rates

Fertility rates are calculated as the number of births divided by the number of females age 15 - 44 years old in the given year(s).

When the numerator is sub-set by mother's age, mother's race, location, or year of birth, then the same sub-set for age, race, location and year applies to the denominator population. If data are sub-set by any other variable, then fertility rates and denominator data are not available.

See Denominator Population Sources below for more information.

Notes:
  • Fertility rates are available for the Natality data since year 2003.
  • Fertility rates are only available for the total population, or for mother's age, mother's race, mother's place of residence and the year of birth. If data are grouped by any other variable, or limited for any other variable, then fertility rates are not calculated. Comparable denominator data are not available for the other variables.


Denominator Population Sources

The current population sources for calculating birth and fertility rates come from two sources:

About Archive Rates and Populations

Archive rates and populations are available, in order to allow researchers to reproduce previously published rates. The archive populations displayed and used as the denominator for rates are bridged-race postcensal population estimates. Each year of archive population data is taken from the corresponding series year. For example, archive population data for 2009 is from the vintage 2009 series of postcensal population estimates, and the archive population data for 2008 is from the vintage 2008 series.

The archive denominator population sources for each year:
  • 2009 from the 2000-2009 (Vintage 2009) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on July 23, 2010.
  • 2008 from the 2000-2008 (Vintage 2008) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on September 2, 2009.
  • 2007 from the 2000-2007 (Vintage 2007) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on September 5, 2008.
  • 2006 from the 2000-2006 (Vintage 2006) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on August 16, 2007.
  • 2005 from the 2000-2005 (Vintage 2005) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on August 16, 2006.
  • 2004 from the 2000 to 2004 (Vintage 2004) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on September 8, 2005.
  • 2003 from the 2000 to 2003 (Vintage 2003) postcensal estimates of the July 1 resident population by year, county, single-year age groups, bridged-race, sex, and Hispanic origin released by NCHS on September 14, 2004.


Step 2. Select maternal residence:

Limit the population to specific location here. Alternately, you can leave the settings at the default values (the United States) and choose to organize or group the data results by State to show these stratifications.


Location

This dataset includes states (no territories) and counties with a total population over 100,000 persons. Limit your data request to specific locations here. You can select any number of locations. The default settings are national data, the United States.

How?  
  1. Click the round radio button to choose between Regions (includes Divisions, States and Counties) or States (includes Counties).
  2. See How do I use a Finder? for hints on how to search for and select your desired locations.
    • The default value is all locations (the United States).
    • See Finder Tool help for more hints.

Notes:   

  •  About Counties: County-level data are shown for counties with populations of 100,000 persons or more. All counties with fewer than 100,000 persons are shown combined together under the label "Unknown County."
    • 1995-2002 data show counties with a population of 100,000 persons or more in the year 1990 Census.
    • 2003-2010 data show counties with a population of 100,000 persons or more in the year 2000 Census.
    • There are 66 more counties in the 2003-2010 data than in the 1995-2002 data.
  •  About Regions and Divisions:
    • Regions and Divisions include States and Counties.
    • The regions and divisions are from the United States Census Bureau.

      Region 1 - Northeast:

      • Division 1 - New England:    Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont.
      • Division 2 - Middle Atlantic:    New Jersey, New York, Pennsylvania.

       

      Region 2 - Midwest:

      • Division 3 - East North Central:    Illinois, Indiana, Michigan, Ohio, Wisconsin.
      • Division 4 - West North Central:    Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota.

       

      Region 3 - South:

      • Division 5 - South Atlantic:    Delaware, District of Columbia reporting area, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia.
      • Division 6 - East South Central:    Alabama, Kentucky, Mississippi, Tennessee.
      • Division 7 - West South Central:    Arkansas, Louisiana, Oklahoma, Texas.

       

      Region 4 - West:

      • Division 8 - Mountain:    Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming.
      • Division 9 - Pacific:    Alaska, California, Hawaii, Oregon, Washington.

       

    • When you export the results of your data request, the location name and the codes are in separate columns.
    • To see the full list of locations and codes, group your data by Region and by Division.
  •  About state and county codes:
    • State and County codes are Federal Information Processing Standard (FIPS) codes. See the Locations section below for more information about how county FIPS codes have been modified for use in the Natality public-use data.
    • When you export the results of your data request, the location names and the FIPS codes are in separate columns.
    • To see the full list of locations and codes, group your data by State and by County.
    • See About Counties for more information about county-level data.

 



Step 3. Select other maternal characteristics:

Limit your data for any of the following data elements:

Available Years 1995-2002:
  • Maternal Race - American Indian or Alaska Native; Black; Chinese; Filipino; Guamanian; Hawaiian (includes part-Hawaiian); Japanese; Other Asian; White.
Available Years 2003-2010:
  • Maternal Race - American Indian or Alaska Native; Asian or Pacific Islander; Black or African American; White.
Available Years 1995-2006:
  • Maternal Education 0 - 8 years; 9 - 11 years; 12 years; 13 - 15 years; 16 years and over; Not stated/Not on Certificate; Excluded.
Available Years 2007-2010:
  • Maternal Education 8th grade or less; 9th through 12th grade with no diploma; High school graduate or GED completed; Some college credit, but not a degree; Associate degree (AA, AS); Bachelor's degree (BA, AB, BS); Master's degree (MA, MS); Doctorate (PHD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD); Unknown/Not on certificate; Excluded.
  1. Maternal Hispanic Origin - Central or South American; Cuban; Mexican; Non-Hispanic Black; Non - Hispanic other races; Non-Hispanic White; Origin unknown or not stated; Other and Unknown Hispanic; Puerto Rican;
  2. Marital Status - Married; Unmarried; Unknown or not stated.
  3. Age of Mother - under 15 years; 5 year age groups through age 54.

 


Maternal Hispanic Origin

This field indicates the Hispanic ethnicity of the mother.

How?     See How do I select items from the list box?.

Note:    When you export the results of your data request, the labels and the codes are in separate columns. Public Health Information Network (PHIN) compatible codes are used when applicable. 


  Code Label
  "2148-5"  Mexican 
  "2180-8"  Puerto Rican 
  "2182-4"  Cuban 
  "4"  Central or South American 
  "5"  Other and Unknown Hispanic 
  "6"  Non-Hispanic White 
  "7"  Non-Hispanic Black 
  "8"  Non - Hispanic other races 
  "9"  Origin unknown or not stated 

 


Maternal Race

Limit your data to the selected categories for the race of the mother. The default option is all race groups. Two sets of race groups are available:

  1. 1995-2002 data: American Indian or Alaska Native; Black; Chinese; Filipino; Guamanian; Hawaiian (includes part-Hawaiian); Japanese; Other Asian; White.
  2. 2003-2010 data: American Indian or Alaska Native; Asian or Pacific Islander; Black or African American; White.
How?     See How do I select items from the list box?.

Note:    When you export the results of your data request, the labels and the codes are in separate columns. Public Health Information Network (PHIN) compatible codes are used when applicable. 


Maternal Race years 1999-2002:
  Code Label
  "1002-5"  American Indian or Alaska Native 
  "2054-5"  Black or African American 
  "2034-7"  Chinese 
  "2036-2"  Filipino 
  "2076-8"  Hawaiian 
  "2039-6"  Japanese 
  "2028-9"  Other Asian 
  "2106-3"  White 


Maternal Race years 2003 and later:
  Code Label
  "1002-5"  American Indian or Alaska Native 
  "2054-5"  Black or African American 
  "A-PI"  Asian or Pacific Islander 
  "2106-3"  White 

 


Marital Status

This field indicates the marital status of the mother as recorded on the birth certificate.

How?     See How do I select items from the list box?.

Notes:   

  • Data for the mother's marital status are derived from the "DMAR" variable in the public use data for years 1995-2002, and from the "MAR" variable in the public use data for years 2003-2010.
  • When the data results are exported to a file, the Marital Status code is shown in a separate column from the category label. 


      Code Label
      "01"  Married 
      "02"  Unmarried 
      "9"  Unknown or not stated 

 


Age of Mother

This field indicates the age group of the mother at the time of birth.

How?     See How do I select items from the list box?.

Note:    When the data results are exported to a file, two columns are provided, a column for the Age group code value and a column for the label. 


  Code Label
  "15"  Under 15 years 
  "15-19"  15 - 19 years 
  "20-24"  20 - 24 years 
  "25-29"  25 - 29 years 
  "30-34"  30 - 34 years 
  "35-39"  35 - 39 years 
  "40-44"  40 - 44 years 
  "45-49"  45 - 49 years 
  "50-54"  50 - 54 years 

 


Maternal Education

This field indicates a range for the number of years of education received by the mother at the time of birth.

How?     See How do I select items from the list box?.

Notes:   

  • About Maternal Education data:
    Education information from the 2003 revision of the birth certificate is not comparable to the information based on the earlier certificate. Thus, Education data have been recoded to "Excluded" for births, based on the form of the birth certificate used in the mother's state of residence in the year the birth occurred.
    • For years 1995-2002, all states used the 1989 U.S. Standard Certificate of Live Birth and no data are recoded to "Excluded."
    • For years 2003-2006, the majority of states continued to use the 1989 U.S. Standard Certificate of Live Birth, and Education data from those states that had implemented the 2003 U.S. Standard Certificate of Live Birth are recoded to "Excluded." Note that prior to the February 2012 data update, these "Excluded" data were recoded to "Not on Certificate."
    • For years 2007-2010, Education data from those states that continued to use the 1989 U.S. Standard Certificate of Live Birth are recoded to "Excluded." Note the change from preceding years: Natality data for years 2003-2006 in CDC WONDER excluded Education data for the states that had adopted the 2003 U.S. Standard Certificate of Live Birth.
    The following states have Education data coded to "Excluded:"
    • For births that occurred in 2003: Pennsylvania and Washington state (2 states);
    • For births that occurred in 2004: Florida, Idaho, Kentucky, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee and Washington state (9 states);
    • For births that occurred in 2005: Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee, Texas, Vermont and Washington state (13 states);
    • For births that occurred in 2006: California, Delaware, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), North Dakota, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington state and Wyoming (19 states);
    • For births that occurred in 2007: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nevada, New Jersey, New Mexico, New York (New York City only), North Carolina, Oklahoma, Oregon, Rhode Island, Utah, Virginia, West Virginia and Wisconsin (30 states);
    • For births that occurred in 2008: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Utah, Virginia, West Virginia and Wisconsin (24 states).
    • For births that occurred in 2009: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia and Wisconsin (23 states).
    • For births that occurred in 2010: Alabama, Alaska, Arizona, Arkansas, Connecticut, Hawaii, Louisiana, Maine, Massachusetts, Minnesota, Mississippi, New Jersey, North Carolina, Rhode Island, Virginia, West Virginia and Wisconsin (17 states).
  • When the data results are exported to a file, two columns are provided for Maternal Education, the code value and the label.


Maternal Education years 1999-2006:
  Code  Label
  "1"  0 - 8 years 
  "2"  9 - 11 years 
  "3"  12 years 
  "4"  13 - 15 years 
  "5"  16 years and over 
  "6"  Not stated/Not on Certificate 
  "999"  Excluded 



Maternal Education years 2007 and later:
  Code  Label
  "1"  8th grade or less 
  "2"  9th through 12th grade with no diploma 
  "3"  High school graduate or GED completed 
  "4"  Some college credit, but not a degree 
  "5"  Associate degree (AA, AS) 
  "6"  Bachelor's degree (BA, AB, BS) 
  "7"  Master's degree (MA, MS) 
  "8"  Doctorate (PHD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD) 
  "Unk"  Unknown/Not on certificate 
  "999"  Excluded 

 



Step 4. Select birth characteristics:

Limit your data for any of the following data elements:

Available only for Years 1999-2002:
  • Gestational Age at Birth - Under 20 weeks; 20 - 27 weeks; 28 - 31 weeks; 32 - 35 weeks; 36 weeks; 37 - 39 weeks; 40 weeks; 41 weeks; 42 weeks; Not stated.
Only available year 2003 and later:
  • Month February - December.
  • Weekday Sunday - Saturday.
  • Gestational Age Group1: Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-33 weeks; 34-36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 or more weeks; Unknown.
  • Gestational Age Group2: Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-35 weeks; 36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 weeks; Not stated.
  • Gestational Age Weekly: 17 weeks; 18 weeks; 19 weeks; 20 weeks; 21 weeks; 22 weeks; 23 weeks; 24 weeks; 25 weeks; 26 weeks; 27 weeks; 28 weeks; 29 weeks; 30 weeks; 31 weeks; 32 weeks; 33 weeks; 34 weeks; 35 weeks; 36 weeks; 37 weeks; 38 weeks; 39 weeks; 40 weeks; 41 weeks; 42 weeks; 43 weeks; 44 weeks; 45 weeks; 46 weeks; 47 weeks; Unknown.
  • Month Prenatal Care Began - No prenatal care; 1st month through 10th month of pregnancy; Not stated/Not on Certificate; Excluded.
  • Live Birth Order - One child born alive to mother through six live births, and Not Stated.
  • Birthplace - In Hospital; Not in Hospital; Unknown or Not Stated.
  • Delivery Method - Vaginal, Cesarean, and Not Stated.
  • Medical Attendant - Doctor of Medicine (MD); Doctor of Osteopathy (DO); Certified Nurse Midwife (CNM); Other Midwife; Other; Unknown or Not Stated.
Only available year 2007 and later:
  1.          Available for all years:
  2. Year 1995-2002, or 2003-2006, or 2007-2010.
  3. Gender (Sex) of Child - Female; Male.
  4. Plurality or Multiple Birth - Single; Twin; Triplet; Quadruplet; Quintuplet or more.
  5. Birth Weight - 499 grams or less; 500 - 999 grams; 1000 - 1499 grams; 1500 - 1999 grams; 2000 - 2499 grams; 2500 - 2999 grams; 3000 - 3499 grams; 3500 - 3999 grams; 4000 - 4499 grams; 4500 - 4999 grams; 5000 - 8165 grams; not stated.

 


Year

Pick any combination of years desired.
How?    See How do I select items from the list box?.

Gender of Child

This field indicates the gender of the child at birth.

How?     See How do I select items from the list box?.

Note:    When the data results are exported to a file, two columns are shown, a column for the code value and a column for the label.


  Code Label
  "F"  Female 
  "M"  Male 


Plurality or Multiple Birth

The Plurality field indicates if more than one infant shared the gestation and birth.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. 


  Code Label
  "1"  Single 
  "2"  Twin 
  "3"  Triplet 
  "4"  Quadruplet 
  "5"  Quintuplet or higher 

 


Gestational Age at Birth

Select a range of weeks that represents the duration of the pregnancy at the time of birth. "Gestational Age at Birth" for years 1995-2002 was renamed in year 2003 and later years to "Gestational Age - Group1". Data for years 2003 and later are organized in three groupings:

  1. Gestational Age - Group1 (Gestational Age at Birth for 1995-2002): Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-35 weeks; 36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 weeks; Not stated.
  2. Gestational Age - Group2: Under 20 weeks; 20-27 weeks; 28-31 weeks; 32-33 weeks; 34-36 weeks; 37-39 weeks; 40 weeks; 41 weeks; 42 or more weeks; Unknown.
  3. Gestational Age - Weekly: 17 weeks; 18 weeks; 19 weeks; 20 weeks; 21 weeks; 22 weeks; 23 weeks; 24 weeks; 25 weeks; 26 weeks; 27 weeks; 28 weeks; 29 weeks; 30 weeks; 31 weeks; 32 weeks; 33 weeks; 34 weeks; 35 weeks; 36 weeks; 37 weeks; 38 weeks; 39 weeks; 40 weeks; 41 weeks; 42 weeks; 43 weeks; 44 weeks; 45 weeks; 46 weeks; 47 weeks; Unknown.

 

How?  
  • Click a round button to switch between the gestational age lists for years 1999 and later.
  • Select your desired age groups. See How do I select items from the list box?.
  • Hint:
    For years 2003 and later you can only pick 1 gestational age list for any data request. The round radio button indicates the selected list, any selections in the other gestational age at birth lists are ignored. The gestational age at birth "by-variables" in section 1 and the radio button selections must match. For example, if you select to group by "Gestational Age - Weekly," then the radio button automatically sets to the "Gestational Age - Weekly" selection list. If you change the radio button to a different list, then the "by-variable" also changes to match.

Notes:   

  • The "Gestational Age at Birth" data for years 1995-2002 are derived from the "GESTAT10" variable in the public use data for years 1995-2002
  • The "Gestational Age - Group1", "Gestational Age - Group2", and "Gestational Age - Weekly" data are derived from the "COMBGEST" variable in the public use data for years 1999 and later.
  • When the data results are exported to a file, the Gestational Age codes are shown in a separate column from the category label.


Gestational Age - Group1 (Gestational Age at Birth for 1995-2002):
  Code Label
  "1"  Under 20 weeks  
  "2"  20 - 27 weeks  
  "3"  28 - 31 weeks  
  "4"  32 - 35 weeks  
  "5"  36 weeks  
  "6"  37 - 39 weeks  
  "7"  40 weeks  
  "8"  41 weeks  
  "9"  42 weeks or more 
  "10"  Unknown  


Gestational Age - Group2:
  Code Label
  "1"  Under 20 weeks  
  "2"  20-27 weeks  
  "3"  28-31 weeks  
  "4"  32-33 weeks  
  "5"  34-36 weeks  
  "6"  37-39 weeks  
  "7"  40 weeks  
  "8"  41 weeks  
  "9"  42 or more weeks 
  "10"  Unknown  


Gestational Age - Weekly:
  Code Label
  "17"  17 weeks  
  "18"  18 weeks  
  "19"  19 weeks  
  "20"  20 weeks  
  "21"  21 weeks  
  "22"  22 weeks  
  "23"  23 weeks  
  "24"  24 weeks  
  "25"  25 weeks  
  "26"  26 weeks  
  "27"  27 weeks  
  "28"  28 weeks  
  "29"  29 weeks  
  "30"  30 weeks  
  "31"  31 weeks  
  "32"  32 weeks  
  "33"  33 weeks  
  "34"  34 weeks  
  "35"  35 weeks  
  "36"  36 weeks  
  "37"  37 weeks  
  "38"  38 weeks  
  "39"  39 weeks  
  "40"  40 weeks  
  "41"  41 weeks  
  "42"  42 weeks  
  "43"  43 weeks  
  "44"  44 weeks  
  "45"  45 weeks  
  "46"  46 weeks  
  "47"  47 weeks  
  "99"  Unknown  

 


Birth Weight

The Birth Weight field indicates weight ranges for the child at birth.

How?     See How do I select items from the list box?.

Notes:   

  • The categories in gram intervals and their equivalents in pounds and ounces are as follows:
    Less than 500 grams = 1 lb 1 oz or less
    500 - 999 grams = 1 lb 2 oz - 2 lb 3 oz
    1,000 - 1,499 grams = 2 lb 4 oz - 3 lb 4 oz
    1,500 - 1,999 grams = 3 lb 5 oz - 4 lb 6 oz
    2,000 - 2,499 grams = 4 lb 7 oz - 5 lb 8 oz
    2,500 - 2,999 grams = 5 lb 9 oz - 6 lb 9 oz
    3,000 - 3,499 grams = 6 lb 10 oz - 7 lb 11 oz
    3,500 - 3,999 grams = 7 lb 12 oz - 8 lb 13 oz
    4,000 - 4,499 grams = 8 lb l4 oz - 9 lb l4 oz
    4,500 - 4,999 grams = 9 lb 15 oz - 11 lb 0 oz
    5,000 grams or more = 11 lb l oz or more
  • About low birth weight: 
    ICD - 9 and ICD - 10 define low birth weight as less than 2,500 grams. This is a shift of 1 gram from the previous criterion of 2,500 grams or less, which was recommended by the American Academy of Pediatrics in 1935 and adopted in 1948 by the World Health Organization in the International Lists of Diseases and Causes of Death, Sixth Revision.
  • When the data results are exported to a file, two columns are provided, a column for the code and a column for the label. 

       Code Label
       "01"  499 grams or less 
       "02"  500 - 999 grams 
       "03"  1000 - 1499  
       "04"  1500 - 1999 grams 
       "05"  2000 - 2499 grams 
       "06"  2500 - 2999 grams 
       "07"  3000 - 3499 grams 
       "08"  3500 - 3999 grams 
       "09"  4000 - 4499 grams 
       "10"  4500 - 4999 grams 
       "11"  5000 - 8165 grams 
       "12"  Not stated 

     


Month

This field indicates month of birth.
How?    See How do I select items from the list box?.

Notes:   

  • For years 2003 and later the month data is derived from the "dob_mm" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "1"   February  
      "2"   February  
      "3"   March  
      "4"   April  
      "5"   May  
      "6"   June  
      "7"   July  
      "8"   August  
      "9"   September  
      "10"   October  
      "11"   November  
      "12"   December  

 


Weekday

This field indicates weekday of birth.
How?    See How do I select items from the list box?.

Notes:   

  • For years 2003 and later the month data is derived from the "dob_wk" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "1"   Sunday  
      "2"   Monday  
      "3"   Tuesday  
      "4"   Wednesday  
      "5"   Thursday  
      "6"   Friday  
      "7"   Saturday  
      "9"   Unknown  

 


Month Prenatal Care Began

This field indicates the month in the pregnancy when prenatal care began.

How?     See How do I select items from the list box?.

Notes:   

  • About Prenatal Care data: 
    All states collect information on Prenatal care, but information from the 2003 revision of the birth certificate is not comparable to the information based on the earlier certificate. Thus, Prenatal Care data have been recoded to "Excluded" for births, based on the form of the birth certificate used in the mother's state of residence in the year the birth occurred.
    • For the years 1995-2002, all states used the 1989 U.S. Standard Certificate of Live Birth. No Prenatal Care data are recoded to "Excluded."
    • For the years 2003-2006, Prenatal care data are recoded to "Excluded" for births to mothers residing in a state that used the 2003 U.S. Standard Certificate of Live Birth in the specified year. Note that prior to the February 2012 data update, these "Excluded" data were recoded to "Not on Certificate."
    • For years 2007-2010, Prenatal care data are recoded to "Excluded" for births to mothers residing in a state that continued to use the 1989 U.S. Standard Certificate of Live Birth in the specified year. Note the change from the preceding years: Natality data for years 2003-2006 in CDC WONDER recoded Prenatal Care data for the states that had adopted the 2003 U.S. Standard Certificate of Live Birth.
    The following states have Prenatal Care data coded to "Excluded:"
    • For births that occurred in 2003: Pennsylvania and Washington state (2 states);
    • For births that occurred in 2004: Florida, Idaho, Kentucky, New Hampshire, New York(excluding New York City), Pennsylvania, South Carolina, Tennessee, and Washington state (9 states);
    • For births that occurred in 2005: Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee, Texas, Vermont and Washington state (13 states);
    • For births that occurred in 2006: California, Delaware, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), North Dakota, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington state and Wyoming. (19 states).
    • For births that occurred in 2007: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nevada, New Jersey, New Mexico, New York (New York City only), North Carolina, Oklahoma, Oregon, Rhode Island, Utah, Virginia, West Virginia, and Wisconsin (30 states);
    • For births that occurred in 2008: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Utah, Virginia, West Virginia and Wisconsin (24 states).
    • For births that occurred in 2009: Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia and Wisconsin (23 states).
    • For births that occurred in 2010: Alabama, Alaska, Arizona, Arkansas, Connecticut, Hawaii, Louisiana, Maine, Massachusetts, Minnesota, Mississippi, New Jersey, North Carolina, Rhode Island, Virginia, West Virginia and Wisconsin (17 states).
  • When you export the results of your data request, the labels and the codes are in separate columns.


  Code Label
  "00"  No prenatal care  
  "01"  1st month  
  "02"  2nd month  
  "03"  3rd month  
  "04"  4th month  
  "05"  5th month  
  "06"  6th month  
  "07"  7th month  
  "08"  8th month  
  "09"  9th month  
  "10"  10th month  
  "99"  Not stated/Not on Certificate
  "98"   Excluded  

 


Live Birth Order

This field indicates the mother's total number of live births, including this birth. Live birth order data are only included for data in the year 2003 and later.

How?     See How do I select items from the list box?

Notes:   

  • For years 2003 and later the live birth order data is derived from the "LBO_REC" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "01"  1st child born alive to mother 
      "02"  2nd children born alive to mother 
      "03"  3rd children born alive to mother 
      "04"  4th children born alive to mother 
      "05"  5th children born alive to mother 
      "06"  6th or more children born alive to mother 
      "99"  Unknown or not stated 

 


Birthplace

This field indicates the place of birth. Birthplace data are only included for data in the year 2003 and later.

How?     See How do I select items from the list box?.

Notes:   

  • For years 2003 and later the birthplace data is derived from the "BFACIL3" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "1"   In Hospital  
      "2"   Not in Hospital  
      "3"   Unknown or Not Stated  

 


Delivery Method

This field indicates whether the baby was born by Cesarean section or vaginal birth. Method of delivery data are only included for data in the year 2003 and later.

How?     See How do I select items from the list box?.

Notes:   

  • For years 2003 and later the method of delivery data is derived from the "DMETH_REC" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "1"   Vaginal  
      "2"   Cesarean  
      "9"   Not Stated  

 


Medical Attendant

This field indicates the attendant at the time of birth. Birth attendant data are only included for data in the year 2003 and later.

How?     See How do I select items from the list box?.

Notes:   

  • For years 2003 and later the birth Attendant data is derived from the "ATTEND" field found in the public use data.
  • When the data results are exported to a file, the code is shown in a separate column from the category label. 


      Code Label
      "1"   Doctor of Medicine (MD)  
      "2"   Doctor of Osteopathy (DO)  
      "3"   Certified Nurse Midwife (CNM)  
      "4"   Other Midwife  
      "5"   Other  
      "9"   Unknown or Not Stated  

 


Birth Anomalies

Note that data for birth anomalies are only available for years 2007 and later. Birth anomaly data for New Mexico in 2007 are coded to "Not Reported."



Congenital Anomaly of the Newborn Codes
  Code Label
  "1"  Yes 
  "2"  No 
  "9"  Not Stated 
  "10"  Not Reported 

 


Anencephalus

This field indicates whether Anencephalus is reported as a congenital anomaly of the newborn. Note that data for birth anomalies are only available for years 2007 and later.

How?     See How do I select items from the list box?.

Notes:   

  • New Mexico has Anencephalus data coded to "Not Reported" for births that occurred in 2007.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showing Congenital Anomaly of the Newborn Codes.

 


Cleft Lip / Palate

This field indicates whether Cleft Lip / Palate is reported as a congenital anomaly of the newborn. Note that data for birth anomalies are only available for years 2007 and later.

How?     See How do I select items from the list box?.

Notes:   

  • New Mexico has Cleft Lip / Palate data coded to "Not Reported" for births that occurred in 2007.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showing Congenital Anomaly of the Newborn Codes.

 


Down Syndrome

This field indicates whether Down Syndrome is reported as a congenital anomaly of the newborn. Note that data for birth anomalies are only available for years 2007 and later.

How?     See How do I select items from the list box?.

Notes:   

  • New Mexico has Down Syndrome data coded to "Not Reported" for births that occurred in 2007.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showing Congenital Anomaly of the Newborn Codes.

 


Omphalocele / Gastroschisis

This field indicates whether Omphalocele / Gastroschisis is reported as a congenital anomaly of the newborn. Note that data for birth anomalies are only available for years 2007 and later.

How?     See How do I select items from the list box?.

Notes:   

  • New Mexico has Omphalocele / Gastroschisis data coded to "Not Reported" for births that occurred in 2007.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showing Congenital Anomaly of the Newborn Codes.

 


Spina Bifida / Meningocele

This field indicates whether Spina Bifida / Meningocele is reported as a congenital anomaly of the newborn. Note that data for birth anomalies are only available for years 2007 and later.

How?     See How do I select items from the list box?.

 

Notes:   

  • New Mexico has Spina Bifida / Meningocele data coded to "Not Reported" for births that occurred in 2007.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showing Congenital Anomaly of the Newborn Codes.

 

  1. Anencephalus Yes, No, Not Stated, Not Reported
  2. Cleft Lip / Palate Yes, No, Not Stated, Not Reported
  3. Down Syndrome Yes, No, Not Stated, Not Reported
  4. Omphalocele / Gastroschisis Yes, No, Not Stated, Not Reported
  5. Spina Bifida / Meningocele Yes, No, Not Stated, Not Reported


Step 5. Select maternal risk factors:

Limit your data for any of the following data elements:

Only available before year 2003:
  1. Chronic Hypertension - Yes; No; Not Stated; Not Reported.
  2. Diabetes - Yes; No; Not Stated; Not Reported.
  3. Pregnancy-associated Hypertension - Yes; No; Not Stated; Not Reported.
  4. Eclampsia - Yes; No; Not Stated; Not Reported.
  5. Tobacco Use - Yes; No; Not Stated; Not Reported.

 


Maternal Risk Factor Codes and Labels
  Code Label
  "1"  Yes 
  "2"  No 
  "9"  Not Stated 
  "10"  Not Reported 

 


Chronic Hypertension Disease

This field indicates whether Chronic Hypertension Disease is reported as a maternal risk factor.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Diabetes

This field indicates whether Diabetes is reported as a maternal risk factor.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Pregnancy-associated Hypertension

This field indicates whether Pregnancy-associated Hypertension is reported as a maternal risk factor.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Eclampsia

This field indicates whether Eclampsia is reported as a maternal risk factor.

How?     See How do I select items from the list box?.

 

Notes:   

  • The following states have Eclampsia data coded to "Not Reported" for births that occurred in 2007: Idaho, Kentucky, Michigan, Nebraska, Pennsylvania, South Carolina, Tennessee, and Washington (8 states).
  • The following states have Eclampsia data coded to "Not Reported" for births that occurred in 2008: Idaho, Kentucky, Michigan, Nebraska, New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington (9 states).
  • The following states have Eclampsia data coded to "Not Reported" for births that occurred in 2009: Idaho, Kentucky, Michigan, Nebraska, New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington (9 states).
  • The following states have Eclampsia data coded to "Not Reported" for births that occurred in 2010: Idaho, Kentucky, Michigan, Nebraska, New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington (9 states).
  • Please see the errata below for changes in the Eclampsia data for the New York city area births that occurred in 2008.
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.

 


Tobacco use during pregnancy

This field indicates whether tobacco use was reported during the pregnancy.

How?     See How do I select items from the list box?.

Notes:   

  • All states, except California, routinely collect information on Maternal Tobacco Use, but information from the 2003 revision of the birth certificate, is not comparable to the information based on the earlier certificate. Thus Tobacco Use data are recoded, based on the form of birth certificate used by the mother's state of residence in the year of birth.
  • In the reporting years 1995-2002, data for tobacco use during pregnancy are recoded for the state of California, because California did not require reporting this risk factor. Tobacco Use data for California are recoded in WONDER as "Not Stated" for births occurring in years 1995-2002. Prior to the February 2012 data update, these data were recoded as "Unknown."
  • In the reporting years 2003-2006, data for tobacco use during pregnancy are recoded for several states, as noted below. Tobacco Use data for these states are recoded in WONDER as "Not Reported." California does not routinely collect Maternal Tobacco Use data. The other states listed have implemented the 2003 revised birth certificate, which collects smoking information differently and the data are not comparable to the data from the previous 1989 U.S. Standard Certificate of Live Birth. Prior to the February 2012 data update, these data were recoded as "Not on Certificate."
    • In reporting year 2003, maternal tobacco use data for 3 states, California, Pennsylvania and Washington state, have been recoded as "Not Reported."
    • In reporting year 2004, Maternal Tobacco Use data for 10 states, California, Florida, Idaho, Kentucky, New Hampshire, New York State (excluding New York City), Pennsylvania, South Carolina, Tennessee and Washington state, have been recoded as "Not Reported."
    • In reporting year 2005, Maternal Tobacco Use data for 14 states, California, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York State (excluding New York City), Pennsylvania, South Carolina, Tennessee, Texas, Vermont and Washington state have been recoded as "Not Reported."
    • In reporting year 2006, Maternal Tobacco Use data for 19 states, California, Delaware, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), North Dakota, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington state and Wyoming, have been recoded as "Not Reported."
  • In the reporting years 2007-2010, data have been recoded to "Not Reported" for births to mothers residing in a state that used the 1989 U.S. Standard Certificate of Live Birth, or did not report Tobacco Use in the specified data year. Note the change from preceding years, which recoded data from the 2003 revision of the birth certificate.
    • In reporting year 2007, Maternal Tobacco Use data for 31 states, Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nevada, New Jersey, New Mexico, New York (New York City only), North Carolina, Oklahoma, Oregon, Rhode Island, Virginia, West Virginia, and Wisconsin have been recoded as "Not Reported."
    • In reporting year 2008, Maternal Tobacco Use data for 27 states, Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia, and Wisconsin have been recoded as "Not Reported."
    • In reporting year 2009, Maternal Tobacco Use data for 26 states, Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia, and Wisconsin have been recoded as "Not Reported."
    • In reporting year 2010, Maternal Tobacco Use data for 20 states, Alabama, Alaska, Arizona, Arkansas, Connecticut, Florida, Georgia, Hawaii, Louisiana, Maine, Massachusetts, Michigan, Minnesota, Mississippi, New Jersey, North Carolina, Rhode Island, Virginia, West Virginia, and Wisconsin have been recoded as "Not Reported."
  • When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.

     


Anemia

This field indicates whether anemia is reported as a maternal risk factor. These data are only available before year 2003.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Cardiac (Heart) Disease

This field indicates whether Cardiac (Heart) Disease is reported as a maternal risk factor. These data are only available before year 2003.

How?     See How do I select items from the list box?.

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Hydramnios/Oligohydramnios

This field indicates whether Hydramnios/Oligohydramnios is reported as a maternal risk factor. These data are only available before year 2003.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Incompetent Cervix

This field indicates whether Incompetent Cervix is reported as a maternal risk factor. These data are only available before year 2003.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.


Lung disease (acute or chronic)

This field indicates whether Lung disease (acute or chronic) is reported as a maternal risk factor. These data are only available before year 2003.

How?     See How do I select items from the list box?.

 

Note:    When you export the results of your data request, the labels and the codes are in separate columns. See the table showingMaternal Risk Factor Codes.



Step 6. Other options:
Export Results   If checked, then query results are exported to a local file. More information on how to import this file into other applications can be found here.
How?   See CheckBox.
Show Totals   If checked, then totals and sub-totals display in the results table.
How?   See CheckBox.
Show Zero Values   If checked, then rows containing zero counts display in the results table. If unchecked, then zero count rows do not appear.
How?   See CheckBox.
Precision   Precision is not available for this dataset because population counts are whole numbers.
Data Access Timeout   This value specifies the maximum time to wait for the data access for a query to complete. If the data access takes too long to complete, a message will be displayed and you can increase the timeout or simplify your request. If you can't complete a request using the maximum timeout, contact user support and we will try to run a custom data request for you.

Additional Information

Suggested Citation
United States Department of Health and Human Services (US DHHS), 
Centers for Disease Control and Prevention (CDC), 
National Center for Health Statistics (NCHS), 
Division of Vital Statistics, 
Natality public-use data on CDC WONDER Online Database, 
for years 1995-2002 published November 2005, 
for years 2003-2006 published March 2009, and 
for years 2007-2010 published December 2012.

Each original annual dataset has a unique suggested citation. Please refer to Data Source Information to reference the original technical reference notes and citation for a specific year. See also National Vital Statistics - Birth Data for published reports and more.

Contact
The National Center for Health Statistics welcomes comments and questions at nchsQuery@cdc.gov.
Notes
  • Assurance of Confidentiality Constraints:   
    Data reports for years 1989 and later must meet the NCHS data use restrictions. Vital statistics data are suppressed due to confidentiality constraints, in order to protect personal privacy. 
    The term "Suppressed" replaces sub-national births counts, birth rates and fertility rates, when the figure represents zero to nine (0-9) persons. Corresponding population denominator data are also suppressed when the figure represents fewer than ten persons. 
    Prior to May 23, 2011, county-level data values representing fewer than 3 births were suppressed for years 2003 and later. 
    Totals and sub-totals are suppressed when the value falls within scope of the suppression criteria, or when the summary value includes a single suppressed figure, in order to prevent the inadvertent disclosure of suppressed values. 
    The confidentiality constraints are established by the original data providers. For more information, please contact thedata providers.
  • Natality data for the United States are limited to births occurring within the United States to U.S. residents and non-residents. Births to non-residents of the United State are excluded from all tabulations in WONDER by place of residence. Births occurring to U.S. citizens outside the United States are not included.
  • All Natality data included in WONDER are reported in conformance to the reporting criteria of the mother's state of residence, rather than the actual criteria on the birth certificate issued by the state of occurrence. For example, if a baby was born in Texas to a mother legally residing in California, then only those data items reported by California are included in the WONDER data set. The original Natality 2004 public use dataset includes data fields from the birth certificate, with an associated reporting flag for each field to indicate whether the mother's state of residence reports this item.
  • About "Unidentified Counties:"
    The label "Unidentified Counties" designates data for combined counties in the indicated state with less than 100,000 population. Note that not all counties in a given state show births that were registered in that county. The state total displays all tabulated births for a given state. Some counties are not associated with tabulated births due to confidentiality concerns. Counties with a total population less than 100,000 report births under "Unidentified Counties" to protect personal privacy.
  • About Maternal Tobacco Use:
    • In the years 1995-2002, data for tobacco use during pregnancy are excluded for the state of California, because California did not require reporting this risk factor. Tobacco Use data for California for years 1995-2002 has been recoded to "Not Stated." Prior to the February 2012 data update, these data were recoded as "Unknown."
    • In the years 2003 - 2006, tobacco use data are not available for all states. California did not routinely collect Maternal Tobacco Use data. The other states listed below have implemented the 2003 revised birth certificate, which collects smoking information differently and the data are not comparable to the data from the previous 1989 standard birth certificate. Prior to the February 2012 data update, these data were recoded as "Not on Certificate."
      • In 2003, Maternal Tobacco Use data for 3 states have been recoded as "Not Reported:"  California, Pennsylvania, and Washington state.
      • In 2004, Maternal Tobacco Use data for 10 states have been recoded as "Not Reported:"  California, Florida, Idaho, Kentucky, New Hampshire, New York State (excluding New York City), Pennsylvania, South Carolina, Tennessee and Washington state.
      • In 2005, Maternal Tobacco Use data for 14 states have been recoded as "Not Reported:"  California, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee, Texas, Vermont, and Washington state.
      • In 2006, Maternal Tobacco Use data for 19 states have been recoded as "Not Reported:" California, Delaware, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), North Dakota, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington state and Wyoming.
    • In the years 2007 - 2008, tobacco use data have been recoded to "Not Reported" for births to mothers residing in a state that used the 1989 U.S. Standard Certificate of Live Birth, or did not report Tobacco Use in the specified data year. Note the change from preceding years, which recoded data from the 2003 revision of the birth certificate.
      • In 2007, Maternal Tobacco Use data for 31 states have been recoded as "Not Reported:"  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nevada, New Jersey, New Mexico, New York (New York City only), North Carolina, Oklahoma, Oregon, Rhode Island, Virginia, West Virginia, and Wisconsin.
      • In 2008, Maternal Tobacco Use data for 27 states have been recoded as "Not Reported:"  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia, and Wisconsin.
      • In 2009, Maternal Tobacco Use data for 26 states have been recoded as "Not Reported:"  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Florida, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia, and Wisconsin.
      • In 2010, Maternal Tobacco Use data for 20 states have been recoded as "Not Reported:"  Alabama, Alaska, Arizona, Arkansas, Connecticut, Florida, Georgia, Hawaii, Louisiana, Maine, Massachusetts, Michigan, Minnesota, Mississippi, New Jersey, North Carolina, Rhode Island, Virginia, West Virginia, and Wisconsin.
  • About Education and Prenatal Care data: 
    Education and Prenatal Care data from the 2003 revision of the birth certificate are not comparable to the information based on the earlier certificate. Education and Prenatal Care data have been recoded to "Excluded" for births, based on the form of the birth certificate used in the mother's state of residence in the year the birth occurred.
    • For years 1995-2002, all states used the 1989 U.S. Standard Certificate of Live Birth and no data are recoded to "Excluded."
    • For years 2003-2006, Education and Prenatal Care data from those states that had implemented the 2003 U.S. Standard Certificate of Live Birth are recoded to "Excluded." Note that prior to the February 2012 data update, these "Excluded" data were recoded to "Not on Certificate." Education and Prenatal Care data for births to mothers residing in a state that used the 2003 U.S. Standard Certificate of Live Birth are available online using the VitalStats data access tool at http://www.cdc.gov/nchs/VitalStats.htm.
      • For births in 2003, Education and Prenatal Care data are recoded to "Excluded" for 2 states:  Pennsylvania and Washington state.
      • For births in 2004, Education and Prenatal Care data are recoded to "Excluded" for 9 states:  Florida, Idaho, Kentucky, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee and Washington state.
      • For births in 2005, Education and Prenatal Care data are recoded to "Excluded" for 13 states:  Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), Pennsylvania, South Carolina, Tennessee, Texas, Vermont and Washington state.
      • For births in 2006, Education and Prenatal Care data are recoded to "Excluded" for 19 states:  California, Delaware, Florida, Idaho, Kansas, Kentucky, Nebraska, New Hampshire, New York (excluding New York City), North Dakota, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington state and Wyoming.
    • For years 2007-2008, Education and Prenatal Care data from those states that continued to use the 1989 U.S. Standard Certificate of Live Birth are recoded to "Excluded." Note the change from preceding years: Natality data for years 2003-2006 in CDC WONDER excluded Education and Prenatal Care data for the states that had adopted the 2003 U.S. Standard Certificate of Live Birth.
      • For births in 2007, Education and Prenatal Care data are recoded to "Excluded" for 30 states:  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Georgia, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nevada, New Jersey, New Mexico, New York (New York City only), North Carolina, Oklahoma, Oregon, Rhode Island, Utah, Virginia, West Virginia and Wisconsin.
      • For births in 2008, Education and Prenatal Care data are recoded to "Excluded" for 24 states:  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Utah, Virginia, West Virginia and Wisconsin.
      • For births in 2009: Education and Prenatal Care data are recoded to "Excluded" for 23 states:  Alabama, Alaska, Arizona, Arkansas, Connecticut, District of Columbia reporting area, Hawaii, Illinois, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Nevada, New Jersey, North Carolina, Oklahoma, Rhode Island, Virginia, West Virginia and Wisconsin.
      • For births in 2010: Education and Prenatal Care data are recoded to "Excluded" for 17 states:  Alabama, Alaska, Arizona, Arkansas, Connecticut, Hawaii, Louisiana, Maine, Massachusetts, Minnesota, Mississippi, New Jersey, North Carolina, Rhode Island, Virginia, West Virginia and Wisconsin.
  • About Eclampsia reporting in years 2007-2010:
    • The following 8 states have Eclampsia data coded to "Not Reported" for births that occurred in 2007:  Idaho, Kentucky, Michigan, Nebraska, Pennsylvania, South Carolina, Tennessee, and Washington.
    • The following 9 states have Eclampsia data coded to "Not Reported" for births that occurred in 2008:  Idaho, Kentucky, Michigan, Nebraska, New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington.
    • The following 9 states have Eclampsia data coded to "Not Reported" for births that occurred in 2009:  Idaho, Kentucky, Michigan, Nebraska, New York New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington.
    • The following 9 states have Eclampsia data coded to "Not Reported" for births that occurred in 2010:  Idaho, Kentucky, Michigan, Nebraska, New York New York (counties comprising New York City only), Pennsylvania, South Carolina, Tennessee, and Washington.
    • Please see the errata below for changes in the Eclampsia data for the New York city area births that occurred in 2008.
  • About the reporting of birth anomalies in New Mexico in year 2007:
    Data for New Mexico in 2007 has been coded to "Not Reported" for these fields:  Anencephalus, Cleft Lip / Palate, Down Syndrome, Omphalocele / Gastroschisis, and Spina Bifida.
  • About data updates released February 2012 for births in years 2003-2006:
    The February 2012 data update added the following 8 fields to the Natality 2003-2006 online database: Month, Weekday, Gestational Age Group1, Gestational Age Weekly, Live Birth Order, Birthplace, Delivery method, Medical Attendant. In addition, the label changed for recoded Education, Prenatal Care and Tobacco Use data.
  • Errata:
    • The August 2004 release of the Natality Online database in CDC WONDER erroneously displayed the births by place of occurrence, instead of place of mother's legal residence, for the years 1999 - 2002. The August 2004 release of the Natality online database was taken off-line November 12, 2004, and this problem was corrected with the February 2005 release. We apologize, and ask that any data obtained from the Natality online database August 2004 release (available on the internet from September 10, 2004 to November 12, 2004) for the years 1999, 2000, 2001 and 2002 is reviewed, or documented with a note stating that for the years 1999-2002, the birth counts indicate the county of birth place, rather than the county of mother's legal residence.
    • With the release of the Natality data for the 2010 year in December 2012, revisions were made to the 2008 year data for the 5 counties that comprise New York City. The Eclampsia data for these 5 counties was recoded to “Not Reported” for those births that occurred in 2008. Before the December 2012 release, the Eclampsia data was mostly coded to “No” in the New York City area for births in 2008, with 23,139 births reported as “No” and suppressed values in other categories in Bronx county (Bronx Borough); 41,729 births reported as “No” and suppressed values in other categories in Kings county (Brooklyn Borough); 20,469 births reported as “No” and suppressed values in other categories New York county (Manhattan); 31,088 births reported as “No” and suppressed values in other categories in Queens; and 5,816 births reported as “No” and suppressed values in other categories in Richmond county (Staten Island).
  • Please see Frequently Asked Questions about Natality below.
  • See Locations for more information about changes to state and county areas and codes over time.

Frequently Asked Questions about Natality
Why are the data results sometimes very slow in returning?
Some queries may take longer to run, such as 5-way tables or queries that sort through every record.

Why are there no records found for a specific county?

When viewing births sorted "by County," please note that not all counties in a given state show births that were registered in that county. The state total displays all tabulated births for a given state. Some counties are not associated with tabulated births due to confidentiality concerns. Counties with a total population less than 100,000 persons report births under "Unidentified Counties" to protect personal privacy. The label "Unidentified Counties" designates the combined counties with less than 100,000 population in the indicated state. 1995-2002 data show counties with a population of 100,000 persons or more in the year 1990 Census. 2003-2010 data show counties with a population of 100,000 persons or more in the year 2000 Census. There are 66 more counties in the 2003-2009 data than in the 1995-2002 data. If there are no records of births for a specific county, then request data for the entire state, grouped by County, to see the state total and the reporting counties for each state.

What data elements are available in the Natality data? 

Please see Natality Data Items for more information.

What other technical notes for this data are available?

Please see the following sources:
National Vital Statistics - Birth Data
Vital Stats Online Downloadable Data Files
Scientific Data Documentation - Natality

What are the data privacy restrictions for Natality data?

  • Counties with a total population less than 100,000 persons report births under "Unidentified Counties" to protect personal privacy. The label "Unidentified Counties" designates the combined counties with less than 100,000 population in the indicated state. 1995-2002 data show counties with a population of 100,000 persons or more in the year 1990 Census. 2003-2010 data show counties with a population of 100,000 persons or more in the year 2000 Census.
  • Sub-national data representing fewer than ten persons are suppressed. See Assurance of Confidentiality for more information.
  • See also CDC WONDER and NCHS data use restrictions.

What about data from the August 2004 release?

The August 2004 release of the Natality Online database in CDC WONDER erroneously displayed the births by place of occurrence, instead of place of mother's legal residence, for the years 1999 - 2002. The Natality online database was taken off line November 12, 2004, and this problem was corrected with the February 2005 release. We apologize, and ask that any data obtained from the Natality online database August 2004 release (available on the internet from September 10, 2004 to November 12, 2004) for the years 1999, 2000, 2001 and 2002 is reviewed, or documented with a note stating that for the years 1999-2002, the birth counts report the county of birth place, rather than the county of mother's legal residence.

 


Locations - About FIPS State and County Codes

The FIPS state and county codes were established by the National Bureau of Standards, U.S. Department of Commerce in 1968. This standard set of codes provides names and codes for counties and county equivalents of the 50 states of the United States and the District of Columbia reporting area. Counties are considered to be the "first order subdivisions " of each State, regardless of their local designation (county, parish, borough). Washington, D.C.; the consolidated government of Columbus, Georgia; the independent cities of the States of Maryland, Missouri, Nevada, and Virginia; the census areas and boroughs of Alaska; and that part of Yellowstone Park in Montana are identified as county equivalents. The system is standard throughout the Federal Government. The State codes are ascending, two-digit numbers; the county codes are ascending three-digit numbers. For both the State and county codes, space has been left for new States or counties. Some changes in the FIPS codes have occurred since 1968.

A modified version of the FIPS state and county codes is used to identify states and counties on NCHS public-use data files. The modifications as implemented in the Natality public-use data are described below.

Modifications of FIPS State and County Codes
  1. Counties with populations under 100,000 persons are grouped into "Unidentified Counties" (FIPS code 999) to protect privacy.
    • 1995-2002 data shows counties with a population of 100,000 persons or more in the year 1990 Census.
    • 2003-2010 data shows counties with a population of 100,000 persons or more in the year 2000 Census.
    • There are 66 more counties in the 2003-2010 data than in the 1995-2002 data.
  2. A portion of Adams county, Colorado (FIPS code 08001) was moved into Broomfield County, Colorado (FIPS code 08014), which was created effective November 15, 2001 from parts of four counties: Adams, Boulder, Jefferson, and Weld. However, this new county does not appear on this file.
  3. A portion of Boulder county, Colorado was moved into Broomfield County, Colorado (FIPS code 08014), which was created effective November 15, 2001 from parts of four counties: Adams, Boulder, Jefferson, and Weld. However, this new county does not appear on this file.
  4. A portion of Weld county, Colorado was moved into Broomfield County, Colorado (FIPS code 08014), which was created effective November 15, 2001 from parts of four counties: Adams, Boulder, Jefferson, and Weld. However, this new county does not appear on this file.
  5. Dade county, Florida (FIPS code 12025) was renamed Miami-Dade County (FIPS code 12086) effective November 13, 1997. However, the area is coded to Dade (12025) for all years.
  6. The independent city of Baltimore, Maryland (FIPS code 24510) is reported separately from Baltimore county (FIPS code 24005).
  7. The independent city of St. Lois, Missouri (FIPS code 29510) is reported separately from St. Louis county (FIPS code 29189).
  8. St. Louis county (FIPS code 29189) is reported separately from the independent city of St. Lois, Missouri (FIPS code 29510).
  9. Bronx, New York (FIPS code 36005) represents Bronx Borough, New York City.
  10. Kings county, New York (FIPS code 36047) represents Brooklyn Borough, New York City.
  11. New York county, New York (FIPS code 36061) represents Manhattan Borough, New York City.
  12. Queens, New York (FIPS code 36081) represents Queens Borough, New York City.
  13. Richmond county, New York (FIPS code 36085) represents Staten Island Borough, New York City.

 

 


This page last reviewed: Tuesday, December 18, 2012

This information is provided as technical reference material. Please contact us at cwus@cdc.gov to request a simple text version of this document.

Slides

Slides

Slide 1 Harnessing Health.Data.gov Data to Address Diabetes in the US

http://semanticommunity.info/
http://gov.aol.com/bloggers/brand-niemann/
http://semanticommunity.info/Health_Datapalooza_IV#Health.Data.gov

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Slide 2 Background

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Slide 3 HealthData.gov and Health Datapalooza III Knowledge Base

http://semanticommunity.info/HealthData.gov

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Slide 4 HealthData.gov and Health Datapalooza III Spotfire Data Ecosystem

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Slide 6 Vocab.Data.gov: Government Data Vocabulary

http://vocab.data.gov/gd

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Slide 8 IBM Watson at RPI

http://semanticommunity.info/A_NITRD_Dashboard/Semantic_Medline
http://semanticommunity.info/Emerging_Technology_SIG_Big_Data_Committee/Government_Challenges_With_Big_Data
http://watson.rpi.edu/

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Slide 9 Health.Data.gov

http://www.healthdata.gov/

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Slide 11 HealthData.gov Catalog Hub

http://hub.healthdata.gov/

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Slide 12 HealthData.gov Catalog Hub: CDC WONDER Births

http://hub.healthdata.gov/dataset/wonder-births

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Slide 13 HealthData.tw.rpi.edu Catalog Hub: CDC WONDER Births

http://healthdata.tw.rpi.edu/hub/dat...onder-births-1

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Slide 14 CDC WONDER: Natality Information Live Births

http://wonder.cdc.gov/natality.html

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Slide 15 CDC WONDER: Natality Data Live Births - Diabetes

http://wonder.cdc.gov/controller/dat...A61D7BF9C5EC5C

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Slide 16 CDC WONDER: Natality Data Live Births - Diabetes

http://wonder.cdc.gov/controller/dat...A61D7BF9C5EC5C

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Slide 17 Harness Health.Data.gov Data to Address Diabetes in the US Knowledge Base

http://semanticommunity.info/Health_Datapalooza_IV#Health.Data.gov

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Slide 18 Diabetes Data Ecosystem Spreadsheet

http://semanticommunity.info/@api/deki/files/23811/Diabetes.xlsx

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Slide 19 NHQR State Snapshots 2009

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Slide 20 AHRQ State Snapshots Conclusion

http://statesnapshots.ahrq.gov/snaps...=AL#conclusion

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Slide 21 AHRQ Quality of Care for Diabetes by Region and State for 2005-2006 by Conditions

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Slide 22 CDC WONDER Births Natality Diabetes

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Slide 23 Diabetes Data Ecosystem Spotfire

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Slide 24 Conclusions and Recommendations

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Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPa d App

Error: Embedded data could not be displayed. Use Google Chrome

First Lady Michelle Obama on Exercise and Dr. Amen on Natural Supplements Data in Preventing and Treating Diabetes

First Lady Michele Obama is focusing on excercise and proper diet for childrenhttp://www.suntimes.com/news/metro/18537958-418/michelle-obama-announces-effort-to-help-kids-exercise-at-school.html

A key ingredient to keeping a sane mind in a tough neighborhood and learning leadership and team principles is through competitive sports and staying active, she said. Mrs. Obama announced a public-private partnership with Nike called “Let’s Move. Active Schools,” which uses private money to bring physical ed and activities into schools and before and after programs.

Mrs. Obama gave a passionate pitch to kids, telling them essentially to look at her achievement as a guide.

“I am you!” she told them. The first lady told them she grew up without much money. Her family lived in a small apartment, and she shared a tiny room with her brother, Craig. She said it was so noisy sometimes, she couldn’t think. When she asked the students if they knew what she was talking about, they cheered and nodded.

Mrs. Obama told the students it’s their choice to eat candy and chips instead of fruits and vegetables. END OF STORY

Dr. Amen is focusing on the use of natural supplements and brain scans to help everyone:

http://www.amenclinics.com/natural-supplement-research/

http://www.amenclinics.com/?p=6090&option=com_wordpress&Itemid=204

http://www.amenclinics.com/natural-supplement-research/

So we need celebrities, parents, and teachers to set a good example and motivate our children. I also think we regulatory science like I learned from 30+ years at the US EPA to label food products in a simple way as to their potential for causing obesity and diabetes.

So I am going to look for scientific data on this starting with Dr. Amen's Appendix on Natural Supplements in his new book Unlease the Power of the Female Brain (2013) as follows:

 

Email Invitation

Source: http://campaign.r20.constantcontact....qV5JFWxn-2Y%3D

March 22, 2013 
Just Released--New Data from the 2011/12 National Survey of Children's Health (NSCH) 
 
In This Letter
 
My Note: This caught my eye and led to the link to the right and the note below to sign up for the data in Excel!
 
 
 
 
Webinar Announcements
 
April 16th
3 PM EDT
MCHB DataSpeak:
Get an in-depth view of the 2011/12 NSCH
 
April 25th
12 PM CDT
Register Today
How to Use Data to Improve Care Delivery
part of the 2013 Medical Home in Pediatrics 
Webinar Series
 
Got Data?
DRC Enhanced Data Set for the 2011/12 NSCH Coming Soon-Late April
 
 
View Your State's Medical Home Profile
 
Compare States by Medical Home
 

Test Drive our Free Online Tool for 
Parents of Young Children
wvp images
 
Try it & Send to 
Friends & Family
 Parents, did you know
 there are 13 well-child care visits in the first 4 years of your child's life? The CAHMI's Well-Visit Planner is designed to help you learn about and prepare for your child's well-care. 
  
Join us on facebook andtwitter.
Health Care Providers 
You can use the Well-Visit Planner to engage and empower parents as proactive partners in improving well-child care and insert parent data into the electronic medical record.
 
About Us
The DRC is a project of the CAHMI
The DRC is a project of the CAHMI supported by the Health Resources and Services Administration, 
 
newNSCHNow Available: 2011/12 NSCH State Profiles

State-by-state snapshots of new data from the 2011/12 National Survey of Children's Health (NSCH) are now accessible on the Data Resource Center (DRC) website. Start browsing the new data now. My Note: 
 
Highlights

Health Status

Childhood Obesity: An estimated 31.3% of US children age 10-17 were reported as overweight or obese, ranging from 22.1% to 39.8% across US states.

NEW TOPIC!  Premature Birth:  Nearly 12% of US children have parents who report they were born premature, ranging from 8.5% to 15.7% across US states. 

Missing School:  Over 1 in 7 US children with special health care needs (CSHCN) miss more than 2 weeks of school in a year due to their health, ranging from 5.9% to 25.5% across US states.

Autism Spectrum Disorder:  As reported in this HRSA/CDC report, prevalence of ASD increased to 1 in 50 US children age 2-17. Read this report for more information!

Health Insurance and Quality

Consistent Insurance:  An estimated 88.7% of US children had consistent health insurance coverage in the past year, with a wide range between 78.2% to 95.9% across US states.

Developmental Screening:  As reported in the HRSA/CDC report on ASD, nearly 31% of young US children are reported to have received standardized developmental screening. Ranging from 17.5% to 58.0% across US states.

Medical Home:  About half of US children meet minimum criteria for receiving care within a Medical Home, 54.4%. Rates vary widely across states and fall to 36.4% and 46.8% for children in poverty and children with special health care needs, respectively.

Positive Health and Risks

NEW TOPIC! Adverse Childhood Events: Nearly a third of US youth age 12-17 have experienced two or more adverse childhood events (30.5%), with a range of 23.0% to 44.4% across US states.

Developmental Risk for Young Children:  Over 1 in 4 US children under age 6 meet criteria for risk for developmental problems or delays.  This ranges from 18.0% to 33.2% across US states.

Family and Home Health

Mother's Health:  An estimated 56.7% of mothers of US children experience excellent or very good physical and mental health, dropping to 45.8% for mothers of children with special health care needs.

Smoking In the Home:  Nearly 1 in 4 US children live in households where someone smokes (24.1%), ranging from 12.4% to 41.0% across US states. 

Neighborhoods

Neighborhood Resources: While over half of US children live in neighborhoods with all four amenities assessed, this varies from 29.4% to 75.7% across US states.  

---
Stay tuned over the coming weeks as the DRC enables interactive queries for all 2011/12 NSCH content. 

 

SAVE THE DATE: 3 PM EDT, April 16, 2013
Get an in-depth view of the 2011/12 NSCH at the MCHB DataSpeak.
Registration opens soon and will be announced here.
 
LearnLearn More About the 2011/12 NSCH
The Data Resource Center partners with the Maternal and Child Health Bureau to make data and information about the national surveys accessible and understandable to all.

 
MHNew State-By-State Medical Home Performance Profiles for Children with Special Health Care Needs (CSHCN)
 
Now available on the DRC's Medical Home Data Portal...
 
Select the map and choose your state to see how many children with special health care needs in your state are receiving care that meets the American Academy of Pediatric criteria for having a medical home. 
State Profiles 
Find out which states are top performers on prevalence of children who receive coordinated, ongoing, comprehensive care within a medical home. Select a population: CSHCN or ALL Children
 
PASSign Up to Join Us at the PAS Annual Meeting

Will you be in Washington, D.C. at the Pediatric Academic Societies (PAS) Annual Meeting May 4-7? 

 

Here's what we are up to this year at PAS:

 

Sunday May 5th, 12-3 PM 

Location: Washington Convention Center Room 143A

Using the National Survey of Children's health and the National Survey of Children with Special Health Care Needs, co-hosted with the CDC and CityMatch

  

Saturday May 4th 12:00-4:00 PM (lunch provided)

Location: Renaissance Washington Hotel, Mt. Vernon Square A

RSVP for An Applied Conversation on Leveraging Mind-Body Neuroscience and Mindfulness to Improve Pediatrics 

 

Monday May 6th, 8:30-11:30 AM

Leveraging the CSHCN Screener to Identify Complex CSHCN, a presentation by the CAHMI as part of the Complex Care Special Interest Group (SIG) Meeting
 
Using the DRC and National Data Sets to
Teach Graduate Students & Jumpstart Careers

The Data Resource Center has been a useful resource for many undergraduate and graduate programs across the U.S. Here is one example to help inspire the work-force development application of the DRC!  

 

Russell Kirby, Professor and Merrell Endowed Chair at the University of South Florida College of Public Health, has used the Data Resource Center for Child and Adolescent Health to teach his second year master's and doctoral students since 2009. In his

Secondary Data Analysis for Maternal and Child Health class, students use data from www.childhealthdata.org to generate research hypotheses. Then, using the DRC-produced datasets, fully coded and labeled with approximately 100 variables, Dr. Kirby works with his students through the research and analysis process focusing on their chosen topic. The class culminates in a presentation and manuscript, which some students take to national conferences. 

 
Sara Kennedy is a former student of Dr. Kirby. "He is just a great professor. More than any other class, I use skills learned in his [class] every day. Secondary Data Analysis was very focused on practical data applications and skills needed for a career in research." In Dr. Kirby's class Sara used the National Survey of Children's Health to explore how health insurance status relates to patient-provider communication. She won an award for her work from Delta Omega, an Honorary Society in Public Health, which sent her to the American Public Health Association conference to present the findings. [read more]
 
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This email was sent to bniemann@cox.net by cahmi@ohsu.edu  
CAHMI | Mail Code CDRC-P | 707 SW Gaines Street | Portland | OR | 97239

 

 

Request For Data Resource Center Indicator Data Set

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Select the data set(s) you are requesting:
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2003  2007 03 & 07 merged—Please note that only variables that were identical between 2003 and 2007 are included in this dataset
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Data Resource Center Indicator data sets contain constructed measures that were developed by CAHMI in collaboration
with a national technical advisory panel for the Data Resource Center for Child and Adolescent Health. The purpose of
this project is to provide support and technical assistance to states for interpretation and utilization of results of the
National Survey of Children’s Health and National Survey of Children with Special Health Care Needs. 10-02-12
 
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Source: Email with PDF
 
2011-2012 National Survey of Children’s Health (available Spring 2013)
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1. Licensee: ___________________________________________________________________
2. Licensor: Child and Adolescent Health Measurement Initiative (CAHMI), Oregon Health and Science University, Department of Pediatrics, 707 SW Gaines St., Portland, OR 97239.
3. Data Set: DRC Indicator Refined Data Set for: 2011-2012 National Survey of Children’s Health, 2009-2010 National Survey of Children with Special Health Care Needs, 2003 & 2007 National Survey of Children’s Health Merged, 2007 National Survey of Children’s Health, 2005-2006 National Survey of Children with Special Health Care Needs, 2003 National Survey of Children’s Health and/or 2001 National Survey of Children with Special Health Care Needs.
4. Ownership: CAHMI is the owner of Data Set which was developed in the course of research at CAHMI.
5. Public Benefit: CAHMI wants this Data Set to be utilized for the public benefit to the fullest extent possible.
6. Publications: Recipient agrees to acknowledge the Provider with appropriate citations in any publications or presentations using results from this Data Set. The suggested citation format is:
Child and Adolescent Health Measurement Initiative (CAHMI). {Year and name of survey} Indicator Data Set. Data Resource Center for Child and Adolescent Health. www.childhealthdata.org
Please initial here to acknowledge citation request ______________
7. Field of Use (how you intend to use these data): _____________________________________ ______________________________________________________________________________ ______________________________________________________________________________
Terms
1. Grant of License: Subject to the terms and conditions of this licensee, Licensor grants to Licensee a non-exclusive, non-sub licensable, non-transferable license to use the Data Set provided herein and any associated documentation. Licensor is not obligated to provide upgrades to the Data Set or technical support beyond assistance in installing the Data Set.
 
2. Ownership of Data Set: This License gives the Licensee limited use of the Data Set. This License is not a sale of the Data Set and Licensor retains all title to all rights and interests in the Data Set. The Data Set is protected by U.S. Copyright laws, international treaty provisions and applicable laws of the country in which it is being used.
 
3. Permitted Use: Licensee may use the Data Set in the Field of Use for academic and research purposes only.
 
4. Non-permitted Uses: Licensee may not
a. Use the data in the Data Set for any purpose other than statistical reporting and analysis;
b. Make any effort to determine the identity of any reported case in the Data Set;
c. Disclose or make use of the identity of any person or establishment discovered inadvertently, and will advise the Director, National Center for Health Statistics (NCHS), of any such discovery;
d. Link this Data Set with individually identifiable data from any other Data Sets;
e. Use the Data Set at any other location than that specified above;
f. Rent, lease, lend, sell, transmit or otherwise distribute or dispose of the Data Set temporarily or permanently without written consent of Licensor;
g. Create or permit third parties to create derivative works based on the Data Set;
h. Remove, modify, alter or obscure the copyright notices or any other proprietary notices contained in or on the Data Set;
i. Sell derivative works based on the Data Set.
 
5. Term and Termination: This License shall commence on the date of delivery of the Data Set to Licensee and shall terminate automatically upon breach of this License by Licensee.
 
6. Confidentiality: Recipient and Recipient Scientist agree to hold the Data in confidence and not disclose to anyone except to such of its employees, consultants and agents as may be necessary to make the determination required under this agreement, providing said employees, consultants and agents are bound by the terms of this Agreement.
 
7. Publications: Recipient agrees to acknowledge the Provider with appropriate citations in any publications or presentations using results from this Data Set.
 
8. Warrants: Licensor warrants that it has the lawful right to grant the license set forth in this Agreement.
 
9. NO REPRESENTATIONS OR WARRANTIES: Except as expressly provided in section 8, the parties acknowledge and agree that licensor, its trustees, directors, officers, employees, and affiliates make no representations and extend no warranties of any kind, either express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, non-infringement and the absence of latent or other defects, whether or not discoverable. Nothing in the license agreement shall be construed as a representation made or warranty given by licensor that the practice by licensee of the license granted hereunder shall not infringe the patent rights or copyright rights of any third party. In no event shall licensor, its trustees, directors, officers, employees and affiliates be liable for incidental or consequential damages of any kind, including economic damage or injury to property and lost profits, regardless of whether licensor shall be advised, shall have other reason to know, or in fact shall know of the possibility. Licensee assumes the entire risk associated with licensee’s use of the Data Set.
 
10. Complete Agreement: This License is a complete and exclusive statement of the terms and conditions of the agreement between Licensee and Licensor.
 
LICENSEE:
Signature Date
Name
Title
Email
Address

Send completed DUA by fax - 503-494-2475, or email - cahmi@ohsu.edu

Thank you for sending in the DUA.

In order to access these data, please visit:

2009-10 CSHCN FTP  (requires Data Use Agreement)   

2003 and 2007 NSCH Merged FTP (requires Data Use Agreement)

(The 2011-2012 NSCH data will be sent to you as soon as it becomes available this spring)

Each site has compressed folders containing the DRC Indicator Datasets. In addition to the data files, the folders for the surveys also contain supplemental documents and information.  For this reason, the Dataset must be extracted from the compressed folder before it can be opened in SAS, SPSS, or other statistical software.

If your colleagues or students will be working with any data files received from the Data Resource Center, be mindful that you are responsible for assuring that they have first read and consented to abide by the terms of the data use agreement you signed.  This can be done by you independently, or by requiring them to make separate applications through the DRC.

Referencing the DRC Indicator Datasets:

Please be sure to use appropriate citation in any materials you publish, distribute or display, which report results from datasets provided by the Data Resource Center and CAHMI. (We never get tired of reminding people to do this!)  Citation language for each survey is listed here: 

2009/10 National Survey of Children with Special Health Care Needs. Maternal and Child Health Bureau in collaboration with the National Center for Health Statistics. 2009/10 NS-CSHCN [Insert SPSS/SAS/Stata] Indicator Data Set prepared by the Data Resource Center for Child and Adolescent Health, Child and Adolescent Health Measurement Initiative. www.childhealthdata.org

2003 & 2007 National Surveys of Children's Health. Maternal and Child Health Bureau in collaboration with the National Center for Health Statistics. 2003_2007 Merged NSCH [Insert SPSS/SAS/Stata] Indicator Data Set prepared by the Data Resource Center for Child and Adolescent Health, Child and Adolescent Health Measurement Initiative. www.childhealthdata.org

We encourage you to keep us informed about your publications and presentations based on these data.  To facilitate that, someone from our staff may be contacting you in a few months.  Since the main mission of the Data Resource Center is to facilitate dissemination and utilization of the results of the National Surveys, we are always delighted to be able to identify real-life examples of how planners, grant writers, researchers and child health policy advocates are using survey results to promote better health and improve access to and quality of children’s health care services.

If we can be of further assistance, please don't hesitate to let us know!

Best, Pete

Peter Fisher

Student Intern

CAHMI-Child and Adolescent Health Measurement Initiative
Oregon Health & Science University
707 SW Gaines Road, Mail Code CDRCP
Portland, Oregon 97239-2998
(206) 227-7608

fax: (503) 494-2475
fishepe@ohsu.edu

http:/www.childhealthdata.org

http://www.wellvisitplanner.org

http://www.yourchildshealthcare.org

http://www.cahmi.org

Present at Health Datapalooza

This is your chance to speak or demo at Health Datapalooza IV! The submission portal for individuals and organizations interested in participating in the 2013 Health Datapalooza IV apps expo is now open.

Want to present your organization’s app or product at the two-day event dedicated to showcasing the meaningful use of health data? Have an interesting perspective on a hot topic dealing with health data? Create an account and complete the electronic submission form in order to be considered for a speaking or presentation opportunity.

This year’s event will have more main stage addresses and panel presentation spots, as well as opportunities for demonstration of apps and products on the main stage, in breakout panel discussions, and in the apps demo sessions. As the key conference that brings together government health data owners, state and local health data enthusiasts, as well as international health data policymakers, the Health Datapalooza 2013 is an event you won’t want to miss!

Deadline for submission of materials for consideration to be a speaker or an exhibitor is April 5, 2013.

As an incentive to submit an app demo, those who complete the submission will receive a discount code that reduces the event price to $195 per person. Submit your app now to receive the code!

Process
Once submissions are received and initially screened, a number of “virtual auditions” will be scheduled. If selected, you will deliver your presentation to judges via webinar technology. The judges will rate the product and your presentation. Those submissions receiving high scores will be invited to showcase during the Main Stage sessions. Others receiving favorable scores will be scheduled to present during the Apps Demo and Breakout Panel sessions.

Entries will be judged based on the following criteria:

  • The extent to which the application or activity uses health data (does not have to be publicly available data)
  • How the application addresses an explicit problem or health issue
  • Whether the application is newly created (developed within the past 12 months) or, if exhibited during last year’s Datapalooza, features enhanced capabilities
  • Whether the application has demonstrated utility (improved health outcomes, reduction in health care costs, etc.)
  • Whether the application has a sustainability plan or future plan of use

Contact healthdatapalooza@healthdataconsortium.org with any questions about the virtual audition submission process. Good luck and see you at the 2013 Health Datapalooza IV!

Register now for the 2013 Health Data Initiative IV: Health Datapalooza 2013
June 3 – 4, 2013
Omni Shoreham Hotel
Washington, DC

Submission

Abstract Collection
Submission Title: Making data accessible to all. It’s your data…your story!
Reference ID: 0270-000127
 
 

Submitter
 
Submitter Prefix* Dr.

Submitter First Name* Brand

Submitter Last Name* Niemann

Submitter Email* bniemann@cox.net

Submitter Work Phone* 703-268-9314

Submitter Cell Phone* 703-268-9314

Submitter Company* Semantic Community

Submitter Job Title* Director and Senior Data Scientist

Submitter Company Website* http://semanticommunity.info


Presenter
 
Presenter Prefix* Dr.

Presenter First Name* Brand

Presenter Last Name* Niemann

Presenter Email* bniemann@cox.net

Presenter Work Phone* 703-268-9314

Presenter Cell Phone* 703-268-9314

Presenter Company* Semantic Community

Presenter Job Title* Director and Senior Data Scientist

Presenter Company Website* http://semanticommunity.info

Presenter Twitter bniemannsr




Type of Presentation
 
Abstract Title* Making data accessible to all. It’s your data…your story!

You can choose to be a speaker and / or a presenter.

Speaking* Breakout session panel discussion

Presenting* Breakout session panel discussion, Apps demo session

Topics* Big Data and Analytics, Data Use in Quality Improvement, Data Analytics to Support Accountable Care Organizations, Integration of Data and EHR Systems, Consumer Health Management and Decision Making Tools Using Data, Healthcare Provider Tools Using Data, Community and Population Health, Data Policy – Federal, State and City

Abstract Description*

As Steve Covey said in his book "The 7 Habits of Highly Effective People Habit 2: Begin with the End in Mind. So my end is to present at the Health Datapalooza IV and enter the Redesigning Data competition using the the National Survey of Children’s Health and National Survey of Children with Special Health Care Needs provided by the Data Resource Center for Child and Adolescent Health

The Health Datapalooza IV presentation criteria are (and my answers):

  • The extent to which the application or activity uses health data (does not have to be publicly available data) (Yes, uses CAHMI indicator data)
  • How the application addresses an explicit problem or health issue (Knowledge Base and Dashboard of Child and Adolescent Heatht data)
  • Whether the application is newly created (developed within the past 12 months) or, if exhibited during last year’s Datapalooza, features enhanced capabilities (Yes, newly created)
  • Whether the application has demonstrated utility (improved health outcomes, reduction in health care costs, etc.) (The CAHMI program has and improved use of their data should help their success)
  • Whether the application has a sustainability plan or future plan of use (Yes, part of a new Data Science Company)

I both formally requested the CAHMI data and extratced it it from the CAHMI web site. So far my knowledge base contains Words: 38555, Chars: 263398, and can be semantically searched. My spreadsheets contain 14 tabs of data sets and my dashboard contains 11 tabs of visualizations. It should be emphasized that the author is focusing on creating web-linked data with strong relationships (see Semantic Medline and our Semantic Web Strategy for Data).

My Conclusions and Recommendations are:

  • The New Digital Government Strategy of treating all content as data has been applied to the CAHMI Web content
  • The CAHMI has been turned into data in spreadsheets and statistical visualizations in Spotfire 5.
  • This simplifies the complex CAHMI interface which requires lots of extra mouse clicks and provides no faceted semantic search.
  • The CAHMI Data Use Agreement for the Data Resource Center Indicator Data Sets provides for additional data access that will be used to supplement this work.
  • This process provides the beginning of a Unified Data Architecture and Ecosystem for Data Integration using the View Data function in Spotfire 5.

The CAHMI Data Sets and others will be used to supplement this work for the Health Datapalooza presentation and Redesigning Data  concept.

See Slide 16: http://semanticommunity.info/@api/deki/files/23610/BrandNIemann03252013.pptx for supplemental conclusions and recommendations based on additional data.

My Note: The author attended the The World Congress Leadership Summit on Building the Data Infrastructure to Drive Evidence-Based Improvements in Health Care where he was impressed with a presentation on "Use of diabetes data to invent and operationalize a “Diabetes Tune-Up” program for “Extreme Diabetics” that provided three examples of Using Data to Drive Better Care at Medstar Health. The author also demonstrated the Medicare Zombie Hunter application at Health Datapalooza II in 2011.


Supplementary Abstract Upload*

Supplementary Abstract URL http://semanticommunity.info/Health_Datapalooza_IV

Presentation upload


Previous Speaking Engagements
 
Previous Presented* No

Previous Applied* No

Other Conferences* No

Start Up* Yes

Year* 2010

Presentation platform Require internet access to the web (wireless is standard), PowerPoint

Other Presentation Platform Needs None

Health Data* No

Public Health Data* Yes


Private Health Data* No

Exhibitor* No
 

PROVE IT! Win $100,000

Source: http://healthdatapalooza.org/prove-it/

Submissions open for the 2013 Data Design Diabetes Innovation Challenge – Prove It!

In partnership with the Health Data Consortium, the Redesigning Data Challenge Series applies the best practices of the leading tech incubators and accelerators to identify the most innovative uses of data services to improve health or healthcare. The Redesigning Data Challenge Series will move beyond theoretical applications of big data to inform practical business solutions that produce tangible improvements. Redesigning Data is looking for the best thinkers. The most innovative solutions. The most actionable outcomes.

The Sanofi US 2013 Data Design Diabetes Innovation Challenge – Prove It! kicks off the Series, inviting innovators to develop solutions that use or produce data for decision-making to help improve health outcomes for people living with diabetes. Through baseline knowledge models, evidence-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the US.

The strongest solutions will advance to a virtual incubator, followed by a live pitch to an expert panel of judges on the mainstage at Health Datapalooza IV on June 3 in Washington, DC. The winning solution will be presented with $100,000 on June 4.

The Redesigning Data Challenge Series is under the auspices of the Health Data Consortium, featured at Health Datapalooza, and powered by Luminary Labs.

Submit a concept now through April 7, 2013.

The Redesigning Data Challenge Series brings entrepreneurs and industry together to accelerate innovative uses of health data.

  • OPEN CALL FOR ENTRIES MARCH 2013
  • STRONGEST TEAMS ADVANCE TO THE MENTORSHIP PHASE
  • FINALISTS PRESENT ON THE MAINSTAGE AT HEALTH DATAPALOOZA JUNE 3, 2013

2013 DATA DESIGN DIABETES INNOVATION CHALLENGE PROVE IT!

Calling all entrepreneurs, data scientists, and designers!
We challenge you to create the evidence needed to make better decisions across the entire spectrum of diabetes.

SCHEDULE

March 18, 2013
Submissions period open for entry.

April 7, 2013
Last day to submit an entry.

April 18, 2013
Challenge finalists announced at TEDMED.

RULES

 All intellectual property and equity will remain the property of its creators.

 Challenges are open to all individuals 18 years of age or older at the time of entry.

 At least one team member must be a legal U.S. Resident or Citizen throughout the duration of the Challenge.

BLOG

March 19, 2013 

PROVE IT! OPEN FOR SUBMISSION

Sanofi invites the innovator community to use or produce data that may improve health outcomes for the millions of people living with diabetes in the US, for a chance to win $100,000.

Prove It! 2013 Data Design Diabetes Innovation Challenge is Open for Submissions

Source: http://www.redesigningdata.com/blog/2013-ddd/

by Dennis Urbaniak

It is my pleasure to announce that the Sanofi US 2013 Data Design Diabetes Innovation Challenge is open and accepting submissions as part of the Redesigning Data Challenge Series.

This year’s challenge, Prove It!, asks innovators to develop solutions that use or produce data for decision-making to help improve health outcomes for people living with diabetes. Through baseline knowledge models, evidenced-based practice, or predictive analysis, Prove It! asks innovators to think creatively about how to effectively harness data to address diabetes in the US.

Now is a critical time for focusing on innovation in diabetes care — currently, one in ten U.S. adults is living with diabetes, and the CDC projects that this number could reach one in three by 2050 given current trends. Sanofi has seen tremendous human-centered solutions come to fruition during the challenge in past years, illustrating the collective power of the innovator community to improve diabetes care. This year we are asking innovators to submit Prove It! concepts that have the potential to create real change with real knowledge, making use of or producing the data that will fuel the tools to help make the right decisions with the right information at the right time to improve diabetes care in the US.

Now through April 7, 2013, anyone—innovators, entrepreneurs, providers, or patients — can submit a concept.

CONCEPT CRITERIA*

EVIDENCE-BASED HEALTH OUTCOMES: Ability to demonstrate in an evidence-based way how the concept can improve the outcomes and/or experience of people living with diabetes in the US.

TARGET AUDIENCE: Ability to support one or more members of the healthcare ecosystem and provide them with data-driven tools or evidence-based insight that can help them make better contributions to staving the diabetes epidemic in the US.

DECISION-MAKING: Ability to illustrate how the concept can enable better data-driven decision-making at a particular stage across the spectrum of type 1 or type 2 diabetes, from lifestyle and environmental factors to diagnosis, treatment, maintenance, and beyond.

DATA SCIENCE: Utilize new or traditional data methodology — such as baseline knowledge models, evidence-based practice, and predictive analysis — to create a tool that may change the landscape of diabetes management through richer insight, more timely information, or better sets of decisions.

*For full criteria, visit http://www.redesigningdata.com/DDD.

Finalists will present their concepts to the judges during a live Demo Day event at Health Datapalooza IV in Washington, DC on June 3, 2013. One (1) winning team will be selected and awarded $100,000 to further develop its solution.

2013 REDESIGNING DATA TIMELINE

  • March 18: Challenge opens for submissions
  • April 7: Last day to submit to the Challenge
  • April 18: Finalists announced during TEDMED
  • April 18-June 2: Finalists participate in virtual incubator
  • April 26-28: Innovators’ bootcamp in San Francisco, CA
  • June 3: Finalists present at Health Datapalooza IV
  • June 4: Winner announced at Health Datapalooza IV

Now is your chance to unlock the power of open data to potentially help the millions of people living with diabetes. Help spread the word and submit your concept before April 7, 2013.

ABOUT DENNIS URBANIAK

Dennis UrbaniakDennis Urbaniak is Vice President and Head of the U.S. Diabetes Patient Centered Unit for Sanofi US, an integrated platform consisting of diabetes products, devices and services. He also serves as a member of the leadership team for the Sanofi global diabetes division. Prior to this role, he held positions such as Vice President of Innovation and New Customer Channels and Vice President, Lovenox® marketing in the U.S.  Dennis joined Sanofi US in 1994 and has held various positions within marketing, sales operations, sales management, and sales. 

 

Site Map

Source: http://childhealthdata.org/home/site-map

My Note: Copied the Site Map and added structure to it to make the content all data.

Home

Sign up for E-Updates

At the Data Resouce Center for Child and Adolescent Health we partner with you--the users of this site--to create a community of individuals committed to improving the health and well-being of children through the use of national and state level data.

As part of our community you will receive periodic updates on our current activities, projects, new data features and future initiatives. Join our community now!

Coming soon! In October 2011 we will introduce our new, enhanced "join our community." The new features will include the ability to personalize portions of the site, keep data queries stored in an online briefcase and customize reports you would like to receive from the Data Resource Center.

About CAHMI

 

 
Home::About Us::Newsletters::Presentations::Getting Help::Contact Us::Link to Us::Search

The Child and Adolescent Health Measurement Initiative (CAHMI) developed and maintains a number of quality measurement tools and strategies that assess the quality of care provided to children and young adults.  To learn more about the CAHMI consumer-centered quality measurement strategies, click on the topic areas below.

CAHMI Quality Measurement Tools: By Topic Area
Spotlight On:
Featuring point-and-click national and state data snapshots from the 2007 National Survey of Children’s Health (NSCH) on:
The Mental and Emotional Well-Being of Children profile compares results on the prevalence of emotional, behavioral, and developmental conditions among children 2-17 years old, plus indicators of health care quality and access for children with these conditions.
The Child Health and System Performance Profile highlights health disparities among children with special health care needs (CSHCN) and children who have public insurance.
Examples from the Field:
Applications of the CAHMI Tools
  Search this site   
© Important Note About Copyrighted Materials: All CAHMI quality measures (including sampling, administration, analysis and reporting specifications) available on this website are copyrighted by the CAHMI. Should you use any of the material from this site, please cite it appropriately.

Contact Us

The Child and Adolescent Health Measurement Initiative (CAHMI)

Oregon Health & Science University
Department of Pediatrics, School of Medicine
707 SW Gaines Road, Mail Code CDRC-P
Portland, OR 97239-2998
(503) 494-1930
Email us at: cahmi@ohsu.edu

Have a Question?

Use our Ask a Question feature to ask the Data Resource Center staff for more assistance.

Stay in Touch!

Receive E-updates from the Data Resource Center about current projects, new data features, and future initiatives.

Accessibility

The Data Resource Center for Child and Adolescent Health (DRC) provides an interactive website with graphs, charts, tables and other interactive media. The site seeks to provide actionable data and information that is easy for all to use.

This website strives to ensure that its design meets the accessibility requirements outlined in Section 508 of the Americans with Disabilities Act.

Site Design Guidelines

We strive to meet accessibility requirements in the following areas:

  • Images. This Web site provides equivalent text for images that convey information.
  • Style sheets. This Web site does not require associated style sheets (i.e., pre-designated templates that define the layout of a Web page) to be viewed.
  • Image maps. This Web site provides equivalent text for images (e.g., navigation bars) that perform functions when selected (e.g., open a new window, navigate through the site).
  • Tables. This Web site provides row and column headers for data tables.
  • Frames. This Web site does not include frames (i.e., separate sections of the display area that are generated from different Web pages).
  • Motion. This Web site sites does not include motion that causes the screen to flicker outside an acceptable range (i.e., higher than 2Hz and lower than 55Hz).
  • Text-only pages. This Web site follows HRSA guidance in adding links to the Adobe Acrobat™ Accessibility site to provide users with a tool for converting PDF files to HTML.
  • Scripts. This Web site identifies the functionality of any scripting languages (e.g., Java and Javascript) used to display content or to create interface elements.
  • Applets and plug-ins. This Web site avoids the use of applets (i.e., programs designed to be executed from within another program) and plug-ins (i.e., programs that add features to a standard browser), and, when such programs are used, the site includes links to external Web sites that provide such applets or plug-ins via download.
  • Forms. This Web site allows for easy access to and completion of forms.
Accessibility Assistance

If you need information from a Web page that is not easily accessible, choose one of the following two options:

(1) Use access.adobe.com 
If you are unable to access PDF files on this Web site, type the URL of the inaccessible file into the Adobe Acrobat™ Accessibility site (access.adobe.com/access), to convert the PDF file to an HTML format that your screen reader can read.

(2) E-mail us
If you experience problems converting PDF files to an HTML format that your screen reader can read, e-mail our Webmaster with the URL of the page you wish to access, the technology you are using, and your phone number and e-mail address. We will work with you to make the files available in a format you can use.

Change the Font Size

You can use the instructions below to change the way your Web browser renders size of a font.

Internet Explorer (all versions)

You can increase or decrease the font size in Internet Explorer.

    • Increase Font Size: click View | Text Size | Larger
    • Decrease Font Size: click View | Text Size | Medium

Netscape Navigator and Communicator (version 4.xx)

You can increase or decrease the font size in Netscape Navigator and Communicator.

    • Increase Font Size: click View | Increase Font
    • Decrease Font Size: click View | Decrease Font

Netscape Navigator (version 7) and Mozilla (version 1.0+)

You can increase or decrease the font size in Netscape Navigator.

    • Increase Font Size: click View | Text Zoom | 120%
    • Decrease Font Size: click View | Text Zoom | 100%

If you have comments or suggestions about the accessibility of this Web site, please contact the Webmaster.

General Browser Requirements for Optimal Viewing of the Web Site

General browser requirements are Netscape Firefox 4.7 or higher or Microsoft Internet Explorer 5 or higher. For the best experience, we recommend updating your browser to the most recent version.

Download Internet Explorer

Download Firefox

To view .PDF files, you need Adobe Acrobat or the free Adobe Reader installed on your computer.

Download Adobe Reader

Non-compliant documents

Due to their large size or complexity, we unfortunately are not able to make some documents compliant with Section 508 standards of the amended Rehabilitation Act of 1973. If you are a visually impaired assistive technology user, please contact the Child and Adolescent Health Measurement Initiative (CAHMI) directly at cahmi@ohsu.edu for assistance.

Legal and Privacy Policy

This website was developed and is maintained by the Child and Adolescent Health Measurement Initiative.

We do not display data about individual children on our website. All graphs, charts and tables include aggregrated data concerning groups of children. The Data Resource Center complies with all local, state, and federal laws and regulations.

Any information you have provided on our site will not be sold or given to any third parties. All information will be used only to help improve the site or to provide a customized experience of the site.

For more information regarding specific policies, please visit Oregon Health & Sciences University's Office of Integrity website.

About the Data Resource Center

About the Data Resource Center

Making data accessible to all. It’s your data…your story!

Mission

The mission of the Data Resource Center for Child and Adolescent Health (DRC) is to advance the effective use of public data on the status of children’s health and health-related services for children, youth and families in the United States. The DRC does this by providing hands-on access to national, state, and regional data findings from large population-based surveys. Data are collected from parents and thus contribute a much needed voice in the drive to improve the quality of health care for children and youth.

The data come from you. It’s your data, it’s your story.

The DRC provides easily accessible data that do not require statistical expertise. The DRC also provides technical assistance on the use of this data and contributes to the maternal and child health (MCH) field through publications and research on the quality of health care systems for children and children with special health care needs.

The DRC promotes active understanding and use of this data by policymakers, MCH program leaders and professionals, family and child health advocates and researchers in order to inform and advance key national and state child and youth health goals. Together we can use data to improve the quality of health care delivery in the United States for children and youth.

Available Data on the DRC Website

The DRC website includes national and state-level data on hundreds of child health indicators from the National Survey of Children’s Health (NSCH) and the National Survey of Children with Special Health Care Needs (NS-CSHCN). You can browse or search by keywords and topics to retrieve interactive data tables and graphs which allow users to select, view, compare, and download survey data results for the nation, all 50 states plus the District of Columbia and the 10 HRSA regions.

These standardized national, state and regional level population data are specifically designed to assist states with child health needs assessment, program planning and evaluation, policy and standards development, monitoring, training, applied research and development of systems of care for children and youth.

What You Can Do on the DRC Website
Sponsors of the Data Resource Center

The Data Resource Center for Child and Adolescent Health (DRC) is a project of the Child and Adolescent Health Measurement Initiative (CAHMI) housed at the Oregon Health & Science University. The DRC is supported by Cooperative Agreement 1-U59-MC06890-01 from the U.S. Department of Health and Human ServicesHealth Resources and Services AdministrationMaternal and Child Health Bureau (MCHB). With funding and direction from the MCHB, the surveys on the DRC website were conducted by the Centers for Disease Control and Prevention's National Center for Health Statistics. The Data Resource Center is responsible for the analyses, interpretations, presentations and conclusions included on this website.

Our Partners

In addition to our parternship with the Maternal and Child Health Bureau, the DRC partners with the National Center for Health Statistics (NCHS) at the CDC to provide this data. NCHS collects the data for both the National Survey of Children's Health and the National Survey of Children with Special Health Care Needs. Measures which derive from both surveys are developed collaboratively through a partnership between MCHB, NCHS and a Technical Expert Panel, in which the DRC is a member.

The DRC functions as a partnership with the Maternal and Child Health Bureau and the many users of the site. Numerous individuals, organizations and health agencies participate in the DRC Advisory Group which provides ongoing guidance and input on the features and implementation of the DRC website and other DRC activities.

We proudly partner with users of this website. Please tell us what you think and how we can improve.

Data Resource Center Staff

Use our Ask a Question feature for the quickest assistance from DRC staff.

Main office line: 503-494-1930
Fax: 503-494-2475

Christina Bethell, PhD, MPH, MBA
Director of the CAHMI 
Professor, School of Medicine 
Department of Pediatrics
Oregon Health and Science University
503-494-1892
bethellc@ohsu.edu
 
Darika Batbayar
CAHMI Student Intern

batbayar@ohsu.edu
Lewis Notestine, MA
CAHMI Research Assistant II
503-494-5092
notestin@ohsu.edu
Carol Coello
CAHMI Senior Software Engineer
503-494-4571

coello@ohsu.edu
Julie Robertson, MSW, MPH
CAHMI Research Associate
503-494-9963

roberjul@ohsu.edu
Brianna Duffy, MPH
CAHMI Senior Research Assistant
503-494-1276

duffyb@ohsu.edu
Quinn Rohlf
CAHMI Student Web Development Intern

 
Peter Fisher
CAHMI Student Intern
fishepe@ohsu.edu

 
Jana Smilanich-Rose, MPA
CAHMI Program Administrator
503-494-1862

smilanic@ohsu.edu
Naraa Gombojav, PhD
CAHMI Senior Research Assistant
503-494-4304

gombojav@ohsu.edu
Scott Stumbo, MA
CAHMI Senior Research Associate
503-494-5942

stumbos@ohsu.edu
Richard LeDonne
CAHMI Research Assistant II
503-494-7984

ledonne@ohsu.edu
 
Bryn Wilson
CAHMI Administrative Assistant
503-494-1930

wilsobry@ohsu.edu
Olivia Lindly, MPH
CAHMI Research Associate
503-494-5508
lindly@ohsu.edu


 
Michael Winther
CAHMI Software Engineer
503-494-4419
wintherm@ohsu.edu
Katharine Zuckerman, MD, MPH
CAHMI Research Scholar
Assistant Professor, School of Medicine
Oregon Health and Science University
503-494-6726
zuckerma@ohsu.edu

DRC News

The latest announcements and news on child and adolescent health from the Data Resource Center.

Newsletters & Announcements from the DRC

New Data from the 2011/12 National Survey of Children's Health (03/22/2013)

State-by-state snapshots of new data from the 2011/12 National Survey of Children's Health (NSCH) arenow accessible on the Data Resource Center (DRC) website. Highlighted topics include childhood obesity,premature birthadverse childhood eventsmother's health, and as reported in this HRSA/CDC report, an increase in prevalence of ASD among US children age 2-17. 

Archived: MCHB DataSpeak "From Theory to Data to Practice - Practical Applications of The Life Course Approach" (02/26/2013)

Advancing the Maternal & Child Health Vision (02/07/2013)

The Data Resource Center for Child & Adolescent Health is committed to advancing the vision of maternal and child health programs. Join us at the Association of Maternal & Child Health Programs Annual Conference in Washington, DC from February 9 to 12, 2013.

Celebrate MCH Epi with Data and Tools from the DRC (12/07/2012)

Prevention and wellness across the life course is a national priority for the Maternal and Child Health Bureau. This includes a focus on environmental factors, systems of care, critical time-periods for development, and health equity. The DRC Web site contains point and click data from the National Survey of Children's Health and the National Survey of Children with Special Health Care Needs that map to key concepts embedded in the life course perspective, which promote a whole child approach to health over the life span. Here is a quick prieview of the data relevant to life course, found on the DRC.

Get Ready for APHA with a Life Course Perspective (10/25/2012)

The 140th annual meeting of the American Public Health Association is just around the corner! This year's theme brings attention to prevention and wellness across the life span, a national priority for the Maternal and Child Health Bureau. In this issue, the Data Resource Center for Child and Adolescent Health (DRC) highlights data from the national surveys relevant to implementing a life course approach for children--the timeline of their lives; the timing of their experiences; their physical, psychosocial and cultural environments; and issues of equity.

View full online newsletter archive

DRC in the News

A wide range of people and organizations use the Data Resource Center for research, policy development, education and more. Below are links to press coverage featuring the Data Resource Center on important topics related to children's health. Please see the examples of data usearticlespresentations and chartbooks sections of our website for additional resources on how others are using this important data.

February 22, 2013

Christina Bethell of the DRC presented at the 18th Annual Maternal and Child Health Epidemiology (MCH EPI) Conference, Engaging Families and Leveraging National and State Data to Advance Quality Improvement Partnerships. This interactive tool provides coverage of the entire presentation!

Select picture above or view other presentations 
at 2012 MCH EPI, Advancing Partnership: Data, Practice, and Policy.

January 15, 2013

The CAHMI worked with the Lucile Packard Foundation for Children’s Health (LPFCH) to create this comprehensive report on the health and well-being of the state’s children with special health care needs (CSHCN) population. This report features data from the 2009/10 NS-CSHCN and provides a profile of demographic characteristics, physical, mental, and social functioning, and health and community service needs of CSHCN in California. It is a companion piece and update to the 2010 report written in collaboration with LPFCH. The report is explain in this video and has been picked up by the press.

October 09, 2012

The DRC's Complementary and Alternative Medicine (CAM) Profiles were recently featured in a "twitter chat" on use of CAM therapies for children lead by AAP's Section for Integrative Medicine and the National Center for Complementary and Alternative Medicine (NCCAM)! See Dr. Lawrence D. Rosen's debriefing on the conversation,"Wanted: Increased Focus on Pediatric CAM Research".

August, 2012

Dr. Bethell was featured in AMCHP's monthly newsletter, PULSE, in a discussion of mindfulness techniques and their relation to maternal and child health. Mindfulness and MCH: Cultivating the Art and Science of Inside-Out Leadership

May 15, 2012

Dr. Bethell discusses environmental factors that impact obesity and family health with KPTV in Oregon. From KPTV:Is your neighborhood making you fat? Research shows that where you live could actually impact your health.

February 13, 2012

GoBeyond interviewed Dr. Bethell at this year's AMCHP Conference. Dr. Bethell discusses the importance of a resource like the DRC for advancing the quality of child and adolescent health.

December 5th 2011

The Oregon Health & Science University, Health Care, Home, School differ for Children With Special Health Care Needs

May 12th 2011

The Oregon Health & Science University, Study Finds States Vary in Children’s Health; Gaps Exist in Insurance, Quality Care Across Sectors

November 17th 2010

Lucile Packard Foundation for Children’s Health, California Ranks Last in U.S. on Index Measuring System of Care for Children with Special Health Care Needs, Study Finds 

March 2nd 2010

U.S. News & World Report, Child Obesity Rates Going Up

March 2nd 2010

Reuters, Snacks mean U.S. kids moving toward "constant eating"

January 14th 2010

United Press International, 6.7M children have no health insurance

November 23rd 2009

The National Initiative for Children's Healthcare Quality, Child Policy Research Center and Child and Adolescent Health Measurement Initiative Release

May 27th 2009

Kansas Health Institute, New child health data available

June 19th 2008

KATU.com, Oregon falling behind in children’s health care

Contact Us

The Child and Adolescent Health Measurement Initiative (CAHMI)

Oregon Health & Science University
Department of Pediatrics, School of Medicine
707 SW Gaines Road, Mail Code CDRC-P
Portland, OR 97239-2998
(503) 494-1930
Email us at: cahmi@ohsu.edu

Have a Question?

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Learn About the Surveys

National Survey of Children’s Health (NSCH)

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Data at a Glance

At your fingertips–easy-to-read data snapshots for each state

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childhealthdata @NCCAM the CAHMI will host workshop at#PAS2013 mind-body neuroscience and mindfulness to improve pediatrics. RSVP:childhealthdata.org/docs/drc/pas-w…2 days ago · reply · retweet · favorite

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The National Survey of Children's Health

The National Survey of Children’s Health (NSCH) touches on multiple, intersecting aspects of children’s lives—including physical and mental health, access to quality health care, and the child’s family, neighborhood and social context.

The Data Resource Center takes the results from the NSCH and makes them easily accessible to parents, researchers, community health providers and anyone interested in maternal and child health. Data on this site are for the nation and each of the 50 states plus the District of Columbia. State and national data can be further refined to assess differences by race/ethnicity, income, special health care needs status and a variety of other important demographic and health status characteristics.

The survey provides a broad range of information about children’s health and well-being collected in a standardized manner that allows comparisons across states as well as comparisons to the nation. It also serves to complement the National Survey of Children with Special Health Care Needs (NS-CSHCN) by providing data on the health of the general U.S. child population. Results in the NSCH can be stratified by CSHCN status to compare children with special health care needs to those without special needs.

The NSCH is a telephone survey conducted by the National Center of Health Statistics at the Centers for Disease Control under the direction and sponsorship of the federal Maternal and Child Health Bureau.  Survey results are weighted to represent the population of non-institutionalized children ages 0-17 nationally and in each state. Click here for more detailed information about how the survey is conducted.

Learn about the NSCH

National Survey of Children with Special Health Care Needs (NS-CSHCN)

Same as Above

The National Survey of Children with Special Health Care Needs (NS-CSHCN) takes a close look at the health and functional status of children with special health care needs in the U.S.—their physical, emotional and behavioral health, along with critical information on access to quality health care, care coordination of services, access to a medical home, transition services for youth, and the impact of chronic condition(s) on the child’s family.  

The Data Resource Center takes the results from the NS-CSHCN and makes them easily accessible to parents, researchers, community health providers and anyone interested in maternal and child health. Data on this site are for the nation and each of the 50 states plus the District of Columbia. State and national data can be further refined to assess differences by race/ethnicity, income, type of health insurance, and a variety of other important demographic and health status characteristics.

The survey provides a broad range of information about the health and well-being of CSHCN collected in a standardized manner that allows comparisons across states as well as comparisons with the nation. It also serves to complement the National Survey of Children’s Health (NSCH) by providing in-depth data on the unique health experiences of children with special health care needs (CSHCN).

The NS-CSHCN is a telephone survey conducted by the National Center of Health Statistics at the Centers for Disease Control under the direction and sponsorship of the federal Maternal and Child Health Bureau. Survey results are weighted to represent the population of non-institutionalized children ages 0-17 who are classified as having one or more special health care needs (CSHCN) nationally and in each state. Click here for more detailed information about how the survey is conducted.

Guide to Topics and Questions

The following Guides to Topics and Questions are interactive catalogs of the questions asked in each section of the NSCH or NS-CSHCN. The interactive web pages allow the user to click on a question to easily view the full question and answer options rather than have to look through a long and complex interview instruement. For the complete full-length instrument or survey sampling and administration of each survey, visit the Survey Methods and Documentation page.

National Survey of Children's Health

2011/12 NSCH Guide to Topics and Questions

2007 NSCH Guide to Topics and Questions

2003 NSCH Guide to Topics and Questions

National Survey of Children with Special Health Care Needs

2009/10 NS-CSHCN Guide to Topics and Questions

2005/06 NS-CSHCN Guide to Topics and Questions

2001 NS-CSHCN Guide to Topics and Questions

Fast Facts about the Survey

These quick guides help orient users of the national surveys to their content and design.  For a more detailed look at the content of each survey, please see the Guide to Topics and Questions. For more on the sampling and methodology of the survey, please see the Survey Methods and Documentation page.

Survey Methods and Documentation

Survey Sampling & Administration Process

A picture is worth a thousand words. These one-page diagrams summarize the sampling and administration steps for the national surveys.

Full Length Survey Instruments

Both the NSCH and NS-CSHCN use Computer-Assisted Telephone Interviewing (CATI) to conduct the surveys. The full length instruments are available in English and Spanish.

National Survey of Children's Health
National Survey of Children with Special Health Care Needs
Survey Design and Operations Manuals

Design and Operations Manuals are technical reports authored by the National Center for Health Statistics which describe the methodological details of survey sampling and data collection procedures.

Methods for analyzing complex sample survey data

Presentation from the National Center for Health Statistics  with guidelines and examples of SAS, SUDAAN and STATA code for obtaining estimates of variability and statistical significance from the NSCH and NS-CSHCN.(document non-508 compliant).

SAS & SPSS Codebooks

The codebooks below are developed by the Data Resource Center. These codebooks provide the SPSS or SAS syntax and documentation used to create all major indicators and subgroup variables that are displayed on the Data Resource Center interactive data query. For NSCH Codebooks, syntax is provided to create over 80 Key Child Health Indicators and demographic groups. For NS-CSHCN Codebooks, syntax is provided to create the 15 Child Health Indicators and the six Maternal and Child Health Bureau Outcome measures plus demographic subgroups. For additional information on variable construction, please see our Ask a Question feature. 

Browse the Data

Browse by Survey & Topic

To begin your interactive data search: 1) Select a Survey, Survey Year, and State or Region. 2) Select your desired Topic/Starting Point. 3) Select your indicator or measure.

This will direct you to a results page where you can compare across states, regions and by numerous subgroups.

1. Select a Survey, Year, and Geographic Area

Get State Snapshots

Data Snapshots are national and state-level profiles featuring whole child overviews or topic-specific reports. You can view an available snapshot or customize your own data snapshot. Customizable profiles, where you can choose your own indicators, are marked with an asterisk*.

To view your data snapshot, follow the two easy steps below:

  1. Click on the map (a state, region or nationwide) to view your snapshot.

  2. Select a snapshot from the list of categories below.

1. Click on your state, HRSA Region, or Nationwide to view your snapshot.

 

2. Select a Snapshot from the Categories below. Customizable profiles, where you can choose your own indicators, are marked with an asterisk*. 
Nationwide
  • Overall Health and Health Care Topics
    Key Indicators of child health status, insurance and health care acccess, and family/social content
    • 2011/12 NSCH National and State Profile Pages
    • 2009/10 NS-CSHCN National and State Profile Pages
    • 2005/06-2009/10 NS-CSHCN Comparison National and State Profile Pages
    • 2007 NSCH National and State Chartbook Pages
    • 2007 NSCH Child Health Indicators Customizable Snapshot*
    • 2003 NSCH National and State Chartbook Pages
    • 2003 NSCH Child Health Indicators Customizable Snapshot*
    • 2003-2007 NSCH Comparison National and State Chartbook Pages
    • 2005/06 NS-CSHCN National and State Chartbook Pages
    • 2001 NS-CSHCN National and State Chartbook Pages
    • 2001-2005/06 NS-CSHCN Comparison National and State Chartbook Pages
  • Health Care System Quality and Performance
    Topic-Specific: Quality indicators, Medical Home & Health People 2010
    • 2007 NSCH Child Health and System Performance Snapshot
    • 2007 NSCH: Medical Home Performance Snapshot - All Children
    • 2007 NSCH: Medical Home Performance Snapshot - Special Needs Status & Insurance Type
    • 2009/10 NS-CSHCN: Medical Home Performance Snapshot - CSHCN Only
    • 2005/06 NS-CSHCN: Medical Home Performance Snapshot - CSHCN Only
  • Health Status and Disparities in Child Health Across Populations
    Topic-Specific: CSHCN vs. Non-CSHCN, Race/Ethnicity & Rural-Urban Status
    • 2007 NSCH Children with Special Health Care Needs in Context: A Portrait of States and the Nation
    • 2007 NSCH Disparities Snapshot: Special Health Care Needs*
    • 2007 NSCH Disparities Snapshot: Health Insurance*
    • 2007 NSCH Disparities Snapshot: Race/Ethnicity*
    • 2007 NSCH National Race/Ethnicity Snapshot* (National Only due to Sample Size)
    • 2007 NSCH National Rural Urban Commuting Area Snapshot* (National Only due to Sample Size)
    • 2005/06 NS-CSHCN: Within State Urban/Rural Snapshot
    • 2005/06 NS-CSHCN: Within State Urban/Rural Customizable Snapshot*
    • 2005/06 NS-CSHCN: CSHCN vs. Non-CSHCN (Referent Sample)
  • Specific Health Problems and Conditions
    Topic-Specific: Mental Health, Obesity, and Selected Conditions
    • 2009/10 NS-CSHCN: Condition-Specific Snapshot
    • 2009/10 NS-CSHCN: Condition-Specific Customizable Snapshot*
    • 2007 NSCH Mental & Emotional Health National and State Chartbook Pages
    • 2007 Childhood Obesity State Report Cards
    • 2005/06 NS-CSHCN: Condition-Specific Snapshot
    • 2005/06 NS-CSHCN: Condition-Specific Customizable Snapshot*
    • 2003 Childhood Obesity State Report Cards

Browse Data Trends

With the release of data from the 2009/10 National Survey of Children with Special Health Care Needs, the comparison of indicators across time—from the 2001, 2005/06 and 2009/10 implementations of the survey—are now possible. Additionally, data can be compared between the 2003 and 2007 National Survey of Children's Health.

Which measures can be compared across survey years in the NS-CSHCN?