1. Logical Edits of Health Insurance Coverage in the ACS and the CPS ASEC American Association for Public Opinion Research Phoenix, Arizona May 13, 2011 Joanna Turner and Michel Boudreaux State Health Access Data Assistance Center (SHADAC) University of Minnesota, School of Public Health Supported by a grant from The Robert Wood Johnson Foundation

2. Outline Motivation Overview of editing and description of logical edits Impact of the edits Preliminary evaluation of the edits using Medicaid administrative records 2

3. Motivation Health insurance coverage is generally underreported in surveys Health insurance is a particularly difficult concept for respondents and is prone to more response error than other socioeconomic concepts To our knowledge no study has carefully examined the quality of the logical edits 3

4. U.S. Census Bureau Surveys Current Population Survey Annual Social and Economic Supplement (CPS ASEC) 20+ years of health insurance coverage data American Community Survey (ACS) Added a question on health insurance coverage in 2008 Implemented logical edits in 2009 4

5. Overview of Editing Survey data are edited prior to release Interview-based verification of coverage Coding of write-in/open-ended responses Imputation edits assign a value to missing responses based on attributes that the respondent shares with other respondents who completed the question 5

7. Deterministically assign public coverage to people who not report it These edits are used as a partial correction for under-reporting types of coverage Coverage is never taken away with these edits Results in fewer uninsured 6

8. Examples of Logical Edit Concepts: ACS Medicare People that are 65 years or older and have Medicaid or Social Security/Railroad retirement benefits Medicaid Foster children SSI enrollees in “SSI states” (if parent not working or disability) Unmarried child (<19 years) with a parent on Medicaid or public assistance Adults receiving public assistance if citizens and parents Spouse of adult receiving Medicaid if citizens and parents TRICARE/Military Health Care People on active duty; the non-privately insured spouse or child (<21 years) of an active duty person 7

9. Development of Logical Edits Agreement among internal, Census Bureau, and external experts ACS edits derived from edits used in the CPS ASEC and recommendations from a Technical Advisory Group Aim to be conservative and avoid edits involving complex coding specifications that vary by sub-group or need frequent updating For example in ACS only the SSI edit is state specific 8

10. Impact of the Logical Edits on Percentage Uninsured: 2008 9 Note: The tabulated ACS population is the civilian non-institutionalized population except for the Military tabulations which include active duty personnel. Source: Lynch, V., Boudreaux, M., and Davern, M., 2010, “Applying and Evaluating Logical Coverage Edits to Health Insurance Coverage in the American Community Survey” available at http://www.census.gov/hhes/www/hlthins/publications/coverage_edits_final.pdf.

11. Impact of the Logical Edits by Coverage Type: 2008 10 About 1.4 million reclassified as insured from uninsured About 3.1 million reclassified as insured from uninsured

12. SNACC Project Multi-phase research project linking survey records to state administrative records from the Medicaid Statistical Information System (MSIS) SHADAC (grant from Robert Wood Johnson Foundation) National Center for Health Statistics (NCHS) Assistant Secretary for Planning and Evaluation (ASPE) Administration for Healthcare Research and Quality (AHRQ) Centers for Medicare and Medicaid Services (CMS) U.S. Census Bureau Medicaid Undercount (survey respondents do not report coverage when administrative records indicate coverage) – about 32% in the CPS ASEC 11

14. False Negative Rate: C/(A+C)

15. Specificity: D/(B+D)

17. False Negative Rate: C/(A+C) 14 Proportion of linked records that are classified in the CPS ASEC as not being Medicaid Enrollees Source: Author calculations from SNACC report available at http://www.census.gov/did/www/snacc

18. Specificity: D/(B+D) 15 Proportion of matchable, but unlinked records*, that are classified in the CPS ASEC as not being Medicaid Enrollees Source: Author calculations from SNACC report available at http://www.census.gov/did/www/snacc

19. Positive Predictive Value: A/(A+B) 16 Proportion of CPS ASEC records classified as having Medicaid that are found on MSIS having full benefits Source: Author calculations from SNACC report available at http://www.census.gov/did/www/snacc

20. Conclusions The logical edits seems to be working as well as the survey questions themselves and should be continued Room for improvement in the logical edits The Affordable Care Act will have far-reaching impacts on health insurance coverage and the logical edits may need to be revised based on implementation 17

21. SHADAC Resources SHADAC’s Data Center:http://www.shadac.org/datacenter SHADAC Brief: “A Summary of the American Community Survey Logical Edits Applied to Health Insurance Coverage” available at: http://www.shadac.org/publications/summary-american-community-survey-logical-edits-applied-health-insurance-coverage 18

22. 19 Contact Information Joanna Turner State Health Access Data Assistance Center University of Minnesota, Minneapolis, MN www.shadac.org turn0053@umn.edu 612-624-1632 ©2002-2009 Regents of the University of Minnesota. All rights reserved.The University of Minnesota is an Equal Opportunity Employer