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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
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Transcript of Pres aapor2011 may13_turner
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 BoudreauxState Health Access Data Assistance Center (SHADAC)University of Minnesota, School of Public Health
Supported by a grant from The Robert Wood Johnson Foundation
www.shadac.org
Outline
• Motivation
• Overview of editing and description of logical edits
• Impact of the edits
• Preliminary evaluation of the edits using Medicaid administrative records
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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
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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
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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
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Logical Edits
• Goal is to correct individual cases for inconsistent information in the survey
• 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
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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
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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
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Impact of the Logical Edits on Percentage Uninsured: 2008
ACS CPS ASEC
No Edit Edit Diff No Edit Edit Diff
All Edits 15.1 14.6 0.5 16.4 15.4 1.0
Medicare 15.1 15.0 0.1 16.4 16.3 0.1
Medicaid 15.1 14.7 0.4 16.4 15.7 0.7
Military 15.0 15.0 0.0 16.4 16.3 0.1
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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.
Note: The tabulated ACS population is the civilian non-institutionalized population except for the Military tabulations which include active duty personnel.
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Impact of the Logical Edits by Coverage Type: 2008
Med-icaid86%
Medicare12%
Military2%
ACS
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Medicaid72%
Medicare13%
Military15%
CPS ASEC
About 1.4 million reclassified as insured from uninsured
About 3.1 million reclassified as insured from uninsured
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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
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Preliminary Evaluation of the Logical Edits using the SNACC Data
MSIS
Yes No
CPSASEC
Yes A B
No C D
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• Sensitivity: A/(A+C)
• False Negative Rate: C/(A+C)
• Specificity: D/(B+D)
• Positive Predictive Value (PPV): A/(A+B)
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Sensitivity: A/(A+C)
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Pre-Edit Post-Edit % Change
Total 55.2 57.1 3.4
Under Age 5 59.8 61.7 3.1
Age 6 to 14 57.2 58.6 2.5
Age 15 to 17 51.6 54.5 5.6
Age 18 to 44 47.5 49.8 4.9
Age 45 to 64 64.0 65.9 2.9
Age 65+ 56.7 59.3 4.5
Proportion of linked records that are classified in the CPS ASEC as Medicaid Enrollees
Source: Author calculations from SNACC report available at http://www.census.gov/did/www/snacc
MSIS
Yes No
CPSASEC
Yes A B
No C D
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False Negative Rate: C/(A+C)
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Pre-Edit Post-Edit % Change
Total 44.8 42.9 -4.2
Under Age 5 40.2 38.3 -4.7
Age 6 to 14 42.8 41.4 -3.3
Age 15 to 17 48.4 45.5 -5.9
Age 18 to 44 52.5 50.2 -4.4
Age 45 to 64 36.0 34.1 -5.2
Age 65+ 43.3 40.7 -5.9
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
MSIS
Yes No
CPSASEC
Yes A B
No C D
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Specificity: D/(B+D)
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Pre-Edit Post-Edit % Change
Total 97.8 97.4 -0.5
Under Age 5 95.4 95.7 -0.8
Age 6 to 14 96.4 95.8 -0.6
Age 15 to 17 97.5 97.0 -0.5
Age 18 to 44 98.6 98.2 -0.4
Age 45 to 64 98.7 98.2 -0.5
Age 65+ 96.2 96.0 -0.3
* Cases in the CPS ASEC with valid linking identification, but were not found in the MSIS, or were not full benefit enrollees in the MSIS
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
MSIS
Yes No
CPSASEC
Yes A B
No C D
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Positive Predictive Value: A/(A+B)
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Pre-Edit Post-Edit % Change
Total 78.3 76.2 -2.7
Under Age 5 86.1 85.0 -1.3
Age 6 to 14 83.1 81.6 -1.8
Age 15 to 17 81.7 80.9 -1.0
Age 18 to 44 78.3 75.6 -3.5
Age 45 to 64 72.9 68.2 -6.4
Age 65+ 57.6 58.0 0.8
Proportion of CPS ASEC records classified as having Medicaidthat are found on MSIS having full benefits
Source: Author calculations from SNACC report available at http://www.census.gov/did/www/snacc
MSIS
Yes No
CPSASEC
Yes A B
No C D
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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
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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
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Contact Information
©2002-2009 Regents of the University of Minnesota. All rights reserved.The University of Minnesota is an Equal Opportunity Employer
Joanna TurnerState Health Access Data Assistance Center
University of Minnesota, Minneapolis, MNwww.shadac.org
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