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Health Serv Res ; 44(3): 965-87, 2009 Jun.
Article in English | MEDLINE | ID: mdl-19187185

ABSTRACT

OBJECTIVE: To assess reasons why survey estimates of Medicaid enrollment are 43 percent lower than raw Medicaid program enrollment counts (i.e., "Medicaid undercount"). DATA SOURCES: Linked 2000-2002 Medicaid Statistical Information System (MSIS) and the 2001-2002 Current Population Survey (CPS). DATA COLLECTION METHODS: Centers for Medicare and Medicaid Services provided the Census Bureau with its MSIS file. The Census Bureau linked the MSIS to the CPS data within its secure data analysis facilities. STUDY DESIGN: We analyzed how often Medicaid enrollees incorrectly answer the CPS health insurance item and imperfect concept alignment (e.g., inclusion in the MSIS of people who are not included in the CPS sample frame and people who were enrolled in Medicaid in more than one state during the year). PRINCIPAL FINDINGS: The extent to which the Medicaid enrollee data were adjusted for imperfect concept alignment reduces the raw Medicaid undercount considerably (by 12 percentage points). However, survey response errors play an even larger role with 43 percent of Medicaid enrollees answering the CPS as though they were not enrolled and 17 percent reported being uninsured. CONCLUSIONS: The CPS is widely used for health policy analysis but is a poor measure of Medicaid enrollment at any time during the year because many people who are enrolled in Medicaid fail to report it and may be incorrectly coded as being uninsured. This discrepancy should be considered when using the CPS for policy research.


Subject(s)
Health Care Surveys , Medicaid/statistics & numerical data , Medical Record Linkage , Adolescent , Adult , Aged , Bias , Censuses , Centers for Medicare and Medicaid Services, U.S. , Child , Child, Preschool , Community Health Planning , Data Collection/methods , Data Collection/standards , Female , Health Care Surveys/methods , Health Care Surveys/standards , Health Policy , Humans , Infant , Insurance Coverage/statistics & numerical data , Male , Medical Record Linkage/methods , Medically Uninsured/statistics & numerical data , Middle Aged , Poverty/statistics & numerical data , Unemployment/statistics & numerical data , United States
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