@article {9155, title = {Using self-reports or claims to assess disease prevalence: It{\textquoteright}s complicated.}, journal = {Medical Care}, volume = {55}, year = {2017}, pages = {782-788}, abstract = {

BACKGROUND: Two common ways of measuring disease prevalence include (1) using self-reported disease diagnosis from survey responses; (2) using disease-specific diagnosis codes found in administrative data. Because they do not suffer from self-report biases, claims are often assumed to be more objective. However, it is not clear that claims always produce better prevalence estimates.

OBJECTIVE: Conduct an assessment of discrepancies between self-report and claims-based measures for 2 diseases in the US elderly to investigate definition, selection, and measurement error issues which may help explain divergence between claims and self-report estimates of prevalence.

DATA: Self-reported data from 3 sources are included: the Health and Retirement Study, the Medicare Current Beneficiary Survey, and the National Health and Nutrition Examination Survey. Claims-based disease measurements are provided from Medicare claims linked to Health and Retirement Study and Medicare Current Beneficiary Survey participants, comprehensive claims data from a 20\% random sample of Medicare enrollees, and private health insurance claims from Humana Inc.

METHODS: Prevalence of diagnosed disease in the US elderly are computed and compared across sources. Two medical conditions are considered: diabetes and heart attack.

RESULTS: Comparisons of diagnosed diabetes and heart attack prevalence show similar trends by source, but claims differ from self-reports with regard to levels. Selection into insurance plans, disease definitions, and the reference period used by algorithms are identified as sources contributing to differences.

CONCLUSIONS: Claims and self-reports both have strengths and weaknesses, which researchers need to consider when interpreting estimates of prevalence from these 2 sources.

}, keywords = {Medicare linkage, Medicare/Medicaid/Health Insurance, Survey Methodology}, issn = {1537-1948}, doi = {10.1097/MLR.0000000000000753}, author = {Patricia A St Clair and Gaudette, {\'E}tienne and Zhao, Henu and Tysinger, Bryan and Seyedin, Roxanna and Dana P Goldman} }