Using Self-reports or Claims to Assess Disease Prevalence: It’s Complicated

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Abstract

Background:

Two common ways of measuring disease prevalence include: (1) using self-reported disease diagnosis from survey responses; and (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.

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