Using Health Insurance Claims Data to Estimate Epidemiological Indicators: Application Pathways and Key Challenges

AIM: To synthesize empirical methodological evidence on how health insurance claims data can be converted into prevalence and incidence estimates. METHODS: PubMed and Web of Science Core Collection were searched on July 10, 2026, for reports in English or Chinese published from January 1, 2016, to June 30, 2026. Eligible studies evaluated claims-based methods for prevalence, incidence, or epidemiological case identification. Data on study context, population definition, case algorithms, numerator/denominator construction, validation, and robustness were extracted, descriptively summarized, and narratively synthesized. RESULTS: 217 studies (217 reports), contributing 331 method instances, met the final methods-focused criteria. The review found that valid estimation of prevalence and incidence from health insurance claims data depends on population representativeness, accurate case identification, and appropriate data linkage and validation. Methodological choices, including denominator definition, case algorithms, look-back periods, index-date rules, and validation sources, substantially influenced estimates. CONCLUSIONS: Valid prevalence and incidence estimation from health insurance claims data requires alignment of the target population, observable membership time, recorded-disease rule, denominator or disease-free risk set, and event time or person-time within the same population and period. Each analytical extract nevertheless requires explicit assessment of coverage, continuity, service capture, coding, and linkage.