Real-world data from the Japanese National Health Insurance System enable fine phenotyping in a 14K-scale population-based study

Abstract

The use of medical databases, known as real-world data (RWD), enables accurate and efficient estimation of disease prevalence in population-based studies, making it a potential game-changer in epidemiology. However, the lack of standardized data formats across hospitals complicates integration across institutions. In Japan, health insurance claims data are standardized under a nationally unified format, providing a reliable source of structured RWD. We evaluated the utility of insurance claims data in epidemiological research. Incorporating both diagnosis and prescription information into case definitions resulted in four- and six-fold increases in the estimated prevalence of Alzheimers disease (AD) and Parkinsons disease (PD), respectively, compared with conventional self-reported definitions. Subsequent genome-wide association studies (GWAS) for AD showed increased model log-likelihood and identified a characteristic APOE signal, findings observed only with extended case definitions. The APOE effect size was consistent with large case-control studies, while standard errors remained comparable to smaller studies. These results indicate that claims-based phenotyping improves case identification without loss of accuracy and supports scalable approaches for genomic epidemiology and public health surveillance. Patient consent statementAll participants provided written informed consent and the study protocol was approved by the Institutional Review Board of Iwate Medical University (Approval number HG2021-009) Permission to reproduce material from other sourcesNo materials requiring permission from other sources have been used in this manuscript. Clinical trial registrationThis study does not involve interventional components and therefore was not registered as a clinical trial.