論文ID: 27004
Administrative claims data have become central resources for epidemiology and health services research in Japan. These datasets provide large sample sizes, longitudinal coverage, and standardized coding, and are increasingly treated as “ready-made” sources for descriptive, causal, and predictive research. However, they are by-products of an insurance and reimbursement system, not purpose-built research registries. What is captured, and what is missing, is determined by institutional arrangements, fee schedules, and clinical workflows. Without understanding how these data are generated and processed, investigators risk misinterpreting variables, underestimating bias, and over-trusting sophisticated analyses.
In this article, we argue that rigorous research using Japanese claims data must begin with an explicit understanding of data provenance—how and why information enters the database, and data processing —how heterogeneous records are transformed into analytic datasets. We outline key elements of provenance for Japanese health insurance claims, including insurer types, separation of medical and dispensing claims, and the evolution of coding and reimbursement rules. We then describe typical processing steps required to convert raw claims files into analysis-ready datasets, namely parsing file structures, constructing relational databases, linking to code master tables, and building cohorts tailored to specific research questions.
By making provenance and processing clearer, researchers can better recognize limitations, design appropriate epidemiological studies, and interpret findings in light of how the data came to exist. Rather than treating claims data as neutral “healthcare data”, this perspective highlights their origins as administrative records and emphasizes that understanding their generation and transformation is a prerequisite for valid epidemiology.
