Claims-Based Enumeration Sampling (CBES): Utilizing Administrative Claims Data as a Sampling Frame for Patient...

Evidence-based policymaking increasingly demands integrating real-world data with patient perspectives. However, a methodological gap exists: administrative claims data provide comprehensive coverage but lack subjective insights, whereas patient experience surveys frequently experience selection bias owing to the lack of reliable sampling frames. This study introduces Claims-Based Enumeration Sampling (CBES), a methodology utilizing administrative claims data as a sampling frame to substitute for clinical registries.By linking survey responses with claims data at the individual level, CBES facilitates the calculation of sampling probabilities and the application of weighting for ensuring representativeness. We demonstrate the practical feasibility of this design through a case study conducted in Tsukuba City, Japan. Targeting National Health Insurance beneficiaries with diabetes, the survey was implemented as an insurer-commissioned administrative operation, successfully overcoming privacy- and data access-related legal hurdles. The resulting dataset revealed crucial insights into patient stigma and socioeconomic disparities, factors invisible to claims analysis alone.This study discusses the theoretical framework, statistical advantages, and legal solutions related to CBES. We conclude that CBES provides a robust and scalable alternative to conventional methods, empowering policymakers to capture the "silent voices" of patients and advancing patient-centered healthcare policy.