Comparative predictive performance of the PopGrouper: internal validation of a population-based classification system for morbidity adjustment using German administrative data

Background To compare the predictive performance of a newly developed population classification system, the PopGrouper, with established morbidity measures used in administrative data research for 1-year mortality, hospitalisation, and healthcare costs. Methods This study presents an internal validation using an independent hold-out sample derived from 2022 to 2023 claims data from BARMER, a large German statutory health insurer covering about 10% of the national population (> 8 million). All adults (≥ 18 years) continuously insured in 2022–2023 or deceased in 2023 were included of which a 20%-random sample ( n = 1,310,273) was used as validation cohort. Regression models were estimated for different outcomes, using age-sex groups, the Charlson Comorbidity Index, Elixhauser comorbidities, PopGroup severity quintiles, Macro PopGroups, or PopGroups as morbidity adjustment methods. For mortality and hospitalisation, sparsely populated PopGroups were hierarchically aggregated to ensure a minimum number of events per group. Predictive performance was assessed using the coefficient of determination (R²) and root mean square error (RMSE) for log-transformed cost outcomes, and c-statistics for mortality and hospitalisation, based on 1,000 bootstrap samples. Results The PopGroup model explained 35.8% of the variation in log-transformed total healthcare costs, compared with 11.5% for age-sex groups, 13.2% for Charlson, and 18.6% for Elixhauser. Discrimination for mortality was excellent (c-statistic = 0.884 [95% CI: 0.882–0.886]) and acceptable for hospitalisation (0.714 [0.712–0.715]). Differences compared with conventional morbidity measures were most pronounced for healthcare cost prediction, whereas gains for mortality and hospitalisation discrimination were smaller. Sensitivity analyses yielded consistent findings, confirming robustness across model specifications. Conclusions The PopGrouper is an open and reproducible classification system which captures multimorbidity by building clinically and economically meaningful groups derived from administrative data. In this internal validation, it demonstrated strong performance across both clinical and economic outcomes, with the largest improvements observed for healthcare cost prediction. The PopGrouper may therefore provide a useful alternative tool for morbidity adjustment, particularly in applications involving healthcare planning, resource allocation, and population health management.