Comparison of pharmacy‐based and diagnosis‐based comorbidity measures from medical administrative data

Purpose Health status is sometimes quantified by chronic condition (CC) scores calculated from medical administrative data. We sought to modify two pharmacy‐based comorbidity measures and compare their performance in predicting hospitalization and/or death. The reference was a diagnosis‐based score. Methods One of the two measures applied an updated approach linking specific ATC codes of dispensed drugs to 22 CCs; the other used a list of 37 drug categories, without linking them to specific CCs. Using logistic regressions that took repeated measures into account and hospitalization and/or death the following year as the outcome, we assigned weights to each CC/drug category. Comorbidity scores were calculated as the weighted sum of the 22 CCs/37 drug categories. We compared the performance of both measures in predicting hospitalization and/or death with that of a diagnosis‐based score based on 30 groups of long‐term illnesses (LTIs), a status granted in France to exempt beneficiaries with chronic diseases from copayments. We assessed the predictive performance of the scores with the quasi‐likelihood under the independence model criterion (QIC), the c statistic and the Brier score. Results The two pharmacy‐based scores performed better than the LTI score, with lower QIC and Brier scores and higher c statistics. Their predictive performance was very similar. Conclusions While there is no clear consensus or recommendations about the optimal choice of comorbidity measure, both pharmacy‐based scores may be useful for limiting confounding in observational studies among general populations of adults from health insurance databases. Copyright © 2016 John Wiley & Sons, Ltd.