title: Comparison of pharmacy-based and diagnosis-based comorbidity measures from medical administrative data.
abstract: OBJECTIVE
  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.
authors.affiliation: INSERM, UMR912 (SESSTIM), Marseille, France.|Aix Marseille Université, UMR_S912, IRD, Marseille, France.|Observatoire Régional de la Santé Provence-Alpes-Côte d'Azur, Marseille, France.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Cortaredona
authors.affiliation: Univ. Bordeaux, U657, Bordeaux, France.|INSERM, U657, Bordeaux, France.
authors.forename: Elodie
authors.initials: E
authors.lastname: Pambrun
authors.affiliation: Univ. Bordeaux, U657, Bordeaux, France.|INSERM, U657, Bordeaux, France.|Centre Hospitalier Charles Perrens, Bordeaux, France.
authors.forename: Hélène
authors.initials: H
authors.lastname: Verdoux
authors.affiliation: INSERM, UMR912 (SESSTIM), Marseille, France.|Aix Marseille Université, UMR_S912, IRD, Marseille, France.|Observatoire Régional de la Santé Provence-Alpes-Côte d'Azur, Marseille, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Verger
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease; D015897:Comorbidity; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D005602:France; D006304:Health Status; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010593:Pharmaceutical Services / Q000706:statistics & numerical data*; D017891:Pharmacoepidemiology / Q000379:methods*; D055815:Young Adult
keywords: administrative claims; chronic disease; comorbidity; health; healthcare; pharmacoepidemiology