Methods for identifying 30 chronic conditions: application to administrative data

TLDR

Multimorbidity is common and linked to poor outcomes and high costs, and administrative data offer a promising means to study its epidemiology. The study aimed to develop and apply a new administrative‑data scheme to identify chronic conditions and multimorbidity by deriving validated ICD‑9/ICD‑10 algorithms for 40 morbidities. Algorithms were graded as high or moderate validity based on PPV and sensitivity thresholds, then applied to inpatient and outpatient claims for 574,409 Edmonton residents in 2008/2009 to identify 30 of the 40 morbidities. The 30‑condition panel identified 25 % of participants with multimorbidity, 25 % with a single morbidity, and 50 % with none, demonstrating the feasibility of using validated algorithms for multimorbidity surveillance and encouraging broader adoption.

Abstract

Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity.We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as "high validity"; those with positive predictive value ≥70% and sensitivity <70% were graded as "moderate validity". To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year.Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities.We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions.