Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads

Multimorbidity measures from health administrative data using ICD system codes: A systematic review

BACKGROUND

We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification.

METHODS

We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria.

RESULTS

Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed.

CONCLUSION

There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.