• Lauren E Griffith, Andrea Gruneir, Kathryn A Fisher, Ross Upshur, Christopher Patterson, Richard Perez, Lindsay Favotto, Maureen Markle-Reid, and Jenny Ploeg.
• Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. Electronic address: griffith@mcmaster.ca.
• J Clin Epidemiol. 2020 Aug 1; 124: 163-172.

ObjectiveThe objective of the study was to compare multimorbidity prevalence using self-reported and administrative data and identify factors associated with agreement between data sources.Study Design And SettingSelf-reported cross-sectional data from four Canadian Community Health Survey waves were linked to administrative data in Ontario, Canada. Multimorbidity prevalence was examined using two definitions, 2+ and 3+ chronic conditions (CCs). Agreement between data sources was assessed using Kappa and Phi statistics. Logistic regression was used to estimate associations between agreement and sociodemographic, health behavior, and health status variables for each multimorbidity definition.ResultsRegardless of multimorbidity definition, prevalence was higher using administrative data (2+ CCs: 55.5% vs. 47.1%; 3+ CCs: 30.0% vs. 24.2%). Agreement between data sources was moderate (2+ CCs K = 0.482; 3+ CCs K = 0.442), and while associated with sociodemographic, health behavior, and health status factors, the magnitude and sometimes direction of association differed by multimorbidity definition.ConclusionA better understanding is needed of what factors influence individuals' reporting of CCs and how they align with what is in administrative data as policy makers need a solid evidence base on which to make decisions for health planning. Our results suggest that data sources may need to be triangulated to provide accurate estimates of multimorbidity for health services planning and policy.Copyright © 2020 Elsevier Inc. All rights reserved.

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