Lauren E Griffith1, Andrea Gruneir2, Kathryn A Fisher3, Ross Upshur4, Christopher Patterson5, Richard Perez6, Lindsay Favotto7, Maureen Markle-Reid8, Jenny Ploeg3. 1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. Electronic address: griffith@mcmaster.ca. 2. Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada; ICES, Toronto, Ontario, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada. 3. School of Nursing, McMaster University, Hamilton, Ontario, Canada. 4. Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Bridgepoint Collaboratory for Research and Innovation, Sinai Health System, Toronto, Ontario, Canada. 5. Department of Medicine, McMaster University, Hamilton, Ontario, Canada. 6. ICES, McMaster University, Hamilton, Ontario, Canada. 7. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; ICES, McMaster University, Hamilton, Ontario, Canada. 8. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; School of Nursing, McMaster University, Hamilton, Ontario, Canada.
Abstract
OBJECTIVE: The 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 SETTING: Self-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. RESULTS: Regardless 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. CONCLUSION: A 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.
OBJECTIVE: The 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 SETTING: Self-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. RESULTS: Regardless 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. CONCLUSION: A 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.
Authors: Lauren E Griffith; Andrea Gruneir; Kathryn A Fisher; Ross Upshur; Christopher Patterson; Richard Perez; Lindsay Favotto; Maureen Markle-Reid; Jenny Ploeg Journal: J Comorb Date: 2020-06-26