Related Experiment Video
Updated: Oct 28, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Examine the association between key determinants identified by the chronic disease indicator framework and
John S Moin1, Richard H Glazier2,3,4, Kerry Kuluski1,5
1University of Toronto, Institute of Health Policy Management and Evaluation (Dalla Lana School of Public Health), Toronto, ON, Canada.
Background:
Multimorbidity, often defined as having two or more chronic conditions is a global phenomenon. This study examined the association between key determinants identified by the chronic disease indicator framework and multimorbidity by rural and urban settings. The prevalence of individual diseases was also investigated by age and sex.
Methods:
The Canada Community Health Survey and linked health administrative databases were used to examine the association between multimorbidity, sociodemographic, behavioral, and other risk factors in the province of Ontario. A multivariable logistic regression model was used to conduct the main analysis.
Results:
Analyses were stratified by age (20-64 and 65-95) and area of residence (rural and urban). A total sample of n = 174,938 residents between the ages of 20-95 were examined in the Ontario province, of which 18.2% (n = 31,896) were multimorbid with 2 chronic conditions, and 23.4% (n = 40,883) with 3+ chronic conditions. Females had a higher prevalence of 2 conditions (17.9% versus 14.6%) and 3+ conditions (19.7% vs. 15.6%) relative to males. Out of all examined variables, poor self-perception of health, age, Body Mass Index, and income were most significantly associated with multimorbidity. Smoking was a significant risk factor in urban settings but not rural, while drinking was significant in rural and not urban settings. Income inequality was associated with multimorbidity with greater magnitude in rural areas. Prevalence of multimorbidity and having three or more chronic conditions were highest among low-income populations.
Conclusion:
Interventions targeting population weight, age/sex specific disease burdens, and additional focus on stable income are encouraged.
Related Concept Videos
Factors Affecting Illness
For instance, risk factors are connected to illness,...
Dimensions of Health and Illness
Models of Health Promotion and Illness Prevention II
The agent-host-environment model states that disease results...
Bias in Epidemiological Studies
Criteria for Causality: Bradford Hill Criteria - II
Lifestyle Factors and Health
Benefits of Physical Activity
Physical activity, whether through structured exercise or casual activities like walking, biking, or dancing, is a cornerstone of a...

