Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jan 31, 2025
Date Accepted: Aug 12, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Multilevel estimation of the relative impacts of social determinants on income-related health inequalities in urban Canada: Protocol for the Canadian Social Determinants Urban Laboratory
ABSTRACT
Background:
Two decades of research have highlighted persistent income-related health inequities in Canada at municipal, provincial, and national levels, but it remains underutilized.
Objective:
This project aims to examine how social, economic, and political factors create conditions that shape health inequalities, and investigate how structural and intermediate determinants explain health disparities across national, provincial, city, neighbourhood, and individual levels.
Methods:
We will create the Canadian Social Determinants Urban Laboratory (CSDUL), a multilevel, longitudinal virtual environment combining multiple surveys and administrative databases, guided by the WHO Social Determinants of Health framework. Initially covering 2011-2015, CSDUL will expand as more data becomes available. Organized in a hub-and-node model, it will include a central hub and five project nodes. We will develop and validate area-based indicators, merged with data to provide a comprehensive understanding of social determinants of health at micro, meso, and macro levels.
Results:
The primary research deliverables of this project will be to critically analyze the strengths and limitations of survey and administrative databases for health research and develop methods for deriving variables from them. After developing CSDUL, we will replicate WHO/Europe's income-related health inequality analysis for urban Canada and report on the impact of social determinants on health outcomes. A key strength of the proposed virtual data laboratory is its ability to examine how various determinants affect health at different levels and explore their impact on identifiable groups (e.g., by gender). It highlights the multifactorial nature of health and identifies the factors most likely to drive health outcomes, such as what makes Canadians healthy or sick.
Conclusions:
Multisectoral interventions are most effective when they are customized to meet the unique needs of specific sub-populations, using robust and multilevel data sources like CSDUL. Clinical Trial: N/A
Citation
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