1 Saskatchewan Health Authority, Saskatoon, SK, Canada.
2 Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada.
3 School of Public Health, University of Saskatchewan, Saskatoon, SK, Canada.
4 Department of Political Science, McGill University, Montreal, QC, Canada.
5 Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, QC, Canada.
6 Johnson Shoyama Graduate School of Public Policy, University of Regina, Regina, SK, Canada.
7 Toronto Public Health, Toronto, ON, Canada.
8 Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada.
9 Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
10 Research Department, Saskatchewan Health Authority, Saskatoon, SK, Canada.
11 Graduate School of Public and International Affairs, University of Ottawa, Ottawa, ON, Canada.
12 Department of Sociology and Anthropology, Concordia University, Montreal, ON, Canada.
13 School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
14 Department of Sociology, McMaster University, Hamilton, ON, Canada.
15 Department of Agricultural and Resource Economics, University of Saskatchewan, Saskatoon, SK, Canada.

Background: Two decades of research have highlighted persistent income-related health inequities in Canada across municipal, provincial, and national levels. While there is broad consensus among researchers, advocates, and health professionals that social determinants are the primary drivers of health, the empirical foundation supporting this remains relatively limited. A current renaissance in health system data access offers an opportunity to assess the multilevel impact of social factors on health inequalities, yet this potential remains underused.

Objective: This project aims to examine how social, economic, and political conditions shape health inequalities and investigate how structural and intermediate determinants explain disparities across national, provincial, city, neighborhood, and individual levels.

Methods: We will create the Canadian Social Determinants Urban Laboratory (CSDUL), a multilevel, longitudinal, virtual data environment that integrates 15 existing databases from Statistics Canada, the Canadian Institute for Health Information, the Canadian Urban Environmental Health Research Consortium, and DMTI Spatial. Guided by the World Health Organization social determinants of health framework, CSDUL will initially cover 2011 to 2015 due to data completeness and expand as additional years become available. CSDUL builds on Statistics Canada's Canadian Population Health Survey and will link survey data to administrative and health records, including hospital discharges, ambulatory care, mortality, cancer registries, and longitudinal tax files. Area-level indicators will be added using historical postal codes and geospatial boundaries. Organized through a hub-and-node model, CSDUL includes a central hub and 5 research nodes. We will develop and validate area-based indicators to study social determinants at micro (individual), meso (neighborhood, city, and province), and macro (national) levels. A core deliverable is to assess the strengths and limitations of survey and administrative data for health research and derive variables accordingly. After developing CSDUL, we will replicate World Health Organization Regional Office for Europe income-related health inequality analysis for urban Canada and analyze the impact of social determinants on outcomes. We will apply a 2-fold Oaxaca-Blinder decomposition between the lowest and highest urban income quintiles. A major strength of CSDUL is its capacity to analyze how diverse determinants shape health across subgroups (eg, gender), identifying key drivers of health outcomes.

Results: The indicators to be used in CSDUL are being developed and validated by the contributing nodes. In collaboration with node 3, we are constructing measures of social capital using DMTI Spatial Points of Interest data. A prototype version of CSDUL incorporating a limited set of indicators has been developed in Statistics Canada's Research Data Centre. We anticipate receiving the finalized indicators from the nodes by August 2025 to September 2025 and aim to complete the decomposition analysis by December 2025.

Conclusions: Multisectoral interventions are most effective when they are customized to meet the unique needs of specific subpopulations using robust and multilevel data sources such as CSDUL.

International registered report identifier (irrid): DERR1-10.2196/71929.