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
Bibliographic record
Abstract
Abstract Building on Canadian data at the provincial, regional, community, and personal levels, the Canadian Social Determinants Urban Laboratory (CSDUL) will enable multilevel and longitudinal investigation of how social determinants of health (SDOH) impact population health (both mental and physical) and health inequities in Canada. Utilizing administrative data linkage, CSDUL will be developed by combining social, economic, and political mechanisms at multiple levels, from national to individual, following the World Health Organization (WHO) SDOH framework. Organized using a hub-and-node model, CSDUL will be created by validating unit and area-level indicators and merging survey and administrative data to provide a comprehensive understanding of SDOH at micro, meso, and macro levels. The project will replicate WHO/Europe’s decomposition analysis of income-related inequalities in self-reported health, assessing the relative impact of social determinants on health outcomes. Extended Abstract Introduction Two decades of research have highlighted persistent income-related health inequities in Canada at municipal, provincial, and national levels. 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 1 . 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. Discussion 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. Conclusion 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. Key Messages Building on Canadian data, the Canadian Social Determinants Urban Laboratory (CSDUL) is the first initiative of its kind to provide a comprehensive understanding of how social determinants impact health outcomes in Canadian cities. CSDUL will be a multilevel, longitudinal data Laboratory, organized using a hub-and-node model, operationalizing the WHO Social Determinants of Health framework After developing CSDUL, we will replicate WHO/Europe’s decomposition analysis of income-related inequalities in self-reported health for urban Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".