Advancing the measurement of health inequalities in Canada with linked health and social data
Bibliographic record
Abstract
IntroductionLinking health and socio-demographic data is providing new opportunities for measuring health inequalities in Canada. Measuring inequalities across relevant population sub-groups is a key step for understanding progress towards achieving health equity and informing interventions to reduce inequities. Objectives and ApproachThis study uses two linked national datasets (excluding Quebec): 1) Statistics Canada’s linkage of the 2006 long-form Census to hospital data from the Canadian Institute for Health Information’s 2006-2008 Discharge Abstract Database (DAD); 2) hospital data linked to area-level income using Statistics Canada’s Postal Code Conversion File tool. Study objectives are to compare hospitalization rates between the linked census cohort and the general population and to inform the measurement of inequalities by income and education. Analysis was based on three hospitalization indicators: chronic obstructive pulmonary disease (COPD) among ResultsThe linked cohort weighted to the population represented 67\% of DAD cases for asthma, 77\% for COPD and 80\% for heart attack. The lower coverage for asthma was because babies born after Census day were not linked. For all three indicators, rates were lower in the linked cohort than the DAD, likely because the linked cohort and DAD capture somewhat different populations (e.g., institutionalized population is in DAD but not Census). Large income and education-related inequalities were observed for all three indicators. Further analyses revealed inequalities of similar magnitude using before and after-tax income data, and larger inequalities by individual-level income compared to area-level (for COPD only). Inequalities were similar using individual and household education, with a clear gradient using multiple education categories. Conclusion/ImplicationsUpdating and expanding existing linkages to include recent data has the potential to expand and integrate health inequalities measurement into routine health system performance reporting. Challenges include measuring inequalities for smaller populations (e.g., health regions) and gaps in Census coverage (e.g., newborns/newcomers, urban indigenous populations, people experiencing homelessness, institutionalized population).
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.031 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".