Premature and Avoidable Mortality by Neighbourhood Income in Urban Canada
Bibliographic record
Abstract
Background: Canadians are among the healthiest people in the world. Policy initiatives, such as implementing universal healthcare, have been employed to distribute this health equally among different groups of people in the country. Yet, health inequalities still exist and, in some cases, are growing across Canada. A key step to address health inequalities is to improve the measurement, monitoring, and reporting of social determinants of health, population health status, and the extent of health disparities.\nPurpose: The underlying purpose of this study is to provide evidence on health inequalities and how these inequalities are related to income disparities in urban Canada, at the municipal, provincial, and national levels, to empower policymakers in evidence-based decision-making to implement policies to tackle health disparity in Canada.\nMethods: This study was conducted in two phases. The first phase was a scoping review on application of the concept 'avoidable mortality' in socioeconomic-related health inequality research. The second phase of this study was a retrospective population-based analysis, conducted in two parts; first, the Canadian Vital Statistics - Death Database (CVSD) from 2011 to 2015 and Canadian Censuses 2011 and 2016 were used to calculate all-cause, premature, avoidable, preventable, and treatable mortality rates at municipal, provincial, and national levels; then, the National Household Survey (NHS) 2011 was used to measure income inequality in mortality by calculating concentration index, at municipal, provincial, and national levels.\nResults: Findings spanning the period of 2011 to 2015 suggest that overall, income inequality in mortality exists at every level of geography in Canada. This study found a higher inequality in preventable mortality as compared to the inequality in all-cause, premature, avoidable, and treatable mortality, at all geographic levels in Canada. Another finding of this study shows that, at the provincial level, Saskatchewan was the only province with an inequality higher than national level inequality in all the mortality indicators. At the Census Metropolitan Area (CMA) level, Winnipeg was the most unequal CMA in premature, avoidable, preventable, and treatable mortality indicators over the study period.\nConclusion: Findings of this study highlight the need for an improvement in policies whose primary purpose is prevention, to curb the preventive mortality burden and reduce disparity in mortality. Additionally, the geographic variation in inequality found in this study points to the need for future research on identifying the factors leading to this difference in inequality across jurisdictions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".