Income inequality and ‘hospitalisations of despair’ in Canada: a study on longitudinal, population-based data
Bibliographic record
Abstract
BACKGROUND: Rates of drug overdoses, alcohol-related liver disease and suicide attempts represent a major public health burden in Canada. While the existing literature does highlight some evidence of association between income inequality and mental health and deaths of despair, no existing research has investigated more intermediate events. As such, the objective of the current study is to investigate the association between income inequality and hospitalisations of despair over time. METHODS: Data from the 2006 Canadian Census, the 2007/2008 Canadian Community Health Survey and the 2007-2018 Discharge Abstract Database were linked. Data were analysed using Cox proportional hazards modelling accounting for robust standard errors at the area level to investigate associations between income inequality at baseline and hazards for hospitalisations of despair, hospitalisations attributable to drug overdose, alcohol-related liver disease and suicide attempts, and all-cause hospitalisations, while controlling for sociodemographics characteristics (including income) and relevant area-level variables. RESULTS: The results highlighted statistically significant associations between income inequality and hazard of hospitalisations of despair (HR 1.38, 95% CI 1.06 to 1.80), hospitalisations related to drug overdose (HR 1.51, 95% CI 1.07 to 2.13) and all-cause hospitalisations (HR 1.17, 95% CI 1.05 to 1.30). The association between income inequality and hospitalisations related to alcohol-related liver disease and suicide attempts/self-harm were not statistically significant. CONCLUSION: Overall, the results showed evidence of associations between income inequality and hospitalisations of despair, drug overdose-related hospitalisations and all-cause hospitalisations. These findings are applicable to upstream policy discussion regarding reducing income inequality and identify potential points of intervention for prevention of drug overdose, alcohol-related liver disease and suicide attempts/self-harm.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".