Understanding, predicting, and preventing mortality from deaths of despair in Canada: a population-based approach
Bibliographic record
Abstract
Declines in life expectancy in the US have been attributed to increases in mortality from drug poisoning, alcohol abuse, and suicide, deaths that have collectively been referred to as “deaths of despair” (DoD). To date, there is very little knowledge of DoD in Canada in terms of its patterns, impact on life expectancy, and potential risk factors. This thesis addresses these important knowledge gaps through three distinct aims. The first aim examined patterns in DoD in Canada between 2001 and 2017 overall and separately by age, sex, province, and marital status. Results showed that between 2001 and 2017, the rate of mortality from drug poisoning increased from 4.3 to 14.9 per 100,000, representing an increase of 10.6 per 100,000 (or 246%). Comparatively, little change (<25%) was observed in rates of alcohol- or suicide-related deaths. The second aim estimated contributions of DoD to life expectancy in Canada between 2001 and 2017 overall and separately by sex. Results suggest that if there was no change in the incidence of drug poisoning mortality between 2001 and 2017, the increase in life expectancy would have been 0.26 years greater overall, 0.38 years greater among males, and 0.11 years greater among females. Changes in rates of mortality from alcohol abuse and suicide were associated with much smaller changes in life expectancy (<0.06 years among males and <0.03 years among females) during the same period. The third aim developed and evaluated prediction models for drug poisoning mortality. The most important predictors of drug poisoning mortality were age, sex, province, smoking status, home ownership status, self-rated general health, employment status, and marital status. The evaluation of the prediction models showed that socioeconomic data and machine learning methods could be leveraged to improve the prediction of drug poisoning mortality. By providing substantial novel insight into DoD in Canada, this thesis represents a significant leap forward in the understanding, prediction, and prevention of DoD among Canadians. The findings can help inform public health policy and resource allocation, provide directions for studies on causal pathways, and improve the prediction of drug poisoning mortality in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 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".