The contributions of avoidable causes of death to gender gap in life expectancy and life disparity in the US and Canada: 2001–2019
Bibliographic record
Abstract
OBJECTIVES: This study measures public health policies' and healthcare system's influence, by assessing the contributions of avoidable deaths, on the gender gaps in life expectancy and disparity (GGLD and GGLD, respectively) in the United States (US) and Canada from 2001 to 2019. METHODS: To estimate the GGLE and GGLD, we retrieved age- and sex-specific causes of death from the World Health Organization's mortality database. By employing the continuous-change model, we decomposed the GGLE and GGLD by age and cause of death for each year and over time using females as the reference group. RESULTS: In Canada and the US, the GGLE (GGLD) narrowed (increased) by 0.9 (0.2) and 0.2 (0.3) years, respectively. Largest contributor to the GGLE was non-avoidable deaths in Canada and preventable deaths in the US. Preventable deaths had the largest contributions to the GGLD in both countries. Ischemic heart disease contributed to the narrowing GGLE/GGLD in both countries. Conversely, treatable causes of death increased the GGLE/GGLD in both countries. In Canada, "treatable & preventable" as well as preventable causes of death narrowed the GGLE while opposite was seen in the US. While lung cancer contributed to the narrowing GGLE/GGLD, drug-related death contributed to the widening GGLE/GGLD in both countries. Injury-related deaths contributed to the narrowing GGLE/GGLD in Canada but not in the US. The contributions of avoidable causes of death to the GGLE declined in the age groups 55-74 in Canada and 70-74 in the US, whereas the GGLE widened for ages 25-34 in the US. CONCLUSION: Canada experienced larger reduction in the GGLE compared to the US attributed mainly to preventable causes of death. To narrow the GGLE and GGLD, the US needs to address injury deaths. Urgent interventions are required for drug-related death in both countries, particularly among males aged 15-44 years.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".