Contributions of injury deaths to changes in life expectancy and disparity: A comparative analysis of G7 countries over two decades
Bibliographic record
Abstract
BACKGROUND: Despite the high level of economic development in the Group of Seven (G7) countries, injury deaths remain a public health concern in these countries. This paper examines the contribution of injury deaths to changes in life expectancy (LE) and life disparity (LD) in the G7 countries. METHODS: We used annual data from the WHO mortality database to compute LE and LD during 2001-03 and 2017-19. The contributions of injury deaths to LE and LD changes for each sex were decomposed by age and cause using a continuous-change model. RESULTS: Across the G7 countries combined, LE (LD) increased by 2.12 (0.25) and 2.73 (0.16) years for females and males, respectively. While most injury-related deaths contributed to increases in LE and decreases in LD, these gains were offset by negative contributions of unintentional poisoning, resulting in an overall negligible net contributions of injury deaths to changes in LE/LD across the G7 countries combined. The country-specific patterns revealed notable variations. Positive contributions of injury-related causes to changes in LE were more prominent in France (+ 0.38/+0.64 years for females/males), while negative contributions were most evident in the USA (-0.23/-0.42 years for females/males). Transport accidents emerged as the leading contributors to improvements in both LE and LD among both sexes in all countries, with more pronounced effects in males. In contrast, unintentional poisoning had a substantial negative impact, particularly among younger populations in the USA, UK, and Canada. CONCLUSION: Injury deaths made negligible contributions to overall changes in LE and LD across the G7 countries combined during the study period. However, there were important variations by sex, age, cause and country. Specifically, unfavourable contributions of injury deaths were mainly observed in the USA, UK, and Canada. These findings highlight the need for targeted, country-specific injury prevention strategies to mitigate premature and unequal mortality.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".