Global risk factor rankings: the importance of age-based health loss inequities caused by alcohol and other risk factors
Bibliographic record
Abstract
BACKGROUND: Achieving health equity is a priority of the World Health Organization; however, there is a scant amount of literature on this topic. As the underlying influences that determine health loss caused by risk factors are age-dependent, the aim of this paper is to examine how the risk factor rankings for health loss differ by age. METHODS: Rankings were based on data obtained from the 2010 Global Burden of Disease study. Health loss (as measured by Disability Adjusted Life Years lost) by risk factor was estimated using Population-Attributable Fractions, years of life lost due to premature mortality, and years lived with disability, which were calculated for 187 countries, 20 age groups and both sexes. Uncertainties of the risk factor rankings were estimated using 1,000 simulations taken from posterior distributions RESULTS: The top risk factors by age were: household air pollution for neonates 0-6 days of age [95% uncertainty interval (UI): 1 to 1]; suboptimal breast feeding for children 7-27 days of age (95% UI: 1-1); childhood underweight for children 28 days to less than 1 year of age and 1-4 years of age (95% UI: 1-2 and 1-1, respectively); iron deficiency for children and youth 5-14 years of age (95% UI: 1-1); alcohol use for people 15-49 years of age (95% UI: 1-2); and dietary risks for people 50 years of age and older (95% UI: 1-1). Rankings of risk factors varied by sex among the older age groups. Alcohol and smoking were the most important risk factors among men 15 years of age and older, and high body mass and intimate partner violence were some of the most important risk factors among women 15 years of age and older. CONCLUSIONS: Our analyses confirm that the relative importance of risk factors is age-dependent. Therefore, preventing harms caused by various modifiable risk factors using interventions that target people of different ages should be a priority, especially since easily implemented and cost-effective public health interventions exist.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".