Comparing US prevention efforts to other high-income countries
Bibliographic record
Abstract
Life expectancy in the USA is considerably lower than in most high-income countries, with many deaths considered preventable. The extent by which poor performance on prevention measures and public health policies in the USA could be contributing to this issue is not well understood. To address this issue, we compared publicly available population-based indicators of health care across different levels of prevention in the USA and six high-income countries (ie, Australia, Canada, Germany, France, Sweden, and the UK) and Organisation for Economic Co-operation and Development countries between 2010 and 2023. Relative to comparator countries, the USA had a younger population and lower smoking rates, but it had higher obesity prevalence, calorie intake, illicit drug use, and gun and vehicle ownership. Regarding public health policies that lie largely outside the health-care system, the USA compared unfavourably to comparator countries. For measures dependent on the health-care system, the USA performed well across several measures of clinical prevention, including screening rates and diagnosis and control of chronic conditions. However, the USA was worse on measures of access to health care and coverage. While the USA performs well in prevention efforts within the health-care system compared with other countries for people with access to the system, it faces greater risk from external factors, generally worse dietary intake, and implements weaker public health prevention and regulation against harmful products that might exacerbate these issues. To improve population health, policy makers should prioritise multi-sectoral investments in prevention policies and improve access to health care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".