Analyzing Whether Countries Are Equally Efficient at Improving Longevity for Men and Women
Bibliographic record
Abstract
OBJECTIVES: We examined the efficiency of country-specific health care spending in improving life expectancies for men and women. METHODS: We estimated efficiencies of health care spending for 27 Organisation for Economic Co-operation and Development (OECD) countries during the period 1991 to 2007 using multivariable regression models, including country fixed-effects and controlling for time-varying levels of national social expenditures, economic development, and health behaviors. RESULTS: Findings indicated robust differences in health-spending efficiency. A 1% annual increase in health expenditures was associated with percent changes in life expectancy ranging from 0.020 in the United States (95% confidence interval [CI] = 0.008, 0.032) to 0.121 in Germany (95% CI = 0.099, 0.143). Health-spending increases were associated with greater life expectancy improvements for men than for women in nearly every OECD country. CONCLUSIONS: This is the first study to our knowledge to estimate the effect of country-specific health expenditures on life expectancies of men and women. Future work understanding the determinants of these differences has the potential to improve the overall efficiency and equity of national health systems.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".