A Global Analysis of Within-Country Health Inequalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Importance: Health inequalities are a defining social and policy concern. However, understanding whether a specific country's inequalities are large or small is limited without a comparative perspective with other countries. Objective: To systematically compare health inequalities in 181 countries and territories from 1960 to 2021 by developing a Health Inequality Normalized Index (HINI) that does not rely on secondary variables such as income, education, or race and ethnicity. Design, Setting, and Participants: This repeated cross-sectional study including demographic cross-national comparative analysis used age-at-death distributions from life tables for 181 countries and territories from 1960 to 2021. All county and territory health inequalities were ranked, and a random forest analysis was conducted of 191 factors to identify their relative role. Special attention was given to trends in health inequalities in the US. Data were analyzed between October 2023 and January 2025. Exposures: HINI was constructed by placing observed age-at-death distributions between perfect equality (conceptualized as everyone living exactly to the most common age at death) and the worst possible state of inequality (the largest variance in age-at-death distribution observed for each country). Main Outcomes and Measures: The primary outcome was the HINI, which measures within-country inequality in age-at-death distribution consistently for all countries and territories. Additional analyses explored the importance of potential factors (eg, infant mortality, wealth inequality, governance quality) associated with health inequalities. Results: Of 181 countries and territories between 1960 and 2021, in 2019 (pre-COVID-19), Turkmenistan had the highest HINI (most unequal), while Hong Kong had the lowest (most equal). Random forest analysis revealed that infant mortality and life expectancy were the primary factors associated with cross-country variation in HINI. Globally, health inequality decreased from 1960 to 2021, consistent with improvements in infant mortality and life expectancy. However, inequality trends diverged by country income group, improving more rapidly in high-income than in low-income countries. In the US, health inequality decreased but less than in other high-income nations: it ranked 19th in 1960 but 77th in 2021 among 181 countries and territories, and 55th among 59 high-income countries. Conclusions and Relevance: Results of this study suggest that infant mortality and life expectancy are critical factors in shaping countries' health inequalities. Sustained improvements in countries with high infant mortality may have the potential to further reduce inequality at a global scale. In the US, comparatively slow progress on health inequality underscores its salience in national health policy discussions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it