A Global Analysis of Within-Country Health Inequalities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».