{"title":"健康寿命差距的大小和疾病成因在世界各区域有所不同。","authors":"Armin Garmany, Andre Terzic","doi":"10.1038/s43856-025-01111-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Longevity gains have not been matched by equivalent advances in healthy longevity, giving rise to the healthspan-lifespan gap. This study maps, by world region, the healthspan-lifespan gap; identifies gap-associated demographic, economic, and health indicators; and deciphers disease burden patterns contributing to gap profiles.</p><p><strong>Methods: </strong>World Health Organization (WHO) Global Health Observatory, United Nations World Population Prospects and Global Health Expenditure Database were interrogated. The healthspan-lifespan gap was quantified from estimates of life expectancy and health-adjusted life expectancy. Regression analysis evaluated healthspan-lifespan gap correlates with a spatial error model used to adjust for confounders arising from geographic proximity. Dimensionality reduction by principal component analysis and clustering by machine learning discriminated disease burden patterns linked to healthspan-lifespan gap identity. Supervised machine learning enabled validation of disease burden pattern distinctness.</p><p><strong>Results: </strong>Charted for six WHO-designated regions, comprising 183 member states, the healthspan-lifespan gap differs in size across regions. Life expectancy, gross domestic product, and noncommunicable disease burden most consistently correlate with the healthspan-lifespan gap. Unsupervised machine learning identifies three clusters delineating global morbidity patterns. Cluster-informed stratification discerns inter- and intra-regional gap heterogeneity. Africa, although exhibiting the narrowest healthspan-lifespan gap, is overrepresented in countries with larger than predicted healthspan-lifespan gaps and shows the greatest gap expansion and disease burden pattern restructuring. In contrast, Europe is overrepresented in countries with healthspan-lifespan gaps smaller than anticipated. Projections into 2100 forecast continuous widening of the healthspan-lifespan gap across regions.</p><p><strong>Conclusions: </strong>The healthspan-lifespan gap is universal yet differs in magnitude and disease contribution among world regions. Gap identities imposed by distinct disease burden patterns caution against global generalization, necessitating region-informed solutions to maximize equitable healthy longevity.</p>","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":"5 1","pages":"381"},"PeriodicalIF":5.4000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12402540/pdf/","citationCount":"0","resultStr":"{\"title\":\"Healthspan-lifespan gap differs in magnitude and disease contribution across world regions.\",\"authors\":\"Armin Garmany, Andre Terzic\",\"doi\":\"10.1038/s43856-025-01111-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Longevity gains have not been matched by equivalent advances in healthy longevity, giving rise to the healthspan-lifespan gap. This study maps, by world region, the healthspan-lifespan gap; identifies gap-associated demographic, economic, and health indicators; and deciphers disease burden patterns contributing to gap profiles.</p><p><strong>Methods: </strong>World Health Organization (WHO) Global Health Observatory, United Nations World Population Prospects and Global Health Expenditure Database were interrogated. The healthspan-lifespan gap was quantified from estimates of life expectancy and health-adjusted life expectancy. Regression analysis evaluated healthspan-lifespan gap correlates with a spatial error model used to adjust for confounders arising from geographic proximity. Dimensionality reduction by principal component analysis and clustering by machine learning discriminated disease burden patterns linked to healthspan-lifespan gap identity. Supervised machine learning enabled validation of disease burden pattern distinctness.</p><p><strong>Results: </strong>Charted for six WHO-designated regions, comprising 183 member states, the healthspan-lifespan gap differs in size across regions. Life expectancy, gross domestic product, and noncommunicable disease burden most consistently correlate with the healthspan-lifespan gap. Unsupervised machine learning identifies three clusters delineating global morbidity patterns. Cluster-informed stratification discerns inter- and intra-regional gap heterogeneity. Africa, although exhibiting the narrowest healthspan-lifespan gap, is overrepresented in countries with larger than predicted healthspan-lifespan gaps and shows the greatest gap expansion and disease burden pattern restructuring. In contrast, Europe is overrepresented in countries with healthspan-lifespan gaps smaller than anticipated. Projections into 2100 forecast continuous widening of the healthspan-lifespan gap across regions.</p><p><strong>Conclusions: </strong>The healthspan-lifespan gap is universal yet differs in magnitude and disease contribution among world regions. Gap identities imposed by distinct disease burden patterns caution against global generalization, necessitating region-informed solutions to maximize equitable healthy longevity.</p>\",\"PeriodicalId\":72646,\"journal\":{\"name\":\"Communications medicine\",\"volume\":\"5 1\",\"pages\":\"381\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12402540/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s43856-025-01111-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43856-025-01111-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Healthspan-lifespan gap differs in magnitude and disease contribution across world regions.
Background: Longevity gains have not been matched by equivalent advances in healthy longevity, giving rise to the healthspan-lifespan gap. This study maps, by world region, the healthspan-lifespan gap; identifies gap-associated demographic, economic, and health indicators; and deciphers disease burden patterns contributing to gap profiles.
Methods: World Health Organization (WHO) Global Health Observatory, United Nations World Population Prospects and Global Health Expenditure Database were interrogated. The healthspan-lifespan gap was quantified from estimates of life expectancy and health-adjusted life expectancy. Regression analysis evaluated healthspan-lifespan gap correlates with a spatial error model used to adjust for confounders arising from geographic proximity. Dimensionality reduction by principal component analysis and clustering by machine learning discriminated disease burden patterns linked to healthspan-lifespan gap identity. Supervised machine learning enabled validation of disease burden pattern distinctness.
Results: Charted for six WHO-designated regions, comprising 183 member states, the healthspan-lifespan gap differs in size across regions. Life expectancy, gross domestic product, and noncommunicable disease burden most consistently correlate with the healthspan-lifespan gap. Unsupervised machine learning identifies three clusters delineating global morbidity patterns. Cluster-informed stratification discerns inter- and intra-regional gap heterogeneity. Africa, although exhibiting the narrowest healthspan-lifespan gap, is overrepresented in countries with larger than predicted healthspan-lifespan gaps and shows the greatest gap expansion and disease burden pattern restructuring. In contrast, Europe is overrepresented in countries with healthspan-lifespan gaps smaller than anticipated. Projections into 2100 forecast continuous widening of the healthspan-lifespan gap across regions.
Conclusions: The healthspan-lifespan gap is universal yet differs in magnitude and disease contribution among world regions. Gap identities imposed by distinct disease burden patterns caution against global generalization, necessitating region-informed solutions to maximize equitable healthy longevity.