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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.
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http://dx.doi.org/10.1038/s43856-025-01111-2 | DOI Listing |
Commun Med (Lond)
September 2025
Mayo Clinic Alix School of Medicine, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, USA.
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.
Age Ageing
March 2025
Queen's University, Department of Political Studies, 99 University Ave Kingston, Ontario K7L 3N6, Canada.
Within a week of his 20 January 2025 inauguration, US President Donald J. Trump issued an order that froze all federal grants and loans, creating confusion and anxiety about the future of research and development in US biomedical science. The politicisation of science creates significant challenges not only for the researchers who depend on public funding to undertake their research, but also for the public understanding of why basic research is so important to the health and economic prosperity of the world's ageing populations.
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Marriott Heart Disease Research Program, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Importance: Health-adjusted life expectancy, a measure of healthy longevity, lags longevity gains, resulting in a healthspan-lifespan gap.
Objective: To quantify the healthspan-lifespan gap across the globe, investigate for sex disparities, and analyze morbidity and mortality associations.
Design, Setting, And Participants: This retrospective cross-sectional study used the World Health Organization (WHO) Global Health Observatory as the global data source and acquired national-level data covering all continents.
NPJ Regen Med
September 2021
Center for Regenerative Medicine, Marriott Family Comprehensive Cardiac Regenerative Medicine, Marriott Heart Disease Research Program, Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN, USA.