Healthspan-lifespan gap differs in magnitude and disease contribution across world regions.

IF 5.4 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Armin Garmany, Andre Terzic
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Abstract

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.

健康寿命差距的大小和疾病成因在世界各区域有所不同。
背景:寿命的增加并没有与健康寿命的同等进步相匹配,从而产生了健康寿命差距。这项研究按世界地区绘制了健康寿命差距图;确定与差距有关的人口、经济和健康指标;并解读导致差距概况的疾病负担模式。方法:对世界卫生组织(WHO)全球卫生观察站、联合国世界人口展望和全球卫生支出数据库进行问卷调查。健康寿命差距是通过预期寿命和健康调整预期寿命的估计来量化的。回归分析评估了健康寿命差距与空间误差模型的相关性,该模型用于调整地理邻近引起的混杂因素。主成分分析的降维和机器学习的聚类区分了与健康寿命差距身份相关的疾病负担模式。有监督的机器学习能够验证疾病负担模式的独特性。结果:由183个会员国组成的六个世卫组织指定区域的图表显示,各区域的健康寿命差距大小不同。预期寿命、国内生产总值和非传染性疾病负担最一致地与健康寿命差距相关。无监督机器学习确定了描述全球发病率模式的三个集群。集群信息分层区分区域间和区域内的差距异质性。非洲虽然表现出最小的健康寿命差距,但在寿命寿命差距大于预期的国家中,非洲的比例过高,而且差距扩大和疾病负担格局调整幅度最大。相比之下,欧洲国家的健康寿命差距比预期的要小。对2100年的预测预测,各区域之间的健康寿命差距将继续扩大。结论:健康寿命差距是普遍存在的,但在大小和疾病成因方面,世界各地区存在差异。不同的疾病负担模式造成的差异身份提醒人们不要在全球普遍化,需要有区域知情的解决方案,以最大限度地公平健康长寿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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