ArticleBMC public health2024
Does living longer mean living healthier? A comprehensive analysis of 204 countries and regions from 1990 to 2019.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Domain-specific trends in healthy life expectancy among Chinese older adults: Compression, expansion, and dynamic equilibrium.SSM - population health · 2026Article
- Review
- Socioeconomic and geographic inequalities in unmet healthcare needs in Cambodia: evidence from a national cross-sectional study.International journal for equity in health · 2026Article
- Article
- Targeting biological age with bioactive, microbiota-accessible nutritional complexes: a pilot study on healthspan extension in medically healthy adults.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe data from the WHO showed that life expectancy (LE) and health-adjusted life expectancy (HALE) at global level both increased from 2000 to 2019, while they did not increase at the same rate. Therefore, our study aims to explore the relationship and changing trends between LE and HALE, and further comprehensively analyze the factors influencing the difference between the two.
methodsThis paper uses data from the Global Burden of Disease (GBD) database in LE and HALE across 204 countries and regions from 1990 to 2019. It analyses the trends of the relationship between LE and HALE at birth and at 65 and over across 204 countries and regions from 1990 to 2019 and classifies all the countries into three types: optimization countries, low deterioration countries and high deterioration countries. Then this paper uses the random effects model to analyze the factors that influence the three types of countries and regions in terms of environmental/occupational risk, behavioral risk, and metabolic risk.
resultsFirst, for males, for indicator of age at birth, 8 optimization countries were with "drug use" having the greatest impact, 98 low deterioration countries were with "low physical activity" being the most significant; 98 high deterioration countries were with "childhood sexual abuse and bullying" being the most significant; for indicator of age at 65 and over, 18 optimization countries were with "low physical activity" having the greatest impact, 98 low deterioration countries were with "drug use" being the most significant; 88 high deterioration countries were with "occupational risk" being the most significant. Second, for females, for indicator of age at birth, 6 optimization countries were with "drug use" having the greatest impact, 107 low deterioration countries were with "child and maternal malnutrition" being the most significant; 91 high deterioration countries were with "drug use" being the most significant; for indicator of age at 65 and over, 14 optimization countries were with "occupational risks" having the greatest impact, 109 low deterioration countries were with "occupational risk" being the most significant; 81 high deterioration countries were with "drug use" being the most significant.
conclusionsBased on the results, it suggested that the policy need to focus on the facts of drug use, occupational risk and low physical activity to narrow the gap between LE and HALE.
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