ArticleJournal of global health2024
Assessing health determinants worldwide: Econometric analysis of the Global Burden of Diseases Study 2000-18 - Highlighting impactful factors on DALY, YLL, and YLD indicators.
Article in Journal of global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of life expectancy determinants among gulf cooperation council members.BMC public health · 2025Pooled it
- Geospatial inequalities in severe leprosy disability in Pernambuco before and during COVID-19.The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases · 2026Article
- The global burden of ischemic heart disease attributable to trans fatty acid in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.Human genomics · 2026Article
- Global and regional impact of health determinants on life expectancy and health-adjusted life expectancy, 2000-2018: an econometric analysis based on the Global Burden of Disease study 2019.Frontiers in public health · 2025Article
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Authors and funding
4 authors.
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Abstract
Background: As the health status of a population is influenced by a variety of health determinants, we sought to assess their impact on health outcomes, both at the global and regional levels. Methods: This ecological study encompassed all 194 member countries of the World Health Organization (WHO) from 2000 to 2018. We first identified all health determinants and then retrieved the related data from various global databases. We additionally considered three indicators - disability-adjusted life years (DALYs), years of life lost (YLL), and years lived with disability (YLD) - in evaluating health outcomes; we extracted their data from the Global Burden of Disease (GBD) 2019 study. We then applied econometric analyses using a multilevel mixed-effects linear regression model. Results: The analysis using the DALY indicator showed that the variables of sexually transmitted infections, injuries prevalence, and urbanisation had the highest effect size or regression coefficients (β) for health outcomes. The variables of sexually transmitted infection (β = 0.75, P < 0.001) in the African region; drinking water (β = -0.60, P < 0.001), alcohol use (β = 0.20, P < 0.001), and drug use (β = 0.05, P = 0.036) in the Americas region; urbanisation (β = -0.34, P < 0.001) in the Eastern Mediterranean region; current health expenditure (β = -0.21, P < 0.001) in the Europe region; injuries (β = 0.65, P < 0.001), air pollution (β = 0.29, P < 0.001), and obesity (β = 0.92, P < 0.001) in the South-East Asia region; and gross domestic product (β = -0.25, P < 0.001), education (β = -0.90, P < 0.001), and smoking (β = 0.28, P < 0.001) in the Western Pacific region had the most significant role in explaining global health outcomes. Except for the drug use variable in regional findings, the role of other variables in explaining the YLL indicator was greater than that of the YLD indicator. Conclusions: To address global health disparities and optimise resource allocation, global and interregional policymakers should focus on determinants that had the highest β with health outcomes in each region compared to other regions. These determinants likely have a higher marginal health product, and investing in them is likely to be more cost-effective.
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