Longitudinal analysis of the determinants of life expectancy and healthy life expectancy: A causal approach

Abstract

Understanding the determinants of health is essential for designing effective strategies to advance economic growth, reduce disease and disability, and enhance quality of life. We undertake a comprehensive outlook on public health by incorporating three metrics — life expectancy (LE), healthy life expectancy (HLE), and the discrepancy between the two. We investigate the effects of various health and socio-economic factors on these metrics and employ causal machine learning and statistical methods such as propensity score matching, X-learners, and causal forests to calculate treatment effects. An increase in basic water services and public health expenditure significantly increased average LE whereas high human immunodeficiency virus (HIV) prevalence rates and poverty rates reduced average LE. High gross national income (GNI) per capita and moderate body mass index (BMI) increased HLE whilst high HIV prevalence rates decreased HLE. High public health expenditure and high GNI per capita expand the gap between HLE and LE whereas high HIV prevalence rates and moderate BMI diminish this gap. Results suggest that policymakers should utilize governmental resources to improve public health infrastructure rather than provide fiscal incentives to encourage private healthcare infrastructure. Additionally, more emphasis should be placed on increasing educational levels of the general public by increasing educational expenditure and making educational institutions, public and private, more accountable.

Keywords

• Healthy life expectancy
• Socio-economic indicators
• Public health
• Treatment effects
• Causal machine learning