Unravelling the determinants of life expectancy during and after the COVID-19 pandemic: a qualitative comparative analysis.

Abstract

Background: Disparities in life expectancy persist across countries, despite overall improvements in recent years. The COVID-19 pandemic further exacerbated these disparities. While substantial research has investigated life expectancy determinants, the factors driving variations across countries remain insufficiently explored.

Methods: This study innovatively employed Qualitative Comparative Analysis with data from 2020-2022, integrating multiple global data sources. We examined the complex causal patterns among conditions, including educational attainment, economic prosperity, environmental quality, social stability, urban development, and public health capacity within a case-oriented framework. Variables were calibrated into fuzzy sets to analyse necessary and sufficient conditions, with intermediate results tested across cases per solution, including robustness tests to validate the findings.

Results: Environmental quality, represented by access to electricity, consistently emerged as a necessary and sufficient condition across seven key case scenarios for achieving high life expectancy. Each case highlights unique pathways that align with different combinations of socioeconomic and policy conditions, illustrating that diverse approaches can lead to positive outcomes. In addition to environmental quality, factors such as mean years of schooling, gross national income per capita, urban population density, and measles immunisation were found to be influential in various combinations within these cases, underscoring the complexity of life expectancy determinants.

Conclusions: Our findings indicate that while core determinants like environmental quality are crucial, countries can enhance life expectancy through unique, context-dependent pathways that integrate environmental, educational, economic, and public health factors. Specifically, countries may focus on different policy areas based on their socio-economic conditions and development priorities to optimise life expectancy outcomes.