Assessment of Factors Associated with Life Expectancy Gap Based on the Preston Curve: A Cross-Sectional Study

Fatemeh  Yari; Lotfali  Agheli; Hossein  Sadeghi; Sajjad  Faraji Dizaji

doi:10.18502/jchr.v12i26.14417

Fatemeh Yari Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares Univrersity, Tehran, Iran
Lotfali Agheli Economic Research Institute, Tarbiat Modares Univrersity, Tehran, Iran
Hossein Sadeghi Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares Univrersity, Tehran, Iran
Sajjad Faraji Dizaji Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares Univrersity, Tehran, Iran

DOI: https://doi.org/10.18502/jchr.v12i26.14417

Keywords: Life Expectancy, Income, Population Health

Abstract

Background: Some countries experience lower or higher life expectancy than what is predicted based on their income. This study examines why life expectancy deviation is experienced with the aim of exploring which factors and conditions contribute to better health outcome (life expectancy) at low cost.

Methods: In this study at the first stage, the well-known Preston curve is reproduced and updated using the cross-sectional data of variables of life expectancy at birth (years) and per capita gross domestic product (GDP) by purchasing power parity (PPP) of 182 countries around the World in 2018 based on the latest available data. After estimating the deviation of each countries life expectancy from the curve, the characteristics of countries with more than four years of positive (group 1) and negative (group 2) gaps from the curve were compared by applying the mean comparison test of two independent groups (t-test).

Results: The identified drivers of gains or losses in longevity relative to income included using at least basic sanitation (P = 0.012) and drinking water services (P = 0.045), Universal Health Coverage (UHC) (P = 0.012), access to electricity (P = 0.004), CO2 emissions (P = 0.037), inequality in income (P = 0.003), health expenditure per capita (P = 0.000), non-communicable (P = 0.000) and communicable diseases and maternal, prenatal, and nutrition conditions (P = 0.000), literacy rate (P = 0.057), and road injuries (P = 0.001).

Conclusion: Better health outcome in countries and regions with relatively low income or few resources can be achieved that would be critical for global improvement in population health. However, it needs to take effective measures and is of great importance for policy-making.