Estimating Life Expectancy in the U.S.: Identifying Environmental Determinants and Disparities (2003 to 2019)

We processed life expectancy data of an age group less than 1 years old from the Institute for Health Metrics and Evaluation (IHME) in the contiguous USA, as well as a set of 33 environmental variables (or features) from the European Center for Medium-Range Weather Forecasts (ECMWF) from the years 2003 through 2019. Visualizing the IHME data, we identified a massive disparity in life expectancy of the age group less than 1 years old in the contiguous USA, where counties in southern states have relatively lower life expectancies compared to counties in northern states. Our use of machine learning to estimate the life expectancy of the sub-1-year-old age group is moderately accurate, as the coefficient of determination (R²) and Root Mean Square Error (RMSE) between the true and estimated values was found to be 0.81 and 1.06 years, respectively, in an independent test set using only a set of five environmental variables. Our key finding shows that apart from well-known pollutants, such as particulate matter (PM), ozone, and carbon monoxide, a reduction in pollutants such as formaldehyde, sulphate aerosols, and dust aerosols; an increase in vegetation areas; and good environmental conditions, such as lower wet-bulb temperature, could potentially increase life expectancy in the US. Future work may include socio-economic variables such as household income, poverty rate, and other relevant features to create a comprehensive set of variables to improve the results and individuals' quality of life.