Multi-Modal Sensing for Estimating U.S. Life Expectancy: Identifying Environmental Determinants and Disparities (2003-2019)

We processed the life expectancy data of age group less than 1 year old from the Institute for Health Metrics and Evaluation (IHME) in contiguous USA and a set of 33 environmental variables (or features ) from the European Centre for Medium-Range Forecasts (ECMWF) from the years 2003 through 2019. Visualizing the IHME data we identified the massive disparity in life expectancy in contiguous USA where counties in southern states have relatively less life expectancy compared to counties in northern states. We made use of machine learning to estimate the life expectancy and obtained moderate accuracy as coefficient of determination (R2) and Root Mean Square Error (RMSE) between the true and estimated values were found to be 0.77 and 1.18 year respectively in an independent test set using only a set of 5 environmental variables. Our key finding shows that apart from well-known pollutants such as particulate matter (PM), ozone, carbonmonoxide, it is essential to reduce pollutants such as formaldehyde, sulphate aerosols, dust aerosols; increase vegetation areas, and good working condition such as lower wet-bulb temperature can potentially increase life expectancy in the US. Future work can 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 livelihood of people.