Since the implementation of the Affordable Care Act (ACA) and the resulting Medicaid expansion beginning in 2014, public health insurance coverage in the USA has increased by over 35 million people. The ACA Medicaid expansion had an intervening Supreme Court decision which permitted individual states to choose whether to implement it, yielding a scenario to test the impact of Medicaid expansion on public health. Lung cancer continues to be the deadliest form of cancer domestically and worldwide, with West Virginia having the highest rate of lung cancer deaths of any state in 2022. The purpose of our work is to investigate whether the ACA reduced lung cancer mortality, and to evaluate various modeling techniques both established and novel to healthcare policy analysis. We have identified obesity, smoking, air pollution (PM2.5), and ACA implementation year as key predictor variables and utilized public data sources from the CDC (WONDER, NHANES, BRFSS) and the EPA. Additionally, preliminary analysis has led us to consider measures for obesity other than simple BMI, such as waist circumference, based on literature indicating that abdominal obesity may have a higher correlation with lung cancer mortality than BMI. This analysis compares models from several methodologies: difference-in-difference, GLM, PAR(p), and ARIMA. Monte Carlo simulations are utilized to propagate uncertainty from the predictor variables through these models to determine the impact of ACA on lung cancer mortality and provide a quantitative tool for analyzing public healthcare policy.
