In American journal of epidemiology ; h5-index 65.0
Mixed evidence exists of associations between mobility data and COVID-19 case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall 2020 in the United States and to demonstrate modified treatment policies (MTPs) to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indices on the number of newly reported cases per 100,000 residents two weeks ahead. Primary analyses used targeted minimum loss-based estimation (TMLE) with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indices and subsequent new case rates. However, after confounder adjustment, none of the indices showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health.
Nugent Joshua R, Balzer Laura B
COVID-19 Pandemic, Machine Learning, modified treatment policy, targeted learning