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Previous research has shown mixed evidence on the associations betweenmobility data and COVID-19 case rates, analysis of which is complicated bydifferences between places on factors influencing both behavior and healthoutcomes. We aimed to evaluate the county-level impact of shifting thedistribution of mobility on the growth in COVID-19 case rates from June 1 -November 14, 2020. We utilized a modified treatment policy (MTP) approach,which considers the impact of shifting an exposure away from its observedvalue. The MTP approach facilitates studying the effects of continuousexposures while minimizing parametric modeling assumptions. Ten mobilityindices were selected to capture several aspects of behavior expected toinfluence and be influenced by COVID-19 case rates. The outcome was defined asthe number of new cases per 100,000 residents two weeks ahead of each mobilitymeasure. Primary analyses used targeted minimum loss-based estimation (TMLE)with a Super Learner ensemble of machine learning algorithms, considering over20 potential confounders capturing counties' recent case rates as well associal, economic, health, and demographic variables. For comparison, we alsoimplemented unadjusted analyses. For most weeks considered, unadjusted analysessuggested strong associations between mobility indices and subsequent growth incase rates. However, after confounder adjustment, none of the indices showedconsistent associations after hypothetical shifts to reduce mobility. Whileidentifiability concerns limit our ability to make causal claims in thisanalysis, MTPs are a powerful and underutilized tool for studying the effectsof continuous exposures.

Evaluating shifts in mobility and COVID-19 case rates in U.S. counties:  A demonstration of modified treatment policies for causal inference with  continuous exposures