  Previous research has shown mixed evidence on the associations between
mobility data and COVID-19 case rates, analysis of which is complicated by
differences between places on factors influencing both behavior and health
outcomes. We aimed to evaluate the county-level impact of shifting the
distribution 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 observed
value. The MTP approach facilitates studying the effects of continuous
exposures while minimizing parametric modeling assumptions. Ten mobility
indices were selected to capture several aspects of behavior expected to
influence and be influenced by COVID-19 case rates. The outcome was defined as
the number of new cases per 100,000 residents two weeks ahead of each mobility
measure. Primary analyses used targeted minimum loss-based estimation (TMLE)
with a Super Learner ensemble of machine learning algorithms, considering over
20 potential confounders capturing counties' recent case rates as well as
social, economic, health, and demographic variables. For comparison, we also
implemented unadjusted analyses. For most weeks considered, unadjusted analyses
suggested strong associations between mobility indices and subsequent growth in
case rates. However, after confounder adjustment, none of the indices showed
consistent associations after hypothetical shifts to reduce mobility. While
identifiability concerns limit our ability to make causal claims in this
analysis, MTPs are a powerful and underutilized tool for studying the effects
of continuous exposures.
