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

TL;DR

This study applied a modified treatment policy approach to assess how changes in mobility affected COVID-19 case growth in U.S. counties, revealing that after adjusting for confounders, mobility shifts did not show consistent causal effects.

Contribution

It demonstrates the use of modified treatment policies and targeted minimum loss-based estimation to analyze continuous exposures like mobility in epidemiological studies.

Findings

Unadjusted analyses showed strong associations between mobility and case growth.

Adjusted analyses found no consistent effects of mobility shifts on case rates.

Highlights the importance of confounder adjustment in causal inference.

Abstract

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.