Asynchrony Between Individual and Government Actions Accounts for Disproportionate Impact of COVID-19 on Vulnerable Communities

Abstract

Introduction: Previously estimated effects of social distancing do not account for changes in individual behavior before the implementation of stay-at-home policies or model this behavior in relation to the burden of disease. This study aims to assess the asynchrony between individual behavior and government stay-at-home orders, quantify the true impact of social distancing using mobility data, and explore the sociodemographic variables linked to variation in social distancing practices.

Methods: This study was a retrospective investigation that leveraged mobility data to quantify the time to behavioral change in relation to the initial presence of COVID-19 and the implementation of government stay-at-home orders. The impact of social distancing that accounts for both individual behavior and testing data was calculated using generalized mixed models. The role of sociodemographics in accounting for variation in social distancing behavior was modeled using a 10-fold cross-validated elastic net (linear machine learning model). Analysis was conducted in April‒July 2020.

Results: Across all the 1,124 counties included in this analysis, individuals began to socially distance at a median of 5 days (IQR=3-8) after 10 cumulative cases of COVID-19 were confirmed in their state, with state governments taking a median of 15 days (IQR=12-19) to enact stay-at-home orders. Overall, people began social distancing at a median of 12 days (IQR=8-17) before their state enacted stay-at-home orders. Of the 16 studies included in the review, 13 exclusively used government dates as a proxy for social distancing behavior, and none accounted for both testing and mobility. Using government stay-at-home dates as a proxy for social distancing (10.2% decrease in the number of daily cases) accounted for only 55% of the true impact of the intervention when compared with estimates using mobility (18.6% reduction). Using 10-fold cross-validation, 23 of 43 sociodemographic variables were significantly and independently predictive of variation in individual social distancing, with delays corresponding to an increase in a county's proportion of people without a high school diploma and proportion of racial and ethnic minorities.

Conclusions: This retrospective analysis of mobility patterns found that social distancing behavior occurred well before the onset of government stay-at-home dates. This asynchrony leads to the underestimation of the impact of social distancing. Sociodemographic characteristics associated with delays in social distancing can help explain the disproportionate case burden and mortality among vulnerable communities.

Figure 1

7 × 7 panel of states residential mobility over time with the highlighted period of government stay-at-home order. Note: The red line represents the state-wide data generated by Google (a composite of all the counties within that state). Blue color denotes the period and duration of government-mandated social distancing. Apr, April; Mar, March.

Figure 2

Differences in time delay between individual and government action in response to locally prevalent disease across 1,124 counties. (A) Time to mobility changepoint after the disease becomes locally prevalent (i.e., Public Response); n=1,124 counties grouped by 42 states. Each point denotes a county. (B) Time to government stay-at-home order after the disease becomes locally prevalent (i.e., Government Response); n=42 states. (C) The time between mobility change point and government stay-at-home order (i.e., Unaccounted Distancing); n=1,124 counties grouped by 42 states. Each point denotes a county.