key: cord-0753780-0yjzxrlg authors: Kavanagh, Nolan M.; Goel, Rishi R.; Venkataramani, Atheendar S. title: County-Level Socioeconomic and Political Predictors of Distancing for COVID-19 date: 2021-03-24 journal: Am J Prev Med DOI: 10.1016/j.amepre.2021.01.040 sha: 79eafb955b68005e8a1c296ad7b6872de20c3c3b doc_id: 753780 cord_uid: 0yjzxrlg Introduction In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have implemented social distancing measures to slow viral transmission. This work aims to determine the extent to which socioeconomic and political conditions have shaped community-level distancing behaviors during the COVID-19 pandemic, especially how these dynamics have evolved over time. Methods This study used daily data on physical distancing based on 15‒17 million cell phone users in 3,037 U.S. counties. County-level changes in the average distance traveled per person were estimated relative to pre-pandemic weeks as a proxy for physical distancing. Pooled ordinary least squares regressions estimated the association between physical distancing and a variety of county-level demographic, socioeconomic, and political characteristics by week from March 9, 2020 through January 17, 2021. Data were collected until January 2021, at which point the analyses were finalized. Results Lower per capita income and greater Republican orientation were associated with significantly reduced physical distancing throughout nearly all of the study period. These associations persisted after adjusting for a variety of county-level demographic and socioeconomic characteristics. Other county-level characteristics, such as the share of Black and Hispanic residents, were associated with reduced distancing at various points during the study period. Conclusions These results highlight the importance of dynamic socioeconomic and political gradients in preventive behavior and imply the need for nimble policy responses. The U.S. is the epicenter of the coronavirus disease 2019 (COVID- 19) pandemic. 1 At the time of writing, there were >25 million cases and 418,000 reported deaths. Early modeling studies suggested that, without mitigation efforts, rising numbers of COVID-19 cases could overwhelm the health system's capacity and cause millions of deaths. 2 In response, federal, state, and local governments implemented a patchwork of public health measures to slow transmission through social distancing. These included orders to stay at home and close schools and non-essential businesses. Similar policies have been implemented for previous pandemics. 3, 4 Recent findings suggest that physical distancing and shelter-in-place orders have been effective at reducing growth rates for COVID-19, 5, 6 and that proxy measures of social distancing, 3 including decreases in physical movement, have been associated with reductions in case growth across U.S. counties. 7, 8 Such policy and behavioral interventions have been especially important in the absence of vaccines and effective treatments. However, the effectiveness of social distancing policies requires significant engagement by communities, which may face barriers to successful adherence depending on prevailing socioeconomic conditions and political beliefs. 9 Indeed, several studies have shown that low-income areas, as well as areas that supported thenpresidential candidate Donald J. Trump, were less likely to engage in social distancing. 5, 10, 11 However, most of these studies were conducted early in the COVID-19 pandemic. The associations between physical distancing and socioeconomic and political characteristics may have evolved owing to the changing geographic nature of the pandemic, the implementation of new mitigation strategies, and the politicization of these strategies. 12 Evaluating the dynamics of physical distancing is critical for updating public health and social policy responses to respond to new patterns of transmission. Such analyses may also provide insight into how risk mitigation behaviors have shifted across population groups as the pandemic has drawn on. Therefore, this study examines the demographic, socioeconomic, and political determinants of physical distancing for the COVID-19 pandemic among U.S. counties from March 2020 through January 2021. Data for the main outcome, physical distancing, were obtained from Unacast. 13 County-level averages of distance traveled per person were estimated using location data from 15-17 million 4 anonymized cell phone users per day. A device was assigned to a county on a given day if a majority of the day was spent in that county. Distance traveled was then summed and averaged across the total number of devices assigned to that county. Physical distancing was conceptualized as the percentage change in average movement each day relative to 4 pre-COVID- 19 14, 15 and been shown to predict case growth in the U.S. 7, 8 The validity of the measure was also Data on the county-level share of residential plots in rural areas were obtained from the 2010 Census. These county-level characteristics were chosen a priori based on their expected contribution to a community's ability to physically distance. Contemporaneous media reports 5 suggested that structural barriers, work-related barriers (especially in specific industries), and political attitudes were shaping how communities responded to the pandemic. [16] [17] [18] As such, covariates that reflected these barriers and attitudes at the county level were included in the models (Appendix Table 1 ). Correlations between the county-level characteristics are shown in Appendix Figure 2 . First, for each calendar day, bivariable associations between physical distancing and each county-level demographic, socioeconomic, and political characteristic were estimated using Pearson correlations with Bonferroni correction for multiple comparisons. Then, using data from the entire study period, a pooled multivariable ordinary least squares regression estimated the association between average movement and each county-level characteristic, allowing the association to vary by week. For this model, distancing was averaged by week at the county level to produce 136,665 county-weeks. The aforementioned county-level characteristics were standardized using the equivalent of a z-score with their median and IQR to compare the relative strengths of their associations. The model was adjusted by all county-level characteristics (to estimate the association of each with distancing, having adjusted for all others), as well as state fixed effects (to account for state-level differences in degree of preparation and mitigation measures for COVID -19) . The estimating equation was as follows: where j denotes the week, i denotes the county, and s denotes the state for a given county. SEs were clustered at the county level to account for temporal dependence in physical distancing. 19 Analyses were not weighted by county population. All analyses were performed in R, version 3.6.3. Replication code is available at github.com/nolankav/socialdistancing. To test the possibility that any observed associations merely reflected rational responses to the local risk of infection, county COVID-19 case rates were added to the model as a sensitivity check. County-level cases per million residents reported over the previous week were obtained from The New York Times, standardized by week, and included as a proxy for the perceived local severity of the pandemic, relative to the rest of the country. The observed associations between the county-level characteristics and distancing were robust to their inclusion (Appendix Figure 2 ). To ensure that the results were robust to alternative assumptions about independence between observations, SEs were clustered at the state level to account for any spatial dependence in physical distancing (e.g., state policy environments may have led to correlations in the main outcome). The alternative specification also provided support for the main findings (Appendix Figure 3 ). Descriptive analyses showed a sharp reduction in average movement among U.S. counties corresponding with the start of the COVID-19 pandemic and the declaration of a national emergency in mid-March 2020 ( Figure 1 ). Physical distancing was most pronounced from late March to early June and returned close to pre-COVID-19 baseline levels by July 2020. Engagement in distancing then began to increase again in early September. Even as the national trend evolved, however, there was substantial variability in physical distancing across counties. Bivariable analyses showed that greater county-level income per capita was highly correlated with more physical distancing (i.e., decreased movement relative to other counties), whereas the county-level share of votes for President Trump in 2016 was highly correlated with less physical distancing (i.e., increased movement) (Appendix Figure 2) . Other county-level characteristics, including shares of racial and ethnic minorities (Black and Hispanic), immigrants, rurality, and employment in transportation, were also correlated with changes in average movement on many days. Figure 2 plots the monthly trajectory of physical distancing by quintile of county income per capita (A) and Republican political orientation (B). Gradients in distancing by income and political orientation persisted throughout the entire study period. A multivariable regression analysis emphasized the degree to which SES and political orientation were associated with physical distancing. The model adjusted for all specified county-level demographic, socioeconomic, and political characteristics. Figure 3 presents the adjusted estimates for the association between each county-level characteristic and physical distancing over time. All estimates were standardized based on IQR to allow for meaningful comparisons of their magnitudes. Full summary statistics for the covariates are provided in Appendix Other county-level characteristics varied over time in their degree of association with physical distancing. During the early months of the study period, counties with greater shares of Black and Hispanic residents were less likely to engage in distancing. These adjusted racial and ethnic differences closed during the summer months before rising again in the fall. Similarly, rural counties were less likely to engage in distancing early on. This trend reversed during June and July, yet by the end of the summer, rurality became the strongest negative predictor of physical distancing. Some characteristics, such as the share of male residents and the share of employment in retail business, had little predictive value of physical distancing in the adjusted models. The observed associations were also robust to the inclusion of perceived pandemic severity, as measured by weekly COVID-19 cases per million county residents (Appendix Figure 3 ). In this national study using county-level data, lower SES and greater Republican orientation were strongly associated with reduced physical distancing throughout the COVID-19 pandemic to date. These patterns persisted over a period during which geographic patterns in transmission risk and policy responses shifted rapidly. County-level shares of racial and ethnic minorities, as well as rurality, were also associated with reduced physical distancing at various times, although these associations did not persist throughout the entire study period. These findings suggest that income-related barriers could lead to significant socioeconomic gradients in COVID-19, similar to those for other health conditions. 20 The associations may implicate a number of potential underlying factors. For example, lower-income or gig jobs may necessitate continued work and be incompatible with working from home, raising the movement profile of a low-income community. Moreover, lower-income households may not have the necessary liquidity or savings to purchase essential goods in bulk, requiring more trips to business and other vendors. 21 These factors are consistent with previous studies showing that lower-income communities often struggle to engage in social distancing. A study of the 2009 H1N1 flu epidemic using TV ratings data suggested that higher-SES groups were better able to engage in behavioral responses (i.e., home TV viewing) that were associated with social distancing. 22 Studies of COVID-19 have demonstrated that social distancing responses to emergency declarations were strongly differentiated by income, 14 and that differences in mobility between low-and high-income neighborhoods have been driven by work-related needs. 11 In addition to work-related barriers, communities of lower SES have also faced less access to health care during the pandemic. A recent study showed extreme disparities in access to critical care services, with 49% of the lowest-income communities having no available intensive care unit beds-compared with only 3% of the highest-income communities. 23 Such areas are acutely vulnerable to COVID-19. Lower-income communities also have a greater burden of comorbidities that predispose to severe disease. 24 In these ways, structural barriers have led to inequitable burdens in COVID-19 mortality, and the inability of lower-income communities to fully engage in physical distancing may deepen existing inequities affecting these populations. At various points during the study period, communities with greater shares of Black and Hispanic residents were less likely to engage in physical distancing. In the U.S., race and ethnicity intersect with many structural barriers, including income, access to health care, and the burden of chronic health conditions. 24, 25 These barriers are then compounded by racial bias within governmental and healthcare institutions. 26, 27 As a result, racial and ethnic minorities have experienced inequitably higher rates of cases, hospitalizations, and deaths due to COVID-19. 28, 29 In the context of this study, it is likely that the county-level associations between race, ethnicity, and physical distancing primarily reflect the uncontrolled confounding of structural barriers and the influence of racial bias, rather than cultural differences between racial and ethnic groups. Throughout the majority of the study period, greater Republican orientation was associated with reduced engagement in physical distancing. Partisanship has dramatically shaped the government and public's responses to COVID-19 in the U.S. Political leaders have been polarized in their handling of the pandemic, 12 and some have even spread misinformation about the virus, treatments, and policy responses. [30] [31] [32] Public polls have shown divergent attitudes about the severity of the pandemic based on political affiliation, 9 and political messaging appears to partly explain variation in prevention behaviors, cases, and deaths. 10, 33 For example, a recent study suggested that Democratic-leaning counties were more likely to reduce their mobility in response to governors' recommendations to stay home. 10 Another study showed that partisan differences in distancing partly explain disparities in case and fatality growth across U.S. counties. 34 Building on these findings, this study shows that political differences have continued to shape social distancing behavior, months into the pandemic. Early on, largely Democratic cities were most burdened by COVID-19 and consequently engaged in more distancing. However, even as cases and deaths have risen in rural Republican areas, support for Trump has continued to predict less distancing, albeit at a lower magnitude. These partisan influences have persisted beyond the 2020 general election and were robust to the inclusion of local COVID-19 case rates in the model. Such differences are concerning during a pandemic that requires a coordinated public health response. That they persist even in the face of a rapidly evolving pandemic is more concerning still. Other health conditions disparately impact Republican communities, 35, 36 and gradients in social distancing could result in the same for COVID-19. This study has several limitations. First, even though physical movement predicts case growth at the community level, 7, 8 it is one of many social distancing behaviors that also include limiting visits to non-essential businesses, maintaining physical space from other people, and working from home. Moreover, the risk of transmission due to mobility may depend on local social and geographical contexts; that is, a given percentage-point change in distance traveled in one county may not translate to the same risk of transmission as in another county. Thus, the proxy metric used here may not fully capture the degree of risk faced by different demographic, socioeconomic, and political groups. Second, physical distancing may have been measured with error, given that the data did not sample all cell phone users and did not reflect non-users. Third, this study has the potential for omitted-variable and ecological biases due to aggregated, crosssectional data. Lastly, though a pooled regression with errors clustered at the county level accounts for some temporal dependence in the measurement of physical distancing, 19 some dependence may remain and bias the inferences in the analysis. The literature on confidence bands may offer more advanced corrections for multiple comparisons and temporal dependence in future studies. 37, 38 Despite these limitations, this study underscores the heterogeneity of communities' engagement in public health responses to COVID-19. It documents gradients by socioeconomic and political conditions that may partly explain inequities in COVID-19 cases and mortality. These patterns may also help policymakers and health professionals to identify communities that are most vulnerable to transmission and to direct resources and communications accordingly. County-level physical distance travelled was averaged by week for N=136,665 county-weeks. Coefficients were estimated using a pooled OLS regression, constructed by interacting all state fixed effects and county-level characteristics with each week throughout the study period. 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Data were obtained from Unacast Coefficients are aligned with the start of the pooled week We thank the Unacast team for providing us their physical distancing data set for research use.None of the authors have any conflicts of interest to report, nor any funding for this project.