Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

Abstract

Background The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents mobility across neighborhoods of different levels of socioeconomic disadvantage. Methods This was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. Results On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Conclusions Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Combating coronavirus disease (COVID-19) in rural areas of Malawi: Factors affecting the fight

“…Moreover, some previous studies have shown that social distancing is more difficult in socio-economically disadvantaged neighbourhoods where crowding results in individuals or communities to be more likely to be exposed to COVID-19. 38 , 39 , 40 , 41 With most participants living below the international poverty line of $1.90 per day, 42 the fight against the pandemic becomes more complex and problematic. The impact of the economy on the COVID-19 fight has been also reported elsewhere in the world.…”

Association of Elementary School Reopening Status and County COVID-19 Incidence

“…There are several potential reasons for the differences observed between younger and older children. Studies have shown age-related differences in transmission risk within households, 9 and an increased risk of COVID-19 transmission from secondary schools versus primary schools. 4 We speculate that younger children may be more likely than older children to contract COVID-19 at home than school.…”

“…These models were designed to address confounding by transmission to children from adults and the effects of SES (which is independently associated with COVID-19 rates) on transmission. 9 Models included: (1) an unadjusted model including only ESOS; (2) an adult-adjusted model including ESOS and the log 2 incidence of COVID-19 in adults aged 20-59 (to account for every doubling of local adult incidence); and (3) a full model adding SES to the adult-adjusted model. The adult-adjusted and full models did not include counties with no adult cases of COVID-19 as modeling the effect of adult COVID-19 transmission does not make sense when there is no transmission.…”