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 2087 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 (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 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.

Does Influenza vaccination reduce the risk of contracting COVID-19?

“…The COVID-19 pandemic has had a range of impacts, and populations with the poorest socioeconomic status have been the most affected. Populations with the greatest deprivation (measured according to the MEDEA index), have a lower percentage of flu vaccination and a greater probability of presenting with COVID-19, apparently consistent with results published by some authors (Mena et al, 2021; Riou et al, 2021; Hawkins et al, 2020; Chang et al, 2021; Morrissey et al, 2021). This relationship may be due to the fact that the highest levels of deprivation include the worst socioeconomic conditions, housing characteristics, types of employment and access to education and culture.…”

The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador

“…The GDP per capita coefficient is also positive but is significant only for El Salvador. This finding confirms the critical role of socio-economic attributes in NPIs adherence reported in previous works [11,13,[53][54][55]. Looking at sociodemographic features, we find that both population and density have a positive significant coefficient in all countries.…”

Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review

“…They concluded that incidences were higher in more affluent populations in the first wave of the pandemic but inverted with ongoing progression, manifesting in higher rates in socioeconomically disadvantaged populations in waves 2 and 3. Chang et al, Neelon et al, and Clouston et al investigated infection rates in the US and found similar results at the county level [ 13 , 36 , 58 ]. Furthermore, Neelon et al showed sinusoidally shaped temporal dynamics of socioeconomic inequalities in COVID-19 incidence, indicating several crossovers over time [ 58 ] similar to one study from the UK [ 65 ].…”

“…However, when mobility restrictions were implemented, populations in lower-paid occupations, precarious jobs, and those living in higher-density housing or using public transport may have been less able to maintain physical distancing, while better-off populations tend to have more resources to follow infection protection restrictions, such as the ability to work remotely [ 13 , 36 , 42 – 44 , 49 , 60 , 66 , 74 ]. Furthermore, populations of lower education may have had insufficient information about the pandemic situation.…”