Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States

  1. Hsien-Yen Chang
  2. Wenze Tang
  3. Elham Hatef
  4. Christopher Kitchen
  5. Jonathan P. Weiner
  6. Hadi Kharrazi

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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.

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  1. Version published to 10.1186/s12889-021-11149-1
  2. ScreenIT

    SciScore for 10.1101/2020.10.20.20216119: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We derived the daily SDI from the COVID-19 Impact Analysis Platform (CIAP) created by the Maryland Transportation Institute at the University of Maryland.[26] We determined the SES of a county using ADI.[27, 28] We derived the county-specific characteristics, such as the percentage of the elderly, from the US Census American Community Survey (ACS) data of 2018.
    ADI.
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    It is possible that residents in less disadvantaged neighborhoods practiced self-protection measures better (e.g., able to purchase face masks or hand sanitizers), as various studies have shown that higher-resource neighborhoods were associated with less COVID-19 transmission.[11-14] Our findings also align with another study that pointed out health disparities may play a more important role in COVID-19 transmission than government interventions (such as SAH order) and community-level compliance to such interventions.[13] Our study had some limitations. First, this was an ecological study and results from the aggregated data might not be generalizable to individuals. Second, some counties implemented various policies aiming at reducing COVID-19 transmission during our study period (e.g., shelter-in-place or stay-at-home orders; restricting dine-in at restaurants; closing nonessential business such as bars, entertainment venues, and gyms; banning large social gatherings; and closing public schools). Although we adopted the random intercept method to control for the fixed state effects, our results may not be attributed to the SAH order alone. Third, we used ADI to represent the disadvantage level of a neighborhood (representing the overall social determinants of health), and SDI to represent mobility. While both measures, especially ADI, have been examined extensively, these measures may still not capture the concepts they aim to represent completely. For example, SDI is based...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.10.20.20216119v1 on medRxiv