Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

  1. Bhuma Krishnamachari
  2. Alexander Morris
  3. Diane Zastrow
  4. Andrew Dsida
  5. Brian Harper
  6. Anthony J. Santella

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Abstract

COVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.22.20110460: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While our study had many strengths, there were weaknesses as well. The biggest issue is the use of aggregate data from a variety of external sources. Each source has its own strength and limitations. Ideally, socio-economic variables should be evaluated by using individual level data. In terms of social distancing laws, it is unclear how each state and/or city reported their data, which may lead to a great deal of heterogeneity. Similarly, the definitions for social distancing measures were vastly different between locations. We did not distinguish between these details, and they are likely important. Another potential limitation was our use of binary variables using median values as cutoffs for analysis. There are several ways data can be analyzed, and it is possible that there is a superior approach to analyzing social distancing variables. Despite the limitations in categorizing the data, we opted for this method because of sample size limitations which could affect statistical power. We initially conducted analyses evaluating each variable as a continuous variable but opted not to report on these results due to the complexities of reporting the effects of a change in 1 unit of a variable on a 1 point change in cumulative incidence, as is obtained from using continuous predictor variables in regression. We opted to pursue an approach that could be meaningfully understood and used by city and state governments, both in numerical and visual format. Future studies should inve...

    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.22.20110460v1 on medRxiv