Prevalence of Occupation Associated with Increased Mobility During COVID-19 Pandemic

  1. Enbal Shacham
  2. Stephen Scroggins
  3. Matthew Ellis
  4. Alexander Garza

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Abstract

Objective

Identifying geographic-level prevalence of occupations associated with mobility during local stay-at-home pandemic mandate.

Methods

A spatio-temporal ecological framework was applied to determine census-tracts that had significantly higher rates of occupations likely to be deemed essential: food-service, business and finance, healthcare support, and maintenance. Real-time mobility data was used to determine the average daily percent of residents not leaving their place of residence. Spatial regression models were constructed for each occupation proportion among census-tracts within a large urban area.

Results

After adjusting for demographics, results indicate census-tracts with higher proportion of food-service workers, healthcare support employees, and office administration staff are likely to have increased mobility.

Conclusions

Increased mobility among communities is likely to exacerbate COVID-19 mitigation efforts. This increase in mobility was also found associated with specific demographics suggesting it may be occurring among underserved and vulnerable populations. We find that prevalence of essential employment presents itself as a candidate for driving inequity in morbidity and mortality of COVID-19.

Three-question Summary

  • Employees and workers deemed essential during the COVID-19 pandemic are likely to endure additional risk of infection due to community exposure. While preliminary reports are still quantifying this risk, we set out to examine if prevalence of specific occupations could be used to evaluate overall community-level risk based on stay-at-home mandate adherence.

  • Study results suggest that that not only are certain occupation geo-spatially associated with movement outside the home but are also associated with demographic characteristics likely to contribute to inequity of COVID-19 morbidity.

  • Often, nuanced inequities are lost in the larger data samples, being able to identify possible inequities from other sources such as prevalence of occupation among communities, remains an important and applicable alternative.

    • Article activity feed

    1. Version published to 10.1101/2020.12.11.20245357v2 on medRxiv
    2. ScreenIT

      SciScore for 10.1101/2020.12.11.20245357: (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: 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      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 10.1101/2020.12.11.20245357v1 on medRxiv