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

Shacham, E.; Scroggins, S.; Ellis, M.; Garza, A.

2021-01-21 epidemiology
10.1101/2020.12.11.20245357 medRxiv
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ObjectiveIdentifying geographic-level prevalence of occupations associated with mobility during local stay-at-home pandemic mandate. MethodsA 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. ResultsAfter 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. ConclusionsIncreased 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 SummaryO_LIEmployees 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. C_LIO_LIStudy 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. C_LIO_LIOften, 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. C_LI

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