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Mar 24, 2020 · California About 39.6 million people. California, America’s most populous state, was the first to order all residents to stay home. Others quickly followed suit. “This is not a permanent state ...
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Based on location data from mobile devices, in 97.6% of counties with mandatory stay-at-home orders issued by states or territories, these orders were associated with decreased median population movement after the order start date, relative to the period before the order was implemented. Reduced population movement helps prevent close contact among persons outside the household, potentially limiting exposure to persons infected with SARS-CoV-2. This suggests that stay-at-home orders can help protect the public’s health by limiting potential exposure to SARS-CoV-2 and reducing community transmission of COVID-19.
The implementation of stay-at-home orders might affect population movement differently depending on when and where orders are issued and to whom they apply. The observed decrease in population movement after the implementation of the first state-issued mandatory stay-at-home order in California suggests that the implementation of certain public health policies might influence behaviors in other areas, in addition to persons directly subject to the action. However, this observation occurred in the context of other variables, which might have influenced behaviors, including the declaration of COVID-19 as a pandemic, declaration of national or state emergencies, media attention to fatalities and increased demands on hospitals, gathering bans, closures of schools and businesses, and cancellation of sporting events.
Increases in population movement were evident among counties in jurisdictions where stay-at-home orders were lifted, as well as in other communities as orders began to lift nationwide. Such increases might be driven in part by persons resuming preorder movement behaviors in response to the lifting of orders where they lived, or in response to perceived reduced risk associated with the lifting of orders elsewhere. Many other factors might have also played a role, and additional studies are needed to determine which factors caused population movement to increase across jurisdictions after the first state stay-at-home order ended.§§§
Further research is needed to assess the impact of reduced population movement and other community mitigation strategies on the spread of COVID-19. For example, understanding the relationship between stay-at-home orders in contiguous counties and movement might explain how same-state and neighboring-state policy changes can affect public health by mitigating or exacerbating external environmental and social factors affecting population movement.¶¶¶ As the pandemic continues and jurisdictions consider reimplementing mitigation policies, additional studies are needed to assess the impact of reissuing stay-at-home orders.
The findings in this report are subject to at least five limitations. First, although relative device coverage largely correlates with U.S. population density, some regions or demographic groups might be over- or underrepresented.**** Second, persons might have multiple mobile devices and might not take certain devices with them when they leave the home (e.g., tablets) or might take multiple devices with them simultaneously (e.g., phones and smart watches). Third, although the clustered Wilcoxon signed rank test is used with counties as clusters because each county’s median pre- and postorder values are paired comparisons rather than independent observations, potential spatial dependence among counties is not addressed. Fourth, this report does not assess whether population movement was affected by nationwide protests during the observation period.†††† Finally, this report analyzes the relationship between stay-at-home orders and population movement and does not assess the complex relationship between stay-at-home orders and illness incidence rates or deaths.
Mandatory stay-at-home orders can help reduce activities associated with community spread of COVID-19, including population movement and close person-to-person contact outside the household. Mandatory stay-at-home orders were associated with reduced population movement in most counties during the early months of the COVID-19 pandemic, and the relaxation of those orders was associated with increased movement. Although stay-at-home orders might assist in limiting potential exposure to SARS-CoV-2 and have had public support (7), such orders substantially disrupt daily life and have resulted in adverse economic impact (8). Further studies are needed to assess the timing and conditions under which stay-at-home orders might be best used to protect health, minimize negative impacts, and ensure equitable enforcement of community mitigation policies. These findings can inform public policies to potentially slow the spread of COVID-19 and control other communicable diseases in the future.
1.CDC. How COVID-19 spreads. Atlanta, GA: US. Department of Health and Human Services, CDC; 2020. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html
2.CDC. Implementation of mitigation strategies for community COVID-19 transmission. Atlanta, GA: US. Department of Health and Human Services, CDC; 2020. https://www.cdc.gov/coronavirus/2019-ncov/community/community-mitigation.html
3.Lasry A, Kidder D, Hast M, et al.; CDC Public Health Law Program; New York City Department of Health and Mental Hygiene; Louisiana Department of Health; Public Health – Seattle & King County; San Francisco COVID-19 Response Team; Alameda County Public Health Department; San Mateo County Health Department; Marin County Division of Public Health. Timing of community mitigation and changes in reported COVID-19 and community mobility—four U.S. metropolitan areas, February 26–April 1, 2020. MMWR Morb Mortal Wkly Rep 2020;69:451–7. CrossRefexternal icon PubMedexternal icon
4.CDC. State, territorial, and county COVID-19 orders and proclamations for individuals to stay home. Atlanta, GA: US. Department of Health and Human Services, CDC; 2020. https://ephtracking.cdc.gov/DataExplorer/index.html?c=33&i=160&m=927
5.SafeGraph. Social distancing metrics. San Francisco, CA: SafeGraph Inc.; 2020. https://docs.safegraph.com/docs/social-distancing-metricsexternal icon
6.Rosner B, Glynn RJ, Lee M-LT. The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics 2006;62:185–92. CrossRefexternal icon PubMedexternal icon
- Amanda Moreland, Christine Herlihy, Michael A Tynan, Gregory Sunshine, Russell F McCord, Charity Hil...
- 2020
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