- Record: found
- Abstract: found
- Article: found
Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement - United States, March 1-May 31, 2020.
Author(s):
Amanda Moreland ,
Christine Herlihy ,
Michael A Tynan ,
Gregory Sunshine ,
Russell F McCord ,
Charity Hilton ,
Jason Poovey ,
Angela K Werner ,
Christopher D Jones ,
Erika B Fulmer ,
Adi V Gundlapalli ,
Heather Strosnider ,
Aaron Potvien ,
Macarena C García ,
Sally Honeycutt ,
Grant Baldwin
Publication date (Electronic):
Sep 04 2020
Publisher:
Centers for Disease Control MMWR Office
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact (1). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing person-to-person interactions (2). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions (3). Each state or territory has authority to enact its own laws and policies to protect the public's health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-at-home orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1-May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state- and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stay-at-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household.
Related collections
Most cited references4
- Record: found
- Abstract: found
- Article: found
The Socio-Economic Implications of the Coronavirus and COVID-19 Pandemic: A Review
Maria R.C. Nicola, Zaid Alsafi, Catrin Sohrabi … (2020)
The COVID-19 pandemic has resulted in over 1.4 million confirmed cases and over 83,000 deaths globally. It has also sparked fears of an impending economic crisis and recession. Social distancing, self-isolation and travel restrictions forced a decrease in the workforce across all economic sectors and caused many jobs to be lost. Schools have closed down, and the need of commodities and manufactured products has decreased. In contrast, the need for medical supplies has significantly increased. The food sector has also seen a great demand due to panic-buying and stockpiling of food products. In response to this global outbreak, we summarise the socio-economic effects of COVID-19 on individual aspects of the world economy.
- Record: found
- Abstract: found
- Article: found
Public Attitudes, Behaviors, and Beliefs Related to COVID-19, Stay-at-Home Orders, Nonessential Business Closures, and Public Health Guidance — United States, New York City, and Los Angeles, May 5–12, 2020
Mark Czeisler, Michael A Tynan, Mark E Howard … (2020)
SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to be transmitted mainly by person-to-person contact ( 1 ). Implementation of nationwide public health orders to limit person-to-person interaction and of guidance on personal protective practices can slow transmission ( 2 , 3 ). Such strategies can include stay-at-home orders, business closures, prohibitions against mass gatherings, use of cloth face coverings, and maintenance of a physical distance between persons ( 2 , 3 ). To assess and understand public attitudes, behaviors, and beliefs related to this guidance and COVID-19, representative panel surveys were conducted among adults aged ≥18 years in New York City (NYC) and Los Angeles, and broadly across the United States during May 5–12, 2020. Most respondents in the three cohorts supported stay-at-home orders and nonessential business closures* (United States, 79.5%; New York City, 86.7%; and Los Angeles, 81.5%), reported always or often wearing cloth face coverings in public areas (United States, 74.1%, New York City, 89.6%; and Los Angeles 89.8%), and believed that their state’s restrictions were the right balance or not restrictive enough (United States, 84.3%; New York City, 89.7%; and Los Angeles, 79.7%). Periodic assessments of public attitudes, behaviors, and beliefs can guide evidence-based public health decision-making and related prevention messaging about mitigation strategies needed as the COVID-19 pandemic evolves. During May 5–12, 2020, a total of 4,042 adults aged ≥18 years in the United States were invited to complete a web-based survey administered by Qualtrics, LLC. † Surveys were conducted among residents of NYC and Los Angeles to enable comparison of the two most populous cities in the United States with each other and with the nationwide cohort ( 4 ). The nationwide survey did not exclude respondents from NYC and Los Angeles, but no respondent was counted in more than one cohort. Invited participants were recruited using methods to create panels representative of the 2010 U.S. Census by age, gender, race, and ethnicity ( 5 ). Overall, 2,402 respondents completed surveys (response rate = 59.4%); of these, 2,221 (92.5%) (United States cohort = 1,676, NYC cohort = 286, and Los Angeles cohort = 259) passed quality screening procedures § ( 5 ); sample sizes provided a margin of error at 95% confidence levels of 2.4%, 5.7%, and 5.9%, respectively. Questions about the effects of the COVID-19 pandemic focused on public attitudes, behaviors, and beliefs regarding stay-at-home orders, nonessential business closures, and public health guidance. Chi-squared statistics (threshold of α = 0.05) were calculated to examine differences between the survey cohorts and to examine potential associations between reported characteristics (gender, age, race, ethnicity, employment status, essential worker status, rural-urban residence, knowing someone with COVID-19, and knowing someone who had died from COVID-19). Jupyter Notebook (version 6.0.0; Project Jupyter) was used to conduct statistical analyses. Among respondents in the U.S. cohort (1,676), 16.8% knew someone who had positive test results for COVID-19, compared with 42.0% of respondents in NYC and 10.8% in Los Angeles (Table 1); 5.9% of respondents in the U.S. survey cohort knew someone who had died from COVID-19, compared with 23.1% in NYC and 7.3% in Los Angeles. TABLE 1 Self-reported characteristics of invited participants and survey respondents — United States, New York City, and Los Angeles,* May 5–12, 2020 Characteristic %† United States New York City Los Angeles Invited Responded Invited Responded Invited Responded (N = 3,010) (N = 1,676) (N = 507) (N = 286) (N = 525) (N = 259) Gender Female 55.9 56.1 52.9 55.2 52.4 52.9 Male 44.0 43.9 47.1 44.8 47.6 47.1 Other 0.1 0.0 0.0 0.0 0.0 0.0 Age group (yrs) 18–24 11.4 3.9 11.2 4.2 11.0 5.8 25–34 14.8 8.5 18.5 11.5 18.1 10.4 35–44 17.6 15.0 15.6 14.0 17.5 12.4 45–54 17.6 19.0 15.0 13.6 16.4 18.5 55–64 18.0 23.4 19.3 26.9 17.1 22.0 ≥65 20.6 30.2 20.3 29.7 19.8 30.9 Race White 78.4 84.7 72.6 82.5 74.3 80.7 Black or African American 9.2 5.0 11.2 4.5 9.1 4.6 Asian 5.7 6.2 6.1 7.3 5.7 7.3 Multiple Race/other § 6.7 4.2 10.1 5.6 10.9 7.3 Ethnicity Hispanic or Latino 8.8 5.9 13.6 8.0 17.1 10.8 Not Hispanic or Latino 91.2 94.1 86.4 92.0 82.9 89.2 Rural-urban residence classification¶ Rural 15.3 15.5 0.8 1.4 0.8 0.4 Urban 84.7 84.5 99.2 98.6 99.2 99.6 Employment status** Employed †† 62.9 49.6 71.2 58.7 68.6 52.5 Essential — 23.4 — 16.1 — 23.2 Nonessential — 26.2 — 42.7 — 29.3 Retired 24.4 34.9 19.9 29.4 21.0 32.8 Unemployed 12.8 15.5 8.9 11.9 10.5 14.7 Know someone with positive test results for COVID-19 — 16.8 — 42.0 — 10.8 Know someone who died from COVID-19 — 5.9 — 23.1 — 7.3 Abbreviation: COVID-19 = coronavirus disease 2019. * The U.S. survey group did not exclude respondents from New York City and Los Angeles. † Totals might not all sum to 100 because of rounding. § The multiple race/other category includes respondents who self-reported as a race with 87% in each area) and limiting gatherings to fewer than 10 persons (>82% in each area). At the time of the survey, most also agreed that dining inside restaurants should not be allowed, with agreement higher in NYC (81.5%) than in Los Angeles (71.8%) and in the United States overall (66.6%). TABLE 2 Attitudes, behaviors, and beliefs related to COVID-19, stay-at-home orders, nonessential business closures, and public health guidance — United States (U.S.),* New York City (NYC), and Los Angeles (LA), May 5–12, 2020 Attitudes, behaviors, and beliefs U.S. NYC LA p-value† p-value† p-value† (N = 1,676) (N = 286) (N = 259) U.S. versus NYC U.S. versus LA NYC versus LA Attitudes, no. of respondents (%) Support stay-at-home order and nonessential business closures Yes 1,332 (79.5) 248 (86.7) 211 (81.5) 85% of adults in the three cohorts. Approximately 90% of respondents reported having been in a public area during the preceding week; among those, 74.1% nationwide reported always or often wearing cloth face coverings when in public, with higher percentages reporting this behavior in NYC (89.6%) and Los Angeles (89.8%). Overall, 84.3% of adults in the U.S. survey cohort believed their state’s COVID-19 community mitigation strategies were the right balance or not restrictive enough, compared with 89.7% in NYC and 79.7% in Los Angeles. As well, 74.3% of respondents in the United States reported they would not feel safe if these restrictions were lifted nationwide at the time the survey was conducted, compared with 81.5% in NYC and 73.4% in Los Angeles. In addition, among those who reported that they would not feel safe, some indicated that they would nonetheless want community mitigation strategies lifted and would accept associated risks (17.1%, 12.6%, and 12.7%, respectively). Reported prevalence of self-isolation and feeling safe if community mitigation strategies were lifted differed significantly by age, employment status, and essential worker status among adults in the U.S. survey cohort (Table 3). The percentage of respondents who reported that they were in self-isolation was highest among persons aged 18–24 years (92.3%) and lowest among those aged 45–54 years (71.5%). The percentage who reported that they would feel safe if community mitigation strategies were lifted was approximately twice as high among persons aged 18–24 as it was among those aged ≥65 years (43.1% versus 19.2%). Respondents who reported that they were essential workers** accounted for 47.2% of employed respondents in the U.S. cohort and were significantly less likely than were nonessential workers to report self-isolating (63.1% versus 80.6%). Essential workers were also significantly more likely than were nonessential workers to report that they would feel safe if COVID-19 community mitigation strategies were lifted (37.7% versus 23.7%). TABLE 3 Attitudes, behaviors, and beliefs related to COVID-19, stay-at-home orders, nonessential business closures, and public health guidance, by respondent characteristics* — United States, May 5–12, 2020 By gender, age group, and ethnicity, % Attitudes, behaviors and, beliefs Gender Age group (yrs) Ethnicity Male Female 18–24 25–34 35–44 45–54 55–64 ≥65 Hispanic Non- Hispanic Attitudes Support stay-at-home orders and nonessential business closures Yes 76.3 81.9 84.6 85.2 83.7 75.2 76.0 80.4 83.8 79.2 p-value† 0.0521 0.1803 1.0 Nonessential workers should stay home Agree 64.9 69.2 55.4 76.8 72.2 62.7 62.0 70.8 72.7 67.0 Disagree 17.8 14.2 13.8 7.7 11.5 20.7 19.6 14.4 11.1 16.1 p-value† 0.9043 <0.05§ 1.0 Persons should always keep ≥6-ft of physical distance Agree 86.5 88.6 73.8 82.4 86.9 85.0 91.1 90.5 77.8 88.3 Disagree 4.8 4.7 4.6 5.6 2.8 7.2 4.8 3.8 6.1 4.6 p-value† 1.0 <0.05§ <0.05§ Groups of 10 or more persons should not be allowed Agree 80.4 84.0 70.8 80.3 83.7 76.8 82.9 87.0 80.8 82.5 Disagree 9.9 7.0 10.8 8.5 6.0 11.9 9.2 6.1 5.1 8.5 p-value† 0.7238 <0.05§ 1.0 Dining inside restaurants should not be allowed Agree 62.2 70.1 67.7 72.5 68.3 60.8 65.6 68.6 66.7 66.6 Disagree 21.8 16.5 9.2 12.0 15.9 23.8 23.2 16.8 14.1 19.1 p-value† <0.05§ <0.05§ 1.0 Behaviors In self-isolation Yes 75.8 78.5 92.3 81.7 77.8 71.5 72.7 81.2 87.9 76.7 p-value† 1.0 <0.05§ 0.1246 Keep ≥6 ft apart from others Always 54.6 61.0 29.2 56.3 60.3 55.2 56.4 64.6 54.5 58.4 Often 22.6 20.3 30.8 23.2 18.3 21.6 23.5 19.2 18.2 21.5 Sometimes 9.0 7.7 26.2 7.0 9.1 9.1 7.7 5.7 14.1 7.9 Rarely 5.0 3.4 9.2 5.6 2.8 4.4 4.6 3.2 7.1 3.9 Never 8.8 7.7 4.6 7.7 9.5 9.7 7.9 7.3 6.1 8.3 p-value† 0.7508 <0.05§ 0.8299 Avoid groups of 10 or more persons Always 72.5 77.2 52.3 68.3 74.2 73.4 73.7 82.6 63.6 75.8 Often 12.2 9.7 15.4 18.3 11.9 8.8 12.0 7.9 14.1 10.6 Sometimes 3.9 3.2 15.4 2.1 4.4 4.4 3.1 1.8 6.1 3.4 Rarely 2.4 2.2 15.4 2.8 0.4 2.2 2.0 1.8 6.1 2.1 Never 8.8 7.8 1.5 8.5 9.1 11.3 9.2 5.9 10.1 8.1 p-value† 1.0 <0.05§ 0.1843 Been to a public area in the preceding week Yes 94.7 88.9 96.9 88.0 92.5 90.6 94.4 89.5 90.9 91.5 p-value† <0.05§ 0.3145 1.0 Wear cloth face covering when in public¶ Always 54.6 65.1 44.4 59.2 57.9 56.1 55.1 71.1 57.8 60.5 Often 14.9 12.9 15.9 16.0 12.9 13.1 17.6 10.8 13.3 13.9 Sometimes 10.1 7.6 15.9 8.8 8.6 8.7 10.3 6.6 13.3 8.5 Rarely 4.6 3.7 12.7 4.0 4.7 4.5 3.5 2.9 4.4 4.1 Never 15.8 10.6 11.1 12.0 15.9 17.6 13.5 8.6 11.1 13.1 p-value† <0.05§ <0.05§ 1.0 Beliefs State restrictions are The right balance 64.5 67.8 61.5 57.0 65.1 63.3 67.3 71.3 60.6 66.7 Not restrictive enough 18.0 18.1 21.5 31.7 19.0 16.9 16.1 15.4 26.3 17.5 p-value† 1.0 <0.05§ 0.7720 Would feel safe if restrictions were lifted nationwide at the time the survey was conducted Yes 28.8 23.3 43.1 26.8 27.4 30.1 26.3 19.2 25.3 25.7 p-value† 0.1019 <0.05§ 1.0 By race, employment status, and essential worker status, % Attitudes, behaviors, and beliefs Race** Employment status Essential worker†† White Black Asian Multiple race/Other Unemployed Retired Employed Yes No Attitudes Support stay-at-home orders and nonessential business closures Yes 77.9 89.2 90.4 84.3 81.9 80.0 78.4 75.6 80.9 p-value† <0.05§ 1.0 0.6953 Nonessential workers should stay home Agree 66.4 63.9 78.8 72.9 68.3 69.9 65.1 58.3 71.3 Disagree 16.8 16.9 4.8 11.4 13.9 14.9 17.1 19.6 14.8 p-value† 0.4225 1.0 <0.05§ Persons should always keep ≥6-ft of physical distance Agree 88.2 81.9 89.4 81.4 83.0 92.5 85.8 81.7 89.5 Disagree 4.9 6.0 1.9 4.3 8.1 2.1 5.5 7.1 4.1 p-value† 1.0 <0.05§ <0.05§ Groups of 10 or more persons should not be allowed Agree 82.0 84.3 89.4 78.6 79.5 87.5 79.7 74.8 84.1 Disagree 8.9 7.2 1.9 7.1 9.7 5.8 9.6 10.7 8.7 p-value† 1.0 <0.05§ <0.05§ Dining inside restaurants should not be allowed Agree 65.8 75.9 72.1 64.3 66.0 69.6 64.8 59.5 69.5 Disagree 20.5 7.2 6.7 15.7 19.3 16.9 20.0 22.4 17.8 p-value† <0.05§ 1.0 0.0899 Behaviors In self-isolation Yes 77.2 78.3 73.1 84.3 81.1 82.7 72.4 63.1 80.6 p-value† 1.0 <0.05§ <0.05§ Keep ≥6 ft apart from others Always 58.2 48.2 67.3 55.7 58.3 65.8 52.8 44.8 59.9 Often 21.6 20.5 17.3 21.4 21.6 19.0 22.8 26.0 20.0 Sometimes 8.0 14.5 4.8 11.4 5.8 5.5 10.9 13.0 9.1 Rarely 3.9 9.6 1.0 5.7 5.4 2.9 4.6 6.6 2.7 Never 8.2 7.2 9.6 5.7 8.9 6.8 8.9 9.7 8.2 p-value† 0.5507 <0.05§ <0.05§ Avoid groups of 10 or more persons Always 76.2 56.6 77.9 71.4 73.0 81.2 71.5 65.6 76.8 Often 10.8 15.7 6.7 11.4 10.8 8.2 12.6 16.0 9.6 Sometimes 3.0 12.0 1.9 5.7 4.2 2.2 4.2 5.6 3.0 Rarely 2.0 8.4 1.9 2.9 2.3 2.1 2.5 4.1 1.1 Never 8.0 7.2 11.5 8.6 9.7 6.3 9.1 8.7 9.6 p-value† <0.05§ 0.1179 <0.05§ Been to a public area in the preceding week Yes 91.8 91.6 87.5 91.4 88.4 89.1 94.1 97.5 91.1 p-value† 1.0 <0.05§ <0.05§ Wear cloth face covering when in public¶ Always 60.1 55.3 71.4 54.7 58.5 70.4 54.2 49.3 58.8 Often 13.7 19.7 9.9 14.1 10.0 11.1 16.7 20.4 13.3 Sometimes 8.4 13.2 8.8 10.9 10.5 5.6 10.3 9.7 11.0 Rarely 3.8 7.9 3.3 7.8 2.2 3.1 5.4 6.5 4.3 Never 14.0 3.9 6.6 12.5 18.8 9.8 13.4 14.1 12.8 p-value† 0.3708 <0.05§ 0.1843 Beliefs State restrictions are The right balance 66.7 65.1 67.3 60.0 67.6 68.7 64.3 64.9 63.8 Not restrictive enough 16.7 28.9 22.1 25.7 18.5 17.4 18.3 14.5 21.6 p-value† 0.0523 1.0 0.0563 Would feel safe if restrictions were lifted nationwide at the time the survey was conducted Yes 25.8 37.3 15.4 25.7 22.4 20.7 30.3 37.7 23.7 p-value† 0.0765 <0.05§ <0.05§ * Nationwide cohort (n = 1,676) only unless otherwise specified. The six respondent characteristic categories shown in the table (gender, age, ethnicity, race, employment status, and essential worker status) account for 32 of 34 significant associations among the 108 potential interactions evaluated. Responses and p-values values for significant associations with characteristics not presented in the table that are associated with the attitudes, behaviors, and beliefs include the following: Use of cloth face coverings when in public × Rural-urban classification, (p-value = 0.0324); Rural: Always = 51.4%, Often = 15.5%, Sometimes = 10.2%, Rarely = 7.8%, Never = 15.1%; Urban: Always = 62.0%, Often = 13.5%, Sometimes = 8.5%, Rarely = 3.4%, Never = 12.6%; attitude that dining inside restaurants should not be allowed × Know someone with COVID-19 (p-value = 0.0243), Know someone: Agree = 75.1%, Disagree = 12.5%; Do not know someone: Agree = 64.9%, Disagree = 20.1%. † Calculated with Chi-squared test of independence. § P-value is statistically significant. ¶ Of respondents who reported having been in a public area in the preceding week. ** The multiple race/other category includes respondents who self-reported as a race with <2.5% of respondents in any cohort (e.g., American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or more than one race). †† Of 832 employed respondents in the U.S. cohort. Reported prevalences of always or often wearing a cloth face covering in public and maintaining ≥6 feet of physical distance also varied significantly across respondent demographics and characteristics. Respondents who were male, employed, or essential workers were significantly more likely to report having been in public areas in the past week. Among respondents who had been in public areas during the preceding week, significantly higher percentages of women, adults aged ≥65 years, retired persons, and those living in urban areas reported wearing cloth face coverings. A significantly higher percentage of adults aged ≥65 years and nonessential workers reported maintaining 6 feet of physical distance between themselves and others and abiding by the recommendation to avoid gatherings of 10 or more persons than did others. Adherence to recommendations to maintain 6 feet of physical distance and limit gatherings to fewer than 10 persons also differed significantly by employment status and race, respectively, with employed persons less likely than were retired persons to have maintained 6 feet of distance and black persons less likely than were white or Asian persons to have limited gatherings to fewer than 10 persons. Discussion There was broad support for stay-at-home orders, nonessential business closures, and adherence to public health recommendations to mitigate the spread of COVID-19 in early- to mid-May 2020. Most adults reported they would not feel safe if government-ordered community mitigation strategies such as stay-at-home orders and nonessential business closures were lifted nationwide at the time the survey was conducted, although a minority of these adults who did not feel safe wanted these restrictions lifted despite the risks. There was a significant association between age and feeling safe without community mitigation strategies, with younger adults feeling safer than those aged ≥65 years, which might relate to perceived risk for infection and severe disease. As of May 16, adults aged ≥65 years accounted for approximately 80% of reported COVID-19–associated deaths, compared with those aged 15–24 years, who accounted for 0.1% of such deaths ( 6 ). Identifying variations in public attitudes, behaviors, and beliefs by respondent characteristics can inform tailored messaging and targeted nonpharmacological interventions that might help to reduce the spread of COVID-19. Other variations in attitudes, behaviors, and beliefs by respondent characteristics have implications for implementation of COVID-19 mitigation strategies and related prevention messaging. For example, a lower percentage of respondents in the U.S. survey cohort reported wearing cloth face coverings and self-isolating than did those in NYC and Los Angeles. However, although use of cloth face coverings in NYC and Los Angeles were similar, NYC experienced substantially higher COVID-19-related mortality during the initial months of the pandemic than did Los Angeles ( 4 ). Nationwide, higher percentages of respondents from urban areas reported use of cloth face coverings than did rural area respondents. Because outbreaks have been reported in rural communities and among certain populations since March 2020 ( 7 , 8 ), these data suggest a need for additional and culturally effective messaging around the benefits of cloth face coverings targeting these areas. Essential workers also reported lower adherence to recommendations for self-isolation, 6 feet of physical distancing, and limiting gatherings to fewer than 10 persons. These behaviors might be related to job requirements and other factors that could limit the ability to effectively adhere to these recommendations. Nevertheless, the high rate of person-to-person contact associated with these behaviors increases the risk for widespread transmission of SARS-CoV-2 and underscores the potential value of tailored and targeted public health interventions. The findings in this report are subject to at least four limitations. First, behaviors and adherence to recommendations were self-reported; therefore, responses might be subject to recall, response, and social desirability biases. Second, responses were cross-sectional, precluding inferences about causality. Third, respondents were not necessarily representative among all groups; notably a lower percentage of African Americans responded than is representative of the U.S. population. In addition, participation might have been higher among persons who knew someone who had tested positive or had died from COVID-19, which could have affected support for and adherence to mitigation efforts. Finally, given that the web-based survey does not recruit participants using population-based probability sampling and respondents might not be fully representative of the U.S. population, findings might have limited generalizability. However, this survey did apply screening procedures to address issues related to web-based panel quality. Widespread support for community mitigation strategies and commitment to COVID-19 public health recommendations indicate that protecting health and controlling disease are public priorities amid this pandemic, despite daily-life disruption and adverse economic impacts ( 5 , 9 ). These findings of high public support might inform reopening policies and the timelines and restriction levels of these mitigation strategies as understanding of public support for and adherence to these policies evolves. Absent a vaccine, controlling COVID-19 depends on community mitigation strategies that require public support to be effective. As the pandemic progresses and mitigation strategies evolve, understanding public attitudes, behaviors, and beliefs is critical. Adherence to recommendations to wear cloth face coverings and physical distancing guidelines are of public health importance. Strong public support for these behaviors suggests an opportunity to normalize safe practices and promote continued use of these and other recommended personal protective behaviors to minimize further spread of COVID-19 as jurisdictions reopen. These findings and periodic assessments of public attitudes, behaviors, and beliefs can also inform future planning if subsequent outbreak waves occur, and if additional periods of expanded mitigation efforts are necessary to prevent the spread of COVID-19 and save lives. Summary What is already known about this topic? Stay-at-home orders and recommended personal protective practices were disseminated to mitigate the spread of COVID-19 in the United States. What is added by this report? During May 5–12, 2020, a survey among adults in New York City and Los Angeles and broadly across the United States found widespread support of stay-at-home orders and nonessential business closures and high degree of adherence to COVID-19 mitigation guidelines. Most respondents reported that they would feel unsafe if restrictions were lifted at the time of the survey. What are the implications for public health practice? Routine assessment of public priorities can guide public health decisions requiring collective action. Current levels of public support for restrictions and adherence to mitigation strategies can inform decisions about reopening and balancing duration and intensity of restrictions.
- Record: found
- Abstract: found
- Article: found
Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility ― Four U.S. Metropolitan Areas, February 26–April 1, 2020
Arielle Lasry, Daniel Kidder, Marisa Hast … (2020)
Community mitigation activities (also referred to as nonpharmaceutical interventions) are actions that persons and communities can take to slow the spread of infectious diseases. Mitigation strategies include personal protective measures (e.g., handwashing, cough etiquette, and face coverings) that persons can use at home or while in community settings; social distancing (e.g., maintaining physical distance between persons in community settings and staying at home); and environmental surface cleaning at home and in community settings, such as schools or workplaces. Actions such as social distancing are especially critical when medical countermeasures such as vaccines or therapeutics are not available. Although voluntary adoption of social distancing by the public and community organizations is possible, public policy can enhance implementation. The CDC Community Mitigation Framework ( 1 ) recommends a phased approach to implementation at the community level, as evidence of community spread of disease increases or begins to decrease and according to severity. This report presents initial data from the metropolitan areas of San Francisco, California; Seattle, Washington; New Orleans, Louisiana; and New York City, New York* to describe the relationship between timing of public policy measures, community mobility (a proxy measure for social distancing), and temporal trends in reported coronavirus disease 2019 (COVID-19) cases. Community mobility in all four locations declined from February 26, 2020 to April 1, 2020, decreasing with each policy issued and as case counts increased. This report suggests that public policy measures are an important tool to support social distancing and provides some very early indications that these measures might help slow the spread of COVID-19. When a novel virus with pandemic potential emerges, community mitigation strategies often are the most readily available interventions to slow transmission. CDC-recommended community mitigation interventions for COVID-19, caused by the SARS-CoV-2 virus, are based on evidence for other viral respiratory illnesses and emerging data on SARS-CoV-2 transmission and epidemiology, including groups at highest risk for hospitalization and death from COVID-19 ( 1 , 2 ). Public policies to implement social distancing include emergency declarations, bans on gatherings of certain sizes, school closures, restrictions on businesses, and stay-at-home or shelter-in-place of residence orders. These strategies can substantially disrupt daily life; therefore, the intensity of their implementation should align with progression and severity of disease ( 1 ). Understanding the timing and potential impact of policies designed to increase compliance with mitigation strategies will assist in guiding modification of those policies over the course of the COVID-19 pandemic as well as increasing the understanding of when and how to fully implement these strategies in future outbreaks where community mitigation is required. Data from February 26–April 1, 2020 were examined from the core metropolitan statistical areas (MSAs) of Seattle, San Francisco, and New Orleans, and from the five boroughs of New York City ( 3 ). These areas were selected because each had substantial numbers of reported COVID-19 cases during the early stages of the U.S. epidemic ( 4 ). For each locality, the following data were analyzed: 1) types and timing of public policies issued to promote community mitigation interventions at the national, state, and local government levels; 2) cumulative number of reported COVID-19 cases; 3) average 3-day percentage change in reported cases; and 4) community mobility. The types and timing of public policies issued were collected by using Google Alerts and targeted Google searches for news media coverage of state and local COVID-19 orders and proclamations, followed by searching state, county, parish, and city government websites to locate official copies of each order. Confirmed cumulative COVID-19 case count data were collected from USAFacts ( 4 ), which aggregates data on cases by date of report from CDC and state- and local-level public health agencies. The 3-day average percentage change in cumulative case count was calculated after the cumulative case count was >20 and is presented to describe more completely the trend in the epidemic growth rate. Community mobility was defined as the percentage of personal mobile devices (e.g., mobile phones, tablets, and watches) leaving home, using publicly accessible data from SafeGraph, a data company that aggregates anonymized location data from mobile devices ( 5 ). The percentage leaving home measure is the inverse of the SafeGraph “completely home” metric, an indicator that a device has not moved throughout the day beyond approximately 150 m (492 ft) of its common nighttime location. The average number of devices included in daily reporting was 80,095 in New Orleans (6.4% of population); 336,783 devices in New York City (4.0% of population); 163,981 devices in San Francisco (3.6% of population); and 177,027 devices in Seattle (4.8% of population). In each of the four locations, a combination of state and local community mitigation policies was issued (Table). All four metropolitan areas were in states that declared a state of emergency and put local limits on mass gatherings, although these varied by numbers of people allowed and, in some cases, changed over time. All four issued school closure and stay-at-home orders at state or local levels, and three parishes in the New Orleans MSA were the only areas in this study to implement a curfew. TABLE Public policies ordering COVID-19 community mitigation interventions and dates of issuance* — four U.S. metropolitan areas, February 26–April 1, 2020 Mandatory intervention New Orleans MSA parishes: Jefferson, Orleans, Plaquemines, St. Bernard, St. Charles, St. James, St. John the Baptist, St. Tammany New York City boroughs: The Bronx, Brooklyn, Manhattan, Queens, Staten Island San Francisco MSA counties: Alameda, Contra Costa, San Francisco, San Mateo, Marin Seattle MSA counties: King, Snohomish, Pierce State declaration of emergency March 11 March 7 March 4 February 29 Local declaration of emergency March 11: Orleans March 12: New York City February 25: San Francisco March 2: King March 12: Jefferson March 1: Alameda March 3: City of Seattle March 13: St. Tammany, St. James March 3: San Mateo, Marin March 4: Snohomish March 14: St. Charles March 10: Contra Costa March 15: Plaquemines, St. John the Baptist March 16: St. Bernard State limits on mass gatherings March 13: limiting to 20. § San Francisco metropolitan statistical area (MSA) counties include Alameda, Contra Costa, San Francisco, San Mateo, and Marin; Seattle MSA counties include King, Snohomish, and Pierce; New York City boroughs include The Bronx, Brooklyn, Manhattan, Queens, and Staten Island; New Orleans MSA parishes include Jefferson, Orleans, Plaquemines, St. Bernard, St. Charles, St. James, St. John the Baptist, and St. Tammany. ¶ The primary and secondary vertical axis are different across locations and set according to each location’s data. The figure is four combination line charts, epidemiologic curves, and timelines showing the selected community mitigation interventions, cumulative COVID-19 case counts, average 3-day percentage change in case counts, and percentage leaving home during February 26–April 1, 2020, in four U.S. metropolitan areas. Discussion During February 26–April 1, 2020, as cumulative cases increased and community mitigation policies were implemented, community mobility declined in four U.S. metropolitan areas. With the exception of emergency declarations, which were implemented as cases increased in other regions and internationally, these policies were implemented during the period when case counts were increasing in each location, but the timing in relation to cumulative case counts varied. Public policies to increase compliance with social distancing, including limits on mass gatherings, school closures, business restrictions, and stay-at-home or shelter-in-place orders appear to be associated with decreases in mobility. Policies related to specific locations or community organizations (e.g., mass gatherings, schools, restaurants, and bars) were often implemented within one or two weeks of mid-March, likely a result of increased awareness and concern about the potential scope of the outbreak in the absence of mitigation. This awareness and concern also likely impacted the public, potentially leading to further decreases in mobility. Thus, the potential impact of interventions on mobility as well as this increased awareness of community spread of disease appears to be cumulative over time. Monitoring adherence to community mitigation strategies through mobility measures could improve the understanding of the types, combinations, and timing of policies that are associated with slowing the spread of COVID-19 as well as other infectious diseases. Finally, there appears to be very early indications of potential impact of policies and social distancing on later changes in cases. There are likely a variety of contributors to these changes, including public health efforts to contain spread and individual efforts to increase personal protective practices. However, both policies related to community mitigation and social distancing, operationalized here as community mobility, could have contributed to these changes. The findings in this report are subject to at least four limitations. First, these data suggest temporal correlations between issuance of public policies to increase mitigation strategies and rising case counts, on one hand, and decreases in mobility, on the other as well as first indications that these changes might impact growth of infections. The trends suggest an association but cannot prove causality. Second, although mobile device data can be used to understand movement within a community, the characteristics of those persons using these devices (e.g., age, gender, race, and ethnicity) are not known, so the results might not be generalizable or reflective of actual mobility patterns. Further, mobile phone coverage was limited to 3%–6% of the population in each location. In addition, the data presented here track mobile devices, not persons, who might have multiple devices (e.g., phone and tablet), who might not take their devices when they leave the home, or who might travel outside their home but remain within 150 m (492 ft) of their usual nighttime location. Third, confirmed cumulative cases of COVID-19 might not reflect the actual number of cases because of variability in access to testing and recommendations for who should be tested during this period. Finally, these four urban metropolitan areas are not representative of communities across the United States, and community mitigation policies might have a very different impact on mobility in suburban and rural communities. These temporal trend data provide a preliminary examination of local timing of community mitigation measures and potential impacts on community mobility as well as very early indications of the impact of community mitigation on disease growth. As the COVID-19 pandemic spreads across the United States, the ability to assess the impact of mitigation strategies on reducing COVID-19 transmission will improve. Decreasing numbers of new cases are needed to curtail the COVID-19 pandemic in communities and relieve pressure on the health care system. Better understanding of the short- and long-term impact of the community disruption that results from these measures is critical. However, this analysis suggests that policies to increase social distancing when case counts are increasing can be an important tool for communities as changes in behavior result in decreased spread of COVID-19. Summary What is already known on this topic? Implementing community mitigation strategies, including personal protective measures persons should adopt in community settings, social distancing, and environmental cleaning in community settings, during a pandemic can slow the spread of infections. What is added by this report? During February 26–April 1, 2020, community mobility (a proxy measure for social distancing) in the metropolitan areas of Seattle, San Francisco, New York City, and New Orleans declined, decreasing with each community mitigation policy issued and as case counts increased. What are the implications for public health practice? Public policies to increase compliance with community mitigation strategies might be effective in decreasing community mobility; however, more information is needed to assess impact on disease transmission.
Author and article information
Journal
Journal ID (iso-abbrev): MMWR Morb Mortal Wkly Rep
Title:
MMWR. Morbidity and mortality weekly report
Publisher:
Centers for Disease Control MMWR Office
ISSN
(Electronic):
1545-861X
ISSN
(Print):
0149-2195
Publication date
(Electronic):
Sep 04 2020
Volume: 69
Issue: 35
Article
DOI: 10.15585/mmwr.mm6935a2
PMC ID: 7470456
PubMed ID: 32881851
SO-VID: d34d8ed7-c67c-47ba-8a7b-0c881c78c20a
History
Data availability: