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Purpose: To examine changes in obesity prevalence among US adults after the COVID-19 pandemic by level of stay-at-home order and sociodemographic characteristics. Design: Quasi-experimental study using repeated cross-sectional data. Setting: Behavioral Risk Factor Surveillance System (BRFSS). Sample: Pooled data for US adults ages ≥26 years (n = 1,107,673) from BRFSS (2018-2021). Measures: States/territories were classified into three levels of stay-at-home order: none, advisory/only for persons at risk, or mandatory for all. Individual-level sociodemographic characteristics were self-reported. Analysis: The difference-in-differences method was conducted with weighted multiple logistic regression analysis to examine obesity (body mass index ≥30 kg/m2) prevalence by stay-at-home order level and sociodemographic characteristics before/after the COVID-19 pandemic (January 2018-February 2020 vs March 2020-February 2022). Results: After adjusting for a secular trend and multiple covariates, adults in states/territories with mandatory stay-at-home orders experienced a larger increase in obesity prevalence (adjusted odds ratio: 1.05; 95% confidence interval: 1.01, 1.11) than adults in states/territories with no stay-at-home order. Younger adults (vs ≥65 years) and individuals with

Initial research on the effect of pandemic related stay-at-home orders (SAHO) on subsequent US state unemployment rates found inconclusive results regarding the magnitude of the effect. This research helps to clarify the debate, finding that while own-state SAHOs affected unemployment outcomes, it was actually the national level of SAHO implementation across the country that had an even greater impact. While these results do not offer direct guidance on when or whether SAHOs should have been issued in any given state, they do help to clarify the impact of SAHOs on various measures of US unemployment. [ABSTRACT FROM AUTHOR]

Background: After COVID-19 was declared a Public Health Emergency of International Concern by WHO, several non-pharmaceutical interventions were adopted for containing the virus. Success to which largely depend upon citizens' compliance to these measures. There is growing body of evidence linking social support with health promoting behaviour. Hence, this research aimed to study the effects on compliance with stay-at-home order in relation to their perceived social support. Methods: A web-based cross-sectional study was conducted among adult participants aged 18 years and above residing in Bagmati Province, Nepal. A convenient non-probability sampling method was adopted to select the required number of samples. The questionnaire was developed through an extensive review of literature, and consultations with the research advisor, subject experts, as well as peers and converted to online survey form using Google Forms. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS) scale whereas compliance was assessed using a single screening question. Statistical analysis was performed using SPSS version 20 involving both the descriptive and inferential statistics. Results: Two fifth (40.2%) of the participants reported poor compliance with stay-at-home order which was found higher among participants who were not vaccinated against COVID-19 compared to those vaccinated (p value < 0.05). A significant difference was observed between sex and perceived support (p value < 0.05) with higher proportion (80.8%) of female participants reporting perceived support from family, friends, and significant others in comparison to male participants. Conclusion: Overall, the results of this study suggest that the perceived support from family is higher compared to others. Further evidence might be helpful to understand contextual factors on compliance with public health measures. Tailoring behaviour change messages as per the community needs would help the response in such emergencies. The findings from this study might be useful as one of the evidence base for formulating plans and policy during emergencies of similar nature. [ABSTRACT FROM AUTHOR]

To clarify associations between changes in going out after lifting a stay-at-home order and decrease in higher-level functional capacities among older adults with impairments. This study was conducted using questionnaires completed by people registered in two day-service facilities and a nursing station. A baseline survey was conducted during Japan's declaration of emergency for the coronavirus disease 2019, and follow-up surveys were conducted in 4, 8, and 12 months after the declaration was lifted. We performed binary logistic regression analyses to clarify the above associations There were significant associations between Δ sum of purposes of going out and the decrease in higher-level functional capacities in 4 months (OR 0.57) and in 8 months (OR 0.47). Increasing the sum of purposes of going out after lifting a stay-at-home order was associated with lower odds for a decrease in higher-level functional capacities in 4 and 8 months. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected the world in extraordinary ways. Various measures were taken by state and local governments, including the introduction of stay-at-home orders and closures of nonessential businesses, as well as recommendations related to social distancing and the wearing of face coverings. The rollout of testing and vaccination programs were also key actions aimed at limiting the spread of COVID-19. Collectively, these restrictions have resulted in marked changes in travel behavior and patterns throughout the world. In this study, mixed-effects linear regression models are estimated to assess the impacts of COVID-19–related travel restrictions and vaccination rates on daily travel across the United States from January 1, 2020, through August 15, 2021. The results show that daily trips per person were reduced by 15% and 31% in March and April of 2020, respectively, prior to considering the impacts of any governmentimposed restrictions. This suggests that government and media coverage of the pandemic played an important role in reducing travel levels. When accounting for the introduction of interventions, ranging from travel advisories to mandatory stay-at-home orders, travel was reduced by an additional 2%–9%. Interestingly, the reductions were less pronounced in areas that strongly supported the Republican candidate in the 2020 presidential election, raising important concerns as to the role of politics and trust in government. Along these same lines, as the duration of mandatory stay-at-home orders increased, travel tended to revert toward prepandemic levels, which may be attributed to quarantine fatigue. Travel levels were also higher among areas with lower median income, as well as those counties that exhibited greater variability in income, illustrating the inequitable impacts of the pandemic on these areas, which tend to include larger proportions of workers in essential industries. The results also show that trip-making increased with vaccination rates, particularly during the early stages of large-scale vaccination programs. Collectively, these insights are important in informing future strategies to mitigate the adverse impacts associated with future outbreaks of new COVID-19 strains and variants. [ABSTRACT FROM AUTHOR]

Early in the coronavirus pandemic, U.S. states implemented several different types of containment measures to slow the disease's spread. Early evidence indicates containment measures were associated with changes in individuals' mental health. This study explores the associations between U.S. state containment measures and older adults' mental health and importantly, whether the associations vary by living arrangement and gender. The study analyzed national sample of adults aged 50 or older from 12 waves (April-July 2020) of the U.S. Household Pulse Survey (N = 394,934). State fixed-effects models linked four state containment measures (stay-at-home order, restaurant closure, bar closure, and movie theater closure) to levels of depression and anxiety across different types of living arrangements, net of controls. Men and women were analyzed separately. Stay-at-home order and restaurant and bar closure, but not movie theater closure, were associated with higher levels of depression and anxiety in older adults. Living arrangements moderated the associations for women but not men. For women, compared to living alone, living with a spouse or intergenerational family was associated with higher levels of anxiety and depression during stay-at-home order and restaurant closure. The associations between containment measures and mental health vary by type of living arrangement and were gendered, likely because household situations create different demands and supports that men and woman experience differently. Although containment measures are necessary to protect public health, paying attention to these underlying dynamics can inform policymakers' efforts to implement policies that balance harms and benefits for older adults. [ABSTRACT FROM AUTHOR]

Road traffic accidents are one of the leading causes of death for the young segment of the population. Prevention strategies can mitigate the occurrence of road accidents, and successful actions should take into account the most relevant causes of accidents. We use a comprehensive dataset which reports information from police records on all road traffic accidents with injures and/or deaths in Italy between 2014 and 2020 to explore the relationships among economic forces, driving behaviours and accident frequency and severity. More specifically, the natural experiment setting provided by the 2020 COVID-19-related movement restrictions allows to capture the effect of traffic intensity and other factors on the frequency of serious accidents and mortality. The empirical findings confirm a direct relationship between the level of economic activity and the frequency of accidents, whereas there is no clear link with their severity. Instead, lockdown periods are associated with a relatively higher occurrence of fatal road traffic accidents, due to both riskier driving behaviours at times of reduced traffic, and changes in transportation habits reflected by a higher probability of accidents involving bicycles and motorcycles. • Economic variables are a major determinant of road traffic accidents (RTAs) in Italy. • RTA frequency follows the economic cycle, but mortality is unrelated. • COVID-19-related stay-at-home orders reduced the number of RTAs, injuries and deaths. • Lockdown periods were associated with a higher rate of fatal accidents. • Road safety strategies should consider behavioral responses to movement restrictions. [ABSTRACT FROM AUTHOR]

Objective: In this study, we estimated the relative contribution of 4 Reasoned Action Approach (RAA) belief determinants in explaining intention to stay home during the COVID-19 pandemic. Methods: Data were obtained from a survey of a nationally representative sample of US adults (N = 942) conducted April 10-20, 2020 (about one-month after initial stay-at-home orders were implemented) using a probability-based Internet household panel (Ipsos KnowledgePanel). Multiple regression analysis tested the association between attitude, injunctive norm, descriptive norm, and self-efficacy and intention to stay home for the next month while controlling for demographic factors. We tested for a moderating effect of worker status on the relationships between the 4 RAA beliefs and intention. Results: Instrumental attitude, injunctive norm, descriptive norm, and self-efficacy demonstrated statistically significant independent associations with intention to stay home. Self-efficacy showed the highest independent association. However, this relation was modified by an interaction between self-efficacy and worker status, revealing that self-efficacy is particularly important for essential workers. Conclusions: These findings suggest that public health strategies to increase individuals' intention to stay home and encourage adherence to stay-at-home policies should focus on enhancing self-efficacy with communication and policy supports. To be most effective, interventions should be targeted based on worker status. [ABSTRACT FROM AUTHOR]

We assess the treatment effect of juvenile stay-at-home orders (JSAHO) on reducing the rate of SARS-CoV-2 infection spread in Saline County ("Saline"), Arkansas, by examining the difference between Saline's and control Arkansas counties' changes in daily and mean log infection rates of pretreatment (March 28–April 5, 2020) and treatment periods (April 6–May 6, 2020). A synthetic control county is constructed based on the parallel-trends assumption, least-squares fitting on pretreatment and socio-demographic covariates, and elastic-net-based methods, from which the counterfactual outcome is predicted and the treatment effect is estimated using the difference-in-differences, the synthetic control, and the changes-in-changes methodologies. Both the daily and average treatment effects of JSAHO are shown to be significant. Despite its narrow scope and lack of enforcement for compliance, JSAHO reduced the rate of the infection spread in Saline. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: Lockdowns amid the COVID-19 pandemic drastically reduced the possibility of undertaking physical activity (PA) in gyms, swimming pools, or work-related PA, e.g., active commuting. However, the stay-at-home order could have reduced PA the most, i.e., the ban of unnecessary outdoor activities. It affected free walking, running, skiing, active tourism, etc. It is, therefore, crucial to estimate how the stay-at-home order affected PA. We estimated how the stay-at-home order affected perceived PA and sedentary behavior compared to the pre-pandemic time in Poland. Methods: We used a self-reported International Physical Activity Questionnaire--Long Form (IPAQ-LF) to estimate the time (minutes per day) of vigorous and moderate PA and walking and sitting time. Results: We gathered data from 320 Polish participants. Bayesian approaches, including t-test and Bayesian correlations, were used to find differences and correlations between PA before and during the stay-at-home lockdown. Our data supported the hypotheses that vigorous PA, as well as walking, declined during the lockdown. Surprisingly, our data did not support the hypothesis that moderate physical activity was reduced. We found that moderate PA during lockdown increased compared to the pre-lockdown PA. As hypothesized, our data strongly evinced that sitting time inclined during the lockdown. PA decline was not correlated with the available living space. People who had access to gardens did not demonstrate a higher PA level than those without. Discussion: Walking and sitting time have drastically changed during the stay-athome lockdown, decreasing and increasing, respectively. Given results from studies focusing on lockdowns without the stay-at-home restriction, it may be assumed that letting people go outside is crucial in keeping them more active and less sedentary. Authorities should take into account the effect the stay-at-home order may have on PA and sedentary behavior and as a result, on health. Stay-at-home orders should be the last considered restriction, given its detrimental consequences. [ABSTRACT FROM AUTHOR]

Purpose: Early COVID-19 mitigation relied on people staying home except for essential trips. The ability to stay home may differ by sociodemographic factors. We analyzed how factors related to social vulnerability impact a community's ability to stay home during a stay-at-home order.Methods: Using generalized, linear mixed models stratified by stay-at-home order (mandatory or not mandatory), we analyzed county-level stay-at-home behavior (inferred from mobile devices) during a period when a majority of United States counties had stay-at-home orders (April 7-April 20, 2020) with the Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI).Results: Counties with higher percentages of single-parent households, mobile homes, and persons with lower educational attainment were associated with lower stay-at-home behavior compared with counties with lower respective percentages. Counties with higher unemployment, higher percentages of limited-English-language speakers, and more multi-unit housing were associated with increases in stay-at-home behavior compared with counties with lower respective percentages. Stronger effects were found in counties with mandatory orders.Conclusions: Sociodemographic factors impact a community's ability to stay home during COVID-19 stay-at-home orders. Communities with higher social vulnerability may have more essential workers without work-from-home options or fewer resources to stay home for extended periods, which may increase risk for COVID-19. Results are useful for tailoring messaging, COVID-19 vaccine delivery, and public health responses to future outbreaks. [ABSTRACT FROM AUTHOR]

The United States is bearing the brunt of coronavirus disease 2019 (COVID-19). The spatially uneven viral spread and community inequality will jointly bring about worse consequences. The combined effects on U.S. communities remain unclear, however. Given spatially heterogeneous compliance with the stay-at-home orders and the varying timing of local directives, the uneven spread should be further examined. In this research, we first exploited county-level data to study the spatiotemporal pattern of viral transmission by a Bayesian approach. We then examined the uneven effects of socioeconomic and demographic variables on viral transmission across U.S. counties using geographically weighted panel regressions. Our results show that, first, the early epicenters shifted from the West Coast to the East Coast with a transmission rate of over 2.5 and continued to expand into Midwestern states in May, although the spread in the majority of counties had been greatly mitigated since the middle of April. Second, increased stay-at-home behaviors reduced the transmission of COVID-19 across the United States. The effects of socioeconomic and demographic variables varied from place to place, except that high household income was more consistently associated with a reduction in viral transmission. Finally, when the order was lifted, high household income was found to increase the viral transmission in the Midwestern United States and the high unemployment rate contributed to the viral spread in the Western United States. The knowledge obtained from this study can offer new insights for the containment actions of COVID-19. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the COVID-related Stay-at-Home order on hospitality sales and automobile traffic counts in the State of Maine, USA. Empirical results show that the Stay-at-Home order did not impact either measure of state economic activity. Instead, households adjusted their behavior as a result of COVID-19 in advance of the Stay-at-Home order. These results are similar to those found in other states, where Stay-at-Home orders did not impact hospitality sales. This is an important public policy issue given the large health and economic impacts of the pandemic, and widespread use of Stay-at-Home orders. Even beyond the COVID pandemic, however, the extent to which people respond to government restrictions is important for policy development and implementation. [ABSTRACT FROM AUTHOR]

Background: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage.Methods: This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.Results: On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.Conclusions: Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control. [ABSTRACT FROM AUTHOR]

Objective: To estimate the association between weather and COVID-19 fatality rates during US stay-at-home orders. Methods: With a county-level longitudinal design, this study analyzed COVID-19 deaths from public health departments' daily reports and considered exposure as the 18 to 22 day-period before death. Models included state-level social distancing measures, Census Bureau demographics, daily weather information, and daily air pollution. The primary measures included minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V. light index. Results: A 1 °F increase in the minimum temperature was associated with 1.9% (95% CI, 0.2% to 3.6%) increase in deaths 20days later. An ozone concentration increase of 1 ppb (part per billion) decreased daily deaths by 2.0% (95% CI, 0.1% to 3.6%); ozone levels below 38 ppb negatively correlated with deaths. Conclusions: Increased mobility may drive the observed association of minimum daily temperature on COVID-19 deaths. [ABSTRACT FROM AUTHOR]

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals' voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of selfinterested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders. [ABSTRACT FROM AUTHOR]

• Child maltreatment reporting dropped by as much as 31 percent in Colorado during the pandemic. • Counterfactual estimates predict thousands of referrals and victims went unreported. • Victims of neglect were most likely being missed. • Data and clever techniques to measure child maltreatment need to be readily available. As a result of the COVID-19 pandemic, schools closed abruptly in March 2020, and Colorado issued a stay-at-home order during the month of April. Subsequently, child maltreatment reporting dropped by 31 percent. This article documents the decline in referrals and reports during 2020 and 2021 in Colorado and predicts counterfactual estimates using two strategies. One strategy assumes the underlying behavior for child maltreatment was unchanged from 2019 to 2020 and 2021, while the second strategy assumes the economic distress and protective factors brought about by the pandemic altered the underlying prevalence of child maltreatment. Consequently, these two approaches yield similar results when investigating referrals, but they differ when investigating screened-in referrals and substantiated reports. I find that the largest reduction in reporting comes from the stay-at-home order, followed by school closings. Lastly, counterfactual estimates suggest that these missed children were suffering from neglect and not abuse. These findings quantify another hardship brought about by the pandemic, underreporting child maltreatment, and underscore the role mandatory reporters play in detecting child maltreatment. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in US counties. Design/methodology/approach: The following two sets of daily restaurant demand data were collected for each US county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020. Findings: Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.25% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits and restaurant diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders. Practical implications: These results characterize the regional restaurant industry's resilience to COVID-19 and identify particularly vulnerable areas that may require pubic policies and managerial strategies for intervention. Originality/value: This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has dramatically altered life globally during the first 4 months of 2020. Many countries, including the United States, responded to the pandemic by issuing stay-at-home orders/shelter-in-place orders (SaHOs/SiPOs) to their citizens. By April 2020, more than 90% of the U.S. population was subject to an order. SaHOs/SiPOs raise a number of complex issues for the police, ranging from concerns about infringement of constitutional rights to potential sanctions for violations of an order. This article delves into the issues surrounding SaHOs/SiPOs and highlights their complexity for the police. First, we examine the "why the police?" question, and point to key features of their role which make enforcement of SaHOs/SiPOs the proper business of the police. Second, we examine the relevant legal doctrines that can serve as the basis for police actions against violators of orders, most notably the special needs doctrine. Last, we offer police legitimacy as a lens for viewing the appropriateness of police responses to violators of SaHOs/SiPOs. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, strict stay-at-home orders have been implemented in many Chinese universities in virus-hit regions. While changes in electricity consumption in the residential sector caused by COVID-19 have been thoroughly analysed, there is a lack of insight into the impact of the stay-at-home order on electricity consumption in university dormitory buildings. Based on questionnaire survey results, this study adopted the statistical Kaplan–Meier survival analysis to analyse the energy-use behaviours of university students in dormitories during the COVID-19 pandemic. The electricity load profiles of the dormitory buildings before and during the implementation of the stay-at-home order were generated and compared to quantitatively analyse the influence of COVID-19 pandemic on the energy-use behaviours of university students, and the proposed load forecasting method was validated by comparing the forecasting results with monitoring data on electricity consumption. The results showed that: 1) during the implementation of the stay-at-home order, electricity consumption in the university dormitory buildings increased by 41.05%; 2) due to the increased use of illuminating lamps, laptops, and public direct drinking machines, the daily electricity consumption increased most significantly from 13:00 to 18:00, with an increase rate of 97.15%; and 3) the morning peak shifted backward and the evening peak shifted forward, demonstrating the effect of implementing the stay-at-home order on reshaping load profiles. [ABSTRACT FROM AUTHOR]

Background: The surge of COVID-19 infections has prompted many countries to take unprecedented policy measures. In Belgium, the authorities implemented a nation-wide stay-at-home order for several months. Evidence of the mental health effect of such measures is scarce. A recent review by Brooks et al. has compiled a defined list of stressors affecting people's mental health under quarantine during previous epidemic settings. This study aims to test the association between these stressors and the mental health of students attending higher education during the stay-at-home order in Belgium.Methods: In this cross-sectional study, 18,301 students from 13 higher education institutions (HEI) participated in an online survey between 26 April and 11 May 2020. We assessed the association between potential stressors and depressive symptoms severity scores and structural equation modeling was used to assess how stressors may mediate the association between duration of exposure and depressive symptoms severity.Results: The stressors proposed by Brooks et al. were found to be associated with depressive symptoms severity. The stressors 'perceived academic stress', 'institutional dissatisfaction' and 'fear of being infected' were associated with substantially higher depressive symptoms severity scores. The association between duration of exposure and depressive symptoms severity was mediated by 'academic stress'. Being in a steady relationship and living together with others were both associated with a lower depressive symptoms severity.Conclusion: Findings underline the need for a student-centered approach and mental health prevention. Authorities and HEIs should consider whether and if so, how a stay-at-home order should be implemented. [ABSTRACT FROM AUTHOR]

The 2019 Coronavirus Disease (COVID-19) pandemic has triggered stay-at-home orders in 43 states since March 19, 2020. Evidence is limited on how these orders affect peoples' behaviors and mental distress. We used a nationally representative survey of 1094 American adults collected between March 19 and March 31, 2020 to compare risk-reduction behaviors and mental distress in states with and without orders. Risk reduction behaviors included hand washing, wearing face mask and social distancing, and the mental distress was assessed by the four-item version of the Patient Health Questionnaire (PHQ-4). We predicted the probability of a person adopting risk reduction behaviors and the mental distress status using the number of days since their state issued the order relative to those in the non-order states, controlling for COVID-19 prevalence in the state, self-reported symptoms and demographic characteristics. The analysis was conducted in April 2020. Results show that the probability of adopting risk reduction behaviors increased between 8 (avoidance of people with high risk, 87% to 95%) and 27 (use of face mask, 18% to 45%) percentage points in the response period. Mental distress increased by 1.0 point on the PHQ-4 score (from 2.4 to 3.5 point) in the first week and started to drop afterwards. In summary, stay-at-home orders were associated with a differential increase in risk-reduction behaviors. People's mental distress rose in the first week under order and dropped afterwards. While in need for confirmation in longitudinal data, these results suggest that residents are responsive to orders. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, a growing number of states, counties and cities in the United States issued mandatory stay-at-home orders as part of their efforts to slow down the spread of the virus. We argue that the consequences of this one-size-fits-all order will be differentially distributed among economic groups. In this paper, we examine social distance behavior changes for lower income populations. We conduct a comparative analysis of responses between lower-income and upper-income groups and assess their relative exposure to COVID-19 risks. Using a difference-in-difference-in-differences analysis of 3140 counties, we find social distance policy effect on the lower-income group is smaller than that of the upper-income group, by as much as 46% to 54%. Our explorations of the mechanisms behind the disparate effects suggest that for the work-related trips the stay-at-home orders do not significantly reduce low income work trips and this result is statistically significant. That is, the share of essential business defined by stay-at-home orders is significantly negatively correlated with income at county level. In the non-work-related trips, we find that both the lower-income and upper-income groups reduced visits to retail, recreation, grocery, and pharmacy visits after the stay-at-home order, with the upper-income group reducing trips more compared to lower-income group. [ABSTRACT FROM AUTHOR]