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(1) Background: The association of COVID-19 infection and mortality with mobility and weather in Indiana and Kentucky was compared for the period from 1 March to 15 May 2020. (2) Methods: The risk of COVID-19 infection and mortality was evaluated using Cox regressions with the following covariates: mobility (retail/recreation, grocery/pharmacy, and workplace), weather (precipitation, minimum and maximum temperature, ultraviolet [UV] index), and metropolitan status. (3) Results: A higher maximum temperature (adjusted hazard ratioinfection (aHRi) = 1.01; adjusted hazard ratiodeath (aHRd) = 1.001), metropolitan status (aHRi = 1.12; aHRd = 2.05), and a higher minimum temperature (aHRi = 1.01) were associated with increased risks of COVID-19 infection and/or mortality. Protection against COVID-19 infection and/or mortality was associated with retail/recreation (aHRi = 0.97; aHRd = 0.937), grocery/pharmacy (aHRi = 0.991; aHRd = 0.992), workplace (aHRi = 0.99; aHRd = 0.965), precipitation (aHRi = 0.999; aHRd = 0.9978), UV index (aHRi = 0.37; aHRd = 0.748), and a higher minimum temperature (aHRd = 0.994). COVID-19 infection (aHRi = 1.18) and mortality (aHRd = 1.59) risks were higher in Indiana compared to Kentucky. (4) Conclusions: COVID-19 infection and mortality were 18% and 59% more likely among Indiana residents compared to Kentucky residents, respectively. This may be attributed to variations in stay-at-home order compliance and enforcement between Indiana and Kentucky. [ABSTRACT FROM AUTHOR]

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

I use 2013–2020 state-month-year data from official maternal death records to quantify the effect of the pandemic on maternal deaths in Mexico. Using a Poisson count regression model that accounts for seasonal trends and unobserved differences across states, I show maternal deaths started increasing meaningfully in April-May 2020. More specifically, I find that the expected number of maternal deaths increased by a factor of 2 during May-August 2020. Moreover, this increase factor remained above 1.6 after September 2020, indicating that the effect of the pandemic on maternal deaths continued to be meaningful at the end of year. I also provide evidence of the key mechanism that could plausibly explain this increase: COVID-19 was the main underlying cause of 26.5% of maternal deaths in 2020. This accounts for nearly all of the increase in maternal deaths from 2019 to 2020. [ABSTRACT FROM AUTHOR]

Abstract 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

Background: After decades of navigating HIV and other sexually transmitted infections, gay and bisexual men are responding to new and uncertain risks presented by the coronavirus (COVID-19) pandemic by adapting their sexual behavior. Methods: This paper uses data from a national sample of 728 gay and bisexual men collected from April 10 to May 10, 2020, to examine changes to sexual behavior in response to the first wave of the pandemic in the USA. We also assess whether behavior modifications are associated with exposure to statewide public health measures, including Stay-at-Home orders. Results: Sexual minority men report significant changes to their sexual behavior and partner selection during the first wave. Nine out of 10 men reported having either one sexual partner or no sexual partner in the last 30 days at the time of interview, a decrease compared to just before the pandemic for nearly half of men surveyed. Reporting no sexual partners in the last 30 days was significantly predicted by increased exposure to a Stay-at-Home order. Sexual minority men also reduced interactions with casual partners, increased no-contact sexual behaviors (e.g., masturbation and virtual sex), and engaged in new strategies to reduce their risks of infection from partners. HIV-positive men were particularly likely to adopt strategies including avoiding casual partners and avoiding public transportation to meet sexual partners. Conclusion: Sexual minority men's behavior changes during the first wave may have reduced the impact of the coronavirus pandemic on their communities. Despite substantial changes in sexual behavior for most men in our sample during the initial first wave, we identify some concerns around the sustainability of certain behavioral changes over time and nondisclosure of COVID-19 symptoms to partners. [ABSTRACT FROM AUTHOR]

Using a data-modified version of the relative-price-of-a-substitute method, we distinguish the consequences of the sharp decline in US automotive fuel demand from the consequences of nonethanol demand changes in the US corn market. Our results suggest that--due to the renewable fuel standard and ethanol-gas price linkages--the COVID-19 pandemic affected corn markets more than it affected other agricultural commodities. The onset of the pandemic reduced Illinois cash prices for corn by approximately 18%. The majority of this impact (approximately 16%) was driven by pandemic-induced reductions in ethanol demand. Ethanol-driven and total impacts were greater in locations farther from terminal markets. [ABSTRACT FROM AUTHOR]

Background: Changes in injury patterns during the COVID pandemic have been reported in other states. The objective was to explore changes to trauma service volume and admission characteristics at a trauma center in northeast Ohio during a stay-at-home order (SAHO) and compare the 2020 data to historic trauma census data. Methods: Retrospective chart review of adult trauma patients admitted to a level I trauma center in northeast Ohio. Trauma admissions from January 21 to July 21, 2020 (COVID period) were compared to date-matched cohorts of trauma admissions from 2018 to 2019 (historic period). The COVID period was further categorized as pre-SAHO, active-SAHO, and post-SAHO. Results: The SAHO was associated with a reduction in trauma center admissions that increased after the SAHO (P =.0033). Only outdoor recreational vehicle (ORV) injuries (P =.0221) and self-inflicted hanging (P =.0028) mechanisms were increased during the COVID period and had substantial effect sizes. Glasgow Coma Scores were lower during the COVID period (P =.0286) with a negligible effect size. Violence-related injuries, injury severity, mortality, and admission characteristics including alcohol and drug testing and positivity were similar in the COVID and historic periods. Discussion: The SAHO resulted in a temporary decrease in trauma center admissions. Although ORV and hanging mechanisms were increased, other mechanisms such as alcohol and toxicology proportions, injury severity, length of stay, and mortality were unchanged. [ABSTRACT FROM AUTHOR]

The Covid-19 stay-at-home restrictions put in place in New York City were followed by an abrupt shift in movement away from public spaces and into the home. This study used interrupted time series analysis to estimate the impact of these changes by crime type and location (public space vs. residential setting), while adjusting for underlying trends, seasonality, temperature, population, and possible confounding from the subsequent protests against police brutality in response to the police-involved the killing of George Floyd. Consistent with routine activity theory, we found that the SAH restrictions were associated with decreases in residential burglary, felony assault, grand larceny, rape, and robbery; increases in non-residential burglary and residential grand larceny motor vehicle; and no change in murder and shooting incidents. We also found that the protests were associated with increases in several crime types: felony assault, grand larceny, robbery, and shooting incidents. Future research on Covid-19's impact on crime will need to account for these potentially confounding events. [ABSTRACT FROM AUTHOR]

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]

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase.

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]

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]

During the COVID-19 pandemic, many firms began operating in a working-from-home environment (WFH). This study focuses on the relationship between WFH and small business performance during the pandemic. We built a theoretical framework based on firm profit maximization, compiled an up-to-date (March through November) real-time daily and weekly multifaceted data set, and empirically estimated fixed-effect panel data, fractional logit, and multilevel mixed effects models to test our hypotheses. We find that in states with higher WFH rates, small businesses performed better overall with industry variations, controlling for the local pandemic, economic, demographic, and policy factors. We also find that WFH rates increased even after stay-at-home orders (SHOs) were rescinded. With the ready technology and practice of WFH in the pandemic, our robust empirics confirm our theory and hypotheses and demonstrate WFH as a potential force that may expedite "creative destruction" instance and permanently impact industrial structure and peoples' work lives. Plain English summary: The Rise of Working from Home (WFH) as a Silver Lining and "Creative Destruction" in the Pandemic: WFH Helps Small Businesses Perform Better with Industry Variations and Continues to Shine after Stay-at-Home Orders Ended. This study focuses on the role of working from home (WFH) for small business performance during the COVID-19 pandemic. We built a theoretical framework based on firm profit maximization and identified WFH as a rational business choice. We then compiled a real-time multifaceted data set, estimated panel fixed-effect, fractional logit, and multilevel mixed effects models, and find that (1) small businesses in states with higher WFH rates performed better with industry variations, controlling for local pandemic and socioeconomic factors; and (2) WFH rates increased after stay-at-home orders were rescinded. Our study demonstrates WFH as a potential "creative destruction" force that may expedite our technologically ready WFH adoption and permanently impact industrial structure and peoples' work lives. [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]

Many public institutions and settings have taken action to limit exposure to and slow the spread of the novel coronavirus (COVID-19). We sought to characterize the impact of stay-at-home orders on our study of cerebral autoregulation and its association with developmental delays in infants with congenital heart disease compared with healthy controls. We calculated the number of participants recruited (i.e., not enrolled in the study) and assessed (i.e., currently enrolled) before March 2020 (pre-COVID-19) and the number of participants that we could not recruit or assess between March and July 2020 (missed due to COVID-19), separately for congenital heart disease and healthy control infants, in reference to the impacts of COVID-19. We used negative binomial regressions to determine incidence rate ratios which compared participants recruited and assessed pre-COVID-19 and missed due to COVID-19. Recruitment and assessments significantly decreased following the pandemic, i.e., participants were more likely to be recruited or be assessed pre-COVID-19 compared to during the pandemic. Study participants were 3.3 times as likely to have assessments performed pre-COVID-19 compared to during the COVID-19 pandemic (p < 0.001). Clinical research teams may consider making protocol modifications such as virtual visits or video recordings explaining the study, for example, to adjust to the restrictions caused by COVID-19. The COVID-19 pandemic drastically reduced recruitment and assessments completed in our study. Study teams will need to continue to modify procedures for recruitment and assessments that align with COVID-19 regulations to facilitate research progress during the pandemic. • We describe the impact of the COVID-19 pandemic on pediatric clinical research at a single tertiary institution. • The COVID-19 pandemic drastically reduced our participant recruitment and assessments. • Study modifications, such as virtual visits, must be implemented to adjust to COVID-19 precautions. • Emphasis on our institution and study's COVID-19 safeguards have helped family's return for follow-up assessments. [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]

• 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]

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data., Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented., Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results., Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends., (© 2022 The Author(s).)

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]

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gao, Zhai and Fu.)

Using the 8th wave of the SHARE and the SHARE Corona Survey, we investigated whether the disruption of parent–adult child contacts due to social distancing restrictions increased the symptoms of depression among old age individuals during the first wave of the COVID-19 pandemic. We model the relationship between the disruption of parent–adult child contacts and the mental health of the elderly using a recursive simultaneous equation model for binary variables. Our findings show that the likelihood of disruption of parent–adult child contacts was higher with adult children who do not live with or close to their parents (i.e., in the same household or in the same building) for whom contact disruption increases about 15 %. The duration of restrictions to movement and lockdowns also has a positive and significant effect on parent-child contact disruption: an additional week of lockdown significantly increases the probability of parent-child contact disruption, by about 1.5 %. The interventions deemed essential to reduce the spread of the pandemic, such as the "stay-at-home" order, necessarily disrupted personal parent–child contacts and the social processes that facilitate psychological well-being, increasing the probability of suffering from a deepening depressed mood by about 17 % for elderly parents. • We explore whether the disruption of the parent–child contacts, because of lockdown, increases depression in older persons. • We use a joint model of contacts disruption and mental health to takes into account for unobserved heterogeneity. • Our evidence reflects that parent-adult child contacts disruption due to COVID‐19's worsens mental health of older people. • Our study provides additional insights into the psychological status and strain on older during the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights. UsaGeoCov19 link: http://yunhefeng.me/geo-tagged&#95;twitter&#95;datasets/., (© 2021 Elsevier Ltd. All rights reserved.)