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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.

Purpose 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. Design and methods 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. Results 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 Clinical implications 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. Conclusion 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.

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.

Background Various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures have been employed to limit the spread of Coronavirus disease (COVID-19). This study measures the impact of social distancing policies on COVID-19 transmission in US states during the early outbreak phase to assess which policies were most effective. Methods To measure transmissibility, we analyze the average effective reproductive number (Rt) in each state the week following its 500th case and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time to 100 deaths with several healthcare infrastructure control variables. Results States with stay-at-home orders in place at the time of their 500th case were associated with lower average Rt the following week compared to states without them (p1 (OR 0.07, 95% CI 0.01-0.37, p=0.004). These states also experienced longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17-0.72, p=0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06-0.53, p=0.002). Conclusions Stay-at-home orders had the largest effect of any policy analyzed. Multivariate analyses with cellphone tracking data suggest social distancing adherence drives these effects. States that plan to scale back such measures should carefully monitor transmission metrics.

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., Highlights • State stay-at-home orders are associated with increasing risk reduction behaviors. • The difference spills over to behaviors not included in the orders. • Orders are associated with mental distress that peaks within a week and then declines.

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.

The decline in human mobility and socioeconomic activities during the COVID-19 pandemic has been accompanied by reports of significant improvements in air quality. We evaluate whether there was a uniform improvement in air quality across neighborhoods, with a special attention on differences by race. We focus on the COVID-19 lockdown in New York State, an early epicenter of the pandemic in the United States. Using a triple difference-in-differences model, we find that, despite the seasonal decline in particulate matter pollution starting late March (concurrent with the lockdown period), the lockdown narrowed the disparity in air quality between census tracts with high and low shares of non-white population in rural New York, whereas the racial gap in air quality remained unchanged in urban New York.