Shelter-In-Place Orders Reduced COVID-19 Mortality And Reduced The Rate Of Growth In Hospitalizations

Systematic review of empiric studies on lockdowns, workplace closures, and other non-pharmaceutical interventions in non-healthcare workplaces during the initial year of the COVID-19 pandemic: benefits and selected unintended consequences

BACKGROUND

We conducted a systematic review aimed to evaluate the effects of non-pharmaceutical interventions within non-healthcare workplaces and community-level workplace closures and lockdowns on COVID-19 morbidity and mortality, selected mental disorders, and employment outcomes in workers or the general population.

METHODS

The inclusion criteria included randomized controlled trials and non-randomized studies of interventions. The exclusion criteria included modeling studies. Electronic searches were conducted using MEDLINE, Embase, and other databases from January 1, 2020, through May 11, 2021. Risk of bias was assessed using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analysis and sign tests were performed.

RESULTS

A total of 60 observational studies met the inclusion criteria. There were 40 studies on COVID-19 outcomes, 15 on anxiety and depression symptoms, and five on unemployment and labor force participation. There was a paucity of studies on physical distancing, physical barriers, and symptom and temperature screening within workplaces. The sign test indicated that lockdown reduced COVID-19 incidence or case growth rate (23 studies, p < 0.001), reproduction number (11 studies, p < 0.001), and COVID-19 mortality or death growth rate (seven studies, p < 0.05) in the general population. Lockdown did not have any effect on anxiety symptoms (pooled standardized mean difference = -0.02, 95% CI: -0.06, 0.02). Lockdown had a small effect on increasing depression symptoms (pooled standardized mean difference = 0.16, 95% CI: 0.10, 0.21), but publication bias could account for the observed effect. Lockdown increased unemployment (pooled mean difference = 4.48 percentage points, 95% CI: 1.79, 7.17) and decreased labor force participation (pooled mean difference = -2.46 percentage points, 95% CI: -3.16, -1.77). The risk of bias for most of the studies on COVID-19 or employment outcomes was moderate or serious. The risk of bias for the studies on anxiety or depression symptoms was serious or critical.

CONCLUSIONS

Empiric studies indicated that lockdown reduced the impact of COVID-19, but that it had notable unwanted effects. There is a pronounced paucity of studies on the effect of interventions within still-open workplaces. It is important for countries that implement lockdown in future pandemics to consider strategies to mitigate these unintended consequences.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration # CRD42020182660.

The Impact Of Provincial Proof- Of-Vaccination Policies On Age-Specific First-Dose Uptake Of COVID-19 Vaccines In Canada

Requirements of proof of COVID-19 vaccination were mandated for nonessential businesses and venues by Canada's ten provinces throughout the fall of 2021. Leveraging variations in the timing of these measures across the provinces, we applied event study regression to estimate the impact the announcement of these measures had nationally on age-specific first-dose uptake in the subsequent seven-week period. Proof-of-vaccination mandate announcements were associated with a rapid, significant increase in first-dose uptake, particularly in people younger than age fifty. However, these behavioral changes were short-lived, with uptake returning to preannouncement levels-or lower-in all age groups within six weeks, despite mandates remaining in place for at least four months; this decline occurred earlier and was more apparent among adolescents ages 12-17. We estimated that nationally, 290,168 additional people received their first dose in the seven weeks after provinces announced proof-of-vaccination policies, for a 17.5 percent increase over the number of vaccinations estimated in the absence of these policies. This study provides novel age-specific evidence showing that proof-of-vaccination mandates led to an immediate, significant increase in national first-dose uptake and were particularly effective for increasing vaccination uptake in younger to middle-aged adults. Proof-of-vaccination mandates may be effective short-term policy measures for increasing population vaccination uptake, but their impact may differ across age groups.

The impact of COVID-19 shelter-in-place policy responses on excess mortality

As a way of slowing COVID-19 transmission, many countries and U.S. states implemented shelter-in-place (SIP) policies. However, the effects of SIP policies on public health are a priori ambiguous. Using an event study approach and data from 43 countries and all U.S. states, we measure changes in excess deaths following the implementation of COVID-19 shelter-in-place (SIP) policies. We do not find that countries or U.S. states that implemented SIP policies earlier had lower excess deaths. We do not observe differences in excess deaths before and after the implementation of SIP policies, even when accounting for pre-SIP COVID-19 death rates.

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Event Study Design for Modeling Early Relaxation in Turkish Public with COVID-19 Vaccine

OBJECTIVE

Vaccination is crucial to fighting the coronavirus disease (COVID-19) pandemic. A large body of literature investigates the effect of the initiation of the COVID-19 vaccination in case numbers in Turkey, including the resistance and willingness to taking the vaccine. The effect of early relaxation in the Turkish public with the initiation of vaccination on new daily cases is unknown.

METHODS

This study performs an event study analysis to explore the pre-relaxation effect of vaccination on the Turkish public by using daily data of new cases, stringency index, and residential mobility. Two events are comparatively defined as the vaccination of the health personnel (Event 1) and the citizens age 65 and over (Event 2). The initial dates of these events are January 13 and February 12, 2021, respectively. The length of the estimation window is determined as 14 days for the 2 events. To represent only the early stages of the vaccination, the study period ends on April 12, 2021. Thus, whereas the event window of Event 1 includes 90 observations, Event 2 covers 60 observations.

RESULTS

While average values of residential mobility, stringency index, and daily numbers of cases are 15.36, 71.03, and 11 978.93 in the estimation window for Event 1, these averages are 8.89, 70.88, and 17 303.20 in the event window. For Event 2, the same average values are 9.14, 69.38, and 7 664.93 in the estimation window and 8.25, 71.12, and 22 319.10 in the event window. When 14-day abnormal growth rates of the daily number of cases for Event 1 and Event 2 are compared, it is observed that Event 1 has negative growth rates initially and reaches a 7.59% growth at most. On the other hand, Event 2 starts with a 1.11% growth rate, and having a steady increase, it reaches a 23.70% growth in the last 14 days of the study period.

CONCLUSION

The preliminary result shows that, despite taking more strict governmental measures, while residential mobility decreases, the daily number of COVID-19 cases increases in the early stages of vaccination compared to short pre-periods of it. This indicates that the initiation of vaccination leads to early behavioral relaxation in public. Moreover, the effect of Event 2 on the case numbers is more significant and immediate, compared to that of Event 1, which may be linked to the characteristic of the Turkish culture being more sensitive to the older adult population.

Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Predictors of General Deviance in the Context of COVID-19

This study examined predictors of individual general deviance (i.e., substance use, risk-taking, property crime, and interpersonal conflict/violence) within the context of COVID-19, focusing on the role of prior deviance, opportunities for crime, and levels of COVID-19- related stress. Our study showed that while some predictors relating to opportunity and strain were predictive of general deviance during the pandemic, few maintained statistical significance once controls for deviant behavior before the pandemic were included in the analyses, indicating the importance of within-individual behavioral stability over time. Further, respondents who participated in deviance prior to the pandemic were more likely to engage in other forms of criminal and high-risk activities during the pandemic. The close connections between criminal and high-risk behavior may imply that even if overall crime rates decreased during the pandemic, within-person behavioral patterns remained stable.

The Effects of Non-Pharmaceutical Interventions on COVID-19 Cases, Hospitalizations, and Mortality: A Systematic Literature Review and Meta-Analysis

Introduction

To assess the effects of various non-pharmaceutical interventions (NPI) on cases, hospitalizations, and mortality during the first wave of the COVID-19 pandemic.

Methods

To empirically investigate the impacts of different NPIs on COVID-19-related health outcomes, a systematic literature review was conducted. We studied the effects of 10 NPIs on cases, hospitalizations, and mortality across three periodic lags (2, 3, and 4 weeks-or-more following implementation). Articles measuring the impact of NPIs were sourced from three databases by May 10, 2022, and risk of bias was assessed using the Newcastle-Ottawa scale.

Results

Across the 44 papers, we found that mask wearing corresponded to decreased per capita cases across all lags (up to -2.71 per 100,000). All NPIs studied except business and bar/restaurant closures corresponded to reduced case growth rates in the two weeks following implementation, while policy stringency and travelling restrictions were most effective after four. While we did not find evidence of reduced deaths in our per capita estimates, policy stringency, masks, SIPOs, limited gatherings, school and business closures were associated with decreased mortality growth rates. Moreover, the two NPIs studied in hospitalizations (SIPOs and mask wearing) showed negative estimates.

Conclusions

When assessing the impact of NPIs, considering the duration of effectiveness following implementation has paramount significance. While some NPIs may reduce the COVID-19 impact, others can disrupt the mitigative progression of containing the virus. Policymakers should be aware of both the scale of their effectiveness and duration of impact when adopting these measures for future COVID-19 waves.

Opening of hotels and ski facilities: Impact on mobility, spending, and Covid-19 outcomes

This paper investigates how reopening hotels and ski facilities in Poland impacted tourism spending, mobility, and COVID-19 outcomes. We used administrative data from a government program that subsidizes travel to show that the policy increased the consumption of tourism services in ski resorts. By leveraging geolocation data from Facebook, we showed that ski resorts experienced a significant influx of tourists, increasing the number of local users by up to 50%. Furthermore, we confirmed an increase in the probability of meetings between pairs of users from distanced locations and users from tourist and non-tourist areas. As the policy impacted travel and gatherings, we then analyzed its effect on the diffusion of COVID-19. We found that counties with ski facilities experienced more infections after the reopening. Moreover, counties strongly connected to the ski resorts during the reopening had more subsequent cases than weakly connected counties.

Nonpharmaceutical Interventions in Georgia: Public Health Implications

OBJECTIVES

As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths.

METHODS

Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate.

RESULTS

We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome.

CONCLUSIONS

Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.

Combined effect of early diagnosis and treatment on the case fatality risk of COVID-19 in Japan, 2020

Japanese government initially enforced restrictions on outpatient attendances among febrile individuals suspected of having COVID-19, asking everyone to remain at home for at least 4 days from the onset of fever. This restriction was cancelled on 8 May 2020, and a new antiviral, remdesivir, was approved from 7 May 2020. To investigate how this policy change influenced the prognosis of people with COVID-19, we estimated the case fatality risk as a function of the date of illness onset from April to June 2020. We used an interrupted time-series analysis model with an intervention date of 8 May 2020, and estimated time-dependent case fatality risk by age group. The case fatality risk showed a decreasing trend in all groups, and models were favored accounting for an abrupt causal effect, i.e., immediate decline in fatality risk. The trend was estimated at - 1.1% (95% CI [confidence interval]: - 3.9, 3.0) among people aged 60-69 years, - 7.2% (95% CI - 11.2, - 2.4) among those aged 70-79 years, - 7.4% (95% CI - 14.2, 0.2) among those aged 80-89 years, and - 10.3% (95% CI - 21.1, 2.7) among those aged 90 and over. Early diagnosis and treatment greatly contributed to reducing the case fatality risk.

Political Economy of the COVID-19 Pandemic: How State Policies Shape County-Level Disparities in COVID-19 Deaths

The authors examine how two state-level coronavirus disease 2019 (COVID-19) policy indices (one capturing economic support and one capturing stringency measures such as stay-at-home orders) were associated with county-level COVID-19 mortality from April through December 2020 and whether the policies were more beneficial for certain counties. Using multilevel negative binominal regression models, the authors found that high scores on both policy indices were associated with lower county-level COVID-19 mortality. However, the policies appeared to be most beneficial for counties with fewer physicians and larger shares of older adults, low-educated residents, and Trump voters. They appeared to be less effective in counties with larger shares of non-Hispanic Black and Hispanic residents. These findings underscore the importance of examining how state and local factors jointly shape COVID-19 mortality and indicate that the unequal benefits of pandemic policies may have contributed to county-level disparities in COVID-19 mortality.

Exposure to the early COVID-19 pandemic and early, moderate and overall preterm births in the United States: A conception cohort approach

BACKGROUND

The United States (US) data suggest fewer-than-expected preterm births in 2020, but no study has examined the impact of exposure to the early COVID-19 pandemic at different points in gestation on preterm birth.

OBJECTIVE

Our objective was to determine-among cohorts exposed to the early COVID-19 pandemic-whether observed counts of overall, early and moderately preterm birth fell outside the expected range.

METHODS

We used de-identified, cross-sectional, national birth certificate data from 2014 to 2020. We used month and year of birth and gestational age to estimate month of conception for birth. We calculated the count of overall (<37 weeks gestation), early (<33 weeks gestation) and moderately (33 to <37 weeks gestation) preterm birth by month of conception. We employed time series methods to estimate expected counts of preterm birth for exposed conception cohorts and identified cohorts for whom the observed counts of preterm birth fell outside the 95% detection interval of the expected value.

RESULTS

Among the 23,731,146 births in our study, the mean prevalence of preterm birth among monthly conception cohorts was 9.7 per 100 live births. Gestations conceived in July, August or December of 2019-that is exposed to the early COVID-19 pandemic in the first or third trimester-yielded approximately 3245 fewer moderately preterm and 3627 fewer overall preterm births than the expected values for moderate and overall preterm. Gestations conceived in August and October of 2019-that is exposed to the early COVID-19 pandemic in the late second to third trimester-produced approximately 498 fewer early preterm births than the expected count for early preterm.

CONCLUSIONS

Exposure to the early COVID-19 pandemic may have promoted longer gestation among close-to-term pregnancies, reduced risk of later preterm delivery among gestations exposed in the first trimester or induced selective loss of gestations.

Pre-pandemic factors associated with delayed health care among US older adults during the COVID-19 pandemic

Background

During the first year of the COVID-19 pandemic, more than one-third of US older adults (aged 65 years and older) reported delaying medical care. Delayed health care may exacerbate short- and long-term health changes in older adults. Older adults more likely to delay health care may benefit from targeted follow-up to return these individuals to the health care system.

Objective

The aim of this study was to describe pre-pandemic sociodemographic, psychological, cognitive, and medical factors associated with delayed health care among US older adults during the COVID-19 pandemic.

Design

We conducted a secondary analysis of 2905 participants from the National Health and Aging Trends Study (NHATS), a nationally representative, prospective cohort of US older adult Medicare beneficiaries.

Methods

Pre-pandemic factors were reported at the Round 9 interview (2019). Delayed health care, including medical (e.g. usual doctor) and supplementary (e.g. dental) care, was reported on the COVID-19 questionnaire (2020). We calculated adjusted odds ratios using weighted logistic regression, accounting for the NHATS sampling design.

Results

Overall, 40% of participants reported delayed care. After adjustment, female participants and those reporting fair (vs good) health were consistently more likely to delay health care while persons with lower income or excellent health were less likely to delay care. Other associations varied by care type.

Conclusion

Women and those with higher income or fair health before the COVID-19 pandemic were more likely to delay care during the pandemic. Our results may inform targeted outreach to older adults who delayed care during the COVID-19 pandemic, or other disruptions to the health care system, to return these individuals to care and promote better management of their health needs.

Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level mobility data from 26 US cities between February 2 â€" August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June - August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

The Impact of the Early COVID-19 Pandemic on Inpatient Clinical Experience for Physical Medicine and Rehabilitation Resident Physicians

ABSTRACT

The objective of this retrospective, observational study was to quantitatively study the impact of the early COVID-19 pandemic on the inpatient clinical experience of Physical Medicine and Rehabilitation resident physicians in an inpatient rehabilitation facility setting. Inpatient clinical experience as evidenced by admissions, rehabilitation diagnosis, medical emergencies, acute care transfers, and resident work hours from January to June 2019 (prepandemic) were compared January to June 2020 (immediately before and during pandemic). There was a statistically significant decrease in the mean daily admissions in April 2020 and a significant increase in medically complex admissions in June 2020, reflective of medical patterns due to the pandemic. There was a decrease in mean work hours during the pandemic, but no statistically significant difference in admission rate of other rehabilitation diagnoses, medical emergencies, or transfers to acute care. This study demonstrates no substantial pandemic-related impact on inpatient clinical experience for physical medicine and rehabilitation residents in the studied program.

Impact of mandatory COVID-19 shelter-in-place order on controlled substance use among rural versus urban communities in the United States

PURPOSE

Mandatory COVID-19 shelter-in-place (SIP) orders have been imposed to fight the pandemic. They may also have led to unintended consequences of increased use of controlled substances especially among rural communities due to increased social isolation. Using the data from the American Association of Poison Control Centers, this study tests the hypothesis that the poison control centers received higher rates of calls related to exposures to controlled substances from rural counties than they did from urban counties during the SIP period.

METHODS

Call counts received by the poison control centers between October 19, 2019 and July 6, 2020 due to exposure to controlled substance (methamphetamine, opioids, cocaine, benzodiazepines, and other narcotics) were aggregated to per-county-per-month-per-10,000 population exposure rates. A falsification test was conducted to reduce the possibility of spurious correlations.

FINDINGS

During the study period, 2,649 counties in the United States had mandatory SIP orders. The rate of calls reporting exposure to any of the aforementioned controlled substances among the rural counties was higher (14%; P = .047) relative to the urban counties. This overall increase was due to increases in the rates of calls reporting exposure to opioids (26%; P = .017) and methamphetamine (39%; P = .077). Moreover, the rate of calls reporting exposures at home was also higher among the rural counties (14%; P = .069).

CONCLUSION

The mandatory SIP orders may have had an unintended consequence of exacerbating the use of controlled substances at home in rural communities relative to urban communities.

The Utility of a Novel Definition of Health Care Regions in the United States in the Era of COVID-19: A Validation of the Pittsburgh Atlas Using Pneumonia Admissions

STUDY OBJECTIVE

The COVID-19 pandemic in the United States has underscored the need to understand health care in a regional context. However, there are multiple definitions of health care regions available for conducting geospatial analyses. In this study, we compare the novel Pittsburgh Atlas, which defined regions for emergency care, with the existing definitions of regions, counties, and the Dartmouth Atlas, with respect to nonemergent acute medical conditions using pneumonia admissions.

METHODS

We identified patients hospitalized with a primary diagnosis of pneumonia or a primary admitting diagnosis of sepsis with a secondary diagnosis of pneumonia in the Agency for Healthcare Research and Quality's State Inpatient Databases. We calculated the percentage of region concordant care, the localization index, and market share for 3 definitions of health care regions (the Pittsburgh Atlas, Dartmouth Atlas, and counties). We used logistic regression identified predictors of region concordant care.

RESULTS

We identified 1,582,287 patients who met the inclusion criteria. We found that the Pittsburgh Atlas and Dartmouth Atlas definitions of regions performed similarly with respect to both localization index (92.0 [interquartile range 87.9 to 95.7] versus 90.3 [interquartile range 81.4 to 94.5]) and market share (8.5 [interquartile range 5.1 to 13.6] versus 9.4 [interquartile range 6.7 to 14.1]). Both atlases outperformed the localization index (67.5 [interquartile range 49.9 to 83.9]) and market share (20.0% [interquartile range 11.4 to 31.4]) of the counties. Within a given referral region, the demographic factors, including age, sex, race/ethnicity, insurance status, and the level of severity, affected concordance rates between residential and hospital regions.

CONCLUSION

Because the Pittsburgh Atlas also has the benefit of respecting state and county boundaries, the use of this definition may have improved policy applicability without sacrificing accuracy in defining health care regions for acute medical conditions.

Impact of COVID-19 social distancing on medical research from the perspective of postgraduate students: a cross-sectional online survey

Objective

To investigate the impact of COVID-19 social distancing on medical research from the perspective of postgraduate students.

Methods

A cross-sectional study using an online survey was conducted from October 31 to November 1, 2021. A questionnaire was used to assess the impact of COVID-19 social distancing on medical research among postgraduate students. The questionnaire included basic information, medical research information, and information about social distancing measures. Participants also completed the self-made Research Work Affected Scale of Postgraduates (RWAS-P; qualitative evaluation: very mildly 0-10; mildly 11-20; moderately 21-30; severely 31-40; very severely 41-50). Logistic regression was used to identify factors related to the impact of COVID-19 social distancing.

Results

A total of 468 participants were analyzed; 95.2% of the participants adhered to social distancing measures. The median total RWAS-P score was 22. The median RWAS-P scores for earlier research data, current research projects, future research plans, paper publication, and graduation schedule were 2, 6, 6, 6, and 4, respectively (score range 0-10). The higher grade of students, experimental research, and existence of inappetence or sleeplessness were related to negative attitude towards COVID-19 social distancing (odd ratio = 6.35, 9.80, 2.31, 2.15, 1.95, respectively).

Conclusions

Participants reported that social distancing had a moderate overall impact on their medical research. Social distancing had the greatest impact on current research projects, future research plans, and paper publications among postgraduate students. Higher grade level, experimental research type, inappetence, and sleeplessness were related to the impact of social distancing on their medical research.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study

OBJECTIVE

We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.

DESIGN

We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.

INTERVENTIONS

Issuance of stay-at-home orders.

PRIMARY AND SECONDARY OUTCOME MEASURES

Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.

RESULTS

Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.

CONCLUSION

Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Large Gathering Attendance is Associated with Increased Odds of Contracting COVID-19: A Survey Based Study

We used a telephone survey to determine risk factors associated with a positive polymerase chain reaction test of a nasopharyngeal swab for severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) at a community hospital in Central New Jersey during the early stages of the pandemic. We compared survey responses of 176 patients in March 2020. Respondents were asked about their living situation, work environment, use of public transportation and attendance at one or more large gatherings (more than 10 people) in the 3 weeks prior to undergoing COVID testing. We found that those who attended a large gathering in the 3 weeks prior to their COVID test had a 2.50 odds ratio (95% CI 1.19, 5.22) of testing positive after controlling for age, sex, race/ethnicity, occupation, living situation and recent visit to a nursing home. The total number of gatherings attended or the number of people in attendance was not associated with a positive test. An association was also seen for specific job types such as factory workers, construction workers, and facilities managers. Attendance at a gathering of more than ten people was associated with testing positive for COVID-19.

Associations of Stay-at-Home Order Enforcement With COVID-19 Population Outcomes: An Interstate Statistical Analysis

In the United States, state governors initially enacted coronavirus diseases 2019 (COVID-19)-mitigation policies with limited epidemiologic data. One prevailing legislative approach, from March to May 2020, was the implementation of "stay-at-home" (SAH) executive orders. Although social distancing was encouraged, SAH orders varied between states, and the associations between potential legal prosecution and COVID-19 outcomes are currently unknown. Here, we provide empirical evidence on how executive enforcement of movement restrictions may influence population health during an infectious disease outbreak. A generalized linear model with negative binomial regression family compared COVID-19 outcomes in states with law-enforceable stay-at-home (eSAH) orders versus those with unenforceable or no SAH orders (uSAH), controlling for demographic factors, socioeconomic influences, health comorbidities, and social distancing. COVID-19 incidence was less by 1.22 cases per day per capita in eSAH states compared with uSAH states (coefficient = -1.22, 95% confidence interval (CI): -1.83, -0.61; P < 0.001), and each subsequent day without an eSAH order was associated with a 0.03 incidence increase (coefficient = 0.03, 95% CI: 0.03, 0.04; P < 0.001). Daily mortality was 1.96 less for eSAH states per capita (coefficient = -1.96, 95% CI: -3.25, -0.68; P = 0.004). Our findings suggest allowing the enforcement of public health violations, compared with community education alone, is predictive of improved COVID-19 outcomes.

Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries

Objectives: To examine the association of non-pharmaceutical interventions (NPIs) with anxiety and depressive symptoms among adults and determine if these associations varied by gender and age. Methods: We combined survey data from 16,177,184 adults from 43 countries who participated in the daily COVID-19 Trends and Impact Survey via Facebook with time-varying NPI data from the Oxford COVID-19 Government Response Tracker between 24 April 2020 and 20 December 2020. Using logistic regression models, we examined the association of [1] overall NPI stringency and [2] seven individual NPIs (school closures, workplace closures, cancellation of public events, restrictions on the size of gatherings, stay-at-home requirements, restrictions on internal movement, and international travel controls) with anxiety and depressive symptoms. Results: More stringent implementation of NPIs was associated with a higher odds of anxiety and depressive symptoms, albeit with very small effect sizes. Individual NPIs had heterogeneous associations with anxiety and depressive symptoms by gender and age. Conclusion: Governments worldwide should be prepared to address the possible mental health consequences of stringent NPI implementation with both universal and targeted interventions for vulnerable groups.

Lonely older adults are more likely to delay or avoid medical care during the coronavirus disease 2019 pandemic

OBJECTIVES

To examine the relationship between loneliness and self-reported delay or avoidance of medical care among community-dwelling older adults during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

Analyses of data from a nationally representative survey administered in June of 2020, in COVID-19 module of the Health and Retirement Study. Bivariate and multivariable analyses determined associations of loneliness with the likelihood of, reasons for, and types of care delay or avoidance.

RESULTS

The rate of care delay or avoidance since March of 2020 was 29.1% among all respondents (n = 1997), and 10.1% higher for lonely (n = 1,150%, 57.6%) versus non-lonely respondents (33.5% vs. 23.4%; odds ratio = 1.59, p = 0.003 after covariate adjustment). The differences were considerably larger among several subgroups such as those with emotional/psychiatric problems. Lonely older adults were more likely to cite "Decided it could wait," "Was afraid to go," and "Couldn't afford it" as reasons for delayed or avoided care. Both groups reported dental care and doctor's visit as the two most common care delayed or avoided.

CONCLUSIONS

Loneliness is associated with a higher likelihood of delaying or avoiding medical care among older adults during the pandemic.

Water Shutoff Moratoria Lowered COVID-19 Infection and Death Across U.S. States

INTRODUCTION

A total of 34 U.S. state governments imposed moratoria on water shutoffs between March and May 2020 to ensure equitable access to water during the COVID-19 pandemic. However, by the end of 2020, most of these moratoria had expired, and millions of people were exposed to the risk of water disconnections. This study examines the linkage between water equity and public health and provides policy recommendations for improving water access and health equity.

METHODS

Event study was used to analyze the impact of a water shutoff moratorium on COVID-19 daily infection growth rate and daily death growth rate from April 17, 2020 to December 31, 2020. The data were collected at the state level. The model controlled for mask mandates, at-risk groups (percentage Hispanic population, percentage essential workers), and percentage health insurance coverage.

RESULTS

During the study period, having a water shutoff moratorium in place significantly lowered the COVID-19 infection daily growth rate by 0.235% and significantly lowered the death growth rate by 0.135%. In addition, a comprehensive moratorium covering all water systems (public and private) significantly lowered the infection growth rate by 0.169% and significantly lowered the death growth rate by 0.228%.

CONCLUSIONS

This study raises attention to the importance of water equity and the need for government actions to create more uniform protections from water shutoffs across all states. A comprehensive approach to water equity can protect the health and safety of all communities.

State social distancing restrictions and nursing home outcomes

The COVID-19 poses a disproportionate threat to nursing home residents. Although recent studies suggested the effectiveness of state social distancing measures in the United States on curbing COVID-19 morbidity and mortality among the general population, there is a lack of evidence as to how these state orders may have affected nursing home patients or what potential negative health consequences they may have had. In this longitudinal study, we evaluated changes in state strength of social distancing restrictions from June to August of 2020, and their associations with the weekly numbers of new COVID-19 cases, new COVID-19 deaths, and new non-COVID-19 deaths in nursing homes of the US. We found that stronger state social distancing measures were associated with improved COVID-19 outcomes (case and death rates), reduced across-facility disparities in COVID-19 outcomes, and somewhat increased non-COVID-19 death rate, although the estimates for non-COVID-19 deaths were sensitive to alternative model specifications.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength

INTRODUCTION

Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment.

METHODS

We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation.

RESULTS

After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.

DISCUSSION

The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures

To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation strategies. To understand individual preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling (RDS) approach to recruit participants from four universities in three countries to complete a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combination, can serve to increase the external validity of a study by enabling recruitment of populations underrepresented in sampling frames, thus allowing preference results to be more generalizable to targeted subpopulations. A total of 99 students or staff members were invited to complete the survey, of which 72% started the survey (n = 71). Sixty-three participants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies. The model estimates indicated that participants preferred mitigation strategies that resulted in lower COVID-19 risk (i.e. sheltering-in-place more days a week), financial compensation from the government, fewer health (mental and physical) problems, and fewer financial problems. The high response rate and survey engagement provide proof of concept that RDS and DCE can be implemented as web-based applications, with the potential for scale up to produce nationally-representative preference estimates.

Did COVID-19 change life insurance offerings?

The profitability of life insurance offerings is contingent on accurate projections and pricing of mortality risk. The COVID-19 pandemic created significant uncertainty, with dire mortality predictions from early forecasts resulting in widespread government intervention and greater individual precaution that reduced the projected death toll. We analyze how life insurance companies changed pricing and offerings in response to COVID-19 using monthly data on term life insurance policies from Compulife. We estimate event-study models that exploit well-established variation in the COVID-19 mortality rate based on age and underlying health status. Despite the increase in mortality risk and significant uncertainty, the results generally indicate that life insurance companies did not increase premiums or decrease policy offerings due to COVID-19. Nonetheless, we find some evidence that premiums differentially increased for individuals with very high risk and that some policies were removed for the oldest of the old.

Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues

Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. However, policy impact evaluations require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate strength of evidence in COVID-19 health policy papers. We (1) introduce the basic suite of policy impact evaluation designs for observational data, including cross-sectional analyses, pre/post, interrupted time-series, and difference-in-differences analysis, (2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19, and (3) provide decision-makers and reviewers a conceptual and graphical guide to identifying these key violations. The overall goal of this paper is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.

Trends in Pediatric Appendicitis and Imaging Strategies During Covid-19 in the United States

RATIONALE AND OBJECTIVES

To determine if, during the first wave of the COVID-19 pandemic, 1) the proportion of complicated appendicitis changed, and 2) if imaging strategies for appendicitis in children changed.

MATERIALS AND METHODS

Retrospective cross-sectional study using administrative data from the Pediatric Health Information System, inclusive of pediatric patients diagnosed with appendicitis from March to May in 2017, 2018, 2019 and 2020. We compared trends during COVID-19 pandemic (March-May 2020) with corresponding pre-COVID-19 periods in 2017-201.9 Study outcomes were the proportion of complicated appendicitis and trends in imaging for appendicitis explained by patient-level variables.

RESULTS

The proportion of complicated appendicitis cases increased by 4.4 percentage points, from 46.5% pre-COVID-19 (2017-2019) to 50.9% during COVID-19 (2020), p < 0.001. Mean count of uncomplicated acute appendicitis cases decreased from pre-COVID-19 to the 2020 COVID-19 period (2017: n = 2555; 2018: n = 2679; 2019: n = 2722; 2020: n = 2231). Mean count of complicated appendicitis was unchanged between study periods (2017: n = 2189; 2018: n = 2302, 2019: n = 2442; 2020: n = 2311). Imaging approaches were largely unchanged between study periods; ultrasound was the most utilized modality in both study periods (68.3%, 70.2%; p = 0.033).

CONCLUSION

During the first wave of the COVID-19 pandemic, the proportion of complicated appendicitis cases increased without an absolute increase in the number of complicated appendicitis cases, but instead a decrease in the number of uncomplicated acute appendicitis diagnoses.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

Mask Mandates and COVID-19 Related Symptoms in the US

PURPOSE

This study investigates the extent to which the Public Mask Mandate, a policy that requires the use of face masks in public, can protect people from developing COVID-19 symptoms during the initial stage of the pandemic from mid-April to early June 2020 in the United States (US).

METHODS

We employ the difference-in-differences model that exploits the differential timing of the mask mandate implementation across states.

RESULTS

Our findings show that the Public Mask Mandate significantly lowers the incidence of developing all COVID-19 symptoms by 0.29 percentage points. The estimate implies an average reduction of 290%, compared to the proportion of the mandate-unaffected individuals who display all symptoms (0.1%).

CONCLUSION

The study provides suggestive evidence for the health benefits of wearing masks in public in the initial stage of the COVID-19 pandemic. The study also highlights the relevance of public mask wearing for the ongoing pandemic where the vaccination rate is precarious and access to vaccines is still limited in many countries.

Impact of essential workers in the context of social distancing for epidemic control

New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such "essential" workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.

Study Designs to Assess Real-World Interventions to Prevent COVID-19

Background: In the face of the novel virus SARS-CoV-2, scientists and the public are eager for evidence about what measures are effective at slowing its spread and preventing morbidity and mortality. Other than mathematical modeling, studies thus far evaluating public health and behavioral interventions at scale have largely been observational and ecologic, focusing on aggregate summaries. Conclusions from these studies are susceptible to bias from threats to validity such as unmeasured confounding, concurrent policy changes, and trends over time. We offer recommendations on how to strengthen frequently applied study designs which have been used to understand the impact of interventions to reduce the spread of COVID-19, and suggest implementation-focused, pragmatic designs that, moving forward, could be used to build a robust evidence base for public health practice. Methods: We conducted a literature search of studies that evaluated the effectiveness of non-pharmaceutical interventions and policies to reduce spread, morbidity, and mortality of COVID-19. Our targeted review of the literature aimed to explore strengths and weaknesses of implemented studies, provide recommendations for improvement, and explore alternative real-world study design methods to enhance evidence-based decision-making. Results:Study designs such as pre/post, interrupted time series, and difference-in-differences have been used to evaluate policy effects at the state or country level of a range of interventions, such as shelter-in-place, face mask mandates, and school closures. Key challenges with these designs include the difficulty of disentangling the effects of contemporaneous changes in policy and correctly modeling infectious disease dynamics. Pragmatic study designs such as the SMART (Sequential, Multiple-Assignment Randomized Trial), stepped wedge, and preference designs could be used to evaluate community re-openings such as schools, and other policy changes. Conclusions: As the epidemic progresses, we need to move from post-hoc analyses of available data (appropriate for the beginning of the pandemic) to proactive evaluation to ensure the most rigorous approaches possible to evaluate the impact of COVID-19 prevention interventions. Pragmatic study designs, while requiring initial planning and community buy-in, could offer more robust evidence on what is effective and for whom to combat the global pandemic we face and future policy decisions.

Association between COVID-19 outcomes and mask mandates, adherence, and attitudes

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.

Unraveling the dynamic importance of county-level features in trajectory of COVID-19

The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.

The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

We estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

"Public Health Behaviors during the COVID-19 Pandemic in Greece and Associated Factors: A Nationwide Cross-sectional Survey"

The objective of this cross-sectional survey was to estimate the association between multiple socioeconomic, and health-related characteristics, COVID-19 related attitudes and adoption of public health preventive behaviors. A national cross-sectional survey among 1205 adults was conducted in April 2020 in Greece. Multivariable ordered logistic regression models were used to estimate the association between COVID-19 related attitudes and knowledge and adoption of preventive behaviors, controlling for socioeconomic and health-related characteristics. A total of 923 individuals fully completed the survey. Individuals who believed that the virus is out of control, is transmitted through the air, and is not similar to the common flu were more likely to adopt public health preventive behaviors more frequently, particularly wearing masks in public spaces, washing their hands, and spending fewer hours out of their homes. Uncertainty about the virus symptomatology was associated with less frequent mask-wearing and handwashing. Increased social support, frequent media use for COVID-19 updates, trust to authorities, older age, worse health status, female gender and being a healthcare professional were also associated with uptake of some preventive health behaviors. Attitudinal and socioeconomic determinants critically affect public engagement in preventive behaviors. Health policy initiatives should focus on community outreach approaches to raise awareness and to strengthen social support mechanisms by integrating multiple stakeholders.

Development of a coronavirus social distance attitudes scale

OBJECTIVE

Our goal was to develop a scale to assess social distance attitudes related to COVID-19.

METHODS

We performed an online national survey of US adults (n = 1,074) to assess social distance attitudes, COVID-19 related beliefs and behaviors, and demographics. We assessed scale structure using confirmatory factor analysis and evaluated internal consistency and validity. We assessed association of scale factors with respondent characteristics.

RESULTS

Confirmatory factor analysis supported a hypothesized two-factor solution. Internal consistency was high for both positive (Alpha = 0.92) and negative (Alpha = 0.91) attitude factors. Analyses supported construct and predictive validity with expected associations between scale factors and perceived norms and behavior (e.g. trips out of the home). We found an interaction suggesting that holding highly negative attitudes reduced the effect of holding positive beliefs. Both attitude factors were related to age, gender, race/ethnicity, and political affiliation. Perceived COVID-19 risk (to others but not for self) and perceived severity were consistently associated with higher positive and lower negative attitudes.

CONCLUSION

This COVID-19 Social Distance Attitude Scale contains positive and negative factors with high internal consistency and construct and predictive validity.

PRACTICE IMPLICATION

A greater understanding and ongoing assessment of COVID-19 social distance attitudes could inform policymakers, researchers, and clinicians who seek to promote protective social distance behaviors.

US Trends in COVID-19-Associated Hospitalization and Mortality Rates Before and After Reopening Economies

Importance

After abrupt closures of businesses and public gatherings in the US in late spring 2020 due to the COVID-19 pandemic, by mid-May 2020, most states reopened their economies. Owing in part to a lack of earlier data, there was little evidence on whether state reopening policies influenced important pandemic outcomes-COVID-19-related hospitalizations and mortality-to guide future decision-making in the remainder of this and future pandemics.

Objective

To investigate changes in COVID-19-related hospitalizations and mortality trends after reopening of US state economies.

Design Setting and Participants

Using an interrupted time series approach, this cross-sectional study examined trends in per-capita COVID-19-related hospitalizations and deaths before and after state reopenings between April 16 and July 31, 2020. Daily state-level data from the University of Minnesota COVID-19 Hospitalization Tracking Project on COVID-19-related hospitalizations and deaths across 47 states were used in the analysis.

Exposures

Dates that states reopened their economies.

Main Outcomes and Measures

State-day observations of COVID-19-related hospitalizations and COVID-19-related new deaths per 100 000 people.

Results

The study included 3686 state-day observations of hospitalizations and 3945 state-day observations of deaths. On the day of reopening, the mean number of hospitalizations per 100 000 people was 17.69 (95% CI, 12.54-22.84) and the mean number of daily new deaths per 100 000 people was 0.395 (95% CI, 0.255-0.536). Both outcomes displayed flat trends before reopening, but they started trending upward thereafter. Relative to the hospitalizations trend in the period before state reopenings, the postperiod trend was higher by 1.607 per 100 000 people (95% CI, 0.203-3.011; P = .03). This estimate implied that nationwide reopenings were associated with 5319 additional people hospitalized for COVID-19 each day. The trend in new deaths after reopening was also positive (0.0376 per 100 000 people; 95% CI, 0.0038-0.0715; P = .03), but the change in mortality trend was not significant (0.0443; 95% CI, -0.0048 to 0.0933; P = .08).

Conclusions and Relevance

In this cross-sectional study conducted over a 3.5-month period across 47 US states, data on the association of hospitalizations and mortality with state reopening policies may provide input to state projections of the pandemic as policy makers continue to balance public health protections with sustaining economic activity.

COVID-19 Risk in Youth Club Sports: A nationwide sample representing over 200,000 Athletes

CONTEXT

The COVID-19 pandemic has affected almost every aspect of life including youth sports. Little data exists on COVID-19 incidences and risk mitigation strategies in youth club sports.

OBJECTIVE

To determine the reported incidence of COVID-19 cases among youth club sport athletes and the information sources used to develop COVID-19 risk mitigation procedures.

DESIGN

Cross-sectional study.

SETTING

Online surveys.

PATIENTS

Soccer and volleyball youth club directors.

INTERVENTION

A survey was completed by directors of youth volleyball and soccer clubs across the country in October 2020. Surveys included self-reported date of re-initiation, number of players, player COVID-19 cases, sources of infection, COVID-19 mitigation strategies, and information sources for the development of COVID-19 mitigation strategies.

MAIN OUTCOME MEASURES

Total number of cases reported, number of players, and days since club re-initiation were used to calculate an incidence rate of cases per 100,000 player-days. To compare reported incidence rates between soccer and volleyball, a negative binomial model was developed to predict player cases with sport and state incidence as covariates and log(player-days) as an offset. Estimates were exponentiated to yield a reported incidence rate ratio (IRR) with Wald confidence intervals.

RESULTS

A total of 205,136 athletes (soccer=165,580; volleyball=39,556) were represented by 437 clubs (soccer=159; volleyball=278). Club organizers reported 673 COVID-19 cases (soccer=322; volleyball=351), for a reported incidence rate of 2.8 cases per 100,000 player-days (soccer=1.7, volleyball=7.9). Volleyball had a significantly higher reported COVID-19 incidence rate compared to soccer (reported IRR = 3.06 [2.0-4.6], p<0.001). Out of 11 possible mitigation strategies, the median number of strategies used by all clubs was 7 with an interquartile range of 2.

CONCLUSIONS

The incidence of self-reported cases of COVID-19 was lower in soccer clubs than volleyball clubs. Most clubs report using many COVID-19 mitigation strategies to reduce the risk of COVID-19.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation (PEACHPIE): A systematic review of study design and evidence strength

Introduction:

Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.

Methods:

We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating.

Results:

After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.

Discussion:

The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policymakers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Role of social distancing in tackling COVID-19 during the first wave of pandemic in Nordic region: Evidence from daily deaths, infections and needed hospital resources

OBJECTIVES

To measure the effect of social distancing on reducing daily deaths, infections and hospital resources needed for coronavirus disease 2019 (COVID-19) patients during the first wave of the pandemic in Nordic countries.

METHODS

The observations of social distancing, daily deaths, infections along with the needed hospital resources for COVID-19 patient hospitalizations including the numbers of all hospital beds, beds needed in ICUs and infection wards, nursing staffs needed in ICUs and infection wards were collected from the Institute for Health Metrics and Evaluation (IHME) by the University of Washington. The observations of social distancing were based on the reduction in human contact relative to background levels for each location quantified by cell phone mobility data collected from IHME. The weighted data per 100,000 population gathered in a 40-day period of the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden. Statistical technique of panel data analysis is used to measure the associations between social distancing and COVID-19 indicators in long-run.

RESULTS

Results of dynamic long-run models confirm that a 1% rise in social distancing by reducing human contacts may decline daily deaths, daily infections, all hospital beds needed, beds/nurses needed in ICUs and beds/nurses needed in infection wards due COVID-19 pandemic by 1.13%, 15.26%, 1.10%, 1.17% and 1.89%, respectively. Moreover, results of error correction models verify that if the equilibriums between these series are disrupted by a sudden change in social distancing, the lengths of restoring back to equilibrium are 67, 62, 40, 22 and 49 days for daily deaths, daily infections, all hospital beds needed, nurses/beds needed in ICUs and nurses/beds needed in infection wards, respectively.

CONCLUSION

Proper social distancing was a successful policy for tackling COVID-19 with falling mortality and infection rates as well as the needed hospital resources for patient hospitalizations in Nordic countries. The results alert governments of the need for continuously implementing social distancing policies while using vaccines to prevent national lockdowns and reduce the burden of patient hospitalizations.

Early relaxation of community mitigation policies and risk of COVID-19 resurgence in the United States

This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country.

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

BACKGROUND

Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels.

OBJECTIVE

COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior-day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels.

METHODS

We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia's 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts.

RESULTS

The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days' counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels.

CONCLUSIONS

For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States

OBJECTIVE

Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic.

METHODS

We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19-related policies with states that did not adopt such policies, before and after these policies took effect during February 15-April 25, 2020.

RESULTS

Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility.

CONCLUSION

Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.

The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States

OBJECTIVE

Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic.

METHODS

We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19-related policies with states that did not adopt such policies, before and after these policies took effect during February 15-April 25, 2020.

RESULTS

Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility.

CONCLUSION

Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.