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In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P  < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]

In the United States, striking racial disparities in COVID-19 infection and mortality rates were one of the core patterns of the virus. These racial disproportionalities were a result of structural factors--laws, rules, and practices embedded in economic, social, and political systems. Public policy is central among such structural features. Policies distribute advantages, disadvantages, benefits, and burdens in ways that generate, reinforce, or redress racial inequities. Crucially, public policy is a function of power relations, so understanding policy decisions requires attentiveness to power. This paper asseses statistical associations between racial power and state anti-eviction policies. Charting the timing of state policy responses between March 2020 and June 2021, I examine correlations between response times and racial power as reflected in state populations, voting constituencies, legislatures, and social movement activities. Ultimately, I do not find any significant associations. The null results underscore the complexities and difficulties of studying race, power, and public policy with theoretical nuance and empirical care. While the findings leave us with much to learn about how racial power operates, the conceptualization and theorizing offered in the paper, instructively underscore the value of centering racial power in analyses of public policy. [ABSTRACT FROM AUTHOR]

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers. [ABSTRACT FROM AUTHOR]

The article presents the discussion on relevance of poverty and inequality as factors affecting the spread and mortality of the COVID-19 pandemic in Latin America. Topics include Peruvians lacking mass diagnostic testing due to the limitations intrinsic to the health system and socioeconomic inequalities; and COVID-19 vaccine strategy against vaccine hesitancy ending the pandemic and achieving sustainable recovery.

In response to the COVID-19 pandemic, large parts of the economy were locked down and, as a result, households' income risk rose sharply. At the same time, policy makers put forward the largest stimulus package in history. In the United States it amounted to $2 trillion, a quarter of which represented transfer payments to households. To the extent that such transfers were (i) announced in advance and (ii) conditional on recipients being unemployed, they mitigated income risk associated with the lockdown—in contrast to unconditional transfers. We develop a baseline scenario for a COVID-19 recession in a medium-scale heterogeneous agent new Keynesian model and use counterfactuals to quantify the impact of transfers. For the short run, we find large differences in the transfer multiplier: it is negligible for unconditional transfers and about unity for conditional transfers. Overall, we find that the transfers reduced the output loss due to the pandemic by some two percentage points at its trough. [ABSTRACT FROM AUTHOR]

We study the role of perceived trade-offs between human lives and economic benefits in shaping policy views. In an online experiment with a representative sample from the United States conducted during the early Covid-19 pandemic, we provide randomised information on the medium-run costs of restricting economic activity to mitigate infections. A one standard deviation lower perceived economic cost of lockdowns increases support by about twice as much as having a Covid at-risk condition, and by half as much as being a Democrat. Varying projected health benefits has a similar effect. Personal exposure to health risks reduces people's responsiveness to cost-benefit considerations. [ABSTRACT FROM AUTHOR]

Background We described the oral nirmatrelvir/ritonavir (NMV/r) and molnupiravir (MOV) uptake among a subgroup of highly vaccinated adults in a US national prospective cohort who were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 12/2021 and 10/2022. Methods We estimate antiviral uptake within 5 days of SARS-CoV-2 infection, as well as age- and gender-adjusted antiviral uptake prevalence ratios by antiviral eligibility (based on age and comorbidities), sociodemographic characteristics, and clinical characteristics including vaccination status and history of long coronavirus disease 2019 (COVID). Results NMV/r uptake was 13.6% (95% CI, 11.9%–15.2%) among 1594 participants, and MOV uptake was 1.4% (95% CI, 0.8%–2.1%) among 1398 participants. NMV/r uptake increased over time (1.9%; 95% CI, 1.0%–2.9%; between 12/2021 and 3/2022; 16.5%; 95% CI, 13.0%–20.0%; between 4/2022 and 7/2022; and 25.3%; 95% CI, 21.6%–29.0%; between 8/2022 and 10/2022). Participants age ≥65 and those who had comorbidities for severe COVID-19 had higher NMV/r uptake. There was lower NMV/r uptake among non-Hispanic Black participants (7.2%; 95% CI, 2.4%–12.0%; relative to other racial/ethnic groups) and among individuals in the lowest income groups (10.6%; 95% CI, 7.3%–13.8%; relative to higher income groups). Among a subset of 278 participants with SARS-CoV-2 infection after 12/2021 who also had a history of prior SARS-CoV-2 infection, those with (vs without) a history of long COVID reported greater NMV/r uptake (22.0% vs 7.9%; P =.001). Among those prescribed NMV/r (n = 216), 137 (63%; 95% CI, 57%–70%) reported that NMV/r was helpful for reducing COVID-19 symptoms. Conclusions Despite proven effectiveness against severe outcomes, COVID-19 antiviral uptake remains low among those with SARS-CoV-2 infection in the United States. Further outreach to providers and patients to improve awareness of COVID-19 oral antivirals and indications is needed. [ABSTRACT FROM AUTHOR]

Background The COVID-19 pandemic led to increase in mental health problems and substance misuse. Yet, little is known about its impact on rates of deaths of despair (death by suicide and drug overdose). Our objective was to determine the impact of COVID-19 stay-at-home orders on deaths of despair using population-level data. We hypothesized that the longer duration of stay-at-home orders would increase rates of deaths of despair. Methods Utilizing quarterly suicide and drug-overdose mortality data from the National Center for Health Statistics from January 2019 through December 2020, we estimated fixed-effects models to examine the effects of the duration of stay-at-home orders as differentially implemented in 51 jurisdictions in the United States on each outcome. Results Controlling for seasonal patterns, the duration of jurisdictional-level stay-at-home order was positively associated with drug-overdose death rates. The duration of stay-at-home orders was not associated with suicide rates when adjusting for calendar quarter. Conclusions Findings suggest an increase in age-adjusted drug-overdose death rates in the United States from 2019 to 2020 possibly attributable to the duration of jurisdictional COVID-19 stay-at-home orders. This effect may have operated through various mechanisms, including increases in economic distress and reduced access to treatment programs when stay-at-home orders were in effect. [ABSTRACT FROM AUTHOR]

Background In 2020, preventive measures were implemented to mitigate the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among 600–700 recruits arriving weekly at a basic combat training (BCT) facility in the southern United States. Trainees were sorted into companies and platoons (cocoons) at arrival, tested, quarantined for 14 days with daily temperature and respiratory-symptom monitoring and retested before release into larger groups for training where symptomatic testing was conducted. Nonpharmaceutical measures, such as masking, and social distancing, were maintained throughout quarantine and BCT. We assessed for SARS-CoV-2 transmission in the quarantine milieu. Methods Nasopharyngeal (NP) swabs were collected at arrival and at the end of quarantine and blood specimens at both timepoints and at the end of BCT. Epidemiological characteristics were analyzed for transmission clusters identified from whole-genome sequencing of NP samples. Results Among 1403 trainees enrolled from 25 August to 7 October 2020, epidemiological analysis identified three transmission clusters (n  = 20 SARS-CoV-2 genomes) during quarantine, which spanned five different cocoons. However, SARS-CoV-2 incidence decreased from 2.7% during quarantine to 1.5% at the end of BCT; prevalence at arrival was 3.3%. Conclusions These findings suggest layered SARS-CoV-2 mitigation measures implemented during quarantine minimized the risk of further transmission in BCT. [ABSTRACT FROM AUTHOR]

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7–11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g. air pollution, neighborhood socioeconomic status), family (e.g. parental mental health), and individuals (e.g. diet, genomics). [ABSTRACT FROM AUTHOR]

This study aimed to examine differences in mental health and alcohol use outcomes across distinct patterns of work, home, and social life disruptions associated with the COVID-19 pandemic. Data from 2093 adult participants were collected from September 2020 to April 2021 as a part of a larger study examining the impacts of the COVID-19 pandemic on substance use. Participants provided data on COVID-19 pandemic experiences, mental health outcomes, media consumption, and alcohol use at baseline. Alcohol use difficulties, including problems related to the use, desire to use alcohol, failure to cut down on alcohol use, and family/friend concern with alcohol use, were measured at 60-day follow-up. Factor mixture modeling followed by group comparisons, multiple linear regressions, and multiple logistic regressions was conducted. A four-profile model was selected. Results indicated that profile membership predicted differences in mental health and alcohol use outcomes above and beyond demographics. Individuals experiencing the most disruption reported the strongest daily impact of COVID-19 and significantly high levels of depression, anxiety, loneliness, overwhelm, alcohol use at baseline, and alcohol use difficulties measured at 60-day follow-up. The findings highlight the need for integrated mental health and/or alcohol services and social services targeting work, home, and social life during public health emergencies in order to respond effectively and comprehensively to the needs of those requiring different types of support. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases. [ABSTRACT FROM AUTHOR]

Mixed evidence exists of associations between mobility data and coronavirus disease 2019 (COVID-19) case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall of 2020 in the United States and to demonstrate modified treatment policies to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indexes on the number of newly reported cases per 100,000 residents 2 weeks ahead. Primary analyses used targeted minimum loss-based estimation with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indexes and subsequent new case rates. However, after confounder adjustment, none of the indexes showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health. [ABSTRACT FROM AUTHOR]

The article discusses vaccination hesitancy among people in Europe and U.S. with reference to the article published in "Journal of Public Health". Topics discussed include development of strategic approach against widespread misinformation and conspiracy theories on COVID-19 vaccines, research published by EU agency Eurofound on the vaccine hesitancy among adults in Europe, and progress on COVID-19 vaccination campaigns in US.

How effective were investments in pandemic preparation? We use a comprehensive and detailed measure of pandemic preparedness, the Global Health Security (GHS) Index produced by the Johns Hopkins Center for Health Security (JHU), to measure which investments in pandemic preparedness reduced infections, deaths, excess deaths, or otherwise ameliorated or shortened the pandemic. We also look at whether values or attitudinal factors such as individualism, willingness to sacrifice, or trust in government—which might be considered a form of cultural pandemic preparedness—influenced the course of the pandemic. Our primary finding is that almost no form of pandemic preparedness helped to ameliorate or shorten the pandemic. Compared to other countries, the United States did not perform poorly because of cultural values such as individualism, collectivism, selfishness, or lack of trust. General state capacity, as opposed to specific pandemic investments, is one of the few factors which appears to improve pandemic performance. Understanding the most effective forms of pandemic preparedness can help guide future investments. Our results may also suggest that either we aren't measuring what is important or that pandemic preparedness is a global public good. [ABSTRACT FROM AUTHOR]

The article informs that the U.S. European centrality is urgently needed toward cohesive and comprehensive transatlantic ‘One Health' approach in addressing global COVID19 response and health security. Topics include multifaceted and multilateral global COVID-19 ‘One Health' approach and develop an enhanced transatlantic post-pandemic recovery; and role of European Union (EU) joint procurement of medical countermeasures to large-scale disease outbreaks with considerable public support.

Background I use Benford's law to assess whether there is misreporting of coronavirus disease of 2019 (COVID-19) deaths in the USA. Methods I use three statistics to determine whether the reported deaths for US states are consistent with Benford's law, where the probability of smaller digits is greater than the probability of larger digits. Results My findings indicate that there is under-reporting of COVID-19 deaths in the USA, although the evidence for and the extent of under-reporting does depend on the statistic one uses to assess conformity with Benford's law. Conclusions Benford's law is a useful diagnostic tool for verifying data and can be used before a more detailed audit or resource intensive investigation. [ABSTRACT FROM AUTHOR]

Background The Coronavirus Disease 2019 (COVID-19) pandemic warranted a myriad of government-ordered business closures across the USA in efforts to mitigate the spread of the virus. This study aims to discover the implications of government-enforced health policies of reopening public businesses amidst the pandemic and its effect on county-level infection rates. Methods Eighty-three US counties (n = 83) that reported at least 20 000 cases as of 4 November 2020 were selected for this study. The dates when businesses (restaurants, bars, retail, gyms, salons/barbers and public schools) partially and fully reopened, as well as infection rates on the 1st and 14th days following each businesses' reopening, were recorded. Regression analysis was conducted to deduce potential associations between the 14-day change in infection rate and mask usage frequency, median household income, population density and social distancing. Results On average, infection rates rose significantly as businesses reopened. The average 14-day change in infection rate was higher for fully reopened businesses (infection rate = +0.100) compared to partially reopened businesses (infection rate = +0.0454). The P -value of the two distributions was 0.001692, indicating statistical significance (P < 0.01). Conclusion This research provides insight into the transmission of COVID-19 and promotes evidence-driven policymaking for disease prevention and community health. [ABSTRACT FROM AUTHOR]

Background Years of Life Lost (YLLs) measure the shortfall in life expectancy due to a medical condition and have been used in multiple contexts. Previously it was estimated that there were 1.2 million YLLs associated with coronavirus disease 2019 (COVID-19) deaths in the USA through 11 July 2020. The aim of this study is to update YLL estimates for the first full year of the pandemic. Methods We employed data regarding COVID-19 deaths in the USA through 31 January 2021 by jurisdiction, gender and age group. We used actuarial life expectancy tables by gender and age to estimate YLLs. Results We estimated roughly 3.9 million YLLs due to COVID-19 deaths, which correspond to roughly 9.2 YLLs per death. We observed a large range across states in YLLs per 10 000 capita, with New York City at 298 and Vermont at 12. Nationally, the YLLs per 10 000 capita were greater for males than females (136.3 versus 102.3), but there was significant variation in the differences across states. Conclusions Our estimates provide further insight into the mortality effects of COVID-19. The observed differences across states and genders demonstrate the need for disaggregated analyses of the pandemic's effects. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic and associated economic crisis have placed millions of US households at risk of eviction. Evictions may accelerate COVID-19 transmission by decreasing individuals' ability to socially distance. We leveraged variation in the expiration of eviction moratoriums in US states to test for associations between evictions and COVID-19 incidence and mortality. The study included 44 US states that instituted eviction moratoriums, followed from March 13 to September 3, 2020. We modeled associations using a difference-in-difference approach with an event-study specification. Negative binomial regression models of cases and deaths included fixed effects for state and week and controlled for time-varying indicators of testing, stay-at-home orders, school closures, and mask mandates. COVID-19 incidence and mortality increased steadily in states after eviction moratoriums expired, and expiration was associated with a doubling of COVID-19 incidence (incidence rate ratio = 2.1; 95% confidence interval (CI): 1.1, 3.9) and a 5-fold increase in COVID-19 mortality (mortality rate ratio = 5.4; CI: 3.1, 9.3) 16 weeks after moratoriums lapsed. These results imply an estimated 433,700 excess cases (CI: 365,200, 502,200) and 10,700 excess deaths (CI: 8,900, 12,500) nationally by September 3, 2020. The expiration of eviction moratoriums was associated with increased COVID-19 incidence and mortality, supporting the public-health rationale for eviction prevention to limit COVID-19 cases and deaths. [ABSTRACT FROM AUTHOR]

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score–adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ. [ABSTRACT FROM AUTHOR]

Background The United States Centers for Disease Control and Prevention (CDC)-sanctioned prevention strategies have included frequent handwashing with soap and water, covering the mouth and nose with a mask when around others, cleaning and disinfecting maintaining a distance of at least 6 feet from others, etc. Although many of these recommendations are based upon observation and past infection control practices, it is important to combine and explore public data sets to identify predictors of infection, morbidity and mortality to develop more finely honed interventions, based on sociodemographic factors. Method Cross-sectional study of both states in the US and counties in NY state. Results Population density was found to be significantly associated with state-level coronavirus infection and mortality rate (b  = 0.49, 95% confidence interval (CI): 0.34, 0.64, P  < .0001). States that have lower socioeconomic status, lower mean age and denser populations are associated with higher incidence rates. In regard to NY state, counties with a higher percentage of minority residents had higher COVID-19 mortality rates (b  = 2.61, 95% CI: 0.36, 4.87, P  = 0.023). Larger population cohorts were associated with lower COVID-19 mortality rates after adjusting for other variables in the model (b  = −1.39, 95% CI: −2.07, −0.71, P  < 0.001). Population density was not significantly associated with COVID-19 mortality rates after adjustment across counties in the NY state. Public ridership was not indicative of cases or mortality across states in the USA; however, it is a significant factor associated with incidence (but not mortality) in NY counties. Conclusion Population density was the only significant predictor of mortality across states in the USA. Lower mean age, lower median household incomes and more densely populated states were at higher risk of COVID-19 infection. Population density was not found to be a significant independent variable compared to minority status and socioeconomic factors in the New York epicenter. Meanwhile, public ridership was found to be a significant factor associated with incidence in New York counties. [ABSTRACT FROM AUTHOR]