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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]

Qualitative research methods, while rising in popularity, are still a relatively underutilized tool in public health research. Usually reserved for small samples, qualitative research techniques have the potential to enhance insights gained from large questionnaires and cohort studies, both deepening the interpretation of quantitative data and generating novel hypotheses that might otherwise be missed by standard approaches; this is especially true where exposures and outcomes are new, understudied, or rapidly changing, as in a pandemic. However, methods for the conduct of qualitative research within large samples are underdeveloped. Here, we describe a novel method of applying qualitative research methods to free-text comments collected in a large epidemiologic questionnaire. Specifically, this method includes: 1) a hierarchical system of coding through content analysis; 2) a qualitative data management application; and 3) an adaptation of Cohen's κ and percent agreement statistics for use by a team of coders, applying multiple codes per record from a large codebook. The methods outlined in this paper may help direct future applications of qualitative and mixed methods within large cohort studies. [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]

It is challenging to quantitatively measure the health vulnerability and risk factors of refugees and migrants residing outside of formal settlement settings. For hard-to-reach populations without available sampling frames, researchers have increasingly turned to novel sampling and statistical methods, like respondent-driven sampling (RDS). "Standard" RDS is typically conducted face-to-face at fixed sites. However, during the coronavirus disease 2019 (COVID-19) pandemic, face-to-face survey methods and recruitment approaches posed high potential risk of virus transmission and infection, making remote RDS approaches optimal. In this paper, we explore the feasibility of implementing telephone and Internet RDS strategies to assess challenges faced by Venezuelan refugees and migrants in the city of Bogotá, Colombia's capital, and the department of Norte de Santander, the main Venezuelan-Colombian border crossing site. We describe RDS assumptions, survey design, formative research, and the implementation of both strategies and present diagnostics for determining whether assumptions are met. Phone-based recruitment strategies in both locations and the Internet strategy in Bogotá achieved their calculated sample size; however, the Internet strategy in Norte de Santander did not. Most RDS assumptions were sufficiently met at sites where sample sizes were reached. These surveys provide valuable lessons for implementing innovative remote strategies with which to study hard-to-reach populations such as refugees and migrants. [ABSTRACT FROM AUTHOR]

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts 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 they 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 the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, 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 with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence. [ABSTRACT FROM AUTHOR]

In their recent article, Dimitris et al. (Am J Epidemiol. 2022;191(6):980–986) presented a series of challenges modern epidemiology has faced during the coronavirus disease 2019 (COVID-19) pandemic, including challenges around the scientific progress, epidemiologic methods, interventions, equity, team science, and training needed to address these issues. Here, 2 social epidemiologists who have been working on COVID-19 inequities reflect on further lessons with an added year of perspective. We focus on 2 key challenges: 1) dominant biomedical individualistic narratives around the production of population health, and 2) the role of profit in policy-making. We articulate a need to consider social epidemiologic approaches, including acknowledging the importance of considering how societal systems lead to health inequities. To address these challenges, future (and current) epidemiologists should be trained in theories of population health distribution and political structures of governance. Last, we close with the need for better investment in public health infrastructure as a crucial step toward achieving population health equity. [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]

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources. [ABSTRACT FROM AUTHOR]

Along with age and race, sex has historically been a core stratification and control variable in epidemiologic research. While in recent decades research guidelines and institutionalized requirements have incorporated an approach differentiating biological sex from social gender, neither sex nor gender is itself a unidimensional construct. The conflation of dimensions within and between sex and gender presents a validity issue wherein proxy measures are used for dimensions of interest, often without explicit acknowledgement or evaluation. Here, individual-level dimensions of sex and gender are outlined as a guide for epidemiologists, and 2 case studies are presented. The first case study demonstrates how unacknowledged use of a sex/gender proxy for a sexed dimension of interest (uterine status) resulted in decades of cancer research misestimating risks, racial disparities, and age trends. The second illustrates how a multidimensional sex and gender framework may be applied to strengthen research on coronavirus disease 2019 incidence, diagnosis, morbidity, and mortality. Considerations are outlined, including: 1) addressing the match between measures and theory, and explicitly acknowledging and evaluating proxy use; 2) improving measurement across dimensions and social ecological levels; 3) incorporating multidimensionality into research objectives; and 4) interpreting sex, gender, and their effects as biopsychosocial. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has underscored the importance of observational studies of real-world vaccine effectiveness (VE) to help answer urgent public health questions. One approach to rapidly answering questions about real-world VE relies on linking data from a population-based registry of vaccinations with a population-based registry of health outcomes. Here we consider some potential sources of bias in linked registry studies, including incomplete reporting to the registries, errors in linking individuals between registries, and errors in the assumed population size of the catchment area of the registries. We show that the direction of the bias resulting from one source of error by itself is predictable. However, if multiple sources of error are present, the direction of the bias can be either upward or downward. The biases can be so strong as to make harmful vaccines appear effective. We provide explicit formulas with which to quantify and adjust for multiple biases in estimates of VE which could be used in sensitivity analyses. While this work was motivated by COVID-19 vaccine questions, the results are generally applicable to studies that link population-based exposure registries with population-based case registries to estimate relative risks of exposures. [ABSTRACT FROM AUTHOR]

We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n  = 1,141; C statistic: C  = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission. [ABSTRACT FROM AUTHOR]

The epidemiologic literature estimating the indirect or secondary effects of the coronavirus disease 2019 (COVID-19) pandemic on pregnant people and gestation continues to grow. Our assessment of this scholarship, however, leads us to suspect that the methods most commonly used may lead researchers to spurious inferences. This suspicion arises because the methods do not account for temporal patterning in perinatal outcomes when deriving counterfactuals, or estimates of the outcomes had the pandemic not occurred. We illustrate the problem in 2 ways. First, using monthly data from US birth certificates, we describe temporal patterning in 5 commonly used perinatal outcomes. Notably, for all but 1 outcome, temporal patterns appear more complex than much of the emerging literature assumes. Second, using data from France, we show that using counterfactuals that ignore this complexity produces spurious results. We recommend that subsequent investigations on COVID-19 and other perturbations use widely available time-series methods to derive counterfactuals that account for strong temporal patterning in perinatal outcomes. [ABSTRACT FROM AUTHOR]

The worldwide shortage of vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection while the pandemic still remains uncontrolled has led many countries to the dilemma of whether or not to vaccinate previously infected persons. Understanding the level of protection conferred by previous infection compared with that of vaccination is important for policy-making. We analyzed an updated individual-level database of the entire population of Israel to assess the protection provided by both prior infection and vaccination in preventing subsequent SARS-CoV-2 infection, hospitalization with coronavirus disease 2019 (COVID-19), severe disease, and death due to COVID-19. Outcome data were collected from December 20, 2020, to March 20, 2021. Vaccination was highly protective, with overall estimated effectiveness of 94.5% (95% confidence interval (CI): 94.3, 94.7) for documented infection, 95.8% (95% CI: 95.2, 96.2) for hospitalization, 96.3% (95% CI: 95.7, 96.9) for severe illness, and 96.0% (95% CI: 94.9, 96.9) for death. Similarly, the overall estimated level of protection provided by prior SARS-CoV-2 infection was 94.8% (95% CI: 94.4, 95.1) for documented infection, 94.1% (95% CI: 91.9, 95.7) for hospitalization, and 96.4% (95% CI: 92.5, 98.3) for severe illness. Our results should be considered by policy-makers when deciding whether or not to prioritize vaccination of previously infected adults. [ABSTRACT FROM AUTHOR]

Nonpharmaceutical interventions, such as social distancing and lockdowns, have been essential to control of the coronavirus disease 2019 (COVID-19) pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention for preventing viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared with larger-scale suppression strategies. Using an integrated data set from Chile (March 3–June 15, 2020) and a novel synthetic control approach, we estimated the effect of localized lockdowns, disentangling its direct and indirect causal effects on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results showed that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down SARS-CoV-2 transmission; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas. [ABSTRACT FROM AUTHOR]

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic—and narrower—credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys. [ABSTRACT FROM AUTHOR]

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the "Missing Link Fallacy." [ABSTRACT FROM AUTHOR]

We compared 3 hypothetical trajectories of change in both general and coronavirus disease 2019 (COVID-19)-specific anxiety during the first wave of the spread in the state of Israel: panic (very high anxiety, either from the outset or rapidly increasing), complacency (stable and low anxiety), and threat-sensitive (a moderate, linear increase compatible with the increase in threat). A representative sample of 1,018 Jewish-Israeli adults was recruited online. A baseline assessment commenced 2 days prior to the identification of the first case, followed by 6 weekly assessments. Latent mixture modeling analyses revealed the presence of 3 trajectories: 1) "threat-sensitivity" (29% and 66%, for general and virus-specific anxiety, respectively), 2) panic (12% and 25%), and 3) complacency (29% and 9%). For general anxiety only, a fourth class representing a stable mid-level anxiety was identified ("balanced": 30%). For general anxiety, women and the initially anxious—both generally and specifically from the spread of the virus—were more likely to belong to the panic class. Men and older participants were more likely to belong to the complacency class. Findings indicate a marked heterogeneity in anxiety responses to the first wave of the spread of COVID-19, including a large group evincing a "balanced" response. [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]

The prevalence of mental health problems represents a significant burden on school and community health resources as early as preschool. Reducing this burden requires a better understanding of the developmental mechanisms linking children's early vulnerabilities with mental health after the transition to formal schooling. The 3D-Transition Study (2017–2021) follows 939 participants from a pregnancy cohort in the province of Québec, Canada, as they transition to kindergarten and first grade to examine these mechanisms. Biannual assessments include completed questionnaires from 2 parents as well as teachers, parent-child observations, anthropometric measurements, and age-sensitive cognitive assessments. Saliva is also collected on 11 days over a 16-month period in a subsample of 384 participants to examine possible changes in child salivary cortisol levels across the school transition and their role in difficulties observed during the transition. A combination of planned missing-data designs is being implemented to reduce participant burden, where incomplete data are collected without introducing bias after the use of multiple imputation. The 3D-Transition Study will contribute to an evidence-based developmental framework of child mental health from pregnancy to school age. In turn, this framework can help inform prevention programs delivered in health-care settings during pregnancy and in child-care centers, preschools, and schools. [ABSTRACT FROM AUTHOR]

Epidemiologists sometimes use external sources of variation to explore highly confounded exposure-outcome relationships or exposures that cannot be randomized. These exogenous sources of variation, or natural experiments, are sometimes proposed as instrumental variables to examine the effects of given exposures on given outcomes. Previous epidemiologic studies have applied this technique using famines, earthquakes, weather events, and previous pandemics as exogenous sources of variation for other exposures; interest in applying this technique using the current severe acute respiratory system coronavirus 2 (SARS-CoV-2) pandemic is already documented. Yet large-scale events like these likely have broad and complicated impacts on human health, which almost certainly violates the exclusion restriction assumption of instrumental variable analyses. We review the assumptions of instrumental variable analyses, highlight previous applications of this method with respect to natural experiments with broad impacts or "shocks," and discuss how these relate to our current observations of the SARS-CoV-2 pandemic. While we encourage thorough investigation of the broad impacts of the SARS-CoV-2 pandemic on human health, we caution against its widespread use as an instrumental variable to study other exposures of interest. [ABSTRACT FROM AUTHOR]

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, |${R}_0$|⁠ , for SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The spread of coronavirus disease 2019 (COVID-19) is causing an unprecedented crisis around the world, with South Korea being no exception. South Korea experienced a surge of COVID-19 cases on February 19, 2020, but was able to flatten the COVID-19 curve in only 20 days without enforcing lockdown measures that restrict the freedom and movement of people. The actual field experience of the Korean government’s COVID-19 response team, who participated in drafting the document “Tackling COVID-19: Health, Quarantine, and Economic Measures: Korean experience,” was examined and an in-depth analysis was conducted of the South Korean government’s experience dealing with the Middle East respiratory syndrome of 2015. Three prominent factors were identified behind Korea’s responses against COVID-19. First, it was crucial that the government responded as 1 team efficiently to cooperate and respond to COVID-19. Second, a novel approach was needed to combat COVID-19 spread. Information and communication technology had a key role in South Korea’s strategy to contain COVID-19. Third, an effective response to COVID-19 was possible due to the successful partnership between the general public and state authorities. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic revealed and exacerbated existing social and economic health disparities, and actionable epidemiologic evidence is needed to identify potential vulnerability factors to help inform targeted responses. In this commentary, methodological challenges and opportunities regarding the links between air pollution and COVID-19 are discussed with a focus on 2 factors: 1) the role of differential exposure to air pollution across populations as an explanation for spatiotemporal variability of the epidemic spread and resultant mortality; and 2) the indirect impacts of interventions to control COVID-19 person-to-person spread treated as natural experiments on air pollution and population health. I first discuss the potential mechanisms between exposure to air pollution and COVID-19 and the opportunity to clearly formulate causal questions of interest through the target trial framework. Then, I discuss challenges regarding the use of quasiexperimental designs that capitalize on the differential timing of COVID-19 policies including the selection of control groups and potential violations of the common shock assumption. Finally, I discuss environmental justice implications of this many-headed beast of a crisis. [ABSTRACT FROM AUTHOR]

More than 1.6 million Americans have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than 10 times that number carry antibodies to it. High-risk patients with progressing symptomatic disease currently have only hospitalization treatment, with its high mortality, available to them. An outpatient treatment that prevents hospitalization is desperately needed. Two candidate medications have been widely discussed: remdesivir and hydroxychloroquine (HCQ) + azithromycin (AZ). Remdesivir has shown mild effectiveness in hospitalized inpatients, but no trials in outpatients have been registered. HCQ + AZ has been widely misrepresented in both clinical reports and public media, and results of outpatient trials are not expected until September. Early outpatient illness is very different from later florid disease requiring hospitalization, and the treatments differ. Evidence about use of HCQ alone, or of HCQ + AZ in inpatients, is irrelevant with regard to the efficacy of HCQ + AZ in early high-risk outpatient disease. Five studies, including 2 controlled clinical trials, have demonstrated significant major outpatient treatment efficacy. HCQ + AZ has been used as the standard of care in more than 300,000 older adults with multiple comorbid conditions; the estimated proportion of such patients diagnosed with cardiac arrhythmia attributable to the medications is 47 per 100,000 users, among whom estimated mortality is less than 20% (9/100,000 users), as compared with the 10,000 Americans now dying each week. These medications need to be made widely available and promoted immediately for physicians to prescribe. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) vaccines have been reported to have a short-term effect on the menstrual cycle, delaying the onset of the next menses. However, the analytical methods that have been used to study this are subject to a statistical phenomenon called "length-biased sampling" that calls the results into question. Those data are important and should be reanalyzed in an unbiased way. [ABSTRACT FROM AUTHOR]

In this issue of the Journal , Dr. Risch (Am J Epidemiol. 2020;189(11):1218–1226) posits that the use of a combination of hydroxychloroquine and azithromycin as an outpatient treatment for high-risk patients with coronavirus 19 should be increased as a way to help curtail the ongoing pandemic. However, a calculation error occurred in the original article, and new data about the studies cited have come to light. Peculiarities in the methods of data collection and reporting in those original sources must be considered when evaluating the evidence for or against hydroxychloroquine and azithromycin bitherapy. [ABSTRACT FROM AUTHOR]