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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]

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]

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]

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]

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 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]

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]

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]