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Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge. [ABSTRACT FROM AUTHOR]

Background: Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design. Methods: We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample data. We apply these methods to data from the Chatham County COVID-19 Cohort (C4), a longitudinal seroprevalence study conducted in central North Carolina. Using simulations, we evaluate bias and confidence interval coverage for the proposed estimator compared with a standard estimator under a stratified, three-stage cluster sample design. Results: We obtained estimates of seroprevalence and corresponding confidence intervals for the C4 study. SARS-CoV-2 seroprevalence increased rapidly from 10.4% in January to 95.6% in July 2021 in Chatham County, North Carolina. In simulation, the proposed estimator demonstrates desirable confidence interval coverage and minimal bias under a wide range of scenarios. Conclusion: We propose a straightforward method for producing valid estimates and confidence intervals when data are based on a complex sample design. The method can be applied to estimate the prevalence of other infections when estimates of test sensitivity and specificity are available. [ABSTRACT FROM AUTHOR]

We describe an approach to sensitivity analysis introduced by Robins et al (1999), for the setting where the outcome is missing for some observations. This flexible approach focuses on the relationship between the outcomes and missingness, where data can be missing completely at random, missing at random given observed data, or missing not at random. We provide examples from HIV that include the sensitivity of the estimation of a mean and proportion under different missingness mechanisms. The approach illustrated provides a method for examining how the results of epidemiologic studies might shift as a function of bias due to missing data. [ABSTRACT FROM AUTHOR]

This study aims to describe the cohort of Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program participants and evaluate whether the ATN's recently completed 5-year cycle recruited study participants who parallel the populations most impacted by HIV in the United States. Harmonized measures across ATN studies collected at baseline were aggregated for participants aged 13–24 years. Pooled means and proportions stratified by HIV status (at risk for or living with HIV) were calculated using unweighted averages of study-specific aggregate data. Medians were estimated using a weighted median of medians method. Public use 2019 Centers for Disease Control and Prevention surveillance data for state-level new HIV diagnoses and HIV prevalence among US youth aged 13–24 years were obtained for use as reference populations for ATN at-risk youth and youth living with HIV (YLWH), respectively. Data from 3,185 youth at-risk for HIV and 542 YLWH were pooled from 21 ATN study phases conducted across the United States. Among ATN studies tailored to at-risk youth, a higher proportion of participants were White and a lower proportion were Black/African American and Hispanic/Latinx compared to youth newly diagnosed with HIV in the United States in 2019. Participants in ATN studies tailored to YLWH were demographically similar to YLWH in the United States. The development of data harmonization guidelines for ATN research activities facilitated this cross-network pooled analysis. These findings suggest the ATN's YLWH are representative, but that future studies of at-risk youth should prioritize recruitment strategies to enroll more participants from African American and Hispanic/Latinx populations. [ABSTRACT FROM AUTHOR]

Background: When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence. Methods: We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas. Results: The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased. Conclusions: If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification. [ABSTRACT FROM AUTHOR]

Examine SARS-CoV-2 seroprevalence and the association of seropositivity with demographic, geographic, and behavioral variables among University of North Carolina Chapel Hill (UNC-CH) undergraduate students enrolled in the fall 2020 semester. All UNC-CH undergraduate students were invited to participate in the Heelcheck study; participants were weighted to the UNC-CH undergraduate population using raking methods. We estimate SARS-CoV-2 seroprevalence at study entrance (11/12/2020–12/10/2020) and bivariable associations using log-binomial regression. SARS-CoV-2 seroprevalence was 7.3% (95% confidence interval (CI): 5.4%–9.2%) at baseline. Compared to students who were living off-campus in the Chapel Hill/Carrboro area (CH) for the Fall 2020 semester (8.6% seroprevalence), students who never returned to CH had lower seroprevalence (1.9%, prevalence ratio (PR), 95% CI: 0.22, 0.06–0.81), whereas, students who started the semester on-campus and moved to off-campus CH housing had 18.9% seroprevalence (PR, 95% CI: 2.21, 1.04–4.72) and students who spent the semester living in a Sorority/Fraternity house had 46.8% seroprevalence (PR, 95% CI: 5.47, 2.62–11.46). Those who predicted they would join an indoor party unmasked had 3.8 times the seroprevalence of those who indicated they would not attend (PR, 95% CI: 3.80, 1.58–9.16). Compared to students who disagreed with the statement "...I am not going to let COVID-19 stop me from having fun...", those who agreed had higher seroprevalence (14.0% vs. 5.7%; (PR, 95% CI: 2.45, 1.13–5.32)). Increased seroprevalence was associated with congregate living and participation (actual or endorsed) in social activities. During pandemics, universities must create safe socializing opportunities while minimizing transmission. [ABSTRACT FROM AUTHOR]

The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets. [ABSTRACT FROM AUTHOR]

• Teachers are central to school-related networks; we surveyed 700 about behaviors. • As schools reopened in the COVID-19 pandemic, close contact was common in schools. • Among teachers and those around them, mask use was suboptimal outside of school. • Survey results underscore the need for multilayered mitigation and messaging. • Survey estimates can inform mathematical models of infection transmission. Teachers are central to school-associated transmission networks, but little is known about their behavioral patterns during the COVID-19 pandemic. We conducted a cross-sectional survey of 700 North Carolina public school teachers in 4 districts open to in-person learning in November-December 2020 (pre-COVID-19 vaccines). We assessed indoor and outdoor time spent, numbers of people encountered at <6 feet ("close contacts"), and mask use by teachers and those around them at specific locations on the most recent weekday and weekend day. Nearly all respondents reported indoor time at home (98%) and school (94%) on the most recent weekday, while 62% reported indoor time at stores, 18% at someone else's home, and 17% at bars/restaurants. Responses were similar for the most recent weekend day, excepting school (where 5% reported indoor time). Most teachers (>94%) reported wearing masks inside school, stores, and salons; intermediate percentages (∼50%-85%) inside places of worship, bars/restaurants, and recreational settings; and few (<25%) in their or others' homes. Approximately half reported daily close contact with students. As schools reopened in the COVID-19 pandemic, potential transmission opportunities arose through close contacts within and outside of school, along with suboptimal mask use by teachers and/or those around them. Our granular estimates underscore the importance of multilayered mitigation strategies and can inform interventions and mathematical models addressing school-associated transmission. [ABSTRACT FROM AUTHOR]