Perceived Importance of Health Concerns Among Lesbian, Gay, Bisexual, and Transgender Adults in a National, Probability-Based Phone Survey, 2017

Perceptions of the importance of health problems can drive advocacy, policy change, resource distribution, and individual behaviors. However, little is known about how lesbian, gay, bisexual, and transgender (LGBT), that is, sexual and gender minority (SGM) adults view the health problems facing SGM populations. In a 2017 national, probability-based survey of U.S. SGM adults (N = 453), we asked respondents to identify the most serious health problem facing SGM people today. Participants also rated the seriousness of five specific health problems (HIV/AIDS, suicide, hate crimes, harmful alcohol use, tobacco use). Analyses accounted for the complex sampling design and were stratified by gender identity. One quarter of U.S. SGM adults identified the most serious health problem facing SGM people to be HIV/AIDS (95% confidence interval [20.3, 31.2]). More respondents stated there were no serious LGBT health differences compared with straight/cisgender adults (4.2%, confidence interval [2.6, 5.9]) than identified tobacco use, hate crimes, chronic diseases, cancer, or suicide as the most serious. Importance ratings differed by gender and tobacco/alcohol use were perceived as less serious compared with HIV/AIDS, suicide, and hate crimes. Attention paid to HIV/AIDS by the SGM public, while important, may hinder efforts to address chronic diseases and other health issues affecting SGM people

Comparison of sampling strategies for tobacco retailer inspections to maximize coverage in vulnerable areas and minimize cost

Introduction In the United States, tens of thousands of inspections of tobacco retailers are conducted each year. Various sampling choices can reduce travel costs, emphasize enforcement in areas with greater noncompliance, and allow for comparability between states and over time. We sought to develop a model sampling strategy for state tobacco retailer inspections. Methods Using a 2014 list of 10,161 North Carolina tobacco retailers, we compared results from simple random sampling; stratified, clustered at the ZIP code sampling; and, stratified, clustered at the census tract sampling. We conducted a simulation of repeated sampling and compared approaches for their comparative level of precision, coverage, and retailer dispersion. Results While maintaining an adequate design effect and statistical precision appropriate for a public health enforcement program, both stratified, clustered ZIP- and tract-based approaches were feasible. Both ZIP and tract strategies yielded improvements over simple random sampling, with relative improvements, respectively, of average distance between retailers (reduced 5.0% and 1.9%), percent Black residents in sampled neighborhoods (increased 17.2% and 32.6%), percent Hispanic residents in sampled neighborhoods (reduced 2.2% and increased 18.3%), percentage of sampled retailers located near schools (increased 61.3% and 37.5%), and poverty rate in sampled neighborhoods (increased 14.0% and 38.2%). Conclusions States can make retailer inspections more efficient and targeted with stratified, clustered sampling. Use of statistically appropriate sampling strategies like these should be considered by states, researchers, and the Food and Drug Administration to improve program impact and allow for comparisons over time and across states. Implications The authors present a model tobacco retailer sampling strategy for promoting compliance and reducing costs that could be used by US states and the Food and Drug Administration (FDA). The design is feasible to implement in North Carolina. Use of the sampling design would help document the impact of FDA's compliance and enforcement program, save money, and emphasize inspections in areas where they are needed most. FDA should consider requiring probability-based sampling in their inspections contracts with states and private contractors

On the Use of Covariate Supersets for Identification Conditions

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

Risk, Resilience, and Smoking in a National, Probability Sample of Sexual and Gender Minority Adults, 2017, USA

Background. There are well-documented inequities in smoking between sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, and transgender [LGBT]) and straight and cisgender people. However, there is less information about risk for and resilience against smoking among SGM people. Such information is critical for understanding etiology and developing interventions. Aims. To conduct a within-group assessment of risks and resiliencies relating to smoking status. Method. In 2017, we conducted a cross-sectional telephone survey with a national, probability-based sample of SGM adults (N = 453). We assessed theory-informed risks (adverse childhood events, substance use–oriented social environment, mental distress, stigma, discrimination, social isolation, and identity concealment) and resiliencies (advertising skepticism, identity centrality, social support, and SGM community participation). We applied survey weights, standardized predictor variables, and fit logistic regression models predicting smoking status. We stratified by age and SGM identity. Results. Patterns of risk and resilience differ by age and identity. Effects were consistently in the same direction for all groups for participating in substance use–oriented social environments, pointing to a potential risk factor for all groups. Advertising skepticism and having people you can talk to about being LGBTQ were potential protective factors. Discussion. Intervention development should address risk and resilience that differs by SGM identity. Additionally, our findings suggest interventionists should consider theoretical frameworks beyond minority stress. Conclusion. While much of the literature has focused on the role of stress from stigma and discrimination in tobacco use, addressing social norms and bolstering protective factors may also be important in SGM-targeted interventions

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

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

SARS-CoV-2 Seroprevalence: Demographic and Behavioral Factors Associated With Seropositivity Among College Students in a University Setting

Purpose: 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. Methods: 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. Results: 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)). Discussion: 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

Missing Outcome Data in Epidemiologic Studies

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology

Illustration of 2 Fusion Designs and Estimators

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information

The impact of greeting personalization on prevalence estimates in a survey of sexual assault victimization

Although personalized invitations tend to increase response rates in web surveys, little is known about how personalization impacts data quality. To evaluate the impact of personalization on survey estimates of sensitive items, the effects of personalized and generic greetings in a survey (n = 9,673) on an extremely sensitive topic-sexual assault victimization-were experimentally compared. Personalization was found to have increased response rates with negligible impact on victimization reporting, and this impact was similar across most demographic groups. The findings suggest that future studies may benefit from the use of a personalized greeting when recruiting sample members to participate in a sensitive survey, but that further research is necessary to better understand how the impact of personalization on reporting may differ across some demographic groups

Permethrin-treated baby wraps for the prevention of malaria in children: Protocol for a double-blind, randomized placebo-controlled controlled trial in western Uganda

This article details the study protocol for a double-blind, randomized placebo-controlled trial to determine the effectiveness of permethrin-treated baby wraps to prevent Plasmodium falciparum malaria infection in children 6–24 months of age. Participating mother-infant dyads will be randomized to receive either a permethrin-treated or a sham-treated wrap, known locally as a “lesu.” After a baseline home visit, during which time all participants will receive new long-lasting insecticidal nets, participants will attend scheduled clinic visits every two weeks for a period of 24 weeks. In the event of an acute febrile illness or other symptoms that may be consistent with malaria (e.g., poor feeding, headache, malaise), participants will be instructed to present to their respective study clinic for evaluation. The primary outcome of interest is the incidence of laboratory-confirmed, symptomatic malaria in participating children. Secondary outcomes of interest include: (1) change in children’s hemoglobin levels; (2) change in children’s growth parameters; (3) prevalence of asymptomatic parasitemia in children; (4) hospitalization for malaria in children; (5) change in the mother’s hemoglobin level; and (6) clinical malaria in the mother. Analyses will be conducted using a modified intent-to-treat approach, with woman-infant dyads who attend one or more clinic visits analyzed according to the arm to which they were randomly assigned. This is the first use of an insecticide-treated baby wrap for prevention of malaria in children. The study began recruitment in June 2022 and is ongoing. ClinicalTrials.gov Identifier: NCT05391230, Registered 25 May 2022