This study aims to explore the perceived effectiveness of waterpipe (WP) tobacco specific health warning labels (HWLs) among young adult WP smokers and nonsmokers in Lebanon. Before participating in focus group discussions, participants (n = 66; WP smokers n = 30; nonsmokers n = 36; age 18–33) completed a brief survey to rate the effectiveness of 12 HWLs’ and rank them according to four risk themes (WP health effects, WP harm to others, WP-specific harm, and WP harm compared to cigarettes). Differences in HWLs ratings by WP smoking status were examined and the top-ranked HWL in each theme were identified. HWLs depicting mouth cancer and harm to babies were rated as the most effective by both WP smokers and non-smokers. WP smokers rated HWLs which depicted harm to children and infants as more effective than non-smokers. The top-ranked HWLs for perceived overall effectiveness were those depicting “oral cancer”, “harm to babies”, “orally transmitted diseases” and “mouth cancer”. HWLs depicting oral lesions and harm to babies were rated as most effective, while HWLs showing the harmful effects of WP secondhand smoke on infants and children were rated as less effective by nonsmokers compared to smokers. Our study provides evidence on the potential effectiveness of HWLs for further evaluation in Lebanon and the Eastern Mediterranean region. The results will inform and guide the development and implementation of tobacco control policy.

This paper deals with the problem of simultaneously making many ($M$) binary decisions based on one realization of a random data matrix $\mathbf{X}$. $M$ is typically large and $\mathbf{X}$ will usually have $M$ rows associated with each of the $M$ decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix $\mathbf{X}$ are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study.

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data “double‐dipping” and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided.

Background: Understanding immune response is essential for preparing for public health crises. COVID-19 vaccination provides robust immunity against SARS-CoV-2, but immunocompromised populations may have weaker immune responses. We assessed SARS-CoV-2 spike (trimer) total IgG/IgM/IgA (total Ig) to investigate immune response to COVID-19 vaccination in people living with HIV (PLWH), considering CD4⁺ T cell count, viral load, substance use, and comorbidities. Methods: This cross-sectional study was conducted in Miami, Florida, between May 2021 and December 2021 as part of the NIH Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) initiative (3U01DA040381-05S1) and the Miami Adult Studies on HIV (MASH) cohort (U01DA040381). Blood samples were collected and SARS-CoV-2 spike (trimer) total Ig was quantified. HIV serostatus, viral load, CD4⁺ T cell count, and COVID-19 vaccinations were abstracted from medical records. Substance use (tobacco, alcohol, and drug use [marijuana, cocaine, heroin, fentanyl, methamphetamine, amphetamine, hallucinogens, ecstasy, or misuse of prescription drugs]), and comorbidities (hypertension, diabetes, autoimmune disease, obesity, chronic kidney disease, and substance use disorders) were assessed via validated questionnaires. Drug use was confirmed via urine toxicology. Multivariable linear regression was conducted. Results: Median age (n = 1317) was 57.8 years, 49.8% were male, 50% were Black non-Hispanic, 66.2% had received ≥1 dose of a COVID-19 vaccine, and 29.6% were PLWH (71.3% virally suppressed and median CD4⁺ T cell count > 500 cells/µL). PLWH, compared to people without HIV, were more likely to have received ≥1 dose of a COVID-19 vaccine (76.2% vs. 62.0%, p < 0.001) and present with substance use (77.2% vs. 42.9%, p < 0.001) and comorbidities (72.8% vs. 48.2%, p < 0.001). Vaccinated PLWH, compared to unvaccinated PLWH, had higher CD4⁺ T cell counts (577.5 vs. 517.5, p = 0.011) and were more likely to be virally suppressed (76.4% vs. 54.8%, p < 0.001). A lower CD4⁺ T cell count (<200 vs. ≥500, β = −0.400, p = 0.033) and higher HIV viral load (≥200–<5000 vs. <200, β = −0.275, p < 0.001) were associated with lower spike (trimer) total Ig titers, indicating a diminished response to COVID-19 vaccination. Conclusions: A lower CD4⁺ T cell count and higher HIV viremia were linked to reduced SARS-CoV-2 immunogenicity in racial/ethnic minority PLWH, a population underrepresented in vaccine clinical trials. HIV care providers should target efforts to maintain viral suppression to avoid diminished responses to COVID-19 vaccination.