BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis.
METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation \"cross-world\" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH).
RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions.
CONCLUSIONS: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

BACKGROUND: Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings.
METHODS: We assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses.
RESULTS: The study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples.
CONCLUSIONS: In conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

BACKGROUND: Accurate prevalence estimates of drug use and its harms are important to characterize burden and develop interventions to reduce negative health outcomes and disparities. Lack of a sampling frame for marginalized/stigmatized populations, including persons who use drugs (PWUD) in rural settings, makes this challenging. Respondent-driven sampling (RDS) is frequently used to recruit PWUD. However, the validity of RDS-generated population-level prevalence estimates relies on assumptions that should be evaluated.
METHODS: RDS was used to recruit PWUD across seven Rural Opioid Initiative studies between 2018-2020. To evaluate RDS assumptions, we computed recruitment homophily and design effects, generated convergence and bottleneck plots, and tested for recruitment and degree differences. We compared sample proportions with three RDS-adjusted estimators (two variations of RDS-I and RDS-II) for five variables of interest (past 30-day use of heroin, fentanyl, and methamphetamine; past 6-month homelessness; and being positive for hepatitis C virus (HCV) antibody) using linear regression with robust confidence intervals. We compared regression estimates for the associations between HCV positive antibody status and (a) heroin use, (b) fentanyl use, and (c) age using RDS-1 and RDS-II probability weights and no weights using logistic and modified Poisson regression and random-effects meta-analyses.
RESULTS: Among 2,842 PWUD, median age was 34 years and 43% were female. Most participants (54%) reported opioids as their drug of choice, however regional differences were present (e.g., methamphetamine range: 4-52%). Many recruitment chains were not long enough to achieve sample equilibrium. Recruitment homophily was present for some variables. Differences with respect to recruitment and degree varied across studies. Prevalence estimates varied only slightly with different RDS weighting approaches, most confidence intervals overlapped. Variations in measures of association varied little based on weighting approach.
CONCLUSIONS: RDS was a useful recruitment tool for PWUD in rural settings. However, several violations of key RDS assumptions were observed which slightly impacts estimation of proportion although not associations.

It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms.

OBJECTIVE: To estimate the association between COVID-19 vaccination status at the time of COVID-19 onset and long COVID prevalence.
METHODS: We used data from the Michigan COVID-19 Recovery Surveillance Study, a population-based probability sample of adults with COVID-19 (n = 4695). We considered 30-day and 90-day long COVID (illness duration ≥30 or ≥90 days, respectively), using Poisson regression to estimate prevalence ratios (PRs) comparing vaccinated (completed an initial series ≥14 days before COVID-19 onset) to unvaccinated individuals (received 0 doses before COVID-19 onset), accounting for differences in age, sex, race and ethnicity, education, employment, health insurance, and rurality/urbanicity. The full unvaccinated comparison group was further divided into historic and concurrent comparison groups based on timing of COVID-19 onset relative to vaccine availability. We used inverse probability of treatment weights to account for sociodemographic differences between groups.
RESULTS: Compared to the full unvaccinated comparison group, the adjusted prevalence of 30-day and 90-day long COVID were lower among vaccinated individuals [PR30-day= 0.57(95%CI:0.49,0.66); PR90-day= 0.42(95%CI:0.34,0.53)]. Estimates were consistent across comparison groups (full, historic, and concurrent).
CONCLUSIONS: Long COVID prevalence was 40-60% lower among adults vaccinated (vs. unvaccinated) prior to their COVID-19 onset. COVID-19 vaccination may be an important tool to reduce the burden of long COVID.

Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates. Together with the RDS designs, we propose a semiparametric analysis approach that efficiently uses all data to estimate the parameters. We describe a numerically stable and computationally efficient EM algorithm to maximize the semiparametric likelihood. We examine the finite sample operating characteristics of the proposed approaches through extensive simulation studies, and compare the efficiency of our designs and analysis approach with existing ones. We illustrate the usefulness of the proposed RDS designs and analysis method in practice by studying the association between a genetic marker and poor lung function among patients enrolled in the Lung Health Study (Connett et al, 1993).

Dried blood microsampling is increasingly used for home-sampling and epidemiological studies because of its multiple advantages, including an often greatly improved analyte stability. However, a critical assessment of the stability under realistic conditions should always be performed as part of the validation, especially for unstable molecules like folates (vitamin B9). Here, the objective was to determine whether folate stability in dried blood microsamples is sufficient to allow the set-up of home-sampling studies for the monitoring of folate status in e.g., women of reproductive age. An extensive set of stability experiments was performed to evaluate the stability of the main folate vitamer 5-methyltetrahydrofolate (5MTHF), its oxidation product MeFOX and the minor non-methyl folate vitamers 10-formylfolic acid (10FoFA), 5,10-methenyltetrahydrofolate (5,10CH+THF) and tetrahydrofolate (THF) in dried blood microsamples using volumetric absorptive microsampling (VAMS) or regular dried blood spots (DBS). The evaluations included (EDTA-anticoagulated blood was collected from a single donor measured in four replicates per condition and time point): (i) the effect of temperature (-20 °C, 4 °C, ambient temperature and 37 °C), (ii) the effect of light (during drying and storage) and humidity, and (iii) the effect of storage under vacuum and pretreatment of the microsamples with stabilizing agents on folate stability. At -20 °C and 4 °C, all folate levels were within 85 to 115% of the baseline value up till two weeks of storage in both VAMS samples and DBS. However, at room temperature the stability of the analyzed folates was only consistently observed up till three days in VAMS samples, and for none of the folates at 37 °C. Humidity had a major impact on 5,10CH+THF stability, but this could be easily improved by using desiccant. Both vacuum treatment and pretreatment of microsamples with 0.1% DL-dithiothreitol and 5% butylated hydroxytoluene improved the stability at room temperature in VAMS samples, but these effects were limited at 37 °C and in DBS. Overall, the stability of the individual folate vitamers proved to be challenging and strongly temperature- and time-dependent. Nonetheless, if controlled transport (temperature and duration) can be assured, the set-up of home-sampling studies to evaluate the folate status using dried blood microsamples can still be beneficial.

Primary healthcare centers (PHCs) serve as the cornerstone of accessible medical services in society, playing a crucial role in screening, detecting, and treating various health issues. This study aimed to investigate the prevalence of psychiatric disorders in middle-aged individuals who refer to PHCs and the potential of PHCs in diagnosing mental disorders.
This cross-sectional study was implemented at PHCs under the supervision of Mashhad University of Medical Sciences (MUMS) in northeast Iran in 2018. The enrolled subjects were middle-aged adults who had electronic medical records in SINA, an integrated health management system, and the electronic medical records of MUMS. The prevalence of psychiatric disorders by type and their relationship with demographic information was evaluated by a Chi-square test using SPSS 22.
This study involved 218,341 middle-aged participants. Prevalence of psychiatric disorders was 8.59%, and depression (53.72%) and anxiety (42.02%) were the most common psychiatric disorders in both males and females. The prevalence of mental disorders was significantly higher in females than in males (88.18% vs. 18.81%; P < 0.0001). Indeed, a significant higher prevalence of depression, anxiety, somatoform, childhood psychiatric disorder, and bipolar disorders was observed in females compared to males (P < 0.05). In addition, individuals between the age of 45-60 years, and those from rural areas showed more prevalence of mental disorders than others, but these differences were not significant.
Considering the previous studies in Iran, the prevalence of mental disorders among patients presenting to PHCs was noticeably lower than expected rates. It seems probable that this huge difference is due to poor screening and detection of mental illness in PHCs of MUMS. It is recommended that health policymakers pursue specific measures to make PHCs more helpful for people with mental health problems in the community.

Structural racism is how society maintains and promotes racial hierarchy and discrimination through established and interconnected systems. Structural racism is theorized to promote alcohol and tobacco use, which are risk factors for adverse health and cancer-health outcomes. The current study assesses the association between measures of state-level structural racism and alcohol and tobacco use among a national sample of 1,946 Black Americans.
An existing composite index of state-level structural racism including five dimensions (subscales; i.e., residential segregation and employment, economic, incarceration, and educational inequities) was merged with individual-level data from a national sample dataset. Hierarchical linear and logistic regression models, accounting for participant clustering at the state level, assessed associations between structural racism and frequency of alcohol use, frequency of binge drinking, smoking status, and smoking frequency. Two models were estimated for each behavioral outcome, one using the composite structural racism index and one modeling dimensions of structural racism in lieu of the composite measure, each controlling for individual-level covariates.
Results indicated positive associations between the incarceration dimension of the structural racism index and binge drinking frequency, smoking status, and smoking frequency. An inverse association was detected between the education dimension and smoking status.
Results suggest that state-level structural racism expressed in incarceration disparities, is positively associated with alcohol and tobacco use among Black Americans.
Addressing structural racism, particularly in incarceration practices, through multilevel policy and intervention may help to reduce population-wide alcohol and tobacco use behaviors and improve the health outcomes of Black populations.

BACKGROUND: Evidence on workplace bullying and harassment (WBH) in the UK has not used probability-sample surveys with robust mental health assessments. This study aimed to profile the prevalence and nature of WBH in England, identify inequalities in WBH exposure, and quantify adjusted associations with mental health.
METHODS: Data were from the 2014 Adult Psychiatric Morbidity Survey, a cross-sectional probability-sample survey of the household population in England, interviewed with verbal informed consent. Criteria for inclusion in the secondary analysis were being aged 16-70 years and in paid work in the past month (n=3838). Common mental disorders were assessed using the Clinical Interview Schedule-Revised and mental wellbeing using the Warwick-Edinburgh Mental Wellbeing Scale. Analyses were weighted. We examined associations between past-year WBH and current common mental disorders using multivariable regression modelling, adjusting for demographic and socioeconomic factors. Interaction terms tested for gender differences in associations. The study received ethics approval (ETH21220-299).
RESULTS: One in ten employees (10·6% (weighted), n=444/3838) reported past-year experience of WBH, with rates higher in women (12·2%, n=284/2189); those of mixed, multiple, and other ethnicity (21·0%, n=15/92); and people in debt (15·2%, n=50/281) or living in cold homes (14·6%, n=42/234). Most commonly identified perpetrators of WBH were line managers (53·6%, n=244/444) or colleagues (42·8%, n=194/444). Excessive criticism (49·3%, n=212/444), verbal abuse (42·6%, n=187/444), and humiliation (31·4%, n=142/444) were the most common types. WBH was associated with all adverse mental health indicators, including common mental disorders (adjusted odds ratio [aOR] 2·65, 95% CI 2·02-3·49), and 11 of 14 mental wellbeing indicators, including lower levels of confidence (aOR 0·57, 0·46-0·72) and of closeness to others (aOR 0·57, 0·46-0·72). Patterns of association between WBH and mental health were similar in men and women.
CONCLUSIONS: These findings reinforce a need for more cohesive UK legislation at the national level; guidance on recognition of bullying behaviours for employees, managers, and human resources at the organisational level, focusing on prevention and early intervention, and increased awareness of the impact of WBH on mental health among health-service practitioners. Study limitations include reliance on cross-sectional data collected before pandemic-related and other major changes in workplace practices. Longitudinal data are needed to improve evidence on causality and the longevity of mental health impacts.
BACKGROUND: UK Prevention Research Partnership.