Validity and reliability of the Neilands sexual stigma scale among Kenyan gay, bisexual, and other men who have sex with men.

BACKGROUND: We evaluated the validity and reliability of the Neilands sexual stigma scale administered to 871 gay, bisexual, and other men who have sex with men (GBMSM) at two research locations in Kenya. METHODS: Using cross-validation, exploratory factor analysis (EFA) was performed on a randomly selected subset of participants and validated using confirmatory factor analysis (CFA) on the remaining participants. Associations of the initial and final stigma scale factors with depressive symptoms, alcohol use, and other substance use were examined for the entire dataset. RESULTS: EFA produced a two-factor scale of perceived and enacted stigma. The CFA model fit to the two-factor scale was improved after removing three cross-loaded items and adding correlated errors (chi-squared = 26.5, df 17, p = 0.07). Perceived stigma was associated with depressive symptoms (beta = 0.34, 95% CI 0.24, 0.45), alcohol use (beta = 0.14, 95% CI 0.03, 0.25) and other substance use (beta = 0.19, 95% CI 0.07, 0.31), while enacted stigma was associated with alcohol use (beta = 0.17, 95% CI 0.06, 0.27). CONCLUSIONS: Our findings suggest enacted and perceived sexual stigma are distinct yet closely related constructs among GBMSM in Kenya and are associated with poor mental health and substance use.

Patterns of Gender-Based Violence and Associations with Mental Health and HIV Risk Behavior Among Female Sex Workers in Mombasa, Kenya: A Latent Class Analysis.

Gender-based violence (GBV) is common among female sex workers (FSWs) and is associated with multiple HIV risk factors, including poor mental health, high-risk sexual behavior, and sexually transmitted infections (STIs). Prior studies have focused on GBV of one type (e.g. physical or sexual) or from one kind of perpetrator (e.g., clients or regular partners), but many FSWs experience overlapping types of violence from multiple perpetrators, with varying frequency and severity. We examined the association between lifetime patterns of GBV and HIV risk factors in 283 FSWs in Mombasa, Kenya. Patterns of GBV were identified with latent class analysis based on physical, sexual, or emotional violence from multiple perpetrators. Cross-sectional outcomes included depressive symptoms, post-traumatic stress disorder (PTSD) symptoms, disordered alcohol and other drug use, number of sex partners, self-reported unprotected sex, prostate-specific antigen (PSA) in vaginal secretions, and a combined unprotected sex indicator based on self-report or PSA detection. We also measured HIV/STI incidence over 12 months following GBV assessment. Associations between GBV patterns and each outcome were modeled separately using linear regression for mental health outcomes and Poisson regression for sexual risk outcomes. Lifetime prevalence of GBV was 87%. We identified 4 GBV patterns, labeled Low (21% prevalence), Sexual (23%), Physical/Moderate Emotional (18%), and Severe (39%). Compared to women with Low GBV, those with Severe GBV had higher scores for depressive symptoms, PTSD symptoms, and disordered alcohol use, and had more sex partners. Women with Sexual GBV had higher scores for disordered alcohol use than women with Low GBV, but similar sexual risk behavior. Women with Physical/Moderate Emotional GBV had more sex partners and a higher prevalence of unprotected sex than women with Low GBV, but no differences in mental health. HIV/STI incidence did not differ significantly by GBV pattern. The prevalence of GBV was extremely high in this sample of Kenyan FSWs, and different GBV patterns were associated with distinct mental health and sexual risk outcomes. Increased understanding of how health consequences vary by GBV type and severity could lead to more effective programs to reduce HIV risk in this vulnerable population.

Latino Cigarette Smoking Patterns by Gender in a US-National Sample.

BACKGROUND: Latino smokers are a rising public health concern who experience elevated tobacco-related health disparities. PURPOSE: Additional information on Latino smoking is needed to inform screening and treatment. ANALYSIS: Latent class analysis using smoking frequency, cigarette preferences, onset, smoking duration, cigarettes per day, and minutes to first cigarette was used to create multivariate latent smoking profiles for Latino men and women. RESULTS: Final models found seven classes for Latinas and nine classes for Latinos. Despite a common finding in the literature that Latino smokers are more likely to be low-risk intermittent smokers, the majority of classes for both males and females described patterns of high-risk daily smoking. Gender variations in smoking classes were noted. CONCLUSIONS: Several markers of smoking risk were identified among both male and female Latino smokers, including long durations of smoking, daily smoking, and preference for specialty cigarettes, all factors associated with long-term health consequences.

Development of a tool to assess HIV prevention readiness of adolescent girls and young women in HPTN 082 study.

BACKGROUND: African adolescent girls and young women (AGYW) represent a large proportion of new HIV infections, a priority population for pre-exposure prophylaxis (PrEP), but adherence remains a challenge. A reliable, valid readiness tool would help identify AGYW motivated to take PrEP who need adherence support. METHODS: In the HPTN 082 open-label PrEP study (2016-2019), South African and Zimbabwean women ages 16-25 were administered an HIV prevention readiness measure (HPRM). The 25 items in the HPRM included medication beliefs, connection with care, disclosure of PrEP use, social support, and housing stability using a 5-point Likert scale. Exploratory factor analysis (EFA) using polychoric correlations, scale reliability, and predictive validity were performed on data from 315 participants who responded to all items. We assessed the predictive value of HPRM scores with PrEP adherence, defined as tenofovir-diphosphate (TFV-DP) concentrations in dried blood spots, as a continuous measure and dichotomized as high PrEP adherence (≥700 fmol/punch). RESULTS: EFA yielded 23 items with three subscales: self-efficacy (16 items), PrEP disclosure (4 items), and social support (3 items). Cronbach's α ranged from 0.71 to 0.92 for the overall scale and the subscales. The average overall scale and the subscales were predictive of 3-month PrEP adherence for TFV-DP concentrations: for each unit increase of the HPRM score, TFV-DP concentration increased by 103 fmol/punch (95% CI: 16, 189, p = 0.02); the highest HPRM score equated with 608 fmol/punch on average. For the self-efficacy subscale, TFV-DP increased by 90 fmol/punch (95% CI: 7, 172, p = 0.03); PrEP disclosure, 68 fmol/punch (95% CI: 19, 117 p = 0.01); and social support, 58fmol/punch (95% CI: 2, 113, p = 0.04). Higher PrEP disclosure suggests high adherence (OR 1.36, 95% CI: 1.00, 1.86, p = 0.05) and predicted persistent high adherence at both months three and six (OR: 1.50, 95% CI: 1.03, 2.21, p = 0.04). CONCLUSIONS: The HPRM scale overall and the subscales individually demonstrated good internal consistency among African young women. PrEP disclosure subscale exhibiting significant association with persistent high PrEP adherence is an important finding for PrEP adherence support programs. Future work will assess replicability and expand self-efficacy and social-support subscales after item revision. TRIAL REGISTRATION: ClinicalTrials.gov NCT02732730.

Optimizing the analysis of adherence interventions using logistic generalized estimating equations.

Interventions aimed at improving HIV medication adherence could be dismissed as ineffective due to statistical methods that are not sufficiently sensitive. Cross-sectional techniques such as t tests are common to the field, but potentially inaccurate due to increased risk of chance findings and invalid assumptions of normal distribution. In a secondary analysis of a randomized controlled trial, two approaches using logistic generalized estimating equations (GEE)-planned contrasts and growth curves-were examined for evaluating percent adherence data. Results of the logistic GEE approaches were compared to classical analysis of variance (ANOVA). Robust and bootstrapped estimation was used to obtain empirical standard error estimates. Logistic GEE with either planned contrasts or growth curves in combination with robust standard error estimates was superior to classical ANOVA for detecting intervention effects. The choice of longitudinal model led to key differences in inference. Implications and recommendations for applied researchers are discussed.

A framework for characterizing heterogeneity in neurodevelopmental data using latent profile analysis in a sample of children with ADHD.

BACKGROUND: Heterogeneity in neurodevelopmental disorders, and attention deficit hyperactivity disorder (ADHD) in particular, is increasingly identified as a barrier to identifying biomarkers and developing standards for clinical care. Clustering analytic methods have previously been used across a variety of data types with the goal of identifying meaningful subgroups of individuals with ADHD. However, these analyses have often relied on algorithmic approaches which assume no error in group membership and have not made associations between patterns of behavioral, neurocognitive, and genetic indicators. More sophisticated latent classification models are often not utilized in neurodevelopmental research due to the difficulty of working with these models in small sample sizes. METHODS: In the current study, we propose a framework for evaluating mixture models in sample sizes typical of neurodevelopmental research. We describe a combination of qualitative and quantitative model fit evaluation procedures. We test our framework using latent profile analysis (LPA) in a case study of 120 children with and without ADHD, starting with well-understood neuropsychological indicators, and building toward integration of electroencephalogram (EEG) measures. RESULTS: We identified a stable five-class LPA model using seven neuropsychological indicators. Although we were not able to identify a stable multimethod indicator model, we did successfully extrapolate results of the neuropsychological model to identify distinct patterns of resting EEG power across five frequency bands. CONCLUSIONS: Our approach, which emphasizes theoretical as well as empirical evaluation of mixture models, could make these models more accessible to clinical researchers and may be a useful approach to parsing heterogeneity in neurodevelopmental disorders.

Heritability and Clinical Characteristics of Neuropsychological Profiles in Youth With and Without Elevated ADHD Symptoms.

OBJECTIVE: In the last decade, there has been an increase in research that aims to parse heterogeneity in attention deficit hyperactivity disorder (ADHD). The current study tests heritability of latent class neuropsychological subtypes. METHOD: Latent class analysis was used to derive subtypes in a sample of school-age twins (N = 2,564) enriched for elevated ADHD symptoms. RESULTS: Five neuropsychological profiles replicated across twin 1 and twin 2 datasets. Latent class membership was heritable overall, but heritability varied by profile and was lower than heritability of ADHD status. Variability in neuropsychological performance across domains was the strongest predictor of elevated ADHD symptoms. Neuropsychological profiles showed distinct associations with age, psychiatric symptoms and reading ability. CONCLUSION: Neuropsychological profiles are associated with unique neurocognitive presentations, but are not strong candidate endophenotypes for ADHD diagnosis.

Systems analysis and improvement approach to optimize outpatient mental health treatment cascades in Mozambique (SAIA-MH): study protocol for a cluster randomized trial.

BACKGROUND: Significant investments are being made to close the mental health (MH) treatment gap, which often exceeds 90% in many low- and middle-income countries (LMICs). However, limited attention has been paid to patient quality of care in nascent and evolving LMIC MH systems. In system assessments across sub-Saharan Africa, MH loss-to-follow-up often exceeds 50% and sub-optimal medication adherence often exceeds 60%. This study aims to fill a gap of evidence-based implementation strategies targeting the optimization of MH treatment cascades in LMICs by testing a low-cost multicomponent implementation strategy integrated into routine government MH care in Mozambique. METHODS: Using a cluster-randomized trial design, 16 clinics (8 intervention and 8 control) providing primary MH care will be randomized to the Systems Analysis and Improvement Approach for Mental Health (SAIA-MH) or an attentional placebo control. SAIA-MH is a multicomponent implementation strategy blending external facilitation, clinical consultation, and provider team meetings with system-engineering tools in an overall continuous quality improvement framework. Following a 6-month baseline period, intervention facilities will implement the SAIA-MH strategy for a 2-year intensive implementation period, followed by a 1-year sustainment phase. Primary outcomes will be the proportion of all patients diagnosed with a MH condition and receiving pharmaceutical-based treatment who achieve functional improvement, adherence to medication, and retention in MH care. The Consolidated Framework for Implementation Research (CFIR) will be used to assess determinants of implementation success. Specific Aim 1b will include the evaluation of mechanisms of the SAIA-MH strategy using longitudinal structural equation modeling as well as specific aim 2 estimating cost and cost-effectiveness of scaling-up SAIA-MH in Mozambique to provincial and national levels. DISCUSSION: This study is innovative in being the first, to our knowledge, to test a multicomponent implementation strategy for MH care cascade optimization in LMICs. By design, SAIA-MH is a low-cost strategy to generate contextually relevant solutions to barriers to effective primary MH care, and thus focuses on system improvements that can be sustained over the long term. Since SAIA-MH is integrated into routine government MH service delivery, this pragmatic trial has the potential to inform potential SAIA-MH scale-up in Mozambique and other similar LMICs. TRIAL REGISTRATION: ClinicalTrials.gov; NCT05103033 ; 11/2/2021.

Modeling Behavior as Dynamic Sequential States: Introduction to the Special Issue.

This special issue of Evaluation and The Health Professions focuses on applications and extensions of latent transition analysis (LTA), a longitudinal parameterization of the latent class (LC) model. LTA is a model of discrete or qualitative change over time among potentially complex states (e.g., patterns of recent drug use or abuse experiences), commonly referred to as latent classes, latent profiles, or latent statuses. Frequently, researchers will distinguish the term "classes" for cross-sectional studies and with LTA use "statuses" to indicate the concept of "dynamic change" with individuals shifting in their response patterns and associated statuses over time. It goes without saying that LTA models are underutilized, although quite flexible. This special issue showcases articles that apply LTA and extend the capabilities of this approach to modeling discrete change in new ways.

What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects.

Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods.

Evaluating Employment Quality as a Determinant of Health in a Changing Labor Market.

The shifting nature of employment in recent decades has not been adequately examined from a public health perspective. To that end, traditional models of work and health research need to be expanded to include the relational and contractual aspects of employment that also affect health. We examine the association of three health outcomes with different types of employment in the contemporary U.S. labor market, as measured by a multidimensional construct of employment quality (EQ) derived from latent class analysis. We find that EQ is associated with self-rated health, mental health, and occupational injury. Further, we explore three proposed mediating mechanisms of the EQ-health relationship (material deprivation, employment-related stressors, and occupational risk factors), and find each to be supported by these data.

Testing the degree of cross-sectional and longitudinal dependence between two discrete dynamic processes.

Developmental research often involves studying change across 2 or more processes or constructs simultaneously. A natural question in this work is whether change in these 2 processes is related or independent. Associative latent transition analysis (ALTA) was designed to test hypotheses about the degree to which change in 2 discrete latent variables is related. The ALTA model is a type of latent class model, which is a categorical latent variable model based on categorical indicators. In the ALTA approach, level and change on 1 variable is predicted by level and change in another. Two types of hypotheses are discussed: (a) broad hypotheses of dependence between the 2 discrete latent variables and (b) targeted hypotheses comparing specific patterns of change between levels of the discrete variables. Both types of hypotheses are tested via nested model comparisons. Analyses of relations between psychological state and substance use illustrate the model. Recent psychological state and recent substance use were found to be associated cross-sectionally and longitudinally, implying that change in recent substance use was related to change in recent psychological state.

A descriptive analysis of alcohol behaviors across gender subgroups within a sample of transgender adults.

OBJECTIVE: Transgender (trans) adults are identified as an at-risk group for problem alcohol use. Descriptive empirical data examining alcohol behaviors among trans adults is limited. The present study investigates alcohol behaviors - quantity, frequency, alcohol-related problems, and drinking to cope motives - across sex assigned at birth, gender expression, and gender identity subgroups within a sample of trans adults. METHOD: A total of 317 trans participants were recruited to complete a cross-sectional battery of online measures assessing alcohol use behaviors, alcohol-related problems, and drinking to cope. Gender identity was assessed through two methods: (1) an open-ended question in which participants wrote-in their primary gender identity; and (2) participants rated the extent to which they identified with 14 gender identity categories. RESULTS: This sample had high rates of alcohol use, alcohol-related problems, and drinking to cope motives relative to the general population. Significant and meaningful differences in drinking frequency, alcohol-related problems and drinking motives were found according to gender expression, but not sex assigned at birth or gender identity. CONCLUSIONS: Future work should examine alcohol behaviors among trans individuals, including investigation of predictors and causal pathways, to inform prevention and intervention work aimed at reducing trans people's risk for alcohol-related problems.

The natural history of college smoking: trajectories of daily smoking during the freshman year.

Although the initiation of cigarette use typically occurs prior to age 18, there is evidence for considerable change in smoking behavior after this age. College may be a particularly important period to study smoking because it is a time when adolescents transition into a new social context where substance use is normative. Using a longitudinal design, daily assessments of smoking were collected during the entire first year of college for a large cohort of freshman (N=496). Findings suggested a weekly cycle of smoking such that the probability of smoking was much higher on weekends (Friday and Saturday) than on remaining days of the week. In addition to this weekly cycle, there was an overall trend for smoking to decline over the course of the year. Substantial individual variability in levels of smoking was observed. These findings provide new insights into college smoking, and have implications for assessment, policy, intervention, and future directions for research.

Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis.

Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concerning stage-sequential change over time in longitudinal data. Both approaches have been used with increasing frequency in the social sciences. The objective of this article is to illustrate data augmentation (DA), a Markov chain Monte Carlo procedure that can be used to obtain parameter estimates and standard errors for LCA and LTA models. By use of DA it is possible to construct hypothesis tests concerning not only standard model parameters but also combinations of parameters, affording tremendous flexibility. DA is demonstrated with an example involving tests of ethnic differences, gender differences, and an Ethnicity x Gender interaction in the development of adolescent problem behavior.

Most smokeless tobacco use does not cause cigarette smoking: results from the 2000 National Household Survey on Drug Abuse.

Those who either never progress from smokeless tobacco (SLT) to smoking or smoked before using SLT logically cannot have smoking caused by SLT use. The prevalence of such use permits strong inferences about the overall importance of the potential causal effects of SLT on cigarette smoking. We found that the majority of SLT ever users (66%) in the 2000 National Household Survey on Drug Abuse (NHSDA) were noncausal users. For these individuals, SLT use cannot have caused them to smoke. We also compared our results in 2000 with a similar sample in 1987, using current SLT users only. Potentially, the causal uses of SLT were in the minority and had not increased significantly over time (24% in 1987 to 29% in 2000, P>.05). Logistic models showed that, when noncausal users were removed, SLT was a minor predictor of current smoking. This is likely due to linked experimentation. We argue that that the majority of SLT use cannot cause smoking, such that SLT effects on smoking initiation are minimal at best. Policy implications of these findings are discussed.

Most smokeless tobacco use is not a causal gateway to cigarettes: using order of product use to evaluate causation in a national US sample.

AIMS: To evaluate non-causal and causal patterns of smokeless tobacco (SLT) and cigarette use; to assess the prevalence of 'non-gateway' and possible 'gateway' patterns of SLT use. DESIGN AND SETTING: Data from the Cancer Control Supplement to the 1987 National Health Interview Survey, a representative survey of non-institutionalized adults in the United States. From reported age at first use, participants were categorized by type and sequence of tobacco product use. SUDAAN 8.0.1 was used for statistical analyses. PARTICIPANTS: Males aged 18-34 (n = 3454), weighted to provide estimates of the US population. A subsample of males aged 23-34 (n = 2614) was analyzed to minimize the possibility of future product switching. MEASUREMENTS: Smoking status, smokeless tobacco (snuff, chewing tobacco, both) use status, age at regular use of cigarettes, age at first use of smokeless tobacco. FINDINGS: Of those 23-34-year-olds who had ever used SLT with or without cigarettes, 77.2% (95% CI: 71.3, 83.3) were classifiable as non-gateway users in that 35.0% (95% CI: 29.9, 40.1) had only used SLT and 42.2% (95% CI: 36.8, 47.7) had used cigarettes first. Cigarette use in younger cohorts was less common, despite increased SLT use. Those who used cigarettes before moist snuff were 2.1 times more likely to have quit smoking (95% CI 1.21,6.39) than cigarette-only users. CONCLUSIONS: The large majority of SLT users are non-gateway users. Causal gateway effects should be of minor concern for policy. SLT may be more likely to prevent smoking than cause it.

Assessing reliability of categorical substance use measures with latent class analysis.

This article illustrates the use of the latent class model to identify classes of individuals and to assess the psychometric reliability of categorical items. The latent class model is a categorical latent variable model used to identify homogeneous classes of respondents such that class membership accounts for item responses. The assessment of measurement reliability comes directly from the estimates of the model. Although not based on classical test theory, the reliability assessment procedures described here answer the same question-that is, how consistent or dependable is measurement? The goal is to identify reliable indicators of a characteristic by examining measurement error and the inter-relatedness of the items. Methods for estimating the reliability of individual items as well as sets of items are presented. These methods are illustrated with data on cigarette smoking from a national sample of adolescents. By using the procedures described here, researchers are able to determine: (1). which classes of people are measured well and which are not; (2). which items perform well and which do not; and (3). whether items need to be altered or added in order to measure and identify particular classes better.