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The SAVA syndemic is frequently used to describe the co-occurrence of HIV, gender-based violence (GBV), and substance use. In this study we determine the extent to which the typologies of the SAVA syndemic can be described and utilized for intervention strategies among youth living in the slums of Kampala, Uganda. We analyzed the "Kampala Youth Survey 2014," a cross-sectional survey conducted in the spring of 2014, consisting of a convenience sample (N = 1134) of urban youth (12-18 years of age). Descriptive statistics were computed for hypothesized risk factors and demographic variables among the 8 typologies of GBV, HIV, and alcohol use. Multinomial logistic regression was conducted to determine statistically significant correlates with each typology. The overall prevalence of GBV was 31.7%, whereas the overall prevalence of alcohol use in the past 12 months was 31.2%. HIV-Positive youth comprised 10.5% of the total sample. Females comprised the majority of the typology with no SAVA components compared to males (55% vs. 45%, respectively), as well as the SAVA syndemic typology (GBV + HIV + ALC +) (58% vs. 42%, respectively). Engaging in commercial sex work (36%), witnessing parental abuse (61%), and depression/suicidality (81%) were all highly prevalent among youth in the SAVA syndemic typology (GBV, HIV, and alcohol use). Sex work and observing parental abuse were associated with the SAVA syndemic typology in the multivariable model. In our study, alcohol rarely coexisted without GBV among the typologies. Therefore, prevention efforts including structural interventions may be particularly warranted in vulnerable populations to address alcohol use, which may directly or indirectly impact GBV and HIV.

Introduction: Blunt and hemp wraps, as a means of consuming cannabis, have emerged into the retail space where the prevalence has been increasing since 2017. There is limited epidemiological research on the prevalence of use of these products across the U.S. particularly among young adults who are at greater risk of tobacco and cannabis use. Methods: This study draws from a U.S. national representative sample of young adults (n = 1178) captured in May 2022. Respondents participated in an online survey about their use of blunt and hemp wraps. Multinomial regression was used to examine differences in sociodemographic characteristics (gender, race/ethnicity, sexual orientation, educational attainment, and region) in relation to use of each wrap type. Results: One quarter (22.7%) of young adults reported ever having used a blunt wrap, 3.2% in the past 30-days. One in seven (14.3%) had ever used a hemp wrap, 2.3% in the past 30-days. Non-Hispanic Black young adults were 1.55 and 2.91 times as likely to have ever used blunt or hemp wraps, respectively, compared to non-Hispanic Whites. Similarly, participants who identified as gay or lesbian or bisexual similarly had greater odds of having ever used blunt or hemp wraps. Hispanic young adults were 2.49 times as likely to have used hempwraps compared to non-Hispanic Whites. Conclusion: Blunt and hemp wrap use is prevalent among young adults, particularly among minoritized populations. Continued research and surveillance of use of these products is needed to fully evaluate the impact their use may have on the broader population. [ABSTRACT FROM AUTHOR]

Using data from the Educational Longitudinal Study (2002), this study employed latent class analysis (LCA) to explore the relationship between students' engagement-related dispositions and their behavioral engagement in home, school, and community activities. The primary aim was to identify and describe the engagement characteristics of distinct subpopulations of students. Our higher-order LCA models yielded five subpopulation profiles of students' social-ecological engagement. In contrast to extant participation-identification models, these new profiles suggested that the relationship between students' behavioral engagement and their school-related identities and dispositions is nonlinear and nonhomogeneous. This major finding recommends comprehensive school-community improvement models as well as nuanced interventions that are tailor-made to fit the engagement needs and identity-related characteristics of each student subpopulation.

Individuals with autism demonstrate atypical and variable responses to social and emotional stimuli, perhaps reflecting heterogeneity of the disorder. The goal of this study was to determine whether unique profiles of psychophysiological responses to such stimuli could be identified in individuals diagnosed with autism spectrum disorder (ASD), with fragile X syndrome (FXS), and with comorbid autism and fragile X syndrome (ASD + FXS), and in typically developing (TYP) individuals. This study included 52 boys (ages 10-17): idiopathic ASD (n = 12), FXS (n = 12), comorbid ASD + FXS (n = 17), and TYP (n = 11). Physiological responses, including potentiated startle, electrodermal response, heart rate variability, and vagal tone, were collected concurrently while participants viewed emotionally evocative pictures of human faces or nonsocial images. Although some of these measures have been utilized separately for investigations on these diagnostic groups, they have not been considered together. Results using Kruskal-Wallis one-way analysis of variance by ranks indicate statistically significant differences in distributions of autonomic regulation responses between groups. The most notable differences were between the ASD group and both the FXS groups on measures of sympathetic activity, with FXS groups evincing increased activity. Also, both the ASD and ASD + FXS groups showed significantly decreased parasympathetic activity compared with FXS and TYP groups. In addition, the ASD + FXS group demonstrated a unique distribution of startle potentiation and arousal modulation. This study provides evidence that autonomic arousal and regulation profiles could be useful for distinguishing subgroups of autism and shed light on the variability underlying emotional responsivity.

This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved hetero-geneity among the subjects using a finite mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identi?es subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.

This study investigates body image concerns and disordered weight- and shape-related behaviors across adolescence and young adulthood in males and how patterns vary by sexual orientation. Participants were 5,388 males from the U.S. national Growing Up Today Study. In 2001, 2003, and 2005 (spanning ages 15-20 years), participants reported sexual orientation, past-year desire for toned/defined muscles and concerns with weight and shape, and past-year binge eating, restrictive dieting, purging (vomiting or laxative use), and use of products to increase muscularity (e.g., creatine, steroids). Latent class analyses identified 2 patterns at ages 15-16 years and 3 patterns at 17-18 and 19-20 years: healthy (all ages; low body image concerns and weight- and shape-related behaviors; 54-74% of observations), muscle-concerned (ages 17-18 and 19-20; relatively high muscularity concern and product use; 18-21% of observations), and lean-concerned (all ages; relatively high weight and shape concern, dieting, and binge eating; 19-28% of observations). Latent transition analyses revealed that sexual minority males (i.e., mostly heterosexual, gay, and bisexual) were more likely than completely heterosexual males to be lean-concerned at ages 17-18 and 19-20 years and to transition to the lean-concerned class from the healthy class. There were no sexual orientation differences in odds of being muscle-concerned. Both heterosexual and sexual minority males are at risk for presenting body image concerns and weight- and shape-related behaviors that may have deleterious health consequences. Results suggest the need for screening for concerns and behaviors related to leanness and muscularity in early adolescence among all males, regardless of sexual orientation.

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.

Background: We used a latent class analysis (LCA) to characterize coping styles of urban youth and examined if coping styles moderated the association between experiencing discrimination and bullying and depressive symptoms. Methods: The data come from the 2006 Boston Youth Survey, where students were asked to select 2 behaviors they do most often when they are upset, from a list of 15 options. A total of 927 (75%) students contributed to the LCA analytic sample (44% non-Hispanic Blacks, 29% Hispanics, and 58% girls). Relative and absolute fit indices determined the number of classes. An interaction term between types of discrimination and bullying experienced and coping style tested for moderation. Results: The LCA revealed that a 3-class solution had the best fit (Lo-Mendell-Rubin likelihood ratio test, 4-class vs 3-class, p-value 0.12). The largest coping style class was characterized by high endorsement of distractive coping strategies (59%), the second class was characterized by using supportive coping strategies (27%), and the third class was characterized by using avoidance coping strategies (12%). We found a significant interaction between discrimination and coping style for depressive symptoms. Conclusions: The relationship between experiencing discrimination and depression varied based on coping style and the type of discrimination and bullying experienced.

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data. [ABSTRACT FROM AUTHOR]

Although studies have established associations between parenting characteristics and adolescent suicidality, the strength of the evidence for these links remains unclear, largely because of methodological limitations, including lack of accounting for possible child effects on parenting. This study addresses these issues by using autoregressive cross-lag models with data on 802 adolescents and their parents across 5 years. Observed parenting behaviors predicted change in adolescent suicidal problems across 1-year intervals even after controlling for adolescents' effects on parenting. Nurturant-involved parenting continued to demonstrate salutary effects after controlling for adolescent and parent internalizing psychopathology: over time, observed nurturant-involved parenting reduced the likelihood of adolescent suicidal problems. This study increases the empirical support implicating parenting behaviors in the developmental course of adolescent suicidality. (Contains 2 figures and 1 table.)

The focus of this study was to prospectively investigate the effect of aggressive behavior and of classroom behavioral context, as measured in the fall of 1st grade, on the timing of 1st school removal across Grades 1-7 in a sample of predominately urban minority youths from Baltimore, Maryland. Using a multilevel discrete-time survival framework, we found that demographic characteristics of the students as well as early individual- and classroom-level of aggression contribute to the onset of school removal. Although early individual aggression was positively associated with the risk of school removal, initially higher levels of classroom aggression corresponded to lower risk of school removal. (Contains 1 footnote, 2 tables, and 6 figures.)

Although suicide ideation, plans, and attempts increase during adolescence, it remains unclear whether boys' and girls' risk for these outcomes peaks at different ages. We used longitudinal categorical data ("never," "once," "2+ times") from the Family Transitions Project (N = 1,248 rural European Americans, ages 11-19) to investigate whether yearly prevalence rates of adolescent suicidal episodes follow different patterns by sex. Multiple-group growth models revealed that peak levels of past-year ideation and plans occurred during mid adolescence for girls, but slowly increased through late adolescence for boys. We found that prevalence patterns for attempts were very similar for boys and girls, with both increasing through mid adolescence and then declining, although girls' risk declined slightly more rapidly. This information may help alert gatekeepers to developmental periods during which boys and girls are particularly vulnerable to suicide-related experiences, and also may help inform the timing of preventive efforts.

This paper provides an introduction to a recently developed conceptual framework--the dimensional-categorical spectrum--for utilizing general factor mixture models to explore the latent structures of psychological constructs. This framework offers advantages over traditional latent variable models that usually employ either continuous latent factors or categorical latent class variables to characterize the latent structure and require an a priori assumption about the underlying nature of the construct as either purely dimension or purely categorical. The modeling process is discussed in detail and then illustrated with data on the delinquency items of Achenbach's child behavior checklist from a sample of children in the National Adolescent and Child Treatment Study.

The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that "softer" drugs lead to "harder," illicit drugs. However, the gateway hypothesis has been notably difficult to directly test--that is, to test as competing hypotheses in a single model that licit drug use might lead to illicit drug use "or" the reverse. This article presents a novel statistical technique, dual-process discrete-time survival analysis, which enables this comparison. This method uses mixture-modeling software to estimate 2 concurrent time-to-event processes and their effects on each other. Using this method, support for the gateway hypothesis in the National Longitudinal Survey of Youth, 1997, was weak. However, this article was not designed as a strong test of causal direction but more as a technical demonstration and suffered from certain technological limitations. Both these limitations and future directions are discussed. (Contains 1 footnote, 3 tables, and 1 figure.)

Developmental scientists frequently seek to understand effects of environmental contexts on development. Traditional analytic strategies assume similar environmental effects for all children, sometimes exploring possible moderating influences or exceptions (e.g., outliers) as a secondary step. These strategies are poorly matched to ecological models of human development that posit complex individual by environment interactions. An alternative conceptual framework is proposed that tests the hypothesis that the environment has differential (nonuniform) effects on children. A demonstration of the utility of this framework is provided by examining the effects of family resources on children's academic outcomes in a multisite study (N = 6,305). Three distinctive groups of children were identified, including 1 group particularly resilient to influence of low levels of family resources. Predictors of group differences including parenting and child demographics are tested, the replicability of the results are examined, and findings are contrasted with those obtained with traditional regression interaction effects. This approach is proposed as a partial solution to advance theories of the environment, social ecological systems research, and behavioral genetics to create well-tailored environments for children. (Contains 2 figures, 5 tables and 4 footnotes.)

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcohol-use data. They guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention. (Contains 11 figures, 6 tables, and 14 footnotes.)

A sense of mastery is an important component of psychological health and wellbeing across the life-span; however, relatively little is known about the development of mastery during childhood and adolescence. Utilizing prospective, longitudinal data from 444 adolescent sibling pairs and their parents, our conceptual model proposes that family socioeconomic status (SES) in the form of parental education promotes effective family problem-solving, which, in turn, fosters adolescent mastery. Results show: (1) a significant increase in mastery for younger and older siblings, (2) parental education promoted effective problem-solving between parents and adolescents and between siblings but not between the parents themselves, and (3) all forms of effective family problem-solving predicted greater adolescent mastery. Parental education had a direct effect on adolescent mastery as well as the hypothesized indirect effect through problem-solving effectiveness, suggesting both a social structural and social process influence on the development of mastery during adolescence. (Contains 2 figures, 2 tables and 1 note.)

Presented are the results of a study using latent profile analysis to describe the self-reported practices of 138 evaluators. Four classes emerged and were labeled (a) indistinct pattern of practice, (b) method-focused, (c) user-focused, and (d) robust pattern of practice. Evaluators in the "indistinct pattern of practice" class had mean item responses closest to zero relative to the other three classes, suggesting relatively weak associations with the practices described by the study instrument. The "method-focused" class had strong and distinct preferences for using particular methods. The "user-focused" class placed high importance on the role of the evaluator as facilitator, and was concerned with attaining a high level of stakeholder/participant involvement. The "robust pattern of practice" class evaluators showed a high degree of rigor in their reported patterns of practice, specifically as they are prescribed by evaluation theorists. The four profiles were distinguished by years of evaluation experience, degree attainment, and internal/external evaluator status. (Contains 4 tables and 3 notes.)

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing an improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through the implementation of moderated non-linear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex model building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided. [ABSTRACT FROM AUTHOR]