Latent Class Analysis Research Articles

Validity of Mehrviru® and Sacace® in molecular detection of HPV: a latent class analysis.

Valid diagnostic tests for human papillomavirus (HPV) detection are crucial to identify individuals at high risk of cervical cancer. We assessed and compared the validity of Mehrviru HPV genotyping and Sacace (HPV Genotypes 14 Real-TM Quant) for molecular detection of 14 high-risk human papillomaviruses. We used three HPV test results to identify HPV-positive individuals (14 high-risk genotypes) in a specialist gynecology clinic. The HPV test results were collected using Mehrviru®, Sacace®, and a third kit from the clinical diagnostic laboratory. We used Latent class analysis to determine the actual status of HPV infection in study participants. The sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, Youden, and area under the ROC curve indices of each diagnostic kit and their 95% confidence intervals were calculated. The agreement between the Mehrviru and Sacace kits was determined using the Kappa statistic. We examined 117 women at high risk of HPV infection. The mean age (SD) was 37.2 (9.1). According to LCA, 28.6% of participants had an HPV infection. The sensitivity and specificity (95% CI) of the Mehrviru were 90.8% (73.7-97.2%) and 90.9% (82.1-95.6%), and corresponding figures for Sacace were 92.0% (72.3-98.1%) and 97.4% (90.2-99.3%). The kappa index between the Mehrviru and Sacace kits was 69.7% (55.6-83.9%). The area under the ROC curve for Mehrviru and Sacace test were 91.4% (85.7-97.1%) and 94.4% (89.3-99.5%). There is an excellent agreement between Mehrviru and Sacace test results, and the diagnostic accuracy indices were similar.

EXPRESS: Beyond the Pair: Media Archetypes and Complex Channel Synergies in Advertising

Prior research on advertising media mixes has mostly focused on single channels (e.g., television), pairwise cross-elasticities, or budget optimization within single campaigns. This is starkly detached from advertising practice where (i) there is an increasingly large number of media channels available to marketers, (ii) media plans employ complex combinations of channels, and (iii) marketers manage complementarities among many (i.e., more than pairs) channels. This research empirically learns complex channel complementaries using Latent Class analysis. Latent classes have three useful properties: (i) they account for non-random selection of channels into campaigns, (ii) they capture pairwise and higher-order interactions between channels, and (iii) they allow for meaningful interpretation. We empirically describe the most common media channel archetypes and estimate their relationship to the effectiveness of a set advertising campaigns on a set of common brand-related performance metrics. We use a dataset of 1,083 advertising campaigns from around the world run between 2008 and 2019. We find that there is not a systematically “best” media mix that correlates to dominant performance across all metrics, but clear patterns emerge given specific metrics. We find that traditional channels (TV, outdoor) are commonly paired with digital channels (Facebook, YouTube) in high-performing campaigns. We also find that current marketing practice appears far from optimal, and simple strategies have the potential to increase brand mindset metric lifts by 50% or more.

A latent class analysis of knowledge and attitudes toward antibiotic use in Vietnam: the challenge of reducing antibiotic resistance

A latent class analysis of knowledge and attitudes toward antibiotic use in Vietnam: the challenge of reducing antibiotic resistance

Latent class analysis of post-acute sequelae of SARS-CoV-2 infection.

Symptoms post-SARS-CoV-2 infection may persist for months and cause significant impairment and impact to quality of life. Acute symptoms of SARS-CoV-2 infection are well studied, yet data on clusters of symptoms over time, or post-acute sequelae of SARS-CoV-2 infection (PASC), are limited. We aim to characterize PASC phenotypes by identifying symptom clusters over a six-month period following infection in individuals vaccinated (boosted and not) and those unvaccinated. Subjects with ≥1 self-reported symptom and positive RT-PCR for SARS-CoV-2 at CVS Health US test sites were recruited between January and April 2022. Patient-reported outcomes symptoms, health-related quality of life (HRQoL), work productivity and activity impairment (WPAI) were captured at 1 month, 3 months, and 6 months post-acute infection. Phenotypes of PASC were determined based on subject matter knowledge and balanced consideration of statistical criteria (lower AIC, lower BIC, and adequate entropy) and interpretability. Generalized estimation equation approach was used to investigate relationship between QoL, WPAI and number of symptoms and identified phenotypes, and relationship between phenotypes and vaccination status as well. LCA identified three phenotypes that are primarily differentiated by number of symptoms. These three phenotypes remained consistent across time periods. Subjects with more symptoms were associated with lower HRQoL, and worse WPAI scores. Vaccinated individuals were more likely to be in the low symptom burden latent classes at all time points compared to unvaccinated individuals.

Heterogeneity in the level of dementia literacy among community doctors in China: A latent profile analysis.

This study aimed to explore the heterogeneity of community doctors based on their knowledge of dementia and the potential factors associated with their dementia literacy. A total of 1288 community doctors completed the Alzheimer disease knowledge scale (ADKS) in a cross-sectional study conducted between December 2021 and January 2022. We used latent class analysis (LCA) to explore potential clusters based on responses to the ADKS. Multivariate multinomial logistic regression analysis was performed to evaluate the associations between potential risk factors and the knowledge of community doctors. Community doctors were divided into four clusters according to their knowledge structure (Akaike information criterion (AIC) = 35672.83, Bayesian information criterion (BIC) = 36307.63, adjusted BIC (aBIC) = 35916.91, entropy = 0.814): the severity-focused subgroup (n = 269), the physical issues-focused group (n = 370), the knowledge uncertainty group (n = 191), and the general-focused group (n = 458). Age, education level, type of practice, and professional title were associated with the knowledge structure of Alzheimer's disease (AD). In addition, the perception that patients seek care in community health centres for physical reasons and community doctors' failure to manage patients with recently identified cognitive impairment were associated with the structures of the ADKS among community doctors (P < 0.05). There is heterogeneity in the level of AD knowledge among community doctors and their demographic characteristics, perceptions, and practices. Further efforts are needed to optimise the knowledge structure of dementia among community health care professionals.

Identifying latent subgroups of primary head injury: an explorative latent class analysis on neuropathologically examined medico-legal autopsy cases.

Traumatic brain injury (TBI) is a significant global health concern and frequently encountered in medico-legal autopsies. Previous studies suggest that certain TBI subtypes are more likely to co-occur than others. Therefore, we aimed to explore the potential of latent class analysis (LCA) to identify and characterize primary head injury combinations in neuropathologically examined medico-legal autopsy cases. The dataset comprised 78 cases from the Forensic Medicine Unit of the Finnish Institute for Health and Welfare over the period of 2016-2022. Data on background and circumstantial characteristics as well as primary and secondary head and brain injuries were collected from police documents, medical records, general autopsy reports and neuropathology reports. Latent class solutions with two to five classes were explored to identify clustering of primary head injuries among the sample. The dataset comprised 69.2% males and the median age was 49 years. In LCA, the solutions appeared reasonable, and each class appeared to represent a distinct TBI profile. The two-class solution was found to fit the present dataset best. Class 1 was characterized by older age, presence of an underlying CNS disease, and less diverse primary head injuries; these were interpreted as suggestive of lower traumatic forces. Class 2 was characterized by male sex and assaults as a prominent injury circumstance; subarachnoid and intracerebral/ventricular haemorrhages and contusions were classified exclusively into this class. In conclusion, this study identified two distinct subgroups of primary head injuries. Understanding typical injury combinations related to distinct circumstances could assist not only forensic pathologists but also clinicians treating TBI patients. However, the present latent class solution should not be interpreted as "ground truth", but instead further research is needed.

Insights into relationship of environmental inequalities and multimorbidity: a population-based study.

Substantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators. This ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency. The overall prevalence of multimorbidity was 33.4% [32.6%-34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02-1.03; OR = 1.01, 95% CI 1.00-1.02 and OR = 1.03, 95% CI 1.02-1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA. There is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.

Military sexual trauma, childhood trauma, and combat trauma: Associations with longitudinal posttraumatic growth among U.S. Veterans.

Military sexual trauma (MST), childhood trauma, and combat trauma are prevalent among U.S. military personnel. Cumulative trauma exposure may hinder posttraumatic growth, a positive psychological change following traumatic events, while social support can facilitate this growth. Understanding the influence of these traumas and social support on longitudinal posttraumatic growth is crucial. We assessed 1,230 veterans at 6, 9, 12, 18, and 24 months postinitial survey. Latent class analysis identified trauma experience classes, and latent growth models examined posttraumatic growth trajectories, incorporating social support as a time-varying covariate. The latent class analysis revealed four classes: high trauma exposure, moderate childhood trauma-moderate combat trauma, high MST-moderate combat trauma, and combat trauma only. Veterans in the combat-only class reported significant posttraumatic growth. The moderate childhood trauma-moderate combat class exhibited consistently low growth. Veterans in the high MST-moderate combat class showed slightly higher initial growth but no significant change over time. The high trauma exposure class experienced a significant decline in growth. Social support significantly predicted posttraumatic growth, with varying impacts across trauma classes. Interventions could be vital for survivors of MST, childhood trauma, or compounded traumas to enhance posttraumatic growth among military veterans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sharing in caring: Family caregiving task sharing patterns for older adults in Singapore.

Research on family caregiving for older adults has largely focused on primary caregivers. We identify caregiving task sharing patterns among multiple caregivers, including family members and live-in hired workers. In addition, we investigate caregiver and care-recipient characteristics associated with these patterns. We interviewed 278 primary family caregivers of home-dwelling older adults in Singapore about who provides what assistance across three domains: activities of daily living, health and social services use, and socio-emotional and other needs. Latent class analysis was used to identify caregiving task sharing patterns. Three patterns were identified: a) 'Shared-Diverse' (39%) - multiple caregivers assisting in all three domains, b) 'Shared-Domestic' (32%) - multiple caregivers assisting with activities of daily living and socio-emotional and other needs, and c) 'Solo-Diverse' (29%) - a sole caregiver assisting in all three domains. 'Solo-Diverse' caregivers were less likely to be employed and had higher depressive symptoms relative to 'Shared-Diverse' primary family caregivers. The predominance of caregiving task sharing patterns involving multiple caregivers calls for expansion of research, policies, and programs beyond primary caregivers. Greater attention should be given to how families and live-in hired workers share caregiving tasks for older adults, and how this varies across cultural settings, especially in familial Asian societies. The findings further highlight the vulnerabilities of solo caregivers, whose employment capacity and mental health may be adversely affected by their caregiving duties. Policymakers should ensure that solo caregivers have access to support programs that address their financial and mental health needs.

Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.

Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.

Abstract 4135206: Patterns of Lifestyle Risk Behaviors for Cardiovascular Disease in Family Caregivers: A Latent Class Analysis

Introduction: Lifestyle risk behaviors for cardiovascular disease (CVD) often co-occur. However, little is known about their behavioral patterns among family caregivers, a population at high risk for CVD. Aims: To identify distinct latent classes of caregivers of lifestyle risk behaviors for CVD and to examine the individual characteristics associated with membership in the identified latent classes. Methods: This study included 643 unpaid family caregivers from the 2019 Health Information National Trends Survey 5 Cycle 3. The assessed lifestyle risk behaviors included cigarette use, non-abstinence from alcohol, low physical activity, long sedentary time, and low fruit and vegetable consumption. These behaviors were dichotomized based on established guidelines or cut-off points. Latent class analysis was performed to identify latent classes based on these lifestyle risk behaviors among caregivers. Subsequently, multinomial logistic regression was used to investigate socio-demographic, health-related, and caregiving characteristics associated with the latent class membership. Results: Three distinct classes were identified (mean age = 55±16 years; 59.3% female; 57.1% non-Hispanic white): “overall healthy, but non-abstainer” (Class 1, 17.1%; reference), “overall unhealthy” (Class 2, 64.1%), and “inactive, but healthy eaters” (Class 3, 18.8%). In unadjusted multinomial logistic regression models, caregivers reporting greater financial hardship (relative risk ratio [95% CI] = 2.5 [1.0-5.9]), higher psychological distress (3.7 [1.9-7.3]), poorer sleep quality (3.3 [1.6-6.8]), longer caregiving hours (2.1 [1.1-3.9]), and providing dementia care (2.3 [1.3-4.3]) were more likely to belong to Class 2, relative to Class 1. In the adjusted model including the significant characteristics above and controlling for age and gender, psychological distress (2.7 [1.2-5.9]) remained significant. No characteristics were significantly associated with membership in Class 3, relative to Class 1. Conclusions: Lifestyle risk behaviors for CVD among caregivers may cluster in distinct patterns that vary by individual characteristics. Caregivers experiencing psychological distress may be particularly at risk for engaging in lifestyle risk behaviors. Our novel findings underscore the importance of adaptive lifestyle modification interventions that target caregiver subgroups with different patterns of health behaviors to promote their cardiovascular health.

Multidimensional gender ideologies: How do they relate to work-family arrangements of mothers with dependent children in Poland and western Germany?

This study uses a multidimensional approach to identify gender ideologies and investigate their association with work-family arrangements among mothers of dependent children in Poland and western Germany. Based on data from the Generations and Gender Survey, we apply ordinal latent class analysis and identify five gender ideology classes: (1) traditional, (2) intensive parenting, (3) ambivalent, (4) moderately egalitarian, and (5) strongly egalitarian. In both countries, we find that the intensive parenting class resembles the traditional class in terms of lower maternal employment, a more gendered division of housework and childcare, and a smaller probability of using formal childcare, whereas the ambivalent respondents mostly practice work-care arrangements that are similar to the moderately egalitarian class. With respect to informal care, mothers who identify with strong egalitarianism significantly differ from the traditional class in western Germany, but they do not in Poland—thus highlighting the role of the cultural and institutional context in explaining the complex links between mothers’ gender ideologies and work-family arrangements.

Abstract 4120274: Symptom cluster profiles predict all-cause mortality among U.S. community-dwelling older adults with heart failure

Background: Individual symptoms are predictors of mortality in adults with heart failure (HF). However, symptom clusters may be more predictive of future risks than isolated symptoms, yet research on these symptom clusters in older adults with HF is limited. Aims: To explore the extent to which symptom cluster profiles predict all-cause mortality among community-dwelling older adults with HF, while adjusting for known risk factors. Methods: We conducted a secondary analysis of data of the 2008 to 2016 surveys of the U.S. Health and Retirement Study using latent class analysis to identify baseline cluster profiles, and survival analysis for time to death with Cox proportional hazard (Cox PH) models, and Kaplan Meier survival analyses. Results: We included 684 participants [mean age=74.9, (SD=10) years, 56.6% female, 16.2% Black/African American]. Three baseline symptom cluster profiles were identified: high-burden (pain, shortness of breath, fatigue, swelling, depressive symptoms, dizziness), low-burden, and cardiopulmonary-depressive (shortness of breath, pain, and dizziness). The estimated median time-to-death was 71 (95% CI=64, 79) months. Of the 364 participants in the cardiopulmonary-depressive profile, 240 (65.9%) died (median time= 65 months, 95% CI= 55, 73) compared to those in the low-burden profile, of which 49% died. Approximately 61% in the high burden profile died (median time=67 months, 95% CI= 51, 90). A significant difference in survival times between the 3 cluster profiles was found (Log-rank= 9.13, p = 0.01). In the adjusted Cox PH model, participants in the high symptom burden and respiratory-depressive distress profiles had adjusted HR of 1.48 (95% CI=1.15, 1.94) and 1.44 (95% CI=1.14, 1.80) compared to those in the low burden symptom cluster profile, after controlling for age, gender, smoking, and comorbidities. Conclusions: The identified symptom cluster profiles predict 8-year all-cause mortality in older adults with HF, after controlling for known risk factors. An evaluation of symptom clusters, rather than individual symptoms, may provide additive prognostic information.

A person-centered approach to understanding psychosocial stressor subgroups and cardiovascular disease: new perspectives from the Multi-Ethnic Study of Atherosclerosis (MESA) study.

This study examined the longitudinal associations of baseline psychosocial stress subgroups with cardiovascular disease (CVD) events and whether social support, neighborhood cohesion, and physical activity modified these associations in the Multi-Ethnic Study of Atherosclerosis (MESA). Data from 6,349 adults (aged: 62.2±10.2 years; 52.9% women) from the MESA study with no prior CVD in 2000-2002 were used in this analysis. Latent class analysis (LCA) was used to specify distinct psychosocial stress subgroups based on self-reported stressors stemming from respondents' neighborhood and social environment. Adjudicated CVD events (fatal and nonfatal events) were ascertained annually through the year 2019. Cox proportional hazards models were used to examine the associations between subgroup membership and CVD events. Five distinct psychosocial stress subgroups were identified via LCA and were labeled 'moderate neighborhood noise' (12.1%), 'excessive neighborhood noise' (6.4%), 'multiple high stressors' (6.3%), 'high discrimination' (21.4%), and 'optimal' (53.8%). By the year 2019, 1,121 participants had experienced a CVD event. Membership in the 'high discrimination' (HR: 1.29; 95%CI: 1.10, 1.51) subgroup was associated with higher risk of a CVD event when adjusted for sociodemographic characteristics and cardiovascular health metrics. Neither social support, neighborhood cohesion, nor physical activity modified this association (ps>0.05). Distinct subgroups of individuals with high self-reported psychological distress-particularly related to discrimination and chronic stress are associated with high incident cardiovascular events.

Classification of long-term disease patterns in inflammatory bowel disease and analysis of their associations with adverse health events.

With existing researches identifying an increased rate of long-term conditions (LTCs) among Inflammatory Bowel Disease (IBD) patients, yet there is a lack of exploration into the patterns of comorbidity and prognostic rates for IBD patients with multiple morbidities. The study included 8,305 participants who self-reported having IBD, comprising ulcerative colitis (UC) and Crohn's disease (CD). Latent class analysis (LCA) was utilized to create optimal categories of LTC combinations for UC and CD patients with additional long-term conditions. Using Cox proportional hazards models, we compared the all-cause mortality rates over a 16-year follow-up among UC and CD patients within different LTC categories, both without LTCs and with the addition of one LTC, the probability of major adverse cardiovascular events (MACE), and the rates of IBD-related surgeries. A total of 5,617 participants reported having two or more LTCs, with the LCA method identifying three prevalence categories among CD patients, and four prevalence categories among UC patients. The highest mortality rate among CD patients was found in category 3: (HR 1.789, 95% CI (1.439-2.224)), and the highest rates of MACE were also in category 3: (HR 11.432, 95% CI (9.332-14.005)), with hypertension being the distinguishing characteristic of this category, and the highest rates of IBD-related surgeries being associated with pain in category 1: (HR 1.217, 95% CI (0.983-1.506)). Among UC patients, the highest mortality rate was in category 3: (HR 2.221, 95% CI (1.837-2.684)), with the highest MACE rates found in category 3: (HR 6.422, 95% CI (5.659-7.288)), and the highest rates of IBD-related surgeries being associated with pain, also in category 3: (HR 1.218, 95% CI (1.041-1.425)). The rates of adverse health outcomes in IBD patients is closely associated with multimorbidity patterns, underscoring the need to fully consider multimorbidity patterns in the assessment, management, and treatment strategies for IBD.

Toward a Typology of the Client: A Latent Class Analysis Approach to the Consumption of Sex Work in the United States

ABSTRACT Previous research on male clients of sex workers has emphasized the diversity of purchasing behaviors; however, we know little about how to organize or categorize this diversity quantitatively. I employed latent class analysis with survey data from cisgender male clients of primarily cisgender female sex workers in the United States to organize client consumption patterns by the type of venue used to contact sex workers and the frequency of using each venue. I found that patterns of consumption emerged around the frequency and context in which sex work is negotiated, generating six distinct patterns of buying: generalists, internet-only buyers, brothel-only buyers, experimenters, routinized buyers, and opportunists. I used multinomial logistic regression to identify relationships between latent class membership, known covariates, and self-reported motivations for purchase. Classes had small but significant demographic differences around age, income, marital status, arrest history, and international sex work engagement. Motivational differences were observed across classes, including the importance of emotional connection, practicing sexual skills, and relaxation. Implications for future research are discussed.

Latent typology of nonsuicidal self‐injury and differences in mental health outcomes

AbstractNonsuicidal self‐injury (NSSI), defined as deliberate acts of self‐harm without suicidal intent, significantly impacts adults. NSSI involves various behaviors, varying in frequency and severity as well as intersecting with one another. To uncover unobserved subtypes of NSSI, this study utilized latent class analysis (LCA) to precisely identify a latent typology of NSSI among adults using a large national sample (N = 18,798; Mage = 21.73; SD = 4.93). Additionally, differences in mental health outcomes, including depression and anxiety, across the identified latent subtypes of NSSI were examined in the LCA model. The findings revealed eight distinct latent subtypes, demonstrating statistically significant differences in depression and anxiety, including multimethod—severe, multimethod—moderate, multimethod—mild, cutting—dominant multimethod, unimethod—pulling hair, unimethod—interfering with wound healing, unimethod—punching/banging, and unimethod—punching/banging an object. These findings underscore the distinct characteristics and mental health needs of each latent subgroup and suggest more tailored and targeted approaches for counseling practice.

Profiles of polysubstance use among people reporting past 12-month sleep-motivated nonmedical use of prescription tranquilizers/sedatives.

Nonmedical use of prescription tranquilizers/sedatives (NMUPTS) is associated with a 15-times increased likelihood of polysubstance use (PSU). This is correlated with an increased risk for use disorders and death, therefore, to inform prevention efforts, we identified PSU profiles among adults reporting sleep-related NMUPTS. National Survey on Drug Use and Health 2015-2019 participants with past 12-month sleep-related NMUPTS and use of an additional drug(s) (n = 2603) were studied. Latent class analyses identified unique latent profiles, and multivariable logistic regressions identified covariates associated with each profile. Alcohol (90.4%), tobacco (61.5%), and marijuana (55.5%) were most commonly reported, with an average of 3.6 additional substances. Two latent PSU profiles were identified: marijuana/alcohol/tobacco (MAT) (68.3%) and MAT + cocaine/hallucinogens/prescription drugs (MAT + CHPR) (31.7%). In comparison to those likely in the MAT profile, those likely in the MAT + CHPR profile had lower odds of being older than 18-25 years (26-34: adjusted odds ratio [AOR]: 0.5, 95% confidence interval [CI]: 0.3, 0.6; 35-49: AOR: 0.2, 95% CI: 0.1, 0.2; 50+: AOR: 0.1, 95% CI: 0.0, 0.1), female (AOR: 0.4, 95% CI: 0.3, 0.6), non-Hispanic Black (AOR: 0.5, 95% CI: 0.4, 0.8) or Hispanic in comparison to non-Hispanic White (AOR: 0.5, 95% CI: 0.4, 0.8), and to not have past 12-month insurance coverage (AOR: 1.7, 95% CI: 1.3, 2.3). Two PSU profiles were identified among people with NMUPTS, with noted variations in sociodemographic characteristics. In comparison to previous studies, sleep-related NMUPTS was associated with higher rates of stimulant use, suggesting stimulant use and sleep-related NMUPTS may be related.

The Impact of Childhood Maltreatment Timing on Adolescents’ Depression and Anxiety

This study examined the relationship between the timing of childhood maltreatment and depression and anxiety in adolescents. Using prospective data from 867 children and their caregivers from the Longitudinal Study of Child Abuse and Neglect (LONGSCAN), the effects of early childhood maltreatment (ages 0–5.99) and middle childhood maltreatment (ages 6–11.99) on depression and anxiety at age 16 were compared. Early childhood and middle childhood maltreatment were measured in three ways, including latent class analysis. Findings from multiple regression analysis revealed that only early childhood maltreatment significantly increased adolescents’ depression and anxiety, even after controlling for multiple covariates such as negative life events, social support, and caregiver depression. Strategies to improve early childhood maltreatment prevention and reduce adolescents’ depression/anxiety are also discussed.

Recidivism risk factors among Mississippi justice-involved youth: latent class analysis of the SAVRY

ABSTRACT The Structured Assessment of Violence Risk in Youth (SAVRY) instrument is a popular risk-need-responsivity tool for justice-involved youth. This study conducted a multi-group (based on sex) latent class analysis model of the SAVRY items to explore heterogeneity in risk/needs among youths involved in the juvenile court in Mississippi between 2017 and 2021. Results indicated a three-class solution best with the data, with Very Low, Low, and Bimodal classes. Generally, these groups were similar across sex, though the Bimodal subgroup was proportionally larger for females than males. Subsequent analyses indicated the Bimodal group was significantly younger and more involved in future one-year recidivism. These findings suggest subgroups of risk exist and should be examined further to assess future recidivism and other risks.