Risk profiles among Black communities in an HIV epicenter: a latent class approach

IntroductionEngaging at‐risk men in HIV prevention programs and services is a current priority, yet there are few effective ways to identify which men are at highest risk or how to best reach them. In this study we generated multi‐factor profiles of HIV acquisition/transmission risk for men in Durban, South Africa, to help inform targeted programming and service delivery.MethodsData come from surveys with 947 men ages 20 to 40 conducted in two informal settlements from May to September 2017. Using latent class analysis (LCA), which detects a small set of underlying groups based on multiple dimensions, we identified classes based on nine HIV risk factors and socio‐demographic characteristics. We then compared HIV service use between the classes.ResultsWe identified four latent classes, with good model fit statistics. The older high‐risk class (20% of the sample; mean age 36) were more likely married/cohabiting and employed, with multiple sexual partners, substantial age‐disparity with partners (eight years younger on‐average), transactional relationships (including more resource‐intensive forms like paying for partner’s rent), and hazardous drinking. The younger high‐risk class (24%; mean age 27) were likely unmarried and employed, with the highest probability of multiple partners in the last year (including 42% with 5+ partners), transactional relationships (less resource‐intensive, e.g., clothes/transportation), hazardous drinking, and inequitable gender views. The younger moderate‐risk class (36%; mean age 23) were most likely unmarried, unemployed technical college/university students/graduates. They had a relatively high probability of multiple partners and transactional relationships (less resource‐intensive), and moderate hazardous drinking. Finally, the older low‐risk class (20%; mean age 29) were more likely married/cohabiting, employed, and highly gender‐equitable, with few partners and limited transactional relationships. Circumcision (status) was higher among the younger moderate‐risk class than either high‐risk class (p < 0.001). HIV testing and treatment literacy score were suboptimal and did not differ across classes.ConclusionsDistinct HIV risk profiles among men were identified. Interventions should focus on reaching the highest‐risk profiles who, despite their elevated risk, were less or no more likely than the lower‐risk to use HIV services. By enabling a more synergistic understanding of subgroups, LCA has potential to enable more strategic, data‐driven programming and evaluation.

Background Adverse childhood experiences (ACEs) have long been known to be related to poorer health across the life course. Previous studies typically relied on cumulative risk scores or individual adversities measured through retrospective self-reports. However, these approaches have important limitations. Cumulative risk scores assume equal weighting of adversities and the single adversity approach ignores the high probability that adversities co-occur. In contrast, latent class analysis (LCA) offers an alternative approach to operationalise ACEs that respects the clustering of adversities and may identify specific patterns of ACEs important for health outcomes. Furthermore, prospective and retrospective reports of ACEs show poor agreement. Therefore, it is important to compare findings based on prospective and retrospective measures in the same individuals. Despite an increasing number of studies applying LCA to ACEs data, no studies have yet simultaneously investigated LCA to cumulative risk and single adversity approaches in their relationships with adult inflammation. Identifying the specific ACEs or combinations of ACEs which are strongly related to inflammation is important for investigating the mechanisms involved and the planning of effective interventions. Methods Using data on 8810 members of the 1958 British birth cohort we investigated 12 ACEs – physical, psychological and sexual abuse, physical and emotional neglect, parental mental health problems, witnessing abuse, parental conflict, parental divorce, parental offending, parental substance misuse and parental death. LCA was applied to explore the clustering of prospectively and retrospectively reported ACEs separately. Associations between latent classes, cumulative risk scores and individual adversities with three inflammatory markers (C-Reactive Protein, fibrinogen and von Willebrand Factor) were tested using linear regression. Results There was co-occurrence between adversities, and particularly for retrospectively reported adversities. Three latent classes were identified in the prospective data – ‘Low ACEs’ (95.7%), ‘Household dysfunction’ (2.8%) and ‘Parental loss’ (1.5%) which were related to increased inflammation in mid-life, as were high ACE scores and individual measures of offending, death, divorce, physical neglect and family conflict. Four latent classes were identified in the retrospective data – ‘Low ACEs’, ‘Parental mental health and substance misuse’, ‘Maltreatment and conflict’ and ‘Polyadversity.’ The latter two (5.2%) were related to raised inflammation in mid-life, as was a retrospective ACE score of 4+ (8.3%) and individual measures of family conflict, psychological and physical abuse, emotional neglect and witnessing abuse. Conclusions Specific ACEs or ACE combinations might be important for chronic inflammation. LCA is an alternative approach to operationalising ACEs data but further research is needed.

BackgroundSexually transmitted and bloodborne infection (STBBI) risk is multifaceted and can involve a complex interplay between sexual behaviours, substance abuse and mental health conditions. In Winnipeg, Manitoba Canada we conducted...

IntroductionFever is a common manifestation of acute illness among children, and it is essential to measure body temperature accurately in pediatric clinical practice. Various methods are in use, but no gold standard exists for body temperature measurement among this population. Latent class analysis (LCA) is increasingly used to assess diagnostic accuracy in the absence of a gold standard. LCA is a method that identifies unobserved groups in populations, allowing diagnostic evaluation even without a reference standard. This study aimed to assess the diagnostic accuracy of the axillary, forehead, and tympanic thermometers in children (one to five years) using LCA. MethodsA cross-sectional study was done to determine the diagnostic accuracy of axillary, forehead, and tympanic thermometers in diagnosing fever among children with LCA as a reference. The digital axillary thermometer and the infrared dual-mode (forehead and tympanic) thermometer were used for the measurements. The study was conducted among 728 in a tertiary care center in Kerala, India. Children were recruited from the pediatric medical wards after 24 hours of admission. ResultsThe study sample consisted of 728 children aged one to five years. LCA had identified two latent classes with a probability of 8.55% for fever and 91.45% for no fever. The sensitivity and specificity of the methods were: axillary method 96.97% and 97.67%, forehead thermometer 98.99% and 100%, and tympanic thermometer 99.7% and 67.44%. The probability of misclassification was minimal (0.10%), and a strong association between the test results and the latent class was observed.ConclusionsThe forehead thermometer demonstrated the highest diagnostic accuracy, followed closely by the axillary thermometer. The tympanic thermometer, though highly sensitive, exhibited a relatively high false-positive rate.

The Latent Class Structure of Exercise and Eating Behavior

To empirically determine the socioeconomic differences in risk profiles of susceptibility and ever use of tobacco among adolescents in India and to investigate the association between the risk profiles and the psychosocial factors for tobacco use. Students in 16 private (higher socioeconomic status [SES]; n = 4,489) and 16 government (lower SES; n = 7,153) schools in two large cities in India were surveyed about their tobacco use and related psychosocial factors in 2004. Latent class analysis was used to identify homogenous, mutually exclusive typologies existing within the data. Overall, 3 and 4 latent classes of susceptibility and ever use of tobacco best described students in higher- and lower- SES schools, respectively. Profiles with various combinations of susceptibility and ever use of tobacco were differentially related to psychosocial factors, with lower- SES students being more vulnerable to increased levels of tobacco use than higher- SES students. Acknowledging the multiple dimensions of tobacco use behaviors and identifying constellations of risk behaviors will enable more accurate understanding of etiological processes and will provide information for refining and targeting preventive interventions. Additionally, identifying the socioeconomic differences in susceptibility and ever use risk profiles and their psychosocial correlates will enable policy makers to address these inequities through improved allocation of resources.

ABSTRACTBackground: Bi/multiracial youth face higher risk of engaging in substance use than most monoracial youth. Objectives: This study contrasts the prevalence of substance use among bi/multiracial youth with that of youth from other racial/ethnic groups, and identifies distinct profiles of bi/multiracial youth by examining their substance use risk. Methods: Using data from the National Survey on Drug Use and Health (collected between 2002 and 2014), we analyze data for 9,339 bi/multiracial youth ages 12–17 living in the United States. Analyses use multinomial regression and latent class analysis. Results: With few exceptions, bi/multiracial youth in general report higher levels of tobacco, alcohol, marijuana, and other illicit drug use compared to other youth of color. Bi/multiracial youth also report higher levels of marijuana use compared to non-Hispanic white adolescents. However, latent class modeling also revealed that a majority (54%) of bi/multiracial youth experience high levels of psychosocial protection (i.e., strong antidrug views and elevated parental engagement) and low levels of psychosocial risk (i.e., low peer substance use, school-related problems, and social-environmental risk), and report very low levels of substance use. Substance use was found to be particularly elevated among a minority of bi/multiracial youth (28%) reporting elevated psychosocial risk and low levels of protection. Bi/multiracial youth characterized by both elevated psychosocial risk and elevated psychosocial protection (22%) reported significantly elevated substance use as well. Conclusions: While bi/multiracial youth in general exhibit elevated levels of substance use, substantial heterogeneity exists among this rapidly-growing demographic.

BackgroundAdverse childhood experiences (ACEs) have long been known to be related to poorer health across the life course. Previous studies typically relied on cumulative risk scores or individual adversities measured through retrospective self-reports. However, these approaches have important limitations. Cumulative risk scores assume equal weighting of adversities and the single adversity approach ignores the high probability that adversities co-occur. In contrast, latent class analysis (LCA) offers an alternative approach to operationalise ACEs that respects the clustering of adversities and may identify specific patterns of ACEs important for health outcomes. Furthermore, prospective and retrospective reports of ACEs show poor agreement. Therefore, it is important to compare findings based on prospective and retrospective measures in the same individuals. Despite an increasing number of studies applying LCA to ACEs data, no studies have yet simultaneously investigated LCA to cumulative risk and single adversity approaches in their relationships with adult inflammation. Identifying the specific ACEs or combinations of ACEs which are strongly related to inflammation is important for investigating the mechanisms involved and the planning of effective interventions. MethodsUsing data on 8810 members of the 1958 British birth cohort we investigated 12 ACEs – physical, psychological and sexual abuse, physical and emotional neglect, parental mental health problems, witnessing abuse, parental conflict, parental divorce, parental offending, parental substance misuse and parental death. LCA was applied to explore the clustering of prospectively and retrospectively reported ACEs separately. Associations between latent classes, cumulative risk scores and individual adversities with three inflammatory markers (C-Reactive Protein, fibrinogen and von Willebrand Factor) were tested using linear regression. ResultsThere was co-occurrence between adversities, and particularly for retrospectively reported adversities. Three latent classes were identified in the prospective data – ‘Low ACEs’ (95.7%), ‘Household dysfunction’ (2.8%) and ‘Parental loss’ (1.5%) which were related to increased inflammation in mid-life, as were high ACE scores and individual measures of offending, death, divorce, physical neglect and family conflict. Four latent classes were identified in the retrospective data – ‘Low ACEs’, ‘Parental mental health and substance misuse’, ‘Maltreatment and conflict’ and ‘Polyadversity.’ The latter two (5.2%) were related to raised inflammation in mid-life, as was a retrospective ACE score of 4+ (8.3%) and individual measures of family conflict, psychological and physical abuse, emotional neglect and witnessing abuse. ConclusionsSpecific ACEs or ACE combinations might be important for chronic inflammation. LCA is an alternative approach to operationalising ACEs data but further research is needed.

Background Adverse childhood experiences (ACEs) have been related to poorer health across the life course. Previous studies typically relied on cumulative risk scores or individual adversities measured through retrospective self-reports. However these approaches have important limitations. Cumulative risk scores assume equal weighting of adversities and the single adversity approach ignores the high probability that adversities co-occur. In contrast, latent class analysis (LCA) offers an alternative approach to operationalise ACEs that respects the clustering of adversities and may identify specific patterns of ACEs important for health outcomes. Furthermore, prospective and retrospective reports of ACEs show poor agreement. Therefore, it is important to compare findings based on prospective and retrospective measures in the same individuals. The aim of this study was to compare LCA, single adversity and cumulative risk approaches to operationalising ACEs with inflammation in mid-life, comparing prospectively and retrospectively-reported ACEs data. Methods Using data on 8,810 members of the 1958 British birth cohort we investigated 12 ACEs – physical, psychological and sexual abuse, physical and emotional neglect, parental mental health problems, witnessing abuse, parental conflict, parental divorce, parental offending, parental substance misuse and parental death. LCA was applied to explore the clustering of prospectively and retrospectively reported ACEs separately. Associations between latent classes, cumulative risk scores and individual adversities with three inflammatory markers (C-Reactive Protein, fibrinogen and von Willebrand Factor) were tested using linear regression. Results There was co-occurrence between adversities, and particularly for retrospectively reported adversities. Three latent classes were identified in the prospective data – ‘Low ACEs’, ‘Household dysfunction’ (2.8%) and ‘Parental loss’ (1.5%) which were related to increased inflammation in mid-life, as were high cumulative risk scores and individual measures of offending, death, divorce, physical neglect and family conflict. Four latent classes were identified in the retrospective data – ‘Low ACEs’, ‘Parental mental health and substance misuse’, ‘Maltreatment and conflict’ and ‘Polyadversity.’ The latter two (5.2%) were related to raised inflammation in mid-life, as was a retrospective ACE score of 4+ (8.3%) and individual measures of family conflict, psychological and physical abuse, emotional neglect and witnessing abuse. Discussion Specific ACEs or ACE combinations might be important for chronic inflammation. LCA is an alternative approach to operationalising ACEs data but further research is needed. Identifying the specific ACEs or combinations of ACEs which are most strongly related to inflammation is important for investigating the mechanisms involved and the planning of effective interventions.

Using Latent Profile Analysis and Related Approaches in Adolescent Health Research

Risk profiles among adolescent nonmedical opioid users in the United States

Background Predicting substance use in adolescence is a difficult yet important task in developing effective prevention. We aim to extend previous findings on the linear associations between familiarity with (knowledge of) substances in childhood and subsequent substance use in adolescence through a latent class analysis (LCA) to create risk profiles based on substance familiarity. Method Using the ABCD Study® sample, we conducted an LCA using 18 binary substance familiarity variables (n = 11,694 substance-naïve youth). Complementary analyses investigated the relationship between LCA groups and (1) longitudinal use, (2) use initiation, and (3) early use. Results The optimal LCA resulted in a four-class solution: Naïve, Common, Uncommon, and Rare, with each group increasing in both the number and rarity of known substances. Analysis 1 revealed an increased risk in use over time among both the Uncommon and Rare groups (ORs = 2.08 and 5.55, respectively, p’s < 0.001) compared to the Common group. Analysis 2 observed a decreased risk for initiation between the Naïve and Common groups (OR = 0.61, p = 0.009); however, the Uncommon and Rare groups were at an increased risk compared to the Common group (ORs = 2.08 and 3.42, respectively, p’s < 0.001). Analysis 3 found an increased risk of early use between the Common and Uncommon groups (OR = 1.92, p < 0.001) with a similar trend between the Common and Rare groups (OR = 1.90, p = 0.06). Conclusion These results highlight distinct risk profiles for adolescent substance use based on substance familiarity in middle childhood. Current work could be applied as an early screening tool for clinicians to identify those at risk for adolescent substance use.

Bullying is a social and health problem that requires appropriate interventions based on valid and fair evaluations of bullying experiences. The validity of interpretations of bullying victimization scores can be compromised by measurement artifacts or biases that may arise during the assessment process. Boys' and girls' bullying experiences could lead to differences in their response processes when they answer the bullying scale items and compromise validity if such differences come from measurement artifacts. The study is intended to illustrate how to obtain validity evidence of response processes for the Students' Experience of Being Bullied Scale from the Programme for International Student Assessment (PISA) 2018 Student Questionnaire and test measurement invariance across gender by latent class analysis (LCA). The sample was taken from the PISA 2018 study and consisted of 11,599 Spanish high school students (50.3% female, 49.7% male). Response patterns were examined through LCA. Four profiles were found: (a) Not Bullied, (b) Bullied (All Types), (c) Relational Bullying, and (d) Potential Friendly Teasing. Measurement invariance across gender was analyzed by a multigroup LCA. LCA results do not guarantee equivalence of measurements. Class prevalence per group and a multinomial logistic regression were calculated to further examine gender differences across latent classes. Boys were more likely to belong to the Bullied (All Types) class and the Potential Friendly Teasing class, whereas girls were more likely to belong to the Relational Bullying class. These findings illustrate the possibilities LCA can offer to provide validity evidence of response processes and suggest that different bullying experiences of girls and boys could compromise the fairness and validity of comparative interpretations of test scores, which is an important issue to address to develop bias-free classroom interventions.

A major reason for the slow progress in identifying susceptibility genes for complex diseases may be that the clinical diagnoses used as phenotypes are genetically heterogeneous. This has led researchers to collect various phenotypes related to the diagnosis, such as detailed symptoms, in the hope that these measurements define more homogeneous disease sub-types, influenced by a smaller number of genes that will thus be more easily detectable. Latent class analysis can be used to define disease sub-types from multivariate symptoms under the assumption that the subjects are independent, an assumption that does not hold between members of the same family. We have recently developed a latent class model allowing dependence between the latent disease class status of relatives within nuclear families. In this paper, we propose approaches to use the resulting latent class probabilities in linkage analysis. We present results from a simulation study showing that the latent class approach can provide a substantial gain in power to detect disease genes over the standard heterogeneity approach of Smith and identity-by-descent sharing methods applied to the disease diagnosis. Taking into account familial dependence in the latent class model generally provides greater power than assuming independence. In an analysis of autism symptoms in families from the Autism Genetics Research Exchange, linkage signals obtained with latent class-derived phenotypes were stronger than those obtained using the original autism spectrum disorder diagnosis.

Autistic youth experience high rates of adverse childhood experiences (ACEs), yet little is known about how different ACEs are associated with mental and physical health outcomes. The aims of this study were to identify latent classes of ACEs in autistic youth and examine how ACEs, measured via cumulative risk or latent class approaches, were associated with mental and physical health outcomes in autistic youth. We conducted a secondary analysis of the 2021-2022 National Survey of Children's Health, a U.S. cross-sectional dataset. The sample included 1,332 autistic youth aged 12-17. A latent class analysis (LCA) of 12 ACE indicators was conducted. Logistic regressions examined associations between ACEs and mental health (anxiety or depression diagnosis) and physical health (poor/fair vs. good/excellent health), controlling for youth characteristics. For each outcome, two regressions were conducted: one using cumulative ACE count (0-12) and one using latent class membership. A 3-class LCA model best fit the data: (1) Low ACEs, (2) Bullying and Discrimination, and (3) Household Disruption and Community Violence. For each outcome, cumulative ACE count and latent classes predicted similar variability. Compared to the Low ACEs class, youth in Class 2 and Class 3 were > 120% more likely to have a diagnosis of anxiety or depression; youth in Class 2 were > 75% more likely to have poor physical health. Findings highlight distinct patterns of ACEs among autistic youth and their links to health outcomes. Tailoring supports to specific types of adversities experienced by youth may foster resilience and promote well-being.