Four-class Solution Research Articles

Trauma exposure, contextual stressors, and PTSD symptoms: patterns in racially and ethnically diverse, low-income postpartum women.

Racial, ethnic, and socioeconomic disparities persist in posttraumatic stress disorder (PTSD), which are partly attributed to minoritized women being trauma-exposed, while also contending with harmful contextual stressors. However, few have used analytic strategies that capture the interplay of these experiences and their relation to PTSD. The current study used a person-centered statistical approach to examine heterogeneity in trauma and contextual stress exposure, and their associations with PTSD and underlying symptom dimensions, in a diverse sample of low-income postpartum women. Using a community-based sample of Black, Hispanic/Latina, and White postpartum women recruited from five U.S. regions (n = 1577), a latent class analysis generated profiles of past-year exposure to traumatic events and contextual stress at one month postpartum. Regression analyses then examined associations between class membership and PTSD symptom severity at six months postpartum as a function of race/ethnicity. A four-class solution best fit the data, yielding High Contextual Stress, Injury/Illness, Violence Exposure, and Low Trauma/Contextual Stress classes. Compared to the Low Trauma/Contextual Stress class, membership in any of the other classes was associated with greater symptom severity across nearly all PTSD symptom dimensions (all ps < 0.05). Additionally, constellations of exposures were differentially linked to total PTSD symptom severity, reexperiencing, and numbing PTSD symptoms across racial/ethnic groups (ps < 0.05). A person-centered approach to trauma and contextual stress exposure can capture heterogeneity of experiences in diverse, low-income women. Moreover, racially/ethnically patterned links between traumatic or stressful exposures and PTSD symptom dimensions have implications for screening and intervention in the perinatal period.

LONELY AND SOCIALLY DISCONNECTED OLDER ADULTS ARE AT HIGHER RISK FOR PREVALENT AND INCIDENT DEMENTIA

Abstract A 2020 report from the National Academies indicates that nearly one in four adults over age 65 are lonely. Loneliness has important health implications, including for cognitive aging. However, loneliness might not manifest uniformly in the population. Herein, we address a gap in the existing literature by examining the link between heterogeneities in the risk of loneliness and dementia outcomes. We used data from n = 8,805 adults aged 65+ from the Health and Retirement Study (average age 74.7; 56.3% women) to estimate latent classifications of loneliness (11 items from the UCLA loneliness index) and tested, using generalized linear regression models, whether different manifestations of loneliness were differentially associated with dementia (Langa-Weir criteria) prevalence and incidence (over 3-waves of data). A four-class solution best fits the data: “No/Low loneliness” (NL; 51.6%); “Socially disconnected” (SD; 18.0%); “Lonely but not socially disconnected” (LNSD; 12.6%), and “Lonely and socially disconnected” (LSD; 17.8%). Notably, these classifications and their prevalence rates were largely consistent across waves. Adjusting for sociodemographic covariates, compared to individuals in the NL group, the LNSD (OR = 1.68; 95%CI = 1.22;2.32) and LSD (OR = 1.71; 95%CI = 1.33;2.20) groups were more likely to meet criteria for prevalent dementia at baseline. The LSD group was also more likely to meet criteria for incident dementia at follow-ups (within 2 and 4 years from baseline). Loneliness is not uniformly distributed in the population and its subtypes confer differential risk for dementia outcomes. Focusing on the subtypes can potentially pinpoint targetable groups for interventions.

Latent Classes of Comorbid Substance Use and Internalizing and Externalizing Symptoms and Their Stability in U.S. Adults Over Time: Findings from the PATH Study Waves 1–3 (2013–2016)

Introduction The co-occurrence of mental disorder symptoms is common in people who use substances. It is unclear whether patterns of comorbidity in a population-based sample of adults are consistent with prior work. The study goal was to identify this comorbidity structure and evaluate its stability over time. Methods Using Waves 1, 2, and 3 of the Population Assessment of Tobacco and Health Study, this study applied latent class analysis to identify comorbid substance use, internalizing and externalizing mental disorder symptoms, and their stability over time. Results A four-class solution was identified for Wave 1 as: (1) low symptom (N = 23,571, 72.9%), (2) internalizing (N = 4,098, 12.7%), (3) externalizing (N = 2,691, 8.3%), and (4) comorbid (N = 1,960, 6.1%). Similar latent profiles emerged across the three waves specifically where the low symptom class was largest (65.5% to 72.9%), and the comorbid class was smallest (6.1% to 8.2%). However, the composition of the classes changed in Wave 3 with low comorbid (N = 5,400, 20.6%) and substance use (N = 1,524, 5.8%) classes emerging. Overall, when individuals transitioned from preceding to subsequent wave, they typically transitioned into the low symptom class. Conclusion The comorbidity structure of substance use behaviors, including cigarette and e-cigarette use, and mental disorder symptoms in a population-based sample of U.S. adults was characterized by four classes. Psychiatric comorbidity may vary in severity within a population and by specific sociodemographic factors. When comparing the latent classes over three years of data, the results suggest that the comorbidity structure may change as participants age.

COVID-19 vaccine hesitancy in Ethiopia: a latent class analysis

BackgroundDespite evidence demonstrating the effectiveness of the COVID-19 vaccine, vaccine hesitancy has emerged as a major challenge for vaccine uptake. The objective of this study was to classify latent typologies of vaccine hesitant adults in Ethiopia and identify predictors of the latent classes.MethodsWe employed a cross-sectional household survey among 1,112 individuals aged 18 and above who were partially vaccinated (one dose) or not vaccinated at the time of the survey. Data was collected in August 2022. We collected information on participant socio-demographics, COVID-19 knowledge, prevention practices, disease history, and vaccine hesitancy. Latent class analysis was used to classify individuals into categories of vaccine hesitancy. We conducted multinomial logistic regression to test the associations between latent typologies and different demographic and COVID-19 related characteristics of study participants.ResultsUsing latent class analysis we found a four-class solution for vaccine hesitancy typologies. The identified classes were strong vaccine acceptors (30%); vaccine acceptors with some concerns (7%); vaccine sceptics (13%); and vaccine rejectors (50%). In adjusted models with vaccine sceptics as the referent group, those with high COVID-19 vaccine knowledge were significantly more likely to belong to the strong vaccine acceptors class compared to those with low vaccine knowledge (adj. RRR: 17.36, 95% CI: 10.94–27.55). Better COVID-19 prevention practices were also significantly associated with belonging to the vaccine acceptors with some concerns class than the vaccine sceptics class (adj. RRR: 2.13, 95% CI: 1.09–4.16). Those who had one dose of the COVID-19 vaccine were significantly more likely to belong in the vaccine acceptors class than the vaccine sceptics class compared to those who had no dose (adj. RRR: 6.82, 95% CI: 3.06–15.21).ConclusionsHalf of the study participants were in the vaccine rejectors class. Individuals in the vaccine sceptics and rejector classes evidenced lower vaccine knowledge and worse COVD-19 prevention practices and were less likely to have been partially vaccinated. Future program interventions should focus on improving knowledge around the vaccine, decrease rumors and misconceptions, and target individuals who may be more amenable to changing their vaccination attitudes or behaviors like vaccine sceptics or acceptors with some concerns.

Using latent variable modelling to identify etiological heterogeneity in preterm delivery.

To identify a subgroup of mothers at high risk of preterm delivery, defined by empirical classes of multimorbidity and recurrence across three consecutive births. The data were extracted from the perinatal data collection (PDC) of all inpatient live births (n = 435 912) occurring in the Australian state of Queensland between January 2009 and December 2015. Within this data, a total of 7714 primiparous mothers delivered three consecutive singleton live births (total births = 23 142), and comprise the sample for all analyses. The LCA indicated a four-class solution fit the data best at each time point, including (i) a 'normative' or healthy class with little morbidity (including >80% of the sample at each birth); (ii) a preterm, high morbidity class (<2% of the sample); (ii) a delivery morbidity class (4-8% of the sample); and (iii) preterm, low morbidity class (5-6% of the sample). Each group exhibited unique and consistent associations with maternal and pregnancy-related factors across births. After accounting for these factors, the high morbidity class and preterm, low morbidity class strongly predicted these same classes across consecutive births, and from birth 1 to birth 3 (second-order transition). A small but highly morbid class of neonatal deliveries emerged, exhibiting strong continuity across consecutive births (odds ratios >10), independent of a range of maternal and pregnancy-related factors. This group of women, if subject to further investigation, could provide valuable insight into the aetiology of prematurity and associated morbidity, perhaps providing information to improve birth outcomes among all women.

Impact of Pre-Adolescent Substance Familiarity on Subsequent Use: Longitudinal Analysis of Risk by Latent Classes in the Adolescent Brain Cognitive Development Sample

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.

Certified Community Behavioral Health Clinic Services for Clients With Co-occurring Disorders: A Latent Class Approach.

Certified community behavioral health clinics (CCBHCs) are designed to provide comprehensive care for individuals with co-occurring mental and substance use disorders. The authors classified outpatient mental health treatment facilities on the basis of provision of services for clients with co-occurring disorders and assessed whether CCBHCs differed from other outpatient mental health facilities in services provided. The authors used latent class analysis to identify distinct services for clients with co-occurring disorders in 5,692 outpatient mental health facilities in the 2021 National Substance Use and Mental Health Services Survey. Nine indicators were included: treatment for clients with substance or alcohol use disorder co-occurring with serious mental illness or serious emotional disturbance, specialized programs or groups for such clients, medication-assisted treatment (MAT) for alcohol use disorder, MAT for opioid use disorder, detoxification, individual counseling, group counseling, case management, and 12-step groups. A multinomial logistic regression was used to estimate whether CCBHCs were associated with any identified classes after analyses controlled for facility characteristics. A four-class solution provided a model with the best fit, comprising comprehensive services (23.4%), case management services (17.7%), counseling and self-help services (58.6%), and professional services (4.3%). Regressing class membership on facility type and covariates, the authors found that compared with community mental health clinics (CMHCs), CCBHCs were more likely to belong to the comprehensive services class than to the case management services, counseling and self-help services, and professional services classes. CCBHCs were more likely than other outpatient programs to offer comprehensive care, and CCBHC status of a CMHC facilitated enhanced service provisions.

Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis

BackgroundOlder adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes.MethodsData are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership.ResultsA four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05–1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05–2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29–3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19–0.77).ConclusionsResults highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework

The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Questionnaire data collected at pre-treatment and 1-year follow up from 11,995 patients from the Swedish Quality Registry for Pain Rehabilitation were used. Indicators measuring pain characteristics, psychological aspects, and social functioning and general health status were used to form subgroups, and pain interference at follow-up was used for the selection and the performance evaluation of models. A nested cross-validation procedure was used for determining the number of classes (inner cross-validation) and the prediction accuracy of the selected model among unseen cases (outer cross-validation). A four-class solution was identified as the optimal model. Identified subgroups were separable on indicators, predictive of long-term outcomes, and related to background characteristics. Results are discussed in relation to previous clustering attempts of patients with diverse chronic pain conditions. Our analytical approach, as the first to combine mixture modeling with supervised, targeted learning, provides a promising framework that can be further extended and optimized for improving accurate prognosis in pain treatment and identifying clinically meaningful subgroups among chronic pain patients.

Unpacking the 'black box' of suicide: A latent class analysis predicting profiles of suicidal ideation in a longitudinal cohort of adolescent girls from India.

Indian women account for 37% of global suicide-related deaths. As suicide is a growing concern among adolescent girls, identifying the social determinants of suicide with this group targeted prevention. We selected social determinants that include intersectional identities and broader syndemics; we then used longitudinal data from a prospective cohort of adolescent girls from Northern India to classify them into unique profiles across multiple socioecological levels. Girls aged 10-19 (N = 11,864) completed self-report questionnaires measuring socio-demographic and trauma exposure variables. At three-year follow-up, they were asked to indicate current suicidal ideation (SI). We conducted latent class analysis (LCA) to classify profiles and then predicted risk of current SI at three-year follow-up. LCA supported a four-class solution: a 'privileged' class (Class 1; n = 1,470), a 'modal' class (Class 2; n = 7,449), an 'intergenerational violence' class (Class 3; n = 2,113), and a 'psychological distress' class (Class 4; n = 732). Classes significantly predicted odds ratios (OR) for SI at follow up; women in Class 4 were associated with the greatest likelihood of SI (OR 1.84, 95% CI 1.38, 2.47), suggesting that psychological distress factors confer greatest risk. Results of the distinct classes of risk and protective factors indicate targets for policy-level interventions. Disrupting cycles of psychological distress and substance use, increasing access to behavioral interventions, and intervening to mitigate intergenerational violence may be particularly impactful with this population.

Trusted Sources of Information and COVID-19 Vaccination Among Black Adults in Chicago.

Examine trust in sources of COVID-19 information and vaccination status. Cross-sectional. Chicago, Illinois. Convenience sample of 538 Black adults surveyed between September 2021 and March 2022. Trust in sources of COVID-19 information, COVID-19 vaccination. Using latent class analysis, we identified classes of trust in sources of COVID-19 information. We considered predictors of class membership using multinomial logistic regression and examined unadjusted and adjusted associations between trust class membership and COVID-19 vaccination while accounting for uncertainty in class assignment. Our analytic sample (n = 522) was predominantly aged 18-34 (52%) and female (71%). Results suggested a four-class solution: (1) low trust, (2) high trust in all sources, (3) high trust in doctor and government, and (4) high trust in doctor, faith leader, and family. Unadjusted odds of vaccination were greater in the high trust in all sources (OR 2.0, 95% CI 1.2-3.2), high trust in doctor and government (OR 2.7, 95% CI 1.4-5.3), and high trust in doctor, faith leader, and family classes (OR 2.1, 95% CI 1.2, 3.9) than the low trust class. However, these associations were not significant after adjustment for sociodemographic and health status factors. Although COVID-19 vaccination varied across trust classes, our adjusted findings do not suggest a direct association between trust and vaccination, reflecting complexities in the vaccine decision-making process.

Childhood adversity is associated with anxiety and depression in older adults: A cumulative risk and latent class analysis

BackgroundThe long-lasting influence of childhood adversity on mental health is well documented; however empirical research examining how this association extends into older adults is limited. This study operationalises adversity using cumulative risk and latent class analysis (LCA) models to assess how adversity exposure and typologies may predict anxiety and depression in older adults. MethodsData came from the Personality and Total Health (PATH) Through Life Project (N = 2551, age 60–66). Participants retrospectively reported their childhood experiences of domestic adversity on a 17-item scale. Mental health was measured using four validated questionnaires of depression and anxiety. ResultsLinear and generalised additive models (GAM) indicated a dose-response relationship, where a greater number of cumulative adversities were associated with poorer scores on all four mental health measures. LCA identified a four-class solution; with high adversity and high parental dysfunction being associated with poorer mental health outcomes while moderate parental dysfunction and low adversity groups scored at healthy levels. Women reported higher overall anxiety than men, but no notable interactions between ACEs and gender were observed. Patterns revealed by LCA were similar to patterns shown by the cumulative risk model. LimitationsThere is a large time gap from childhood to assessment, making our study susceptible to recall bias. Also, our findings were based on cross-sectional data, limiting causal inferences. ConclusionChildhood adversity had independent and additive contributions to depression and anxiety in older adulthood, and both cumulative risk and person-centred approaches captured this relationship.

Repurposing the K-10: The Factor Structure and Latent Profile of COVID-19 Pandemic-Anchored Psychological Distress Symptoms Among Filipino Teachers

The Kessler Psychological Distress Scale (K-10) is widely utilized to assess non-specific psychological distress. However, when applied to COVID-19 pandemic experiences, its psychometric properties have not been examined. This study investigated the factor structure, reliability, and latent profile of K-10 using data from 3032 Filipino teachers who reflected on their pandemic-related adversity prior to completing K-10. Confirmatory factor analyses were conducted to evaluate five extant models of the K-10’s structure, supporting the two-factor model (i.e., anxiety and depression) as best fitting the data. Both factors showed excellent internal reliability, and criterion-related validity was supported by correlations with anxiety and depression measures. Latent profile analysis revealed a four-class solution, identifying four distinct levels of distress severity. Overall, the contextualized K-10 demonstrated sound psychometric properties, which corroborates and extends its reliability and validity as a measure of pandemic-related distress. Further, the results offer insights into the dimensionality and distress profiles of the K-10 among teachers. Broadly, the findings highlight the importance of valid instruments in assessing contextualized psychological distress.

Adolescents' exposure to community violence and associations with after-school activities across two samples.

Exposure to community violence (ECV) continues to be a major public health problem among urban adolescents in the United States. We sought to identify subgroups of adolescents' ECV and examine how after-school activities are related to exposure subgroups across two samples. In Study 1 there were 1432 adolescents (Cohort 9 n = 717, Mage = 11, and Cohort 12 n = 715, Mage = 14; 52% boys) from the Project on Human Development in Chicago Neighborhoods (1994-2002). Study 2 had a more recent sample of 537 adolescents (Mage = 16 years; 54% girls) from the After-School Activity Study (ASAS; 2015-2017) in Chicago and Detroit. Exploratory latent class analyses yielded a three-class solution for Study 1: a"No ECV" class (44%); a "Low ECV" class (36%); and a "High Exposure" class (14%). In Study 2, a four-class solution was the best fit with a "No ECV" class (33%), a "Moderate Witness/Low Victim" class (36%), a "High Witness/Moderate Victim" class (19%), and a "High ECV" class (11%). Home-based activities appeared to be protective against high ECV for adolescents in Study 2. School-based activities were associated with higher ECV across both samples, but community-based activities were only associated with greater violence exposure in Study 1. Adolescents' unstructured socializing in both studies was associated with higher odds of ECV. Results indicate that subgroups of adolescents can be identified based on ECV and highlight the complexity of after-school activities as risk and protective factors in both past and more recent contexts.

The relationship between patterns of negative life experiences and clinical presentation in a psychiatric sample.

Attempts have been made to classify the patterns of polytraumatization using a person-centered approach. However, most studies have only focused on maltreatment and interpersonal trauma and have been unable to examine various clinical symptoms. This study aimed to explore patterns of negative life experiences, including maltreatment, lifetime trauma, and recent stressful life events, and compare diverse dimensions of the clinical manifestations among the subtypes in a clinical sample. We investigated childhood maltreatment, lifetime trauma, and recent stressful events using a self-report method in 1410 psychiatric patients; we classified the patterns of lifelong negative life experiences using latent profile analysis (LPA). We used the rates of psychiatric diagnosis, the Beck Depression Inventory,the Impact of Event Scale-Revised (IES-R-K),and the Multiphasic Minnesota Personality Inventory-2-Restructured Formto compare various symptom dimensions among the derived subtypes. LPA indicated a four-class solution: mild, recent stress, maltreatment, and multiple adversity group. The multiple adversity group experiencing both lifetime trauma and recent stressful life events, in addition to maltreatment, including sexual abuse, had a high rate of severe mental illness and more symptom dimensions of thought and behavior problems. However, the rates of depressive disorders and emotional/internalizing symptoms were not more than those in the other two groups (recent stress and maltreatment groups) experiencing moderate levels of lifetime trauma. There was no significant difference between the recent stress and maltreatment groups for most symptom dimensions. The findings indicate that distinct symptom profiles may be associated with the pattern of negative experiences, suggesting that negative experiences need multidimensional investigation in clinical settings.

Patterns of Adverse Childhood Experiences and Psychiatric Disorders Among Adolescents with ADHD: A Latent Class Analysis.

There is limited existing research on whether specific combinations of Adverse Childhood Experiences (ACEs) are associated with different psychiatric disorders among adolescents with ADHD. This study aimed to address this gap by identifying classes of ACEs and examining their association with behavioral problems, anxiety, and depression in adolescents with ADHD aged 11-17 (n = 1,806), using data from the 2018 National Survey of Children's Health (NSCH). A latent class analysis revealed a four-class solution: (1) low-risk ACEs (61.6%), (2) moderate-risk ACEs (25.2%), (3) high discrimination and neighborhood violence exposure (7.6%), and (4) high-risk ACEs (5.6%). The "high-risk ACEs" and the "high discrimination and neighborhood violence exposure" class showed a higher likelihood of behavioral problems and depression, and anxiety and depression respectively. These findings provide some insight into the ACE patterns that are more likely to be associated with mental health problems among adolescents with ADHD.

Latent trajectories of DSM-5-TR-based Prolonged Grief Disorder: findings from a data pooling project MARBLES

ABSTRACT Background: With the release of the text revision of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5-TR), criteria for Prolonged Grief Disorder (PGD) were included. This necessitates studying grief trajectories based on these criteria. Objective: This is the first study examining latent trajectories of DSM-5-TR-based PGD symptom levels and testing whether specific risk factors (e.g. cause of death) predicted PGD trajectories. Method: We evaluated latent DSM-5-TR PGD trajectories using pooled existing data collected at 6–12, 13–24, and 25–60 months post-loss in Danish and Dutch bereaved adults (N = 398). Latent Growth Mixture Modelling (LGMM) was employed to determine the trajectories. Multinomial logistic regression analyses were used to examine which risk factors predicted class membership. Results: The four-class LGMM solution with a quadratic term was best-fitting the data. This solution represented four trajectories: High stable PGD (6%), High PGD quick recovery (10%), High PGD slow recovery (35%), and Low PGD symptoms (49%). Participants with a higher educational level were more likely to be assigned to the Low PGD symptoms trajectory compared to High stable PGD and High PGD slow recovery trajectories. Unnatural causes of death increased the likelihood of being in the High stable PGD and High PGD slow recovery trajectories compared to the Low PGD symptoms trajectory. Conclusions: Consistent with prior research, the Low PGD symptoms trajectory was the most common. A significant minority experienced high and stable levels of PGD within five years after the loss. About one-third of participants experienced high acute grief levels that decreased slowly; how slow decreasing symptoms relate to an individual’s functioning requires further attention. This study demonstrates that a significant minority of bereaved people develop acute PGD symptomatology that does not diminish within five years post-loss, emphasizing the need for early screening for PGD to prevent long-lasting complaints.

Subtypes of Transitions into a Family Caregiving Role: A Latent Class Analysis.

This paper groups persons who have transitioned into family caregiving using a latent class analysis and examines class differences on measures of well-being. Latent classes were identified for a sample of 251 participants who became family caregivers while participating in a longitudinal national study, and linear regression analyses compared average well-being change scores across classes. Fit indices supported a four-class solution dispersed along two conceptual dimensions: caregiving intensity and caregiving stain. The largest class (35.5%) was characterized as low intensity, low strain. The smallest class (12.7%) was characterized as high intensity, high strain, and these caregivers had significantly worse well-being change scores compared to the other caregiving classes. Categorizing caregivers by differing levels of care intensity and caregiving strain helps identify caregivers who are at most risk for poor psychosocial outcomes, determines which caregivers might benefit from specific caregiver support programs, and informs investigators on possible refinements to interventions.

Associations of Lifestyle Patterns With Overweight and Depressive Symptoms Among United States Emerging Adults With Different Employment Statuses.

Objective: To identify lifestyle patterns in emerging adults and examine the association of lifestyle patterns with overweight and depression. Methods: Data was from the National Health and Nutrition Examination Survey between 2011 and 2018. A latent class analysis (LCA) was conducted with 2,268 US emerging adults based on sedentary behavior, moderate-to-vigorous physical activity, diet, sleep, alcohol drinking, and cigarette smoking. The associations of lifestyle groups with overweight and depression were examined by logistic regression and were further stratified by employment status. Results: The LCA results favored a four-class solution: "unhealthy but non-substance use" (59%), "healthy but sleepless and drinking" (12%), "unhealthy lifestyle" (15%), and "healthy but sedentary" group (14%). Compared to the "unhealthy lifestyle" group, participants in the "unhealthy but non-substance use" (coef. = -1.44, SE = 0.27; OR = 0.40, 95% CI [0.26, 0.61]), "healthy but sleepless and drinking" (coef. = -1.49, SE = 0.35; OR = 0.38, 95% CI [0.20, 0.72]), and "healthy but sedentary" (coef. = -1.97, SE = 0.34; OR = 0.29, 95% CI [0.14, 0.57]) groups had lower depression severity and reported fewer depressive symptoms. Moreover, lifestyle groups and health outcomes showed different relationships among employed and unemployed participants. Conclusion: This study found that the combinations of lifestyle behaviors had synergistic effects on mental health, and such effects differed by employment status.

Abstract 17819: Latent Class Analysis of Heart Health in a Marginalized Black Community

BACKGROUND Mounting evidence associates Black American’s experiences of chronic stress with younger age hypertension (HTN) onset, higher prevalence rates, and poorer treatment outcomes. Epigenetic research findings attribute these trends to DNA methylation, commonly referred to as stress related premature biological aging. Guided by the current American Heart Association (AHA) Science Statement on heart health, this health equity study aims to examine positive Black identity as a measure of positive psychological health in Black adults. METHODS Our community-engagement survey measured self-reports of: (1) environmental wellbeing, positive self-regard, spirituality, chronic stress, Black identity, anxiety, depression, anger, and five AHA essential cardiovascular health (CVH) factors: (2) physical activity, fruit, vegetables, smoking tobacco, and sleep. Multivariable linear regressions examined psychological health and CVH as predictors of systolic blood pressure. Latent class analysis (LCA), using ordinal indicators, assessed for latent sample profiles as statistical probabilities of similar responses and sample heterogeneity. RESULTS In a community sample (N=201) of Black adults (age M=46.7, SD=15.9), primarily female (N=141, 69.5%), increases in systolic blood pressure (Mean=134, SD=20.9) were associated with more chronic stress and less environment wellbeing; decreases were associated with less expressed anger. The LCA results show a four-class statistical solution with greater heterogeneity in spirituality, anger, anxiety, and depression. Among the four profiles, two theoretical latent classes for high and low systolic blood pressure were distinct. Low systolic blood pressure is associated with higher levels of environmental wellbeing, physical activity, and self-regard, both more and less spirituality, more positive Black identity, moderate anger, anxiety, and depression, and checking your BP. CONCLUSION In addition to emotion regulation, physical activity, and self-care, our findings support CVH promotion strategies in marginalized communities of Black Americans designed to reduce chronic stress, improve environmental wellbeing, and support positive Black identity.