Loneliness Trajectories in U.S. Military Veterans: A 3-Year Longitudinal Study of Risk and Protective Factors.

OBJECTIVES: Loneliness is a significant public health concern associated with adverse mental and physical health outcomes in older adults. This study examined the nature and correlates of predominant loneliness trajectories in a nationally representative sample of older U.S. military veterans. METHODS: Participants included 2,441 veterans (mean age = 63, 8% female, 80% white) from the National Health and Resilience in Veterans Study, a 3-year longitudinal cohort study. Growth mixture modeling (GMM) was used to identify distinct trajectory classes of loneliness based on self-reported ratings. Multinomial logistic three-step regression analyses examined potential psychosocial risk and protective factors associated with loneliness trajectories. RESULTS: GMM revealed three distinct loneliness trajectories: Low-decreasing loneliness (61.2%), moderate-increasing loneliness (31.6%), and high-increasing loneliness (7.2%). Being married/partnered and perceiving greater purpose in life emerged as protective factors against elevated levels of loneliness. Worse cognitive functioning was a risk factor for the moderate-increasing loneliness trajectory, while greater psychological distress and more adverse childhood experiences were risk factors for the high-increasing loneliness trajectory. DISCUSSION: Nearly 40% of older U.S. veterans exhibited trajectories characterized by moderate to high levels of loneliness, with both groups showing increases over time. Targeted interventions that promote social connectedness, enhance purpose in life, and address mental health concerns and early life adversities may help mitigate the negative health consequences associated with chronic loneliness in this vulnerable population.

Changes in Skeletal Muscle Mass in the First 3 Months Following Gastrointestinal Cancer Surgery: A Prospective Study.

BACKGROUND: Surgical resection is the primary treatment for gastrointestinal (GI) cancers, but postoperative skeletal muscle loss (SML) is common and linked to poor prognosis. This study aims to identify patterns of muscle change, examine its association with quality of life (QoL), and explore predictors of SML in the first 3 months. PATIENTS AND METHODS: A prospective cohort study was conducted on patients newly diagnosed with GI cancer and undergoing surgery in China between September 2021 and May 2022. Skeletal muscle mass (SMM) and QoL were assessed at admission, 7 days, 1 month, and 3 months post-surgery. Demographic, clinical data, and biomarkers were collected. Missing data were imputed using multiple imputation. Data were analyzed using growth mixture modelling, bivariate analyses, and logistic regression. RESULTS: A total of 483 patients completed baseline assessment. Of the 242 patients with complete muscle assessments, 92% experienced SML. Three distinct patterns of muscle change were identified: 57% had normal preoperative SMM with mild postoperative SML, 16% had low preoperative SMM with moderate SML, and 27% had normal preoperative mass but severe postoperative SML. Moderate/severe SML was associated with more postoperative complications, poorer health, and higher symptom burden. Independent predictors included advanced age, preoperative sarcopenia, advanced cancer stage, and low prognostic nutrition index (PNI ≤ 45). The results did not change when using imputed values. CONCLUSIONS: Although SML is prevalent, patterns of muscle change are heterogeneous among patients. Advanced age, preoperative sarcopenia, advanced cancer stage, and cancer-related inflammation are predictors for moderate/severe SML, highlighting the need for early detection and management.

Developmental trajectories of loneliness in Chinese children: Environmental and personality predictors.

BACKGROUND: This study aimed to identify the developmental trajectories of loneliness in Chinese children and examine the predictive roles of domain-specific environmental factors (i.e., family dysfunction and satisfaction of relatedness needs at school), personality factors (i.e., neuroticism and extraversion), and their interactions in these developmental trajectories. METHODS: A total of 702 Chinese children (Mage = 8.95, SD = 0.76; 54.1 % boys) participated in assessments at six time points over three years at six-month intervals. Growth mixture modeling (GMM) was used to estimate trajectory classes for loneliness, followed by multivariate logistic regression analyses exploring associations between these classes and predictors. RESULTS: GMM analyses identified three distinct trajectories of loneliness: "low-stable" (81.5 %), "moderate-increasing" (9.4 %), and "high-decreasing" (9.1 %). Multivariate logistic regression analyses revealed that family dysfunction and neuroticism served as risk factors for adverse loneliness trajectories, while satisfaction of relatedness needs at school and extraversion acted as protective factors. Furthermore, the interaction between family dysfunction and extraversion indicated that extraversion did not mitigate the adverse effects of high family dysfunction on children's loneliness, emphasizing the vital need to support positive family functioning among all children. LIMITATIONS: This study did not incorporate biological variables (e.g., genetics), which are crucial in the evolutionary theory of loneliness. CONCLUSIONS: The identification of three distinct trajectory groups of children's loneliness, along with key environmental and personality predictors, suggests that interventions should be tailored to each group's unique characteristics.

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors.

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

Distinct trajectories of neuropsychiatric symptoms in the 12 months following traumatic brain injury (TBI): a TRACK-TBI study.

BACKGROUND: Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses. METHODS: Participants were level 1 trauma center patients with TBI (n = 1943), orthopedic trauma controls (n = 257), and non-injured friend controls (n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury. RESULTS: Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups. CONCLUSIONS: Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Trajectories of depressive symptoms and subjective well-being before and after the onset of the COVID-19 pandemic: Two six-year longitudinal studies.

Previous research investigated the trajectories of mental health and well-being during and after the onset of the COVID-19 pandemic. However, less is known about the trajectories of mental health and well-being before, during, and two years after the onset of the pandemic. The aim of the current study was to investigate the trajectory of depression symptoms and subjective well-being (i.e., life satisfaction and positive and negative affect) trajectories over six time points (2017-2022), three before the pandemic and three after the onset of the pandemic. To increase the robustness of our overall conclusions and avoid reliance on data from only one country, we used data from two nationwide representative longitudinal surveys conducted in Germany (GESIS Panel study; N = 5184) and Switzerland (Swiss Household Panel study; N = 17,074). Using covariance pattern mixture models, the results revealed that a four-class model best fit the data. The Stable/resilient trajectory was the most common across outcomes (74.2%-90.1% of participants). Three additional trajectories of Chronic/Low, Upright U-shaped, and Inverted U-shaped emerged in the analysis of negative affect and depression symptoms, while distinct trajectory classes of Worsening, Improving/Stable, and Upright U-shaped also emerged for analyses of positive affect and life satisfaction shaped. In conclusion, there was no evidence of a long-term impact of the pandemic for the vast majority of participants (about 90%). For the remaining participants, the COVID-19 pandemic (along with its exceptional circumstances) was a turning point or a catalyst that reversed, accelerated, or flattened a pre-pandemic trend. These changes in trends were not only negative (e.g., greater depression symptoms), but also positive (e.g., less depression symptoms).

On the detection of population heterogeneity in causation between two variables: Finite mixture modeling of data collected from twin pairs.

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual's data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model's potential to detect and model heterogeneity due to varying causal directions.

Multimodal distribution and its impact on the accurate assessment of spermatozoa morphological data: Lessons from machine learning.

Objective assessment of sperm morphology is an essential component for assessing ejaculate quality. Due to economic limitations, investigators often divert to conducting observational studies instead of experimental ones, which provide the strongest statistical power, yielding more heterogeneous data regardless of the number of data sources (barns/farms). Using such data inevitably leads to higher variances of estimates, which negatively impacts the statistical power of a study. In this article, we describe a statistical methodology called finite mixture modeling (FMM), which, based on the supplied data and assumed number of sub-classes, classifies the data into two or more homogeneous types of distributions and determines their fractional size relative to the entire cohort. The goal is to use statistical methods that will confound the variance of the sample. A figure from a previous publication was used to generate simulated data (n=1559) on the cytoplasmic droplet rate. We identified that a bi-modal distribution with two latent classes best described the simulated data. Post-hoc estimation showed that about 80 % of observations belonged to latent class 1, with 20 % in latent class 2. The FMM methodology identified a cutoff point of 8.7 %. Finally, when estimating the standard error for the total cohort, the FMM methodology yielded a 40 % reduction in the standard error compared to standard methodologies. In conclusion, here we show that FMM successfully confounded the variance of the data and, as such, yielded lower estimates of the variance than standard methodologies, increasing the statistical power of the cohort.

Behind the heterogeneity in the long-term course of first-episode psychosis: Different psychotic symptom trajectories are associated with different patterns of cannabis and stimulant use.

BACKGROUND: Data-driven classification of long-term psychotic symptom trajectories and identification of associated risk factors could assist treatment planning and improve long-term outcomes in psychosis. However, few studies have used this approach, and knowledge about underlying mechanisms is limited. Here, we identify long-term psychotic symptom trajectories and investigate the role of illness-concurrent cannabis and stimulant use. METHODS: 192 participants with first-episode psychosis were followed up after 10 years. Psychotic symptom trajectories were estimated using growth mixture modeling and tested for associations with baseline characteristics and cannabis and stimulant use during the follow-up (FU) period. RESULTS: Four trajectories emerged: (1) Stable Psychotic Remission (54.2 %), (2) Delayed Psychotic Remission (15.6 %), (3) Psychotic Relapse (7.8 %), (4) Persistent Psychotic Symptoms (22.4 %). At baseline, all unfavorable trajectories (2-4) were characterized by more schizophrenia diagnoses, higher symptom severity, and longer duration of untreated psychosis. Compared to the Stable Psychotic Remission trajectory, unstable trajectories (2,3) showed distinct associations with cannabis/stimulant use during the FU-period, with dose-dependent effects for cannabis but not stimulants (Delayed Psychotic Remission: higher rates of frequent cannabis and stimulant use during the first 5 FU-years; Psychotic Relapse: higher rates of sporadic stimulant use throughout the entire FU-period). The Persistent Psychosis trajectory was less clearly linked to substance use during the FU-period. CONCLUSIONS: The risk for an adverse long-term course could be mitigated by treatment of substance use, where particular attention should be devoted to preventing the use of stimulants while the use reduction of cannabis may already yield positive effects.

Who's most at risk? A person-centered approach to understanding the long-term relationship between early social media use and later depression across adolescence.

INTRODUCTION: Person-centered analyses examined the relationship between social media use and depression over an 8-year period. The purpose was to examine the varying ways early social media use was associated with the development of depressive symptoms with a hypothesis that social media would not have a uniform association with depressive symptoms across adolescents. METHODS: Participants included 488 adolescents (52% female), living in the United States, who were surveyed once a year for 8 years (beginning in 2010 when the average age for participants was 13.33 years old). RESULTS: Longitudinal mixture regression was used to identify classes of adolescents representing unique ways their early social media use was related to the development of depressive symptoms over an 8-year period. Five classes were found representing unique ways social media use was related to depression. Findings suggest social media use does not impact all adolescents in the same way. Social media use was related to increased depression for adolescents with greater parental hostility, peer bullying, anxiety, reactivity to stressors, and lower parental media monitoring. In other instances, social media use was related to less depression or was unrelated to depression. CONCLUSIONS: By identifying which adolescents may be most at risk from social media use, health providers, schools, and caregivers can tailor interventions to fit the needs of each adolescent.

Trajectories of Anhedonia Symptoms after Traumatic Injury.

Anhedonia describes the inability or difficulty of experiencing or seeking pleasure. Previous research has demonstrated a relationship between posttraumatic stress disorder (PTSD) or experiencing trauma and anhedonia symptoms; however, little to no work has been done to understand the evolution of anhedonia symptoms after trauma. We aimed to identify anhedonia trajectories following traumatic injury. One hundred ninety-five participants were recruited from the emergency department of a Level-1 Trauma Center after experiencing a traumatic injury. To measure anhedonia symptoms, participants completed the Snaith-Hamilton Pleasure Scale (SHAPS) at 2-weeks, 3-months, and 6-months post-injury. Using latent class mixture modeling, we ran a trajectory analysis with three timepoints of SHAPS scores and compared mental and physical health outcomes across trajectories. Most of the sample fell in the resilient trajectory (85%), while the remainder were in a remitting trajectory (7%) where symptoms decreased over time, and a delayed (6%) trajectory where symptoms did not emerge until 3-months after injury. In the resilient trajectory, there was consistently low levels of PTSD, pain, depression, and anxiety relative to the other trajectories. In the delayed trajectory, depression and PTSD were chronically elevated and pain levels were consistent but mild. In the remitting trajectory, PTSD and depression symptoms decreased over time. Identified anhedonia trajectories mirrored trajectories commonly reported for PTSD symptoms after injury. Evaluating anhedonia trajectories and how they relate to mental health outcomes may inform targeted interventions for traumatic injury patients.

Influence of early childhood parental hostility and socioeconomic stress on children's internalizing symptom trajectories from childhood to adolescence.

Introduction: Children and adolescents with elevated internalizing symptoms are at increased risk for depression, anxiety, and other psychopathology later in life. The present study examined the predictive links between two bioecological factors in early childhood-parental hostility and socioeconomic stress-and children's internalizing symptom class outcomes, while considering the effects of child sex assigned at birth on internalizing symptom development from childhood to adolescence. Materials and Methods: The study used a sample of 1,534 children to test the predictive effects of socioeconomic stress at ages 18 and 27 months; hostile parenting measured at child ages 4-5; and sex assigned at birth on children's internalizing symptom latent class outcomes at child ages 7-9, 10-12, 13-15, and 16-19. Analyses also tested the mediating effect of parenting on the relationship between socioeconomic stress and children's symptom classes. Other covariates included parent depressive symptoms at child ages 4-5 and child race and ethnicity. Results: Analyses identified three distinct heterogenous internalizing symptom classes characterized by relative symptom levels and progression: low (35%); moderate and increasing (41%); and higher and increasing (24%). As anticipated, higher levels of parental hostility in early childhood predicted membership in the higher and increasing symptom class, compared with the low symptom class (odds ratio (OR) = .61, 95% confidence interval (CI) [.48,.77]). Higher levels of early childhood socioeconomic stress were also associated with the likelihood of belonging to the higher-increasing symptom class compared to the low and moderate-increasing classes (OR = .46, 95% CI [.35,.60] and OR = .56, 95% CI [.44,.72], respectively). The total (c = .61) and direct (c' = .57) effects of socioeconomic stress on children's symptom class membership in the mediation analysis were significant (p <.001). Discussion: Study findings suggest that intervening on modifiable bioecological stressors-including parenting behaviors and socioeconomic stressors-may provide important protective influences on children's internalizing symptom trajectories.

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.

Analysis of trajectory changes and predictive factors of sense of coherence in patients after colorectal cancer surgery.

OBJECTIVE: To investigate the trajectories and potential categories of changes in the sense of coherence (SOC) in patients after colorectal cancer surgery and to analyze predictive factors. METHODS: From January to July 2023, 175 patients with colorectal cancer treated at a tertiary Grade A oncology hospital in Jiangsu Province were selected as the study subjects. Prior to surgery, SOC-13 scale, Patient-Generated Subjective Global Assessment (PG-SGA), Brief Illness Perception Questionnaire (BIPQ), and Social Support Rating Scale (SSRS) were used to survey the patients. SOC levels were measured multiple times at 1 week, 1 month, and 3 months post-surgery. Growth Mixture Modeling (GMM) was applied to fit the trajectory changes of SOC in patients after colorectal cancer surgery. Multinomial logistic regression was used to analyze the predictive factors of SOC trajectory changes. RESULTS: The SOC scores of patients at points T1-T4 were (65.27 ± 9.20), (63.65 ± 10.41), (63.85 ± 11.84), and (61.56 ± 12.65), respectively. Multinomial logistic regression results indicated that gender, employment status, disease stage, household monthly income, intestinal stoma, nutritional status, illness perception, and social support were predictors of SOC trajectory changes (P < 0.05). CONCLUSION: There is heterogeneity in the trajectory changes of SOC in patients after colorectal cancer surgery. Healthcare professionals should implement early precision interventions based on the patterns of changes and predictive factors in each trajectory category.

From pregnancy to the postpartum: Unraveling the complexities of symptom profiles among trauma-exposed women.

BACKGROUND: Many women experience new onset or worsening of existing posttraumatic stress disorder (PTSD) symptoms during pregnancy and the early postpartum period. However, perinatal PTSD symptom profiles and their predictors are not well understood. METHODS: Participants (N = 614 community adults) completed self-report measures across three methodologically similar longitudinal studies. Mixture modeling was used to identify latent subgroups of trauma-exposed women with distinct patterns of symptoms at pregnancy, 1-month, and 3-month postpartum. RESULTS: Mixture modeling demonstrated two classes of women with relatively homogenous profiles (i.e., low vs. high symptoms) during pregnancy (n = 237). At 1-month postpartum (n = 391), results suggested a five-class solution: low symptoms, PTSD only, depression with primary appetite loss, depression, and comorbid PTSD and depression. At 3-months postpartum (n = 488), three classes were identified: low symptoms, elevated symptoms, and primary PTSD. Greater degree of exposure to interpersonal trauma and reproductive trauma, younger age, and minoritized racial/ethnic identity were associated with increased risk for elevated symptoms across the perinatal period. LIMITATIONS: Only a subset of potential predictors of PTSD symptoms were examined. Replication with a larger and more racially and ethnically diverse sample of pregnant women is needed. CONCLUSIONS: Results highlight limitations of current perinatal mental health screening practices, which could overlook women with elevations in symptoms (e.g., intrusions) that are not routinely assessed relative to others (e.g., depressed mood), and identify important risk factors for perinatal PTSD symptoms to inform screening and referral.

A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions.

Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism and do not allow to take the uncertainty in classification into account. We present a response-time-based model-based mixture modeling approach that overcomes this limitation. The approach (a) facilitates disentangling item omissions stemming from disengagement from those going back to solution behavior, (b) considers the uncertainty in omission classification, (c) allows for omission mechanisms to vary on the item-by-examinee level, (d) supports investigating person and item characteristics associated with different types of omission behavior, and (e) gives researchers flexibility in deciding on how to handle different types of omissions. The approach exhibits good parameter recovery under realistic research conditions. We illustrate the approach on data from the Programme for the International Assessment of Adult Competencies 2012 and compare it against previous classification approaches for item omissions.

Prospective trajectories of depression predict mortality in cancer patients.

An ever-growing body of empirical evidence has demonstrated the relationship between depression and cancer. The objective of this study was to examine whether depression trajectories predict mortality risk above and beyond demographics and other general health-related factors. Participants (n = 2,345) were a part of the Health and Retirement Study. The sample consisted of patients who were assessed once before their cancer diagnosis and thrice after. Depressive symptoms and general health-related factors were based on self-reports. Mortality risk was determined based on whether the patient was alive or not at respective time points. Latent Growth Mixture Modeling was performed to map trajectories of depression, assess differences in trajectories based on demographics and general health-related factors, and predict mortality risk. Four trajectories of depression symptoms emerged: resilient (69.7%), emerging (13.5%), recovery (9.5%), and chronic (7.2%). Overall, females, fewer years of education, higher functional impairment at baseline, and high mortality risk characterized the emerging, recovery, and chronic trajectories. In comparison to the resilient trajectory, mortality risk was highest for the emerging trajectory and accounted for more than half of the deaths recorded for the participants in emerging trajectory. Mortality risk was also significantly elevated, although to a lesser degree, for the recovery and chronic trajectories. The data highlights clinically relevant information about the depression-cancer association that can have useful implications towards cancer treatment, recovery, and public health.

BMI Growth Profiles Among Black Children from Immigrant and US-Born Families.

A large body of research has documented racial/ethnic disparities in childhood obesity in the United States (US) but less work has sought to understand differences within racial groups. Longitudinal studies are needed to describe BMI trajectories across development, particularly for Black children from immigrant families who have been underrepresented in childhood obesity research. The current study utilizes BMI data collected longitudinally from ages 5 to 8 years and growth mixture modeling to (1) identify and visualize growth patterns among Black children from primarily Caribbean immigrant families, and (2) to compare these patterns to growth trajectories among Black children from US-born families. First, we identified four classes or trajectories of growth for Black children from immigrant families. The largest trajectory (70% of the sample) maintained non-overweight throughout the study period. A second trajectory developed overweight by age 8 (25%). Two small trajectory groups demonstrated high rates of moderate and severe obesity-i.e., specifically, a trajectory of accelerated weight gain ending in moderate/severe obesity (3%), and a trajectory of early severe obesity with BMI decreasing slightly with age (2%). We identified a very similar four class/trajectory model among Black children from US-born families, and compared the model to the one for children from immigrant families using multi-group growth mixture modeling. We found that the patterns of growth did not differ significantly between the populations, with two notable exceptions. Among Black children from immigrant families, ∼ 5% were classified into the two heavier BMI trajectories, compared to ∼ 11% of children from US-born families. Additionally, among children with an accelerated weight gain trajectory, children from immigrant families had lower BMIs on average at each time point than children from US-born families. These findings describe the multiple trajectories of weight gain among Black children from immigrant families and demonstrate that although these trajectories are largely similar to those of Black children from US-born families, the differences provide some evidence for lower obesity risk among Black children from immigrant families compared to Black children from US-born families. As this study is the first to describe BMI trajectories for Black children from immigrant families across early and middle childhood, future work is needed to replicate these results and to explore differences in heavier weight trajectories between children from immigrant and US-born families.

PTSD, depression, and treatment outcomes: A latent profile analysis among active duty personnel in a residential PTSD program.

Depression frequently co-occurs with posttraumatic stress disorder (PTSD), including among active duty service members. However, symptom heterogeneity of this comorbidity is complex and its association with treatment outcomes is poorly understood, particularly among active duty service members in residential treatment. This study used latent profile analysis (LPA) to identify symptom-based subgroups of PTSD and depression among 282 male service members in a 10-week, residential PTSD treatment program with evidence-based PTSD psychotherapies and adjunctive interventions. The PTSD Checklist-Military Version and Patient Health Questionnaire-8 were completed by service members at pre- and posttreatment and weekly during treatment. Multilevel models compared subgroups on PTSD and depression symptom change across treatment. LPA indicated four subgroups provided optimal fit: Depressive (high depression severity, low PTSD avoidance; n = 33, 11.7%), Avoidant (high PTSD avoidance, moderate depression severity; n = 89, 31.6%), Moderate (moderate PTSD and depression severity; n = 27, 9.6%), and Distressed (high PTSD and depression severity; n = 133, 47.2%). Treatment response differed across classes for both PTSD and depression outcomes (time × LPA class interaction ps < 0.001). In PTSD models, post-hoc comparisons indicated the Moderate class was associated with less PTSD symptom improvement relative to the other classes (ps < 0.006). In depression models, symptom reduction was greatest for the Distressed and Depressive subgroups relative to the other two classes (ps < 0.009). Study results provide an initial model for two prevalent, impairing disorders among service members and show how these symptom-based subgroups may differentially respond to residential PTSD treatment.

Trajectories of depressive and anxiety symptoms and associated risk factors during the COVID-19 pandemic in Germany: A longitudinal cohort study.

BACKGROUND: Most COVID-19-related mental health research focused on average levels of mental health parameters in the general population. However, considering heterogeneous groups and their long-term responses could deepen our understanding of mental health during community crises. This four-wave study aimed to (1) identify subgroups with different trajectories of depressive and anxiety symptoms in the German general population, and (2) investigate associated risk factors. METHODS: We analyzed self-report data from N = 1257 German adults participating in a European cohort study, assessed in summer 2020 (T1), and at 6 (T2), 12 (T3), and 30 months (T4). Depressive and anxiety symptoms were measured using the PHQ-4. Sociodemographic, health-related, and pandemic-related variables were assessed at baseline. We applied growth mixture modeling to identify subgroups of symptom trajectories and conducted multinomial logistic regression to examine factors associated with class membership. RESULTS: We identified six symptom trajectories: Low-stable (n = 971, 77.2 %), Continuous deterioration (n = 30, 2.4 %), Transient deterioration (n = 75, 6.0 %), Continuous improvement (n = 97, 7.7 %), Transient improvement (n = 38, 3.0 %) and Chronicity (n = 46, 3.7 %). Age, education, work status, mental health diagnoses, self-reported health, and pandemic-related news consumption were significantly associated with subgroup membership. LIMITATIONS: The generalizability of the study is constrained by an unrepresentative sampling method, a notable dropout rate, and limited consideration of risk factors. CONCLUSION: Most people experienced low symptoms or improvement during the pandemic, while others experienced chronic or transient symptoms. Specific risk factors were associated with these trajectories, revealing nuanced mental health dynamics.