Latent Class Analysis Analysis Research Articles

Symptom Network and Subgroup Analysis in Patients with Exacerbation of Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study.

This study aims to construct a contemporaneous symptom network of inpatients with Exacerbation of Chronic Obstructive Pulmonary Disease (ECOPD) based on the symptom cluster, identify core and bridge symptoms, and patient subgroups with different symptom clusters based on individual differences in the intensity of patient symptom experiences. This study used convenience sampling to collect demographic, symptom, auxiliary examination, and prognosis information of 208 inpatients with ECOPD from April 2022 to October 2023. The data underwent exploratory factor analysis (EFA), symptom network analysis, latent class analysis (LCA), Spearman correlation analysis, Wilcoxon signed-rank test, single-factor regression and multiple-factor stepwise regression. In hospitalized patients with ECOPD, symptom network analysis revealed that loss of appetite was the core symptom, while chest distress was the bridge symptom. Through LCA analysis, two symptom subgroups were identified: a high-symptom group (53.8%) and a low-symptom group (46.2%). This suggests that there is significant heterogeneity in symptom experience among ECOPD individuals. Patients in the high-symptom group had a higher probability of experiencing symptom clusters related to nutrition-sleep. The combination of symptom network analysis and LCA comprehensively captures the symptom/symptom cluster characteristics and accounts for the heterogeneity of ECOPD patients from both individual and group perspectives. This study identifies core symptoms, bridge symptoms, and symptom subgroups, offering valuable insights for precision symptom management in ECOPD.

Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

BackgroundUnderstanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. MethodsA comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. ResultsIndividuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. ConclusionsThese findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

Identification of the health education targeted susceptible population of tuberculosis in Ningxia, Northwest China

Knowledge, Attitude, and Practice (KAP) survey, as an effective measure tool, is of practical significance for identifying the susceptible population in high-incidence regions of tuberculosis (TB). We aim to identify the health education targeted susceptible population of TB and discuss the acting pathway of KAP in Ningxia. A multistage random sampling method was used to conduct a face-to-face questionnaire survey for residents. The latent class analysis (LCA) model was used to classify susceptible populations of TB, and the structural equation modeling (SEM) model was also employed to investigate the interaction mechanisms of KAP (mediation analysis). We further applied the ordered logistic regression model to explore the associated factors. A total of 973 residents were enrolled, 70.6% were male, aged from 16 to 89. The LCA analysis demonstrated that 3 categories of susceptible populations of TB ("overall good", "positive attitude" and "overall poor") have optimal goodness of fit (BIC = 7889.5, Entropy = 0.923). SEM model indicated that the attitude plays a significant mediation effect from knowledge to practice toward TB (an indirect effect of 0.038, and a direct effect of 0.138). The ordered logistic regression results found that age, sex, marital status, education level, occupation, family income, self-perceived health status, having a family member or friend with TB, and knowing the DOTS strategy were significantly associated with classifications of KAP level towards TB. Based on the LCA model, we accurately classified the susceptible population of TB into 3 groups with different degrees of KAP. We found that TB attitude plays a mediating role between knowledge and practice. Therefore, we should pay more attention and carry out targeted health education in the community to these populations with overall poor KAP towards TB, and develop effective strategies and measures to realize the End TB Plan.

A latent class analysis of depressive symptoms among rural Chinese adolescents and their association with psychological resilience

Numerous studies have demonstrated the heterogeneity of depressive symptoms, but few studies have focused on the heterogeneity of depressive symptoms among rural Chinese adolescents. In November to December 2022, multistage sampling was employed to administer questionnaires to 1,816 rural adolescents aged 11–19 years from six schools in Henan Province, China. Depressive symptoms were measured using the Chinese version of the Children’s Depression Inventory Scale. Latent class analysis (LCA) was utilized to identify subgroups of depressive symptoms. The investigation of subgroup characteristics and associated factors was conducted through χ2 tests, ANOVA, and multinomial logistic regression analyses. The findings revealed a 24.24 % detection rate of depressive symptoms among Chinese rural adolescents. LCA analysis of responses to the 27 items in the Depressive Symptoms Scale led to the classification of depressive symptoms into four subgroups based on severity: “no depressive symptoms group” (22.5 %), a “low depressive symptoms group” (35.7 %), a “transition group” (31.6 %), and a “high depressive symptoms group” (10.2 %). Gender, grade level, academic performance, academic stress, family environment, and level of psychological resilience are associated factors for subgroups of depressive symptoms among rural adolescents. There should be increased training of rural educators to enable early recognition of depressive characteristics and risk factors, facilitating targeted prevention and intervention strategies.

Exploring multimorbidity clusters in relation to healthcare use and its impact on self-rated health among older people in India.

The conventional definition of multimorbidity may not address the complex treatment needs resulting from interactions between multiple conditions, impacting self-rated health (SRH). In India, there is limited research on healthcare use and SRH considering diverse disease combinations in individuals with multimorbidity. This study aims to identify multimorbidity clusters related to healthcare use and determine if it improves the self-rated health of individuals in different clusters. This study extracted information from cross-sectional data of the first wave of the Longitudinal Ageing Study in India (LASI), conducted in 2017-18. The study participants were 31,373 people aged ≥ 60 years. A total of nineteen chronic diseases were incorporated to identify the multimorbidity clusters using latent class analysis (LCA) in the study. Multivariable logistic regression was used to examine the association between identified clusters and healthcare use. A propensity score matching (PSM) analysis was utilised to further examine the health benefit (i.e., SRH) of using healthcare in each identified cluster. LCA analysis identified five different multimorbidity clusters: relatively healthy' (68.72%), 'metabolic disorder (16.26%), 'hypertension-gastrointestinal-musculoskeletal' (9.02%), 'hypertension-gastrointestinal' (4.07%), 'complex multimorbidity' (1.92%). Older people belonging to the complex multimorbidity [aOR:7.03, 95% CI: 3.54-13.96] and hypertension-gastrointestinal-musculoskeletal [aOR:3.27, 95% CI: 2.74-3.91] clusters were more likely to use healthcare. Using the nearest neighbor matching method, results from PSM analysis demonstrated that healthcare use was significantly associated with a decline in SRH across all multimorbidity clusters. Findings from this study highlight the importance of understanding multimorbidity clusters and their implications for healthcare utilization and patient well-being. Our findings support the creation of clinical practice guidelines (CPGs) focusing on a patient-centric approach to optimize multimorbidity management in older people. Additionally, finding suggest the urgency of inclusion of counseling and therapies for addressing well-being when treating patients with multimorbidity.

Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis

Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC). To identify the latent phenotypes of patients with SIC. Retrospective cohort study. We obtained data of patients with SIC from the Medical Information Mart for Intensive Care IV database. SIC subphenotypes were identified by latent class analysis (LCA) and K-means clustering. Clinical and laboratory variables were obtained in patients who met the diagnostic criteria for SIC. The baseline characteristics of the patients and the association between the heterogeneity of anticoagulant therapy and clinical outcomes (28-day and in-hospital mortality) were compared between the subphenotypes. We identified 4,993 patients with SIC. The LCA and K-means clustering analysis robustly identified three subphenotypes of SIC. Class 1 patients (n = 1,808) had the lowest blood cell counts (leukocytes, erythrocytes, and platelets). Class 2 patients (n = 1,157) had severe coagulopathy with a high prothrombin time and international normalized ratio, multiple-organ dysfunction, high lactate, sequential organ failure assessment score, and mortality. Class 3 (n = 2,028) were older, had more comorbidities, a higher fibrinogen concentration, and lower plasma and platelet infusion rates. After variable adjustments, heparin therapy reduced the 28-day mortality (odds ratio [OR] 0.39, 0.30-0.49, p < 0.001) and in-hospital mortality (OR 0.42, 0.33-0.53, p < 0.001) only in class 2. Three SIC subphenotypes were defined using clinical findings and laboratory variables. The effects of heparin treatment differ between the subphenotypes. This finding will facilitate the identification of target patients with SIC who should receive anticoagulant therapy.

Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis.

Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC). This study aimed to identify the latent phenotypes of patients with SIC. Retrospective cohort study. We obtained data of patients with SIC from the Medical Information Mart for Intensive Care IV database. SIC subphenotypes were identified by latent class analysis (LCA) and K-means clustering. Clinical and laboratory variables were obtained in patients who met the diagnostic criteria for SIC. The baseline characteristics of the patients and the association between the heterogeneity of anticoagulant therapy and clinical outcomes (28-day and in-hospital mortality) were compared between the subphenotypes. We identified 4,993 patients with SIC. The LCA and K-means clustering analysis robustly identified three subphenotypes of SIC. Class 1 patients (n = 1,808) had the lowest blood cell counts (leukocytes, erythrocytes, and platelets). Class 2 patients (n = 1,157) had severe coagulopathy with a high prothrombin time and international normalized ratio, multiple-organ dysfunction, high lactate, sequential organ failure assessment score, and mortality. Class 3 (n = 2,028) were older, had more comorbidities, a higher fibrinogen concentration, and lower plasma and platelet infusion rates. After variable adjustments, heparin therapy reduced the 28-day mortality (odds ratio [OR] 0.39, 0.30-0.49, p < 0.001) and in-hospital mortality (OR 0.42, 0.33-0.53, p < 0.001) only in class 2. Three SIC subphenotypes were identified using clinical and laboratory variables. The effects of heparin treatment varied among subphenotypes. This finding will facilitate the identification of target patients with SIC who should undergo anticoagulant therapy.

The perceived work environment and well-being: A survey of emergency health care workers during the COVID-19 pandemic.

BackgroundDuring the COVID‐19 pandemic, health care provider well‐being was affected by various challenges in the work environment. The purpose of this study was to evaluate the relationship between the perceived work environment and mental well‐being of a sample of emergency physicians (EPs), emergency medicine (EM) nurses, and emergency medical services (EMS) providers during the pandemic.MethodsWe surveyed attending EPs, resident EPs, EM nurses, and EMS providers from 10 academic sites across the United States. We used latent class analysis (LCA) to estimate the effect of the perceived work environment on screening positive for depression/anxiety and burnout controlling for respondent characteristics. We tested possible predictors in the multivariate regression models and included the predictors that were significant in the final model.ResultsOur final sample included 701 emergency health care workers. Almost 23% of respondents screened positive for depression/anxiety and 39.7% for burnout. Nurses were significantly more likely to screen positive for depression/anxiety (adjusted odds ratio [aOR] 2.04, 95% confidence interval [CI] 1.11–3.86) and burnout (aOR 2.05, 95% CI 1.22–3.49) compared to attendings. The LCA analysis identified four subgroups of our respondents that differed in their responses to the work environment questions. These groups were identified as Work Environment Risk Group 1, an overall good work environment; Risk Group 2, inadequate resources; Risk Group 3, lack of perceived organizational support; and Risk Group 4, an overall poor work environment. Participants in the two groups who perceived their work conditions as most adverse were significantly more likely to screen positive for depression/anxiety (aOR 1.89, 95% CI 1.05–3.42; and aOR 2.04, 95% CI 1.14–3.66) compared to participants working in environments perceived as less adverse.ConclusionsWe found a strong association between a perceived adverse working environment and poor mental health, particularly when organizational support was deemed inadequate. Targeted strategies to promote better perceptions of the workplace are needed.

Relationships among adverse childhood experience patterns, psychological resilience, self-esteem and depressive symptoms in Chinese adolescents: A serial multiple mediation model

Adverse childhood experiences (ACEs) and adolescent depression are both prevalent social problems that can increase the risk of several negative health consequences throughout life. The original definition of ACEs misdirects the focus of intervention efforts for ACEs to only family dysfunction and parenting practices. We used a broader definition of ACEs and a latent class analysis (LCA) model to examine ACE patterns, aiming to overcome the shortcomings of cumulative and single adversity approaches based on the special social context of China. The data were derived from a middle school in Huaibei City of Anhui Province in 2019 and 2020, which was a prospective study involving 1687 junior high school students. At the initial evaluation (T1), ACEs, psychological resilience, self-esteem, and depressive symptoms were assessed by the students. At Time 2 (T2), the depressive symptoms of students were assessed. LCA and mediation analyses were conducted with Mplus version 8.2. The LCA identified the following three heterogeneous ACE classes: “low adversity” (36.4%), “moderate adversity” (44.2%), and “high adversity” (19.4%). The mediation analysis showed that the ACE patterns affected depressive symptoms through the following two mediation paths only in the moderate but not in the high adversity class: self-esteem alone and a path combining psychological resilience and self-esteem. Psychological resilience separately did not mediate the association between ACE patterns and depressive symptoms. To reduce depressive symptoms, interventions for students with ACEs need to improve self-esteem through many channels.

A data‐driven examination of clustering of apathy and depression symptoms in people with dementia

AbstractBackgroundApathy is one of the most common Neuropsychiatric Symptom (NPS) and is associated with poor clinical outcomes. Apathy remains under‐researched and its medical classification remains unclear, potentially associated with the fact that apathy is commonly a comorbidity with depression. Research that helps define the apathy phenotype is urgently needed, particularly for clinical and biomarker studies.MethodUsing latent class analysis (LCA) we analysed data from the KCL Care Home Research Network study with 349 participants (an observational study of people living with dementia in care homes). The aim was to understand how apathy and depression phenotypes cluster together. Apathy and depression were defined on the basis of item G (Apathy) and D (Depression) of the NPI‐NH scale, combining the 7 apathy subquestions with the 8 depression subquestions, rated as yes or no. Predictors of class membership were then analysed, including other NPS, demographics, medication and Clinical Dementia Rating (CDR).ResultThe LCA analysis showed a 4‐cluster group which was considered the best model: No symptoms, predominantly depression, apathy/depression, and a predominantly apathy group. Those in the apathy/depression class were more likely to experience psychosis symptoms compared to the no symptoms class, whilst those in the predominantly apathy class were more likely to have a higher CDR score compared to the no symptoms class.ConclusionUsing a data driven method, we show distinct clustering of apathy and depression symptoms in this cohort. There was evidence that these clusters have different clinical associations which may help inform diagnostic categories for research studies and clinical practice. Potentially, the four subtypes may be used in future studies to further evaluate their utility in biomarker studies in dementia.

Clustering of asthma and related comorbidities and their association with maternal health during pregnancy: evidence from an Australian birth cohort

BackgroundThe population-based classification of asthma severity is varied and needs further classification. This study identified clusters of asthma and related comorbidities of Australian children aged 12–13 years; determined health outcome differences among clusters; and investigated the associations between maternal asthma and other health conditions during pregnancy and the children’s clustered groups.MethodsParticipants were 1777 children in the birth cohort of the Longitudinal Study of Australian Children (LSAC) who participated in the Health CheckPoint survey and the LSAC 7th Wave. A latent class analysis (LCA) was conducted to identify clusters of children afflicted with eight diseases, such as asthma (ever diagnosed or current), wheezing, eczema, sleep problem/snoring/breathing problem, general health status, having any health condition and food allergy. Multinomial logistic regression was used to investigate the association between maternal asthma or other health conditions and LCA clusters.ResultsThe study identified four clusters: (i) had asthma – currently healthy (11.0%), (ii) never asthmatic & healthy (64.9%), (iii) early-onset asthmatic or allergic (10.7%), and (iv) asthmatic unhealthy (13.4%). The asthmatic unhealthy cluster was in poor health in terms of health-related quality of life, general wellbeing and lung functions compared to other clusters. Children whose mothers had asthma during pregnancy were 3.31 times (OR 3.31, 95% CI: 2.06–5.30) more likely to be in the asthmatic unhealthy cluster than children whose mothers were non-asthmatic during pregnancy.ConclusionUsing LCA analysis, this study improved a classification strategy for children with asthma and related morbidities to identify the most vulnerable groups within a population-based sample.

Psychometric Testing of the Modified Dental Anxiety Scale among Iranian Adolescents during COVID-19 Pandemic.

(1) Background: The present study aimed to examine the psychometric properties of the Persian adaptation of the Modified Dental Anxiety Scale (MDAS) in Iranian adolescents. (2) Methods: Adolescents with a mean age of 15.10 (n = 3197; 47.1% males) were recruited from Qazvin city of Iran using a stratified cluster random sampling technique. All children completed the five-item Persian MDAS and information related to background characteristics. Psychometric testing was conducted using classical test theory (CTT) and Rasch models. For CTT, an item-total correlation of >0.4 was considered satisfactory while for Rasch analysis, infit and outfit mean squares (Mnsq) ranging from 0.5–1.5 were considered satisfactory. Confirmatory Factor Analysis (CFA) was conducted to confirm the unidimensional structure of MDAS using various fit indices. Differential item functioning (DIF) was evaluated based on gender and time since last dental visit. Moreover, latent class analysis (LCA) was used to classify the participants into different levels of dental fear based on their pattern of responses. Both item level reliability using Cronbachs alpha (α) and test-reliability using intraclass correlation coefficients were evaluated. (3) Results: Item-total correlations ranged from 0.69–0.78, infit MnSq ranged from 0.80 to 1.11 and the range of outfit MnSq was 0.84–1.10. The data confirmed a one-factor structure of MDAS with satisfactory fit indices. DIF analysis indicated that the scale was interpreted similarly across the genders and time since dental visit groups. LCA analysis identified three levels, low, moderate and high levels of dental anxiety. The groups with moderate and high levels of dental anxiety had more females (44.6% and 36.7%) than the group with low level of dental anxiety (18.8%; p < 0.001). α of the total scale was 0.89 and item test-retest reliability ranged from 0.72–0.86. (4) Conclusions: The Persian MDAS was unidimensional with satisfactory psychometric properties evaluated using both CTT and Rasch analysis among Iranian adolescents. The scale was stable across the genders and individuals with different dental visiting patterns. The Persian MDAS also demonstrated excellent reliability.

Adherence to treatment in Behçet's syndrome: a multi-faceted issue.

The aim of this study is to explore the adherence to treatment in patients with Behçet's syndrome (BS), to identify the diverse adherence profiles and their correlations. A cross-sectional study among adult BS patients was conducted administering an ad-hoc questionnaire to BS patients with the aim of investigating several dimensions related to BS management, including attitudes towards treatment. A Latent Class Analysis (LCA) was performed to identify adherence profiles and associated characteristics were identified using logistic regression analysis. A total of 207 patients answered the survey and 180 of them declared to take medication for BS, thus representing the study population. More than a third of the respondents have declared that they have skipped treatments before and autonomously modified (reduced or increased) the dosage of the treatment without medical consultation. LCA analysis allowed the identification of two distinct profiles, one more stick with recommended medication and the other less adherent to treatment. The less-adherent BS patient profile seems to be related with being in the third decade of life, being diagnosed with BS for more than 5 years and perceiving greater psychological impact of the disease. Addressing adherence in BS is not only related to measuring treatment adherence and identifying the barriers and the limitations; in fact, it should also encompass a wider approach that includes the awareness, the socio-psychological impact of the disease as well as patient education.

Co-infection status of novel parvovirus's (PPV2 to 4) with porcine circovirus 2 in porcine respiratory disease complex and porcine circovirus-associated disease from 1997 to 2012.

As global pig health diseases, porcine respiratory disease complex (PRDC) and porcine circovirus-associated disease (PCVAD) generate substantial economic losses despite pigs been vaccinated against the primary causative virus, highlighting the importance of understanding virome interactions and specifically co-factor infections. Established primary endemic pathogens for PRDC include porcine circovirus 2 (PCV2), porcine reproductive and respiratory syndrome virus (PRRSv) and swine influenza virus (SIV), and PCV2 aetiology in interaction with other co-infecting viruses can result in PCVAD. Porcine parvovirus (PPV) 1 is a well-characterized virus with an available vaccine preventing reproductive failure in sows. However, whilst novel PPV 2 to 7 viruses have been identified since 2001, their viral pathogenic potential in clinical and subclinical disease remains to be determined. Therefore, this study has sought to develop a better understanding of their potential role as associated co-infections in PRDC and PCVAD by examining archival samples for the presence of PCV2 and the novel parvoviruses PPV2-4 from clinically diseased pigs across production age stages. Epidemiologically, the novel PPV2 was found to be the most prevalent within the fattener age group with PPV2-4 statistically associated with pig respiratory disease and enteric ulcers. Additionally, statistical modelling by latent class analysis (LCA) on veterinary pathology scored pigs found a clustering co-factor association between PPV2 and PCV2, suggesting the novel PPV may be involved in PRDC and PCVAD. Phylogenetic analysis of novel PPVs revealed the PPV2 capsid evolution to be diverged from the original strains with a low nucleotide homology of 88%-96% between two distinct clades. These findings determine that novel PPV 2-4 viruses are statistically associated as co-infectors in a diseased pig population, and significantly detected PPV2 clustering co-infection frequency with PCV2 in PRDC and PCVAD diseased pigs through LCA analysis.

The Structure and Determinants of Intergenerational Support Exchange Flows in an Eastern European Setting.

Although the provision, receipt, and bidirectionality of support exchanges are important for generational well-being, our understanding of them is lacking in Eastern Europe, a region undergoing swift population aging and social change. This study links intergenerational support exchanges to determinants in Romania, with a focus on proximity of adult children. Data are from the Romanian Aging and Migration Survey (N = 1,398). Analyses involve two stages. First, latent class analysis (LCA) is conducted to develop an intergenerational support typology. The second uses the typology as a dependent variable in multivariate equations predicting exchange determinants. LCA analysis yielded six propensity classes. Physical distance strongly predicts class membership. Having coresident adult children increases the likelihood of bidirectional exchange. Having an international migrant adult child reduces the chances, even with coresident adult children present. International migrant children lead to a higher probability of being a nonexchanger or receiving monetary support. There is a need for continued consideration of bidirectional exchange models in rapidly developing contexts.

The structure of ICD-11 PTSD and Complex PTSD in adolescents exposed to potentially traumatic experiences

BackgroundThe recently released 11th edition of International Classification of Diseases (ICD-11) included new definitions of disorders specifically associated with stress. Complex post-traumatic stress disorder (CPTSD) was included in ICD-11 as a new trauma-related disorder which could develop following prolonged or reoccurring traumatic experiences. Research on ICD-11 PTSD and CPTSD validity and epidemiology has, so far, mostly been conducted in adult population. This is the first study to explore the construct validity of the Child and Adolescent version of International Trauma Questionnaire (ITQ-CA) as a measure of ICD-11 CPTSD symptoms. MethodsThe study was based on a sample of 932 adolescents from the general population aged 12–16 (M = 14.25, SD = 1.27) years exposed to various traumatic experiences. We used confirmatory factor analysis (CFA) and latent class analysis (LCA) to test validity of the ITQ-CA scores from adolescents. ResultsThe best fitting measurement model included six correlated factors representing the three PTSD and three DSO symptom clusters. LCA analysis revealed four classes whose symptom profiles were reflective of ‘CPTSD’, ‘PTSD’, ‘DSO only’, and ‘Baseline’. ConclusionsFindings of the study provide support for the construct validity of the ICD-11 PTSD and CPTSD among adolescents.

Exploring Evolutionary and Transmission Dynamics of HIV Epidemic in Serbia: Bridging Socio-Demographic With Phylogenetic Approach.

Previous molecular studies of Serbian HIV epidemic identified the dominance of subtype B and presence of clusters related HIV-1 transmission, in particular among men who have sex with men (MSM). In order to get a deeper understanding of the complexities of HIV sub-epidemics in Serbia, epidemic trends, temporal origin and phylodynamic characteristics in general population and subpopulations were analyzed by means of mathematical modeling, phylogenetic analysis and latent class analysis (LCA). Fitting of the logistic curve of trends for a cumulative annual number of new HIV cases in 1984–2016, in general population and MSM transmission group, was performed. Both datasets fitted the logistic growth model, showing the early exponential phase of the growth curve. According to the suggested model, in the year 2030, the number of newly diagnosed HIV cases in Serbia will continue to grow, in particular in the MSM transmission group. Further, a detailed phylogenetic analysis was performed on 385 sequences from the period 1997–2015. Identification of transmission clusters, estimation of population growth (Ne), of the effective reproductive number (Re) and time of the most recent common ancestor (tMRCA) were estimated employing Bayesian and maximum likelihood methods. A substantial proportion of 53% of subtype B sequences was found within transmission clusters/network. Phylodynamic analysis revealed Re over one during the whole period investigated, with the steepest slopes and a recent tMRCA for MSM transmission group subtype B clades, in line with a growing trend in the number of transmissions in years approaching the end of the study period. Contrary, heterosexual clades in both studied subtypes – B and C – showed modest growth and stagnation. LCA analysis identified five latent classes, with transmission clusters dominantly present in 2/5 classes, linked to MSM transmission living in the capital city and with the high prevalence of co-infection with HBV and/or other STIs.Presented findings imply that HIV epidemic in Serbia is still in the exponential growth phase, in particular, related to the MSM transmission, with estimated steep growth curve until 2030. The obtained results imply that an average new HIV patient in Serbia is a young man with concomitant sexually transmitted infection.

Relationship and communication characteristics associated with agreement between heart failure patients and their Carepartners on patient depressive symptoms

Objectives: Informal caregivers who recognize patients’ depressive symptoms can better support self-care and encourage patients to seek treatment. We examined patient-caregiver agreement among patients with heart failure (HF). Our objectives were to (1) identify distinct groups of HF patients and their out-of-home informal caregivers (CarePartners) based on their relationship and communication characteristics, and (2) compare how these groups agree on the patients’ depressive symptoms.Method: We used baseline data from a comparative effectiveness trial of a self-care support program for veterans with HF treated in outpatient clinics from 2009-2012. We used a cross-sectional design and latent class analysis (LCA) approach to identify distinct groups of patient-CarePartner dyads (n = 201) based on relationship and communication characteristics then evaluated agreement on patients’ depressive symptoms within these groups.Results: The LCA analysis identified four groups: Collaborative (n = 102 dyads, 51%), Avoidant (n = 33 dyads, 16%), Distant (n = 35 dyads, 17%), and Antagonistic (n = 31 dyads, 15%). Dyadic agreement on the patients’ depressive symptoms was highest in the Distant (Kappa (κ) = 0.44, r = 0.39) and Collaborative groups (κ = 0.19, r = 0.32), and relatively poor in the Avoidant (κ = –0.20, r = 0.17) and Antagonistic (κ =–0.01, r = 0.004) groups. Patients in Avoidant (61%) and Antagonistic groups (74%) more frequently had depression based on self-report than patients in Collaborative (46%) and Distant (34%) groups.Conclusion: Caregiver relationships in HF tend to be either Collaborative, Avoidant, Distant, or Antagonistic. Patients’ depressive symptoms may negatively affect how they communicate with their caregivers. At the same time, improved patient-caregiver communication could enhance dyadic consensus about the patient’s depressive symptoms.

DETERMINATION OF STUDENTS’ PROFILES ACCORDING TO HOME EDUCATIONAL RESOURCES AND MATHEMATICS ACHIEVEMENT VIA LATENT CLASS ANALYSIS

The purpose of this study was to examine the correlation between mathematics achievements and ownership of the home educational resources of students via Latent Class Analysis (LCA) using Programme for International Student Assessment (PISA) 2015 study. Although 5895 15-year-old Turkish students were participated in PISA study, the data of 5355 students were included in the analysis because of the missing data of the selected variables. 12 items which were related to students’ home educational resources were used in LCA analysis. As a result, 3 classes were determined; 32% of students were in 1st latent class, 29% of students were in 2nd latent class and 39% of students were in 3rd latent class. It was revealed that students in 1st latent class had only “a desk to study at”, “a quiet place to study”, “books to help with your school work” and “a dictionary” while students in 3rd latent class had all the home educational resources (12 items). Furthermore, it was shown that students in 3rd latent class had the highest mathematics score while students in 1st latent class had the lowest mathematics scores.

The structure of ICD-11 PTSD and complex PTSD in Lithuanian mental health services

ABSTRACTBackground: The updated 11th edition of International Classification of Diseases (ICD-11) is expected to be released by the WHO in 2018. Disorders specifically associated with stress will be included in a separate chapter in ICD-11, and will include a revision of ICD-10 PTSD as well as a new diagnosis of complex posttraumatic stress disorder (CPTSD). The proposed symptom structures of ICD-11 PTSD and CPTSD have been validated in several studies previously, however few studies have used the International Trauma Questionnaire (ITQ), a specific measure for ICD-11 PTSD and CPTSD. Given that ICD-11 PTSD and CPTSD diagnoses are intended to be applicable across different cultures and nations, it is important that the constructs be evaluated across diverse populations and languages.Objective: Study of the psychological impact of trauma is relatively new in Lithuania, coinciding with its independence from the Soviet Union in the 1990s. Studies thus far reveal a population suffering from the effects of long-term and systematic political oppression and violence. The aim of this study was to assess the validity of the symptoms and structure of PTSD and CPTSD in a Lithuanian treatment-seeking sample as measured by the ITQ.Method: A total of 280 patients from outpatient mental health centres participated in this study. PTSD and CPTSD symptoms were measured with the ITQ. We applied confirmatory factor analysis (CFA) and latent class analysis (LCA) for analysis of data.Results and conclusions: Our study supported the ICD-11 factor structure of CPTSD, and a three-class model was supported in LCA analysis with a PTSD class, a CPTSD class, and a low symptom class. Findings support the factorial and discriminant validity of the ICD-11 proposals for PTSD and CPTSD in a unique clinical population.