Patterns and predictors of PTSD in treatment-seeking African refugees and asylum seekers: A latent class analysis

2020 ◽  
pp. 002076402095909
Author(s):  
Alberto Barbieri ◽  
Federica Visco-Comandini ◽  
Danilo Alunni Fegatelli ◽  
Anna Dessì ◽  
Giuseppe Cannella ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Asylum Seekers ◽  
Latent Class ◽  
Treatment Seeking ◽  
Class Analysis ◽  
Ptsd Symptoms ◽  
Class Membership ◽  
African Refugees ◽  
Refugees And Asylum Seekers ◽  
High Threat

Background: Despite the empirical and clinical relevance of understanding posttraumatic stress disorder (PTSD) heterogeneity in refugees and asylum-seekers, very few studies have examined the manner in which PTSD symptoms manifest in such populations. Aims: This study sought to investigate patterns and predictors of DSM-5 PTSD in a treatment-seeking sample of African refugees. Methods: Participants were 122 African refugees and asylum-seekers living in Italy who completed measures of trauma exposure and PTSD symptoms. Latent class analysis (LCA) was used to identify PTSD symptom profiles, and predictors of class membership were identified via multinomial logistic regression. Results: Among participants, 79.5% had a probable diagnosis of PTSD. Three PTSD classes were identified by LCA: Pervasive (32.0%) with high probabilities of all symptoms, high-Threat (45.9%) with higher probabilities of intrusions and avoidance symptoms, moderate-Avoidance (22.1%) with high probability of thoughts/feelings avoidance. None of the examined variables (legal status, gender, age, education, months spent in Italy, number of traumatic events, employment) significantly predicted class membership with the relevant exception of reception conditions. Specifically, living in large reception centres (over 1,000 people) significantly predicted Pervasive PTSD class membership compared to high/Threat PTSD class and to moderate/Avoidance class. Conclusion: This study provides evidence for distinct patterns of PTSD symptomatology in refugees and asylum seekers. We identified three classes which present both qualitative and quantitative differences in symptoms: Pervasive class, high-Threat class and a new moderate class, characterised by avoidance symptoms. Reception conditions contributed to the emergence of the Pervasive PTSD profile characterised by the symptoms highest severity. These findings highlight that stressors in the post-migration environment, as inadequate reception conditions in large facilities, may have detrimental effect on refugees’ mental health. We emphasise the importance for host countries to implement reception models that provide effective protection and integration to this vulnerable population.

scholarly journals Testing the validity of ICD-11 PTSD and CPTSD among refugees in treatment using latent class analysis

2020 ◽  
Vol 29 (3) ◽  
pp. 27-45 ◽  
Author(s):  
Maria Louison Vang ◽  
Sabrina Brødsgaard Nielsen ◽  
Mikkel Auning-Hansen ◽  
Ask Elklit
Keyword(s):  
Latent Class Analysis ◽  
Posttraumatic Stress ◽  
Latent Class ◽  
Treatment Center ◽  
Treatment Seeking ◽  
Complex Ptsd ◽  
Class Analysis ◽  
Ptsd Symptoms ◽  
Affective Dysregulation ◽  
Ptsd Group

Introduction: The WHO has proposed posttraumatic stress (PTSD) and Complex PTSD (CPTSD) trauma-related ‘sibling’-disorders in ICD-11. The proposal has received support from research among clinical and community samples alike but only few studies have tested the validity of these disorders in a sample of refugees using the International Trauma Questionnaire especially designed for assessment of ICD-11 PTSD and CPTSD.    Methods: Latent class analysis was used to test the validity of the ICD-11 PTSD and CPTSD distinction in a heterogeneous group of 284 highly symptomatic refugees registered for treatment at a Danish treatment-center.   Results: A two-class solution fit the data best. One group reported elevated levels of PTSD-symptoms and symptoms of affective dysregulation, and one group reported elevated levels of symptoms corresponding to CPTSD. The CPTSD group was considerably larger than the PTSD-group. Discussion: The current study supports the ICD-11 distinction between PTSD and CPTSD in a sample of treatment-seeking refugees. The assistance of interpreters was needed for some of the participants which affected the reliability of the assessment. Conclusion: The ICD-11 proposal for PTSD and CPTSD is supported in a heterogenous sample of refugees using the ITQ.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

scholarly journals Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Adult Population ◽  
Class Analysis ◽  
Class Membership ◽  
Anthropometric Measures ◽  
Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

scholarly journals A primer to latent profile and latent class analysis

2021 ◽  
Author(s):  
Johannes Bauer
Keyword(s):  
Individual Differences ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
External Variables ◽  
Response Profiles ◽  
Person Centered Approach ◽  
Conceptual Overview ◽  
Latent Profile

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data, and to estimate the proportional size and response profiles of these classes in the population. Moreover, latent class membership can serve as predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centered approach that is useful for analyzing individual differences in prerequisites, processes, or outcomes of learning. The chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using freely available data and the R statistical environment.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study (Preprint)

2020 ◽  
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

BACKGROUND eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. OBJECTIVE The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. METHODS Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. RESULTS Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. CONCLUSIONS Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

scholarly journals Self-Reported Mental Health During the COVID-19 Pandemic and its Association with Alcohol and Cannabis Use: A Latent Class Analysis

2021 ◽  
Author(s):  
Nibene Habib Somé ◽  
Samantha Wells ◽  
Daniel Felsky ◽  
Hayley A. Hamilton ◽  
Shehzad Ali ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Alcohol Use ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Mental Health Problems ◽  
Cannabis Use ◽  
Class Analysis ◽  
Class Membership ◽  
Anxiety Depression

Abstract Background: Mental health problems and substance use co-morbidities during the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing these problems can directly inform mitigating strategies. We aimed to identify distinct groups of individuals (i.e., latent classes) based on patterns of self-reported mental health symptoms and investigate associations with alcohol and cannabis use.Methods: We used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses.Results: We identified two distinct classes: 1) individuals with low scores on all three mental health indicators (no/low-symptoms) and 2) those reporting high scores (high-symptoms). Those at greater risk of being in the high-symptoms class were likely to be women (adjusted odds ratio (aOR) =1.34, 95%CI:1.18-1.52), people worried about getting COVID-19 (aOR=2.39, 95%CI:2.02-2.82), and those with post-secondary education (aOR=1.26, 95%CI:1.02-1.55). Asian ethnicity (aOR=0.78, 95%CI:0.62-0.97), married status (aOR=0.71, 95%CI:0.59-0.85), seniors (aOR=0.38, 95%CI:0.32-0.47), individuals in households with income higher than CAD$40,000: $40,000-$79,000 (aOR=0.73, 95%CI:0.60-0.90), $80,000-$119,000 (aOR=0.60, 95%CI:0.48-0.74) and $120,000+ (aOR=0.47, 95%CI:0.37-0.59) were at lower odds of being in the high-symptoms class. Individuals in the high-symptoms class were more likely to use cannabis at least once a week (aOR=2.25, 95%CI:1.90-2.67), drink alcohol heavily (aOR=1.69, 95%CI:1.47-1.95); and increase the use of cannabis (aOR=3.48, 95%CI:2.79-4.35) and alcohol (aOR=2.37, 95%CI:2.05-2.73) during the pandemic. Women in the high-symptoms class had higher odds of increasing alcohol use than men.Conclusions: We identified the determinants of experiencing high-symptoms of anxiety, depression, and loneliness, and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities, particularly regarding alcohol use in women.

scholarly journals Towards a typology of prisoners’ awareness of and familiarity with prison inspection and monitoring bodies

2021 ◽  
pp. 147737082199894
Author(s):  
Sophie van der Valk ◽  
Eva Aizpurua ◽  
Mary Rogan
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Personal Characteristics ◽  
Sentence Length ◽  
Class Analysis ◽  
Multinomial Regression ◽  
Key Factors ◽  
Class Membership ◽  
The Relationship

Inspection and monitoring bodies have an important role in the protection of prisoners’ rights. Although these bodies are seen as widely beneficial, there is limited research examining their operations in practice. This study addresses this gap in the existing literature by identifying unique profiles of prisoners based on their familiarity with prison oversight bodies. In addition, the relationship between profiles and key factors (personal characteristics, sentence-related variables and those related to life in prison) was examined using multinomial regression. Participants were 508 males randomly selected from three prisons in Ireland. Data were collected between November 2018 and February 2019, using self-administered surveys. Latent class analysis revealed four subgroups of prisoners characterized by distinct patterns of awareness and contact with prison oversight bodies: (1) Low familiarity (44.1 percent); (2) High awareness with low contact (26.4 percent); (3) High familiarity with the Visiting Committees but low with other oversight bodies (14.2 percent); and (4) High familiarity (15.4 percent). Notably, the largest group was the low familiarity group, and few prisoners belonged to the high familiarity group. Nationality, sentence length, confidence in staff and complaint usage were linked to class membership. The results of this study point to the importance of increasing awareness of inspection and monitoring bodies among prisoners in general, and among certain groups in particular.

scholarly journals Multidimensional dependency subgroups in community-dwelling older adults: A latent class analysis

2020 ◽  
Vol 52 (2) ◽  
pp. 101-109
Author(s):  
Kely Rely ◽  
Delfino Vargas-Chanes ◽  
Carmen García-Peña ◽  
Guillermo Salinas-Escudero ◽  
Luis-Miguel Gutiérrez-Robledo ◽  
...  
Keyword(s):  
Older Adults ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Community Dwelling ◽  
Future Research ◽  
Class Analysis ◽  
Class Membership ◽  
Fit Statistics ◽  
Nationally Representative ◽  
Using Data

Objectives: Use latent class analysis (LCA) to identify patterns of multidimensional dependency in a sample of older adults and assess sociodemographic, predictors of class membership. Material and methods: Longitudinal data were usedfrom the Mexican Health and Aging Study (MHAS). 7,920 older adults, 55% women, were recruited. LCA were used to identify meaningful subgroups. LCA was conducted using MPlus version. The final class model was chosen based on the comparison of multiple fit statistics and theoretical parsimony, with models of increasing complexity analyzed sequentially until the best fitting model was identified. Covariates were incorporated to explore the association between these variables and class membership. Results: Three classes groups based on the nine indicators were identified: “Active older adults” was comprised of 64% of the sample participants, “Relatively independent” and “Physically impaired” were comprised of 26% and 10% of the sample. The “Active older adults” profile comprised the majority of respondents who exhibited high endorsement rates across all criteria. The profiles of the “Active older adults” and “Relatively independent” were comparatively more uniform. Finally, respondents belonging to the “Physically impaired” profile, the smallest subgroup, encompassed the individuals most susceptible to a poor dependency profile. Conclusions: These findings highlighted the usefulness to adopt a person-centered approach rather than a variable-centered approach, suggesting directions for future research and tailored interventions approaches to older adults with particular characteristics. Based on patterns of multidimensional dependency, this study identified a typology of dependency using data from a large, nationally representative survey.

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