Patterns of Trauma Exposure, Emotion Dysregulation, and Mental Health Symptoms: A Latent Class Analysis

The purpose of this study is to understand the availability of employee wellness programs within law enforcement agencies (LEAs) across the United States, including physical fitness, resilience/wellness, coping skills, nutrition, mental health treatment, and substance use treatment. The research team investigated whether patterns of LEA wellness programming are identifiable and, if so, what characteristics describe these patterns. We assess using latent class analysis whether there are distinct profiles of agencies with similar patterns offering different types of wellness programs and explore what characteristics distinguish agencies with certain profiles of wellness programming. Data were from a nationally representative sample of 1135 LEAs: 80.1% municipal, 18.6% county and 1.3% other agencies (state-level and Bureau of Indian Affairs LEAs). We found that many agencies (62%) offer no wellness programming. We also found that 23% have comprehensive wellness programming, and that another group of agencies specialize in specific wellness programming. About 14% of the agencies have a high probability of providing resilience coping skill education, mental health and/or substance use treatment services programming. About 1% of the agencies in the United States limit their programming to fitness and nutrition, indicating that fitness and nutrition programs are more likely to be offered in concert with other types of wellness programs. The analyses revealed that agencies offering broad program support are more likely to be large, municipal LEAs located in either the West, Midwest or Northeast (compared with the southern United States), and not experiencing a recent budget cut that impacted wellness programming.

Supplemental Material for Latent Class Analysis of COVID-19 Experiences, Social Distancing, and Mental Health

American Journal of Orthopsychiatry ◽

10.1037/ort0000593.supp ◽

2021 ◽

Keyword(s):

Mental Health ◽

Latent Class Analysis ◽

Latent Class ◽

Class Analysis ◽

Social Distancing

Application of latent class analysis in assessing the mental health of medical students during the COVID-19 Epidemic (Preprint)

10.2196/preprints.26804 ◽

2020 ◽

Author(s):

Fei Wang

Keyword(s):

Mental Health ◽

Medical Students ◽

High Risk ◽

Latent Class Analysis ◽

Latent Class ◽

Risk Group ◽

Multinomial Logistic Regression ◽

High Risk Group ◽

Class Analysis ◽

Related Factors

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

Risk Factors Explaining Military Deaths From Suicide, 2008–2017: A Latent Class Analysis

Armed Forces & Society ◽

10.1177/0095327x211046976 ◽

2021 ◽

pp. 0095327X2110469

Author(s):

Scott D. Landes ◽

Janet M. Wilmoth ◽

Andrew S. London ◽

Ann T. Landes

Keyword(s):

Mental Health ◽

Risk Factors ◽

Latent Class Analysis ◽

Military Service ◽

Latent Class ◽

Service Providers ◽

Service Members ◽

Latent Classes ◽

Class Analysis ◽

Military Service Members

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.

Snapshot of a social movement: mental health and protest classes in Hong Kong

10.31234/osf.io/hkf3p ◽

2021 ◽

Author(s):

Adrienne Wing Yee Li ◽

Wai Ho CHAK ◽

Kenneth S.L. Yuen

Keyword(s):

Mental Health ◽

Hong Kong ◽

Latent Class ◽

Demographic Data ◽

Mental Health Symptoms ◽

Health Symptoms ◽

Class Differences ◽

Potentially Traumatic Events ◽

Political Views ◽

Tear Gas

Background: Popular protests have broken out in worldwide, particularly in the last few years. In 2019, numerous demonstrations against an extradition bill occurred in Hong Kong until pandemic restrictions were imposed. The policing response relied heavily on methods such as batons, tear gas and rubber bullets. Given the relevance for other geographical contexts, the current study investigated the mental health impacts on protest participants and spillover to community members.Methods: Surveys were disseminated on social media in August and October 2019 to collect demographics, political views, protest participation, exposure to (protest-related) potentially traumatic events (PTEs) and mental health symptoms. A latent class analysis (LCA) was conducted using demographic data and inter-class differences in PTEs and mental health symptoms were examined.Results: There were 37,541 (59.8% female) and 40,703 (50.0% female) responses in August and October. Respondents, even those with low participation, reported significant levels of depression, anxiety, and symptoms of traumatic stress (STS). The LCA suggested a 5-class solution (youth, allies, supporters, sympathizers, and frontliners). Mental health symptoms and PTEs varied with class membership, with 50.8% of frontliners reporting severe STS.Limitations: The non-random sampling and self-reported measures may over-estimate the prevalence of mental distress in the wider population.Conclusions: Large numbers of pro-democracy supporters in Hong Kong reported high rates of depression, anxiety and STS during mass protests. Younger and more heavily involved respondents faced the highest mental health risks, however elevated rates were also observed for respondents with low participation.

Associative stigma among mental health professionals in Singapore: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2018-028179 ◽

2019 ◽

Vol 9 (7) ◽

pp. e028179 ◽

Cited By ~ 2

Author(s):

Louisa Picco ◽

Sherilyn Chang ◽

Edimansyah Abdin ◽

Boon Yiang Chua ◽

Qi Yuan ◽

...

Keyword(s):

Mental Health ◽

Job Satisfaction ◽

Latent Class Analysis ◽

Mental Health Professionals ◽

Health Professionals ◽

Online Survey ◽

Latent Class ◽

Multinomial Logistic Regression ◽

Class Analysis ◽

Associative Stigma

Objectives(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.DesignCross-sectional online survey.ParticipantsDoctors, nurses and allied health staff, working in Singapore.MethodsStaff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.ResultsThe latent class analysis revealed that items formed a three-class model where the classes were classified as ‘no/low associative stigma’, ‘moderate associative stigma’ and ‘high associative stigma’. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.ConclusionAssociative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.

A latent class analysis of the co-occurrence of sexual violence, substance use, and mental health in youth

Substance Use & Misuse ◽

10.1080/10826084.2019.1618337 ◽

2019 ◽

Vol 54 (12) ◽

pp. 1938-1944 ◽

Cited By ~ 4

Author(s):

Jordan L. Nelon ◽

Kris T. De Pedro ◽

Tamika D. Gilreath ◽

Megan S. Patterson ◽

Caitlin B. Holden ◽

...

Keyword(s):

Mental Health ◽

Substance Use ◽

Latent Class Analysis ◽

Sexual Violence ◽

Latent Class ◽

Class Analysis

An examination of generalized anxiety disorder and dysthymic disorder by latent class analysis

Psychological Medicine ◽

10.1017/s0033291713002225 ◽

2013 ◽

Vol 44 (8) ◽

pp. 1701-1712 ◽

Cited By ~ 8

Author(s):

D. Rhebergen ◽

I. M. van der Steenstraten ◽

M. Sunderland ◽

R. de Graaf ◽

M. ten Have ◽

...

Keyword(s):

Mental Health ◽

Anxiety Disorder ◽

Latent Class Analysis ◽

Generalized Anxiety Disorder ◽

Clinical Characteristics ◽

Latent Class ◽

Depressive Symptomatology ◽

Dysthymic Disorder ◽

Generalized Anxiety ◽

Class Analysis

BackgroundThe nosological status of generalized anxiety disorder (GAD) versus dysthymic disorder (DD) has been questioned. The aim of this study was to examine qualitative differences within (co-morbid) GAD and DD symptomatology.MethodLatent class analysis was applied to anxious and depressive symptomatology of respondents from three population-based studies (2007 Australian National Survey of Mental Health and Wellbeing; National Comorbidity Survey Replication; and Netherlands Mental Health Survey and Incidence Study-2; together known as the Triple study) and respondents from a multi-site naturalistic cohort [Netherlands Study of Depression and Anxiety (NESDA)]. Sociodemographics and clinical characteristics of each class were examined.ResultsA three-class (Triple study) and two-class (NESDA) model best fitted the data, reflecting mainly different levels of severity of symptoms. In the Triple study, no division into a predominantly GAD or DD co-morbidity subtype emerged. Likewise, in spite of the presence of pure GAD and DD cases in the NESDA sample, latent class analysis did not identify specific anxiety or depressive profiles in the NESDA study. Next, sociodemographics and clinical characteristics of each class were examined. Classes only differed in levels of severity.ConclusionsThe absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.