Latent Cluster Analysis Research Articles

Car sharing user groups and their changes in car ownership: A latent cluster analysis

Car sharing contributes to reducing emissions, resource depletion, and land take, by promoting more efficient use of vehicles that on average sit idle 96% of the time. But consumer adoption remains limited. User segmentation studies can inform efforts to mobilize potential users to join car sharing, and to anticipate what their needs and usage characteristics will be. This paper proposes a comprehensive segmentation method based on socio-demographic variables, which reveals differences in preferences for car sharing platform type, usage characteristics and societal outcomes of car sharing use. To do so, we apply a three step Latent Cluster Analysis to a nationwide sample of active car sharing users. This method results in a set of user segments that reflect distinct life phases, with different needs in terms of the frequency and type of trips made by car sharing. This is reflected in different preferences for car sharing platform types, as the likelihood to use peer-to-peer car sharing as opposed to business-to-consumer car sharing differs by user segment. The segments do not differ substantially in their motivations for engaging with car sharing. Yet, environmental motivations for car sharing outrank financial motivations, while social motivations play no substantial role. The clusters do show substantial differences in the changes in car ownership that occur while being an active car sharing member as well as the likelihood to use car sharing in place of a private car rather than public transport or walking and biking. We conclude that a comprehensive segmentation method based on socio-demographic variables provides actionable insights for upscaling car sharing adoption, as well as targeting specific user segments in order to optimize societal outcomes from car sharing use.

Rural households' livelihood transitions in China: processes, drivers and outcomes

PurposeThis study aims to comprehensively examine the transitions in household livelihood strategies within rural China, including the underlying processes, drivers and outcomes.Design/methodology/approachThis study uses two waves (2010 and 2018) of longitudinal data from the China Family Panel Studies (CFPS), employing latent cluster analysis, regression models and cumulative distribution function within a dynamic household livelihood strategy framework.FindingsThe results show that (1) households’ livelihood strategies can be categorized into four distinct types, i.e. agricultural dominated, agricultural dominated with non-agricultural supplementation, non-agricultural dominated with agricultural supplementation and employment oriented. (2) During 2010–2018, approximately 60% of households underwent transitions in their livelihoods, encompassing both upward and downward trajectories, with a prevalence of upward transitions. (3) Various forms of livelihood capital significantly contribute to upward transitions, while the availability of land resources and exposure to shocks impede the potential for upward mobility. (4) The transition towards non-agricultural livelihood strategies by households leads to a notable enhancement in their livelihood welfare.Research limitations/implicationsIn the context of urbanization, industrialization and globalization, rural areas in China are undergoing a gradual socioeconomic transformation, which has also led to changes in rural households’ livelihood strategies. Nevertheless, a dearth of empirical investigation exists regarding the dynamics of rural households’ livelihood strategies, the determinants behind such transitions and the resulting outcomes on their livelihoods. A comprehensive understanding of livelihood transitions can provide valuable insights for policymakers in their endeavors to promote rural revitalization in China.Originality/valueBased on the nationwide representative datasets in China, it examines the micro-level livelihood transitions of rural households within the broader context of socioeconomic transformation that presents both opportunities and challenges, as well as vulnerable contexts, shaped by various government policies. This exploration would offer valuable theoretical and empirical evidence to advance our understanding of the process, driver and outcome of rural households’ livelihood transition in developing countries.

Patterns of adverse childhood experiences and community environments

This study examined patterns of adverse childhood experience and its association with community environments. Using the 2020 National Survey of Children’s Health (NSCH) data, 30,056 children who are 6–17 years of age were our samples for the investigation. Among them, 14,026 children with at least one Adverse Childhood Experience (ACE) were profiled through latent cluster analysis. We found four classes of ACEs: separation/divorce, multiple low-risk, multiple high-risk, and alcohol/drug problem. Moreover, controlling for socio-demographic variables (i.e., race/ethnicity, the highest education level of primary caregiver in a household), we found significant differences in the quality of supportive neighborhoods and the number of positive and adverse community amenities across children categorized into four classes of ACEs and no ACE class (i.e., children without ACEs, n = 16,030). Implications for research on ACEs and their association with the community environment are discussed.

High-density lipoprotein metrics during midlife and future subclinical atherosclerosis in women: the SWAN HDL study.

The clinical utility of high-density lipoprotein cholesterol (HDL-C) in risk classification is limited, especially in midlife women. Novel metrics of HDL may better reflect this risk. We clustered a comprehensive profile of HDL metrics into favorable and unfavorable clusters and assessed how these two clusters are related to future subclinical atherosclerosis (carotid intima media thickness [cIMT], interadventitial diameter [IAD], and carotid plaque presence) in midlife women. Four hundred sixty-one women (baseline age: 50.4 [2.7] years; 272 White, 137 Black, 52 Chinese) from the Study of Women's Health Across the Nation HDL ancillary study who had baseline measures of HDL cholesterol efflux capacity (HDL-CEC), lipid contents (HDL-phospholipids [HDL-PL] and HDL triglycerides [HDL-Tg]), and HDL particle (HDL-P) distribution and size, followed by carotid ultrasound (average 12.9 [SD: 2.6] years later), were included. Using latent cluster analysis, women were clustered into a favorable (high HDL-CEC, HDL-PL, large and medium HDL-P, less HDL-Tg and small HDL-P, larger size) or an unfavorable HDL cluster (low HDL-CEC, HDL-PL, large and medium HDL-P, more HDL-Tg, and small HDL-P, smaller size) and then linked to future subclinical atherosclerosis using linear or logistic regression. The favorable HDL cluster was associated with lower cIMT, IAD, and odds of carotid plaque presence. These associations were attenuated by body mass index, except in Chinese women where the association with cIMT persisted (0.72 [0.63, 0.83]). The association between favorable HDL clusters and a better postmenopausal subclinical atherosclerosis profile is largely explained by body mass index; however, racial/ethnic differences may exist.

Teachers as Readers: Baseline Profile Types with Regard to Skills, Habits, and Dispositions

This paper shares findings drawn from data generated over a period of five years on self-reported reading skills, habits, and dispositions of pre-service and in-service K-12 teachers (primarily middle and high school) across content areas (N = 854). Using latent cluster analyses, we created a Teacher Reader Profile Type (TRPT) model that illuminates distinct differences between teachers in different content areas and across status (i.e. undergraduate v. graduate). Our results suggest five distinct TRPTs, each with relative strengths and areas for growth. We provide five recommendations for teacher education programs and policy makers to consider. In addition, the measurement tool in this study could be used for key assessment related to teachers as readers. Future research could focus on how to improve teacher progress toward reading maturity, and the extent to which TRPTs relate to their own students’ literacy development.

Patterns of improvement in functional ability and predictors of responders to dual-task exercise: A latent class analysis.

Exercise is the pillar for healthy aging. "Non-responders" may be due to a mismatch in exercise prescription. A latent cluster analysis (LCA) profile can be useful to uncover subpopulations sharing similar profiles or outcomes. We aim to use the LCA to develop a response prediction model for older adults who would benefit from The Healthy Aging Promotion Program for You, a community-embedded dual-task exercise program. A total of 197 participants completed the 3-month follow-up, and the complete data were available for 136 community-dwelling older adults. Inclusion criteria were age ≥60 years, pre-frail or frail and ambulant, mild cognitive impairment, and ability to provide consent. Data collected include demographics, education, falls, physical function (Katz ADL scale and Lawton's IADL scale), physical activity (rapid assessment of physical activity), cognition (Montreal Cognitive Assessment; MoCA), frailty (FRAIL scale), and perceived health, pain, anxiety/depression, fear of falling, and social isolation (Lubben Social Network Scale). The body mass index (BMI), handgrip strength, and short physical performance battery (SPPB) were measured. Those who improved in frailty, anxiety/depression, pain, Lubben, MoCA, SPPB, fear-of-falling, physical activity, falls, and HGS were classified as responders. The mean age was 74.7 years, BMI 23.5 kg/m2, 23.5% were male, 96.3% were of Chinese ethnicity, 61% were pre-frail, education level of 4.3 years, and the MoCA score of 23.3 ± 4.8. Two clusters were identified: non-responders (61.8%) and responders (38.2%). Responders had significant improvement in cognition (44.2% vs. 0, p < 0.001) and SPPB (gait:28.8% vs. 0, p < 0.001; balance:42.3% vs. 15.5%, p = 0.001; chair-stand:65.4% vs. 4.8%, p < 0.001). Responders were significantly older (76.9 vs. 73.3 years, p = 0.005), had higher BMI (24.8 vs. 22.8 kg/m2, p = 0.007), lower education (3.4 vs. 4.9 years, p = 0.021), lower MoCA scores (21.8 vs. 24.3, p = 0.002), and lower SPPB scores (8.7 vs. 10.6, p < 0.001). The predictive variables for the responder cluster were age ≥75 years, BMI ≥23 kg/m2, robust, no anxiety, pain, fear of falling, MoCA ≤22, Lubben ≤12, SPPB score: chair-stand ≤2, balance ≤2, gait >2, handgrip strength <20 kg, no falls and RAPA >3. With an optimal cut-off of ≥12, this prediction model had sensitivity of 76.9%, specificity of 70.2%, positive predictive value 61.5%, and negative predictive value of 83.1%. Response to dual-task exercise was influenced by age, SPPB, BMI, and cognition. Prospective longitudinal studies are needed to validate this LCA model and guide the development of public health strategies.

Patterns of participation restriction among older adults at risk of falls and relationship with intrinsic capacity: A latent cluster analysis.

The concept of participation restriction was first described by the World Health Organization in 2001 as a component of The International Classification of Functioning, Disability and Health Framework. Both falls and fear of falling (FOF) are associated with social isolation, depression, anxiety, poor quality of life and cognitive impairment resulting in participation restriction. Life-space mobility (LSM) is an important indicator for participation restriction which depends on multiple inter-related factors. We aimed to determine participation patterns using latent cluster analysis (LCA) in older adults at risk of falls, its relationship with intrinsic capacity (IC) and its risk prediction. Cross-sectional study of 154 community dwelling older adults ≥ 60 years with falls or risk of falls was conducted. Questionnaires were administered on demographics, hearing, LSM, frailty (FRAIL scale), anorexia of aging (SNAQ), cognition (Montreal Cognitive Assessment, MoCA), FOF (Falls Efficacy Scale-International), physical function, and assessment for handgrip strength (HGS), gait speed, 5-times sit to stand (STS), vision and times-up-and-go (TUG) were performed. Six IC domains (vision and hearing, cognition, nutrition, mobility and depression) were measured. Three pattern of participation cluster were identified, high (n = 63, 40.9%), moderate (n = 83, 53.9%) and low (n = 8, 33 5.2%). Individuals in the high participation cluster were significantly younger, had higher LSM scores and lower FES-I scores, more robust, fewer ADL and IADL limitations, lower prevalence of low HGS, higher gait speed and shorter TUG. In the fully adjusted model compared to the high participation cluster, moderate participation was significantly associated with low MoCA scores (OR 4.2, 95% CI 1.7-10.4, p = 0.02), poor STS (OR 7.1, 95% CI 3.0-17.0, p < 0.001) whereas low participation was associated with anorexia of aging (OR 9.9, 95% CI 1.6-60.9, p = 0.014), poor STS (OR 19.1, 95% CI 2.0-187.5, p = 0.011) and hearing impairment (OR 9.8, 95% CI 1.4-70.8, p = 0.024). Participants with 3 out of 6 IC decline had a probability of greater than 80% to belong to the low/moderate participation class. Physical function, cognition, hearing and nutrition were significantly associated with low and/or moderate participation class. Future studies are needed to evaluate improvement in participation of those with falls or at risk for falls through restoration of IC.

Combination of WFDC2, CHI3L1, and KRT19 in Plasma Defines a Clinically Useful Molecular Phenotype Associated with Prognosis in Critically Ill COVID-19 Patients

BackgroundCOVID-19 is now a common disease, but its pathogenesis remains unknown. Blood circulating proteins reflect host defenses against COVID-19. We investigated whether evaluation of longitudinal blood proteomics for COVID-19 and merging with clinical information would allow elucidation of its pathogenesis and develop a useful clinical phenotype.MethodsTo achieve the first goal (determining key proteins), we derived plasma proteins related to disease severity by using a first discovery cohort. We then assessed the association of the derived proteins with clinical outcome in a second discovery cohort. Finally, the candidates were validated by enzyme-linked immunosorbent assay in a validation cohort to determine key proteins. For the second goal (understanding the associations of the clinical phenotypes with 28-day mortality and clinical outcome), we assessed the associations between clinical phenotypes derived by latent cluster analysis with the key proteins and 28-day mortality and clinical outcome.ResultsWe identified four key proteins (WFDC2, GDF15, CHI3L1, and KRT19) involved in critical pathogenesis from the three different cohorts. These key proteins were related to the function of cell adhesion and not immune response. Considering the multicollinearity, three clinical phenotypes based on WFDC2, CHI3L1, and KRT19 were identified that were associated with mortality and clinical outcome.ConclusionThe use of these easily measured key proteins offered new insight into the pathogenesis of COVID-19 and could be useful in a potential clinical application.

Nurses’ professional identity and information needs in the time of Covid-19: A latent cluster analysis

The aims of this study are to find out, how nurses differ regarding their professional identity in times of Covid-19 and if nurses of different clusters of professional identity also differ regarding their satisfaction with their information needs. To get more insights in nurses work situation in Covid-19, we asked nurses about information sources they use to get information about Covid-19, by whom they feel supported, and if they feel fear working in times of Covid-19. We conducted a cross-sectional study. We used online questionnaires. 266 Nurses in Germany (N = 266) participated in our study. The study was conducted during Covid-19 pandemic. Our results show that most nurses use information about Covid-19 provided by their organisation. Most of them find social support through their colleagues. There are two clusters of nurses with significantly different professional identities; these two clusters show significant differences regarding their satisfaction with information needs. The results of the study are important for nursing organisations that should provide information for their nurses and by this can have influence on their coping strategies.

Children's shyness and frontal electroencephalogram delta–beta correlation in the pediatric surgical setting

The prospect of surgery is a unique psychologically threatening context for children, often leading to experiences of preoperative anxiety. Recent research suggests that individual differences in children's temperament may influence responses to the surgical setting. In the present study, we examined whether individual differences in shyness were related to differences in frontal electroencephalogram (EEG) delta-beta correlation, a proposed neural correlate of emotion regulation and dysregulation, among children anticipating surgery. Seventy-one children (36 boys, Mage =10.3 years, SDage =1.7 years) undergoing elective surgery self-reported on their own shyness, and their parents also reported on their child's shyness. Using a mobile, dry sensor EEG headband, frontal EEG measures were collected and self- and observer-reported measures of state anxiety were obtained at the children's preoperative visit (Time 1) and on the day of surgery (Time 2). A latent cluster analysis derived classes of low shy (n=37) and high shy (n=34) children using the child- and parent-reported shyness measures. We then compared the two classes on frontal EEG delta-beta correlation using between- and within-subjects analyses. Although children classified as high versus low in shyness had higher self- and observer-reported state anxiety across both time periods, frontal EEG delta-beta correlation increased from T1 to T2 only among low shy children using a between-subjects delta-beta correlation measure. We discuss the interpretation of a relatively higher delta-beta correlation as a correlate of emotion regulatory versus dysregulatory strategies for some children in a "real-world," surgical context.

Latent pattern analysis of conscious cooperation for developing sustainable transport

This study explores the emerging pattern of conscious cooperation in travel mode selection for developing sustainable transport. A conceptual model for investigating the formation of conscious cooperation is examined based on a multi-factor latent cluster analysis of the survey data from Chinese cites. The study identifies three dominant patterns including the diversity average pattern, the pro-social pattern and the pro-environment pattern. An analysis of such patterns reveals that improving mobility and accessibility is more important than increasing availability of transport facilities for enhancing conscious cooperation. It finds out that there is much more to be done in addressing the concern of travelers on social equity and environment for sustainable transport development. These findings above provide better understanding of the formation of conscious cooperation for developing sustainable transport.

Investigating injury severity of pedestrian–vehicle crashes by integrating latent class cluster analysis and unbalanced panel mixed ordered probit model

In recent years the pedestrian deaths have been declining, but the pedestrian–vehicle death rate in Croatia is still pretty high. This study intended to investigate the injury severity of pedestrian–vehicle crashes and identify the influencing factors. To achieve this goal, the dataset was firstly collected from Traffic Accident Database System maintained by the Ministry of the Interior, Republic of Croatia from 2015 to 2019, and then latent cluster analysis was employed to identify homogenous clusters from heterogeneous dataset. Based on the classified dataset, unbalanced panel mixed ordered probit model was proposed. By analyzing the classes with different vehicles, the proposed model revealed a more complete understanding of significant variables and showed beneficial performance from the goodness-of-fit, while capturing the impact of exogenous variables to vary among different places, as well as accommodating the heterogeneity issue due to unobserved effects. Findings revealed that the proposed model can be considered as an alternative to determine the factors of injury severity and to deal with the heterogeneity issue. The results may provide potential insights for reducing the injury severity of pedestrian-vehicle crashes.

A Latent Class Analysis of Mental Health Severity and Alcohol Consumption: Associations with COVID-19-Related Quarantining, Isolation, Suicidal Ideations, and Physical Activity.

The present study examined latent class cluster group patterns based on measures of depression and anxiety symptom severity and alcohol consumption during the COVID-19 pandemic. Hypothesized correlates with latent class cluster groups including quarantining, self-isolation, suicidal ideations, sitting hours per day, and physical activity (vigorous intensity exercise in minutes per week) were examined. The delimited participant sample consisted of 606 university young adults 18 to 25 years of age (M = 21.24 ± 1.62). Latent cluster analysis (LCA) modeled patterns of depression and anxiety symptom severity and alcohol consumption during the COVID-19 pandemic. Between group analysis and multinomial logistic regression analysis were used to examine relationships between latent class clusters and correlates including quarantining, self-isolation, suicidal ideations, sitting hours per day, and physical activity (vigorous intensity exercise in minutes per week). LCA results showed that six latent cluster groups provided optimal model-to-date fit based on mental health symptom severity and alcohol consumption (L2 = 56.31, BIC = 5012.79, AIC = 4849.74, and the bootstrap L2 p-value = .88; Entropy R2 = .89). Identified latent class clusters were as follows: cluster one = moderate anxiety and depression severity and moderate alcohol consumption (n = 156; 25.7%); cluster two = high mental health severity and alcohol consumption (n = 133; 21.9%); cluster three = low mental health symptoms and moderate alcohol consumption (n = 105; 17.3%); cluster four = lowest mental health severity and alcohol consumption (n = 95; 15.7%); cluster five = moderate depression severity, low anxiety severity, and low alcohol consumptions (n = 74; 12.2%); and cluster six = moderate anxiety severity, low depression severity, and low alcohol consumption (n = 43; 7.1%). Multinomial logistic regression analysis results found that quarantining, self-isolation, suicidal ideations, sedentary behavior, and physical activity were differentially associated with cluster group membership. Findings from this study demonstrate associations between COVID-19 public health restrictions, suicidal ideations, and declines in mental health and increases in alcohol consumption among young adult university students.

Caregiver Task Difficulties at the End of Life: A Latent Cluster Analysis

Caregivers engage in myriad tasks from household help to complex medical care. However, little information is available on how caregivers experience individual tasks – particularly key end-of-life tasks such as managing breathing problems or patients' sadness and anxiety. The purpose of this study was therefore to assess task difficulty. Using data from the National Health and Aging Trends Survey and the National Survey on Caregivers (2015-2017), we assessed eleven caregiving tasks in 241 primary caregivers of care recipients in their last month of life. A latent cluster analysis revealed three key clusters: 1) pervasive difficulties, in which caregivers reported difficulty across most or all of the tasks; 2) minimal difficulties; and 3) emotional management difficulties, in which caregivers reported difficulty with managing sadness and anxiety and lower levels of difficulty on the other tasks. Weighted frequency analyses revealed that caregivers in the pervasive difficulties cluster were most likely to be filial caregivers (85% versus 63% of the full sample, p<0.05) or co-residing with the care recipient (49% versus 37% of the full sample, p<0.05). Caregivers identified as having pervasive difficulties were also more likely to report providing intensive care, more than 100 hours per week (54% versus 36% of the full sample, p<0.05). Care recipient condition was not associated with cluster membership. The findings highlight the need to consider caregiver coping at the task-level and have implications for understanding unmet needs. Future research will assess predictors of cluster membership and how task difficulties are associated with symptoms and well-being outcomes.

Latent class cluster analysis of knowledge on acute myocardial infarction symptoms and risk factors in community residents

Abstract Background Knowledge of Acute Myocardial Infarction (AMI) symptoms and risk factors (RF) have been studied worldwide in various populations. However, the studied specific symptom and RF of different surveys varies, the number of surveyed symptom and RF varies, and the criteria for judging good/high knowledge and bad/low knowledge are different (group classification criteria include the number, the percentages or the scores), which makes the results difficult to compare. Purpose The aim was to identify clusters of knowledge on symptoms and modifiable RFs using Latent Cluster Analysis, and to further compare characteristics and the awareness of the need for prompt treatment among different clusters. Methods A structured questionnaire was used to interview 4122 community residents over 35 years old on their knowledge about AMI symptoms and RFs. Latent GOLD Statistical Package was used to identify and classify the respondent subtypes (latent classes) of the knowledge on AMI symptoms or modifiable RFs. An individual's class membership probabilities are computed from the estimated model parameters and the observed scores. The multivariable logistic regression was performed in order to identify factors that predicted High knowledge membership. Results Two and three distinct clusters were identified in terms of knowledge of symptoms and RFs. 62.7% (Symptom High Knowledge Cluster) and 39.5% (RF High Knowledge Cluster) of the respondents were able to identify most symptoms and modifiable RFs. A number of common differences were observed with respect to higher probability of having high knowledge on symptom and RF, which was mostly better among those with higher education and average monthly income, insured, having annual physical examination, have disease history of AMI RFs, AMI history in immediate family member or acquaintance and have been exposed to AMI related public education. There was also significant difference in awareness of the prompt treatment in case of AMI occurs among different symptom or RF clusters. Conclusions A moderate or relatively low knowledge regarding AMI symptoms and modifiable RFs was observed in our study. Identification of Symptom or RF knowledge clusters can be a way to detect specific targeted groups that are most likely to possess lower knowledge of AMI with readily identifiable characteristics, which may facilitate health education and further reduce the pre-hospital delay and improve patient outcomes. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Key Project of Scientific and Technological Support Plan of Tianjin in 2020 Cluster category chartProfile plot

IDENTIFICATION OF PHENOTYPES AMONG COVID-19 DEATH USING LATENT CLASS ANALYSIS

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Certain risk factors and prognostic indicators have been identified in patients with coronavirus disease 2019 (COVID-19). However, no study has addressed the risk factor distribution pattern among patients who die from COVID-19. In this study, we used latent class analysis (LCA) to identify phenotypes and risk factor distribution patterns in hospitalized patients who died from COVID-19. METHODS: We reviewed the charts of patients who died from COVID-19 at Greenwich Hospital from February 1 to May 30, 2020. We performed LCA based on well-documented prognostic factors of COVID-19. We also compared the in-hospital laboratory results, and treatment information among the different clusters identified by LCA. To validate of the clustering results, we conducted a robust LCA of the entire COVID-19 cohort. RESULTS: 483 patients who were admitted for COVID-19 infection from February 1 to May 30, 2020, 81 patients died. Using latent cluster analysis, we identified two risk factor clusters among COVID-19 death: C1 (n=58) and C2 (n=23). In C1, patients were older (p<0.001) with a higher proportion of comorbidities such as hypertension (82.8% vs. 39.1%, p<0.001), CAD (43.1% vs. 0%, p<0.001), CHF (22.4% vs. 0%, p=0.015), and pre-existing respiratory disease (32.8% vs. 0%, p=0.004) than in C2. In C2, patients were significantly younger and were more likely to be obese (BMI ≥30 kg/m2;56.5% vs. 24.1%, p=0.012), male (87.0% vs. 58.6%, p=0.018), and non-white (60.9% vs. 15.8%, p<0.001) than in C1. Compared with patients in C1, patients in C2 showed a pattern of increased expression of inflammatory and hypercoagulable markers, including C-reactive protein (84.4±117 vs. 12.7±10.7 mg/L, p=0.008) and D-dimer (17.8±13.6 vs. 8.0±9.9 mg/L, p=0.004). The robust-test by using the entire cohort of patients hospitalized with COVID-19 had identified two clusters of risk factor patterns similar to those identified in the cohort of patients who died from COVID-19. CONCLUSIONS: Our study suggests that there are two patterns of risk factors that contributed to death in patients with COVID-19. These results indicate that different pathophysiologic processes lead to COVID-19 death and may be useful in identifying treatment targets and selecting patients with severe COVID-19 disease for future clinical trials. CLINICAL IMPLICATIONS: The results indicate that each phenotype of patients who died from COVID-19 has its distinct feature and underlying pathophysiology. Research focused on targeted therapy for each phenotype may help decrease the mortality rate among patients with severe COVID-19. DISCLOSURES: No relevant relationships by Deepa Jansen, source=Web Response No relevant relationships by Pengyang Li, source=Web Response No relevant relationships by Catherine Teng, source=Web Response

Stigma toward individuals with mental disorders among Brazilian psychiatrists: a latent class analysis

Objective:The stigma toward individuals with mental disorders is highly prevalent, not only in the general population but among health care providers as well. The aim of this study was to identify subgroups based on stigmatizing beliefs related to psychiatric disorders among Brazilian psychiatrists, as well as to investigate their association with clinical and personality characteristics.Methods:Latent cluster analysis was used to find subgroups of cases in multivariate data according to a psychotic (schizophrenia) and a nonpsychotic disorder (attention-deficit hyperactivity disorder). The clusters for each psychiatric disorder were compared according to sociodemographic, emotional traits, and personality characteristics.Results:A total of 779 psychiatrists answered the questionnaire. Three different subgroups of stigma levels were identified regarding schizophrenia: the highest (n=202 [51.7%]), intermediate (108 [27.6%]), and the lowest (81 [20.7%]). Participants from the highest stigma group had a significantly longer time since graduation, higher anxiety-state scores, and lower positive affect. Two subgroups were identified with respect to attention-deficit hyperactivity disorder, although there were no differences between them in sociodemographic or clinical variables.Conclusion:There were more subgroups of stigmatizing beliefs regarding psychotic disorders. Individual characteristics, such as those related to trait anxiety and affect, can be associated with high stigma toward schizophrenia.

Stepfamily Involvement in and out of School and (Step)children’s College Preparation Behaviors: A Latent Cluster Analysis

Using a latent class analysis (LCA) with data from a subsample of children in stepfamilies ( N = 6,637) from the 2009 High School Longitudinal Study (HSLS), this study examined how stepfamily involvement in their (step)child’s education in and outside of school influenced their (step)child’s college preparation. Stepfamily involvement in their (step)child’s education in school (e.g., help with homework) and outside of school (e.g., educational experiences such as going to a museum) may help overcome challenges associated with academic and college preparation for children in stepfamilies. Results broadly indicate students with higher stepfamily involvement in education in and out of school had (step)parents who believed that college was attainable, students engaged in more activities that would prepare them for their future, and students took more AP/IB level courses and tests. Together, findings suggest that stepfamily involvement in education both in and out of school is important for their (step)child’s college preparation behaviors.

Profiles of Emotion Dysregulation Among University Students Who Self-Injure: Associations with Parent-Child Relationships and Non-Suicidal Self-Injury Characteristics.

More research is needed to understand the different vulnerability profiles of university students who engage in non-suicidal self-injury (NSSI). This study sought to classify university students (n = 479; 83.8% female) aged 17-25 years (M = 18.77; SD = 1.42) who had engaged in NSSI within the past year into latent profiles based on their self-perceived difficulties in regulating both positive and negative emotions. Independent samples of students who had a past history of NSSI but had not self-injured within the previous year (n = 439; 82.9% females; Mage = 19.03, SD = 1.62) and who had no history of NSSI (n = 1551; 69.9% females; Mage = 19.02, SD = 1.55) were recruited for comparison purposes. Latent cluster analyses revealed three emotion regulation profiles within the NSSI sample-the Average Difficulties (47.4%), Dysregulated (33.0%), and Low Difficulties (19.6%) profiles-each of which differed meaningfully from both comparison samples on mean emotion regulation difficulties. Students across profiles also differed in their self-reported experiences with parents, particularly with fathers (pressure, antipathy, unresolved attachment, psychological control), and in the extent to which they felt alienated from parents. Lastly, students across profiles differed in the frequency, methods, functions, and addictive properties of their NSSI. Findings highlight that university students who self-injure experience distinct patterns of difficulties with emotion regulation, which are associated with variation in parent-child relational risk factors and NSSI outcomes.

Working in the Sport Industry: A Classification of Human Capital Archetypes

As thousands of professionals are drawn to work in the sport industry known for celebrity, action, and excitement, a growing body of literature on the industry’s culture describes a field fraught with burnout, stress, and difficulty balancing work–family responsibilities. Given this contradiction, there is a need to better understand employee experiences. Thus, the authors utilized a human capital framework to develop employee archetypes. Results from a latent cluster analysis of National Collegiate Athletic Association athletics department employees (N = 4,324) revealed five distinct employee archetypes utilizing inputs related to human capital development and work experiences (e.g., work–family interface, work engagement, age). Consistent with creative nonfiction methodology, results are presented as composite narratives. Archetypes follow a career arc from early-career support staff to late-career senior leaders and portray an industry culture wherein the human capital is largely overworked, underpaid, and replete with personal sacrifice and regret.