Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

PubMed Central

BOYSAN, Murat

2014-01-01

Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

PubMed Central

Collins, John; Huynh, Minh

2014-01-01

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification. PMID:24910172

Using Latent Class Analysis to Model Temperament Types

ERIC Educational Resources Information Center

Loken, Eric

2004-01-01

Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks…

A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification

ERIC Educational Resources Information Center

Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi

2012-01-01

This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

PubMed Central

2013-01-01

Background Falls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique which could better estimate fall probability is both timely and needed. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly. Methods Using a retrospective data-set a two-step LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients’ prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons. Results A review of the model fit indices with covariates shows that a six-class solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good at 90%. Specific prescription medications were found to strongly influence group membership. Conclusions In conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data. PMID:23705639

A Flexible Latent Class Approach to Estimating Test-Score Reliability

ERIC Educational Resources Information Center

van der Palm, Daniël W.; van der Ark, L. Andries; Sijtsma, Klaas

2014-01-01

The latent class reliability coefficient (LCRC) is improved by using the divisive latent class model instead of the unrestricted latent class model. This results in the divisive latent class reliability coefficient (DLCRC), which unlike LCRC avoids making subjective decisions about the best solution and thus avoids judgment error. A computational…

Large-scale weakly supervised object localization via latent category learning.

PubMed

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

The job content questionnaire in various occupational contexts: applying a latent class model.

PubMed

Santos, Kionna Oliveira Bernardes; Araújo, Tânia Maria de; Carvalho, Fernando Martins; Karasek, Robert

2017-05-17

To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into accountthe latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers-groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Latent trajectory studies: the basics, how to interpret the results, and what to report.

PubMed

van de Schoot, Rens

2015-01-01

In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000). The method used to evaluate such trajectories is called Latent Growth Mixture Modeling (LGMM) or Latent Class Growth Modeling (LCGA). The difference between the two models is whether variance within latent classes is allowed for (Jung & Wickrama, 2008). The default approach most often used when estimating such models begins with estimating a single cluster model, where only a single underlying group is presumed. Next, several additional models are estimated with an increasing number of clusters (latent groups or classes). For each of these models, the software is allowed to estimate all parameters without any restrictions. A final model is chosen based on model comparison tools, for example, using the BIC, the bootstrapped chi-square test, or the Lo-Mendell-Rubin test. To ease the use of LGMM/LCGA step by step in this symposium (Van de Schoot, 2015) guidelines are presented which can be used for researchers applying the methods to longitudinal data, for example, the development of posttraumatic stress disorder (PTSD) after trauma (Depaoli, van de Schoot, van Loey, & Sijbrandij, 2015; Galatzer-Levy, 2015). The guidelines include how to use the software Mplus (Muthén & Muthén, 1998-2012) to run the set of models needed to answer the research question: how many latent classes exist in the data? The next step described in the guidelines is how to add covariates/predictors to predict class membership using the three-step approach (Vermunt, 2010). Lastly, it described what essentials to report in the paper. When applying LGMM/LCGA models for the first time, the guidelines presented can be used to guide what models to run and what to report.

Evidence for Latent Classes of IQ in Young Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Munson, Jeffrey; Dawson, Geraldine; Sterling, Lindsey; Beauchaine, Theodore; Zhou, Andrew; Koehler, Elizabeth; Lord, Catherine; Rogers, Sally; Sigman, Marian; Estes, Annette; Abbott, Robert

2008-01-01

Autism is currently viewed as a spectrum condition that includes strikingly different severity levels; IQ is consistently described as one of the primary aspects of the heterogeneity in autism. To investigate the possibility of more than one distinct subtype of autism based on IQ, both latent class analysis and taxometrics methods were used to…

Multilevel Latent Class Analysis: An Application of Adolescent Smoking Typologies with Individual and Contextual Predictors

ERIC Educational Resources Information Center

Henry, Kimberly L.; Muthen, Bengt

2010-01-01

Latent class analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this…

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

ERIC Educational Resources Information Center

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background: Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods: Sibs-A (N = 204) were assessed…

Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

ERIC Educational Resources Information Center

Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta

2011-01-01

"Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…

Patterns of Chronic Conditions and Their Associations With Behaviors and Quality of Life, 2010

PubMed Central

Mitchell, Sandra A.; Thompson, William W.; Zack, Matthew M.; Reeve, Bryce B.; Cella, David; Smith, Ashley Wilder

2015-01-01

Introduction Co-occurring chronic health conditions elevate the risk of poor health outcomes such as death and disability, are associated with poor quality of life, and magnify the complexities of self-management, care coordination, and treatment planning. This study assessed patterns of both singular and multiple chronic conditions, behavioral risk factors, and quality of life in a population-based sample. Methods In a national survey, adults (n = 4,184) answered questions about the presence of 27 chronic conditions. We used latent class analysis to identify patterns of chronic conditions and to explore associations of latent class membership with sociodemographic characteristics, behavioral risk factors, and health. Results Latent class analyses indicated 4 morbidity profiles: a healthy class (class 1), a class with predominantly physical health conditions (class 2), a class with predominantly mental health conditions (class 3), and a class with both physical and mental health conditions (class 4). Class 4 respondents reported significantly worse physical health and well-being and more days of activity limitation than those in the other latent classes. Class 4 respondents were also more likely to be obese and sedentary, and those with predominantly mental health conditions were most likely to be current smokers. Conclusions Subgroups with distinct patterns of chronic conditions can provide direction for screening and surveillance, guideline development, and the delivery of complex care services. PMID:26679491

Application of Latent Class Analysis to Identify Behavioral Patterns of Response to Behavioral Lifestyle Interventions in Overweight and Obese Adults.

PubMed

Fitzpatrick, Stephanie L; Coughlin, Janelle W; Appel, Lawrence J; Tyson, Crystal; Stevens, Victor J; Jerome, Gerald J; Dalcin, Arlene; Brantley, Phillip J; Hill-Briggs, Felicia

2015-08-01

Examining responders and non-responders to behavioral lifestyle interventions among overweight/obese adults with additional comorbidities may aid in refining and tailoring obesity treatment. The purpose of this study is to demonstrate the use of latent class analysis to identify patterns of response to behavioral lifestyle interventions based on adherence to diet and exercise recommendations. Repeated measures latent class analysis was applied to two clinical trial datasets, combination of two active interventions in the PREMIER Trial (n = 501) and phase 1 of the Weight Loss Maintenance Trial (WLM; n = 1685), to identify patterns of response to behavioral lifestyle interventions. Treatment response was based on adherence to daily recommendations for fruit/vegetable, fat, saturated fat, sodium, and exercise at baseline and 6 months. In PREMIER, three distinct latent classes emerged: responders (45.9%), non-responders (23.6%), and early adherers (30.5%). Responders and Early Adherers had greater weight loss at 6 and 18 months and were more likely to meet behavioral recommendations at 18 months than Non-responders. For WLM, there were four latent classes: partial responders (16%), non-responders (40%), early adherers (2%), and fruit/veggie only responders (41%). Non-responders in WLM had significantly less weight loss at 6 months compared to that of the other three latent classes. Latent class analysis is a useful method to apply to clinical trial data to identify distinct patterns of response to behavioral interventions. Overweight/ obese participants who respond to behavioral lifestyle treatment (i.e., meet behavioral recommendations) have significantly greater weight loss than that of participants who do not make behavioral changes.

Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

ERIC Educational Resources Information Center

DiStefano, Christine; Kamphaus, R. W.

2006-01-01

Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

ERIC Educational Resources Information Center

Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

2013-01-01

This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

Examining the Stability of "DSM-IV" and Empirically Derived Eating Disorder Classification: Implications for "DSM-5"

ERIC Educational Resources Information Center

Peterson, Carol B.; Crow, Scott J.; Swanson, Sonja A.; Crosby, Ross D.; Wonderlich, Stephen A.; Mitchell, James E.; Agras, W. Stewart; Halmi, Katherine A.

2011-01-01

Objective: The purpose of this investigation was to derive an empirical classification of eating disorder symptoms in a heterogeneous eating disorder sample using latent class analysis (LCA) and to examine the longitudinal stability of these latent classes (LCs) and the stability of DSM-IV eating disorder (ED) diagnoses. Method: A total of 429…

Latent Classes of Adolescent Posttraumatic Stress Disorder Predict Functioning and Disorder after 1 Year

ERIC Educational Resources Information Center

Ayer, Lynsay; Danielson, Carla Kmett; Amstadter, Ananda B.; Ruggiero, Ken; Saunders, Ben; Kilpatrick, Dean

2011-01-01

Objective: To identify latent classes of posttraumatic stress disorder (PTSD) symptoms in a national sample of adolescents, and to test their associations with PTSD and functional impairment 1 year later. Method: A total of 1,119 trauma-exposed youth aged 12 through 17 years (mean = 14.99 years, 51% female and 49% male) participating in the…

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia

PubMed Central

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-01-01

Background Scant information exists on PTSD symptoms and structure in youth from developing countries. Methods We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Results Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Conclusions Results suggest that PTSD is a continuously distributed latent trait. PMID:25382359

Simple Estimators for the Simple Latent Class Mastery Testing Model. Twente Educational Memorandum No. 19.

ERIC Educational Resources Information Center

van der Linden, Wim J.

Latent class models for mastery testing differ from continuum models in that they do not postulate a latent mastery continuum but conceive mastery and non-mastery as two latent classes, each characterized by different probabilities of success. Several researchers use a simple latent class model that is basically a simultaneous application of the…

Job Satisfaction among Health-Care Staff in Township Health Centers in Rural China: Results from a Latent Class Analysis.

PubMed

Wang, Haipeng; Tang, Chengxiang; Zhao, Shichao; Meng, Qingyue; Liu, Xiaoyun

2017-09-22

Background : The lower job satisfaction of health-care staff will lead to more brain drain, worse work performance, and poorer health-care outcomes. The aim of this study was to identify patterns of job satisfaction among health-care staff in rural China, and to investigate the association between the latent clusters and health-care staff's personal and professional features; Methods : We selected 12 items of five-point Likert scale questions to measure job satisfaction. A latent-class analysis was performed to identify subgroups based on the items of job satisfaction; Results : Four latent classes of job satisfaction were identified: 8.9% had high job satisfaction, belonging to "satisfied class"; 38.2% had low job satisfaction, named as "unsatisfied class"; 30.5% were categorized into "unsatisfied class with the exception of interpersonal relationships"; 22.4% were identified as "pseudo-satisfied class", only satisfied with management-oriented items. Low job satisfaction was associated with specialty, training opportunity, and income inequality. Conclusions : The minority of health-care staff belong to the "satisfied class". Three among four subgroups are not satisfied with income, benefit, training, and career development. Targeting policy interventions should be implemented to improve the items of job satisfaction based on the patterns and health-care staff's features.

When do latent class models overstate accuracy for diagnostic and other classifiers in the absence of a gold standard?

PubMed

Spencer, Bruce D

2012-06-01

Latent class models are increasingly used to assess the accuracy of medical diagnostic tests and other classifications when no gold standard is available and the true state is unknown. When the latent class is treated as the true class, the latent class models provide measures of components of accuracy including specificity and sensitivity and their complements, type I and type II error rates. The error rates according to the latent class model differ from the true error rates, however, and empirical comparisons with a gold standard suggest the true error rates often are larger. We investigate conditions under which the true type I and type II error rates are larger than those provided by the latent class models. Results from Uebersax (1988, Psychological Bulletin 104, 405-416) are extended to accommodate random effects and covariates affecting the responses. The results are important for interpreting the results of latent class analyses. An error decomposition is presented that incorporates an error component from invalidity of the latent class model. © 2011, The International Biometric Society.

A Note on Cluster Effects in Latent Class Analysis

ERIC Educational Resources Information Center

Kaplan, David; Keller, Bryan

2011-01-01

This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under…

Use of Systematic Methods to Improve Disease Identification in Administrative Data: The Case of Severe Sepsis.

PubMed

Shahraz, Saeid; Lagu, Tara; Ritter, Grant A; Liu, Xiadong; Tompkins, Christopher

2017-03-01

Selection of International Classification of Diseases (ICD)-based coded information for complex conditions such as severe sepsis is a subjective process and the results are sensitive to the codes selected. We use an innovative data exploration method to guide ICD-based case selection for severe sepsis. Using the Nationwide Inpatient Sample, we applied Latent Class Analysis (LCA) to determine if medical coders follow any uniform and sensible coding for observations with severe sepsis. We examined whether ICD-9 codes specific to sepsis (038.xx for septicemia, a subset of 995.9 codes representing Systemic Inflammatory Response syndrome, and 785.52 for septic shock) could all be members of the same latent class. Hospitalizations coded with sepsis-specific codes could be assigned to a latent class of their own. This class constituted 22.8% of all potential sepsis observations. The probability of an observation with any sepsis-specific codes being assigned to the residual class was near 0. The chance of an observation in the residual class having a sepsis-specific code as the principal diagnosis was close to 0. Validity of sepsis class assignment is supported by empirical results, which indicated that in-hospital deaths in the sepsis-specific class were around 4 times as likely as that in the residual class. The conventional methods of defining severe sepsis cases in observational data substantially misclassify sepsis cases. We suggest a methodology that helps reliable selection of ICD codes for conditions that require complex coding.

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis.

PubMed

Boysan, Murat

2014-09-01

There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson's product-moment correlation coefficients. The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism.

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

ERIC Educational Resources Information Center

Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

2011-01-01

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

ERIC Educational Resources Information Center

Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho; Bottge, Brian

2010-01-01

A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation…

Data-driven subtypes of major depressive disorder: a systematic review

PubMed Central

2012-01-01

Background According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression. Methods We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses. Results In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial. Conclusions The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis. PMID:23210727

Lay Americans' views of why scientists disagree with each other.

PubMed

Johnson, Branden B; Dieckmann, Nathan F

2017-10-01

A survey experiment assessed response to five explanations of scientific disputes: problem complexity, self-interest, values, competence, and process choices (e.g. theories and methods). A US lay sample ( n = 453) did not distinguish interests from values, nor competence from process, as explanations of disputes. Process/competence was rated most likely and interests/values least; all, on average, were deemed likely to explain scientific disputes. Latent class analysis revealed distinct subgroups varying in their explanation preferences, with a more complex latent class structure for participants who had heard of scientific disputes in the past. Scientific positivism and judgments of science's credibility were the strongest predictors of latent class membership, controlling for scientific reasoning, political ideology, confidence in choice, scenario, education, gender, age, and ethnicity. The lack of distinction observed overall between different explanations, as well as within classes, raises challenges for further research on explanations of scientific disputes people find credible and why.

Associations Between Neurotransmitter Genes and Fatigue and Energy Levels in Women Following Breast Cancer Surgery

PubMed Central

Eshragh, Jasmine; Dhruva, Anand; Paul, Steven M.; Cooper, Bruce A.; Mastick, Judy; Hamolsky, Deborah; Levine, Jon D.; Miaskowski, Christine; Kober, Kord M.

2016-01-01

Context Fatigue is a common problem in oncology patients. Less is known about decrements in energy levels and the mechanisms that underlie both fatigue and energy. Objectives In patients with breast cancer, variations in neurotransmitter genes between Lower and Higher Fatigue latent classes and between the Higher and Lower Energy latent classes were evaluated. Methods Patients completed assessments prior to and monthly for 6 months following surgery. Growth mixture modeling was used to identify distinct latent classes for fatigue severity and energy levels. Thirty candidate genes involved in various aspects of neurotransmission were evaluated. Results Eleven single nucleotide polymorphisms (SNPs) or haplotypes (i.e., ADRB2 rs1042718, BDNF rs6265, COMT rs9332377, CYP3A4 rs4646437, GALR1 rs949060, GCH1 rs3783642, NOS1 rs9658498, NOS1 rs2293052, NPY1R Haplotype A04, SLC6A2 rs17841327 and 5HTTLPR + rs25531 in SLC6A4) were associated with latent class membership for fatigue. Seven SNPs or haplotypes (i.e., NOS1 rs471871, SLC6A1 rs2675163, SLC6A1 Haplotype D01, SLC6A2 rs36027, SLC6A3 rs37022, SLC6A4 rs2020942, and TAC1 rs2072100) were associated with latent class membership for energy. Three of thirteen genes (i.e., NOS1, SLC6A2, SLC6A4) were associated with latent class membership for both fatigue and energy. Conclusions Molecular findings support the hypothesis that fatigue and energy are distinct, yet related symptoms. Results suggest that a large number of neurotransmitters play a role in the development and maintenance of fatigue and energy levels in breast cancer patients. PMID:27720787

A Latent Transition Model with Logistic Regression

ERIC Educational Resources Information Center

Chung, Hwan; Walls, Theodore A.; Park, Yousung

2007-01-01

Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…

Risk-taking behaviors and subgrouping of suicide in Iran: A latent class analysis of national registries data.

PubMed

Hajebi, Ahmad; Abbasi-Ghahramanloo, Abbas; Hashemian, Seyed Sepehr; Khatibi, Seyed Reza; Ghasemzade, Masomeh; Khodadost, Mahmoud

2017-09-01

Suicide is one the most important public health problem which is rapidly growing concerns. The aim of this study was to subgroup suicide using LCA method. This cross-sectional study was conducted in Iran based on 66990 records registered in Ministry of Health in 2014. A case report questionnaire in the form of software was used for case registries. Latent class analysis was used to achieve the research objectives. Four latent classes were identified; (a) Non-lethal attempters without a history of psychiatric disorders, (b) Non-lethal attempters with a history of psychiatric disorders, (c) Lethal attempters without a history of psychiatric disorders, (d) Lethal attempters with a history of psychiatric disorders. The probability of completed/an achieved suicide is high in lethal attempter classes. Being male increases the risk of inclusion in lethal attempters' classes (OR = 4.93). Also, being single (OR = 1.16), having an age lower than 25 years (OR = 1.14) and being a rural citizen (OR = 2.36) associate with lethal attempters classes. The males tend to use more violent methods and have more completed suicide. Majority of the individuals are non-lethal attempters who need to be addressed by implementing preventive interventions and mental support provision. Copyright © 2017. Published by Elsevier B.V.

Paths to tobacco abstinence: A repeated-measures latent class analysis.

PubMed

McCarthy, Danielle E; Ebssa, Lemma; Witkiewitz, Katie; Shiffman, Saul

2015-08-01

Knowledge of smoking change processes may be enhanced by identifying pathways to stable abstinence. We sought to identify latent classes of smokers based on their day-to-day smoking status in the first weeks of a cessation attempt. We examined treatment effects on class membership and compared classes on baseline individual differences and 6-month abstinence rates. In this secondary analysis of a double-blind randomized placebo-controlled clinical trial (N = 1,433) of 5 smoking cessation pharmacotherapies (nicotine patch, nicotine lozenge, bupropion SR, patch and lozenge, or bupropion SR and lozenge), we conducted repeated-measures latent class analysis of daily smoking status (any smoking vs. none) for the first 27 days of a quit attempt. Treatment and covariate relations with latent class membership were examined. Distal outcome analysis compared confirmed 6-month abstinence rates among the latent classes. A 5-class solution was selected. Three-quarters of smokers were in stable smoking or abstinent classes, but 25% were in classes with unstable abstinence probabilities over time. Active treatment (compared to placebo), and particularly the patch and lozenge combination, promoted early quitting. Latent classes differed in 6-month abstinence rates and on several baseline variables, including nicotine dependence, quitting history, self-efficacy, sleep disturbance, and minority status. Repeated-measures latent class analysis identified latent classes of smoking change patterns affected by treatment, related to known risk factors, and predictive of distal outcomes. Tracking behavior early in a change attempt may identify prognostic patterns of change and facilitate adaptive treatment planning. (c) 2015 APA, all rights reserved).

Examining Factor Score Distributions to Determine the Nature of Latent Spaces

ERIC Educational Resources Information Center

Steinley, Douglas; McDonald, Roderick P.

2007-01-01

Similarities between latent class models with K classes and linear factor models with K-1 factors are investigated. Specifically, the mathematical equivalence between the covariance structure of the two models is discussed, and a Monte Carlo simulation is performed using generated data that represents both latent factors and latent classes with…

Evidence of Associations between Cytokine Genes and Subjective Reports of Sleep Disturbance in Oncology Patients and Their Family Caregivers

PubMed Central

Miaskowski, Christine; Cooper, Bruce A.; Dhruva, Anand; Dunn, Laura B.; Langford, Dale J.; Cataldo, Janine K.; Baggott, Christina R.; Merriman, John D.; Dodd, Marylin; Lee, Kathryn; West, Claudia; Paul, Steven M.; Aouizerat, Bradley E.

2012-01-01

The purposes of this study were to identify distinct latent classes of individuals based on subjective reports of sleep disturbance; to examine differences in demographic, clinical, and symptom characteristics between the latent classes; and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. Among 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 of their FCs, growth mixture modeling (GMM) was used to identify latent classes of individuals based on General Sleep Disturbance Scale (GSDS) obtained prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in candidate cytokine genes were interrogated for differences between the two latent classes. Multiple logistic regression was used to assess the effect of phenotypic and genotypic characteristics on GSDS group membership. Two latent classes were identified: lower sleep disturbance (88.5%) and higher sleep disturbance (11.5%). Participants who were younger and had a lower Karnofsky Performance status score were more likely to be in the higher sleep disturbance class. Variation in two cytokine genes (i.e., IL6, NFKB) predicted latent class membership. Evidence was found for latent classes with distinct sleep disturbance trajectories. Unique genetic markers in cytokine genes may partially explain the interindividual heterogeneity characterizing these trajectories. PMID:22844404

A general class of multinomial mixture models for anuran calling survey data

USGS Publications Warehouse

Royle, J. Andrew; Link, W.A.

2005-01-01

We propose a general framework for modeling anuran abundance using data collected from commonly used calling surveys. The data generated from calling surveys are indices of calling intensity (vocalization of males) that do not have a precise link to actual population size and are sensitive to factors that influence anuran behavior. We formulate a model for calling-index data in terms of the maximum potential calling index that could be observed at a site (the 'latent abundance class'), given its underlying breeding population, and we focus attention on estimating the distribution of this latent abundance class. A critical consideration in estimating the latent structure is imperfect detection, which causes the observed abundance index to be less than or equal to the latent abundance class. We specify a multinomial sampling model for the observed abundance index that is conditional on the latent abundance class. Estimation of the latent abundance class distribution is based on the marginal likelihood of the index data, having integrated over the latent class distribution. We apply the proposed modeling framework to data collected as part of the North American Amphibian Monitoring Program (NAAMP).

Latent Class Analysis of Differential Item Functioning on the Peabody Picture Vocabulary Test-III

ERIC Educational Resources Information Center

Webb, Mi-young Lee; Cohen, Allan S.; Schwanenflugel, Paula J.

2008-01-01

This study investigated the use of latent class analysis for the detection of differences in item functioning on the Peabody Picture Vocabulary Test-Third Edition (PPVT-III). A two-class solution for a latent class model appeared to be defined in part by ability because Class 1 was lower in ability than Class 2 on both the PPVT-III and the…

Polydrug use and HIV risk among people who inject heroin in Tijuana, Mexico: A Latent class analysis

PubMed Central

Meacham, M.C.; Rudolph, A.E.; Strathdee, S.A.; Rusch, M.L.; Brouwer, K.C.; Patterson, T.L.; Vera, A.; Rangel, G.; Roesch, S.C.

2016-01-01

Background Although most people who inject drugs (PWID) in Tijuana, Mexico, primarily inject heroin, injection and non-injection use of methamphetamine and cocaine is common. We examined patterns of polydrug use among heroin injectors to inform prevention and treatment of drug use and its health and social consequences. Methods Participants were PWID residing in Tijuana aged ≥ 18 years who reported heroin injection in the past 6 months and were recruited through respondent driven sampling (n=1025). Latent class analysis was conducted to assign individuals to classes on a probabilistic basis, using four indicators of past 6 month polydrug and polyroute use: cocaine injecting, cocaine smoking or snorting, methamphetamine injecting, methamphetamine smoking or snorting. Latent class membership was regressed onto covariates in a multinomial logistic regression. Results Latent class analyses testing 1, 2, 3, and 4 classes were fit, with the 3-class solution fitting best. Class 1 was defined by predominantly heroin use (50.2%, n=515); class 2 by methamphetamine and heroin use (43.7%, n=448), and class 3 by methamphetamine, cocaine, and heroin use (6.0%, n=62). Bivariate and multivariate analyses indicated a group of methamphetamine and cocaine users that exhibited higher risk sexual practices and lower heroin injecting frequency, and a group of methamphetamine users who were younger and more likely to be female. Conclusions Discrete subtypes of heroin PWID were identified based on methamphetamine and cocaine use patterns. These findings have identified subtypes of heroin injectors who require more tailored interventions to reduce the health and social harms of injecting drug use. PMID:26444185

Scale Reliability Evaluation with Heterogeneous Populations

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A latent variable modeling approach for scale reliability evaluation in heterogeneous populations is discussed. The method can be used for point and interval estimation of reliability of multicomponent measuring instruments in populations representing mixtures of an unknown number of latent classes or subpopulations. The procedure is helpful also…

Examining the Latent Class Structure of CO2 Hypersensitivity using Time Course Trajectories of Panic Response Systems

PubMed Central

Roberson-Nay, Roxann; Beadel, Jessica R.; Gorlin, Eugenia I.; Latendresse, Shawn J.; Teachman, Bethany A.

2014-01-01

Background and Objectives Carbon dioxide (CO2) hypersensitivity is hypothesized to be a robust endophenotypic marker of panic spectrum vulnerability. The goal of the current study was to explore the latent class trajectories of three primary response systems theoretically associated with CO2 hypersensitivity: subjective anxiety, panic symptoms, and respiratory rate (fR). Methods Participants (n=376; 56% female) underwent a maintained 7.5% CO2 breathing task that included three phases: baseline, CO2 air breathing, and recovery. Growth mixture modeling was used to compare response classes (1..n) to identify the best-fit model for each marker. Panic correlates also were examined to determine class differences in panic vulnerability. Results For subjective anxiety ratings, a three-class model was selected, with individuals in one class reporting an acute increase in anxiety during 7.5% CO2 breathing and a return to pre-CO2 levels during recovery. A second, smaller latent class was distinguished by elevated anxiety across all three phases. The third class reported low anxiety reported during room air, a mild increase in anxiety during 7.5% CO2 breathing, and a return to baseline during recovery. Latent class trajectories for fR yielded one class whereas panic symptom response yielded two classes. Limitations This study examined CO2 hypersensitivity in one of the largest samples to date, but did not ascertain a general population sample thereby limiting generalizability. Moreover, a true resting baseline measure of fR was not measured. Conclusions Two classes potentially representing different risk pathways were observed. Implications of results will be discussed in the context of panic risk research. PMID:25496936

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

PubMed Central

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods Sibs-A (n=204) were assessed with the Mullen Scales of Early Learning from age 6–36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (n=52); non-ASD social/communication delay (broader autism phenotype; BAP) (n=31); and unaffected (n=121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. Results A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Conclusions Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. PMID:22574686

Progressive Elaboration and Cross-Validation of a Latent Class Typology of Adolescent Alcohol Involvement in a National Sample

PubMed Central

Donovan, John E.; Chung, Tammy

2015-01-01

Objective: Most studies of adolescent drinking focus on single alcohol use behaviors (e.g., high-volume drinking, drunkenness) and ignore the patterning of adolescents’ involvement across multiple alcohol behaviors. The present latent class analyses (LCAs) examined a procedure for empirically determining multiple cut points on the alcohol use behaviors in order to establish a typology of adolescent alcohol involvement. Method: LCA was carried out on six alcohol use behavior indicators collected from 6,504 7th through 12th graders who participated in Wave I of the National Longitudinal Study of Adolescent Health (AddHealth). To move beyond dichotomous indicators, a “progressive elaboration” strategy was used, starting with six dichotomous indicators and then evaluating a series of models testing additional cut points on the ordinal indicators at progressively higher points for one indicator at a time. Analyses were performed on one random half-sample, and confirmatory LCAs were performed on the second random half-sample and in the Wave II data. Results: The final model consisted of four latent classes (never or non–current drinkers, low-intake drinkers, non–problem drinkers, and problem drinkers). Confirmatory LCAs in the second random half-sample from Wave I and in Wave II support this four-class solution. The means on the four latent classes were also generally ordered on an array of measures reflecting psychosocial risk for problem behavior. Conclusions: These analyses suggest that there may be four different classes or types of alcohol involvement among adolescents, and, more importantly, they illustrate the utility of the progressive elaboration strategy for moving beyond dichotomous indicators in latent class models. PMID:25978828

Predictors of Patterns of Alcohol-Related Blackouts Over Time in Youth From the Collaborative Study of the Genetics of Alcoholism: The Roles of Genetics and Cannabis

PubMed Central

Schuckit, Marc A.; Smith, Tom L.; Shafir, Alexandra; Clausen, Peyton; Danko, George; Gonçalves, Priscila Dib; Anthenelli, Robert M.; Chan, Grace; Kuperman, Samuel; Hesselbrock, Michie; Hesselbrock, Victor; Kramer, John; Bucholz, Kathleen K.

2017-01-01

Objective: Alcohol-related blackouts (ARBs) are anterograde amnesias related to heavy alcohol intake seen in about 50% of drinkers. Although a major determinant of ARBs relates to blood alcohol concentrations, additional contributions come from genetic vulnerabilities and possible impacts of cannabis use disorders (CUDs). We evaluated relationships of genetics and cannabis use to latent class trajectories of ARBs in 829 subjects from the Collaborative Study of the Genetics of Alcoholism (COGA). Method: The number of ARBs experienced every 2 years from subjects with average ages of 18 to 25 were entered into a latent class growth analysis in Mplus, and resulting class membership was evaluated in light of baseline characteristics, including CUDs. Correlations of number of ARBs across assessments were also compared for sibling pairs versus unrelated subjects. Results: Latent class growth analysis identified ARB-based Classes 1 (consistent low = 42.5%), 2 (moderate low = 28.3%), 3 (moderate high = 22.9%), and 4 (consistent high = 6.3%). A multinomial logistic regression analysis within latent class growth analysis revealed that baseline CUDs related most closely to Classes 3 and 4. The number of ARBs across time correlated .23 for sibling pairs and -.10 for unrelated subjects. Conclusions: Baseline CUDs related to the most severe latent ARB course over time, even when considered along with other trajectory predictors, including baseline alcohol use disorders and maximum number of drinks. Data indicated significant roles for genetic factors for alcohol use disorder patterns over time. Future research is needed to improve understanding of how cannabis adds to the ARB risk and to find genes that contribute to risks for ARBs among drinkers. PMID:27936363

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

ERIC Educational Resources Information Center

Park, Jungkyu; Yu, Hsiu-Ting

2016-01-01

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

On Local Homogeneity and Stochastically Ordered Mixed Rasch Models

ERIC Educational Resources Information Center

Kreiner, Svend; Hansen, Mogens; Hansen, Carsten Rosenberg

2006-01-01

Mixed Rasch models add latent classes to conventional Rasch models, assuming that the Rasch model applies within each class and that relative difficulties of items are different in two or more latent classes. This article considers a family of stochastically ordered mixed Rasch models, with ordinal latent classes characterized by increasing total…

Latent Cognitive Phenotypes in De Novo Parkinson's Disease: A Person-Centered Approach.

PubMed

LaBelle, Denise R; Walsh, Ryan R; Banks, Sarah J

2017-08-01

Cognitive impairment is an important aspect of Parkinson's disease (PD), but there is considerable heterogeneity in its presentation. This investigation aims to identify and characterize latent cognitive phenotypes in early PD. Latent class analysis, a data-driven, person-centered, cluster analysis was performed on cognitive data from the Parkinson's Progressive Markers Initiative baseline visit. This analytic method facilitates identification of naturally occurring endophenotypes. Resulting classes were compared across biomarker, symptom, and demographic data. Six cognitive phenotypes were identified. Three demonstrated consistent performance across indicators, representing poor ("Weak-Overall"), average ("Typical-Overall"), and strong ("Strong-Overall") cognition. The remaining classes demonstrated unique patterns of cognition, characterized by "Strong-Memory," "Weak-Visuospatial," and "Amnestic" profiles. The Amnestic class evidenced greater tremor severity and anosmia, but was unassociated with biomarkers linked with Alzheimer's disease. The Weak-Overall class was older and reported more non-motor features associated with cognitive decline, including anxiety, depression, autonomic dysfunction, anosmia, and REM sleep behaviors. The Strong-Overall class was younger, more female, and reported less dysautonomia and anosmia. Classes were unrelated to disease duration, functional independence, or available biomarkers. Latent cognitive phenotypes with focal patterns of impairment were observed in recently diagnosed individuals with PD. Cognitive profiles were found to be independent of traditional biomarkers and motoric indices of disease progression. Only globally impaired class was associated with previously reported indicators of cognitive decline, suggesting this group may drive the effects reported in studies using variable-based analysis. Longitudinal and neuroanatomical characterization of classes will yield further insight into the evolution of cognitive change in the disease. (JINS, 2017, 23, 551-563).

Common Mental Disorders among Occupational Groups: Contributions of the Latent Class Model

PubMed Central

Martins Carvalho, Fernando; de Araújo, Tânia Maria

2016-01-01

Background. The Self-Reporting Questionnaire (SRQ-20) is widely used for evaluating common mental disorders. However, few studies have evaluated the SRQ-20 measurements performance in occupational groups. This study aimed to describe manifestation patterns of common mental disorders symptoms among workers populations, by using latent class analysis. Methods. Data derived from 9,959 Brazilian workers, obtained from four cross-sectional studies that used similar methodology, among groups of informal workers, teachers, healthcare workers, and urban workers. Common mental disorders were measured by using SRQ-20. Latent class analysis was performed on each database separately. Results. Three classes of symptoms were confirmed in the occupational categories investigated. In all studies, class I met better criteria for suspicion of common mental disorders. Class II discriminated workers with intermediate probability of answers to the items belonging to anxiety, sadness, and energy decrease that configure common mental disorders. Class III was composed of subgroups of workers with low probability to respond positively to questions for screening common mental disorders. Conclusions. Three patterns of symptoms of common mental disorders were identified in the occupational groups investigated, ranging from distinctive features to low probabilities of occurrence. The SRQ-20 measurements showed stability in capturing nonpsychotic symptoms. PMID:27630999

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

PubMed Central

Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

2018-01-01

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues. PMID:29713300

A Multidimensional Item Response Model: Constrained Latent Class Analysis Using the Gibbs Sampler and Posterior Predictive Checks.

ERIC Educational Resources Information Center

Hoijtink, Herbert; Molenaar, Ivo W.

1997-01-01

This paper shows that a certain class of constrained latent class models may be interpreted as a special case of nonparametric multidimensional item response models. Parameters of this latent class model are estimated using an application of the Gibbs sampler, and model fit is investigated using posterior predictive checks. (SLD)

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Parametric embedding for class visualization.

PubMed

Iwata, Tomoharu; Saito, Kazumi; Ueda, Naonori; Stromsten, Sean; Griffiths, Thomas L; Tenenbaum, Joshua B

2007-09-01

We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

Latent Growth and Dynamic Structural Equation Models.

PubMed

Grimm, Kevin J; Ram, Nilam

2018-05-07

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

Instructor and peer bullying in college students: Distinct typologies based on Latent Class Analysis.

PubMed

Marraccini, Marisa E; Brick, Leslie Ann D; Weyandt, Lisa L

2018-03-22

Although bullying is traditionally considered within the context of primary and secondary school, recent evidence suggests that bullying continues into college and workplace settings. Participants/Method: Latent class analysis (LCA) was employed to classify college bullying involvement typologies among 325 college students attending a northeastern university. Four classes concerning bullying involvement were revealed: Non-involved (36%); Instructor victim (30%); Peer bully-victim (22%); and Peer bully-victim/ Instructor victim (12%). Findings from this study, which classified college bullying experiences by incorporating both peer and instructor (teacher and professor) bullying, add substantially to the literature by providing insight into patterns of relatively unexplored bullying behaviors.

Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study

ERIC Educational Resources Information Center

Li, Ming; Harring, Jeffrey R.

2017-01-01

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more…

Assigning Cases to Groups Using Taxometric Results: An Empirical Comparison of Classification Techniques

ERIC Educational Resources Information Center

Ruscio, John

2009-01-01

Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic…

A Latent Class Approach to Fitting the Weighted Euclidean Model, CLASCAL.

ERIC Educational Resources Information Center

Winsberg, Suzanne; De Soete, Geert

1993-01-01

A weighted Euclidean distance model is proposed that incorporates a latent class approach (CLASCAL). The contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. A model selection strategy is proposed and illustrated. (SLD)

A Latent Class Unfolding Model for Analyzing Single Stimulus Preference Ratings.

ERIC Educational Resources Information Center

De Soete, Geert; Heiser, Willem J.

1993-01-01

A latent class unfolding model is developed for single stimulus preference ratings. One advantage is the possibility of testing the spatial unfolding model against the unconstrained latent class model for rating data. The model is applied to data about party preferences of members of the Dutch parliament. (SLD)

Managerial performance and cost efficiency of Japanese local public hospitals: a latent class stochastic frontier model.

PubMed

Besstremyannaya, Galina

2011-09-01

The paper explores the link between managerial performance and cost efficiency of 617 Japanese general local public hospitals in 1999-2007. Treating managerial performance as unobservable heterogeneity, the paper employs a panel data stochastic cost frontier model with latent classes. Financial parameters associated with better managerial performance are found to be positively significant in explaining the probability of belonging to the more efficient latent class. The analysis of latent class membership was consistent with the conjecture that unobservable technological heterogeneity reflected in the existence of the latent classes is related to managerial performance. The findings may support the cause for raising efficiency of Japanese local public hospitals by enhancing the quality of management. Copyright © 2011 John Wiley & Sons, Ltd.

Substance use, mental illness, and familial conflict non-negotiation among HIV-positive African-Americans: latent class regression and a new syndemic framework.

PubMed

Robinson, Allysha C; Knowlton, Amy R; Gielen, Andrea C; Gallo, Joseph J

2016-02-01

We evaluated a synergistic epidemic (syndemic) of substance use, mental illness, and familial conflict non-negotiation among HIV-positive injection drug users (IDU). Baseline BEACON study data was utilized. Latent class analyses identified syndemic classes. These classes were regressed on sex, viral suppression, and acute care non-utilization. Females were hypothesized to have higher syndemic burden, and worse health outcomes than males. Nine percent of participants had high substance use/mental illness prevalence (Class 4); 23 % had moderate levels of all factors (Class 3); 25 % had high mental illness (Class 2); 43 % had moderate substance use/mental illness (Class 1; N = 331). Compared to Classes 1-3, Class 4 was mostly female (p < .05), less likely to achieve viral suppression, and more likely to utilize acute care (p < .05). Interventions should target African-American IDU females to improve their risk of negative medical outcomes. Findings support comprehensive syndemic approaches to HIV interventions, rather than singular treatment methods.

Applying Latent Class Analysis to Risk Stratification for Perioperative Mortality in Patients Undergoing Intraabdominal General Surgery.

PubMed

Kim, Minjae; Wall, Melanie M; Li, Guohua

2016-07-01

Perioperative risk stratification is often performed using individual risk factors without consideration of the syndemic of these risk factors. We used latent class analysis (LCA) to identify the classes of comorbidities and risk factors associated with perioperative mortality in patients presenting for intraabdominal general surgery. The 2005 to 2010 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a cohort of patients undergoing intraabdominal general surgery. Risk factors and comorbidities were entered into LCA models to identify the latent classes, and individuals were assigned to a class based on the highest posterior probability of class membership. Relative risk regression was used to determine the associations between the latent classes and 30-day mortality, with adjustments for procedure. A 9-class model was fit using LCA on 466,177 observations. After combining classes with similar adjusted mortality risks, 5 risk classes were obtained. Compared with the class with average mortality risk (class 4), the risk ratios (95% confidence interval) ranged from 0.020 (0.014-0.027) in the lowest risk class (class 1) to 6.75 (6.46-7.02) in the highest risk class. After adjusting for procedure and ASA physical status, the latent classes remained significantly associated with 30-day mortality. The addition of the risk class variable to a model containing ASA physical status and surgical procedure demonstrated a significant increase in the area under the receiver operator characteristic curve (0.892 vs 0.915; P < 0.0001). Latent classes of risk factors and comorbidities in patients undergoing intraabdominal surgery are predictive of 30-day mortality independent of the ASA physical status and improve risk prediction with the ASA physical status.

A Latent Class Regression Analysis of Men's Conformity to Masculine Norms and Psychological Distress

ERIC Educational Resources Information Center

Wong, Y. Joel; Owen, Jesse; Shea, Munyi

2012-01-01

How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse…

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Patterns of Adolescent Sexual Behavior Predicting Young Adult Sexually Transmitted Infections: A Latent Class Analysis Approach

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Butera, Nicole M.; Lanza, Stephanie T.

2014-01-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity. PMID:24449152

Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: a latent class analysis approach.

PubMed

Vasilenko, Sara A; Kugler, Kari C; Butera, Nicole M; Lanza, Stephanie T

2015-04-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity.

Structural Relationships between Social Activities and Longitudinal Trajectories of Depression among Older Adults

ERIC Educational Resources Information Center

Hong, Song-Iee; Hasche, Leslie; Bowland, Sharon

2009-01-01

Purpose: This study examines the structural relationships between social activities and trajectories of late-life depression. Design and Methods: Latent class analysis was used with a nationally representative sample of older adults (N = 5,294) from the Longitudinal Study on Aging II to classify patterns of social activities. A latent growth curve…

Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

ERIC Educational Resources Information Center

Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk

2005-01-01

Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…

Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

PubMed

Depaoli, Sarah

2013-06-01

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis.

PubMed

Eppig, Joel S; Edmonds, Emily C; Campbell, Laura; Sanderson-Cimino, Mark; Delano-Wood, Lisa; Bondi, Mark W

2017-08-01

Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes. A total of 806 participants diagnosed by means of the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on "robust" normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer's disease CSF biomarkers than LPA-derived normal subjects. Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent "false-positive" diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564-576).

Exploring heterogeneity in clinical trials with latent class analysis

PubMed Central

Abarda, Abdallah; Contractor, Ateka A.; Wang, Juan; Dayton, C. Mitchell

2018-01-01

Case-mix is common in clinical trials and treatment effect can vary across different subgroups. Conventionally, a subgroup analysis is performed by dividing the overall study population by one or two grouping variables. It is usually impossible to explore complex high-order intersections among confounding variables. Latent class analysis (LCA) provides a framework to identify latent classes by observed manifest variables. Distal clinical outcomes and treatment effect can be different across these classes. This paper provides a step-by-step tutorial on how to perform LCA with R. A simulated dataset is generated to illustrate the process. In the example, the classify-analyze approach is employed to explore the differential treatment effects on distal outcomes across latent classes. PMID:29955579

A latent transition analysis of bullying and victimization in Chinese primary school students

PubMed Central

Lau, Puiyi; Luo, Fang

2017-01-01

Bullying is a social phenomenon that impacts a large number of children and young people, worldwide. This study aimed to longitudinally examine the development of bullying and victimization in Chinese students in grades 4, 5, and 6. We used latent class analysis to empirically identify groups of youth with different bullying and victimization patterns, and then used latent transition analysis to explore the movement of children between these latent classes over time. Results showed that: (1) across the three time points, students could be classified into four classes: bullies, victims, bully-victims, and non-involved children; and (2) students in the non-involved class tended to remain in that class when moving to higher grades, students in the bully and victims classes tended to transition to the non-involved class, while students in the bully-victims class tended to transition to the bullies class. Thus, future intervention should be implemented to prevent bully-victims from bullying behaviors. PMID:28837571

Growth Modeling with Non-Ignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

PubMed Central

Muthén, Bengt; Asparouhov, Tihomir; Hunter, Aimee; Leuchter, Andrew

2011-01-01

This paper uses a general latent variable framework to study a series of models for non-ignorable missingness due to dropout. Non-ignorable missing data modeling acknowledges that missingness may depend on not only covariates and observed outcomes at previous time points as with the standard missing at random (MAR) assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework using the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling using latent trajectory classes. A new selection model allows not only an influence of the outcomes on missingness, but allows this influence to vary across latent trajectory classes. Recommendations are given for choosing models. The missing data models are applied to longitudinal data from STAR*D, the largest antidepressant clinical trial in the U.S. to date. Despite the importance of this trial, STAR*D growth model analyses using non-ignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. PMID:21381817

A Latent Class Analysis of Dyadic Perfectionism in a College Sample

ERIC Educational Resources Information Center

Lopez, Frederick G.; Fons-Scheyd, Alia; Bush-King, Imelda; McDermott, Ryon C.

2011-01-01

A latent class analysis of dyadic perfectionism scores within a college sample (N = 369) identified four classes of participants. Controlling for gender and current dating status, class membership was associated with significant differences on several measures of relationship attitudes. Gender and class membership also significantly interacted in…

Sexual Behavior Latent Classes Among Men Who Have Sex With Men: Associations With Sexually Transmitted Infections.

PubMed

Rice, Cara E; Norris Turner, Abigail; Lanza, Stephanie T

2017-01-01

Men who have sex with men (MSM) are at disproportionate risk of acquisition of sexually transmitted infections (STIs). We used latent class analysis (LCA) to examine patterns of sexual behavior among MSM and how those patterns are related to STIs. We examined patterns of sexual behavior using behavioral and clinical data from a cross-sectional study of 235 MSM who presented to an urban sexual health clinic for STI testing. Analyzed data were collected using a combination of interviewer- and self-administered surveys and electronic health records. We used LCA to identify underlying subgroups of men based on their sexual behavior, described the demographics of the latent classes, and examined the association between the latent classes and STI status. We identified three latent classes of sexual behavior: Unprotected Anal Intercourse (UAI) Only (67%), Partner Seekers (14%), and Multiple Behaviors (19%). Men in the Multiple Behaviors class had a 67% probability of being STI positive, followed by men in the UAI Only class (27%) and men in the Partner Seekers class (22%). Examining the intersection of a variety of sexual practices indicates particular subgroups of MSM have the highest probability of being STI positive.

Latent class analysis of eating and impulsive behavioral symptoms in Taiwanese women with bulimia nervosa.

PubMed

Tseng, Mei-Chih Meg; Hu, Fu-Chang

2012-01-01

The implications of impulsivity in its relationship with binge-eating or purging behaviors remain unclear. This study examined the patterns of eating behaviors and co-morbid impulsive behaviors in individuals with bulimia nervosa n optimally homogeneous classes using latent class analysis (LCA). All participants (n=180) were asked to complete a series of self-reported inventories of impulsive behaviors and other psychological measures. Information regarding the lifetime presence of symptoms of eating disorder was assessed by clinical interviews. LCA was conducted using eating disorder symptoms, impulsive behaviors, and the number of purging methods. Three latent classes of bulimic women were identified. These were women who exhibited relatively higher rates of purging, symptoms of impulsive behavior, and multiple purging methods (17.8%), women who used no more than one purging method with a low occurrence of impulsive behavior (41.7%), and women who showed higher rates of purging behaviors and the use of multiple purging methods with a low rate of impulsive behavior (41.7%). The impulsive sub-group had comparable severity of eating-related measures, frequency of binge-eating, and higher levels of general psychopathology than that of the other two sub-groups. This study provides empirical support for the existence of an impulsive subgroup with distinctive features among a non-Western group of BN patients. This study also suggests that mechanisms other than impulse dysregulation may exist for the development of binge-eating and purging behaviors in bulimia nervosa patients, or the mechanisms contributing to binge-eating and impulsive behaviors may be different. Copyright © 2011 Elsevier Inc. All rights reserved.

Job Satisfaction among Health-Care Staff in Township Health Centers in Rural China: Results from a Latent Class Analysis

PubMed Central

Wang, Haipeng; Tang, Chengxiang; Zhao, Shichao; Meng, Qingyue; Liu, Xiaoyun

2017-01-01

Background: The lower job satisfaction of health-care staff will lead to more brain drain, worse work performance, and poorer health-care outcomes. The aim of this study was to identify patterns of job satisfaction among health-care staff in rural China, and to investigate the association between the latent clusters and health-care staff’s personal and professional features; Methods: We selected 12 items of five-point Likert scale questions to measure job satisfaction. A latent-class analysis was performed to identify subgroups based on the items of job satisfaction; Results: Four latent classes of job satisfaction were identified: 8.9% had high job satisfaction, belonging to “satisfied class”; 38.2% had low job satisfaction, named as “unsatisfied class”; 30.5% were categorized into “unsatisfied class with the exception of interpersonal relationships”; 22.4% were identified as “pseudo-satisfied class”, only satisfied with management-oriented items. Low job satisfaction was associated with specialty, training opportunity, and income inequality. Conclusions: The minority of health-care staff belong to the “satisfied class”. Three among four subgroups are not satisfied with income, benefit, training, and career development. Targeting policy interventions should be implemented to improve the items of job satisfaction based on the patterns and health-care staff’s features. PMID:28937609

Consistent latent position estimation and vertex classification for random dot product graphs.

PubMed

Sussman, Daniel L; Tang, Minh; Priebe, Carey E

2014-01-01

In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $(k)$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.

Studying Psychosocial Barriers to Drug Treatment Among Chinese Methamphetamine Users Using A 3-Step Latent Class Analysis.

PubMed

Wang, Jichuan; Kelly, Brian C; Liu, Tieqiao; Hao, Wei

2016-03-01

Given the growth in methamphetamine use in China during the 21st century, we assessed perceived psychosocial barriers to drug treatment among this population. Using a sample of 303 methamphetamine users recruited via Respondent Driven Sampling, we use Latent Class Analysis (LCA) to identify possible distinct latent groups among Chinese methamphetamine users on the basis of their perceptions of psychosocial barriers to drug treatment. After covariates were included to predict latent class membership, the 3-step modeling approach was applied. Our findings indicate that the Chinese methamphetamine using population was heterogeneous on perceptions of drug treatment barriers; four distinct latent classes (subpopulations) were identified--Unsupported Deniers, Deniers, Privacy Anxious, and Low Barriers--and individual characteristics shaped the probability of class membership. Efforts to link Chinese methamphetamine users to treatment may require a multi-faceted approach that attends to differing perceptions about impediments to drug treatment. Copyright © 2015. Published by Elsevier Inc.

A Latent Class Analysis of Maternal Depressive Symptoms over 12 Years and Offspring Adjustment in Adolescence

PubMed Central

Campbell, Susan B.; Morgan-Lopez, Antonio A.; Cox, Martha J.; McLoyd, Vonnie C.

2009-01-01

We used data from the NICHD Study of Early Child Care and Youth Development and latent class analysis to model patterns of maternal depressive symptoms from infant age 1 month to the transition to adolescence (age 12), and then examined adolescent adjustment at age 15 as a function of the course and severity of maternal symptoms. We identified five latent classes of symptoms in these 1357 women while also taking into account sociodemographic measures: never depressed; stable subclinical; early-decreasing; moderately elevated; chronic. Women with few symptoms were more likely to be married, better educated, and in better physical health than women with more elevated symptoms. Family size and whether the pregnancy was planned also differentiated among classes. At age 15, adolescents whose mothers were in the chronic, elevated, and stable subclinical latent classes reported more internalizing and externalizing problems and acknowledged engaging in more risky behavior than did children of never-depressed mothers. Latent class differences in self-reported loneliness and dysphoria were also found. Finally, several significant interactions between sex and latent class suggested that girls whose mothers reported elevated symptoms of depression over time experienced more internalizing distress and dysphoric mood relative to their male counterparts. Discussion focuses on adolescent adjustment, especially among offspring whose mothers report stable symptoms of depression across their childhoods. PMID:19685946

Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

ERIC Educational Resources Information Center

Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

2011-01-01

The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

A latent transition model of the effects of a teen dating violence prevention initiative.

PubMed

Williams, Jason; Miller, Shari; Cutbush, Stacey; Gibbs, Deborah; Clinton-Sherrod, Monique; Jones, Sarah

2015-02-01

Patterns of physical and psychological teen dating violence (TDV) perpetration, victimization, and related behaviors were examined with data from the evaluation of the Start Strong: Building Healthy Teen Relationships initiative, a dating violence primary prevention program targeting middle school students. Latent class and latent transition models were used to estimate distinct patterns of TDV and related behaviors of bullying and sexual harassment in seventh grade students at baseline and to estimate transition probabilities from one pattern of behavior to another at the 1-year follow-up. Intervention effects were estimated by conditioning transitions on exposure to Start Strong. Latent class analyses suggested four classes best captured patterns of these interrelated behaviors. Classes were characterized by elevated perpetration and victimization on most behaviors (the multiproblem class), bullying perpetration/victimization and sexual harassment victimization (the bully-harassment victimization class), bullying perpetration/victimization and psychological TDV victimization (bully-psychological victimization), and experience of bully victimization (bully victimization). Latent transition models indicated greater stability of class membership in the comparison group. Intervention students were less likely to transition to the most problematic pattern and more likely to transition to the least problem class. Although Start Strong has not been found to significantly change TDV, alternative evaluation models may find important differences. Latent transition analysis models suggest positive intervention impact, especially for the transitions at the most and the least positive end of the spectrum. Copyright © 2015. Published by Elsevier Inc.

Substance Use Profiles of Urban American Indian Adolescents: A Latent Class Analysis.

PubMed

Kulis, Stephen S; Jager, Justin; Ayers, Stephanie L; Lateef, Husain; Kiehne, Elizabeth

2016-07-28

A growing majority of American Indian adolescents now live in cities and are at high risk of early and problematic substance use and its negative health effects. This study used latent class analysis to empirically derive heterogeneous patterns of substance use among urban American Indian adolescents, examined demographic correlates of the resulting latent classes, and tested for differences among the latent classes in other risk behavior and prosocial outcomes. The study employed a representative sample of 8th, 10th, and 12th grade American Indian adolescents (n = 2,407) in public or charter schools in metropolitan areas of Arizona in 2012. Latent class analysis examined eight types of last 30 day substance use. Four latent classes emerged: a large group of "nonusers" (69%); a substantial minority using alcohol, tobacco, and/or marijuana [ATM] (17%); a smaller group of polysubstance users consuming, alcohol, tobacco, marijuana, other illicit drugs, and prescription or OTC drugs in combination (6%); and a "not alcohol" group reporting combinations of tobacco, marijuana, and prescription drug use, but rarely alcohol use (4%). The latent classes varied by age and grade level, but not by other demographic characteristics, and aligned in highly consistent patterns on other non-substance use outcomes. Polysubstance users reported the most problematic and nonusers the least problematic outcomes, with ATM and "not alcohol" users in the middle. Urban AI adolescent substance use occurs in three somewhat distinctive patterns of combinations of recent alcohol and drug consumption, covarying in systematic ways with other problematic risk behaviors and attitudes.

A Discrete Latent State Approach to Diagnostic Testing. Final Report on Contract Number N00014-81-K-0564.

ERIC Educational Resources Information Center

Paulson, James A.

This paper reports on a project which has developed the general latent class model as a framework for representation of item responses. This framework can be used to represent data in applications such as mastery tests and other kinds of achievement tests, where there is reason to believe that current foundations are deficient. Methods of…

Discordant inflammation and pain in early and established rheumatoid arthritis: Latent Class Analysis of Early Rheumatoid Arthritis Network and British Society for Rheumatology Biologics Register data.

PubMed

McWilliams, Daniel F; Ferguson, Eamonn; Young, Adam; Kiely, Patrick D W; Walsh, David A

2016-12-13

Rheumatoid arthritis (RA) disease activity is often measured using the 28-joint Disease Activity Score (DAS28). We aimed to identify and independently verify subgroups of people with RA that may be discordant with respect to self-reported and objective disease state, with potentially different clinical needs. Data were derived from three cohorts: (1) the Early Rheumatoid Arthritis Network (ERAN) and the British Society for Rheumatology Biologics Register (BSRBR), (2) those commencing tumour necrosis factor (TNF)-α inhibitors and (3) those using non-biologic drugs. In latent class analysis, we used variables related to pain, central pain mechanisms or inflammation (pain, vitality, mental health, erythrocyte sedimentation rate, swollen joint count, tender joint count, visual analogue scale of general health). Clinically relevant outcomes were examined. Five, four and four latent classes were found in the ERAN, BSRBR TNF inhibitor and non-biologic cohorts, respectively. The proportions of people assigned with >80% probability into latent classes were 76%, 58% and 72% in the ERAN, TNF inhibitor and non-biologic cohorts, respectively. The latent classes displayed either concordance between measures indicative of mild, moderate or severe disease activity; discordantly worse patient-reported measures despite less markedly elevated inflammation; or discordantly less severe patient-reported measures despite elevated inflammation. Latent classes with discordantly worse patient-reported measures represented 12%, 40% and 21% of the ERAN, TNF inhibitor and non-biologic cohorts, respectively; contained more females; and showed worse function. In those latent classes with worse scores at baseline, DAS28 and function improved over 1 year (p < 0.001 for all comparisons), and scores differed less at follow-up than at baseline. Discordant latent classes can be identified in people with RA, and these findings are robust across three cohorts with varying disease duration and activity. These findings could be used to identify a sizeable subgroup of people with RA who might gain added benefit from pain management strategies.

Patterns of Physical Activity Among Older Adults in New York City

PubMed Central

Mooney, Stephen J.; Joshi, Spruha; Cerdá, Magdalena; Quinn, James W.; Beard, John R.; Kennedy, Gary J.; Benjamin, Ebele O.; Ompad, Danielle C.; Rundle, Andrew G.

2015-01-01

Introduction Little research to date has explored typologies of physical activity among older adults. An understanding of physical activity patterns may help to both determine the health benefits of different types of activity and target interventions to increase activity levels in older adults. This analysis, conducted in 2014, used a latent class analysis approach to characterize patterns of physical activity in a cohort of older adults. Methods A total of 3,497 men and women aged 65–75 years living in New York City completed the Physical Activity Scale for the Elderly (PASE) in 2011. PASE scale items were used to classify subjects into latent classes. Multinomial regression was then used to relate individual and neighborhood characteristics to class membership. Results Five latent classes were identified: “least active,” “walkers,” “domestic/gardening,” “athletic,” and “domestic/gardening athletic.” Individual-level predictors, including more education, higher income, and better self-reported health, were associated with membership in the more-active classes, particularly the athletic classes. Residential characteristics, including living in single-family housing and living in the lower-density boroughs of New York City, were predictive of membership in one of the domestic/gardening classes. Class membership was associated with BMI even after controlling for total PASE score. Conclusions This study suggests that individual and neighborhood characteristics are associated with distinct physical activity patterns in a group of older urban adults. These patterns are associated with body habitus independent of overall activity. PMID:26091927

The Latent Class Structure of Chinese Patients with Eating Disorders in Shanghai.

PubMed

Zheng, Yuchen; Kang, Qing; Huang, Jiabin; Jiang, Wenhui; Liu, Qiang; Chen, Han; Fan, Qing; Wang, Zhen; Chen, Jue; Xiao, Zeping

2017-08-25

Eating disorder is culture related, and the clinical symptoms are different between eastern and western patients. So the validity of feeding and eating disorders in the upcoming ICD-11 guide for Chinese patients is unclear. To explore the latent class structure of Chinese patients with eating disorder and the cross-cultural validity of the eating disorder section of the new ICD-11 guide in China. A total of 379 patients with eating disorders at Shanghai Mental Health Center were evaluated using the EDI questionnaire and a questionnaire developed by researchers from 2010 to 2016. SPSS 20.0 was used to enter data and analyze demographic data, and Latent GOLD was employed to conduct latent profile analysis. According to the results of latent profile analysis, patients with eating disorder were divided into five classes: low-weight fasting class (23.1%), non-fat-phobic binge/purge class (21.54%), low-fat-phobic binge class (19.27%), fat-phobic binge class (19.27%), and non-fat-phobic low-weight class (16.76%). Among the clinical symptoms extracted, there were significant differences in Body Mass Index (BMI), binge eating behavior, self-induced vomiting, laxative use and fat-phobic opinion; while there was no significant difference in restrictive food intake. Based on the clinical symptoms, there are five latent classes in Chinese patients with eating disorder, which is in accordance with the diagnostic categories of feeding and eating disorder in ICD-11. However, further work is needed in improving the fat-phobic opinion of patients with eating disorder and clarifying the BMI standard of thinness in the Chinese population.

Heterogeneity in risk of prostate cancer: A Swedish population-based cohort study of competing risks and type 2 diabetes mellitus.

PubMed

Häggström, Christel; Van Hemelrijck, Mieke; Garmo, Hans; Robinson, David; Stattin, Pär; Rowley, Mark; Coolen, Anthony C C; Holmberg, Lars

2018-05-09

Most previous studies of prostate cancer have not taken into account that men in the studied populations are also at risk of competing event, and that these men may have different susceptibility to prostate cancer risk. The aim of this study was to investigate heterogeneity in risk of prostate cancer, using a recently developed latent class regression method for competing risks. We further aimed to elucidate the association between type 2 diabetes mellitus (T2DM) and prostate cancer risk, and to compare the results with conventional methods for survival analysis. We analysed the risk of prostate cancer in 126,482 men from the comparison cohort of the Prostate Cancer Data base Sweden (PCBaSe) 3.0. During a mean follow-up of 6 years 6,036 men were diagnosed with prostate cancer and 22,393 men died. We detected heterogeneity in risk of prostate cancer with two distinct latent classes in the study population. The smaller class included 9% of the study population in which men had a higher risk of prostate cancer and the risk was stronger associated with class membership than any of the covariates included in the study. Moreover, we found no association between T2DM and risk of prostate cancer after removal of the effect of informative censoring due to competing risks. The recently developed latent class for competing risks method could be used to provide new insights in precision medicine with the target to classify individuals regarding different susceptibility to a particular disease, reaction to a risk factor or response to treatment. This article is protected by copyright. All rights reserved. © 2018 UICC.

Spurious Latent Classes in the Mixture Rasch Model

ERIC Educational Resources Information Center

Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.

2011-01-01

Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…

Cross-Informant Agreement on Child and Adolescent Withdrawn Behavior: A Latent Class Approach

ERIC Educational Resources Information Center

Rubin, David H.; Althoff, Robert R.; Walkup, John T.; Hudziak, James J.

2013-01-01

Withdrawn behavior (WB) relates to many developmental outcomes, including pervasive developmental disorders, anxiety, depression, psychosis, personality disorders and suicide. No study has compared the latent profiles of different informants' reports on WB. This study uses multi-informant latent class analyses (LCA) of the child behavior checklist…

The effects of rurality on substance use disorder diagnosis: A multiple-groups latent class analysis.

PubMed

Brooks, Billy; McBee, Matthew; Pack, Robert; Alamian, Arsham

2017-05-01

Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities. Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership. Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P value<0.0001). Overall the rural class structure exhibited less diversity in class structure and lower prevalence of SUD in multiple drug categories (e.g. cocaine, hallucinogens, and stimulants). This result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

PubMed Central

Arndt, Alice; Rubel, Julian; Berger, Thomas; Schröder, Johanna; Späth, Christina; Meyer, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen

2017-01-01

Background Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment. Objective The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects. Methods We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression. Results Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04). Conclusions These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources. PMID:28600278

Understanding the Heterogeneity of BPD Symptoms through Latent Class Analysis: Initial Results and Clinical Correlates among Inner-City Substance Users

ERIC Educational Resources Information Center

Bornovalova, Marina A.; Levy, Roy; Gratz, Kim L.; Lejuez, C. W.

2010-01-01

The current study investigated the heterogeneity of borderline personality disorder (BPD) symptoms in a sample of 382 inner-city, predominantly African American male substance users through the use of latent class analysis. A 4-class model was statistically preferred, with 1 class interpreted to be a baseline class, 1 class interpreted to be a…

Discontinuous Patterns of Cigarette Smoking From Ages 18 to 50 in the United States: A Repeated-Measures Latent Class Analysis.

PubMed

Terry-McElrath, Yvonne M; O'Malley, Patrick M; Johnston, Lloyd D

2017-12-13

Effective cigarette smoking prevention and intervention programming is enhanced by accurate understanding of developmental smoking pathways across the life span. This study investigated within-person patterns of cigarette smoking from ages 18 to 50 among a US national sample of high school graduates, focusing on identifying ages of particular importance for smoking involvement change. Using data from approximately 15,000 individuals participating in the longitudinal Monitoring the Future study, trichotomous measures of past 30-day smoking obtained at 11 time points were modeled using repeated-measures latent class analyses. Sex differences in latent class structure and membership were examined. Twelve latent classes were identified: three characterized by consistent smoking patterns across age (no smoking; smoking < pack per day; smoking pack + per day); three showing uptake to a higher category of smoking across age; four reflecting successful quit behavior by age 50; and two defined by discontinuous shifts between smoking categories. The same latent class structure was found for both males and females, but membership probabilities differed between sexes. Although evidence of increases or decreases in smoking behavior was observed at virtually all ages through 35, 21/22 and 29/30 appeared to be particularly key for smoking category change within class. This examination of latent classes of cigarette smoking among a national US longitudinal sample of high school graduates from ages 18 to 50 identified unique patterns and critical ages of susceptibility to change in smoking category within class. Such information may be of particular use in developing effective smoking prevention and intervention programming. This study examined cigarette smoking among a national longitudinal US sample of high school graduates from ages 18 to 50 and identified distinct latent classes characterized by patterns of movement between no cigarette use, light-to-moderate smoking, and the conventional definition of heavy smoking at 11 time points via repeated-measures latent class analysis. Membership probabilities for each smoking class were estimated, and critical ages of susceptibility to change in smoking behaviors were identified. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Applying the Mixed Rasch Model to the Runco Ideational Behavior Scale

ERIC Educational Resources Information Center

Sen, Sedat

2016-01-01

Previous research using creativity assessments has used latent class models and identified multiple classes (a 3-class solution) associated with various domains. This study explored the latent class structure of the Runco Ideational Behavior Scale, which was designed to quantify ideational capacity. A robust state-of the-art technique called the…

Use of Latent Class Analysis to define groups based on validity, cognition, and emotional functioning.

PubMed

Morin, Ruth T; Axelrod, Bradley N

Latent Class Analysis (LCA) was used to classify a heterogeneous sample of neuropsychology data. In particular, we used measures of performance validity, symptom validity, cognition, and emotional functioning to assess and describe latent groups of functioning in these areas. A data-set of 680 neuropsychological evaluation protocols was analyzed using a LCA. Data were collected from evaluations performed for clinical purposes at an urban medical center. A four-class model emerged as the best fitting model of latent classes. The resulting classes were distinct based on measures of performance validity and symptom validity. Class A performed poorly on both performance and symptom validity measures. Class B had intact performance validity and heightened symptom reporting. The remaining two Classes performed adequately on both performance and symptom validity measures, differing only in cognitive and emotional functioning. In general, performance invalidity was associated with worse cognitive performance, while symptom invalidity was associated with elevated emotional distress. LCA appears useful in identifying groups within a heterogeneous sample with distinct performance patterns. Further, the orthogonal nature of performance and symptom validities is supported.

[Poverty profile regarding households participating in a food assistance program].

PubMed

Álvarez-Uribe, Martha C; Aguirre-Acevedo, Daniel C

2012-06-01

This study was aimed at establishing subgroups having specific socioeconomic characteristics by using latent class analysis as a method for segmenting target population members of the MANA-ICBF supplementary food program in the Antioquia department of Colombia and determine their differences regarding poverty and health conditions in efficiently addressing pertinent resources, programs and policies. The target population consisted of 200,000 children and their households involved in the MANA food assistance program; a representative sample by region was used. Latent class analysis was used, as were the expectation-maximization and Newton Raphson algorithms for identifying the appropriate number of classes. The final model classified the households into four clusters or classes, differing according to well-defined socio-demographic conditions affecting children's health. Some homes had a greater depth of poverty, therefore lowering the families' quality of life and affecting the health of the children in this age group.

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

PubMed

Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

2015-05-01

We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Short-term memory development: differences in serial position curves between age groups and latent classes.

PubMed

Koppenol-Gonzalez, Gabriela V; Bouwmeester, Samantha; Vermunt, Jeroen K

2014-10-01

In studies on the development of cognitive processes, children are often grouped based on their ages before analyzing the data. After the analysis, the differences between age groups are interpreted as developmental differences. We argue that this approach is problematic because the variance in cognitive performance within an age group is considered to be measurement error. However, if a part of this variance is systematic, it can provide very useful information about the cognitive processes used by some children of a certain age but not others. In the current study, we presented 210 children aged 5 to 12 years with serial order short-term memory tasks. First we analyze our data according to the approach using age groups, and then we apply latent class analysis to form latent classes of children based on their performance instead of their ages. We display the results of the age groups and the latent classes in terms of serial position curves, and we discuss the differences in results. Our findings show that there are considerable differences in performance between the age groups and the latent classes. We interpret our findings as indicating that the latent class analysis yielded a much more meaningful way of grouping children in terms of cognitive processes than the a priori grouping of children based on their ages. Copyright © 2014 Elsevier Inc. All rights reserved.

Informing the Discussion on Evaluator Training: A Look at Evaluators' Course Taking and Professional Practice

ERIC Educational Resources Information Center

Christie, Christina A.; Quiñones, Patricia; Fierro, Leslie

2014-01-01

This classification study examines evaluators' coursework training as a way of understanding evaluation practice. Data regarding courses that span methods and evaluation topics were collected from evaluation practitioners. Using latent class analysis, we establish four distinct classes of evaluator course-taking patterns: quantitative,…

Evaluation by latent class analysis of a magnetic capture based DNA extraction followed by real-time qPCR as a new diagnostic method for detection of Echinococcus multilocularis in definitive hosts.

PubMed

Maas, Miriam; van Roon, Annika; Dam-Deisz, Cecile; Opsteegh, Marieke; Massolo, Alessandro; Deksne, Gunita; Teunis, Peter; van der Giessen, Joke

2016-10-30

A new method, based on a magnetic capture based DNA extraction followed by qPCR, was developed for the detection of the zoonotic parasite Echinococcus multilocularis in definitive hosts. Latent class analysis was used to compare this new method with the currently used phenol-chloroform DNA extraction followed by single tube nested PCR. In total, 60 red foxes and coyotes from three different locations were tested with both molecular methods and the sedimentation and counting technique (SCT) or intestinal scraping technique (IST). Though based on a limited number of samples, it could be established that the magnetic capture based DNA extraction followed by qPCR showed similar sensitivity and specificity as the currently used phenol-chloroform DNA extraction followed by single tube nested PCR. All methods have a high specificity as shown by Bayesian latent class analysis. Both molecular assays have higher sensitivities than the combined SCT and IST, though the uncertainties in sensitivity estimates were wide for all assays tested. The magnetic capture based DNA extraction followed by qPCR has the advantage of not requiring hazardous chemicals like the phenol-chloroform DNA extraction followed by single tube nested PCR. This supports the replacement of the phenol-chloroform DNA extraction followed by single tube nested PCR by the magnetic capture based DNA extraction followed by qPCR for molecular detection of E. multilocularis in definitive hosts. Copyright © 2016 Elsevier B.V. All rights reserved.

A latent class regression analysis of men's conformity to masculine norms and psychological distress.

PubMed

Wong, Y Joel; Owen, Jesse; Shea, Munyi

2012-01-01

How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse sample of 223 men. The authors identified a 2-class solution. Both latent classes demonstrated very different associations between conformity to masculine norms and psychological distress. In Class 1 (labeled risk avoiders; n = 133), conformity to the masculine norm of risk-taking was negatively related to psychological distress. In Class 2 (labeled detached risk-takers; n = 90), conformity to the masculine norms of playboy, self-reliance, and risk-taking was positively related to psychological distress, whereas conformity to the masculine norm of violence was negatively related to psychological distress. A post hoc analysis revealed that younger men and Asian American men (compared with Latino and White American men) had significantly greater odds of being in Class 2 versus Class 1. The implications of these findings for future research and clinical practice are examined. (c) 2012 APA, all rights reserved.

A Latent Transition Analysis of Academic Intrinsic Motivation from Childhood through Adolescence

ERIC Educational Resources Information Center

Marcoulides, George A.; Gottfried, Adele Eskeles; Gottfried, Allen W.; Oliver, Pamella H.

2008-01-01

A longitudinal modeling approach was utilized to determine the existence of latent classes with regard to academic intrinsic motivation and the points of stability and transition of individuals between and within classes. A special type of latent Markov Chain model using "Mplus" was fit to data from the Fullerton Longitudinal Study, with…

Empirically derived lifespan polytraumatization typologies: A systematic review.

PubMed

Contractor, Ateka A; Caldas, Stephanie; Fletcher, Shelley; Shea, M Tracie; Armour, Cherie

2018-07-01

Polytraumatization classes based on trauma endorsement patterns relate to distinct clinical outcomes. Person-centered approaches robustly evaluate the nature, and construct validity of polytraumatization classes. Our review examined evidence for the nature and construct validity of lifespan polytraumatization typologies. In September 2016, we searched Pubmed, PSYCINFO, PSYC ARTICLES, Academic Search Complete, PILPTS, Web of Science, CINAHL, Medline, PsycEXTRA, and PBSC. Search terms included "latent profile," "latent class," "latent analysis," "person-centered," "polytrauma," "polyvictimization," "traumatization," "lifetime," "cooccurring," "complex," "typology," "multidimensional," "sequential," "multiple," "subtype," "(re)victimization," "cumulative," "maltreatment," "abuse," and "stressor." Inclusionary criteria included: peer-reviewed; latent class/latent profile analyses (LCA/LPA) of lifespan polytrauma classes; adult samples of size greater than 200; only trauma types as LCA/LPA indicators; mental health correlates of typologies; and individual-level trauma assessment. Of 1,397 articles, nine met inclusion criteria. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, research assistants completed a secondary reference search, and independently extracted data with standardized coding forms. Three-class (n = 5) or four-class (n = 4) solutions were found. Seven studies found a class characterized by higher trauma endorsement (high-trauma). All studies found a class characterized by lower trauma endorsement (low-trauma), and predominance of specific traumas (specific-trauma; e.g., childhood maltreatment). High-trauma versus low-trauma classes and specific-trauma versus low-trauma classes differed on mental health correlates. Evidence supports the prevalence of a high-trauma class experiencing poorer mental health, and the detrimental impact of aggregated interpersonal and other traumas. We highlight the clinical importance of addressing polytraumatization classes, and comprehensively assessing the impact of all traumas. © 2018 Wiley Periodicals, Inc.

Modern Methods for Modeling Change in Obesity Research in Nursing.

PubMed

Sereika, Susan M; Zheng, Yaguang; Hu, Lu; Burke, Lora E

2017-08-01

Persons receiving treatment for weight loss often demonstrate heterogeneity in lifestyle behaviors and health outcomes over time. Traditional repeated measures approaches focus on the estimation and testing of an average temporal pattern, ignoring the interindividual variability about the trajectory. An alternate person-centered approach, group-based trajectory modeling, can be used to identify distinct latent classes of individuals following similar trajectories of behavior or outcome change as a function of age or time and can be expanded to include time-invariant and time-dependent covariates and outcomes. Another latent class method, growth mixture modeling, builds on group-based trajectory modeling to investigate heterogeneity within the distinct trajectory classes. In this applied methodologic study, group-based trajectory modeling for analyzing changes in behaviors or outcomes is described and contrasted with growth mixture modeling. An illustration of group-based trajectory modeling is provided using calorie intake data from a single-group, single-center prospective study for weight loss in adults who are either overweight or obese.

Class Extraction and Classification Accuracy in Latent Class Models

ERIC Educational Resources Information Center

Wu, Qiong

2009-01-01

Despite the increasing popularity of latent class models (LCM) in educational research, methodological studies have not yet accumulated much information on the appropriate application of this modeling technique, especially with regard to requirement on sample size and number of indicators. This dissertation study represented an initial attempt to…

An Assessment of Character and Leadership Development Latent Factor Structures through Confirmatory Factor, Item Response Theory, and Latent Class Analyses

ERIC Educational Resources Information Center

Higginbotham, David L.

2013-01-01

This study leveraged the complementary nature of confirmatory factor (CFA), item response theory (IRT), and latent class (LCA) analyses to strengthen the rigor and sophistication of evaluation of two new measures of the Air Force Academy's "leader of character" definition--the Character Mosaic Virtues (CMV) and the Leadership Mosaic…

Longitudinal Physical Activity Patterns Among Older Adults: A Latent Transition Analysis.

PubMed

Mooney, Stephen J; Joshi, Spruha; Cerdá, Magdalena; Kennedy, Gary J; Beard, John R; Rundle, Andrew G

2018-05-14

Most epidemiologic studies of physical activity measure either total energy expenditure or engagement in a single activity type, such as walking. These approaches may gloss over important nuances in activity patterns. We performed a latent transition analysis to identify patterns of activity types as well as neighborhood and individual determinants of changes in those activity patterns over two years in a cohort of 2,023 older adult residents of New York City, NY, surveyed between 2011 and 2013. We identified seven latent classes: 1) Mostly Inactive, 2) Walking, 3) Exercise, 4) Household Activities and Walking, 5) Household Activities and Exercise, 6) Gardening and Household Activities, and 7) Gardening, Household Activities, and Exercise. The majority of subjects retained the same activity patterns between waves (54% unchanged between waves 1 and 2, 66% unchanged between waves 2 and 3).Most latent class transitions were between classes distinguished only by one form of activity, and only neighborhood unemployment was consistently associated with changing between activity latent classes. Future latent transition analyses of physical activity would benefit from larger cohorts and longer follow-up periods to assess predictors of and long-term impacts of changes in activity patterns.

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

PubMed Central

Ann Lyons, Heidi

2017-01-01

Casual sex is common during the emerging adult life course stage, but little research has taken a person-centered approach to investigate if casual sexual behavior influences STI rates. Using a nationally representative sample and latent class analysis, results showed three distinctive latent classes. Abstainers were the least likely to have an STI, followed by the casual sex experienced, and then the casual sex risk-takers. Once other covariates were included in the model, there was no significant difference between the abstainers and casual sex experienced classes. These results highlight the need for future research to include diverse samples of emerging adults. PMID:29276549

Exploring the latent trait of Opioid Use Disorder criteria among frequent nonmedical prescription opioid users

PubMed Central

Castaldelli-Maia, João Mauricio; Andrade, Laura H.; Keyes, Katherine M.; Cerdá, Magdalena; Pilowsky, Daniel J.; Martins, Silvia S.

2016-01-01

Background There is a need to explore the dimensional and categorical phenotypes of criteria of opioid use disorder among frequent nonmedical users of prescription opioids (NMUPO) users. Methods We used pooled data of 2011–2012 National Survey on Drug Use and Health to examine reliability and phenotypic variability in the diagnosis of OUD secondary to NMUPO in a nationally-representative sample of 18+ years-old frequent past-year NMUPO users (120+ days, n=806). Through exploratory factor analysis (EFA) and latent class analysis (LCA), we examined 10 past-year OUD criteria. We examined associations between the latent classes and sociodemographic/psychiatric/NMUPO correlates. Results OUD criteria were unidimensional, and a three-class model was the overall best fitting solution for characterizing individuals into phenotypes along this unidimensional continuum: a “non-symptomatic class” (40.7%), “Tolerance-Time spent class” (29.0%) with high probability of endorsing Tolerance/Time Spent criteria, and a “High-moderate symptomatic class” (30.1%). The last class was significantly associated with being male, having insurance and obtaining prescription opioids (PO) nonmedically via “doctor shopping” as compared to the non-symptomatic class. “Tolerance-Time spent class” was significantly associated with being younger (18–25 years) and obtaining PO nonmedically from family/friends as compared to the non-symptomatic class. Conclusion This study revealed the different characteristics and routes of access to PO of different classes of frequent NMUPO users. It is possible that these groups may respond to different interventions, however such conclusions would require a clinical study. PMID:27302873

Detecting Math Anxiety with a Mixture Partial Credit Model

ERIC Educational Resources Information Center

Ölmez, Ibrahim Burak; Cohen, Allan S.

2017-01-01

The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…

Classes in the Balance: Latent Class Analysis and the Balance Scale Task

ERIC Educational Resources Information Center

Boom, Jan; ter Laak, Jan

2007-01-01

Latent class analysis (LCA) has been successfully applied to tasks measuring higher cognitive functioning, suggesting the existence of distinct strategies used in such tasks. With LCA it became possible to classify post hoc. This important step forward in modeling and analyzing cognitive strategies is relevant to the overlapping waves model for…

Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

ERIC Educational Resources Information Center

Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

2010-01-01

This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

Examining Variation in Adolescent Bystanders' Responses to Bullying

ERIC Educational Resources Information Center

Waasdorp, Tracy Evian; Bradshaw, Catherine P.

2018-01-01

Latent class analysis was used to examine whether patterns of bystander responses varied as a function of both student- and school-level characteristics. Data from 18,863 high school students from 58 schools who "ever witnessed bullying" were used to identify five latent classes of bystander behavior. Three of the classes identified…

Latent Class Analysis of Peer Conformity: Who Is Yielding to Pressure and Why?

ERIC Educational Resources Information Center

Kosten, Paul A.; Scheier, Lawrence M.; Grenard, Jerry L.

2013-01-01

This study used latent class analysis to examine typologies of peer conformity in a community sample of middle school students. Students responded to 31 items assessing diverse facets of conformity dispositions. The most parsimonious model produced three qualitatively distinct classes that differed on the basis of conformity to recreational…

A Latent Class Analysis of Weight-Related Health Behaviors among 2-and 4-Year College Students and Associated Risk of Obesity

ERIC Educational Resources Information Center

Mathur, Charu; Stigler, Melissa; Lust, Katherine; Laska, Melissa

2014-01-01

Little is known about the complex patterning of weight-related health behaviors in 2-and 4-year college students. The objective of this study was to identify and describe unique classes of weight-related health behaviors among college students. Latent class analysis was used to identify homogenous, mutually exclusive classes of nine health…

Tracking Children Who Fly below the Radar: Latent Transition Modeling of Students with Late-Emerging Reading Disability

ERIC Educational Resources Information Center

Compton, Donald L.; Fuchs, Douglas; Fuchs, Lynn S.; Elleman, Amy M.; Gilbert, Jennifer K.

2008-01-01

The purpose of this study was to examine (1) the stability of latent classes associated with reading disability (RD) and typical development (TD) across time, (2) the importance of speeded word recognition as a latent class indicator of RD and TD, and (3) possible early indicators of students with late-emerging RD. Analyses were based on a…

Indentifying Latent Classes and Testing Their Determinants in Early Adolescents' Use of Computers and Internet for Learning

ERIC Educational Resources Information Center

Heo, Gyun

2013-01-01

The purpose of the present study was to identify latent classes resting on early adolescents' change trajectory patterns in using computers and the Internet for learning and to test the effects of gender, self-control, self-esteem, and game use in South Korea. Latent growth mixture modeling (LGMM) was used to identify subpopulations in the Korea…

Selection of latent variables for multiple mixed-outcome models

PubMed Central

ZHOU, LING; LIN, HUAZHEN; SONG, XINYUAN; LI, YI

2014-01-01

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them. PMID:27642219

University and Student Segmentation: Multilevel Latent-Class Analysis of Students' Attitudes towards Research Methods and Statistics

ERIC Educational Resources Information Center

Mutz, Rudiger; Daniel, Hans-Dieter

2013-01-01

Background: It is often claimed that psychology students' attitudes towards research methods and statistics affect course enrolment, persistence, achievement, and course climate. However, the inter-institutional variability has been widely neglected in the research on students' attitudes towards research methods and statistics, but it is important…

Growth Mixture Modeling of Academic Achievement in Children of Varying Birth Weight Risk

PubMed Central

Espy, Kimberly Andrews; Fang, Hua; Charak, David; Minich, Nori; Taylor, H. Gerry

2009-01-01

The extremes of birth weight and preterm birth are known to result in a host of adverse outcomes, yet studies to date largely have used cross-sectional designs and variable-centered methods to understand long-term sequelae. Growth mixture modeling (GMM) that utilizes an integrated person- and variable-centered approach was applied to identify latent classes of achievement from a cohort of school-age children born at varying birth weights. GMM analyses revealed two latent achievement classes for calculation, problem-solving, and decoding abilities. The classes differed substantively and persistently in proficiency and in growth trajectories. Birth weight was a robust predictor of class membership for the two mathematics achievement outcomes and a marginal predictor of class membership for decoding. Neither visuospatial-motor skills nor environmental risk at study entry added to class prediction for any of the achievement skills. Among children born preterm, neonatal medical variables predicted class membership uniquely beyond birth weight. More generally, GMM is useful in revealing coherence in the developmental patterns of academic achievement in children of varying weight at birth, and is well suited to investigations of sources of heterogeneity. PMID:19586210

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

PubMed

Huang, Yangxin; Lu, Xiaosun; Chen, Jiaqing; Liang, Juan; Zangmeister, Miriam

2017-10-27

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

Examination of the Predictors of Latent Class Typologies of Bullying Involvement among Middle School Students

ERIC Educational Resources Information Center

Lovegrove, Peter J.; Henry, Kimberly L.; Slater, Michael D.

2012-01-01

This study employs latent class analysis to construct bullying involvement typologies among 3,114 students (48% male, 58% White) in 40 middle schools across the United States. Four classes were constructed: victims (15%); bullies (13%); bully/victims (13%); and noninvolved (59%). Respondents who were male and participated in fewer conventional…

Using Latent Class Analysis to Identify Academic and Behavioral Risk Status in Elementary Students

ERIC Educational Resources Information Center

King, Kathleen R.; Lembke, Erica S.; Reinke, Wendy M.

2016-01-01

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in…

A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors among U.S. Adolescents

ERIC Educational Resources Information Center

Chung, Hwan; Anthony, James C.

2013-01-01

This article presents a multiple-group latent class-profile analysis (LCPA) by taking a Bayesian approach in which a Markov chain Monte Carlo simulation is employed to achieve more robust estimates for latent growth patterns. This article describes and addresses a label-switching problem that involves the LCPA likelihood function, which has…

Life-Course Body Mass Index Trajectories Are Predicted by Childhood Socioeconomic Status but Not Exposure to Improved Nutrition during the First 1000 Days after Conception in Guatemalan Adults.

PubMed

Ford, Nicole D; Martorell, Reynaldo; Mehta, Neil K; Ramirez-Zea, Manuel; Stein, Aryeh D

2016-11-01

Latin America has experienced increases in obesity. Little is known about the role of early life factors on body mass index (BMI) gain over the life course. The objective of this research was to examine the role of early life factors [specifically, nutrition supplementation during the first 1000 d (from conception to 2 y of age) and childhood household socioeconomic status (SES)] on the pattern of BMI gain from birth or early childhood through midadulthood by using latent class growth analysis. Study participants (711 women, 742 men) who were born in 4 villages in Guatemala (1962-1977) were followed prospectively since participating in a randomized nutrition supplementation trial as children. Sex-specific BMI latent class trajectories were derived from 22 possible measures of height and weight from 1969 to 2004. To characterize early life determinants of BMI latent class membership, we used logistic regression modeling and estimated the difference-in-difference (DD) effect of nutrition supplementation during the first 1000 d. We identified 2 BMI latent classes in women [low (57%) and high (43%)] and 3 classes in men [low (38%), medium (47%), and high (15%)]. Nutrition supplementation during the first 1000 d after conception was not associated with BMI latent class membership (DD test: P > 0.15 for men and women), whereas higher SES was associated with increased odds of high BMI latent class membership in both men (OR: 1.98; 95% CI: 1.09, 3.61) and women (OR: 1.62; 95% CI: 1.07, 2.45) for the highest relative to the lowest tertile. In a cohort of Guatemalan men and women, nutrition supplementation provided during the first 1000 d was not significantly associated with higher BMI trajectory. Higher childhood household SES was associated with increased odds of high BMI latent class membership relative to the poorest households. The pathways through which this operates still need to be explored. © 2016 American Society for Nutrition.

Interactions between lower urinary tract symptoms and cardiovascular risk factors determine distinct patterns of erectile dysfunction: a latent class analysis.

PubMed

Barbosa, João A B A; Muracca, Eduardo; Nakano, Élcio; Assalin, Adriana R; Cordeiro, Paulo; Paranhos, Mario; Cury, José; Srougi, Miguel; Antunes, Alberto A

2013-12-01

An epidemiological association between lower urinary tract symptoms and erectile dysfunction is well established. However, interactions among multiple risk factors and the role of each in pathological mechanisms are not fully elucidated We enrolled 898 men undergoing prostate cancer screening for evaluation with the International Prostate Symptom Score (I-PSS) and simplified International Index of Erectile Function-5 (IIEF-5) questionnaires. Age, race, hypertension, diabetes, dyslipidemia, metabolic syndrome, cardiovascular disease, serum hormones and anthropometric parameters were also evaluated. Risk factors for erectile dysfunction were identified by logistic regression. The 333 men with at least mild to moderate erectile dysfunction (IIEF 16 or less) were included in a latent class model to identify relationships across erectile dysfunction risk factors. Age, hypertension, diabetes, lower urinary tract symptoms and cardiovascular event were independent predictors of erectile dysfunction (p<0.05). We identified 3 latent classes of patients with erectile dysfunction (R2 entropy=0.82). Latent class 1 had younger men at low cardiovascular risk and a moderate/high prevalence of lower urinary tract symptoms. Latent class 2 had the oldest patients at moderate cardiovascular risk with an increased prevalence of lower urinary tract symptoms. Latent class 3 had men of intermediate age with the highest prevalence of cardiovascular risk factors and lower urinary tract symptoms. Erectile dysfunction severity and lower urinary tract symptoms increased from latent class 1 to 3. Risk factor interactions determined different severities of lower urinary tract symptoms and erectile dysfunction. The effect of lower urinary tract symptoms and cardiovascular risk outweighed that of age. While in the youngest patients lower urinary tract symptoms acted as a single risk factor for erectile dysfunction, the contribution of vascular disease resulted in significantly more severe dysfunction. Applying a risk factor interaction model to prospective trials could reveal distinct classes of drug responses and help define optimal treatment strategies for specific groups. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Personality and changes in comorbidity patterns among anxiety and depressive disorders.

PubMed

Spinhoven, Philip; de Rooij, Mark; Heiser, Willem; Smit, Jan H; Penninx, Brenda W J H

2012-11-01

This prospective study examined the prognostic value of the Big Five personality model for changes in comorbidity patterns of emotional disorders both from a person- and trait-centered perspective. Moreover, it is investigated whether the predictive effect of personality can be attributed to symptom severity at baseline. We followed a cohort of 2566 persons (18-65 years) recruited in primary and specialized mental health care during two years. Personality dimensions at baseline were assessed with the NEO-FFI. The Diagnostic and Statistical Manual of Mental Disorders (4th ed.)-based diagnostic interviews with the CIDI allowed assessment of changes in comorbidity patterns of anxiety and depressive disorders over two years. Data were analyzed with latent class analysis (LCA) and latent transition analysis (LTA). LCA identified a four-class latent comorbidity class solution (Few Disorders, Fear Disorders, Distress Disorders, and Comorbid Fear and Distress Disorders) and a five-class latent personality class solution (High Resilients, Medium Resilients, Low Overcontrollers, Medium Overcontrollers, and High Overcontrollers). LTA showed that the likelihood of remaining in the same latent class was larger than that of transitioning to a less severe comorbidity class. Also, after correcting for symptom severity, medium and high Overcontrollers as well as participants with lower levels of conscientiousness were less likely to transition to a less severe comorbidity class. In particular, the individual trait of conscientiousness may be less dependent on current levels of anxiety and depressive symptoms and be a key pathoplastic or even predisposing variable in anxiety and depression and needs more theoretical and empirical study. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Maternal Anaemia at Delivery and Haemoglobin Evolution in Children during Their First 18 Months of Life Using Latent Class Analysis

PubMed Central

Koura, Kobto G.; Ouédraogo, Smaïla; Cottrell, Gilles; Le Port, Agnès; Massougbodji, Achille; Garcia, André

2012-01-01

Background Anaemia during pregnancy and at delivery is an important public health problem in low- and middle-income countries. Its association with the children’s haemoglobin level over time remains unclear. Our goals were to identify distinct haemoglobin level trajectories using latent class analysis and to assess the association between these trajectories and maternal anaemia and other risk factors. Method A prospective study of children from birth to 18 months of life was conducted in a rural setting in Tori-Bossito, Benin. The main outcome measure was the haemoglobin levels repeatedly measured at 3, 6, 9, 12, 15 and 18 months. Variables were collected from the mothers at delivery and from their children at birth and during the follow-up. The analyses were performed by means of Latent Class Analysis which has never been used for this kind of data. All the analyses were performed with Stata software, version 11.0, using the generalized linear latent and mixed model (GLLAMM) framework. Results We showed that 33.7% of children followed a low haemoglobin trajectory and 66.3% a high trajectory during the first 18 months of life. Newborn anaemia, placental malaria, malaria attack, sickle cell trait and male gender were significantly associated with a lower children’s haemoglobin level over time, whereas maternal age, children living in a polygamous family and with good feeding practices had a higher Hb level in the first18 months. We also showed that maternal anaemia was a predictor for ‘low haemoglobin level trajectory’ group membership but have no significant effect on children haemoglobin level over time. Conclusion Latent Class Analyses framework seems well suited to analyse longitudinal data under the hypothesis that different subpopulations of subjects are present in the data, each with its own set of parameters, with distinctive evolutions that themselves may reflect distinctive aetiologies. PMID:23185556

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Investigating preferences for mosquito-control technologies in Mozambique with latent class analysis.

PubMed

Smith, Rachel A; Barclay, Victoria C; Findeis, Jill L

2011-07-21

It is common practice to seek the opinions of future end-users during the development of innovations. Thus, the aim of this study is to investigate latent classes of users in Mozambique based on their preferences for mosquito-control technology attributes and covariates of these classes, as well as to explore which current technologies meet these preferences. Surveys were administered in five rural villages in Mozambique. The data were analysed with latent class analysis. This study showed that users' preferences for malaria technologies varied, and people could be categorized into four latent classes based on shared preferences. The largest class, constituting almost half of the respondents, would not avoid a mosquito-control technology because of its cost, heat, odour, potential to make other health issues worse, ease of keeping clean, or inadequate mosquito control. The other three groups are characterized by the attributes which would make them avoid a technology; these groups are labelled as the bites class, by-products class, and multiple-concerns class. Statistically significant covariates included literacy, self-efficacy, willingness to try new technologies, and perceived seriousness of malaria for the household. To become widely diffused, best practices suggest that end-users should be included in product development to ensure that preferred attributes or traits are considered. This study demonstrates that end-user preferences can be very different and that one malaria control technology will not satisfy everyone.

Latent lifestyle preferences and household location decisions

NASA Astrophysics Data System (ADS)

Walker, Joan L.; Li, Jieping

2007-04-01

Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

ERIC Educational Resources Information Center

Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J.

2009-01-01

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

ERIC Educational Resources Information Center

Lubke, Gitta; Tueller, Stephen

2010-01-01

Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent…

Classifying Patients with Chronic Pelvic Pain into Levels of Biopsychosocial Dysfunction Using Latent Class Modeling of Patient Reported Outcome Measures

PubMed Central

Fenton, Bradford W.; Grey, Scott F.; Tossone, Krystel; McCarroll, Michele; Von Gruenigen, Vivian E.

2015-01-01

Chronic pelvic pain affects multiple aspects of a patient's physical, social, and emotional functioning. Latent class analysis (LCA) of Patient Reported Outcome Measures Information System (PROMIS) domains has the potential to improve clinical insight into these patients' pain. Based on the 11 PROMIS domains applied to n=613 patients referred for evaluation in a chronic pelvic pain specialty center, exploratory factor analysis (EFA) was used to identify unidimensional superdomains. Latent profile analysis (LPA) was performed to identify the number of homogeneous classes present and to further define the pain classification system. The EFA combined the 11 PROMIS domains into four unidimensional superdomains of biopsychosocial dysfunction: Pain, Negative Affect, Fatigue, and Social Function. Based on multiple fit criteria, a latent class model revealed four distinct classes of CPP: No dysfunction (3.2%); Low Dysfunction (17.8%); Moderate Dysfunction (53.2%); and High Dysfunction (25.8%). This study is the first description of a novel approach to the complex disease process such as chronic pelvic pain and was validated by demographic, medical, and psychosocial variables. In addition to an essentially normal class, three classes of increasing biopsychosocial dysfunction were identified. The LCA approach has the potential for application to other complex multifactorial disease processes. PMID:26355825

Asymmetric latent semantic indexing for gene expression experiments visualization.

PubMed

González, Javier; Muñoz, Alberto; Martos, Gabriel

2016-08-01

We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture model applied in the genes latent space. We describe the steps of the procedure and we show its utility in the Human Cancer dataset.

Alexithymia and psychosocial problems among Italian preadolescents. A latent class analysis approach.

PubMed

Mannarini, Stefania; Balottin, Laura; Toldo, Irene; Gatta, Michela

2016-10-01

The study, conducted on Italian preadolscents aged 11 to 13 belonging to the general population, aims to investigate the relationship between the emotional functioning, namely, alexithymia, and the risk of developing behavioral and emotional problems measured using the Strength and Difficulty Questionnaire. The latent class analysis approach allowed to identify two latent variables, accounting for the internalizing (emotional symptoms and difficulties in emotional awareness) and for the externalizing problems (conduct problems and hyperactivity, problematic relationships with peers, poor prosocial behaviors and externally oriented thinking). The two latent variables featured two latent classes: the difficulty in dealing with problems and the strength to face problems that was representative of most of the healthy participants with specific gender differences. Along with the analysis of psychopathological behaviors, the study of resilience and strengths can prove to be a key step in order to develop valuable preventive approaches to tackle psychiatric disorders. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Latent Classes of PTSD Symptoms in Vietnam Veterans

ERIC Educational Resources Information Center

Steenkamp, Maria M.; Nickerson, Angela; Maguen, Shira; Dickstein, Benjamin D.; Nash, William P.; Litz, Brett T.

2012-01-01

The authors examined heterogeneity in posttraumatic stress disorder (PTSD) symptom presentation among veterans (n = 335) participating in the clinical interview subsample of the National Vietnam Veterans Readjustment Study. Latent class analysis was used to identify clinically homogeneous subgroups of Vietnam War combat veterans. Consistent with…

Race Differences in Patterns of Risky Behavior and Associated Risk Factors in Adolescence.

PubMed

Childs, Kristina K; Ray, James V

2017-05-01

Using data from the National Longitudinal Study of Adolescent Health (Add Health), this study expands on previous research by (a) examining differences across race in patterns or "subgroups" of adolescents based on nine self-reported behaviors (e.g., delinquency, substance use, risky sexual practices) and (b) comparing the risk factors (e.g., peer association, parenting, neighborhood cohesion), both within and across the race-specific subgroups, related to membership into the identified latent classes. The data used in this study include respondents aged 13 to 17 who participated in Waves 1 and 2 of the Add Health in-home interview. Latent class analysis (LCA) identified key differences in the number and characteristics of the latent classes across the racial subgroups. In addition, both similarities and differences in the risk factors for membership into the latent classes were identified across and within the race-specific subgroups. Implications for understanding risky behavior in adolescence, as well as directions for future research, are discussed.

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.

PubMed

Polcicová, Gabriela; Tino, Peter

2004-01-01

We introduce topographic versions of two latent class models (LCM) for collaborative filtering. Latent classes are topologically organized on a square grid. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. The variation in film rating patterns is modelled by multinomial and binomial distributions with varying independence assumptions. In the first stage of topographic LCM construction, self-organizing maps with neural field organized according to the LCM topology are employed. We apply our system to a large collection of user ratings for films. The system can provide useful visualization plots unveiling user preference patterns buried in the data, without loosing potential to be a good recommender model. It appears that multinomial distribution is most adequate if the model is regularized by tight grid topologies. Since we deal with probabilistic models of the data, we can readily use tools from probability and information theories to interpret and visualize information extracted by our system.

Three-year latent class trajectories of attention-deficit/hyperactivity disorder (ADHD) symptoms in a clinical sample not selected for ADHD.

PubMed

Arnold, L Eugene; Ganocy, Stephen J; Mount, Katherine; Youngstrom, Eric A; Frazier, Thomas; Fristad, Mary; Horwitz, Sarah M; Birmaher, Boris; Findling, Robert; Kowatch, Robert A; Demeter, Christine; Axelson, David; Gill, Mary Kay; Marsh, Linda

2014-07-01

This study aims to examine trajectories of attention-deficit/hyperactivity disorder (ADHD) symptoms in the Longitudinal Assessment of Manic Symptoms (LAMS) sample. The LAMS study assessed 684 children aged 6 to 12 years with the Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS) and rating scales semi-annually for 3 years. Although they were selected for elevated manic symptoms, 526 children had baseline ADHD diagnoses. With growth mixture modeling (GMM), we separately analyzed inattentive and hyperactive/impulsive symptoms, covarying baseline age. Multiple standard methods determined optimal fit. The χ(2) and Kruskal-Wallis analysis of variance compared resulting latent classes/trajectories on clinical characteristics and medication. Three latent class trajectories best described inattentive symptoms, and 4 classes best described hyperactive/impulsive symptoms. Inattentive trajectories maintained their relative position over time. Hyperactive/impulsive symptoms had 2 consistent trajectories (least and most severe). A third trajectory (4.5%) started mild, then escalated; and a fourth (14%) started severe but improved dramatically. The improving trajectory was associated with the highest rate of ADHD and lowest rate of bipolar diagnoses. Three-fourths of the mildest inattention class were also in the mildest hyperactive/impulsive class; 72% of the severest inattentive class were in the severest hyperactive/impulsive class, but the severest inattention class also included 62% of the improving hyperactive-impulsive class. An ADHD rather than bipolar diagnosis prognosticates a better course of hyperactive/impulsive, but not inattentive, symptoms. High overlap of relative severity between inattention and hyperactivity/impulsivity confirms the link between these symptom clusters. Hyperactive/impulsive symptoms wane more over time. Group means are insufficient to understand individual ADHD prognosis. A small subgroup deteriorates over time in hyperactivity/impulsivity and needs better treatments than currently provided. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Heterogeneity of postpartum depression: a latent class analysis

PubMed Central

2016-01-01

Summary Background Maternal depression in the postpartum period confers substantial morbidity and mortality, but the definition of postpartum depression remains controversial. We investigated the heterogeneity of symptoms with the aim of identifying clinical subtypes of postpartum depression. Methods Data were aggregated from the international perinatal psychiatry consortium Postpartum Depression: Action Towards Causes and Treatment, which represents 19 institutions in seven countries. 17 912 unique subject records with phenotypic data were submitted. We applied latent class analyses in a two-tiered approach to assess the validity of empirically defined subtypes of postpartum depression. Tier one assessed heterogeneity in women with complete data on the Edinburgh postnatal depression scale (EPDS) and tier two in those with postpartum depression case status. Findings 6556 individuals were assessed in tier one and 4245 in tier two. A final model with three latent classes was optimum for both tiers. The most striking characteristics associated with postpartum depression were severity, timing of onset, comorbid anxiety, and suicidal ideation. Women in class 1 had the least severe symptoms (mean EPDS score 10·5), followed by those in class 2 (mean EPDS score 14·8) and those in class 3 (mean EPDS score 20·1). The most severe symptoms of postpartum depression were significantly associated with poor mood (mean EPDS score 20·1), increased anxiety, onset of symptoms during pregnancy, obstetric complications, and suicidal ideation. In class 2, most women (62%) reported symptom onset within 4 weeks postpartum and had more pregnancy complications than in other two classes (69% vs 67% in class 1 and 29% in class 3). Interpretation PPD seems to have several distinct phenotypes. Further assessment of PPD heterogeneity to identify more precise phenotypes will be important for future biological and genetic investigations. Funding Sources of funding are listed at the end of the article. PMID:26359613

Prediction of incidence and stability of alcohol use disorders by latent internalizing psychopathology risk profiles in adolescence and young adulthood.

PubMed

Behrendt, Silke; Bühringer, Gerhard; Höfler, Michael; Lieb, Roselind; Beesdo-Baum, Katja

2017-10-01

Comorbid internalizing mental disorders in alcohol use disorders (AUD) can be understood as putative independent risk factors for AUD or as expressions of underlying shared psychopathology vulnerabilities. However, it remains unclear whether: 1) specific latent internalizing psychopathology risk-profiles predict AUD-incidence and 2) specific latent internalizing comorbidity-profiles in AUD predict AUD-stability. To investigate baseline latent internalizing psychopathology risk profiles as predictors of subsequent AUD-incidence and -stability in adolescents and young adults. Data from the prospective-longitudinal EDSP study (baseline age 14-24 years) were used. The study-design included up to three follow-up assessments in up to ten years. DSM-IV mental disorders were assessed with the DIA-X/M-CIDI. To investigate risk-profiles and their associations with AUD-outcomes, latent class analysis with auxiliary outcome variables was applied. AUD-incidence: a 4-class model (N=1683) was identified (classes: normative-male [45.9%], normative-female [44.2%], internalizing [5.3%], nicotine dependence [4.5%]). Compared to the normative-female class, all other classes were associated with a higher risk of subsequent incident alcohol dependence (p<0.05). AUD-stability: a 3-class model (N=1940) was identified with only one class (11.6%) with high probabilities for baseline AUD. This class was further characterized by elevated substance use disorder (SUD) probabilities and predicted any subsequent AUD (OR 8.5, 95% CI 5.4-13.3). An internalizing vulnerability may constitute a pathway to AUD incidence in adolescence and young adulthood. In contrast, no indication for a role of internalizing comorbidity profiles in AUD-stability was found, which may indicate a limited importance of such profiles - in contrast to SUD-related profiles - in AUD stability. Copyright © 2017 Elsevier B.V. All rights reserved.

Associations Between Latent Classes of Interpersonal Polyvictimization and Polyperpetration and Sexual Risk Behaviors Among Young Pregnant Couples: A Dyadic Analysis.

PubMed

Willie, Tiara; Kershaw, Trace S

2018-05-24

Interpersonal violence victimization and perpetration have been associated with sexual risk behaviors among adolescents and young adults, but research is lacking on: (1) how patterns of interpersonal polyvictimization and polyperpetration are associated with sexual risk among young pregnant couples, and (2) how individual and partner experiences of violence differentially impact sexual risk. The current analyses used baseline data from a longitudinal study that followed 296 pregnant young couples from pregnancy to 12 months postpartum. Couples were recruited at obstetrics and gynecology clinics, and an ultrasound clinic in the U.S. Latent class analysis identified subgroups based on polyvictimization and polyperpetration. Using the Actor-Partner Interdependence Model, path analyses assessed actor-partner effects of class membership on sexual risk. Three latent classes were used for women: Class 1: Polyvictim-Polyperpetrator; Class 2: Nonvictim-Nonperpetrator; and Class 3: Community and Prior IPV Victim. Four latent classes were used for men: Class 1: Community and Prior IPV Victim; Class 2: Polyvictim-Nonpartner Perpetrator; Class 3: Prior IPV and Peer Victim; and Class 4: Nonvictim-Nonperpetrator. Path analyses revealed that females in Class 2 and their male partners had higher condom use than females in Class 3. Males in Class 2 had more sexual partners than males in Class 1. Among nonmonogamous couples, males in Class 2 were less likely to be involved with a female partner reporting unprotected sex than males in Class 1. Among nonmonogamous couples, females in Class 2 had more acts of unprotected sex than females in Class 1. Males in Class 4 were less likely to have concurrent sexual partners compared to males in Class 1. Risk reduction interventions should address both victimization and perpetration. Additional research is needed to understand how mechanisms driving differential sexual risk by patterns of interpersonal polyvictimization and polyperpetration.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia.

PubMed

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-11-01

Scant information exists on PTSD symptoms and structure in youth from developing countries. We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Results suggest that PTSD is a continuously distributed latent trait.

Modeling Individual Differences in Unfolding Preference Data: A Restricted Latent Class Approach.

ERIC Educational Resources Information Center

Bockenholt, Ulf; Bockenholt, Ingo

1990-01-01

A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)

Analyzing the Validity of the Adult-Adolescent Parenting Inventory for Low-Income Populations

ERIC Educational Resources Information Center

Lawson, Michael A.; Alameda-Lawson, Tania; Byrnes, Edward

2017-01-01

Objectives: The purpose of this study was to examine the construct and predictive validity of the Adult-Adolescent Parenting Inventory (AAPI-2). Methods: The validity of the AAPI-2 was evaluated using multiple statistical methods, including exploratory factor analysis, confirmatory factor analysis, and latent class analysis. These analyses were…

Video Gaming in a Hyperconnected World: A Cross-sectional Study of Heavy Gaming, Problematic Gaming Symptoms, and Online Socializing in Adolescents

PubMed Central

Colder Carras, Michelle; Van Rooij, Antonius J.; Van de Mheen, Dike; Musci, Rashelle; Xue, Qian-Li; Mendelson, Tamar

2016-01-01

Aims Examining online social interactions along with patterns of video gaming behaviors and game addiction symptoms has the potential to enrich our understanding of disorders related to excessive video game play. Methods We performed latent class analysis in a sample of 9733 adolescents based on heavy use of games, social networking and instant messaging, and game addiction symptoms. We used latent class regression to determine associations between classes, psychosocial well-being and friendship quality. Results We identified two types of heavy gaming classes that differed in probability of online social interaction. Classes with more online social interaction reported fewer problematic gaming symptoms than those with less online social interaction. Most adolescents estimated to be in heavy gaming classes had more depressive symptoms than normative classes. Male non-social gamers had more social anxiety. Female social gamers had less social anxiety and loneliness, but lower self-esteem. Friendship quality attenuated depression in some male social gamers, but strengthened associations with loneliness in some male non-social gamers. Conclusions In adolescents, symptoms of video game addiction depend not only on video game play but also on concurrent levels of online communication, and those who are very socially active online report fewer symptoms of game addiction. PMID:28260834

The Severe 5%: A Latent Class Analysis of the Externalizing Behavior Spectrum in the United States

PubMed Central

Vaughn, Michael G.; DeLisi, Matt; Gunterbh, Tracy; Fu, Qiang; Beaver, Kevin M.; Perron, Brian E.; Howard, Matthew O.

2012-01-01

Objective Criminological research consistently demonstrates that approximately 5% of study populations are comprised of pathological offenders who account for a preponderance of antisocial behavior and violent crime. Unfortunately, there have been no nationally representative epidemiological studies characterizing the severe 5% group. Materials and Methods Data from the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a nationally representative sample of 43,093 non-institutionalized U.S. residents aged 18 years and older were analyzed using latent class analysis to assess sociodemographic, psychiatric, and behavioral characteristics. Results Four-classes of respondents were identified vis-à-vis lifetime externalizing behaviors. A normative class (66.1% of respondents) demonstrated little involvement in antisocial conduct. A low substance use/high antisocial behavior class (20.7% of respondents) and high substance use/moderate antisocial behavior (8.0% of respondents) class evinced diverse externalizing and psychiatric symptoms. Finally, a severe class (5.3% of respondents) was characterized by pathological involvement in more varied and intensive forms of antisocial and externalizing behaviors and extensive psychiatric disturbance. Conclusions The current study is the first nationally representative epidemiological study of criminal careers/externalizing behavior spectrum in the United States and validates the existence of the 5% pathological group demonstrated by prior research. PMID:22942480

Latent Class Subtyping of Attention-Deficit/Hyperactivity Disorder and Comorbid Conditions

ERIC Educational Resources Information Center

Acosta, Maria T.; Castellanos, F. Xavier; Bolton, Kelly L.; Balog, Joan Z.; Eagen, Patricia; Nee, Linda; Jones, Janet; Palacio, Luis; Sarampote, Christopher; Russell, Heather F.; Berg, Kate; Arcos-Burgos, Mauricio; Muenke, Maximilian

2008-01-01

The study attempts to carry out latent class analysis (LCA) in a sample of 1010 individuals, some with Attention-Deficit/Hyperactivity disorder (ADHD) and others normal. Results indicate that LCA can feasibly allow the combination of externalizing and internalizing symptoms for future tests regarding specific genetic risk factors.

Latent Profiles of Temperament and Their Relations of Psychopathology and Wellness

ERIC Educational Resources Information Center

Rettew, David C.; Althoff, Robert R.; Dumenci, Levent; Ayer, Lynsay; Hudziak, James J.

2008-01-01

The study applies latent profiles analysis to a group of children and adolescents to test temperament phenotypes in order to examine their association to wellness and psychopathology. One of the results concluded that lifetime disorder was lower in the steady class as compared to moderate class.

Psychological Distress among Victimized Women on Probation and Parole: A Latent Class Analysis

PubMed Central

Golder, Seana; Engstrom, Malitta; Hall, Martin T.; Higgins, George; Logan, TK

2015-01-01

Latent class analysis was used to identify subgroups of victimized women (N=406) on probation and parole differentiated by levels of general psychological distress. The nine primary symptom dimensions from the Brief Symptom Inventory (BSI) were used individually as latent class indicators (Derogatis, 1993). Results identified three classes of women characterized by increasing levels of psychological distress; classes were further differentiated by posttraumatic stress disorder symptoms, cumulative victimization, substance use and other domains of psychosocial functioning (i.e., sociodemographic characteristics; informal social support and formal service utilization; perceived life stress; and resource loss). The present research was effective in uncovering important heterogeneity in psychological distress using a highly reliable and easily accessible measure of general psychological distress. Differentiating levels of psychological distress and associated patterns of psychosocial risk can be used to develop intervention strategies targeting the needs of different subgroups of women. Implications for treatment and future research are presented. PMID:25915692

Beyond the average marital communication: Latent profiles of the observed interactions among Chinese newlywed couples.

PubMed

Cao, Hongjian; Fang, Xiaoyi; Fine, Mark A; Ju, Xiaoyan; Lan, Jing; Liu, Xuanwen

2015-12-01

Employing a multicontext observational design, using a person-centered approach, and treating the marital dyad as the unit of analysis, this study examined the within-couple communication patterning of 144 Chinese newlywed couples and its association with relationship satisfaction. Latent profile analysis consistently revealed 3 profiles of spouses' interactive behaviors across contexts differing in both topic nature (i.e., problem-solving vs. social support) and initiator (i.e., husbands vs. wives): (a) traditionally undemonstrative profile, (b) emotionally quarrelling profile, and (c) warmly supportive profile. The prevalence of communication profiles changed markedly with the nature of the discussion topic and the topic initiator. Further, using latent class analysis, we classified couples into subgroups based on their identified profile memberships across contexts (i.e., consistency of interaction mode across contexts). Three classes were identified: (a) consistently quarrelling class, (b) consistently supportive class, and (c) modestly traditional class. Both the consistently supportive class and the modestly traditional class reported significantly higher levels of marital satisfaction than did the consistently quarrelling class. (c) 2015 APA, all rights reserved).

Latent Homeless Risk Profiles of a National Sample of Homeless Veterans and Their Relation to Program Referral and Admission Patterns

PubMed Central

Kasprow, Wesley J.; Rosenheck, Robert A.

2013-01-01

Objectives. We identified risk and need profiles of homeless veterans and examined the relation between profiles and referrals and admissions to Department of Veterans Affairs (VA) homeless service programs. Methods. We examined data from the VA’s new Homeless Operations Management and Evaluation System on 120 852 veterans from 142 sites nationally in 2011 and 2012 using latent class analyses based on 9 homeless risk factors. The final 4-class solution compared both referral and admission to VA homeless services. Results. We identified 4 latent classes: relatively few problems, dual diagnosis, poverty–substance abuse–incarceration, and disabling medical problems. Homeless veterans in the first group were more likely to be admitted to the VA’s permanent supportive housing program, whereas those in the second group were more likely to be admitted to more restrictive VA residential treatment. Homeless veterans in the third group were more likely to be admitted to the VA’s prisoner re-entry program, and those in the fourth group were more likely to be directed to VA medical services. Conclusions. The heterogeneous risk and need profiles of homeless veterans supported the diversity of VA homeless services and encouraged the development of specialized services to meet their diverse needs. PMID:24148048

The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities

ERIC Educational Resources Information Center

Bauer, Daniel J.; Curran, Patrick J.

2004-01-01

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model,…

Polydrug Use and HIV Risk Among People Who Inject Heroin in Tijuana, Mexico: A Latent Class Analysis.

PubMed

Meacham, Meredith C; Rudolph, Abby E; Strathdee, Steffanie A; Rusch, Melanie L; Brouwer, Kimberly C; Patterson, Thomas L; Vera, Alicia; Rangel, Gudelia; Roesch, Scott C

2015-01-01

Although most people who inject drugs (PWID) in Tijuana, Mexico, primarily inject heroin, injection and non-injection use of methamphetamine and cocaine is common. We examined patterns of polydrug use among heroin injectors to inform prevention and treatment of drug use and its health and social consequences. Participants were PWID residing in Tijuana, aged ≥18 years who reported heroin injection in the past six months and were recruited through respondent-driven sampling (n = 1,025). Latent class analysis was conducted to assign individuals to classes on a probabilistic basis, using four indicators of past six-month polydrug and polyroute use: cocaine injecting, cocaine smoking or snorting, methamphetamine injecting, and methamphetamine smoking or snorting. Latent class membership was regressed onto covariates in a multinomial logistic regression. Latent class analyses testing 1, 2, 3, and 4 classes were fit, with the 3-class solution fitting best. Class 1 was defined by predominantly heroin use (50.2%, n = 515); class 2 by methamphetamine and heroin use (43.7%, n = 448), and class 3 by methamphetamine, cocaine, and heroin use (6.0%, n = 62). Bivariate and multivariate analyses indicated a group of methamphetamine and cocaine users that exhibited higher-risk sexual practices and lower heroin injecting frequency, and a group of methamphetamine users who were younger and more likely to be female. Discrete subtypes of heroin PWID were identified based on methamphetamine and cocaine use patterns. These findings have identified subtypes of heroin injectors who require more tailored interventions to reduce the health and social harms of injecting drug use.

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

PubMed Central

Kim, Eun Sook; Wang, Yan

2017-01-01

Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

Personality and trajectories of posttraumatic psychopathology: A latent change modelling approach.

PubMed

Fletcher, Susan; O'Donnell, Meaghan; Forbes, David

2016-08-01

Survivors of traumatic events may develop a range of psychopathology, across the internalizing and externalizing dimensions of disorder and associated personality traits. However, research into personality-based internalizing and externalizing trauma responses has been limited to cross-sectional investigations of PTSD comorbidity. Personality typologies may present an opportunity to identify and selectively intervene with survivors at risk of posttraumatic disorder. Therefore this study examined whether personality prospectively influences the trajectory of disorder in a broader trauma-exposed sample. During hospitalization for a physical injury, 323 Australian adults completed the Multidimensional Personality Questionnaire-Brief Form and Structured Clinical Interview for DSM-IV, with the latter readministered 3 and 12 months later. Latent profile analysis conducted on baseline personality scores identified subgroups of participants, while latent change modelling examined differences in disorder trajectories. Three classes (internalizing, externalizing, and normal personality) were identified. The internalizing class showed a high risk of developing all disorders. Unexpectedly, however, the normal personality class was not always at lowest risk of disorder. Rather, the externalizing class, while more likely than the normal personality class to develop substance use disorders, were less likely to develop PTSD and depression. Results suggest that personality is an important mechanism in influencing the development and form of psychopathology after trauma, with internalizing and externalizing subtypes identifiable in the early aftermath of injury. These findings suggest that early intervention using a personality-based transdiagnostic approach may be an effective method of predicting and ultimately preventing much of the burden of posttraumatic disorder. Copyright © 2016 Elsevier Ltd. All rights reserved.

Clustering of modifiable biobehavioral risk factors for chronic disease in US adults: a latent class analysis.

PubMed

Leventhal, Adam M; Huh, Jimi; Dunton, Genevieve F

2014-11-01

Examining the co-occurrence patterns of modifiable biobehavioral risk factors for deadly chronic diseases (e.g. cancer, cardiovascular disease, diabetes) can elucidate the etiology of risk factors and guide disease-prevention programming. The aims of this study were to (1) identify latent classes based on the clustering of five key biobehavioral risk factors among US adults who reported at least one risk factor and (2) explore the demographic correlates of the identified latent classes. Participants were respondents of the National Epidemiologic Survey of Alcohol and Related Conditions (2004-2005) with at least one of the following disease risk factors in the past year (N = 22,789), which were also the latent class indicators: (1) alcohol abuse/dependence, (2) drug abuse/dependence, (3) nicotine dependence, (4) obesity, and (5) physical inactivity. Housing sample units were selected to match the US National Census in location and demographic characteristics, with young adults oversampled. Participants were administered surveys by trained interviewers. Five latent classes were yielded: 'obese, active non-substance abusers' (23%); 'nicotine-dependent, active, and non-obese' (19%); 'active, non-obese alcohol abusers' (6%); 'inactive, non-substance abusers' (50%); and 'active, polysubstance abusers' (3.7%). Four classes were characterized by a 100% likelihood of having one risk factor coupled with a low or moderate likelihood of having the other four risk factors. The five classes exhibited unique demographic profiles. Risk factors may cluster together in a non-monotonic fashion, with the majority of the at-risk population of US adults expected to have a high likelihood of endorsing only one of these five risk factors. © Royal Society for Public Health 2013.

The heterogeneous health latent classes of elderly people and their socio-demographic characteristics in Taiwan.

PubMed

Liu, Li-Fan; Tian, Wei-Hua; Yao, Hui-Ping

2014-01-01

The health care needs of elderly people were influenced by their heterogeneity. This study aimed to identify the health latent classes of elderly people by using latent class analysis to deal with heterogeneity and examine their socio-demographic characteristics. Data came from the 2005 National Health Interview Survey (NHIS) in Taiwan. In total, 2449 elderly individuals with available health indicators were examined in latent class analysis (LCA), and 2217 elderly community-dwellings with complete socio-demographic data were analyzed by multinomial logistic regression. Four health latent classes were identified which included 1066 (43.5%) people in the High Comorbidity (HC), 152 (6.2%) in the Functional Impairment (FI), 252 (10.3%) in the Frail (FR), and 979 (40.0%) in the Relatively Healthy (RH) group. Multinomial logistic regressions revealed socio-demographic characteristics among health classes. The variables associated with an increased likelihood of being in the FR group were age, female, and living with families. They were also correlated to ethnicity and educations. Apart from age and gender, the Functional Impairment group was less likely to be ethnicity of Hakka, more likely to live with others than were the RH group. The HC group tended to be younger, with higher educations, and more likely to live in urban area than the Functional Impairment group. The correlations between health classes and socio-demographic factors were discussed. The health status of elderly people includes a variety of health indicators. A person-centered approach is critical to identify the health heterogeneity of elderly people and manage their care needs by targeting differential aging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Co-occurrence of Victimization from Five Subtypes of Bullying: Physical, Verbal, Social Exclusion, Spreading Rumors, and Cyber

PubMed Central

Iannotti, Ronald J.; Luk, Jeremy W.; Nansel, Tonja R.

2010-01-01

Objective To examine co-occurrence of five subtypes of peer victimization. Methods Data were obtained from a national sample of 7,475 US adolescents in grades 6 through 10 in the 2005/2006 Health Behavior in School-Aged Children (HBSC) study. Latent class analyses (LCA) were conducted on victimization by physical, verbal, social exclusion, spreading rumors, and cyber bullying. Results Three latent classes were identified, including an all-types victims class (9.7% of males and 6.2% of females), a verbal/relational victims class (28.1% of males and 35.1% of females), and a nonvictim class (62.2% of males and 58.7% of females). Males were more likely to be all-type victims. There was a graded relationship between the three latent classes and level of depression, frequency of medically attended injuries, and medicine use, especially among females. Conclusions  Increased co-occurrence of victimization types put adolescents at greater risks for poorer physical and psychological outcomes. PMID:20488883

Longitudinal analysis of latent classes of psychopathology and patterns of class migration in survivors of severe injury.

PubMed

Forbes, David; Nickerson, Angela; Alkemade, Nathan; Bryant, Richard A; Creamer, Mark; Silove, Derrick; McFarlane, Alexander C; Van Hooff, Miranda; Fletcher, Susan L; O'Donnell, Meaghan

2015-09-01

Little research to date has explored the typologies of psychopathology following trauma, beyond development of particular diagnoses such as posttraumatic stress disorder (PTSD). The objective of this study was to determine the longitudinal patterns of these typologies, especially the movement of persons across clusters of psychopathology. In this 6-year longitudinal study, 1,167 hospitalized severe injury patients who were recruited between April 2004-February 2006 were analyzed, with repeated measures at baseline, 3 months, 12 months, and 72 months after injury. All patients met the DSM-IV criterion A1 for PTSD. Structured clinical interviews were used to assess psychiatric disorders at each follow-up point. Latent class analysis and latent transition analysis were applied to assess clusters of individuals determined by psychopathology. The Mini International Neuropsychiatric Interview (MINI) and Clinician-Administered PTSD Scale (CAPS) were employed to complete diagnoses. Four latent classes were identified at each time point: (1) Alcohol/Depression class (3 months, 2.1%; 12 months, 1.3%; and 72 months, 1.1%), (2) Alcohol class (3 months, 3.3%; 12 months, 3.7%; and 72 months, 5.4%), (3) PTSD/Depression class (3 months, 10.3%; 12 months, 11.5%; and 72 months, 6.4%), and (4) No Disorder class (3 months, 84.2%; 12 months, 83.5%; and 72 months, 87.1%). Latent transition analyses conducted across the 2 transition points (12 months and 72 months) found consistently high levels of stability in the No Disorder class (90.9%, 93.0%, respectively) but lower and reducing levels of consistency in the PTSD/Depression class (81.3%, 46.6%), the Alcohol/Depression class (59.7%, 21.5%), and the Alcohol class (61.0%, 36.5%), demonstrating high levels of between-class migration. Despite the array of psychiatric disorders that may develop following severe injury, a 4-class model best described the data with excellent classification certainty. The high levels of migration across classes indicate a complex pattern of psychopathology expression over time. The findings have considerable implications for tailoring multifocused interventions to class type, as well as flexible stepped care models, and for the potential development and delivery of transdiagnostic interventions targeting underlying mechanisms. © Copyright 2015 Physicians Postgraduate Press, Inc.

A latent profile analysis of intimate partner victimization and aggression and examination of between-class differences in psychopathology symptoms and risky behaviors

PubMed Central

Weiss, Nicole H.; Dixon-Gordon, Katherine L.; Peasant, Courtney; Jaquier, Véronique; Johnson, Clinesha; Sullivan, Tami P.

2016-01-01

Objective Intimate partner violence (IPV) is associated with heightened psychopathology symptoms and risky behaviors. However, extant investigations are limited by their focus on IPV victimization, despite evidence to suggest that victimization and aggression frequently co-occur. Further, research on these correlates often has not accounted for the heterogeneity of women who experience victimization. Method The present study utilized latent profile analysis to identify patterns of physical, psychological, and sexual victimization and aggression in a convenience sample of 212 community women experiencing victimization (M age=36.63, 70.8% African American), as well as examined differences in psychopathology symptoms (i.e., posttraumatic stress symptoms and depressive symptoms) and risky behaviors (i.e., drug problems, alcohol problems, deliberate self-harm, HIV-risk behaviors) across these classes. Results Four classes of women differentiated by severities of victimization and aggression were identified. Greater psychopathology symptoms were found among classes defined by greater victimization and aggression, regardless of IPV type. Risky behaviors were more prevalent among classes defined by greater sexual victimization and aggression in particular. Conclusions Findings highlight the importance of developing interventions that target the particular needs of subgroups of women who experience victimization. PMID:27736140

A Latent Class Analysis of Family Characteristics Linked to Youth Offending Outcomes

PubMed Central

Chu, Chi Meng; Zeng, Gerald; Li, Dongdong; Ting, Ming Hwa

2016-01-01

Objectives: There were two aims to this study: firstly, to identify family subtypes of Singaporean youth offenders based on eight family variables. Secondly, the associations of these family subtypes with youth offending outcomes were tested. Methods: With a sample of 3,744 youth, a latent class analysis was first conducted based on eight family variables. Multivariate analyses and a Cox regression were subsequently performed to analyze the associations of the family classes with age at first arrest, age at first charge, and recidivism. Results: A three-class solution was found to have the best fit to the data: (1) intact functioning families had little family risk; (2) families with criminality had higher probabilities of family criminality, of drug/alcohol abuse, and of being nonintact; and (3) poorly managed families received the poorest parenting and were more likely to be nonintact. Youth offenders from the latter two classes were arrested and charged at younger ages. Additionally, they reoffended at a quicker rate. Conclusions: Family backgrounds matter for youth offending outcomes. Interventions have to be multifaceted and targeted at the family in order to mitigate the risk of young offenders from developing into pathological adult criminals. PMID:28736458

Longitudinal Patterns of Health Insurance Coverage Among a National Sample of Children in the Child Welfare System

PubMed Central

Raghavan, Ramesh; Aarons, Gregory A.; Roesch, Scott C.; Leslie, Laurel K.

2008-01-01

Objectives. We sought to describe health insurance coverage over time among a national sample of children who came into contact with child welfare or child protective services agencies. Methods. We used data from 4 waves of the National Survey of Child and Adolescent Well-Being to examine insurance coverage among 2501 youths. Longitudinal insurance trajectories were identified using latent class analyses, a technique used to classify individuals into groupings of observed variables, and survey-weighted logistic regression was used to identify variables associated with class membership. Results. We identified 2 latent insurance classes—1 contained children who gained health insurance, and the other contained children who stably maintained coverage over time. History of sexual abuse, and race/ethnicity other than White, Black, and Hispanic, were associated with membership in the “gainer” class. Foster care placement and poorer health status were associated with membership in the “maintainer” class. Caregiver characteristics were not associated with class membership. Conclusions. The majority of children in child welfare had stable health insurance coverage over time. Given this vulnerable population’s dependence upon Medicaid, protection of existing entitlements to Medicaid is essential to preserve their stable insurance coverage. PMID:18235059

Heterogeneity of sleep quality in relation to circadian preferences and depressive symptomatology among major depressive patients.

PubMed

Selvi, Yavuz; Boysan, Murat; Kandeger, Ali; Uygur, Omer F; Sayin, Ayca A; Akbaba, Nursel; Koc, Basak

2018-08-01

The current study aimed at investigating the latent dimensional structure of sleep quality as indexed by the seven components of the Pittsburgh Sleep Quality Index (PSQI), as well as latent covariance structure between sleep quality, circadian preferences and depressive symptoms. Two hundred twenty-five patients with major depressive disorder (MDD), with an average age of 29.92 ± 10.49 years (aged between 17 and 63), participated in the study. The PSQI, Morningness-Eveningness Questionnaire (MEQ) and Beck Depression Inventory (BDI) were administered to participants. Four sets of latent class analyses were subsequently run to obtain optimal number of latent classes best fit to the data. Mixture models revealed that sleep quality is multifaceted in MDD. The data best fit to four-latent-class model: Poor Habitual Sleep Quality (PHSQ), Poor Subjective Sleep Quality (PSSQ), Intermediate Sleep Quality (ISQ), and Good Sleep Quality (GSQ). MDD patients classified into GSQ latent class (23.6%) reported the lowest depressive symptoms and were more prone to morningness diurnal preferences compared to other three homogenous sub-groups. Finally, the significant association between eveningness diurnal preferences and depressive symptomatology was significantly mediated by poor sleep quality. The cross-sectional nature of the study and the lack of an objective measurement of sleep such as polysomnography recordings was the most striking limitation of the study. We concluded sleep quality in relation to circadian preferences and depressive symptoms has a heterogeneous nature in MDD. Copyright © 2018. Published by Elsevier B.V.

A personality-based latent class typology of outpatients with major depressive disorder: association with symptomatology, prescription pattern and social function.

PubMed

Hori, Hiroaki; Teraishi, Toshiya; Nagashima, Anna; Koga, Norie; Ota, Miho; Hattori, Kotaro; Kim, Yoshiharu; Higuchi, Teruhiko; Kunugi, Hiroshi

2017-08-01

While major depressive disorder (MDD) is considered to be a heterogeneous disorder, the nature of the heterogeneity remains unclear. Studies have attempted to classify patients with MDD using latent variable techniques, yet the empirical approaches to symptom-based subtyping of MDD have not provided conclusive evidence. Here we aimed to identify homogeneous classes of MDD based on personality traits, using a latent profile analysis. We studied 238 outpatients with DSM-IV MDD recruited from our specialized depression outpatient clinic and assessed their dimensional personality traits with the Temperament and Character Inventory. Latent profile analysis was conducted with 7 dimensions of the Temperament and Character Inventory as indicators. Relationships of the identified classes with symptomatology, prescription pattern, and social function were then examined. The latent profile analysis indicated that a 3-class solution best fit the data. Of the sample, 46.2% was classified into a "neurotic" group characterized by high harm avoidance and low self-directedness; 30.3% into an "adaptive" group characterized by high self-directedness and cooperativeness; and 23.5% into a "socially-detached" group characterized by low reward dependence and cooperativeness and high self-transcendence. The 2 maladaptive groups, namely neurotic and socially-detached groups, demonstrated unique patterns of symptom expression, different classes of psychotropic medication use, and lower social functioning. Generalizability of the findings was limited since our patients were recruited from the specialized depression outpatient clinic. Our personality-based latent profile analysis identified clinically meaningful 3 MDD groups that were markedly different in their personality profiles associated with distinct symptomatology and functioning. Copyright © 2017 Elsevier B.V. All rights reserved.

Patterns of cannabis use during adolescence and their association with harmful substance use behaviour: findings from a UK birth cohort

PubMed Central

Collin, Simon M; Munafò, Marcus R; MacLeod, John; Hickman, Matthew; Heron, Jon

2017-01-01

Background Evidence on the role of cannabis as a gateway drug is inconsistent. We characterise patterns of cannabis use among UK teenagers aged 13–18 years, and assess their influence on problematic substance use at age 21 years. Methods We used longitudinal latent class analysis to derive trajectories of cannabis use from self-report measures in a UK birth cohort. We investigated (1) factors associated with latent class membership and (2) whether latent class membership predicted subsequent nicotine dependence, harmful alcohol use and recent use of other illicit drugs at age 21 years. Results 5315 adolescents had three or more measures of cannabis use from age 13 to 18 years. Cannabis use patterns were captured as four latent classes corresponding to ‘non-users’ (80.1%), ‘late-onset occasional’ (14.2%), ‘early-onset occasional’ (2.3%) and ‘regular’ users (3.4%). Sex, mother's substance use, and child's tobacco use, alcohol consumption and conduct problems were strongly associated with cannabis use. At age 21 years, compared with the non-user class, late-onset occasional, early-onset occasional and regular cannabis user classes had higher odds of nicotine dependence (OR=3.5, 95% CI 0.7 to 17.9; OR=12.1, 95% CI 1.0 to 150.3; and OR=37.2, 95% CI 9.5 to 144.8, respectively); harmful alcohol consumption (OR=2.6, 95% CI 1.5 to 4.3; OR=5.0, 95% CI 2.1 to 12.1; and OR=2.6, 95% CI 1.0 to 7.1, respectively); and other illicit drug use (OR=22.7, 95% CI 11.3 to 45.7; OR=15.9, 95% CI 3.9 to 64.4; and OR=47.9, 95% CI 47.9 to 337.0, respectively). Conclusions One-fifth of the adolescents in our sample followed a pattern of occasional or regular cannabis use, and these young people were more likely to progress to harmful substance use behaviours in early adulthood. PMID:28592420

The Latent Classes of Subclinical ADHD Symptoms: Convergences of Multiple Informant Reports

ERIC Educational Resources Information Center

Kobor, Andrea; Takacs, Adam; Urban, Robert; Csepe, Valeria

2012-01-01

The purpose of the present study was to conduct latent class analysis on the Hyperactivity scale of the Strengths and Difficulties Questionnaire in order to identify distinct subgroups of subclinical ADHD in a multi-informant framework. We hypothesized a similar structure between teachers and parents, and differences in symptom severity across…

Some Factor Analytic Approximations to Latent Class Structure.

ERIC Educational Resources Information Center

Dziuban, Charles D.; Denton, William T.

Three procedures, alpha, image, and uniqueness rescaling, were applied to a joint occurrence probability matrix. That matrix was the basis of a well-known latent class structure. The values of the recurring subscript elements were varied as follows: Case 1 - The known elements were input; Case 2 - The upper bounds to the recurring subscript…

Latent Class Analysis of Conduct Problems of Elementary Students Receiving Special Education Services

ERIC Educational Resources Information Center

Toupin, Jean; Déry, Michèle; Verlaan, Pierrette; Lemelin, Jean-Pascal; Lecocq, Aurélie; Jagiellowicz, Jadwiga

2016-01-01

Students with conduct problems (CPs) may present heterogeneity in terms of behavioral manifestations and service needs. Previous studies using Latent Class Analysis (LCA) to capture this heterogeneity have been conducted mostly with community samples and have often applied a narrow definition of CP. Considering this context, this study…

Multilevel Latent Class Analysis: Parametric and Nonparametric Models

ERIC Educational Resources Information Center

Finch, W. Holmes; French, Brian F.

2014-01-01

Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

SOCIAL STABILITY AND HIV RISK BEHAVIOR: EVALUATING THE ROLE OF ACCUMULATED VULNERABILITY

PubMed Central

German, Danielle; Latkin, Carl A.

2011-01-01

This study evaluated a cumulative and syndromic relationship among commonly co-occurring vulnerabilites (homelessness, incarceration, low-income, residential transition) in association with HIV-related risk behaviors among 635 low-income women in Baltimore. Analysis included descriptive statistics, logistic regression, latent class analysis and latent class regression. Both methods of assessing multidimensional instability showed significant associations with risk indicators. Risk of multiple partners, sex exchange, and drug use decreased significantly with each additional domain. Higher stability class membership (77%) was associated with decreased likelihood of multiple partners, exchange partners, recent drug use, and recent STI. Multidimensional social vulnerabilities were cumulatively and synergistically linked to HIV risk behavior. Independent instability measures may miss important contextual determinants of risk. Social stability offers a useful framework to understand the synergy of social vulnerabilities that shape sexual risk behavior. Social policies and programs aiming to enhance housing and overall social stability are likely to be beneficial for HIV prevention. PMID:21259043

Relationships among Behavioral Profiles, Reading Performance, and Disability Labels: A Latent Class Analysis of Students in the ED, LD, and OHI Special Education Categories

ERIC Educational Resources Information Center

Weingarten, Zachary

2018-01-01

The aim of this study was to explore the variation in student behavior across the ED, LD, and OHI disability categories and to examine demographic, behavioral, and academic factors that may place students at risk for negative outcomes. This study used teachers' rating of students' in-class behavior to identify latent classes of students in the ED,…

Latent classes of sexual behaviors: Prevalence, predictors, and consequences

PubMed Central

Wesche, Rose; Lefkowitz, Eva S.; Vasilenko, Sara A.

2016-01-01

Scholars of adolescent and emerging adult sexuality have recently begun to study how diverse patterns of sexual behaviors contribute to development and well-being. A person-oriented approach to studying sexual behaviors provides a nuanced understanding of sexual repertoires. The goals of this paper were to document patterns of sexual behaviors ranging from kissing to penetrative sex, and to examine how latent classes of behaviors, gender, and partner type (romantic vs. nonromantic) predict intra- and interpersonal consequences of sexual behaviors. Latent class analysis of a stratified random sample of U.S. college students revealed four classes of sexual behaviors: Kissing Only, Kissing and Touching, All Behaviors, and Oral and Penetrative Only. Compared to individuals in the All Behaviors class, individuals in the Kissing Only class were less likely to experience a positive or a negative intrapersonal consequence of sexual behaviors. Men were less likely to report a negative intrapersonal consequence than women were. Partner type predicted negative interpersonal consequences for the All Behaviors class. Implications are discussed in terms of normative sexual development, prevention, and sexual and relationship education. PMID:28163800

Academic Trajectories of Newcomer Immigrant Youth

ERIC Educational Resources Information Center

Suarez-Orozco, Carola; Gaytan, Francisco X.; Bang, Hee Jin; Pakes, Juliana; O'Connor, Erin; Rhodes, Jean

2010-01-01

Immigration to the United States presents both challenges and opportunities that affect students' academic achievement. Using a 5-year longitudinal, mixed-methods approach, we identified varying academic trajectories of newcomer immigrant students from Central America, China, the Dominican Republic, Haiti, and Mexico. Latent class growth curve…

Parent–Child Relationships in Stepfather Families and Adolescent Adjustment: A Latent Class Analysis

PubMed Central

Amato, Paul R.; King, Valarie; Thorsen, Maggie L.

2015-01-01

In the current study the authors drew on Waves I and III from Add Health to examine the closeness of parent–adolescent relationships in married mother–stepfather families (N = 1,934). They used latent class analysis to identify family constellations defined by adolescents’ relationships with all of their parents: mothers, stepfathers, and biological nonresident fathers. In particular, the authors (a) identified the most common underlying patterns of adolescent–parent relationships in stepfamilies; (b) determined the background characteristics that predict membership in these groups; and (c) examined how adolescents in these groups fare with respect to depressive symptoms, delinquency, and substance use. The results indicate that adolescents’ relationships can be represented with 4 latent classes. Adolescents in these classes differ on measures of adjustment, and many of these differences persist into the early adult years. PMID:27022199

The association between school exclusion, delinquency and subtypes of cyber- and F2F-victimizations: identifying and predicting risk profiles and subtypes using latent class analysis.

PubMed

Barboza, Gia Elise

2015-01-01

This purpose of this paper is to identify risk profiles of youth who are victimized by on- and offline harassment and to explore the consequences of victimization on school outcomes. Latent class analysis is used to explore the overlap and co-occurrence of different clusters of victims and to examine the relationship between class membership and school exclusion and delinquency. Participants were a random sample of youth between the ages of 12 and 18 selected for inclusion to participate in the 2011 National Crime Victimization Survey: School Supplement. The latent class analysis resulted in four categories of victims: approximately 3.1% of students were highly victimized by both bullying and cyberbullying behaviors; 11.6% of youth were classified as being victims of relational bullying, verbal bullying and cyberbullying; a third class of students were victims of relational bullying, verbal bullying and physical bullying but were not cyberbullied (8%); the fourth and final class, characteristic of the majority of students (77.3%), was comprised of non-victims. The inclusion of covariates to the latent class model indicated that gender, grade and race were significant predictors of at least one of the four victim classes. School delinquency measures were included as distal outcomes to test for both overall and pairwise associations between classes. With one exception, the results were indicative of a significant relationship between school delinquency and the victim subtypes. Implications for these findings are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Heterogeneity in patterns of DSM-5 posttraumatic stress disorder and depression symptoms: Latent profile analyses.

PubMed

Contractor, Ateka A; Roley-Roberts, Michelle E; Lagdon, Susan; Armour, Cherie

2017-04-01

Posttraumatic stress disorder (PTSD) and depression co-occur frequently following the experience of potentially traumatizing events (PTE; Morina et al., 2013). A person-centered approach to discern heterogeneous patterns of such co-occurring symptoms is recommended (Galatzer-Levy and Bryant, 2013). We assessed heterogeneity in PTSD and depression symptomatology; and subsequently assessed relations between class membership with psychopathology constructs (alcohol use, distress tolerance, dissociative experiences). The sample consisted of 268 university students who had experienced a PTE and susequently endorsed clinical levels of PTSD or depression severity. Latent profile analyses (LPA) was used to identify the best-fitting class solution accouring to recommended fit indices (Nylund et al., 2007a); and the effects of covariates was analyzed using a 3-step approach (Vermunt, 2010). Results of the LPA indicated an optimal 3-class solutions: high severity (Class 2), lower PTSD-higher depression (Class 1), and higher PTSD-lower depression (Class 3). Covariates of distress tolerance, and different kinds of dissociative experiences differentiated the latent classes. Use of self-report measure could lead to response biases; and the specific nature of the sample limits generalizability of results. We found evidence for a depressive subtype of PTSD differentiated from other classes in terms of lower distress tolerance and greater dissociative experiences. Thus, transdiagnostic treatment protocols may be most beneficial for these latent class members. Further, the distinctiveness of PTSD and depression at comparatively lower levels of PTSD severity was supported (mainly in terms of distress tolerance abilities); hence supporting the current classification system placement of these disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables

PubMed Central

Corradi, Nicola

2016-01-01

Zipf’s law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf’s law in each of them, those explanations are typically domain specific. Recently, methods from statistical physics were used to show that a fairly broad class of models does provide a general explanation of Zipf’s law. This explanation rests on the observation that real world data is often generated from underlying causes, known as latent variables. Those latent variables mix together multiple models that do not obey Zipf’s law, giving a model that does. Here we extend that work both theoretically and empirically. Theoretically, we provide a far simpler and more intuitive explanation of Zipf’s law, which at the same time considerably extends the class of models to which this explanation can apply. Furthermore, we also give methods for verifying whether this explanation applies to a particular dataset. Empirically, these advances allowed us extend this explanation to important classes of data, including word frequencies (the first domain in which Zipf’s law was discovered), data with variable sequence length, and multi-neuron spiking activity. PMID:27997544

Patterns of Alcohol Policy Enforcement Activities among Local Law Enforcement Agencies: A Latent Class Analysis

PubMed Central

Erickson, Darin J.; Rutledge, Patricia C.; Lenk, Kathleen M.; Nelson, Toben F.; Jones-Webb, Rhonda; Toomey, Traci L.

2015-01-01

Aims We assessed levels and patterns of alcohol policy enforcement activities among U.S. local law enforcement agencies. Design/Setting/Participants We conducted a cross-sectional survey of a representative sample of 1,631 local law enforcement agencies across the 50 states. Measures/Methods We assessed 29 alcohol policy enforcement activities within each of five enforcement domains—underage alcohol possession/consumption, underage alcohol provision, underage alcohol sales, impaired driving, and overservice of alcohol—and conducted a series of latent class analyses to identify unique classes or patterns of enforcement activity for each domain. Findings We identified three to four unique enforcement activity classes for each of the enforcement domains. In four of the domains, we identified a Uniformly Low class (i.e., little or no enforcement) and a Uniformly High enforcement activity class (i.e., relatively high levels of enforcement), with one or two middle classes where some but not all activities were conducted. The underage provision domain had a Uniformly Low class but not a Uniformly High class. The Uniformly Low class was the most prevalent class in three domains: underage provision (58%), underage sales (61%), and overservice (79%). In contrast, less than a quarter of agencies were in Uniformly High classes. Conclusions We identified qualitatively distinct patterns of enforcement activity, with a large proportion of agencies in classes characterized by little or no enforcement and fewer agencies in high enforcement classes. An important next step is to determine if these patterns are associated with rates of alcohol use and alcohol-related injury and mortality. PMID:26877822

A Taxonomy of Latent Structure Assumptions for Probability Matrix Decomposition Models.

ERIC Educational Resources Information Center

Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven

2003-01-01

Proposed a taxonomy of latent structure assumptions for probability matrix decomposition (PMD) that includes the original PMD model and a three-way extension of the multiple classification latent class model. Simulation study results show the usefulness of the taxonomy. (SLD)

On the Estimation of Disease Prevalence by Latent Class Models for Screening Studies Using Two Screening Tests with Categorical Disease Status Verified in Test Positives Only

PubMed Central

Chu, Haitao; Zhou, Yijie; Cole, Stephen R.; Ibrahim, Joseph G.

2010-01-01

Summary To evaluate the probabilities of a disease state, ideally all subjects in a study should be diagnosed by a definitive diagnostic or gold standard test. However, since definitive diagnostic tests are often invasive and expensive, it is generally unethical to apply them to subjects whose screening tests are negative. In this article, we consider latent class models for screening studies with two imperfect binary diagnostic tests and a definitive categorical disease status measured only for those with at least one positive screening test. Specifically, we discuss a conditional independent and three homogeneous conditional dependent latent class models and assess the impact of misspecification of the dependence structure on the estimation of disease category probabilities using frequentist and Bayesian approaches. Interestingly, the three homogeneous dependent models can provide identical goodness-of-fit but substantively different estimates for a given study. However, the parametric form of the assumed dependence structure itself is not “testable” from the data, and thus the dependence structure modeling considered here can only be viewed as a sensitivity analysis concerning a more complicated non-identifiable model potentially involving heterogeneous dependence structure. Furthermore, we discuss Bayesian model averaging together with its limitations as an alternative way to partially address this particularly challenging problem. The methods are applied to two cancer screening studies, and simulations are conducted to evaluate the performance of these methods. In summary, further research is needed to reduce the impact of model misspecification on the estimation of disease prevalence in such settings. PMID:20191614

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

PubMed Central

De Angelis, Marco; Marín Puchades, Víctor; Fraboni, Federico; Pietrantoni, Luca

2017-01-01

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist’s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types. PMID:28158296

Classification and Short-Term Course of DSM-IV Cannabis, Hallucinogen, Cocaine, and Opioid Disorders in Treated Adolescents

ERIC Educational Resources Information Center

Chung, Tammy; Martin, Christoper S.

2005-01-01

This study examined the latent class structure of Diagnostic and Statistical Manual of Mental Disorders (text rev.; DSM-IV; American Psychiatric Association, 2000) symptoms used to diagnose cannabis, hallucinogen, cocaine, and opiate disorders among 501 adolescents recruited from addictions treatment. Latent class results were compared with the…

A Latent Class Analysis of Adolescent Gambling: Application of Resilience Theory

ERIC Educational Resources Information Center

Goldstein, Abby L.; Faulkner, Breanne; Cunningham, Rebecca M.; Zimmerman, Marc A.; Chermack, Stephen; Walton, Maureen A.

2013-01-01

The current study examined the application of resilience theory to adolescent gambling using Latent Class Analysis (LCA) to establish subtypes of adolescent gamblers and to explore risk and promotive factors associated with gambling group membership. Participants were a diverse sample of 249 adolescents ages 14 to 18 (30.1 % female, 59.4 % African…

Hybrid Model of IRT and Latent Class Models.

ERIC Educational Resources Information Center

Yamamoto, Kentaro

This study developed a hybrid of item response theory (IRT) models and latent class models, which combined the strengths of each type of model. The primary motivation for developing the new model is to describe characteristics of examinees' knowledge at the time of the examination. Hence, the application of the model lies mainly in so-called…

Identifying Students' Expectancy-Value Beliefs: A Latent Class Analysis Approach to Analyzing Middle School Students' Science Self-Perceptions

ERIC Educational Resources Information Center

Phelan, Julia; Ing, Marsha; Nylund-Gibson, Karen; Brown, Richard S.

2017-01-01

This study extends current research by organizing information about students' expectancy-value achievement motivation, in a way that helps parents and teachers identify specific entry points to encourage and support students' science aspirations. This study uses latent class analysis to describe underlying differences in ability beliefs, task…

Exploring the Relationship between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis

ERIC Educational Resources Information Center

Cuccaro, Michael L.; Tuchman, Roberto F.; Hamilton, Kara L.; Wright, Harry H.; Abramson, Ruth K.; Haines, Jonathan L.; Gilbert, John R.; Pericak-Vance, Margaret

2012-01-01

Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster…

The Structure of Student Satisfaction with College Services: A Latent Class Model

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

Latent Class Analysis (LCA) was used to identify distinct groups of Community college students based on their self-ratings of satisfaction with student service programs. The programs were counseling, financial aid, health center, student programs and student government. The best fitting model to describe the data was a two Discrete-Factor model…

Patterns of Adolescent Bullying Behaviors: Physical, Verbal, Exclusion, Rumor, and Cyber

ERIC Educational Resources Information Center

Wang, Jing; Iannotti, Ronald J.; Luk, Jeremy W.

2012-01-01

Patterns of engagement in cyber bullying and four types of traditional bullying were examined using latent class analysis (LCA). Demographic differences and externalizing problems were evaluated across latent class membership. Data were obtained from the 2005-2006 Health Behavior in School-aged Survey and the analytic sample included 7,508 U.S.…

Solidarity and Conflict between Adult Children and Parents: A Latent Class Analysis

ERIC Educational Resources Information Center

van Gaalen, Ruben I.; Dykstra, Pearl A.

2006-01-01

Using multiple dimensions of solidarity and conflict in a latent class analysis, we develop a typology of adult child-parent relationships. The data (N = 4,990) are from the first wave of the Netherlands Kinship Panel Study. In descending order of relationship quality, the 5 types are harmonious (akin to relationships with friends), ambivalent…

Tailoring community-based wellness initiatives with latent class analysis--Massachusetts Community Transformation Grant projects.

PubMed

Arcaya, Mariana; Reardon, Timothy; Vogel, Joshua; Andrews, Bonnie K; Li, Wenjun; Land, Thomas

2014-02-13

Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts' municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach. Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains. Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges. Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.

Characterizing Longitudinal Patterns of Physical Activity in Mid-Adulthood Using Latent Class Analysis: Results From a Prospective Cohort Study

PubMed Central

Silverwood, Richard J.; Nitsch, Dorothea; Pierce, Mary; Kuh, Diana; Mishra, Gita D.

2011-01-01

The authors aimed to describe how longitudinal patterns of physical activity during mid-adulthood (ages 31–53 years) can be characterized using latent class analysis in a population-based birth cohort study, the Medical Research Council’s 1946 National Survey of Health and Development. Three different types of physical activity—walking, cycling, and leisure-time physical activity—were analyzed separately using self-reported data collected from questionnaires between 1977 and 1999; 3,847 study members were included in the analysis for one or more types of activity. Patterns of activity differed by sex, so stratified analyses were conducted. Two walking latent classes were identified representing low (52.8% of males in the cohort, 33.5% of females) and high (47.2%, 66.5%) levels of activity. Similar low (91.4%, 82.1%) and high (8.6%, 17.9%) classes were found for cycling, while 3 classes were identified for leisure-time physical activity: “low activity” (46.2%, 48.2%), “sports and leisure activity” (31.0%, 35.3%), and “gardening and do-it-yourself activities” (22.8%, 16.5%). The classes were reasonably or very well separated, with the exception of walking in females. Latent class analysis was found to be a useful tool for characterizing longitudinal patterns of physical activity, even when the measurement instrument differs slightly across ages, which added value in comparison with observed activity at a single age. PMID:22074812

Latent Classes of Adolescent Sexual and Romantic Relationship Experiences: Implications for Adult Sexual Health and Relationship Outcomes

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Lanza, Stephanie T.

2015-01-01

Adolescents’ sexual and romantic relationship experiences are multidimensional, but often studied as single constructs. Thus, it is not clear how different patterns of sexual and relationship experience may interact to differentially predict later outcomes. In this study we used latent class analysis to model patterns (latent classes) of adolescent sexual and romantic experiences, and then examined how these classes are associated with young adult sexual health and relationship outcomes in data from the National Longitudinal Study of Adolescent to Adult Health. We identified six adolescent relationship classes: No Relationship (33%), Waiting (22%), Intimate (38%), Private (3%), Low Involvement (3%), and Physical (2%). Adolescents in the Waiting and Intimate classes were more likely to have married by young adulthood than those in other classes, and those in the Physical class had a greater number of sexual partners and higher rates of STIs. Some gender differences were found; for example, women in the Low-involvement and Physical classes in adolescence had average or high odds of marriage, whereas men in these classes had relatively low odds of marriage. Our findings identify more and less normative patterns of romantic and sexual experiences in late adolescence, and elucidate associations between adolescent experiences and adult outcomes. PMID:26445133

Severity of mental illness as a result of multiple childhood adversities: US National Epidemiologic Survey.

PubMed

Curran, Emma; Adamson, Gary; Stringer, Maurice; Rosato, Michael; Leavey, Gerard

2016-05-01

To examine patterns of childhood adversity, their long-term consequences and the combined effect of different childhood adversity patterns as predictors of subsequent psychopathology. Secondary analysis of data from the US National Epidemiologic Survey on alcohol and related conditions. Using latent class analysis to identify childhood adversity profiles; and using multinomial logistic regression to validate and further explore these profiles with a range of associated demographic and household characteristics. Finally, confirmatory factor analysis substantiated initial latent class analysis findings by investigating a range of mental health diagnoses. Latent class analysis generated a three-class model of childhood adversity in which 60 % of participants were allocated to a low adversity class; 14 % to a global adversities class (reporting exposures for all the derived latent classes); and 26 % to a domestic emotional and physical abuse class (exposed to a range of childhood adversities). Confirmatory Factor analysis defined an internalising-externalising spectrum to represent lifetime reporting patterns of mental health disorders. Using logistic regression, both adversity groups showed specific gender and race/ethnicity differences, related family discord and increased psychopathology. We identified underlying patterns in the exposure to childhood adversity and associated mental health. These findings are informative in their description of the configuration of adversities, rather than focusing solely on the cumulative aspect of experience. Amelioration of longer-term negative consequences requires early identification of psychopathology risk factors that can inform protective and preventive interventions. This study highlights the utility of screening for childhood adversities when individuals present with symptoms of psychiatric disorders.

Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use

PubMed Central

Reboussin, Beth A.; Ialongo, Nicholas S.

2011-01-01

Summary Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is most often diagnosed in childhood with symptoms often persisting into adulthood. Elevated rates of substance use disorders have been evidenced among those with ADHD, but recent research focusing on the relationship between subtypes of ADHD and specific drugs is inconsistent. We propose a latent transition model (LTM) to guide our understanding of how drug use progresses, in particular marijuana use, while accounting for the measurement error that is often found in self-reported substance use data. We extend the LTM to include a latent class predictor to represent empirically derived ADHD subtypes that do not rely on meeting specific diagnostic criteria. We begin by fitting two separate latent class analysis (LCA) models by using second-order estimating equations: a longitudinal LCA model to define stages of marijuana use, and a cross-sectional LCA model to define ADHD subtypes. The LTM model parameters describing the probability of transitioning between the LCA-defined stages of marijuana use and the influence of the LCA-defined ADHD subtypes on these transition rates are then estimated by using a set of first-order estimating equations given the LCA parameter estimates. A robust estimate of the LTM parameter variance that accounts for the variation due to the estimation of the two sets of LCA parameters is proposed. Solving three sets of estimating equations enables us to determine the underlying latent class structures independently of the model for the transition rates and simplifying assumptions about the correlation structure at each stage reduces the computational complexity. PMID:21461139

Microaggressions, Discrimination, and Phenotype among African Americans: A Latent Class Analysis of the Impact of Skin Tone and BMI.

PubMed

Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M

2017-05-01

Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.

Cancer patients' trade-offs among efficacy, toxicity, and out-of-pocket cost in the curative and noncurative setting.

PubMed

Wong, Yu-Ning; Egleston, Brian L; Sachdeva, Kush; Eghan, Naa; Pirollo, Melanie; Stump, Tammy K; Beck, John Robert; Armstrong, Katrina; Schwartz, Jerome Sanford; Meropol, Neal J

2013-09-01

When making treatment decisions, cancer patients must make trade-offs among efficacy, toxicity, and cost. However, little is known about what patient characteristics may influence these trade-offs. A total of 400 cancer patients reviewed 2 of 3 stylized curative and noncurative scenarios that asked them to choose between 2 treatments of varying levels of efficacy, toxicity, and cost. Each scenario included 9 choice sets. Demographics, cost concerns, numeracy, and optimism were assessed. Within each scenario, we used latent class methods to distinguish groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. The median age of the patients was 61 years (range, 27-90 y). Of the total number of patients included, 25% were enrolled at a community hospital, and 99% were insured. Three latent classes were identified that demonstrated (1) preference for survival, (2) aversion to high cost, and (3) aversion to toxicity. Across all scenarios, patients with higher income were more likely to be in the class that favored survival. Lower income patients were more likely to be in the class that was averse to high cost (P<0.05). Similar associations were found between education, employment status, numeracy, cost concerns, and latent class. Even in these stylized scenarios, socioeconomic status predicted the treatment choice. Higher income patients may be more likely to focus on survival, whereas those of lower socioeconomic status may be more likely to avoid expensive treatment, regardless of survival or toxicity. This raises the possibility that insurance plans with greater cost-sharing may have the unintended consequence of increasing disparities in cancer care.

Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland.

PubMed

Sasidharan, Lekshmi; Wu, Kun-Feng; Menendez, Monica

2015-12-01

One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury severities and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Socioeconomic Differences in the Risk Profiles of Susceptibility and Ever Use of Tobacco Among Indian Urban Youth: A Latent Class Approach

PubMed Central

2014-01-01

Purpose: To empirically determine the socioeconomic differences in risk profiles of susceptibility and ever use of tobacco among adolescents in India and to investigate the association between the risk profiles and the psychosocial factors for tobacco use. Methods: Students in 16 private (higher socioeconomic status [SES]; n = 4,489) and 16 government (lower SES; n = 7,153) schools in two large cities in India were surveyed about their tobacco use and related psychosocial factors in 2004. Latent class analysis was used to identify homogenous, mutually exclusive typologies existing within the data. Results: Overall, 3 and 4 latent classes of susceptibility and ever use of tobacco best described students in higher- and lower- SES schools, respectively. Profiles with various combinations of susceptibility and ever use of tobacco were differentially related to psychosocial factors, with lower- SES students being more vulnerable to increased levels of tobacco use than higher- SES students. Conclusions: Acknowledging the multiple dimensions of tobacco use behaviors and identifying constellations of risk behaviors will enable more accurate understanding of etiological processes and will provide information for refining and targeting preventive interventions. Additionally, identifying the socioeconomic differences in susceptibility and ever use risk profiles and their psychosocial correlates will enable policy makers to address these inequities through improved allocation of resources. PMID:24271966

Identifying patterns of item missing survey data using latent groups: an observational study

PubMed Central

McElwee, Paul; Nathan, Andrea; Burton, Nicola W; Turrell, Gavin

2017-01-01

Objectives To examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as ‘item missing’. Design Observational study of longitudinal data. Setting Residents of Brisbane, Australia. Participants 6901 people aged 40–65 years in 2007. Materials and methods We used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants’ characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey. Results Four per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave. Conclusions Examining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data. PMID:29084795

A Bayesian latent class model to estimate the accuracy of pregnancy diagnosis by transrectal ultrasonography and laboratory detection of pregnancy-associated glycoproteins in dairy cows.

PubMed

Fosgate, G T; Motimele, B; Ganswindt, A; Irons, P C

2017-09-15

Accurate diagnosis of pregnancy is an essential component of an effective reproductive management plan for dairy cattle. Indirect methods of pregnancy detection can be performed soon after breeding and offer an advantage over traditional direct methods in not requiring an experienced veterinarian and having potential for automation. The objective of this study was to estimate the sensitivity and specificity of pregnancy-associated glycoprotein (PAG) detection ELISA and transrectal ultrasound (TRUS) in dairy cows of South Africa using a Bayesian latent class approach. Commercial dairy cattle from the five important dairy regions in South Africa were enrolled in a short-term prospective cohort study. Cattle were examined at 28-35days after artificial insemination (AI) and then followed up 14days later. At both sampling times, TRUS was performed to detect pregnancy and commercially available PAG detection ELISAs were performed on collected serum and milk. A total of 1236 cows were sampled and 1006 had complete test information for use in the Bayesian latent class model. The estimated sensitivity (95% probability interval) and specificity for PAG detection serum ELISA were 99.4% (98.5, 99.9) and 97.4% (94.7, 99.2), respectively. The estimated sensitivity and specificity for PAG detection milk ELISA were 99.2% (98.2, 99.8) and 93.4% (89.7, 96.1), respectively. Sensitivity of veterinarian performed TRUS at 28-35days post-AI varied between 77.8% and 90.5% and specificity varied between 94.7% and 99.8%. In summary, indirect detection of pregnancy using PAG ELISA is an accurate method for use in dairy cattle. The method is descriptively more sensitive than veterinarian-performed TRUS and therefore could be an economically viable addition to a reproductive management plan. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of hemoglobin in blood donors using a latent class mixed-effects transition model.

PubMed

Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Rizopoulos, Dimitris; Lesaffre, Emmanuel

2016-02-20

Blood donors experience a temporary reduction in their hemoglobin (Hb) value after donation. At each visit, the Hb value is measured, and a too low Hb value leads to a deferral for donation. Because of the recovery process after each donation as well as state dependence and unobserved heterogeneity, longitudinal data of Hb values of blood donors provide unique statistical challenges. To estimate the shape and duration of the recovery process and to predict future Hb values, we employed three models for the Hb value: (i) a mixed-effects models; (ii) a latent-class mixed-effects model; and (iii) a latent-class mixed-effects transition model. In each model, a flexible function was used to model the recovery process after donation. The latent classes identify groups of donors with fast or slow recovery times and donors whose recovery time increases with the number of donations. The transition effect accounts for possible state dependence in the observed data. All models were estimated in a Bayesian way, using data of new entrant donors from the Donor InSight study. Informative priors were used for parameters of the recovery process that were not identified using the observed data, based on results from the clinical literature. The results show that the latent-class mixed-effects transition model fits the data best, which illustrates the importance of modeling state dependence, unobserved heterogeneity, and the recovery process after donation. The estimated recovery time is much longer than the current minimum interval between donations, suggesting that an increase of this interval may be warranted. Copyright © 2015 John Wiley & Sons, Ltd.

Symptom Trajectories in Children Receiving Treatment for Leukemia: A Latent Class Growth Analysis With Multitrajectory Modeling.

PubMed

Hockenberry, Marilyn J; Hooke, Mary C; Rodgers, Cheryl; Taylor, Olga; Koerner, Kari M; Mitby, Pauline; Moore, Ida; Scheurer, Michael E; Pan, Wei

2017-07-01

Cancer treatment symptoms play a major role in determining the health of children with cancer. Symptom toxicity often results in complications, treatment delays, and therapy dose reductions that can compromise leukemia therapy and jeopardize chances for long-term survival. Critical to understanding symptom experiences during treatment is the need for exploration of "why" inter-individual symptom differences occur; this will determine who may be most susceptible to treatment toxicities. This study examined specific symptom trajectories during the first 18 months of childhood leukemia treatment. Symptom measures included fatigue, sleep disturbances, pain, nausea, and depression. Symptom trajectories of 236 children with leukemia three to 18 years old were explored prospectively over four periods: initiation of post-induction therapy, four and eight post-induction therapy, and the last time point was at the beginning of maintenance/continuation therapy. Latent class growth analysis was used to classify patients into distinctive groups with similar symptom trajectories based on patients' response patterns on the symptom measures over time. Three latent classes of symptom trajectories were identified and classified into mild, moderate, and severe symptom trajectories. The only demographic characteristic with a significant relationship to membership in the latent class symptom trajectories was race/ethnicity. All other demographic characteristics including leukemia risk levels showed no significant relationships. This study is unique in that groups of patients with similar symptoms were identified rather than groups of symptoms. Further research using latent class growth analysis is needed. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

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

PubMed

Rhebergen, D; van der Steenstraten, I M; Sunderland, M; de Graaf, R; Ten Have, M; Lamers, F; Penninx, B W J H; Andrews, G

2014-06-01

The 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. Latent 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. A 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. The absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.

Evaluation of Measurement Instrument Criterion Validity in Finite Mixture Settings

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

2016-01-01

A method for evaluating the validity of multicomponent measurement instruments in heterogeneous populations is discussed. The procedure can be used for point and interval estimation of criterion validity of linear composites in populations representing mixtures of an unknown number of latent classes. The approach permits also the evaluation of…

Configurations of Common Childhood Psychosocial Risk Factors

ERIC Educational Resources Information Center

Copeland, William; Shanahan, Lilly; Costello, E. Jane; Angold, Adrian

2009-01-01

Background: Co-occurrence of psychosocial risk factors is commonplace, but little is known about psychiatrically-predictive configurations of psychosocial risk factors. Methods: Latent class analysis (LCA) was applied to 17 putative psychosocial risk factors in a representative population sample of 920 children ages 9 to 17. The resultant class…

Heterogeneity Moderates Treatment Response among Patients with Binge Eating Disorder

ERIC Educational Resources Information Center

Sysko, Robyn; Hildebrandt, Tom; Wilson, G. Terence; Wilfley, Denise E.; Agras, W. Stewart

2010-01-01

Objective: The purpose of the study was to explore heterogeneity and differential treatment outcome among a sample of patients with binge eating disorder (BED). Method: A latent class analysis was conducted with 205 treatment-seeking, overweight or obese individuals with BED randomized to interpersonal psychotherapy (IPT), behavioral weight loss…

Expressed Emotion-Criticism and Risk of Depression Onset in Children

PubMed Central

Burkhouse, Katie L.; Uhrlass, Dorothy J.; Stone, Lindsey B.; Knopik, Valerie S.; Gibb, Brandon E.

2012-01-01

Objective The primary goal of the current study was to examine the impact of maternal criticism (expressed emotion-criticism; EE-Crit) on the prospective development of depressive episodes in children. In addition to examining baseline levels of EE-Crit, we also sought to determine whether distinct subgroups (latent classes) of mothers could be identified based on the levels of EE-Crit they exhibited over a multi-wave assessment and whether that latent class membership would predict depression onset in children. Finally, we examined whether EE-Crit and maternal depression would independently predict children's depression risk or whether EE-Crit would moderate the link between maternal depression and children's depression onset. Method Children of mothers with or without a history of major depression (N=100) were assessed five times over 20 months. Children completed the Children's Depression Inventory (CDI) and mothers completed the Five Minute Speech Sample and the Beck Depression Inventory (BDI) at the baseline assessment, and at 2, 4, and 6 month follow-up assessments. Children and mothers completed diagnostic interviews assessing children's onsets of depressive episodes at the 20 month follow-up. Results Latent class analysis of the 4 waves of EE-Crit assessments revealed two distinct groups, exhibiting relatively lower versus higher levels of EE-Crit across the first 6 months of follow-up. EE-Crit latent class membership predicted children's depression onset over the subsequent 14 months. This finding was maintained after controlling for mother's and children's depressive symptoms during the initial 6 months of follow-up. Finally, maternal depression did not moderate the link between EE-Crit and childhood depression onset. Conclusions Continued exposure to maternal criticism appears to be an important risk factor for depression in children, risk that is at least partially independent of the risk conveyed by maternal depression. These results highlight the importance of a modifiable risk factor for depression–repeated exposure to maternal criticism. PMID:22838507

A Latent Class Analysis of Online Sexual Experiences and Offline Sexual Behaviors Among Female Adolescents.

PubMed

Maas, Megan K; Bray, Bethany C; Noll, Jennie G

2017-11-20

This study used latent class analysis to identify patterns (i.e., classes) across a broad range of online sexual experiences among female adolescents (n = 312) and to explore offline sexual behavior and substance use correlates of as well as maltreatment differences in class membership. The following four classes were identified: Online Abstinent, Online Inclusive, Attractors, and Seekers. Maltreated female adolescents were more likely to be members of the Online Inclusive class and less likely to be members of the Online Abstinent class than nonmaltreated female adolescents. Offline sexual behaviors and substance use differentially predicted class membership. These results suggest online sexual experiences vary greatly and should not be aggregated together as a global risk factor for all female adolescents. © 2017 Society for Research on Adolescence.

Latent Class Analysis of Antisocial Behavior: Interaction of Serotonin Transporter Genotype and Maltreatment

ERIC Educational Resources Information Center

Li, James J.; Lee, Steve S.

2010-01-01

To improve understanding about genetic and environmental influences on antisocial behavior (ASB), we tested the association of the 44-base pair polymorphism of the serotonin transporter gene (5-HTTLPR) and maltreatment using latent class analysis in 2,488 boys and girls from Wave 1 of the National Longitudinal Study of Adolescent Health. In boys,…

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

ERIC Educational Resources Information Center

Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

2016-01-01

A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

Identifying Students at Risk: An Examination of Computer-Adaptive Measures and Latent Class Growth Analysis

ERIC Educational Resources Information Center

Keller-Margulis, Milena; McQuillin, Samuel D.; Castañeda, Juan Javier; Ochs, Sarah; Jones, John H.

2018-01-01

Multitiered systems of support depend on screening technology to identify students at risk. The purpose of this study was to examine the use of a computer-adaptive test and latent class growth analysis (LCGA) to identify students at risk in reading with focus on the use of this methodology to characterize student performance in screening.…

Developmental Typologies of Identity Formation and Adjustment in Female Emerging Adults: A Latent Class Growth Analysis Approach

ERIC Educational Resources Information Center

Luyckx, Koen; Schwartz, Seth J.; Goossens, Luc; Soenens, Bart; Beyers, Wim

2008-01-01

The developmental interplay between identity and adjustment was examined in a seven-wave longitudinal study of 428 European female college students (M[subscript age] = 18.8 years) over a period of 3 years, with semi-annual measurement waves each year. Latent Class Growth Analysis (LCGA) was used to identify developmental typologies of both…

The Co-Occurrence of Substance Use and Bullying Behaviors among U.S. Adolescents: Understanding Demographic Characteristics and Social Influences

ERIC Educational Resources Information Center

Luk, Jeremy W.; Wang, Jing; Simons-Morton, Bruce G.

2012-01-01

This study examined the co-occurrence of subtypes of substance use and bullying behaviors using latent class analysis and evaluated latent class differences in demographic characteristics, peer and parental influences. Self-reported questionnaire data were collected from a nationally representative sample (N = 7508) of 6-10th grade adolescents in…

Determination of Pain Phenotypes in Knee Osteoarthritis: A Latent Class Analysis using Data from the Osteoarthritis Initiative Study

PubMed Central

Kittelson, Andrew J.; Stevens-Lapsley, Jennifer E.; Schmiege, Sarah J.

2017-01-01

Objective Knee osteoarthritis (OA) is a broadly applied diagnosis that may encompass multiple subtypes of pain. The purpose of this study was to identify phenotypes of knee OA, using measures from the following pain-related domains: 1) knee OA pathology, 2) psychological distress, and 3) altered pain neurophysiology. Methods Data were selected from a total of 3494 participants at Visit #6 of the Osteoarthritis Initiative (OAI) study. Latent Class Analysis was applied to the following variables: radiographic OA severity, quadriceps strength, Body Mass Index (BMI), Charlson Comorbidity Index (CCI), Center for Epidemiologic Studies Depression subscale (CES-D), Coping Strategies Questionnaire-Catastrophizing subscale (CSQ-Cat), number of bodily pain sites, and knee joint tenderness at 4 sites. Resulting classes were compared on the following demographic and clinical factors: age, sex, pain severity, disability, walking speed, and use of arthritis-related healthcare. Results A four-class model was identified. Class 1 (4% of the study population) had higher CCI scores. Class 2 (24%) had higher knee joint sensitivity. Class 3 (10%) had greater psychological distress. Class 4 (62%) had lesser radiographic OA, little psychological involvement, greater strength, and less pain sensitivity. Additionally, Class 1 was the oldest, on average. Class 4 was the youngest, had the lowest disability, and least pain. Class 3 had the worst disability and most pain. Conclusions Four distinct pain phenotypes of knee OA were identified. Psychological factors, comorbidity status, and joint sensitivity appear to be important in defining phenotypes of knee OA-related pain. PMID:26414884

Longitudinal Course of Risk for Parental Post-Adoption Depression

PubMed Central

Foli, Karen J.; South, Susan C.; Lim, Eunjung; Hebdon, Megan

2016-01-01

Objective To determine whether the Postpartum Depression Predictors Inventory-Revised (PDPI-R) could be used to reveal distinct classes of adoptive parents across time. Design Longitudinal data were collected via online surveys at 4-6 weeks pre-placement, 4-6 weeks post-placement, and 5-6 months post-placement. Setting Participants were primarily clients of the largest adoption agency in the United States. Participants Participants included 127 adoptive parents (68 mothers and 59 fathers). Methods We applied a latent class growth analysis to the PDPI-R and conducted mixed effects modeling of class, time, and class×time interaction for the following categories of explanatory variables: parental expectations; interpersonal variables; psychological symptoms; and life orientation. Results Four latent trajectory classes were found. Class 1 (55% of sample) showed a stably low level of PDPI-R scores over time. Class 2 (32%) reported mean scores below the cut-off points at all three time points. Class 3 (8%) started at an intermediate level and increased after post-placement, but decreased at 5-6 months post-placement. Class 4 (5%) had high mean scores at all three time points. Significant main effects were found for almost all explanatory variables for class and for several variables for time. Significant interactions between class and time were found for expectations about the child and amount of love and ambivalence in parent's intimate relationship. Conclusion Findings may assist nurses to be alert to trajectories of risk for post-adoption depression. Additional factors, not included in the PDPI-R, to determine risk for post-adoption depression may be needed for adoptive parents. PMID:26874267

Longitudinal burnout-collaboration patterns in Japanese medical care workers at special needs schools: a latent class growth analysis

PubMed Central

Kanayama, Mieko; Suzuki, Machiko; Yuma, Yoshikazu

2016-01-01

The present study aimed to identify and characterize potential burnout types and the relationship between burnout and collaboration over time. Latent class growth analysis and the growth mixture model were used to identify and characterize heterogeneous patterns of longitudinal stability and change in burnout, and the relationship between burnout and collaboration. We collected longitudinal data at three time points based on Japanese academic terms. The 396 study participants included academic teachers, yogo teachers, and registered nurses in Japanese special needs schools. The best model included four types of both burnout and collaboration in latent class growth analysis with intercept, slope, and quadratic terms. The four types of burnout were as follows: low stable, moderate unstable, high unstable, and high decreasing. They were identified as involving inverse collaboration function. The results indicated that there could be dynamic burnout types, namely moderate unstable, high unstable, and high decreasing, when focusing on growth trajectories in latent class analyses. The finding that collaboration was dynamic for dynamic burnout types and stable for stable burnout types is of great interest. This was probably related to the inverse relationship between the two constructs. PMID:27366107

[Latent Class Analysis of Gambling Activities among Korean Adolescents].

PubMed

Kang, Kyonghwa; Kim, Hyeongsu; Park, Ae Ran; Kim, Hee Young; Lee, Kunsei

2018-04-01

The aim of this study is to identify the types of gambling among adolescents and provide basic prevention information regarding adolescents' gambling problems. Secondary data from representative national survey on 2015 Youth Gambling Problems of Korea Center on Gambling Problems were used. Using latent class analysis (LCA), 13 gambling types such as offline and online games of 14,011 adolescents were classified, and gambling experiences and characteristics were analyzed. The subgroups of adolescent gambling were identified as four latent classes: a rare group (84.5% of the sample), a risk group (1.0%), an offline group (11.9%), and an expanded group (2.6%). The types and characteristics of gambling among the latent classes differed. In the risk group, adolescents participated in online illegal sports betting and internet casino, and gambling time, gambling expenses, and the number of gambling types were higher than other groups. Gambling frequently occur among adolescent, and the subtypes of gambling did not reveal homogeneous characteristics. In order to prevent adolescent gambling problems, it is a necessary to develop tailored prevention intervention in the nursing field, which is appropriate to the characteristics of adolescent gambling group and can help with early identification. © 2018 Korean Society of Nursing Science.

Latent class analysis of early developmental trajectory in baby siblings of children with autism.

PubMed

Landa, Rebecca J; Gross, Alden L; Stuart, Elizabeth A; Bauman, Margaret

2012-09-01

Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Sibs-A (N = 204) were assessed with the Mullen Scales of Early Learning from age 6 to 36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (N = 52); non-ASD social/communication delay (broader autism phenotype; BAP; N = 31); and unaffected (N = 121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

Multilevel Higher-Order Item Response Theory Models

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung

2014-01-01

In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…

Ethnic differences in the clustering and outcomes of health behaviours during pregnancy: results from the Born in Bradford cohort.

PubMed

Petherick, E S; Fairley, L; Parslow, R C; McEachan, R; Tuffnell, D; Pickett, K E; Leon, D; Lawlor, D A; Wright, J

2017-09-01

Pregnancy is a time of optimal motivation for many women to make positive behavioural changes. We aim to describe pregnant women with similar patterns of self-reported health behaviours and examine associations with birth outcomes. We examined the clustering of multiple health behaviours during pregnancy in the Born in Bradford cohort, including smoking physical inactivity, vitamin d supplementation and exposure to second-hand smoke. Latent class analysis was used to identify groups of individuals with similar patterns of health behaviours separately for White British (WB) and Pakistani mothers. Multinomial regression was then used to examine the association between group membership and birth outcomes, which included preterm birth and mean birthweight. For WB mothers, offspring of those in the 'Unhealthiest' group had lower mean birthweight than those in the 'Mostly healthy but inactive' class, although no association was observed for preterm birth. For Pakistani mothers, group membership was not associated with birthweight differences, although the odds of preterm birth was higher in 'Inactive smokers' compared to the 'Mostly healthy but inactive' group. The use of latent class methods provides important information about the clustering of health behaviours which can be used to target population segments requiring behaviour change interventions considering multiple risk factors. Given the dominant negative association of smoking with the birth outcomes investigated, latent class groupings of other health behaviours may not confer additional risk information for these outcomes. © The Author 2016. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Latent profile analysis and principal axis factoring of the DSM-5 dissociative subtype

PubMed Central

Frewen, Paul A.; Brown, Matthew F. D.; Steuwe, Carolin; Lanius, Ruth A.

2015-01-01

Objective A dissociative subtype has been recognized based on the presence of experiences of depersonalization and derealization in relation to DSM-IV posttraumatic stress disorder (PTSD). However, the dissociative subtype has not been assessed in a community sample in relation to the revised DSM-5 PTSD criteria. Moreover, the 20-item PTSD Checklist for DSM-5 (PCL-5) currently does not assess depersonalization and derealization. Method We therefore evaluated two items for assessing depersonalization and derealization in 557 participants recruited online who endorsed PTSD symptoms of at least moderate severity on the PCL-5. Results A five-class solution identified two PTSD classes who endorsed dissociative experiences associated with either 1) severe or 2) moderate PTSD symptom severity (D-PTSD classes). Those in the severe dissociative class were particularly likely to endorse histories of childhood physical and sexual abuse. A principal axis factor analysis of the symptom list identified six latent variables: 1) Reexperiencing, 2) Emotional Numbing/Anhedonia, 3) Dissociation, 4) Negative Alterations in Cognition & Mood, 5) Avoidance, and 6) Hyperarousal. Conclusions The present results further support the presence of a dissociative subtype within the DSM-5 criteria for PTSD. PMID:25854673

Searching for chemical classes among metal-poor stars using medium-resolution spectroscopy

NASA Astrophysics Data System (ADS)

Cruz, Monique A.; Cogo-Moreira, Hugo; Rossi, Silvia

2018-04-01

Astronomy is in the era of large spectroscopy surveys, with the spectra of hundreds of thousands of stars in the Galaxy being collected. Although most of these surveys have low or medium resolution, which makes precise abundance measurements not possible, there is still important information to be extracted from the available data. Our aim is to identify chemically distinct classes among metal-poor stars, observed by the Sloan Digital Sky Survey, using line indices. The present work focused on carbon-enhanced metal-poor (CEMP) stars and their subclasses. We applied the latent profile analysis technique to line indices for carbon, barium, iron and europium, in order to separate the sample into classes with similar chemical signatures. This technique provides not only the number of possible groups but also the probability of each object to belong to each class. The method was able to distinguish at least two classes among the observed sample, with one of them being probable CEMP stars enriched in s-process elements. However, it was not able to separate CEMP-no stars from the rest of the sample. Latent profile analysis is a powerful model-based tool to be used in the identification of patterns in astrophysics. Our tests show the potential of the technique for the attainment of additional chemical information from `poor' data.

Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

PubMed

Kim, Eun Sook; Cao, Chunhua

2015-01-01

Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

University and student segmentation: multilevel latent-class analysis of students' attitudes towards research methods and statistics.

PubMed

Mutz, Rüdiger; Daniel, Hans-Dieter

2013-06-01

It is often claimed that psychology students' attitudes towards research methods and statistics affect course enrollment, persistence, achievement, and course climate. However, the inter-institutional variability has been widely neglected in the research on students' attitudes towards research methods and statistics, but it is important for didactic purposes (heterogeneity of the student population). The paper presents a scale based on findings of the social psychology of attitudes (polar and emotion-based concept) in conjunction with a method for capturing beginning university students' attitudes towards research methods and statistics and identifying the proportion of students having positive attitudes at the institutional level. The study based on a re-analysis of a nationwide survey in Germany in August 2000 of all psychology students that enrolled in fall 1999/2000 (N= 1,490) and N= 44 universities. Using multilevel latent-class analysis (MLLCA), the aim was to group students in different student attitude types and at the same time to obtain university segments based on the incidences of the different student attitude types. Four student latent clusters were found that can be ranked on a bipolar attitude dimension. Membership in a cluster was predicted by age, grade point average (GPA) on school-leaving exam, and personality traits. In addition, two university segments were found: universities with an average proportion of students with positive attitudes and universities with a high proportion of students with positive attitudes (excellent segment). As psychology students make up a very heterogeneous group, the use of multiple learning activities as opposed to the classical lecture course is required. © 2011 The British Psychological Society.

On the explaining-away phenomenon in multivariate latent variable models.

PubMed

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

Classes of conduct disorder symptoms and their life course correlates in a US national sample.

PubMed

Breslau, J; Saito, N; Tancredi, D J; Nock, M; Gilman, S E

2012-05-01

Population data on conduct disorder (CD) symptoms can help determine whether hypothesized subtypes of CD are sufficiently disparate in their familial, psychiatric and life course correlates to distinguish separate diagnostic entities. Latent class analysis (LCA) of CD symptoms occurring before age 15 was conducted in a national sample of adults aged 18-44 years from the National Epidemiological Study of Alcohol and Related Conditions. Associations of latent class membership with parental behavior problems, onset of psychiatric disorders and anti-social behaviors after age 15, adolescent life events (e.g. high school drop-out), and past-year life events (e.g. divorce/separation, bankruptcy) were estimated. LCA identified a no-CD class with low prevalence of all symptoms, three intermediate classes - deceit/theft, rule violations, aggression - and a severe class. The prevalence of CD, according to DSM-IV criteria, was 0% in the no-CD class, between 13.33% and 33.69% in the intermediate classes and 62.20% in the severe class. Latent class membership is associated with all the familial, psychiatric and life course outcomes examined. Among the intermediate classes, risk for subsequent mood/anxiety disorders and anti-social behavior was higher in the deceit/theft and aggressive classes than in the rule violations class. However, risk for adolescent life events is highest in the rule violations class. CD symptoms tend to occur in a partially ordered set of classes in the general population. Prognostically meaningful distinctions can be drawn between classes, but only at low levels of symptoms.

Etiological Beliefs, Treatments, Stigmatizing Attitudes toward Schizophrenia. What Do Italians and Israelis Think?

PubMed

Mannarini, Stefania; Boffo, Marilisa; Rossi, Alessandro; Balottin, Laura

2017-01-01

Background: Although scientific research on the etiology of mental disorders has improved the knowledge of biogenetic and psychosocial aspects related to the onset of mental illness, stigmatizing attitudes and behaviors are still very prevalent and pose a significant social problem. Aim: The aim of this study was to deepen the knowledge of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics, such as culture and religion of the perceiver. More precisely, the main purpose is the definition of a structure of variables, namely perceived dangerousness, social closeness, and avoidance of the ill person, together with the beliefs about the best treatment to be undertaken and the sick person' gender, capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned. Method: The study involved 305 university students, 183 from the University of Padua, Italy, and 122 from the University of Haifa, Israel. For the analyses, a latent class analysis (LCA) approach was chosen to identify a latent categorical structure accounting for the covariance between the observed variables. Such a latent structure was expected to be moderated by cultural background (Italy versus Israel) and religious beliefs, whereas causal beliefs, recommended treatment, dangerousness, social closeness, and public avoidance were the manifest variables, namely the observed indicators of the latent variable. Results: Two sets of results were obtained. First, the relevance of the manifest variables as indicators of the hypothesized latent variable was highlighted. Second, a two-latent-class categorical dimension represented by prejudicial attitudes, causal beliefs, and treatments concerning schizophrenia was found. Specifically, the differential effects of the two cultures and the religious beliefs on the latent structure and their relations highlighted the relevance of the observed variables as indicators of the expected latent variable. Conclusion: The present study contributes to the improvement of the understanding of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics of the perceiver. The definition of a structure of variables capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned was achieved from a cross-cultural perspective.

Stress, Mental Health, and Substance Abuse Problems in a Sample of Diversion Program Youths: An Exploratory Latent Class Analysis

ERIC Educational Resources Information Center

Dembo, Richard; Briones, Rhissa; Gulledge, Laura; Karas, Lora; Winters, Ken C.; Belenko, Steven; Greenbaum, Paul E.

2012-01-01

Reflective of interest in mental health and substance abuse issues among youths involved with the justice system, we performed a latent class analysis on baseline information collected on 100 youths involved in two diversion programs. Results identified two groups of youths: Group 1: a majority of the youths, who had high levels of delinquency,…

Prevention of Substance Use among Adolescents through Social and Emotional Training in School: A Latent-Class Analysis of a Five-Year Intervention in Sweden

ERIC Educational Resources Information Center

Kimber, Birgitta; Sandell, Rolf

2009-01-01

The study considers the impact of a program for social and emotional learning in Swedish schools on use of drugs, volatile substances, alcohol and tobacco. The program was evaluated in an effectiveness study. Intervention students were compared longitudinally with non-intervention students using nonparametric latent class analysis to identify…

A Latent Class Growth Analysis of School Bullying and Its Social Context: The Self-Determination Theory Perspective

ERIC Educational Resources Information Center

Lam, Shui-fong; Law, Wilbert; Chan, Chi-Keung; Wong, Bernard P. H.; Zhang, Xiao

2015-01-01

The contribution of social context to school bullying was examined from the self-determination theory perspective in this longitudinal study of 536 adolescents from 3 secondary schools in Hong Kong. Latent class growth analysis of the student-reported data at 5 time points from grade 7 to grade 9 identified 4 groups of students: bullies (9.8%),…

Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey.

PubMed

Khalagi, Kazem; Mansournia, Mohammad Ali; Rahimi-Movaghar, Afarin; Nourijelyani, Keramat; Amin-Esmaeili, Masoumeh; Hajebi, Ahmad; Sharif, Vandad; Radgoodarzi, Reza; Hefazi, Mitra; Motevalian, Abbas

2016-01-01

Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.

Testing treatment effect in schizophrenia clinical trials with heavy patient dropout using latent class growth mixture models.

PubMed

Kong, Fanhui; Chen, Yeh-Fong

2016-07-01

By examining the outcome trajectories of the dropout patients with different reasons in the schizophrenia trials, we note that although patients are recruited from the same protocol that have compatible baseline characteristics, they may respond differently even to the same treatment. Some patients show consistent improvement while others only have temporary relief. This creates different patient subpopulations characterized by their response and dropout patterns. At the same time, those who continue to improve seem to be more likely to complete the study while those who only experience temporary relief have a higher chance to drop out. Such phenomenon appears to be quite general in schizophrenia clinical trials. This simultaneous inhomogeneity both in patient response as well as dropout patterns creates a scenario of missing not at random and therefore results in biases when we use the statistical methods based on the missing at random assumption to test treatment efficacy. In this paper, we propose to use the latent class growth mixture model, which is a special case of the latent mixture model, to conduct the statistical analyses in such situation. This model allows us to take the inhomogeneity among subpopulations into consideration to make more accurate inferences on the treatment effect at any visit time. Comparing with the conventional statistical methods such as mixed-effects model for repeated measures, we demonstrate through simulations that the proposed latent mixture model approach gives better control on the Type I error rate in testing treatment effect. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Copyright © 2016 John Wiley & Sons, Ltd.

Tri-city study of Ecstasy use problems: a latent class analysis.

PubMed

Scheier, Lawrence M; Ben Abdallah, Arbi; Inciardi, James A; Copeland, Jan; Cottler, Linda B

2008-12-01

This study used latent class analysis to examine distinctive subtypes of Ecstasy users based on 24 abuse and dependence symptoms underlying standard DSM-IV criteria. Data came from a three site, population-based, epidemiological study to examine diagnostic nosology for Ecstasy use. Subject inclusion criteria included lifetime Ecstasy use exceeding five times and once in the past year, with participants ranging in age between 16 and 47 years of age from St. Louis, Miami, U.S. and Sydney, Australia. A satisfactory model typified four latent classes representing clearly differentiated diagnostic clusters including: (1) a group of sub-threshold users endorsing few abuse and dependence symptoms (negatives), (2) a group of 'diagnostic orphans' who had characteristic features of dependence for a select group of symptoms (mild dependent), (3) a 'transitional group' mimicking the orphans with regard to their profile of dependence also but reporting some abuse symptoms (moderate dependent), and (4) a 'severe dependent' group with a distinct profile of abuse and dependence symptoms. A multinomial logistic regression model indicated that certain latent classes showed unique associations with external non-diagnostic markers. Controlling for demographic characteristics and lifetime quantity of Ecstasy pill use, criminal behavior and motivational cues for Ecstasy use were the most efficient predictors of cluster membership. This study reinforces the heuristic utility of DSM-IV criteria applied to Ecstasy but with a different collage of symptoms that produced four distinct classes of Ecstasy users.

Latent class analysis on internet and smartphone addiction in college students

PubMed Central

Mok, Jung-Yeon; Choi, Sam-Wook; Kim, Dai-Jin; Choi, Jung-Seok; Lee, Jaewon; Ahn, Heejune; Choi, Eun-Jeung; Song, Won-Young

2014-01-01

Purpose This study aimed to classify distinct subgroups of people who use both smartphone and the internet based on addiction severity levels. Additionally, how the classified groups differed in terms of sex and psychosocial traits was examined. Methods A total of 448 university students (178 males and 270 females) in Korea participated. The participants were given a set of questionnaires examining the severity of their internet and smartphone addictions, their mood, their anxiety, and their personality. Latent class analysis and ANOVA (analysis of variance) were the statistical methods used. Results Significant differences between males and females were found for most of the variables (all <0.05). Specifically, in terms of internet usage, males were more addicted than females (P<0.05); however, regarding smartphone, this pattern was reversed (P<0.001). Due to these observed differences, classifications of the subjects into subgroups based on internet and smartphone addiction were performed separately for each sex. Each sex showed clear patterns with the three-class model based on likelihood level of internet and smartphone addiction (P<0.001). A common trend for psychosocial trait factors was found for both sexes: anxiety levels and neurotic personality traits increased with addiction severity levels (all P<0.001). However, Lie dimension was inversely related to the addiction severity levels (all P<0.01). Conclusion Through the latent classification process, this study identified three distinct internet and smartphone user groups in each sex. Moreover, psychosocial traits that differed in terms of addiction severity levels were also examined. It is expected that these results should aid the understanding of traits of internet and smartphone addiction and facilitate further study in this field. PMID:24899806

Subgrouping of risky behaviors among Iranian college students: a latent class analysis

PubMed Central

Safiri, Saeid; Rahimi-Movaghar, Afarin; Yunesian, Masud; Sadeghi-Bazargani, Homayoun; Shamsipour, Mansour; Mansournia, Mohammad Ali; Fotouhi, Akbar

2016-01-01

Background Risky behaviors may interrupt development or cause considerable morbidity or mortality. This study’s purpose was to determine subgroups of students based on risky behaviors and assess the prevalence of risky behaviors in each of the subgroups. Participants and methods This anonymous cross-sectional study was carried out in October 2015 and November 2015, with 1,777 students from Tabriz University of Medical Sciences, through multistage random sampling method. The data were analyzed by latent class analysis. Results The prevalence rates of cigarette smoking (more than or equal to ten cigarettes), hookah use (≥1 time/month), and alcohol consumption (≥1 time/month) during the last year were 12.4% (95% confidence interval [CI]: 10.9–14.0), 11.6% (95% CI: 10.0–13.1), and 4.9% (95% CI: 3.8–5.9), respectively. The prevalence rates of illicit opioids (1.8%, 95% CI: 1.2–2.5), cannabis (1.2%, 95% CI: 0.7–1.7), methamphetamine (1.1%, 95% CI: 0.6–1.6), methylphenidate (2.5%, 95% CI: 1.7–3.2), and extramarital sex (5.5%, 95% CI: 4.5–6.6) over the last year were also estimated. Three latent classes were determined: 1) low risk; 2) cigarette and hookah smoker; and 3) high risk. It is worth mentioning that 3.7% of males and 0.4% of females were in the high risk group. Conclusion Subgrouping of college students showed that a considerable percentage of them, especially males, were classified into the high risk and cigarette and hookah smoker groups. Appropriate preventive measures that consider multiple different risky behaviors simultaneously are needed for this part of the population. PMID:27524898

Nonlinear Structured Growth Mixture Models in M"plus" and OpenMx

ERIC Educational Resources Information Center

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2010-01-01

Growth mixture models (GMMs; B. O. Muthen & Muthen, 2000; B. O. Muthen & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models…

Higher-Order Item Response Models for Hierarchical Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming

2013-01-01

Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…

Locally Dependent Latent Trait Model and the Dutch Identity Revisited.

ERIC Educational Resources Information Center

Ip, Edward H.

2002-01-01

Proposes a class of locally dependent latent trait models for responses to psychological and educational tests. Focuses on models based on a family of conditional distributions, or kernel, that describes joint multiple item responses as a function of student latent trait, not assuming conditional independence. Also proposes an EM algorithm for…

Associations among personal care product use patterns and exogenous hormone use in the NIEHS Sister Study.

PubMed

Taylor, Kyla W; Baird, Donna D; Herring, Amy H; Engel, Lawrence S; Nichols, Hazel B; Sandler, Dale P; Troester, Melissa A

2017-09-01

It is hypothesized that certain chemicals in personal care products may alter the risk of adverse health outcomes. The primary aim of this study was to use a data-centered approach to classify complex patterns of exposure to personal care products and to understand how these patterns vary according to use of exogenous hormone exposures, oral contraceptives (OCs) and post-menopausal hormone therapy (HT). The NIEHS Sister Study is a prospective cohort study of 50,884 US women. Limiting the sample to non-Hispanic blacks and whites (N=47,019), latent class analysis (LCA) was used to identify groups of individuals with similar patterns of personal care product use based on responses to 48 survey questions. Personal care products were categorized into three product types (beauty, hair, and skincare products) and separate latent classes were constructed for each type. Adjusted prevalence differences (PD) were calculated to estimate the association between exogenous hormone use, as measured by ever/never OC or HT use, and patterns of personal care product use. LCA reduced data dimensionality by grouping of individuals with similar patterns of personal care product use into mutually exclusive latent classes (three latent classes for beauty product use, three for hair, and four for skin care. There were strong differences in personal care usage by race, particularly for haircare products. For both blacks and whites, exogenous hormone exposures were associated with higher levels of product use, especially beauty and skincare products. Relative to individual product use questions, latent class variables capture complex patterns of personal care product usage. These patterns differed by race and were associated with ever OC and HT use. Future studies should consider personal care product exposures with other exogenous exposures when modeling health risks.

Associations among personal care product use patterns and exogenous hormone use in the NIEHS Sister Study

PubMed Central

Taylor, Kyla W.; Baird, Donna D.; Herring, Amy H.; Engel, Lawrence S.; Nichols, Hazel B.; Sandler, Dale P.; Troester, Melissa A.

2017-01-01

It is hypothesized that certain chemicals in personal care products may alter the risk of adverse health outcomes. The primary aim of this study was to use a data-centered approach to classify complex patterns of exposure to personal care products and to understand how these patterns vary according to use of exogenous hormone exposures, oral contraceptives (OCs) and post-menopausal hormone therapy (HT). The NIEHS Sister Study is a prospective cohort study of 50,884 US women. Limiting the sample to non-Hispanic blacks and whites (N = 47,019), latent class analysis (LCA) was used to identify groups of individuals with similar patterns of personal care product use based on responses to 48 survey questions. Personal care products were categorized into three product types (beauty, hair, and skincare products) and separate latent classes were constructed for each type. Adjusted prevalence differences (PD) were calculated to estimate the association between exogenous hormone use, as measured by ever/never OC or HT use, and patterns of personal care product use. LCA reduced data dimensionality by grouping of individuals with similar patterns of personal care product use into mutually exclusive latent classes (three latent classes for beauty product use, three for hair, and four for skin care. There were strong differences in personal care usage by race, particularly for haircare products. For both blacks and whites, exogenous hormone exposures were associated with higher levels of product use, especially beauty and skincare products. Relative to individual product use questions, latent class variables capture complex patterns of personal care product usage. These patterns differed by race and were associated with ever OC and HT use. Future studies should consider personal care product exposures with other exogenous exposures when modeling health risks. PMID:28120835

Substance Use, Violence, and Antiretroviral Adherence: A Latent Class Analysis of Women Living with HIV in Canada.

PubMed

Carter, Allison; Roth, Eric Abella; Ding, Erin; Milloy, M-J; Kestler, Mary; Jabbari, Shahab; Webster, Kath; de Pokomandy, Alexandra; Loutfy, Mona; Kaida, Angela

2018-03-01

We used latent class analysis to identify substance use patterns for 1363 women living with HIV in Canada and assessed associations with socio-economic marginalization, violence, and sub-optimal adherence to combination antiretroviral therapy (cART). A six-class model was identified consisting of: abstainers (26.3%), Tobacco Users (8.81%), Alcohol Users (31.9%), 'Socially Acceptable' Poly-substance Users (13.9%), Illicit Poly-substance Users (9.81%) and Illicit Poly-substance Users of All Types (9.27%). Multinomial logistic regression showed that women experiencing recent violence had significantly higher odds of membership in all substance use latent classes, relative to Abstainers, while those reporting sub-optimal cART adherence had higher odds of being members of the poly-substance use classes only. Factors significantly associated with Illicit Poly-substance Users of All Types were sexual minority status, lower income, and lower resiliency. Findings underline a need for increased social and structural supports for women who use substances to support them in leading safe and healthy lives with HIV.

Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

ERIC Educational Resources Information Center

Tirri, Henry; And Others

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated…

A Systematic Evaluation of ADHD and Comorbid Psychopathology in a Population-Based Twin Sample

ERIC Educational Resources Information Center

Volk, Heather E.; Neuman, Rosalind J.; Todd, Richard D.

2005-01-01

Objective: Clinical and population samples demonstrate that attention-deficit/hyperactivity disorder (ADHD) occurs with other disorders. Comorbid disorder clustering within ADHD subtypes is not well studied. Method: Latent class analysis (LCA) examined the co-occurrence of DSM-IV ADHD, oppositional defiant disorder (ODD), conduct disorder (CD),…

Some Issues in Item Response Theory: Dimensionality Assessment and Models for Guessing

ERIC Educational Resources Information Center

Smith, Jessalyn

2009-01-01

Currently, standardized tests are widely used as a method to measure how well schools and students meet academic standards. As a result, measurement issues have become an increasingly popular topic of study. Unidimensional item response models are used to model latent abilities and specific item characteristics. This class of models makes…

Investigating Visitor Profiles as a Valuable Addition to Museum Research

ERIC Educational Resources Information Center

Lewalter, Doris; Phelan, Siëlle; Geyer, Claudia; Specht, Inga; Grüninger, Rahel; Schnotz, Wolfgang

2015-01-01

There is a long tradition of museum research assessing visitors' personal background. In this article, we suggest an insightful way to enhance and intensify visitor analyses and adopt a more integrative approach. To this end, we draw attention to Latent Class Analysis (LCA), a classification method that allows us to investigate visitor profiles…

Long-Term Outcomes for the Promoting CARE Suicide Prevention Program

ERIC Educational Resources Information Center

Hooven, Carole; Herting, Jerald R.; Snedker, Karen A.

2010-01-01

Objectives: To provide a long-term look at suicide risk from adolescence to young adulthood for former participants in Promoting CARE, an indicated suicide prevention program. Methods: Five hundred ninety-three suicide-vulnerable high school youth were involved in a long-term follow-up study. Latent class growth models identify patterns of change…

Profiling Students for Remediation Using Latent Class Analysis

ERIC Educational Resources Information Center

Boscardin, Christy K.

2012-01-01

While clinical exams using SPs are used extensively across the medical schools for summative purposes and high-stakes decisions, the method of identifying students for remediation varies widely and there is a lack of consensus on the best methodological approach. The purpose of this study is to provide an alternative approach to identification of…

Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion

PubMed Central

Larose, Chantal; Harel, Ofer; Kordas, Katarzyna; Dey, Dipak K.

2016-01-01

Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC. PMID:27695391

Variation in Latent Classes of Adult Attention-Deficit Hyperactivity Disorder by Sex and Environmental Adversity.

PubMed

Ebejer, Jane L; Medland, Sarah E; van der Werf, Julius; Lynskey, Michael; Martin, Nicholas G; Duffy, David L

2016-11-01

The findings of genetic, imaging and neuropsychological studies of attention-deficit hyperactivity disorder (ADHD) are mixed. To understand why this might be the case we use both dimensional and categorical symptom measurement to provide alternate and detailed perspectives of symptom expression. Interviewers collected ADHD, conduct problems (CP) and sociodemographic data from 3793 twins and their siblings aged 22 to 49 (M = 32.6). We estimate linear weighting of symptoms across ADHD and CP items. Latent class analyses and regression describe associations between measured variables, environmental risk factors and subsequent disadvantage. Additionally, the clinical relevance of each class was estimated. Five classes were found for women and men; few symptoms, hyperactive-impulsive, CP, inattentive, combined symptoms with CP. Women within the inattentive class reported more symptoms and reduced emotional health when compared to men and to women within other latent classes. Women and men with combined ADHD symptoms reported comorbid conduct problems but those with either inattention or hyperactivity-impulsivity only did not. The dual perspective of dimensional and categorical measurement of ADHD provides important detail about symptom variation across sex and with environmental covariates. © The Author(s) 2013.

Maternal eating disorder and infant diet. A latent class analysis based on the Norwegian Mother and Child Cohort Study (MoBa).

PubMed

Torgersen, Leila; Ystrom, Eivind; Siega-Riz, Anna Maria; Berg, Cecilie Knoph; Zerwas, Stephanie C; Reichborn-Kjennerud, Ted; Bulik, Cynthia M

2015-01-01

Knowledge of infant diet and feeding practices among children of mothers with eating disorders is essential to promote healthy eating in these children. This study compared the dietary patterns of 6-month-old children of mothers with anorexia nervosa, bulimia nervosa, binge eating disorder, and eating disorder not otherwise specified-purging subtype, to the diet of children of mothers with no eating disorders (reference group). The study was based on 53,879 mothers in the Norwegian Mother and Child Cohort Study (MoBa). Latent class analysis (LCA) was used to identify discrete latent classes of infant diet based on the mothers' responses to questions about 16 food items. LCA identified five classes, characterized by primarily homemade vegetarian food (4% of infants), homemade traditional food (8%), commercial cereals (35%), commercial jarred baby food (39%), and a mix of all food groups (11%). The association between latent dietary classes and maternal eating disorders were estimated by multinomial logistic regression. Infants of mothers with bulimia nervosa had a lower probability of being in the homemade traditional food class compared to the commercial jarred baby food class, than the referent (O.R. 0.59; 95% CI 0.36-0.99). Infants of mothers with binge eating disorder had a lower probability of being in the homemade vegetarian class compared to the commercial jarred baby food class (O.R. 0.77; 95% CI 0.60-0.99), but only before adjusting for relevant confounders. Anorexia nervosa and eating disorder not otherwise specified-purging subtype were not statistically significantly associated with any of the dietary classes. These results suggest that maternal eating disorders may to some extent influence the child's diet at 6 months; however, the extent to which these differences influence child health and development remains an area for further inquiry. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gender roles and binge drinking among Latino emerging adults: a latent class regression analysis.

PubMed

Vaughan, Ellen L; Wong, Y Joel; Middendorf, Katharine G

2014-09-01

Gender roles are often cited as a culturally specific predictor of drinking among Latino populations. This study used latent class regression to test the relationships between gender roles and binge drinking in a sample of Latino emerging adults. Participants were Latino emerging adults who participated in Wave III of the National Longitudinal Study of Adolescent Health (N = 2,442). A subsample of these participants (n = 660) completed the Bem Sex Role Inventory--Short. We conducted latent class regression using 3 dimensions of gender roles (femininity, social masculinity, and personal masculinity) to predict binge drinking. Results indicated a 3-class solution. In Class 1, the protective personal masculinity class, personal masculinity (e.g., being a leader, defending one's own beliefs) was associated with a reduction in the odds of binge drinking. In Class 2, the nonsignificant class, gender roles were not related to binge drinking. In Class 3, the mixed masculinity class, personal masculinity was associated with a reduction in the odds of binge drinking, whereas social masculinity (e.g., forceful, dominant) was associated with an increase in the odds of binge drinking. Post hoc analyses found that females, those born outside the United States, and those with greater English language usage were at greater odds of being in Class 1 (vs. Class 2). Males, those born outside the United States, and those with greater Spanish language usage were at greater odds of being in Class 3 (vs. Class 2). Directions for future research and implications for practice with Latino emerging adults are discussed.

Semicompeting risks in aging research: methods, issues and needs

PubMed Central

Varadhan, Ravi; Xue, Qian-Li; Bandeen-Roche, Karen

2015-01-01

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class O) and those based on potential (latent) failure times (class L). The classical illness-death model belongs to class O. This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times (L models). These advances notwithstanding, the semi-competing risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community. PMID:24729136

Conflicting views on elder care responsibility in Japan.

PubMed

Lee, Kristen Schultz

2016-05-01

I examine the attitudinal ambivalence created by conflicting social expectations regarding parent-child devotion, filial obligation and family membership, and gender norms in a national population of Japanese adults. I ask: in a context of rapidly changing family and elder care norms, how do different beliefs and attitudes overlap and conflict and how are they related to elder care preferences? I analyze data from the 2006 Japanese General Social Survey and use Latent Class Analysis to identify latent groups in the population defined by their beliefs and examine the relationship between class membership and elder care preferences. I found variation in the population with respect to the measured beliefs as well as a relationship between patterns of beliefs and choice of elder caregiver. I found conflicting expectations regarding elder care responsibility in one latent class and this class also expressed elder care preferences that conflict with at least some of their strongly held beliefs. Copyright © 2016 Elsevier Inc. All rights reserved.

The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

PubMed

Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

2014-08-01

Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

Patterns of HIV Risks and Related Factors among People Who Inject Drugs in Kermanshah, Iran: A Latent Class Analysis.

PubMed

Sharifi, Hamid; Mirzazadeh, Ali; Noroozi, Alireza; Marshall, Brandon D L; Farhoudian, Ali; Higgs, Peter; Vameghi, Meroe; Mohhamadi Shahboulaghi, Farahnaz; Qorbani, Mostafa; Massah, Omid; Armoon, Bahram; Noroozi, Mehdi

2017-01-01

The objective of this study was to explore patterns of drug use and sexual risk behaviors among people who inject drugs (PWID) in Iran. We surveyed 500 PWID in Kermanshah concerning demographic characteristics, sexual risk behaviors, and drug-related risk behaviors in the month prior to study. We used latent class analysis (LCA) to establish a baseline model of risk profiles and to identify the optimal number of latent classes, and we used ordinal regression to identify factors associated with class membership. Three classes of multiple HIV risk were identified. The probability of membership in the high-risk class was 0.33, compared to 0.26 and 0.40 for the low- and moderate-risk classes, respectively. Compared to members in the lowest-risk class (reference group), the highest-risk class members had higher odds of being homeless (OR = 4.5, CI: 1.44-8.22; p = 0.001) in the past 12 months. Members of the high-risk class had lower odds of regularly visiting a needle and syringe exchange program as compared to the lowest-risk class members (AOR = 0.42, CI: 0.2-0.81; p = 0.01). Findings show the sexual and drug-related HIV risk clusters among PWID in Iran, and emphasize the importance of developing targeted prevention and harm reduction programs for all domains of risk behaviors, both sexual and drug use related.

Evidence for the Continuous Latent Structure of Mania in the Epidemiologic Catchment Area from Multiple Latent Structure and Construct Validation Methodologies

PubMed Central

Prisciandaro, James J.; Roberts, John E.

2011-01-01

Background Although psychiatric diagnostic systems have conceptualized mania as a discrete phenomenon, appropriate latent structure investigations testing this conceptualization are lacking. In contrast to these diagnostic systems, several influential theories of mania have suggested a continuous conceptualization. The present study examined whether mania has a continuous or discrete latent structure using a comprehensive approach including taxometric, information-theoretic latent distribution modeling (ITLDM), and predictive validity methodologies in the Epidemiologic Catchment Area (ECA) study. Methods Eight dichotomous manic symptom items were submitted to a variety of latent structural analyses; including factor analyses, taxometric procedures, and ITLDM; in 10,105 ECA community participants. Additionally, a variety of continuous and discrete models of mania were compared in terms of their relative abilities to predict outcomes (i.e., health service utilization, internalizing and externalizing disorders, and suicidal behavior). Results Taxometric and ITLDM analyses consistently supported a continuous conceptualization of mania. In ITLDM analyses, a continuous model of mania demonstrated 6:52:1 odds over the best fitting latent class model of mania. Factor analyses suggested that the continuous structure of mania was best represented by a single latent factor. Predictive validity analyses demonstrated a consistent superior ability of continuous models of mania relative to discrete models. Conclusions The present study provided three independent lines of support for a continuous conceptualization of mania. The implications of a continuous model of mania are discussed. PMID:20507671

Separating "Rotators" from "Nonrotators" in the Mental Rotations Test: A Multigroup Latent Class Analysis

ERIC Educational Resources Information Center

Geiser, Christian; Lehmann, Wolfgang; Eid, Michael

2006-01-01

Items of mental rotation tests can not only be solved by mental rotation but also by other solution strategies. A multigroup latent class analysis of 24 items of the Mental Rotations Test (MRT) was conducted in a sample of 1,695 German pupils and students to find out how many solution strategies can be identified for the items of this test. The…

What Do Test Score Really Mean? A Latent Class Analysis of Danish Test Score Performance

ERIC Educational Resources Information Center

McIntosh, James; Munk, Martin D.

2014-01-01

Latent class Poisson count models are used to analyse a sample of Danish test score results from a cohort of individuals born in 1954-1955, tested in 1968, and followed until 2011. The procedure takes account of unobservable effects as well as excessive zeros in the data. We show that the test scores measure manifest or measured ability as it has…

Latent transition analysis of pre-service teachers' efficacy in mathematics and science

NASA Astrophysics Data System (ADS)

Ward, Elizabeth Kennedy

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the STEBI-B, MTEBI-r, and the ABNTMS instruments. The findings suggest that LTA is a viable technique for use in teacher efficacy research. Teacher efficacy is modeled as a construct with two dimensions: personal teaching efficacy (PTE) and outcome expectancy (OE). Findings suggest that the mathematics and science teaching efficacy (PTE) of pre-service teachers is a multi-class phenomena. The analyses revealed a four-class model of PTE at the beginning and end of the final year of teacher training. Results indicate that when pre-service teachers transition between classes, they tend to move from a lower efficacy class into a higher efficacy class. In addition, the findings suggest that time-varying variables (attitudes and beliefs) and time-invariant variables (previous coursework, previous experiences, and teacher perceptions) are statistically significant predictors of efficacy class membership. Further, analyses suggest that the measures used to assess outcome expectancy are not suitable for LCA and LTA procedures.

Elucidating the association between the self-harm inventory and several borderline personality measures in an inpatient psychiatric sample.

PubMed

Sellbom, Martin; Sansone, Randy A; Songer, Douglas A

2017-09-01

The current study evaluated the utility of the self-harm inventory (SHI) as a proxy for and screening measure of borderline personality disorder (BPD) using several diagnostic and statistical manual of mental disorders (DSM)-based BPD measures as criteria. We used a sample of 145 psychiatric inpatients, who completed the SHI and a series of well-validated, DSM-based self-report measures of BPD. Using a series of latent trait and latent class analyses, we found that the SHI was substantially associated with a latent construct representing BPD, as well as differentiated latent classes of 'high' vs. 'low' BPD, with good accuracy. The SHI can serve as proxy for and a good screening measure for BPD, but future research needs to replicate these findings using structured interview-based measurement of BPD.

Dimensionality of the Latent Structure and Item Selection via Latent Class Multidimensional IRT Models

ERIC Educational Resources Information Center

Bartolucci, F.; Montanari, G. E.; Pandolfi, S.

2012-01-01

With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item…

Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung

2013-01-01

Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian…

Latent Transition Analysis of Pre-Service Teachers' Efficacy in Mathematics and Science

ERIC Educational Resources Information Center

Ward, Elizabeth Kennedy

2009-01-01

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the…

The role of conspicuity in preventing bicycle crashes involving a motor vehicle

PubMed Central

Woodward, Alistair; Ameratunga, Shanthi

2015-01-01

Background: Bicycle use, despite its proven health and other benefits, is rarely part of everyday travel for many people due to the perceived risk of injury from collision crashes. This article investigated the role of physical vs. attention conspicuity in preventing bicycle crashes involving a motor vehicle in New Zealand. Methods: The Taupo Bicycle Study involved 2590 adult cyclists recruited in 2006 (43.1% response rate) and followed for bicycle crash outcomes through linkage to four national databases. A composite measure of physical conspicuity was created using latent class analysis based on the use of fluorescent colours, lights and reflective materials, and the main colour of top, helmet and bike frame. Attention conspicuity was assessed based on regional differences in travel patterns and the amount of riding in a bunch. Cox regression modelling for repeated events was performed with multivariate adjustments. Results: During a median follow-up period of 6.4 years, 162 participants experienced 187 bicycle–motor vehicle crashes. The crash risk was not predicted by the four latent classes identified and the amount of bunch riding but was higher in Auckland, the region with the lowest level of bicycle use relative to car use. In subgroup analyses, compared to other latent classes, the most physically conspicuous group had a higher risk in Auckland but a lower risk in other regions. Conclusion: Conspicuity aids may not be effective in preventing bicycle–motor vehicle crashes in New Zealand, particularly in Auckland, where attention conspicuity is low. PMID:25085469

School Victimization and Substance Use among Adolescents in California

PubMed Central

Astor, Ron A.; Estrada, Joey N.; Benbenishty, Rami; Unger, Jennifer B.

2016-01-01

Substance use and violence co-occur among adolescents. However, the extant literature focuses on the substance use behaviors of perpetrators of violence and not on victims. This study identifies patterns of school victimization and substance use and how they co-occur. The California Healthy Kids Survey was used to identify latent classes/clusters of school victimization patterns and lifetime and frequency of recent (past month) alcohol, tobacco, and marijuana use (N =419,698). Demographic characteristics (age, gender, and race/ethnicity) were included as predictors of latent class membership. Analyses revealed four latent classes of school victimization: low victimization (44.4 %), moderate victimization (22.3 %), verbal/relational victimization (20.8 %), and high victimization (with physical threats; 12.5 %). There were also four classes of substance use: non-users (58.5 %), alcohol experimenters (some recent alcohol use; 25.8 %), mild poly-substance users (lifetime use of all substances with few days of recent use; 9.1 %), and frequent poly-substance users (used all substances several times in the past month; 6.5 %). Those in the high victimization class were twice as likely to be frequent poly-substance users, and mild poly-substance use was most salient for those in the verbal victimization class. Few studies have explored latent patterns of substance use and violence victimization concurrently. The findings indicate substantial heterogeneity in victimization and substance use among youth in California schools with implications for targeted and tailored interventions. Understanding how certain types of victimization are associated with particular patterns of substance use will provide schools with opportunities to screen for concurrent behavioral health problems among youth. PMID:24482139

School victimization and substance use among adolescents in California.

PubMed

Gilreath, Tamika D; Astor, Ron A; Estrada, Joey N; Benbenishty, Rami; Unger, Jennifer B

2014-12-01

Substance use and violence co-occur among adolescents. However, the extant literature focuses on the substance use behaviors of perpetrators of violence and not on victims. This study identifies patterns of school victimization and substance use and how they co-occur. The California Healthy Kids Survey was used to identify latent classes/clusters of school victimization patterns and lifetime and frequency of recent (past month) alcohol, tobacco, and marijuana use (N = 419,698). Demographic characteristics (age, gender, and race/ethnicity) were included as predictors of latent class membership. Analyses revealed four latent classes of school victimization: low victimization (44.4 %), moderate victimization (22.3 %), verbal/relational victimization (20.8 %), and high victimization (with physical threats; 12.5 %). There were also four classes of substance use: non-users (58.5 %), alcohol experimenters (some recent alcohol use; 25.8 %), mild poly-substance users (lifetime use of all substances with few days of recent use; 9.1 %), and frequent poly-substance users (used all substances several times in the past month; 6.5 %). Those in the high victimization class were twice as likely to be frequent poly-substance users, and mild poly-substance use was most salient for those in the verbal victimization class. Few studies have explored latent patterns of substance use and violence victimization concurrently. The findings indicate substantial heterogeneity in victimization and substance use among youth in California schools with implications for targeted and tailored interventions. Understanding how certain types of victimization are associated with particular patterns of substance use will provide schools with opportunities to screen for concurrent behavioral health problems among youth.

Multiple murder and criminal careers: a latent class analysis of multiple homicide offenders.

PubMed

Vaughn, Michael G; DeLisi, Matt; Beaver, Kevin M; Howard, Matthew O

2009-01-10

To construct an empirically rigorous typology of multiple homicide offenders (MHOs). The current study conducted latent class analysis of the official records of 160 MHOs sampled from eight states to evaluate their criminal careers. A 3-class solution best fit the data (-2LL=-1123.61, Bayesian Information Criterion (BIC)=2648.15, df=81, L(2)=1179.77). Class 1 (n=64, class assignment probability=.999) was the low-offending group marked by little criminal record and delayed arrest onset. Class 2 (n=51, class assignment probability=.957) was the severe group that represents the most violent and habitual criminals. Class 3 (n=45, class assignment probability=.959) was the moderate group whose offending careers were similar to Class 2. A sustained criminal career with involvement in versatile forms of crime was observed for two of three classes of MHOs. Linkages to extant typologies and recommendations for additional research that incorporates clinical constructs are proffered.

pong: fast analysis and visualization of latent clusters in population genetic data.

PubMed

Behr, Aaron A; Liu, Katherine Z; Liu-Fang, Gracie; Nakka, Priyanka; Ramachandran, Sohini

2016-09-15

A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixed-membership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixed-membership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining. We introduce pong, a network-graphical approach for analyzing and visualizing membership in latent clusters with a native interactive D3.js visualization. pong leverages efficient algorithms for solving the Assignment Problem to dramatically reduce runtime while increasing accuracy compared with other methods that process output from mixed-membership models. We apply pong to 225 705 unlinked genome-wide single-nucleotide variants from 2426 unrelated individuals in the 1000 Genomes Project, and identify previously overlooked aspects of global human population structure. We show that pong outpaces current solutions by more than an order of magnitude in runtime while providing a customizable and interactive visualization of population structure that is more accurate than those produced by current tools. pong is freely available and can be installed using the Python package management system pip. pong's source code is available at https://github.com/abehr/pong aaron_behr@alumni.brown.edu or sramachandran@brown.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Multiple Chronic Conditions and Hospitalizations Among Recipients of Long-Term Services and Supports

PubMed Central

Van Cleave, Janet H.; Egleston, Brian L.; Abbott, Katherine M.; Hirschman, Karen B.; Rao, Aditi; Naylor, Mary D.

2016-01-01

Background Among older adults receiving long term-services and supports (LTSS), debilitating hospitalizations is a pervasive clinical and research problem. Multiple chronic conditions (MCC) are prevalent in LTSS recipients. However, the combination of MCC and diseases associated with hospitalizations of LTSS recipients is unclear. Objective The purpose of this analysis was to determine the association between classes of MCC in newly enrolled LTSS recipients and the number of hospitalizations over a one-year period following enrollment. Methods This report is based on secondary analysis of extant data from a longitudinal cohort study of 470 new recipients of LTSS, ages 60 years and older, receiving services in assisted living facilities, nursing homes, or through home- and community-based services. Using baseline chronic conditions reported in medical records, latent class analysis (LCA) was used to identify classes of MCC and posterior probabilities of membership in each class. Poisson regressions were used to estimate the relative ratio between posterior probabilities of class membership and number of hospitalizations during the 3 month period prior to the start of LTSS (baseline) and then every three months forward through 12 months. Results Three latent MCC-based classes named Cardiopulmonary, Cerebrovascular/Paralysis, and All Other Conditions were identified. The Cardiopulmonary class was associated with elevated numbers of hospitalization compared to the All Other Conditions class (relative ratio [RR] = 1.88, 95% CI [1.33, 2.65], p < .001). Conclusion Older LTSS recipients with a combination of MCCs that includes cardiopulmonary conditions have increased risk for hospitalization. PMID:27801713

Exploring evidence of a dissociative subtype in PTSD: Baseline symptom structure, etiology, and treatment efficacy for those who dissociate.

PubMed

Burton, Mark S; Feeny, Norah C; Connell, Arin M; Zoellner, Lori A

2018-05-01

With the inclusion of a dissociative subtype, recent changes to the DSM-5 diagnosis of posttraumatic stress disorder (PTSD) have emphasized the role of dissociation in the experience and treatment of the disorder. However, there is a lack of research exploring the clinical impact for highly dissociative groups receiving treatment for PTSD. The current study examined the presence and clinical impact of a dissociative subtype in a sample of individuals receiving treatment for chronic PTSD. This study used latent transition analyses (LTA), an expanded form of latent profile analyses (LPA), to examine latent profiles of PTSD and dissociation symptoms before and after treatment for individuals (N = 200) receiving prolonged exposure (PE) or sertraline treatment for chronic PTSD. The best fitting LTA model was one with a 4-class solution at both pretreatment and posttreatment. There was a latent class at pretreatment with higher levels of dissociative symptoms. However, this class was also marked by higher reexperiencing symptoms, and membership was not predicted by chronic child abuse. Further, although those in the class were less likely to transition to the responder class overall, this was not the case for exposure-based treatment specifically. These findings are not in line with the dissociative-subtype theoretical literature that proposes those who dissociate represent a clinically distinct group that may respond worse to exposure-based treatments for PTSD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Trajectories of depressive symptoms over two years postpartum among overweight or obese women

PubMed Central

Lee, Chien-Ti; Stroo, Marissa; Fuemmeler, Bernard; Malhotra, Rahul; Østbye, Truls

2014-01-01

Background Although depressive symptoms are common postpartum, few studies have followed women beyond 12 months postpartum to investigate changes in the number and severity of these symptoms over time, especially in overweight and obese women. Using two complementary analytical methods, this study aims to identify trajectories of depressive symptoms over two years postpartum among overweight or obese mothers, and assess the demographic, socio-economic , and health covariates for these trajectories. Methods Using longitudinal data from two behavioral intervention studies (KAN-DO and AMP; N = 844), we used latent growth modeling to identify the overall trajectory of depressive symptoms and how it was related to key covariates. Next, we used latent class growth analysis to assess the heterogeneity in the depressive symptom trajectories over time, and thereby, identify subgroups of women with distinct trajectories. Findings The overall trajectory of depressive symptoms over two years postpartum was relatively stable in our sample. However, the presence of three distinct latent class trajectories [stable-low (82.5%), decreasing symptoms (7.3%) and increasing symptoms (10.2%)], identified based on trajectory shape and mean depressive symptom score, supported heterogeneity in depressive symptom trajectories over time. Lower maternal education was related to a higher symptom score, and poorer subjective health status at baseline predicted inclusion in the increasing symptoms trajectory. Conclusions In some overweight or obese mothers postpartum depressive symptoms do not resolve quickly. Practitioners should be aware of this phenomenon and continue to screen for depression for longer periods of time postpartum. PMID:25213748

Evaluating Students' Abilities to Construct Mathematical Models from Data Using Latent Class Analysis

ERIC Educational Resources Information Center

Brandriet, Alexandra; Rupp, Charlie A.; Lazenby, Katherine; Becker, Nicole M.

2018-01-01

Analyzing and interpreting data is an important science practice that contributes toward the construction of models from data; yet, there is evidence that students may struggle with making meaning of data. The study reported here focused on characterizing students' approaches to analyzing rate and concentration data in the context of method of…

Screening for Language Delay: Growth Trajectories of Language Ability in Low- and High-Performing Children

ERIC Educational Resources Information Center

Klem, Marianne; Hagtvet, Bente; Hulme, Charles; Gustafsson, Jan-Eric

2016-01-01

Purpose: This study investigated the stability and growth of preschool language skills and explores latent class analysis as an approach for identifying children at risk of language impairment. Method: The authors present data from a large-scale 2-year longitudinal study, in which 600 children were assessed with a language-screening tool…

Least Squares Distance Method of Cognitive Validation and Analysis for Binary Items Using Their Item Response Theory Parameters

ERIC Educational Resources Information Center

Dimitrov, Dimiter M.

2007-01-01

The validation of cognitive attributes required for correct answers on binary test items or tasks has been addressed in previous research through the integration of cognitive psychology and psychometric models using parametric or nonparametric item response theory, latent class modeling, and Bayesian modeling. All previous models, each with their…

Youth Self-Report of Physical and Sexual Abuse: A Latent Class Analysis

ERIC Educational Resources Information Center

Nooner, Kate B.; Litrownik, Alan J.; Thompson, Richard; Margolis, Benjamin; English, Diana J.; Knight, Elizabeth D.; Everson, Mark D.; Roesch, Scott

2010-01-01

Objective: To determine if meaningful groups of at-risk pre-adolescent youth could be identified based on their self-report of physical and sexual abuse histories. Methods: Youth participating in a consortium of ongoing longitudinal studies were interviewed using an audio-computer assisted self-interview (A-CASI) when they were approximately 12…

Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

ERIC Educational Resources Information Center

Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio

2016-01-01

We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…

Examination of the Predictors of Latent Class Typologies of Bullying Involvement among Middle School Students

PubMed Central

LOVEGROVE, PETER J.; HENRY, KIMBERLY L.; SLATER, MICHAEL D.

2012-01-01

This study employs latent class analysis to construct bullying involvement typologies among 3114 students (48% male, 58% White) in 40 middle schools across the U.S. Four classes were constructed: victims (15%); bullies (13%); bully-victims (13%); and noninvolved (59%). Respondents who were male and participated in fewer conventional activities were more likely to be members of the victims class. Students who were African-American and reported being less successful at school had a higher likelihood of membership in the bullies class. Bully-victims shared characteristics with bullies and victims: Students with more feelings of anger toward others and a higher tendency toward sensation-seeking had a higher likelihood of membership in the bullies and bully-victims classes, whereas lower levels of social inclusion was associated with membership in the victims and bully-victims classes. PMID:22606069

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

PubMed Central

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2014-01-01

Growth mixture models (GMMs; Muthén & Muthén, 2000; Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this paper, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use. PMID:25419006

Negative Treatment by Family as a Predictor of Depressive Symptoms, Life Satisfaction, Suicidality, and Tobacco/Alcohol Use in Vietnamese Sexual Minority Women.

PubMed

Nguyen, Trang Quynh; Bandeen-Roche, Karen; German, Danielle; Nguyen, Nam T T; Bass, Judith K; Knowlton, Amy R

2016-10-01

Research linking family rejection and health outcomes in sexual minority people is mostly limited to North America. We assessed the associations between negative treatment by family members and depressive symptoms, life satisfaction, suicidality, and tobacco/alcohol use in sexual minority women (SMW) in Viet Nam. Data were from an anonymous internet survey (n = 1936). Latent class analysis characterized patterns of negative treatment by family members experienced by respondents. Latent class with distal outcome modeling was used to regress depressive symptoms, life satisfaction, suicidality, and tobacco/alcohol use on family treatment class, controlling for predictors of family treatment and for two other types of sexual prejudice. Five latent family treatment classes were extracted, including four negative classes representing varying patterns of negative family treatment. Overall, more than one negative class predicted lower life satisfaction, more depressive symptoms, and higher odds of attempted suicide (relative to the non-negative class), supporting the minority stress hypothesis that negative family treatment is predictive of poorer outcomes. Only the most negative class had elevated alcohol use. The association between family treatment and smoking status was not statistically significant. The most negative class, unexpectedly, did not have the highest odds of having attempted suicide, raising a question about survivor bias. This population requires public health attention, with emphasis placed on interventions targeting the family to promote acceptance and to prevent negative treatment, and interventions supporting those SMW who encounter the worst types of negative family treatment.

Consumption Patterns of Nightlife Attendees in Munich: A Latent-Class Analysis.

PubMed

Hannemann, Tessa-Virginia; Kraus, Ludwig; Piontek, Daniela

2017-09-19

The affinity for substance use among patrons of nightclubs has been well established. With novel psychoactive substances (NPS) quickly emerging on the European drug market, trends, and patterns of use are potentially changing. (1) The detection of subgroups of consumers in the electronic dance music scene of a major German metropolitan city, (2) describing the consumption patterns of these subgroups, (3) exploring the prevalence and type of NPS consumption in this population at nightlife events in Munich. A total of 1571 patrons answered questions regarding their own substance use and the emergence of NPS as well as their experience with these substances. A latent class analysis was employed to detect consumption patterns within the sample. A four class model was determined reflecting different consumption patterns: the conservative class (34.9%) whose substance was limited to cannabis; the traditional class (36.6%) which especially consumed traditional club drugs; the psychedelic class (17.5%) which, in addition to traditional club drugs also consumed psychedelic drugs; and an unselective class (10.9%) which displayed the greatest likelihood of consumption of all assessed drugs. "Smoking mixtures" and methylone were the new substances mentioned most often, the number of substances mentioned differed between latent classes. Specific strategies are needed to reduce harm in those displaying the riskiest substance use. Although NPS use is still a fringe phenomenon its prevalence is greater in this subpopulation than in the general population, especially among users in the high-risk unselective class.

A Latent Class Analysis of Pathological-Gambling Criteria Among High School Students: Associations With Gambling, Risk and Health/Functioning Characteristics

PubMed Central

Kong, Grace; Tsai, Jack; Krishnan-Sarin, Suchitra; Cavallo, Dana A.; Hoff, Rani A.; Steinberg, Marvin A.; Rugle, Loreen; Potenza, Marc N.

2015-01-01

Objectives To identify subtypes of adolescent gamblers based on the 10 Diagnostic and Statistical Manual of Mental Disorders, fourth edition criteria for pathological gambling and the 9 Diagnostic and Statistical Manual of Mental Disorders, fifth edition criteria for gambling disorder and to examine associations between identified subtypes with gambling, other risk behaviors, and health/functioning characteristics. Methods Using cross-sectional survey data from 10 high schools in Connecticut (N = 3901), we conducted latent class analysis to classify adolescents who reported past-year gambling into gambling groups on the basis of items from the Massachusetts Gambling Screen. Adolescents also completed questions assessing demographic information, substance use (cigarette, marijuana, alcohol, and other drugs), gambling behaviors (relating to gambling formats, locations, motivations, and urges), and health/functioning characteristics (eg, extracurricular activities, mood, aggression, and body mass index). Results The optimal solution consisted of 4 classes that we termed low-risk gambling (86.4%), at-risk chasing gambling (7.6%), at-risk negative consequences gambling (3.7%), and problem gambling (PrG) (2.3%). At-risk and PrG classes were associated with greater negative functioning and more gambling behaviors. Different patterns of associations between at-risk and PrG classes were also identified. Conclusions Adolescent gambling classifies into 4 classes, which are differentially associated with demographic, gambling patterns, risk behaviors, and health/functioning characteristics. Early identification and interventions for adolescent gamblers should be sensitive to the heterogeneity of gambling subtypes. PMID:25275877

Behavioral and Mental Health Correlates of Youth Stalking Victimization

PubMed Central

Reidy, Dennis E.; Smith-Darden, Joanne P.; Kernsmith, Poco D.

2018-01-01

Introduction Although recognized as a public health problem, little attention has been paid to the problem of stalking among youth. Latent profile analysis was used to identify latent groups of adolescent stalking victims and their behavioral and mental health correlates. Methods A cross-sectional sample of 1,236 youths were randomly selected from 13 schools stratified by community risk level (i.e., low, moderate, and high risk) and gender. Students completed surveys assessing behavioral indicators of stalking victimization, as well as substance use, sexual behavior, dating violence, and psychiatric symptoms. Data were collected in 2013 and data analyses were performed in 2015. Results Analysis indicated the presence of a non-victim class, a minimal exposure class, and a victim class for boys and girls alike. Approximately 14% of girls and 13% of boys were in the stalking victim class. Adolescents in the victim class reported more symptoms of post-traumatic stress, mood disorder, and hopelessness, as well as more instances of alcohol use, binge drinking, and physical dating violence victimization. Girls in the victim class also reported engaging in sexting behaviors and oral sex with significantly more partners than their non-victim peers. Conclusions These findings provide valuable knowledge of the prevalence and pertinent health correlates of stalking victimization in adolescence. The data suggest a substantial proportion of adolescents are victims of stalking and are likewise at risk for a number of deleterious health outcomes. As such, this population merits further attention by prevention researchers and practitioners. PMID:27743623

Chronic Disease Risk Typologies among Young Adults in Community College.

PubMed

Jeffries, Jayne K; Lytle, Leslie; Sotres-Alvarez, Daniela; Golden, Shelley; Aiello, Allison E; Linnan, Laura

2018-03-01

To address chronic disease risk holistically from a behavioral perspective, insights are needed to refine understanding of the covariance of key health behaviors. This study aims to identify distinct typologies of young adults based on 4 modifiable risk factors of chronic disease using a latent class analysis approach, and to describe patterns of class membership based on demographic characteristics, living arrangements, and weight. Overall, 441 young adults aged 18-35 attending community colleges in the Minnesota Twin Cities area completed a baseline questionnaire for the Choosing Healthy Options in College Environments and Settings study, a RCT. Behavioral items were used to create indicators for latent classes, and individuals were classified using maximum-probability assignment. Three latent classes were identified: 'active, binge-drinkers with a healthy dietary intake' (13.1%); 'non-active, moderate-smokers and non-drinkers with poor dietary intake' (38.2%); 'moderately active, non-smokers and non-drinkers with moderately healthy dietary intake' (48.7%). Classes exhibited unique demographic and weight-related profiles. This study may contribute to the literature on health behaviors among young adults and provides evidence that there are weight and age differences among subgroups. Understanding how behaviors cluster is important for identifying groups for targeted interventions in community colleges.

Are Informing Knowledge and Supportive Attitude Enough for Tobacco Control? A Latent Class Analysis of Cigarette Smoking Patterns among Medical Teachers in China

PubMed Central

Niu, Lu; Luo, Dan; Silenzio, Vincent M.B.; Xiao, Shuiyuan; Tian, Yongquan

2015-01-01

Background: This study is one part of a five-year tobacco-control project in China, which aimed to gain insight into the smoking behavior, knowledge, and attitudes among medical teachers in China. Methods: In May 2010, a cross-sectional survey was conducted among medical teachers of Xiangya Medical School, Central South University, China. Results: A total number of 682 medical teachers completed the surveys. Latent class analysis indicated the sample of smoking patterns was best represented by three latent subgroups of smoking consumption severity levels. Most respondents were informed of smoking related knowledge, but lack of knowledge on smoking cessation. Most of them held a supportive attitude towards their responsibilities among tobacco control, as well as the social significance of smoking. However, both smoking related knowledge and attitude were not correlated with severity of smoking consumption among medical teachers. Conclusion: The smoking prevalence among medical teachers in China remains high. Programs on smoking cessation training are required. Future study should also develop targeted interventions for subgroups of smokers based on smoking consumption. Persistent and effective anti-tobacco efforts are needed to achieve the goals of creating smoke-free campuses and hospitals. PMID:26404331

Population heterogeneity in trajectories of midlife blood pressure

PubMed Central

Wills, Andrew K; Lawlor, Debbie A; Muniz-Terrera, Graciela; Matthews, Fiona; Cooper, Rachel; Ghosh, Arjun K; Kuh, Diana; Hardy, Rebecca

2012-01-01

Background We investigated whether there are subgroups with different underlying (latent) trajectories of midlife systolic blood pressure (BP), diastolic BP and pulse pressure in a UK cohort. Methods Data are from 1840 men and 1819 women with BP measured at age 36, 43 and 53years. We used unconditional growth mixture models to test for the presence of latent trajectory classes. Extracted classes were described in terms of a number of known lifetime risk factors, and linked to the risk of undiagnosed angina (Rose questionnaire) at age 53 years. Results In both sexes for systolic BP, diastolic BP and pulse pressure, there was a large “normative” class (>90% of the sample) characterized by gentle annual increases (eg an increase in male systolic BP of 0.9mmHg/year [95% confidence interval = 0.9 to1.0]), with a smaller class for whom the rate of increase was high (e.g. an increase in male systolic BP of 3.1mmHg/year [2.8 to3.4]). In women there was an additional class for whom BP was high at age 36 and remained high. Persons in the “normative” classes were, on average, heavier at birth and taller at age 7 years, had a lower midlife body mass index, and were less likely to be on antihypertensive medication compared with those in other classes. Among those with no diagnosed cardiovascular disease, those in the classes with more strongly increasing systolic BP and pulse pressure were at greatest risk of angina. Conclusion Our study suggests that in midlife the majority of the population have a gentle underlying increase in BP, but that there also exists an important subgroup in whom BP increases much more markedly. These classes may be useful for identifying those most at risk of cardiovascular disease. PMID:22249241

Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

PubMed

Zwemmer, J N P; Berkhof, J; Castelijns, J A; Barkhof, F; Polman, C H; Uitdehaag, B M J

2006-10-01

Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

Maltreatment and Mental Health Outcomes among Ultra-Poor Children in Burkina Faso: A Latent Class Analysis

PubMed Central

Ismayilova, Leyla; Gaveras, Eleni; Blum, Austin; Tô-Camier, Alexice; Nanema, Rachel

2016-01-01

Objectives Research about the mental health of children in Francophone West Africa is scarce. This paper examines the relationships between adverse childhood experiences, including exposure to violence and exploitation, and mental health outcomes among children living in ultra-poverty in rural Burkina Faso. Methods This paper utilizes baseline data collected from 360 children ages 10–15 and 360 of their mothers recruited from twelve impoverished villages in the Nord Region of Burkina, located near the Sahel Desert and affected by extreme food insecurity. We used a Latent Class Analysis to identify underlying patterns of maltreatment. Further, the relationships between latent classes and mental health outcomes were tested using mixed effected regression models adjusted for clustering within villages. Results About 15% of the children in the study scored above the clinical cut-off for depression, 17.8% for posttraumatic stress disorder (PTSD), and 6.4% for low self-esteem. The study identified five distinct sub-groups (or classes) of children based on their exposure to adverse childhood experiences. Children with the highest exposure to violence at home, at work and in the community (Abused and Exploited class) and children not attending school and working for other households, often away from their families (External Laborer class), demonstrated highest symptoms of depression and trauma. Despite living in adverse conditions and working to assist families, the study also identified a class of children who were not exposed to any violence at home or at work (Healthy and Non-abused class). Children in this class demonstrated significantly higher self-esteem (b = 0.92, SE = 0.45, p<0.05) and lower symptoms of trauma (b = -3.90, SE = 1.52, p<0.05). Conclusions This study offers insight into the psychological well-being of children in the context of ultra-poverty in Burkina Faso and associated context-specific adverse childhood experiences. Identifying specific sub-groups of children with increased exposure to life stressors has implications for program developers. Study findings indicate a further need to explore the mental health consequences of traumatic experiences within the context of ultra-poverty and to develop integrated economic and psychosocial interventions that prevent or mitigate childhood adversities linked with the family-level poverty and violence in the family. PMID:27764155

Accuracy of p53 Codon 72 Polymorphism Status Determined by Multiple Laboratory Methods: A Latent Class Model Analysis

PubMed Central

Walter, Stephen D.; Riddell, Corinne A.; Rabachini, Tatiana; Villa, Luisa L.; Franco, Eduardo L.

2013-01-01

Introduction Studies on the association of a polymorphism in codon 72 of the p53 tumour suppressor gene (rs1042522) with cervical neoplasia have inconsistent results. While several methods for genotyping p53 exist, they vary in accuracy and are often discrepant. Methods We used latent class models (LCM) to examine the accuracy of six methods for p53 determination, all conducted by the same laboratory. We also examined the association of p53 with cytological cervical abnormalities, recognising potential test inaccuracy. Results Pairwise disagreement between laboratory methods occurred approximately 10% of the time. Given the estimated true p53 status of each woman, we found that each laboratory method is most likely to classify a woman to her correct status. Arg/Arg women had the highest risk of squamous intraepithelial lesions (SIL). Test accuracy was independent of cytology. There was no strong evidence for correlations of test errors. Discussion Empirical analyses ignore possible laboratory errors, and so are inherently biased, but test accuracy estimated by the LCM approach is unbiased when model assumptions are met. LCM analysis avoids ambiguities arising from empirical test discrepancies, obviating the need to regard any of the methods as a “gold” standard measurement. The methods we presented here to analyse the p53 data can be applied in many other situations where multiple tests exist, but where none of them is a gold standard. PMID:23441193

Phenotypic and molecular characteristics associated with various domains of quality of life in oncology patients and their family caregivers.

PubMed

Alexander, Kimberly E; Cooper, Bruce A; Paul, Steven M; Yates, Patsy; Aouizerat, Bradley E; Miaskowski, Christine

2016-11-01

Not all oncology patients and their family caregivers (FCs) experience the same quality of life (QOL). The purposes of this study were to identify latent classes of oncology patients (n = 168) and their FCs (n = 85) with distinct physical, psychological, social, and spiritual well-being trajectories from prior to through 4 months after the completion of radiation therapy and to evaluate for demographic, clinical, and genetic characteristics that distinguished between these latent classes. Using growth mixture modeling, two latent classes were found for three (i.e., physical, psychological, and social well-being) of the four QOL domains evaluated. Across these three domains, the largest percentage of participants reported relatively high well-being scores across the 6 months of the study. Across these three QOL domains, patients and FCs who were younger, female, belonged to an ethnic minority group, had children at home, had multiple comorbid conditions, or had a lower functional status, were more likely to be classified in the lower QOL class. The social well-being domain was the only domain that had a polymorphism in nuclear factor kappa beta 2 (NFKB2) associated with latent class membership. Carrying one or two doses of the rare allele for rs7897947 was associated with a 54 % decrease in the odds of belonging to the lower social well-being class [OR (95 % CI) = .46 (.21, .99), p = .049]. These findings suggest that a number of phenotypic and molecular characteristics contribute to differences in QOL in oncology patients and their FCs.

Differentiation of functional constipation and constipation predominant irritable bowel syndrome based on Rome III criteria: a population-based study.

PubMed

Koloski, N A; Jones, M; Young, M; Talley, N J

2015-05-01

While the Rome III classification recognises functional constipation (FC) and constipation predominant IBS (IBS-C) as distinct disorders, recent evidence has suggested that these disorders are difficult to separate in clinical practice. To identify whether clinical and lifestyle factors differentiate Rome III-defined IBS-C from FC based on gastrointestinal symptoms and lifestyle characteristics. 3260 people randomly selected from the Australian population returned a postal survey. FC and IBS-C were defined according to Rome III. The first model used logistic regression to differentiate IBS-C from FC based on lifestyle, quality-of-life and psychological characteristics. The second approach was data-driven employing latent class analysis (LCA) to identify naturally occurring clusters in the data considering all symptoms involved in the Rome III criteria for IBS-C and FC. We found n = 206 (6.5%; 95% CI 5.7-7.4%) people met strict Rome III FC whereas n = 109 (3.5%; 95% CI 2.8-4.1%) met strict Rome III IBS-C. The case-control approach indicated that FC patients reported an older age at onset of constipation, were less likely to exercise, had higher mental QoL and less health care seeking than IBS-C. LCA yielded one latent class that was predominantly (75%) FC, while the other class was approximately half IBS-C and half FC. The FC-dominated latent class had clearly lower levels of symptoms used to classify IBS (pain-related symptoms) and was more likely to be male (P = 0.046) but was otherwise similar in distribution of lifestyle factors to the mixed class. The latent class analysis approach suggests a differentiation based more on symptom severity rather than the Rome III view. © 2015 John Wiley & Sons Ltd.

General practitioners' knowledge and concern about electromagnetic fields.

PubMed

Berg-Beckhoff, Gabriele; Breckenkamp, Jürgen; Larsen, Pia Veldt; Kowall, Bernd

2014-12-01

Our aim is to explore general practitioners' (GPs') knowledge about EMF, and to assess whether different knowledge structures are related to the GPs' concern about EMF. Random samples were drawn from lists of GPs in Germany in 2008. Knowledge about EMF was assessed by seven items. A latent class analysis was conducted to identify latent structures in GPs' knowledge. Further, the GPs' concern about EMF health risk was measured using a score comprising six items. The association between GPs' concern about EMF and their knowledge was analysed using multiple linear regression. In total 435 (response rate 23.3%) GPs participated in the study. Four groups were identified by the latent class analysis: 43.1% of the GPs gave mainly correct answers; 23.7% of the GPs answered low frequency EMF questions correctly; 19.2% answered only the questions relating EMF with health risks, and 14.0% answered mostly "don't know". There was no association between GPs' latent knowledge classes or between the number of correct answers given by the GPs and their EMF concern, whereas the number of incorrect answers was associated with EMF concern. Greater EMF concern in subjects with more incorrect answers suggests paying particular attention to misconceptions regarding EMF in risk communication.

Substance Use Patterns Among Adolescents in Europe: A Latent Class Analysis.

PubMed

Göbel, Kristin; Scheithauer, Herbert; Bräker, Astrid-Britta; Jonkman, Harrie; Soellner, Renate

2016-07-28

Several researchers have investigated substance use patterns using a latent class analysis; however, hardly no studies exist on substance use patterns across countries. Adolescent substance use patterns, demographic factors, and international differences in the prevalence of substance use patterns were explored. Data from 25 European countries were used to identify patterns of adolescent (12-16 years, 50.6% female) substance use (N = 33,566). Latent class analysis revealed four substance use classes: nonusers (68%), low-alcohol users (recent use of beer, wine, and alcopops; 16.1%), alcohol users (recent use of alcohol and lifetime use of marijuana; 11.2%), and polysubstance users (recent use of alcohol, marijuana, and other illicit drugs; 4.7%). Results support a general pattern of adolescent substance use across all countries; however, the prevalence rates of use patterns vary for each country. The present research provides insight into substance use patterns across Europe by using a large international adolescent sample, multidimensional indicators and a variety of substances. Substance use patterns are helpful when targeting policy and prevention strategies.

Colorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions.

PubMed

Mansfield, Carol; Ekwueme, Donatus U; Tangka, Florence K L; Brown, Derek S; Smith, Judith Lee; Guy, Gery P; Li, Chunyu; Hauber, Brett

2018-05-09

Screening rates for colorectal cancer are below the Healthy People 2020 goal. There are several colorectal cancer screening tests that differ in terms of accuracy, recommended frequency, and administration. In this article, we compare how a set of personal characteristics correlates with preferences for colorectal cancer screening test attributes, past colorectal cancer screening behavior, and future colorectal cancer screening intentions. We conducted a discrete-choice experiment survey to assess relative preferences for attributes of colorectal cancer screening tests among adults aged 50-75 years in USA. We used a latent class logit model to identify classes of preferences and calculated willingness to pay for changes in test attributes. A set of personal characteristics were included in the latent class analysis and analyses of self-reported past screening behavior and self-assessed likelihood of future colorectal cancer screening. Latent class analysis identified three types of respondents. Class 1 valued test accuracy, class 2 valued removing polyps and avoiding discomfort, and class 3 valued cost. Having had a prior colonoscopy and a higher income were predictors of the likelihood of future screening and membership in classes 1 and 2. Health insurance and a self-reported higher risk of developing colorectal cancer were associated with prior screening and higher future screening intentions, but not class membership. We identified distinct classes of preferences focusing on different test features and personal characteristics associated with reported behavior and intentions. Healthcare providers should engage in a careful assessment of patient preferences when recommending colorectal cancer test options to encourage colorectal cancer screening uptake.

Subgroups of Chemotherapy Patients With Distinct Morning and Evening Fatigue Trajectories

PubMed Central

Kober, Kord M.; Cooper, Bruce A.; Paul, Steven M.; Dunn, Laura B.; Levine, Jon D.; Wright, Fay; Hammer, Marilyn J.; Mastick, Judy; Venook, Alan; Aouizerat, Bradley E.; Miaskowski, Christine

2017-01-01

Purpose Purposes of this study were to: identify subgroups of patients with distinct trajectories for morning and evening fatigue; evaluate for differences in demographic and clinical characteristics among these subgroups; and compare and contrast the predictors of subgroup membership for morning and evening fatigue. Methods Outpatients with breast, gastrointestinal, gynecological, or lung cancer (n=582) completed questionnaires a total of six times over two cycles of CTX. Morning and evening fatigue severity were evaluated using the Lee Fatigue Scale. Latent profile analysis (LPA) was used to identify distinct subgroups. Results Three latent classes were identified for morning fatigue (i.e., Low (31.8%), High (51.4%), and Very High (16.8%)) and for evening evening fatigue (i.e., Moderate (20.0%), High (21.8%), and Very High (58.2%)). Most of the disease and treatment characteristics did not distinguish among the morning and evening fatigue classes. Compared to the Low class, patients in the High and Very High morning fatigue class were younger, had a lower functional status and higher level of comorbidity. Compared to the Moderate class, patients in the Very High evening fatigue class were younger, more likely to be female, had child care responsibilities, had a lower functional status, and a higher level of comorbidity. Conclusion LPA allows for the identification of risk factors for more severe fatigue. Since an overlap was not observed across the morning and evening fatigue classes and unique predictors for morning and evening fatigue were identified, these findings suggest that morning and evening fatigue may have distinct underlying mechanisms. PMID:26361758

The heterogeneity of antipsychotic response in the treatment of schizophrenia

PubMed Central

Case, M.; Stauffer, V. L.; Ascher-Svanum, H.; Conley, R.; Kapur, S.; Kane, J. M.; Kollack-Walker, S.; Jacob, J.; Kinon, B. J.

2011-01-01

Background Schizophrenia is a heterogeneous disorder in terms of patient response to antipsychotic treatment. Understanding the heterogeneity of treatment response may help to guide treatment decisions. This study was undertaken to capture inherent patterns of response to antipsychotic treatment in patients with schizophrenia, characterize the subgroups of patients with similar courses of response, and examine illness characteristics at baseline as possible predictors of response. Method Growth mixture modeling (GMM) was applied to data from a randomized, double-blind, 12-week study of 628 patients with schizophrenia or schizo-affective disorder treated with risperidone or olanzapine. Results Four distinct response trajectories based on Positive and Negative Syndrome Scale (PANSS) total score over 12 weeks were identified: Class 1 (420 patients, 80.6%) with moderate average baseline PANSS total score showing gradual symptom improvement; Class 2 (65 patients, 12.5%) showing rapid symptom improvement; Class 3 (24 patients, 4.6%) with high average baseline PANSS total score showing gradual symptom improvement; and Class 4 (12 patients, 2.3%) showing unsustained symptom improvement. Latent class membership of early responders (ER) and early non-responders (ENR) was determined based on 20% symptom improvement criteria at 2 weeks and ultimate responders (UR) and ultimate non-responders (UNR) based on 40% symptom improvement criteria at 12 weeks. Baseline factors with potential influence on latent class membership were identified. Conclusions This study identified four distinct treatment response patterns with predominant representation of responders or non-responders to treatment in these classes. This heterogeneity may represent discrete endophenotypes of response to treatment with different etiologic underpinnings. PMID:20925971

Maximum likelihood estimation of finite mixture model for economic data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

Grouping Youth With Similar Symptoms: A Person-Centered Approach to Transdiagnostic Subgroups.

PubMed

Bonadio, F Tony; Dynes, Morgan; Lackey, Jennifer; Tompsett, Carolyn; Amrhein, Kelly

2016-07-01

The present study extracted symptom profiles based on parent and youth report on a broad symptom checklist. Profiles based on parent-reported symptoms were compared to those based on adolescent self-report to clarify discrepancies. The current study used archival data from 1,269 youth and parent dyads whose youth received services at a community mental health center. The mean age of the sample was 14.31 years (standard deviation = 1.98), and the youth sample was half male (50.1%) and primarily Caucasian (86.8%). Latent profile analysis was used to extract models based on parent and self-reported emotional and behavioral problems. Results indicated that a 5-class solution was the best fitting model for youth-reported symptoms and an adequate fit for parent-reported symptoms. For 46.5% of the sample, class membership matched for both parent and youth. Latent profile analysis provides an alternative method for exploring transdiagnostic subgroups within clinic-referred samples. © 2016 Wiley Periodicals, Inc.

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study

PubMed Central

Fu, Wei; Shi, Qiyuan; Prosperi, Christine; Wu, Zhenke; Hammitt, Laura L.; Feikin, Daniel R.; Baggett, Henry C.; Howie, Stephen R.C.; Scott, J. Anthony G.; Murdoch, David R.; Madhi, Shabir A.; Thea, Donald M.; Brooks, W. Abdullah; Kotloff, Karen L.; Li, Mengying; Park, Daniel E.; Lin, Wenyi; Levine, Orin S.; O’Brien, Katherine L.; Zeger, Scott L.

2017-01-01

Abstract In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case–control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case–control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. PMID:28575370

Latent Class Models in action: bridging social capital & Internet usage.

PubMed

Neves, Barbara Barbosa; Fonseca, Jaime R S

2015-03-01

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one's social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. Copyright © 2014 Elsevier Inc. All rights reserved.

Pathway Evidence of How Musical Perception Predicts Word-Level Reading Ability in Children with Reading Difficulties

PubMed Central

Cogo-Moreira, Hugo; Brandão de Ávila, Clara Regina; Ploubidis, George B.; de Jesus Mari, Jair

2013-01-01

Objective To investigate whether specific domains of musical perception (temporal and melodic domains) predict the word-level reading skills of eight- to ten-year-old children (n = 235) with reading difficulties, normal quotient of intelligence, and no previous exposure to music education classes. Method A general-specific solution of the Montreal Battery of Evaluation of Amusia (MBEA), which underlies a musical perception construct and is constituted by three latent factors (the general, temporal, and the melodic domain), was regressed on word-level reading skills (rate of correct isolated words/non-words read per minute). Results General and melodic latent domains predicted word-level reading skills. PMID:24358358

Maternal eating disorder and infant diet. A latent class analysis based on the Norwegian Mother and Child Cohort Study (MoBa)

PubMed Central

Torgersen, Leila; Ystrom, Eivind; Siega-Riz, Anna Maria; Berg, Cecilie Knoph; Zerwas, Stephanie; Reichborn-Kjennerud, Ted; Bulik, Cynthia M.

2015-01-01

Knowledge of infant diet and feeding practices among children of mothers with eating disorders is essential to promote healthy eating in these children. This study compared the dietary patterns of 6-month-old children of mothers with anorexia nervosa, bulimia nervosa, binge eating disorder, and eating disorder not otherwise specified - purging subtype, to the diet of children of mothers with no eating disorders. The study was based on 53,879 mothers in the Norwegian Mother and Child Cohort Study (MoBa). Latent class analysis (LCA) was used to identify discrete latent classes of infant diet based on the mothers’ responses to questions about 16 food items. LCA identified five classes, characterized by primarily homemade vegetarian food (4% of the infants in the sample), homemade traditional food (8%), commercial infant cereals (35%), commercial jarred baby food (39%), and a mix of all food groups (11%). We then estimated the association between the different latent dietary classes and maternal eating disorders using a multinomial logistic regression model. Infants of mothers with bulimia nervosa had a lower probability of being in the homemade traditional food class compared to the commercial jarred baby food class, than the referent without an eating disorder (O.R. 0.59; 95% CI 0.36–0.99). Infants of mothers with binge eating disorder had a lower probability of being in the homemade vegetarian class compared to the commercial jarred baby food class, than the referent (O.R. 0.77; 95% CI 0.60–0.99), but only before controlling for relevant confounders. Anorexia nervosa and eating disorder not otherwise specified-purging subtype were not statistically significant associated with any of the dietary classes. These results suggest that in the general population, maternal eating disorders may to some extent influence the child’s diet as early as 6 months after birth; however, the extent to which these differences influence child health and development remain an area for further inquiry. PMID:25453594

Are poker players all the same? Latent class analysis.

PubMed

Dufour, Magali; Brunelle, Natacha; Roy, Élise

2015-06-01

Poker is the gambling game that is currently gaining the most in popularity. However, there is little information on poker players' characteristics and risk factors. Furthermore, the first studies described poker players, often recruited in universities, as an homogeneous group who played in only one of the modes (land based or on the Internet). This study aims to identify, through latent class analyses, poker player subgroups. A convenience sample of 258 adult poker players was recruited across Quebec during special events or through advertising in various media. Participants filled out a series of questionnaires (Canadian Problem Gambling Index, Beck Depression, Beck Anxiety, erroneous belief and alcohol/drug consumption). The latent class analysis suggests that there are three classes of poker players. Class I (recreational poker players) includes those who have the lowest probability of engaging intensively in different game modes. Participants in class II (Internet poker players) all play poker on the Internet. This class includes the highest proportion of players who consider themselves experts or professionals. They make a living in part or in whole from poker. Class III (multiform players) includes participants with the broadest variety of poker patterns. This group is complex: these players are positioned halfway between professional and recreational players. Results indicate that poker players are not an homogeneous group identified simply on the basis of the form of poker played. The specific characteristics associated with each subgroup points to vulnerabilities that could potentially be targeted for preventive interventions.

Latent profile analysis of teacher perceptions of parent contact and comfort.

PubMed

Stormont, Melissa; Herman, Keith C; Reinke, Wendy M; David, Kimberly B; Goel, Nidhi

2013-09-01

The purpose of the study was to explore patterns of parent involvement as perceived by teachers and identify correlates of these patterns. Parent involvement indicators and correlates were selected from a review of existing research. Participants included 34 teachers and 577 children in kindergarten through third grade. The vast majority of the sample was African American (78%), followed by Caucasian (19%) and other ethnic backgrounds (2%). Two subscales from the Parent Involvement-Teacher scale, contact and comfort, were entered as indicators in a latent profile analysis to determine the number and types of parent involvement classes. Contact included the frequency of interactions between parents and teachers; comfort included the quality of their relationship with the parent and how well their goals were aligned. Subsequent latent class regressions were conducted to identify student, school, and family characteristics associated with class membership. Three classes provided the optimal solution. This included two classes of parents with low contact with teachers but different comfort levels; one with low contact and low comfort (11%), and one with low contact but high comfort (71%). The remaining class, representing 18% of parents, was rated high on both contact and comfort. Low income status, family problems, and social, emotional, academic, and self-regulation problems distinguished the low comfort class from the other two classes. It is imperative to help teachers feel more comfortable working with families who may be experiencing substantial stressors and who also have children who need support across school and home settings.

A joint latent class model for classifying severely hemorrhaging trauma patients.

PubMed

Rahbar, Mohammad H; Ning, Jing; Choi, Sangbum; Piao, Jin; Hong, Chuan; Huang, Hanwen; Del Junco, Deborah J; Fox, Erin E; Rahbar, Elaheh; Holcomb, John B

2015-10-24

In trauma research, "massive transfusion" (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a "gold standard" for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients. Using the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients' classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models. Out of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. The traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.

Comparing Factor, Class, and Mixture Models of Cannabis Initiation and DSM Cannabis Use Disorder Criteria, Including Craving, in the Brisbane Longitudinal Twin Study

PubMed Central

Kubarych, Thomas S.; Kendler, Kenneth S.; Aggen, Steven H.; Estabrook, Ryne; Edwards, Alexis C.; Clark, Shaunna L.; Martin, Nicholas G.; Hickie, Ian B.; Neale, Michael C.; Gillespie, Nathan A.

2014-01-01

Accumulating evidence suggests that the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying factor. However, it remains possible that models with additional factors, or latent class models or hybrid models, may better explain the data. Using structured interviews, 626 adult male and female twins provided complete data on symptoms of cannabis abuse and dependence, plus a craving criterion. We compared latent factor analysis, latent class analysis, and factor mixture modeling using normal theory marginal maximum likelihood for ordinal data. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-IV criteria and determine whether DSM-5 craving loads onto a general factor. When compared with latent class and mixture models, factor models provided a better fit to the data. When conditioned on initiation and cannabis use, the association between criteria for abuse, dependence, withdrawal, and craving were best explained by two correlated latent factors for males and females: a general risk factor to CUD and a factor capturing the symptoms of social and occupational impairment as a consequence of frequent use. Secondary analyses revealed a modest increase in the prevalence of DSM-5 CUD compared with DSM-IV cannabis abuse or dependence. It is concluded that, in addition to a general factor with loadings on cannabis use and symptoms of abuse, dependence, withdrawal, and craving, a second clinically relevant factor defined by features of social and occupational impairment was also found for frequent cannabis use. PMID:24588857

Patterns of perceived barriers to medical care in older adults: a latent class analysis.

PubMed

Thorpe, Joshua M; Thorpe, Carolyn T; Kennelty, Korey A; Pandhi, Nancy

2011-08-03

This study examined multiple dimensions of healthcare access in order to develop a typology of perceived barriers to healthcare access in community-dwelling elderly. Secondary aims were to define distinct classes of older adults with similar perceived healthcare access barriers and to examine predictors of class membership to identify risk factors for poor healthcare access. A sample of 5,465 community-dwelling elderly was drawn from the 2004 wave of the Wisconsin Longitudinal Study. Perceived barriers to healthcare access were measured using items from the Group Health Association of America Consumer Satisfaction Survey. We used latent class analysis to assess the constellation of items measuring perceived barriers in access and multinomial logistic regression to estimate how risk factors affected the probability of membership in the latent barrier classes. Latent class analysis identified four classes of older adults. Class 1 (75% of sample) consisted of individuals with an overall low level of risk for perceived access problems (No Barriers). Class 2 (5%) perceived problems with the availability/accessibility of healthcare providers such as specialists or mental health providers (Availability/Accessibility Barriers). Class 3 (18%) perceived problems with how well their providers' operations arise organized to accommodate their needs and preferences (Accommodation Barriers). Class 4 (2%) perceived problems with all dimension of access (Severe Barriers). Results also revealed that healthcare affordability is a problem shared by members of all three barrier groups, suggesting that older adults with perceived barriers tend to face multiple, co-occurring problems. Compared to those classified into the No Barriers group, those in the Severe Barrier class were more likely to live in a rural county, have no health insurance, have depressive symptomatology, and speech limitations. Those classified into the Availability/Accessibility Barriers group were more likely to live in rural and micropolitan counties, have depressive symptomatology, more chronic conditions, and hearing limitations. Those in the Accommodation group were more likely to have depressive symptomatology and cognitive limitations. The current study identified a typology of perceived barriers in healthcare access in older adults. The identified risk factors for membership in perceived barrier classes could potentially assist healthcare organizations and providers with targeting polices and interventions designed to improve access in their most vulnerable older adult populations, particularly those in rural areas, with functional disabilities, or in poor mental health.

Latent Classes of Symptoms related to Clinically Depressed Mood in Adolescents.

PubMed

Blom, Eva Henje; Forsman, Mats; Yang, Tony T; Serlachius, Eva; Larsson, Jan-Olov

2014-01-01

The diagnosis of major depressive disorder (MDD), according to the Diagnostic and Statistical Manual of Mental Disorders , is based only on adult symptomatology of depression and not adapted for age and gender. This may contribute to the low diagnostic specificity and validity of adolescent MDD. In this study, we investigated whether latent classes based on symptoms associated with depressed mood could be identified in a sample of adolescents seeking psychiatric care, regardless of traditionally defined diagnostic categories. Self-reports of the Strengths and Difficulties Questionnaire and the Development and Well-Being Assessment were collected consecutively from all new patients between the ages of 13 and 17 years at two psychiatric outpatient clinics in Stockholm, Sweden. Those who reported depressed mood at intake yielded a sample of 21 boys and 156 girls. Latent class analyses were performed for all screening items and for the depression-specific items of the Development and Well-Being Assessment. The symptoms that were reported in association with depressed mood differentiated the adolescents into two classes. One class had moderate emotional severity scores on the Strengths and Difficulties Questionnaire and mainly symptoms that were congruent with the Diagnostic and Statistical Manual of Mental Disorders criteria for MDD. The other class had higher emotional severity scores and similar symptoms to those reported in the first class. However, in addition, this group demonstrated more diverse symptomatology, including vegetative symptoms, suicidal ideation, anxiety, conduct problems, body dysmorphic symptoms, and deliberate vomiting. The classes predicted functional impairment in that the members of the second class showed more functional impairment. The relatively small sample size limited the generalizability of the results of this study, and the amount of items included in the analysis was restricted by the rules of latent class analysis. No conclusions about gender differences between the classes could be could be drawn as a result of the low number of boys included in the study. Two distinct classes were identified among adolescents with depressed mood. The class with highest emotional symptom severity score and the most functional impairment had a more diverse symptomatology that included symptoms that were not congruent with the traditional diagnostic criteria of MDD. However, this additional symptomatology is clinically important to consider. As a result, the clinical usefulness of the Diagnostic and Statistical Manual of Mental Disorders during the diagnostic process of adolescent depression is questioned.

Gradient of association between parenting styles and patterns of drug use in adolescence: A latent class analysis.

PubMed

Valente, Juliana Y; Cogo-Moreira, Hugo; Sanchez, Zila M

2017-11-01

To identify different patterns of drug use in adolescence and determine if these are associated with parenting styles and other sociodemographic factors. A latent class analysis was conducted using baseline data collected in a sample (n=6381) from a randomized controlled trial conducted to evaluate the effectiveness of the #Tamojunto drug-use prevention program, carried out with 7th- and 8th-grade public school students in six Brazilian cities. Three latent classes were identified among the students: 1) abstainers/low users (81.54%), 2) alcohol users/binge drinkers (16.65%), and 3) polydrug users (1.80%). A gradient of inverse association was found between parenting styles (authoritative, authoritarian, and indulgent, with the neglectful style as a reference point) and the classes "alcohol users/binge drinkers" (aOR=0.36, 95%CI=0.27-0.47; aOR=0.56, 95%CI=0.43-0.72; and aOR=0.64, 95%CI=0.51-0.80, respectively) and "polydrug users" (aOR=0.09, 95%CI=0.03-0.24; aOR=0.23, 95%CI=0.11-0.52; and aOR=0.24, 95%CI=0.08-0.74, respectively). Associations were also revealed between the latent classes and the adolescent's age and socioeconomic status. The results suggest that activities to develop parenting skills should be included in school programs aimed at the prevention of drug use among adolescents in order to reduce neglectful practices and thereby possibly reduce drug use among the children. Copyright © 2017. Published by Elsevier B.V.

Patterns of Adolescent Bullying Behaviors: Physical, Verbal, Exclusion, Rumor, and Cyber

PubMed Central

Wang, Jing; Iannotti, Ronald J.; Luk, Jeremy W.

2012-01-01

Patterns of engagement in cyber bullying and four types of traditional bullying were examined using latent class analysis (LCA). Demographic differences and externalizing problems were evaluated across latent class membership. Data were obtained from the 2005–2006 Health Behavior in School-aged Survey and the analytic sample included 7,508 U.S. adolescents in grades 6 through 10. LCA models were tested on physical bullying, verbal bullying, social exclusion, spreading rumors, and cyber bullying behaviors. Three latent classes were identified for each gender: All-Types Bullies (10.5% for boys and 4.0% for girls), Verbal/Social Bullies (29.3% for boys and 29.4% for girls), and a Non-Involved class (60.2% for boys and 66.6% for girls). Boys were more likely to be All-Types Bullies than girls. The prevalence rates of All-Types and Verbal/Social Bullies peaked during grades 6 to 8 and grades 7 & 8, respectively. Pairwise comparisons across the three latent classes on externalizing problems were conducted. Overall, the All-Types Bullies were at highest risk of using substances and carrying weapons, the Non-Involved were at lowest risk, and the Verbal/Social Bullies were in the middle. Results also suggest that most cyber bullies belong to a group of highly aggressive adolescents who conduct all types of bullying. This finding does not only improve our understanding of the relation between cyber bullying and traditional bullying, but it also suggests that prevention and intervention efforts could target cyber bullies as a high-risk group for elevated externalizing problems. PMID:22710019

Gender Differences in Anxiety Trajectories from Middle to Late Adolescence

PubMed Central

Ohannessian, Christine McCauley; Milan, Stephanie; Vannucci, Anna

2016-01-01

Although developmental trajectories of anxiety symptomatology have begun to be explored, most research has focused on total anxiety symptom scores during childhood and early adolescence, using racially/ethnically homogenous samples. Understanding the heterogeneous courses of anxiety disorder symptoms during middle to late adolescence has the potential to clarify developmental risk models of anxiety and to inform prevention programs. Therefore, this study specifically examined gender differences in developmental trajectories of anxiety disorder symptoms (generalized anxiety disorder, panic disorder, and social anxiety disorder) from middle to late adolescence in a diverse community sample (N=1,000; 57% female; 65% White), assessed annually over two years. Latent growth curve modeling revealed that girls exhibited a slight linear decrease in generalized anxiety disorder, panic disorder, and social anxiety disorder symptoms, whereas boys exhibited a stable course. These models suggested that one trajectory was appropriate for panic disorder symptoms in both girls and boys. Growth mixture models indicated the presence of four latent generalized anxiety disorder symptom trajectory classes: low increasing, moderate decreasing slightly, high decreasing, and very high decreasing rapidly. Growth mixture models also suggested the presence of five latent social anxiety disorder symptom trajectory classes: a low stable trajectory class and four classes that were qualitatively similar to the latent generalized anxiety disorder trajectories. For both generalized anxiety disorder and social anxiety disorder symptoms, girls were significantly more likely than boys to be in trajectory classes characterized by moderate or high initial symptoms that subsequently decreased over time. These findings provide novel information regarding the developmental course of anxiety disorder symptoms in adolescents. PMID:27889856

Qualitative and quantitative aspects of information processing in first psychosis: latent class analyses in patients, at-risk subjects, and controls.

PubMed

van Tricht, Mirjam J; Bour, Lo J; Koelman, Johannes H T M; Derks, Eske M; Braff, David L; de Wilde, Odette M; Boerée, Thijs; Linszen, Don H; de Haan, Lieuwe; Nieman, Dorien H

2015-04-01

We aimed to determine profiles of information processing deficits in the pathway to first psychosis. Sixty-one subjects at ultrahigh risk (UHR) for psychosis were assessed, of whom 18 converted to a first episode of psychosis (FEP) within the follow-up period. Additionally, 47 FEP and 30 control subjects were included. Using 10 neurophysiological parameters associated with information processing, latent class analyses yielded three classes at baseline. Class membership was related to group status. Within the UHR sample, two classes were found. Transition to psychosis was nominally associated with class membership. Neurophysiological profiles were unstable over time, but associations between specific neurophysiological components at baseline and follow-up were found. We conclude that certain constellations of neurophysiological variables aid in the differentiation between controls and patients in the prodrome and after first psychosis. Copyright © 2014 Society for Psychophysiological Research.

PTSD symptom severity and psychiatric comorbidity in recent motor vehicle accident victims: a latent class analysis.

PubMed

Hruska, Bryce; Irish, Leah A; Pacella, Maria L; Sledjeski, Eve M; Delahanty, Douglas L

2014-10-01

We conducted a latent class analysis (LCA) on 249 recent motor vehicle accident (MVA) victims to examine subgroups that differed in posttraumatic stress disorder (PTSD) symptom severity, current major depressive disorder and alcohol/other drug use disorders (MDD/AoDs), gender, and interpersonal trauma history 6-weeks post-MVA. A 4-class model best fit the data with a resilient class displaying asymptomatic PTSD symptom levels/low levels of comorbid disorders; a mild psychopathology class displaying mild PTSD symptom severity and current MDD; a moderate psychopathology class displaying severe PTSD symptom severity and current MDD/AoDs; and a severe psychopathology class displaying extreme PTSD symptom severity and current MDD. Classes also differed with respect to gender composition and history of interpersonal trauma experience. These findings may aid in the development of targeted interventions for recent MVA victims through the identification of subgroups distinguished by different patterns of psychiatric problems experienced 6-weeks post-MVA. Copyright © 2014 Elsevier Ltd. All rights reserved.

PTSD Symptom Severity and Psychiatric Comorbidity in Recent Motor Vehicle Accident Victims: A Latent Class Analysis

PubMed Central

Hruska, Bryce; Irish, Leah A.; Pacella, Maria L.; Sledjeski, Eve M.; Delahanty, Douglas L.

2014-01-01

We conducted a latent class analysis (LCA) on 249 recent motor vehicle accident (MVA) victims to examine subgroups that differed in posttraumatic stress disorder (PTSD) symptom severity, current major depressive disorder and alcohol/other drug use disorders (MDD/AoDs), gender, and interpersonal trauma history 6-weeks post-MVA. A 4-class model best fit the data with a resilient class displaying asymptomatic PTSD symptom levels/low levels of comorbid disorders; a mild psychopathology class displaying mild PTSD symptom severity and current MDD; a moderate psychopathology class displaying severe PTSD symptom severity and current MDD/AoDs; and a severe psychopathology class displaying extreme PTSD symptom severity and current MDD. Classes also differed with respect to gender composition and history of interpersonal trauma experience. These findings may aid in the development of targeted interventions for recent MVA victims through the identification of subgroups distinguished by different patterns of psychiatric problems experienced 6-weeks post-MVA. PMID:25124501

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

PubMed Central

Kim, Minjung; Lamont, Andrea E.; Jaki, Thomas; Feaster, Daniel; Howe, George; Van Horn, M. Lee

2015-01-01

Regression mixture models are a novel approach for modeling heterogeneous effects of predictors on an outcome. In the model building process residual variances are often disregarded and simplifying assumptions made without thorough examination of the consequences. This simulation study investigated the impact of an equality constraint on the residual variances across latent classes. We examine the consequence of constraining the residual variances on class enumeration (finding the true number of latent classes) and parameter estimates under a number of different simulation conditions meant to reflect the type of heterogeneity likely to exist in applied analyses. Results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted estimated class sizes and showed the potential to greatly impact parameter estimates in each class. Results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions were made. PMID:26139512

Identifying classes of persons with mild intellectual disability or borderline intellectual functioning: a latent class analysis.

PubMed

Nouwens, Peter J G; Lucas, Rosanne; Smulders, Nienke B M; Embregts, Petri J C M; van Nieuwenhuizen, Chijs

2017-07-17

Persons with mild intellectual disability or borderline intellectual functioning are often studied as a single group with similar characteristics. However, there are indications that differences exist within this population. Therefore, the aim of this study was to identify classes of persons with mild intellectual disability or borderline intellectual functioning and to examine whether these classes are related to individual and/or environmental characteristics. Latent class analysis was performed using file data of 250 eligible participants with a mean age of 26.1 (SD 13.8, range 3-70) years. Five distinct classes of persons with mild intellectual disability or borderline intellectual functioning were found. These classes significantly differed in individual and environmental characteristics. For example, persons with a mild intellectual disability experienced fewer problems than those with borderline intellectual disability. The identification of five classes implies that a differentiated approach is required towards persons with mild intellectual disability or borderline intellectual functioning.

Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

PubMed

Conley, Samantha

2017-12-01

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

Adding to the Pool of Methods for Program Evaluation: A Comparison of Latent Class Analysis and Propensity Score Analysis

ERIC Educational Resources Information Center

Taylor, Lauren Christine

2013-01-01

Considering the amount of funding that is distributed to educational research each year, leaders and policymakers have a vested interest in finding scientifically based evidence that answers causal questions regarding program effectiveness. The importance of program evaluation has long been recognized in many fields of research; however, the most…

Sex-related and non-sex-related comorbidity subtypes of tic disorders: a latent class approach.

PubMed

Rodgers, S; Müller, M; Kawohl, W; Knöpfli, D; Rössler, W; Castelao, E; Preisig, M; Ajdacic-Gross, V

2014-05-01

Recent evidence suggests that there may be more than one Gilles de la Tourette syndrome (GTS)/tic disorder phenotype. However, little is known about the common patterns of these GTS/tic disorder-related comorbidities. In addition, sex-specific phenomenological data of GTS/tic disorder-affected adults are rare. Therefore, this community-based study used latent class analyses (LCA) to investigate sex-related and non-sex-related subtypes of GTS/tic disorders and their most common comorbidities. The data were drawn from the PsyCoLaus study (n = 3691), a population-based survey conducted in Lausanne, Switzerland. LCA were performed on the data of 80 subjects manifesting motor/vocal tics during their childhood/adolescence. Comorbid attention-deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder, depressive, phobia and panic symptoms/syndromes comprised the selected indicators. The resultant classes were characterized by psychosocial correlates. In LCA, four latent classes provided the best fit to the data. We identified two male-related classes. The first class exhibited both ADHD and depression. The second class comprised males with only depression. Class three was a female-related class depicting obsessive thoughts/compulsive acts, phobias and panic attacks. This class manifested high psychosocial impairment. Class four had a balanced sex proportion and comorbid symptoms/syndromes such as phobias and panic attacks. The complementary occurrence of comorbid obsessive thoughts/compulsive acts and ADHD impulsivity was remarkable. To the best of our knowledge, this is the first study applying LCA to community data of GTS symptoms/tic disorder-affected persons. Our findings support the utility of differentiating GTS/tic disorder subphenotypes on the basis of comorbid syndromes. © 2013 The Author(s) European Journal of Neurology © 2013 EFNS.

Examining the Heterogeneity and Cost Effectiveness of a Complex Intervention by Segmentation of Patients with Chronic Obstructive Pulmonary Disease.

PubMed

Sørensen, Sabrina Storgaard; Jensen, Morten Berg; Pedersen, Kjeld Møller; Ehlers, Lars

2018-02-01

To examine the heterogeneity in cost-effectiveness analyses of patient-tailored complex interventions. Latent class analysis (LCA) was performed on data from a randomized controlled trial evaluating a patient-tailored case management strategy for patients suffering from chronic obstructive pulmonary disease (COPD). LCA was conducted on detailed process variables representing service variation in the intervention group. Features of the identified latent classes were compared for consistency with baseline demographic, clinical, and economic characteristics for each class. Classes for the control group, corresponding to the identified latent classes for the intervention group, were identified using multinomial logistic regression. Cost-utility analyses were then conducted at the class level, and uncertainty surrounding the point estimates was assessed by probabilistic sensitivity analysis. The LCA identified three distinct classes: the psychologically care class, the extensive COPD care class, and the limited COPD care class. Patient baseline characteristics were in line with the features identified in the LCA. Evaluation of cost-effectiveness revealed highly disparate results, and case management for only the extensive COPD care class appeared cost-effective with an incremental cost-effectiveness ratio of £26,986 per quality-adjusted life-year gained using the threshold value set by the National Institute of Health and Care Excellence. Findings indicate that researchers evaluating patient-tailored complex interventions need to address both supply-side variation and demand-side heterogeneity to link findings with outcome. The article specifically proposes the use of LCA because it is believed to have the potential to enable more appropriate targeting of complex care strategies. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Who Should Be Targeted for the Prevention of Birth Defects? A Latent Class Analysis Based on a Large, Population-Based, Cross-Sectional Study in Shaanxi Province, Western China.

PubMed

Zhu, Zhonghai; Cheng, Yue; Yang, Wenfang; Li, Danyang; Yang, Xue; Liu, Danli; Zhang, Min; Yan, Hong; Zeng, Lingxia

2016-01-01

The wide range and complex combinations of factors that cause birth defects impede the development of primary prevention strategies targeted at high-risk subpopulations. Latent class analysis (LCA) was conducted to identify mutually exclusive profiles of factors associated with birth defects among women between 15 and 49 years of age using data from a large, population-based, cross-sectional study conducted in Shaanxi Province, western China, between August and October, 2013. The odds ratios (ORs) and 95% confidence intervals (CIs) of associated factors and the latent profiles of indicators of birth defects and congenital heart defects were computed using a logistic regression model. Five discrete subpopulations of participants were identified as follows: No folic acid supplementation in the periconceptional period (reference class, 21.37%); low maternal education level + unhealthy lifestyle (class 2, 39.75%); low maternal education level + unhealthy lifestyle + disease (class 3, 23.71%); unhealthy maternal lifestyle + advanced age (class 4, 4.71%); and multi-risk factor exposure (class 5, 10.45%). Compared with the reference subgroup, the other subgroups consistently had a significantly increased risk of birth defects (ORs and 95% CIs: class 2, 1.75 and 1.21-2.54; class 3, 3.13 and 2.17-4.52; class 4, 5.02 and 3.20-7.88; and class 5, 12.25 and 8.61-17.42, respectively). For congenital heart defects, the ORs and 95% CIs were all higher, and the magnitude of OR differences ranged from 1.59 to 16.15. A comprehensive intervention strategy targeting maternal exposure to multiple risk factors is expected to show the strongest results in preventing birth defects.

Prevalence and co-occurrence of addictive behaviors among former alternative high school youth: A longitudinal follow-up study

PubMed Central

Sussman, Steve; Pokhrel, Pallav; Sun, Ping; Rohrbach, Louise A.; Spruijt-Metz, Donna

2015-01-01

Background and Aims Recent work has studied addictions using a matrix measure, which taps multiple addictions through single responses for each type. This is the first longitudinal study using a matrix measure. Methods We investigated the use of this approach among former alternative high school youth (average age = 19.8 years at baseline; longitudinal n = 538) at risk for addictions. Lifetime and last 30-day prevalence of one or more of 11 addictions reviewed in other work was the primary focus (i.e., cigarettes, alcohol, hard drugs, shopping, gambling, Internet, love, sex, eating, work, and exercise). These were examined at two time-points one year apart. Latent class and latent transition analyses (LCA and LTA) were conducted in Mplus. Results Prevalence rates were stable across the two time-points. As in the cross-sectional baseline analysis, the 2-class model (addiction class, non-addiction class) fit the data better at follow-up than models with more classes. Item-response or conditional probabilities for each addiction type did not differ between time-points. As a result, the LTA model utilized constrained the conditional probabilities to be equal across the two time-points. In the addiction class, larger conditional probabilities (i.e., 0.40−0.49) were found for love, sex, exercise, and work addictions; medium conditional probabilities (i.e., 0.17−0.27) were found for cigarette, alcohol, other drugs, eating, Internet and shopping addiction; and a small conditional probability (0.06) was found for gambling. Discussion and Conclusions Persons in an addiction class tend to remain in this addiction class over a one-year period. PMID:26551909

Latent class profile of psychiatric symptoms and treatment utilization in a sample of patients with co-occurring disorders.

PubMed

Villalobos-Gallegos, Luis; Marín-Navarrete, Rodrigo; Roncero, Calos; González-Cantú, Hugo

2017-01-01

To identify symptom-based subgroups within a sample of patients with co-occurring disorders (CODs) and to analyze intersubgroup differences in mental health services utilization. Two hundred and fifteen patients with COD from an addiction clinic completed the Symptom Checklist 90-Revised. Subgroups were determined using latent class profile analysis. Services utilization data were collected from electronic records during a 3-year span. The five-class model obtained the best fit (Bayesian information criteria [BIC] = 3,546.95; adjusted BIC = 3,363.14; bootstrapped likelihood ratio test p < 0.0001). Differences between classes were quantitative, and groups were labeled according to severity: mild (26%), mild-moderate (28.8%), moderate (18.6%), moderate-severe (17.2%), and severe (9.3%). A significant time by class interaction was obtained (chi-square [χ2[15

Latent profile analysis of sixth graders based on teacher ratings: Association with school dropout.

PubMed

Orpinas, Pamela; Raczynski, Katherine; Peters, Jaclyn Wetherington; Colman, Laura; Bandalos, Deborah

2015-12-01

The goal of this study was to identify meaningful groups of sixth graders with common characteristics based on teacher ratings of assets and maladaptive behaviors, describe dropout rates for each group, and examine the validity of these groups using students' self-reports. The sample consisted of racially diverse students (n = 675) attending sixth grade in public schools in Northeast Georgia. The majority of the sample was randomly selected; a smaller group was identified by teachers as high risk for aggression. Based on teacher ratings of externalizing behaviors, internalizing problems, academic skills, leadership, and social assets, latent profile analysis yielded 7 classes that can be displayed along a continuum: Well-Adapted, Average, Average-Social Skills Deficit, Internalizing, Externalizing, Disruptive Behavior with School Problems, and Severe Problems. Dropout rate was lowest for the Well-adapted class (4%) and highest for the Severe Problems class (58%). However, students in the Average-Social Skills Deficit class did not follow the continuum, with a large proportion of students who abandoned high school (29%). The proportion of students identified by teachers as high in aggression consistently increased across the continuum from none in the Well-Adapted class to 84% in the Severe Problems class. Students' self-reports were generally consistent with the latent profile classes. Students in the Well-Adapted class reported low aggression, drug use, and delinquency, and high life satisfaction; self-reports went in the opposite direction for the Disruptive Behaviors with School Problems class. Results highlight the importance of early interventions to improve academic performance, reduce externalizing behaviors, and enhance social assets. (c) 2015 APA, all rights reserved).

Latent Structure Agreement Analysis

DTIC Science & Technology

1989-11-01

correct for bias in estimation of disease prevalence due to misclassification error [39]. Software Varying panel latent class agreement models can be...D., and L. M. Irwig, "Estimation of Test Error Rates, Disease Prevalence and Relative Risk from Misclassified Data: A Review," Journal of Clinical

Substance use predictors of victimization profiles among homeless youth: a latent class analysis.

PubMed

Bender, Kimberly; Thompson, Sanna; Ferguson, Kristin; Langenderfer, Lisa

2014-02-01

Although a substantial body of literature demonstrates high prevalence of street victimization among homeless youth, few studies have investigated the existence of victimization classes that differ on the type and frequency of victimization experienced. Nor do we know how substance use patterns relate to victimization classes. Using latent class analysis (LCA), we examined the existence of victimization classes of homeless youth and investigated substance use predictors of class membership utilizing a large purposive sample (N=601) recruited from homeless youth-serving host agencies in three disparate regions of the U.S. Results of the LCA suggest the presence of three distinct victimization profiles - youth fit into a low-victimization class, a witness class, or a high-victimization class. These three victimization classes demonstrated differences in their substance use, including rates of substance abuse/dependence on alcohol and/or drugs. The presence of distinct victimization profiles suggests the need for screening and referral for differential services. Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Latent profile analyses of posttraumatic stress disorder, depression and generalized anxiety disorder symptoms in trauma-exposed soldiers.

PubMed

Contractor, Ateka A; Elhai, Jon D; Fine, Thomas H; Tamburrino, Marijo B; Cohen, Gregory; Shirley, Edwin; Chan, Philip K; Liberzon, Israel; Galea, Sandro; Calabrese, Joseph R

2015-09-01

Posttraumatic stress disorder (PTSD) is comorbid with major depressive disorder (MDD; Kessler et al., 1995) and generalized anxiety disorder (GAD; Brown et al., 2001). We aimed to (1) assess discrete patterns of post-trauma PTSD-depression-GAD symptoms using latent profile analyses (LPAs), and (2) assess covariates (gender, income, education, age) in defining the best fitting class solution. The PTSD Checklist (assessing PTSD symptoms), GAD-7 scale (assessing GAD symptoms), and Patient Health Questionnaire-9 (assessing depression) were administered to 1266 trauma-exposed Ohio National Guard soldiers. Results indicated three discrete subgroups based on symptom patterns with mild (class 1), moderate (class 2) and severe (class 3) levels of symptomatology. Classes differed in symptom severity rather than symptom type. Income and education significantly predicted class 1 versus class 3 membership, and class 2 versus class 3. In conclusion, there is heterogeneity regarding severity of PTSD-depression-GAD symptomatology among trauma-exposed soldiers, with income and education predictive of class membership. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling Optimal Cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a Nationwide Representative Sample.

PubMed

Interlenghi, Gabriela S; Reichenheim, Michael E; Segall-Corrêa, Ana M; Pérez-Escamilla, Rafael; Moraes, Claudia L; Salles-Costa, Rosana

2017-07-01

Background: This is the second part of a model-based approach to examine the suitability of the current cutoffs applied to the raw score of the Brazilian Household Food Insecurity Measurement Scale [Escala Brasileira de Insegurança Alimentar (EBIA)]. The approach allows identification of homogeneous groups who correspond to severity levels of food insecurity (FI) and, by extension, discriminant cutoffs able to accurately distinguish these groups. Objective: This study aims to examine whether the model-based approach for identifying optimal cutoffs first implemented in a local sample is replicated in a countrywide representative sample. Methods: Data were derived from the Brazilian National Household Sample Survey of 2013 ( n = 116,543 households). Latent class factor analysis (LCFA) models from 2 to 5 classes were applied to the scale's items to identify the number of underlying FI latent classes. Next, identification of optimal cutoffs on the overall raw score was ascertained from these identified classes. Analyses were conducted in the aggregate data and by macroregions. Finally, model-based classifications (latent classes and groupings identified thereafter) were contrasted to the traditionally used classification. Results: LCFA identified 4 homogeneous groups with a very high degree of class separation (entropy = 0.934-0.975). The following cutoffs were identified in the aggregate data: between 1 and 2 (1/2), 5 and 6 (5/6), and 10 and 11 (10/11) in households with children and/or adolescents <18 y of age (score range: 0-14), and 1/2, between 4 and 5 (4/5), and between 6 and 7 (6/7) in adult-only households (range: 0-8). With minor variations, the same cutoffs were also identified in the macroregions. Although our findings confirm, in general, the classification currently used, the limit of 1/2 (compared with 0/1) for separating the milder from the baseline category emerged consistently in all analyses. Conclusions: Nationwide findings corroborate previous local evidence that households with an overall score of 1 are more akin to those scoring negative on all items. These results may contribute to guide experts' and policymakers' decisions on the most appropriate EBIA cutoffs. © 2017 American Society for Nutrition.

The Overlap of Youth Violence Among Aggressive Adolescents with Past-Year Alcohol Use—A Latent Class Analysis: Aggression and Victimization in Peer and Dating Violence in an Inner City Emergency Department Sample

PubMed Central

Whiteside, Lauren K.; Ranney, Megan L.; Chermack, Stephen T.; Zimmerman, Marc A.; Cunningham, Rebecca M.; Walton, Maureen A.

2013-01-01

Objective: The purpose of this study was to identify overlap and violence types between peer and dating aggression and victimization using latent class analysis (LCA) among a sample of aggressive adolescents with a history of alcohol use and to identify risk and protective factors associated with each violence class. Method: From September 2006 to September 2009, a systematic sample of patients (14–18 years old) seeking care in an urban emergency department were approached. Adolescents reporting any past-year alcohol use and aggression completed a survey using validated measures including types of violence (severe and moderate aggression, severe and moderate victimization with both peers and dating partners). Using LCA, violence classes were identified; correlates of membership in each LCA class were determined. Results: Among this sample (n = 694), LCA identified three classes described as (a) peer aggression (PA) (52.2%), (b) peer aggression + peer victimization (PAPV) (18.6%), and (c) multiple domains of violence (MDV) (29.3%). Compared with those in the PA class, those in the PAPV class were more likely to be male, report injury in a fight, and have delinquent peers. Compared with the PA class, those in the MDV class were more likely to be female, African American, report injury in a fight, carry a weapon, experience negative consequences from alcohol use, and have delinquent peers and more family conflict. Compared with the PAPV class, those in the MDV class were likely to be female, African American, receive public assistance, carry a weapon, experience negative consequences from alcohol use, and use marijuana. Conclusions: There is extensive overlap of victimization and aggression in both peer and dating relationships. Also, those with high rates of violence across relationships have increased alcohol misuse and marijuana use. Thus, violence-prevention efforts should consider addressing concomitant substance use. PMID:23200158

Attachment typologies and posttraumatic stress disorder (PTSD), depression and anxiety: a latent profile analysis approach

PubMed Central

Armour, Cherie; Elklit, Ask; Shevlin, Mark

2011-01-01

Background Bartholomew (1990) proposed a four category adult attachment model based on Bowlby's (1973) proposal that attachment is underpinned by an individual's view of the self and others. Previous cluster analytic techniques have identified four and two attachment styles based on the Revised Adult Attachment Scale (RAAS). In addition, attachment styles have been proposed to meditate the association between stressful life events and subsequent psychiatric status. Objective The current study aimed to empirically test the attachment typology proposed by Collins and Read (1990). Specifically, LPA was used to determine if the proposed four styles can be derived from scores on the dimensions of closeness/dependency and anxiety. In addition, we aimed to test if the resultant attachment styles predicted the severity of psychopathology in response to a whiplash trauma. Method A large sample of Danish trauma victims (N=1577) participated. A Latent Profile Analysis was conducted, using Mplus 5.1, on scores from the RAAS scale to ascertain if there were underlying homogeneous attachment classes/subgroups. Class membership was used in a series of one-way ANOVA tests to determine if classes were significantly different in terms of mean scores on measures of psychopathology. Results The three class solution was considered optimal. Class one was termed Fearful (18.6%), Class two Preoccupied (34.5%), and Class three Secure (46.9%). The secure class evidenced significantly lower mean scores on PTSD, depression, and anxiety measures compared to other classes, whereas the fearful class evidenced significantly higher mean scores compared to other classes. Conclusions The results demonstrated evidence of three discrete classes of attachment styles, which were labelled secure, preoccupied, and fearful. This is in contrast to previous cluster analytic techniques which have identified four and two attachment styles based on the RAAS.In addition, Securely attached individuals display lower levels of psychopathology post whiplash trauma. PMID:22893805

Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies

PubMed Central

2014-01-01

Background The term “atopic march” has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level. Methods and Findings Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts. Conclusions The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors' Summary PMID:25335105

Psychometric properties and a latent class analysis of the 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) in a pooled dataset of community samples.

PubMed

MacLeod, Melissa A; Tremblay, Paul F; Graham, Kathryn; Bernards, Sharon; Rehm, Jürgen; Wells, Samantha

2016-12-01

The 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a brief measurement tool used cross-culturally to capture the multi-dimensional nature of disablement through six domains, including: understanding and interacting with the world; moving and getting around; self-care; getting on with people; life activities; and participation in society. Previous psychometric research supports that the WHODAS 2.0 functions as a general factor of disablement. In a pooled dataset from community samples of adults (N = 447) we used confirmatory factor analysis to confirm a one-factor structure. Latent class analysis was used to identify subgroups of individuals based on their patterns of responses. We identified four distinct classes, or patterns of disablement: (1) pervasive disability; (2) physical disability; (3) emotional, cognitive, or interpersonal disability; (4) no/low disability. Convergent validity of the latent class subgroups was found with respect to socio-demographic characteristics, number of days affected by disabilities, stress, mental health, and substance use. These classes offer a simple and meaningful way to classify people with disabilities based on the 12-item WHODAS 2.0. Focusing on individuals with a high probability of being in the first three classes may help guide interventions. Copyright © 2016 John Wiley & Sons, Ltd.

Transitional Life Events and Trajectories of Cigarette and Alcohol Use During Emerging Adulthood: Latent Class Analysis and Growth Mixture Modeling

PubMed Central

Huh, Jimi; Huang, Zhaoqing; Liao, Yue; Pentz, Maryann; Chou, Chih-Ping

2013-01-01

Objective: Emerging adulthood (ages 18–25 years) has been associated with elevated substance use. Transitional life events (TLEs) during emerging adulthood in relation to substance use are usually examined separately, rather than as a constellation. The purposes of this study were (a) to explore distinct subgroups experiencing various TLEs during emerging adulthood, (b) to identify heterogeneous trajectories of cigarette and alcohol use during emerging adulthood, and (c) to examine the association of TLEs with cigarette and alcohol use trajectories. Method: Five waves of longitudinal data (mean age range: 19.5–26.0 years) were used from a community-based drug prevention program (n = 946, 49.9% female). Distinct subgroups of emerging adults who experienced various TLEs were identified using latent class analysis. Cigarette and alcohol use were examined using a latent growth mixture model. Results: A three-class model fit the data best in identifying TLE subgroups (new family, college attenders [NFCA]; uncommitted relationships, college attenders [URCA]; hibernators [HBN]). Three-trajectory models fit the data best for cigarette and alcohol use during emerging adulthood. The TLE categories were significantly associated with the cigarette (p < .05) and alcohol use groups (p < .001); specifically, the URCA and HBN groups were significantly more likely to be classified as accelerating cigarette users, relative to NFCA (ps < .05). The NFCA and HBN groups were significantly more likely to be classified as accelerating alcohol users, relative to URCA (ps < .01). Conclusions: To characterize an “at-risk” emerging adult group for cigarette and alcohol use over time, a range of life events during emerging adulthood should be considered. Interventions tailored to young adulthood may benefit from targeting the absence of these life events typifying “independence” as a potential marker for underlying substance use problems and provide supplemental screening methods to identify young adults with similar issues. PMID:23948532

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

PubMed Central

Hildebrandt, Tom; Epstein, Elizabeth E.; Sysko, Robyn; Bux, Donald A.

2017-01-01

Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs. PMID:28247423

Hawaiian Residents' Preferences for Miconia Control Program Attributes Using Conjoint Choice Experiment and Latent Class Analysis

NASA Astrophysics Data System (ADS)

Chan-Halbrendt, Catherine; Lin, Tun; Yang, Fang; Sisior, Gwendalyn

2010-02-01

Invasive species control or eradication is an important issue. On the islands of Hawaii, this problem is exceedingly evident when it comes to Miconia calvescens ( Miconia) . Adequate funding is needed to control or eradicate this invasive plant, but with the limited amount of funding available for the fight against Miconia, it is important to make sure that the fund is being spent in a way that addresses the needs or preferences of the Hawaiian residents. Using the conjoint choice experiment method, we designed a survey that would measure the Hawaiian residents’ willingness to support Miconia control program attributes. The attributes focused on were cost, biodiversity loss, extent of spread and soil erosion. Latent class approach was used to assess the surveyed population to see the different preferences by individual classes. The results show three different classes or groups of individuals with varying preferences for a control program of which cost and erosion were the top preferred attributes among the classes. These groups were defined by their socio-demographics of income, the length of residency and exposure to farming/gardening activities. Even with a preference for lower cost, a group showed willingness to pay more (2.40) for a program that reduces erosion from high to low. Finally, the biodiversity attribute had very low consideration from a majority of the respondents showing the need for educating the public regarding its importance in preserving the unique environment in Hawaii.

Organizational Supports for Research Evidence Use in State Public Health Agencies: A Latent Class Analysis.

PubMed

Hu, Hengrui; Allen, Peg; Yan, Yan; Reis, Rodrigo S; Jacob, Rebekah R; Brownson, Ross C

2018-05-30

Use of research evidence in public health decision making can be affected by organizational supports. Study objectives are to identify patterns of organizational supports and explore associations with research evidence use for job tasks among public health practitioners. In this longitudinal study, we used latent class analysis to identify organizational support patterns, followed by mixed logistic regression analysis to quantify associations with research evidence use. The setting included 12 state public health department chronic disease prevention units and their external partnering organizations involved in chronic disease prevention. Chronic disease prevention staff from 12 US state public health departments and partnering organizations completed self-report surveys at 2 time points, in 2014 and 2016 (N = 872). Latent class analysis was employed to identify subgroups of survey participants with distinct patterns of perceived organizational supports. Two classify-analyze approaches (maximum probability assignment and multiple pseudo-class draws) were used in 2017 to investigate the association between latent class membership and research evidence use. The optimal model identified 4 latent classes, labeled as "unsupportive workplace," "low agency leadership support," "high agency leadership support," and "supportive workplace." With maximum probability assignment, participants in "high agency leadership support" (odds ratio = 2.08; 95% CI, 1.35-3.23) and "supportive workplace" (odds ratio = 1.74; 95% CI, 1.10-2.74) were more likely to use research evidence in job tasks than "unsupportive workplace." The multiple pseudo-class draws produced comparable results with odds ratio = 2.09 (95% CI, 1.31-3.30) for "high agency leadership support" and odds ratio = 1.74 (95% CI, 1.07-2.82) for "supportive workplace." Findings suggest that leadership support may be a crucial element of organizational supports to encourage research evidence use. Organizational supports such as supervisory expectations, access to evidence, and participatory decision-making may need leadership support as well to improve research evidence use in public health job tasks.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Anxiety sensitivity class membership moderates the effects of pre-quit reduction in anxiety sensitivity on quit-day tobacco craving.

PubMed

Bakhshaie, Jafar; Zvolensky, Michael J; Langdon, Kirsten J; Leventhal, Adam M; Smits, Jasper A J; Allan, Nicholas; Schmidt, Norman B

2016-04-01

Although anxiety sensitivity has been primarily conceptualized as a dimensional latent construct, empirical evidence suggests that it also maintains a latent class structure, reflecting low-, moderate-, and high-risk underlying classes. The present study sought to explore whether these anxiety sensitivity classes moderated the relations between the degree of pre-quit reductions in anxiety sensitivity and the severity of nicotine withdrawal symptoms and craving experienced on quit-day. Participants included 195 adult smokers (47% female; Mage=39.4) participating in a larger "anxiety sensitivity reduction-smoking cessation" intervention trial. Anxiety sensitivity class significantly moderated relations between pre-quit reduction in anxiety sensitivity and quit-day craving. Specifically, smokers within the anxiety sensitivity high-risk class, who also demonstrated lesser pre-quit reductions in anxiety sensitivity, experienced the highest levels of craving on quit-day. These findings highlight the importance of 'high-risk' classes of anxiety sensitivity to better understand the experience of craving on quit day. Copyright © 2016. Published by Elsevier Ltd.

The Four U's: Latent Classes of Hookup Motivations Among College Students.

PubMed

Uecker, Jeremy E; Pearce, Lisa D; Andercheck, Brita

2015-06-01

College students' "hookups" have been the subject of a great deal of research in recent years. Motivations for hooking up have been linked to differences in well-being after the hookup, but studies detailing college students' motivations for engaging in hookups focus on single motivations. Using data from the 2010 Duke Hookup Survey, we consider how motivations for hooking up cluster to produce different classes, or profiles, of students who hook up, and how these classes are related to hookup regret. Four distinct classes of motivations emerged from our latent class analysis: Utilitarians (50%), Uninhibiteds (27%), Uninspireds (19%), and Unreflectives (4%). We find a number of differences in hookup motivation classes across social characteristics, including gender, year in school, race-ethnicity, self-esteem, and attitudes about sexual behavior outside committed relationships. Additionally, Uninspireds regret hookups more frequently than members of the other classes, and Uninhibiteds report regret less frequently than Utilitarians and Uninspireds. These findings reveal the complexity of motivations for hooking up and the link between motivations and regret.

Behavioral and Mental Health Correlates of Youth Stalking Victimization: A Latent Class Approach.

PubMed

Reidy, Dennis E; Smith-Darden, Joanne P; Kernsmith, Poco D

2016-12-01

Although recognized as a public health problem, little attention has been paid to the problem of stalking among youth. Latent profile analysis was used to identify latent groups of adolescent stalking victims and their behavioral and mental health correlates. A cross-sectional sample of 1,236 youths were randomly selected from 13 schools stratified by community risk level (i.e., low, moderate, and high risk) and gender. Students completed surveys assessing behavioral indicators of stalking victimization, as well as substance use, sexual behavior, dating violence, and psychiatric symptoms. Data were collected in 2013 and data analyses were performed in 2015. Analysis indicated the presence of a non-victim class, a minimal exposure class, and a victim class for boys and girls alike. Approximately 14% of girls and 13% of boys were in the stalking victim class. Adolescents in the victim class reported more symptoms of post-traumatic stress, mood disorder, and hopelessness, as well as more instances of alcohol use, binge drinking, and physical dating violence victimization. Girls in the victim class also reported engaging in sexting behaviors and oral sex with significantly more partners than their non-victim peers. These findings provide valuable knowledge of the prevalence and pertinent health correlates of stalking victimization in adolescence. The data suggest a substantial proportion of adolescents are victims of stalking and are likewise at risk for a number of deleterious health outcomes. As such, this population merits further attention by prevention researchers and practitioners. Published by Elsevier Inc.

Variations in Care Quality Outcomes of Dying People: Latent Class Analysis of an Adult National Register Population.

PubMed

Öhlén, Joakim; Russell, Lara; Håkanson, Cecilia; Alvariza, Anette; Fürst, Carl Johan; Årestedt, Kristofer; Sawatzky, Richard

2017-01-01

Symptom relief is a key goal of palliative care. There is a need to consider complexities in symptom relief patterns for groups of people to understand and evaluate symptom relief as an indicator of quality of care at end of life. The aims of this study were to distinguish classes of patients who have different symptom relief patterns during the last week of life and to identify predictors of these classes in an adult register population. In a cross-sectional retrospective design, data were used from 87,026 decedents with expected deaths registered in the Swedish Register of Palliative Care in 2011 and 2012. Study variables were structured into patient characteristics, and processes and outcomes of quality of care. A latent class analysis was used to identify symptom relief patterns. Multivariate multinomial regression analyses were used to identify predictors of class membership. Five latent classes were generated: "relieved pain," "relieved pain and rattles," "relieved pain and anxiety," "partly relieved shortness of breath, rattles and anxiety," and "partly relieved pain, anxiety and confusion." Important predictors of class membership were age, sex, cause of death, and having someone present at death, individual prescriptions as needed (PRN) and expert consultations. Interindividual variability and complexity in symptom relief patterns may inform quality of care and its evaluation for dying people across care settings. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Stability of ARDS subphenotypes over time in two randomised controlled trials.

PubMed

Delucchi, Kevin; Famous, Katie R; Ware, Lorraine B; Parsons, Polly E; Thompson, B Taylor; Calfee, Carolyn S

2018-05-01

Two distinct acute respiratory distress syndrome (ARDS) subphenotypes have been identified using data obtained at time of enrolment in clinical trials; it remains unknown if these subphenotypes are durable over time. To determine the stability of ARDS subphenotypes over time. Secondary analysis of data from two randomised controlled trials in ARDS, the ARMA trial of lung protective ventilation (n=473; patients randomised to low tidal volumes only) and the ALVEOLI trial of low versus high positive end-expiratory pressure (n=549). Latent class analysis (LCA) and latent transition analysis (LTA) were applied to data from day 0 and day 3, independent of clinical outcomes. In ALVEOLI, LCA indicated strong evidence of two ARDS latent classes at days 0 and 3; in ARMA, evidence of two classes was stronger at day 0 than at day 3. The clinical and biological features of these two classes were similar to those in our prior work and were largely stable over time, though class 2 demonstrated evidence of progressive organ failures by day 3, compared with class 1. In both LCA and LTA models, the majority of patients (>94%) stayed in the same class from day 0 to day 3. Clinical outcomes were statistically significantly worse in class 2 than class 1 and were more strongly associated with day 3 class assignment. ARDS subphenotypes are largely stable over the first 3 days of enrolment in two ARDS Network trials, suggesting that subphenotype identification may be feasible in the context of clinical trials. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Parent-Teen Communication and Pre-College Alcohol Involvement: A Latent Class Analysis

PubMed Central

Abar, Caitlin C.; Fernandez, Anne C.; Wood, Mark D.

2011-01-01

Although parent-adolescent communication has been identified as important in delaying the onset and escalation of alcohol use, both the strength and direction of observed associations has varied in prior research with adolescents and college students. The current study categorizes parents according to alcohol-related communication and relates these categories to other parenting factors and late adolescent alcohol involvement. Method As part of a larger study, 1,007 college-bound teens and their parents were assessed. Teens were asked to report on their drinking behavior, and parents were asked about the occurrence of several specific alcohol-related communications with their teen, as well as additional parenting characteristics. Profiles of parent alcohol-related communication were derived using latent class analysis. Once the best fitting solution was determined, covariates were entered predicting class membership and investigating how classes were associated with additional parenting characteristics and teen alcohol use. Results A five-class solution provided the best fit to the data: Frequent, All Topics (28%); Moderate, All Topics (25%); Frequent, General Topics (25%); Frequent, Consequences and Limits (12%); and Infrequent, All Topics (10%). Covariate analyses demonstrated class differences with regard to parental modeling, monitoring, knowledge, and parent-teen relationship satisfaction, as well as for students’ intentions to join fraternities/sororities and alcohol use. Conclusions Findings from the current study add to a small but growing literature supporting the continuing influence of parents in late adolescence and suggest that the frequency and specificity of parent-teen communication are potentially informative for refined parent-based preventive interventions. PMID:21864983

Multilevel latent class casemix modelling: a novel approach to accommodate patient casemix

PubMed Central

2011-01-01

Background Using routinely collected patient data we explore the utility of multilevel latent class (MLLC) models to adjust for patient casemix and rank Trust performance. We contrast this with ranks derived from Trust standardised mortality ratios (SMRs). Methods Patients with colorectal cancer diagnosed between 1998 and 2004 and resident in Northern and Yorkshire regions were identified from the cancer registry database (n = 24,640). Patient age, sex, stage-at-diagnosis (Dukes), and Trust of diagnosis/treatment were extracted. Socioeconomic background was derived using the Townsend Index. Outcome was survival at 3 years after diagnosis. MLLC-modelled and SMR-generated Trust ranks were compared. Results Patients were assigned to two classes of similar size: one with reasonable prognosis (63.0% died within 3 years), and one with better prognosis (39.3% died within 3 years). In patient class one, all patients diagnosed at stage B or C died within 3 years; in patient class two, all patients diagnosed at stage A, B or C survived. Trusts were assigned two classes with 51.3% and 53.2% of patients respectively dying within 3 years. Differences in the ranked Trust performance between the MLLC model and SMRs were all within estimated 95% CIs. Conclusions A novel approach to casemix adjustment is illustrated, ranking Trust performance whilst facilitating the evaluation of factors associated with the patient journey (e.g. treatments) and factors associated with the processes of healthcare delivery (e.g. delays). Further research can demonstrate the value of modelling patient pathways and evaluating healthcare processes across provider institutions. PMID:21362172

In Search of Teen Dating Violence Typologies

PubMed Central

Reidy, Dennis E.; Ball, Barbara; Houry, Debra; Holland, Kristin M.; Valle, Linda A.; Kearns, Megan C.; Marshall, Khiya J.; Rosenbluth, Barri

2018-01-01

Purpose The goal of the present research was to identify distinct latent classes of adolescents that commit teen dating violence (TDV) and assess differences on demographic, behavioral, and attitudinal correlates. Methods Boys and girls (N = 1,149; Mage = 14.3; Grades 6–12) with a history of violence exposure completed surveys assessing six indices of TDV in the preceding 3 months. Indices of TDV included controlling behaviors, psychological TDV, physical TDV, sexual TDV, fear/intimidation, and injury. In addition, adolescents provided demographic and dating history information and completed surveys assessing attitudes condoning violence, relationship skills and knowledge, and reactive/proactive aggression. Results Latent class analysis indicated a three-class solution wherein the largest class of students was nonviolent on all indices (“nonaggressors”) and the smallest class of students demonstrated high probability of nearly all indices of TDV (“multiform aggressors”). In addition, a third class of “emotional aggressors” existed for which there was a high probability of controlling and psychological TDV but low likelihood of any other form of TDV. Multiform aggressors were differentiated from emotional and nonaggressors on the use of self-defense in dating relationships, attitudes condoning violence, and proactive aggression. Emotional aggressors were distinguished from nonaggressors on nearly all measured covariates. Conclusions Evidence indicates that different subgroups of adolescents engaging in TDV exist. In particular, a small group of youth engaging in multiple forms of TDV can be distinguished from a larger group of youth that commit acts of TDV restricted to emotional aggression (i.e., controlling and psychological) and most youth that do not engage in TDV. PMID:26683984

Exposure to Different Types of Violence and Subsequent Sexual Risk Behavior among Female STD Clinic Patients: A Latent Class Analysis

PubMed Central

Walsh, Jennifer L.; Senn, Theresa E.; Carey, Michael P.

2013-01-01

Objective Diverse forms of violence, including childhood maltreatment (CM), intimate partner violence (IPV), and exposure to community violence (ECV), have been linked separately with sexual risk behaviors. However, few studies have explored multiple experiences of violence simultaneously in relation to sexual risk-taking, especially in women who are most vulnerable to violent experiences. Methods Participants were 481 women (66% African American, Mage = 27 years) attending a publicly-funded STD clinic who reported on their past and current experiences with violence and their current sexual risk behavior. We identified patterns of experience with violence using latent class analysis (LCA) and investigated which combinations of experiences were associated with the riskiest sexual outcomes. Results Four classes of women with different experiences of violence were identified: Low Violence (39%), Predominantly ECV (20%), Predominantly CM (23%), and Multiply Victimized (18%). Women in the Multiply Victimized and Predominantly ECV classes reported the highest levels of sexual risk behavior, including more lifetime sexual partners and a greater likelihood of receiving STD treatment and using substances before sex. Conclusions Women with different patterns of violent experiences differed in their sexual risk behavior. Interventions to reduce sexual risk should address violence against women, focusing on experiences with multiple types of violence and experiences specifically with ECV. Additional research is needed to determine the best ways to address violence in sexual risk reduction interventions. PMID:23626921

Augmenting Latent Dirichlet Allocation and Rank Threshold Detection with Ontologies

DTIC Science & Technology

2010-03-01

Probabilistic Latent Semantic Indexing (PLSI) is an automated indexing information retrieval model [20]. It is based on a statistical latent class model which is...uses a statistical foundation that is more accurate in finding hidden semantic relationships [20]. The model uses factor analysis of count data, number...principle of statistical infer- ence which asserts that all of the information in a sample is contained in the likelihood function [20]. The statistical

Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico.

PubMed

Meacham, Meredith C; Roesch, Scott C; Strathdee, Steffanie A; Lindsay, Suzanne; Gonzalez-Zuniga, Patricia; Gaines, Tommi L

2018-01-01

Patterns of polydrug use among people who inject drugs (PWID) may be differentially associated with overdose and unique human immunodeficiency virus (HIV) risk factors. Subgroups of PWID in Tijuana, Mexico, were identified based on substances used, route of administration, frequency of use and co-injection indicators. Participants were PWID residing in Tijuana age ≥18 years sampled from 2011 to 2012 who reported injecting an illicit substance in the past month (n = 735). Latent class analysis identified discrete classes of polydrug use characterised by 11 indicators of past 6 months substance use. Multinomial logistic regression examined class membership association with HIV risk behaviours, overdose and other covariates using an automated three-step procedure in mplus to account for classification error. Participants were classified into five subgroups. Two polydrug and polyroute classes were defined by use of multiple substances through several routes of administration and were primarily distinguished from each other by cocaine use (class 1: 5%) or no cocaine use (class 2: 29%). The other classes consisted primarily of injectors: cocaine, methamphetamine and heroin injection (class 3: 4%); methamphetamine and heroin injection (class 4: 10%); and heroin injection (class 5: 52%). Compared with the heroin-only injection class, memberships in the two polydrug and polyroute use classes were independently associated with both HIV injection and sexual risk behaviours. Substance use patterns among PWID in Tijuana are highly heterogeneous, and polydrug and polyroute users are a high-risk subgroup who may require more tailored prevention and treatment interventions. [Meacham MC, Roesch SC, Strathdee SA, Lindsay S, Gonzalez-Zuniga P, Gaines TL. Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico. Drug Alcohol Rev 2018;37:128-136]. © 2017 Australasian Professional Society on Alcohol and other Drugs.

The Influence of Static and Dynamic Intrapersonal Factors on Longitudinal Patterns of Peer Victimization through Mid-adolescence: a Latent Transition Analysis.

PubMed

Haltigan, John D; Vaillancourt, Tracy

2018-01-01

Using 6 cycles (grade 5 through grade 10) of data obtained from a large prospective sample of Canadian school children (N = 700; 52.6% girls), we replicated previous findings concerning the empirical definition of peer victimization (i.e., being bullied) and examined static and dynamic intrapersonal factors associated with its emergence and experiential continuity through mid-adolescence. Latent class analyses consistently revealed a low victimization and an elevated victimization class across time, supporting previous work suggesting peer victimization was defined by degree rather than by type (e.g., physical). Using latent transition analyses (LTA), we found that child sex, parent-perceived pubertal development, and internalizing symptoms influenced the probability of transitioning from the low to the elevated victimization class across time. Higher-order extensions within the LTA modeling framework revealed a lasting effect of grade 5 victimization status on grade 10 victimization status and a large effect of chronic victimization on later parent-reported youth internalizing symptoms (net of prior parent-reported internalizing symptoms) in later adolescence (grade 11). Implications of the current findings for the experience of peer victimization, as well as the application of latent transition analysis as a useful approach for peer victimization research, are discussed.

Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients.

PubMed

Rosato, Rosalba; Testa, Silvia; Oggero, Alessandra; Molinengo, Giorgia; Bertolotto, Antonio

2015-09-01

The aim of this study was to estimate preferences related to quality of life attributes in people with multiple sclerosis, by keeping heterogeneity of patient preference in mind, using the latent class approach. A discrete choice experiment survey was developed using the following attributes: activities of daily living, instrumental activities of daily living, pain/fatigue, anxiety/depression and attention/concentration. Choice sets were presented as pairs of hypothetical health status, based upon a fractional factorial design. The latent class logit model estimated on 152 patients identified three subpopulations, which, respectively, attached more importance to: (1) the physical dimension; (2) pain/fatigue and anxiety/depression; and (3) instrumental activities of daily living impairments, anxiety/depression and attention/concentration. A posterior analysis suggests that the latent class membership may be related to an individual's age to some extent, or to diagnosis and treatment, while apart from energy dimension, no significant difference exists between latent groups, with regard to Multiple Sclerosis Quality of Life-54 scales. A quality of life preference-based utility measure for people with multiple sclerosis was developed. These utility values allow identification of a hierarchic priority among different aspects of quality of life and may allow physicians to develop a care programme tailored to patient needs.

A social network-informed latent class analysis of patterns of substance use, sexual behavior, and mental health: Social Network Study III, Winnipeg, Manitoba, Canada.

PubMed

Hopfer, Suellen; Tan, Xianming; Wylie, John L

2014-05-01

We assessed whether a meaningful set of latent risk profiles could be identified in an inner-city population through individual and network characteristics of substance use, sexual behaviors, and mental health status. Data came from 600 participants in Social Network Study III, conducted in 2009 in Winnipeg, Manitoba, Canada. We used latent class analysis (LCA) to identify risk profiles and, with covariates, to identify predictors of class. A 4-class model of risk profiles fit the data best: (1) solitary users reported polydrug use at the individual level, but low probabilities of substance use or concurrent sexual partners with network members; (2) social-all-substance users reported polydrug use at the individual and network levels; (3) social-noninjection drug users reported less likelihood of injection drug and solvent use; (4) low-risk users reported low probabilities across substances. Unstable housing, preadolescent substance use, age, and hepatitis C status predicted risk profiles. Incorporation of social network variables into LCA can distinguish important subgroups with varying patterns of risk behaviors that can lead to sexually transmitted and bloodborne infections.

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

PubMed

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

Coping Styles of Adolescents Experiencing Multiple Forms of Discrimination and Bullying: Evidence from a Sample of Ethnically Diverse Urban Youth

ERIC Educational Resources Information Center

Garnett, Bernice R.; Masyn, Katherine E.; Austin, S. Bryn; Williams, David R.; Viswanath, Kasisomayajula

2015-01-01

Background: We used a latent class analysis (LCA) to characterize coping styles of urban youth and examined if coping styles moderated the association between experiencing discrimination and bullying and depressive symptoms. Methods: The data come from the 2006 Boston Youth Survey, where students were asked to select 2 behaviors they do most often…

Comparative Effectiveness of Medication versus Cognitive-Behavioral Therapy in a Randomized Controlled Trial of Low-Income Young Minority Women with Depression

ERIC Educational Resources Information Center

Siddique, Juned; Chung, Joyce Y.; Brown, C. Hendricks; Miranda, Jeanne

2012-01-01

Objective: To examine whether there are latent trajectory classes in response to treatment and whether they moderate the effects of medication versus psychotherapy. Method: Data come from a 1-year randomized controlled trial of 267 low-income, young (M = 29 years), minority (44% Black, 50% Latina, 6% White) women with current major depression…

Towards Better Computational Models of the Balance Scale Task: A Reply to Shultz and Takane

ERIC Educational Resources Information Center

van der Maas, Han L. J.; Quinlan, Philip T.; Jansen, Brenda R. J.

2007-01-01

In contrast to Shultz and Takane [Shultz, T.R., & Takane, Y. (2007). Rule following and rule use in the balance-scale task. "Cognition", in press, doi:10.1016/j.cognition.2006.12.004.] we do not accept that the traditional Rule Assessment Method (RAM) of scoring responses on the balance scale task has advantages over latent class analysis (LCA):…

Patterns of adolescent bullying behaviors: physical, verbal, exclusion, rumor, and cyber.

PubMed

Wang, Jing; Iannotti, Ronald J; Luk, Jeremy W

2012-08-01

Patterns of engagement in cyber bullying and four types of traditional bullying were examined using latent class analysis (LCA). Demographic differences and externalizing problems were evaluated across latent class membership. Data were obtained from the 2005-2006 Health Behavior in School-aged Survey and the analytic sample included 7,508 U.S. adolescents in grades 6 through 10. LCA models were tested on physical bullying, verbal bullying, social exclusion, spreading rumors, and cyber bullying behaviors. Three latent classes were identified for each gender: All-Types Bullies (10.5% for boys and 4.0% for girls), Verbal/Social Bullies (29.3% for boys and 29.4% for girls), and a Non-Involved class (60.2% for boys and 66.6% for girls). Boys were more likely to be All-Types Bullies than girls. The prevalence rates of All-Types and Verbal/Social Bullies peaked during grades 6 to 8 and grades 7 and 8, respectively. Pairwise comparisons across the three latent classes on externalizing problems were conducted. Overall, the All-Types Bullies were at highest risk of using substances and carrying weapons, the Non-Involved were at lowest risk, and the Verbal/Social Bullies were in the middle. Results also suggest that most cyber bullies belong to a group of highly aggressive adolescents who conduct all types of bullying. This finding does not only improve our understanding of the relation between cyber bullying and traditional bullying, but it also suggests that prevention and intervention efforts could target cyber bullies as a high-risk group for elevated externalizing problems. Copyright © 2012 Society for the Study of School Psychology. All rights reserved.

Race-Specific Transition Patterns among Alcohol Use Classes in Adolescent Girls

ERIC Educational Resources Information Center

Dauber, Sarah E.; Paulson, James F.; Leiferman, Jenn A.

2011-01-01

We used data from the National Longitudinal Study of Adolescent Health to examine transitions among alcohol use classes in 2225 White and African American adolescent girls, and race differences in predictors of transition into and out of problematic drinking classes. Latent class analysis confirmed four classes for White girls and three for AA…

Adolescent stalking and risk of violence✩

PubMed Central

Smith-Darden, Joanne P.; Reidy, Dennis E.; Kernsmith, Poco D.

2018-01-01

Stalking perpetration and the associated risk for violence among adolescents has generally been neglected. In the present study, 1236 youth completed surveys assessing empirically established stalking indicators, threats and aggression toward stalking victims, dating violence, and violent delinquency. Latent Profile Analysis identified 3 latent classes of boys: non-perpetrators (NP), hyper-intimate pursuit (HIP), and comprehensive stalking perpetrators (CSP) and, and 2 classes for girls: NP and HIP. Boys in the CSP class were the most violent youth on nearly all indices with boys in the HIP class demonstrating an intermediate level of violence compared to NP boys. Girls in the HIP class were more violent than NP girls on all indices. These findings suggest stalking in adolescence merits attention by violence prevention experts. In particular, juvenile stalking may signify youth at risk for multiple forms of violence perpetrated against multiple types of victims, not just the object of their infatuation. PMID:27641644

Latino cigarette smoking patterns by gender in a US national sample

PubMed Central

Kristman-Valente, Allison; Flaherty, Brian P.

2015-01-01

Background Latino smokers are a rising public health concern who experience elevated tobacco related health disparities. Purpose Additional information on Latino smoking is needed to inform screening and treatment. Analysis Latent class analysis using smoking frequency, cigarette preferences, onset, smoking duration, cigarettes per day and minutes to first cigarette were used to create multivariate latent smoking profiles for Latino men and women. Results Final models found seven classes for Latinas and nine classes for Latinos. Despite a common finding in the literature that Latino smokers are more likely to be low-risk, intermittent smokers, the majority of classes, for both males and females, described patterns of high-risk, daily smoking. Gender variations in smoking classes were noted. Conclusions Several markers of smoking risk were identified among both male and female Latino smokers including long durations of smoking, daily smoking and preference for specialty cigarettes, all factors associated with long-term health consequences. PMID:26304857

RACE-SPECIFIC TRANSITION PATTERNS AMONG ALCOHOL USE CLASSES IN ADOLESCENT GIRLS

PubMed Central

Dauber, Sarah E.; Paulson, James F.; Leiferman, Jenn A.

2010-01-01

We used data from the National Longitudinal Study of Adolescent Health to examine transitions among alcohol use classes in 2225 White and African American adolescent girls, and race differences in predictors of transition into and out of problematic drinking classes. Latent class analysis confirmed four classes for White girls and three for AA girls, defined in a previous study. Latent transition analysis revealed more stable abstainers and decreasing alcohol use among AA girls, and more increasing alcohol use among White girls, though stable abstainers were the largest group among both races. Increasing use was predicted by delinquency, academic misbehavior, substance use, and peer support for White girls, and by older age and delinquency for AA girls. Decreasing use was predicted by older age and depressive symptoms for White girls, and by family relationship quality and substance use for AA girls. Study limitations and implications of findings are discussed. PMID:20708254

Patterns of Drug Use, Risky Behavior, and Health Status Among Persons Who Inject Drugs Living in San Diego, California: A Latent Class Analysis

PubMed Central

Roth, Alexis M.; Armenta, Richard A.; Wagner, Karla D.; Roesch, Scott C.; Bluthenthal, Ricky N.; Cuevas-Mota, Jazmine; Garfein, Richard S.

2015-01-01

Background Among persons who inject drugs (PWID), polydrug use (the practice of mixing multiple drugs/alcohol sequentially or simultaneously) increases risk for HIV transmission and unintentional overdose deaths. Research has shown local drug markets influence drug use practices. However, little is known about the impact of drug mixing in markets dominated by black tar heroin and methamphetamine, such as the western United States. Methods Data were collected through an ongoing longitudinal study examining drug use, risk behavior, and health status among PWID. Latent class analysis (LCA) was used to identify patterns of substance use (heroin, methamphetamine, prescription drugs, alcohol, and marijuana) via multiple administration routes (injecting, smoking, and swallowing). Logistic regression was used to identify behaviors and health indicators associated with drug use class. Results The sample included 511 mostly white (51.5%) males (73.8%), with mean age of 43.5 years. Two distinct classes of drug users predominated: methamphetamine by multiple routes (51%) and heroin by injection (49%). In multivariable logistic regression, class membership was associated with age, race, and housing status. PWID who were HIV-seropositive and reported prior sexually transmitted infections had increased odds of belonging to the methamphetamine class. Those who were HCV positive and reported previous opioid overdose had an increased odds of being in the primarily heroin injection class (all P-values < .05). Conclusion Risk behaviors and health outcomes differed between PWID who primarily inject heroin vs. those who use methamphetamine. The findings suggest that in a region where PWID mainly use black tar heroin or methamphetamine, interventions tailored to sub-populations of PWID could improve effectiveness. PMID:25313832

Gender Differences in Patterns of Substance Use and Delinquency: A Latent Transition Analysis

PubMed Central

Bright, Charlotte Lyn; Sacco, Paul; Kolivoski, Karen M.; Stapleton, Laura M.; Jun, Hyun-Jin; Morris-Compton, Darnell

2017-01-01

This study explores gender-specific patterns and transitions of adolescent substance use and delinquency in a sample of youths at ages 12, 15, and 18 (N = 803). Latent transition analysis identified “Primary Delinquent,” “Delinquency and Substance Use,” and “Low Risk” classes. Females were less likely to be in the “Primary Delinquent” class at age 12 than males. From 15 to 18, females were approximately equally likely to transition from “Primary Delinquent” to both other classes, whereas males were more likely to transition from “Primary Delinquent” to “Delinquency and Substance Use.” These gender differences in behavior can inform services. PMID:28603406

Dietary and exercise change following acute cardiac syndrome onset: A latent class growth modelling analysis.

PubMed

Bennett, Paul; Gruszczynska, Ewa; Marke, Victoria

2016-10-01

The present study aim determine sub-group trajectories of change on measures of diet and exercise following acute coronary syndrome. 150 participants were assessed in hospital, 1 month and 6 months subsequently on measures including physical activity, diet, illness beliefs, coping and mood. Change trajectories were measured using latent class growth modelling. Multinomial logistic regression was used to predict class membership. These analyses revealed changes in exercise were confined to a sub-group of participants already reporting relatively high exercise levels; those eating less healthily evidenced modest dietary improvements. Coping, gender, depression and perceived control predicted group membership to a modest degree. © The Author(s) 2015.

Measurement of Psychological Disorders Using Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Templin, Jonathan L.; Henson, Robert A.

2006-01-01

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

Latent Profiles of Externalizing Psychopathology and Their Relation to Children's Aggression and Social Behavior.

PubMed

Andrade, Brendan F; Wade, Mark

2016-01-01

This study identified profiles of clinic-referred children with disruptive behavior and determined the association between identified profiles and children's aggression, peer problems, and prosocial skills. Parents and teachers of 208 children (163 boys) aged 6 to 12 years (Mage = 8.80, SD = 1.75) completed measures to assess children's callous-unemotional (CU) traits, inattentive-impulsive-overactive (IO) and oppositional-defiant (OD) behavior, aggression, and social behaviors. Latent class analysis was used to identify the profiles, and the pseudoclass draw method to test the equality of means for each of the aggression and social behavioral outcomes across the latent classes. Five profiles were identified: (1) Low (35.6% of children), with relatively low levels of CU traits and IO and OD behavior; (2) Low-Moderate (30.8%), with low-moderate levels of CU traits, low IO and moderate OD behavior; (3) Moderate (21.6%), with moderate levels of CU traits and IO and moderate-high OD behavior; (4) Moderate-High (7.2%), with moderate-high levels of CU traits, high IO and moderate-high OD behavior; and (5) High (4.8%), with high levels of CU traits, IO and OD behavior. Children categorized into profiles showed important differences in level of aggression and social behavior. The overlap between CU traits, IO, and OD behavior add to understanding of child psychopathology that influences behavior and clinical outcomes.

Patient factors and quality of life outcomes differ among four subgroups of oncology patients based on symptom occurrence.

PubMed

Astrup, Guro Lindviksmoen; Hofsø, Kristin; Bjordal, Kristin; Guren, Marianne Grønlie; Vistad, Ingvild; Cooper, Bruce; Miaskowski, Christine; Rustøen, Tone

2017-03-01

Reviews of the literature on symptoms in oncology patients undergoing curative treatment, as well as patients receiving palliative care, suggest that they experience multiple, co-occurring symptoms and side effects. The purposes of this study were to determine if subgroups of oncology patients could be identified based on symptom occurrence rates and if these subgroups differed on a number of demographic and clinical characteristics, as well as on quality of life (QoL) outcomes. Latent class analysis (LCA) was used to identify subgroups (i.e. latent classes) of patients with distinct symptom experiences based on the occurrence rates for the 13 most common symptoms from the Memorial Symptom Assessment Scale. In total, 534 patients with breast, head and neck, colorectal, or ovarian cancer participated. Four latent classes of patients were identified based on probability of symptom occurrence: all low class [i.e. low probability for all symptoms (n = 152)], all high class (n = 149), high psychological class (n = 121), and low psychological class (n = 112). Patients in the all high class were significantly younger compared with patients in the all low class. Furthermore, compared to the other three classes, patients in the all high class had lower functional status and higher comorbidity scores, and reported poorer QoL scores. Patients in the high and low psychological classes had a moderate probability of reporting physical symptoms. Patients in the low psychological class reported a higher number of symptoms, a lower functional status, and poorer physical and total QoL scores. Distinct subgroups of oncology patients can be identified based on symptom occurrence rates. Patient characteristics that are associated with these subgroups can be used to identify patients who are at greater risk for multiple co-occurring symptoms and diminished QoL, so that these patients can be offered appropriate symptom management interventions.

Predominant typologies of psychopathology in the United States: a latent class analysis.

PubMed

El-Gabalawy, Renée; Tsai, Jack; Harpaz-Rotem, Ilan; Hoff, Rani; Sareen, Jitender; Pietrzak, Robert H

2013-11-01

Latent class analysis (LCA) offers a parsimonious way of classifying common typologies of psychiatric comorbidity. We used LCA to identify the nature and correlates of predominant typologies of Axis I and II disorders in a large and comprehensive population-based sample of U.S. adults. We analyzed data from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (2004-2005; n = 34,653), a population-based sample of U.S. adults. We derived latent classes based on all assessed Axis I and II disorders and examined the relationship between the identified Axis I classes and lifetime psychiatric disorders and suicide attempts, and physical and mental health-related quality of life. A four-class solution was optimal in characterizing predominant typologies of both Axis I and II disorders. For Axis I disorders, these included low psychopathology (n = 28,935, 84.0%), internalizing (n = 3693, 9.9%), externalizing (n = 1426, 4.5%), and high psychopathology (n = 599, 1.6%) classes. For Axis II disorders, these included no/low personality disorders (n = 31,265, 90.9%), obsessive/paranoid (n = 1635, 4.6%), borderline/dysregulated (n = 1319, 3.4%), and highly comorbid (n = 434, 1.1%) classes. Compared to the low psychopathology class, all other Axis I classes had significantly increased odds of mental disorders, elevated Axis II classes, suicide attempts and poorer quality of life, with the high psychopathology class having the overall highest rates of these correlates, with the exception of substance use disorders. Compared to the low psychopathology class, the internalizing and externalizing classes had increased rates of mood and anxiety disorders, and substance use disorders, respectively. Axis I and II psychopathology among U.S. adults may be best represented by four predominant typologies. Characterizing co-occurring patterns of psychopathology using person-based typologies represents a higher-order classification system that may be useful in clinical and research settings. Published by Elsevier Ltd.

The three latent classes of adolescent delinquency and the risk factors for membership in each class.

PubMed

Hasking, Penelope Anne; Scheier, Lawrence M; Abdallah, Arbi Ben

2011-01-01

This study used latent class analysis to examine subpopulation membership based on self-reports of delinquent behaviors obtained from Australian youth. Three discrete identifiable classes were derived based on 51 indicators of physical violence, property damage, minor infractions, drug use, and social delinquency. One class of youth engaged in primarily rule breaking and norm violations including underage alcohol use, typical of this age period. A second class was more actively delinquent emphasizing drug use, trespassing, and various forms of disobedience. A third class of highly delinquent youth differed from their counterparts by endorsing drug use, thievery that involved stealing money, goods, and cars, property damage, gambling, precocious sexual experiences, involvement with pornographic materials, and fighting. Multinomial logistic regression predicting class membership indicated highly delinquent youth were more likely to be older males, use venting coping strategies, and be fun or novelty seeking compared with rule breakers. Findings are discussed in terms of refining current taxonomic arguments regarding the structure of delinquency and implications for prevention of early-stage antisocial behavior. © 2010 Wiley-Liss, Inc.

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study.

PubMed

Kim, Minjung; Lamont, Andrea E; Jaki, Thomas; Feaster, Daniel; Howe, George; Van Horn, M Lee

2016-06-01

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.

Latent Classes of Psychiatric Symptoms among Chinese Children Living in Poverty

ERIC Educational Resources Information Center

Herman, Keith C.; Bi, Yu; Borden, Lindsay A.; Reinke, Wendy M.

2012-01-01

Describing co-occurring symptom patterns among children in nonwestern contexts may have important implications for how emotional and behavior problems are defined, conceptualized, studied, and ultimately prevented. A latent profile analysis (LPA) was conducted on the co-occurring psychiatric symptoms of 196 Chinese children living in poverty.…

Comparisons of Mathematics Intervention Effects in Resource and Inclusive Classrooms

ERIC Educational Resources Information Center

Bottge, Brian A.; Cohen, Allan S.; Choi, Hye-Jeong

2018-01-01

In this article, we describe results of a reanalysis of two randomized studies that tested the effects of enhanced anchored instruction (EAI) on the fractions computation performance of students in special education resource rooms and inclusive mathematics classrooms. Latent class analysis and latent transition analysis classified students…

A person-centered analysis of posttraumatic stress disorder symptoms following a natural disaster: predictors of latent class membership.

PubMed

Rosellini, Anthony J; Coffey, Scott F; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Person-Centered Analysis of Posttraumatic Stress Disorder Symptoms Following a Natural Disaster: Predictors of Latent Class Membership

PubMed Central

Rosellini, Anthony J.; Coffey, Scott F.; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. PMID:24334161

Latent class analysis on internet and smartphone addiction in college students.

PubMed

Mok, Jung-Yeon; Choi, Sam-Wook; Kim, Dai-Jin; Choi, Jung-Seok; Lee, Jaewon; Ahn, Heejune; Choi, Eun-Jeung; Song, Won-Young

2014-01-01

This study aimed to classify distinct subgroups of people who use both smartphone and the internet based on addiction severity levels. Additionally, how the classified groups differed in terms of sex and psychosocial traits was examined. A total of 448 university students (178 males and 270 females) in Korea participated. The participants were given a set of questionnaires examining the severity of their internet and smartphone addictions, their mood, their anxiety, and their personality. Latent class analysis and ANOVA (analysis of variance) were the statistical methods used. Significant differences between males and females were found for most of the variables (all <0.05). Specifically, in terms of internet usage, males were more addicted than females (P<0.05); however, regarding smartphone, this pattern was reversed (P<0.001). Due to these observed differences, classifications of the subjects into subgroups based on internet and smartphone addiction were performed separately for each sex. Each sex showed clear patterns with the three-class model based on likelihood level of internet and smartphone addiction (P<0.001). A common trend for psychosocial trait factors was found for both sexes: anxiety levels and neurotic personality traits increased with addiction severity levels (all P<0.001). However, Lie dimension was inversely related to the addiction severity levels (all P<0.01). Through the latent classification process, this study identified three distinct internet and smartphone user groups in each sex. Moreover, psychosocial traits that differed in terms of addiction severity levels were also examined. It is expected that these results should aid the understanding of traits of internet and smartphone addiction and facilitate further study in this field.

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

NASA Technical Reports Server (NTRS)

Gutmann, Ethan D.; Small, Eric E.

2007-01-01

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

Latent typologies of posttraumatic stress disorder in World Trade Center responders.

PubMed

Horn, Sarah R; Pietrzak, Robert H; Schechter, Clyde; Bromet, Evelyn J; Katz, Craig L; Reissman, Dori B; Kotov, Roman; Crane, Michael; Harrison, Denise J; Herbert, Robin; Luft, Benjamin J; Moline, Jacqueline M; Stellman, Jeanne M; Udasin, Iris G; Landrigan, Philip J; Zvolensky, Michael J; Southwick, Steven M; Feder, Adriana

2016-12-01

Posttraumatic stress disorder (PTSD) is a debilitating and often chronic psychiatric disorder. Following the 9/11/2001 World Trade Center (WTC) attacks, thousands of individuals were involved in rescue, recovery and clean-up efforts. While a growing body of literature has documented the prevalence and correlates of PTSD in WTC responders, no study has evaluated predominant typologies of PTSD in this population. Participants were 4352 WTC responders with probable WTC-related DSM-IV PTSD. Latent class analyses were conducted to identify predominant typologies of PTSD symptoms and associated correlates. A 3-class solution provided the optimal representation of latent PTSD symptom typologies. The first class, labeled "High-Symptom (n = 1,973, 45.3%)," was characterized by high probabilities of all PTSD symptoms. The second class, "Dysphoric (n = 1,371, 31.5%)," exhibited relatively high probabilities of emotional numbing and dysphoric arousal (e.g., sleep disturbance). The third class, "Threat (n = 1,008, 23.2%)," was characterized by high probabilities of re-experiencing, avoidance and anxious arousal (e.g., hypervigilance). Compared to the Threat class, the Dysphoric class reported a greater number of life stressors after 9/11/2001 (OR = 1.06). The High-Symptom class was more likely than the Threat class to have a positive psychiatric history before 9/11/2001 (OR = 1.7) and reported a greater number of life stressors after 9/11/2001 (OR = 1.1). The High-Symptom class was more likely than the Dysphoric class, which was more likely than the Threat class, to screen positive for depression (83% > 74% > 53%, respectively), and to report greater functional impairment (High-Symptom > Dysphoric [Cohen d = 0.19], Dysphoric > Threat [Cohen d = 0.24]). These results may help inform assessment, risk stratification, and treatment approaches for PTSD in WTC and disaster responders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Emotional Psychological and Related Problems among Truant Youths: An Exploratory Latent Class Analysis

ERIC Educational Resources Information Center

Dembo, Richard; Briones-Robinson, Rhissa; Ungaro, Rocio Aracelis; Gulledge, Laura M.; Karas, Lora M.; Winters, Ken C.; Belenko, Steven; Greenbaum, Paul E.

2012-01-01

Intervention Project. Results identified two classes of youths: Class 1(n=9) - youths with low levels of delinquency, mental health and substance abuse issues; and Class 2(n=37) - youths with high levels of these problems. Comparison of these two classes on their urine analysis test results and parent/guardian reports of traumatic events found…

Profiles of Community Violence Exposure Among African American Youth: An Examination of Desensitization to Violence Using Latent Class Analysis.

PubMed

Gaylord-Harden, Noni K; Dickson, Daniel; Pierre, Cynthia

2016-07-01

The current study employed latent class analysis (LCA) to identify distinct profiles of community violence exposure and their associations to desensitization outcomes in 241 African American early adolescents (M age = 12.86, SD = 1.28) in the sixth through eighth grade from under-resourced urban communities. Participants self-reported on their exposure to community violence, as well as on depressive and anxiety symptoms. The LCA revealed three distinct classes: a class exposed to low levels of violence (low exposure class), a class exposed to moderately high levels of victimization (victimization class), and a class exposed to high levels of all types of violence (high exposure class). Consistent with predictions, the high exposure class showed the lowest levels of depressive symptoms, suggesting a desensitization outcome. Gender and age were also examined in relation to the classes, and age was significantly associated with an increased risk of being a member of the high exposure class relative to the low exposure class. Using person-based analyses to examine desensitization outcomes provides useful information for prevention and intervention efforts, as it helps to identify a specific subgroup of youth that may be more likely to show desensitization outcomes in the context of community violence. © The Author(s) 2015.

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study.

PubMed

Deloria Knoll, Maria; Fu, Wei; Shi, Qiyuan; Prosperi, Christine; Wu, Zhenke; Hammitt, Laura L; Feikin, Daniel R; Baggett, Henry C; Howie, Stephen R C; Scott, J Anthony G; Murdoch, David R; Madhi, Shabir A; Thea, Donald M; Brooks, W Abdullah; Kotloff, Karen L; Li, Mengying; Park, Daniel E; Lin, Wenyi; Levine, Orin S; O'Brien, Katherine L; Zeger, Scott L

2017-06-15

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case-control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case-control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

A Three-Step Latent Class Analysis to Identify How Different Patterns of Teen Dating Violence and Psychosocial Factors Influence Mental Health.

PubMed

Choi, Hye Jeong; Weston, Rebecca; Temple, Jeff R

2017-04-01

Although multiple forms (i.e., physical, threatening, psychological, sexual, and relational abuse) and patterns (i.e., perpetration and victimization) of violence can co-occur, most existing research examines these experiences individually. Thus, the purpose of this study is to investigate: (1) homogenous subgroups based on victimization and perpetration of multiple forms of teen dating violence; (2) predictors of membership in these subgroups; and (3) mental health consequences associated with membership in each subgroup. Nine hundred eighteen adolescents in the 9 th or 10 th grade at seven public high schools in Texas participated in the survey (56 % female, White: 30 %, Hispanic: 32 %, African American: 29 %, others: 9 %). A three-step latent class analysis was employed. Five latent teen dating violence classes were identified: (1) nonviolence; (2) emotional/verbal abuse; (3) forced sexual contact; (4) psychological + physical violence; and (5) psychological abuse. Females, African Americans, and youth who had higher acceptance of couple violence scores and whose parents had less education were more likely to members of dating violence classes compared with the nonviolence class. Adolescents who experienced multiple types of dating violence reported greater mental health concerns. Prevention programs may benefit by identifying the homogenous subgroups of teen dating violence and targeting adolescent teen dating violence accordingly.

Symptoms of prolonged grief and posttraumatic stress following loss: A latent class analysis.

PubMed

Maccallum, Fiona; Bryant, Richard A

2018-04-01

Individuals vary in how they respond to bereavement. Those who experience poor bereavement outcomes often report symptoms from more than one diagnostic category. This study sought to identify groups of individuals who share similar patterns of prolonged grief disorder and posttraumatic stress disorder symptoms to determine whether these profiles are differentially related to negative appraisals thought to contribute to prolonged grief disorder and posttraumatic stress disorder symptomatology. Participants were 185 bereaved adults. Latent class analysis was used to identify subgroups of individuals who showed similar patterns of co-occurrence of prolonged grief disorder and posttraumatic stress disorder symptoms. Multinomial regression was used to examine the extent to which appraisal domains and sociodemographic and loss factors predicted class membership. Latent class analysis revealed three classes of participants: a low symptom group, a high prolonged grief disorder symptom group, and a high prolonged grief disorder and posttraumatic stress disorder symptom group. Membership of the prolonged grief disorder group and prolonged grief disorder and posttraumatic stress disorder group was predicted by higher mean negative self-related appraisals. Demographic and loss-related factors did not predict group membership. These findings have implications for understanding co-occurrence of prolonged grief disorder and posttraumatic stress disorder symptoms following bereavement. Findings are consistent with theoretical models highlighting the importance of negative self-related beliefs in prolonged grief disorder.

Mental toughness latent profiles in endurance athletes

PubMed Central

Zeiger, Robert S.

2018-01-01

Mental toughness in endurance athletes, while an important factor for success, has been scarcely studied. An online survey was used to examine eight mental toughness factors in endurance athletes. The study aim was to determine mental toughness profiles via latent profile analysis in endurance athletes and whether associations exist between the latent profiles and demographics and sports characteristics. Endurance athletes >18 years of age were recruited via social media outlets (n = 1245, 53% female). Mental toughness was measured using the Sports Mental Toughness Questionnaire (SMTQ), Psychological Performance Inventory-Alternative (PPI-A), and self-esteem was measured using the Rosenberg Self-Esteem Scale (RSE). A three-class solution emerged, designated as high mental toughness (High MT), moderate mental toughness (Moderate MT) and low mental toughness (Low MT). ANOVA tests showed significant differences between all three classes on all 8 factors derived from the SMTQ, PPI-A and the RSE. There was an increased odds of being in the High MT class compared to the Low MT class for males (OR = 1.99; 95% CI, 1.39, 2.83; P<0.001), athletes who were over 55 compared to those who were 18–34 (OR = 2.52; 95% CI, 1.37, 4.62; P<0.01), high sports satisfaction (OR = 8.17; 95% CI, 5.63, 11.87; P<0.001), and high division placement (OR = 2.18; 95% CI, 1.46,3.26; P<0.001). The data showed that mental toughness latent profiles exist in endurance athletes. High MT is associated with demographics and sports characteristics. Mental toughness screening in athletes may help direct practitioners with mental skills training. PMID:29474398

Delirium superimposed on dementia: defining disease states and course from longitudinal measurements of a multivariate index using latent class analysis and hidden Markov chains.

PubMed

Ciampi, Antonio; Dyachenko, Alina; Cole, Martin; McCusker, Jane

2011-12-01

The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach. Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated. Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement. Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.

Maternal anaemia at delivery and haemoglobin evolution in children during their first 18 months of life using latent class analysis.

PubMed

Koura, Kobto G; Ouédraogo, Smaïla; Cottrell, Gilles; Le Port, Agnès; Massougbodji, Achille; Garcia, André

2012-01-01

Anaemia during pregnancy and at delivery is an important public health problem in low- and middle-income countries. Its association with the children's haemoglobin level over time remains unclear. Our goals were to identify distinct haemoglobin level trajectories using latent class analysis and to assess the association between these trajectories and maternal anaemia and other risk factors. A prospective study of children from birth to 18 months of life was conducted in a rural setting in Tori-Bossito, Benin. The main outcome measure was the haemoglobin levels repeatedly measured at 3, 6, 9, 12, 15 and 18 months. Variables were collected from the mothers at delivery and from their children at birth and during the follow-up. The analyses were performed by means of Latent Class Analysis which has never been used for this kind of data. All the analyses were performed with Stata software, version 11.0, using the generalized linear latent and mixed model (GLLAMM) framework. We showed that 33.7% of children followed a low haemoglobin trajectory and 66.3% a high trajectory during the first 18 months of life. Newborn anaemia, placental malaria, malaria attack, sickle cell trait and male gender were significantly associated with a lower children's haemoglobin level over time, whereas maternal age, children living in a polygamous family and with good feeding practices had a higher Hb level in the first18 months. We also showed that maternal anaemia was a predictor for 'low haemoglobin level trajectory' group membership but have no significant effect on children haemoglobin level over time. Latent Class Analyses framework seems well suited to analyse longitudinal data under the hypothesis that different subpopulations of subjects are present in the data, each with its own set of parameters, with distinctive evolutions that themselves may reflect distinctive aetiologies.

Malaria surveillance in the Democratic Republic of the Congo: comparison of microscopy, PCR, and rapid diagnostic test.

PubMed

Doctor, Stephanie M; Liu, Yunhao; Whitesell, Amy; Thwai, Kyaw L; Taylor, Steve M; Janko, Mark; Emch, Michael; Kashamuka, Melchior; Muwonga, Jérémie; Tshefu, Antoinette; Meshnick, Steven R

2016-05-01

Malaria surveillance is critical for control efforts, but diagnostic methods frequently disagree. Here, we compare microscopy, PCR, and a rapid diagnostic test in 7137 samples from children in the Democratic Republic of the Congo using latent class analysis. PCR had the highest sensitivity (94.6%) and microscopy had the lowest (76.7%). Copyright © 2016 Elsevier Inc. All rights reserved.

What Doesn't Work for Whom? Exploring Heterogeneity in Responsiveness to the Family Check-Up in Early Childhood Using a Mixture Model Approach.

PubMed

Pelham, William E; Dishion, Thomas J; Tein, Jenn-Yun; Shaw, Daniel S; Wilson, Melvin N

2017-11-01

This study applied latent class analysis to a family-centered prevention trial in early childhood to identify subgroups of families with differential responsiveness to the Family Check-Up (FCU) intervention. The sample included 731 families with 2-year-olds randomized to the FCU or control condition and followed through age 5 with yearly follow-up assessments. A two-step mixture model was used to examine whether specific constellations of family characteristics at age 2 (baseline) were related to intervention response across ages 3, 4, and 5. The first step empirically identified latent classes of families based on several family risk and adjustment variables selected on the basis of previous research. The second step modeled the effect of the FCU on longitudinal change in children's problem behavior in each of the empirically derived latent classes. Results suggested a five-class solution, where a significant intervention effect of moderate to large size was observed in one of the five classes-the class characterized by child neglect, legal problems, and parental mental health issues. Pairwise comparisons revealed that the intervention effect was significantly greater in this class of families than in two other classes that were generally less at risk for the development of child disruptive behavior problems, albeit still low-income. Thus, findings suggest that (a) the FCU is most successful in reducing child problem behavior in more highly distressed, low-income families, and (b) the FCU may have little impact for relatively low-risk, low-income families. Future directions include the development of a brief screening process that can triage low-income families into groups that should be targeted for intervention, redirected to other services, monitored prospectively, or left alone.

The Co-occurrence of Substance Use and Bullying Behaviors among U.S. Adolescents: Understanding Demographic Characteristics and Social Influences

PubMed Central

Luk, Jeremy W.; Wang, Jing; Simons-Morton, Bruce G.

2012-01-01

This study examined the co-occurrence of subtypes of substance use and bullying behaviors using latent class analysis and evaluated latent class differences in demographic characteristics, peer and parental influences. Self-reported questionnaire data were collected from a nationally representative sample (N = 7508) of 6–10th grade adolescents in the United States. Four latent classes were identified: the non-involved (57.7%), substance users (19.4%), bullies (17.5%), and substance-using bullies (5.4%). Older and Hispanic adolescents were more likely to be substance users and substance-using bullies, whereas younger and African American adolescents were more likely to be bullies. Females were more likely to be substance users, whereas males were more likely to be bullies and substance-using bullies. Spending more evenings with peers posed greater risks for substance use, bullying, and the co-occurrence of both problem behaviors. Paternal knowledge exerted protective effects over-and-above the effects of maternal knowledge. Implications for prevention and intervention efforts are discussed. PMID:22698675

Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models.

PubMed

Chand, Sai; Dixit, Vinayak V

2018-03-01

The repercussions from congestion and accidents on major highways can have significant negative impacts on the economy and environment. It is a primary objective of transport authorities to minimize the likelihood of these phenomena taking place, to improve safety and overall network performance. In this study, we use the Hurst Exponent metric from Fractal Theory, as a congestion indicator for crash-rate modeling. We analyze one month of traffic speed data at several monitor sites along the M4 motorway in Sydney, Australia and assess congestion patterns with the Hurst Exponent of speed (H speed ). Random Parameters and Latent Class Tobit models were estimated, to examine the effect of congestion on historical crash rates, while accounting for unobserved heterogeneity. Using a latent class modeling approach, the motorway sections were probabilistically classified into two segments, based on the presence of entry and exit ramps. This will allow transportation agencies to implement appropriate safety/traffic countermeasures when addressing accident hotspots or inadequately managed sections of motorway. Copyright © 2017 Elsevier Ltd. All rights reserved.

Literacy Profiles of At-Risk Young Adults Enrolled in Career and Technical Education

ERIC Educational Resources Information Center

Mellard, Daryl F.; Woods, Kari L.; Lee, Jae Hoon

2016-01-01

A latent profile analysis of 323 economically and academically at-risk adolescent and young adult learners yielded two classes: an average literacy class (92%) and a low literacy class (8%). The class profiles significantly differed in their word reading and math skills, and in their processing speeds and self-reported learning disabilities. The…

Pubertal Development and Prepubertal Height and Weight Jointly Predict Young Adult Height and Body Mass Index in a Prospective Study in South Africa.

PubMed

Stein, Aryeh D; Lundeen, Elizabeth A; Martorell, Reynaldo; Suchdev, Parminder S; Mehta, Neil K; Richter, Linda M; Norris, Shane A

2016-07-01

Height and adiposity track over childhood, but few studies, to our knowledge, have longitudinally examined the mediating relation of the timing and progression of puberty. We assessed interrelations between prepubertal height and body mass index, the progression through puberty, and young adult height and adiposity. We analyzed data from the Birth to Twenty Plus study (females, n = 823; males, n = 765). Serial measures of anthropometry and pubertal development were obtained between ages 9 and 16 y. We used latent class growth analysis to categorize pubertal development with respect to pubic hair (females and males), breasts (females), and genitalia (males) development. Adult height and weight were obtained at ages 18 to 20 y. Among females, higher latent class (earlier initiation and faster progression through puberty) was associated with an increased risk of obesity [pubic hair class 3 compared with class 1: RR, 3.41 (95% CI: 1.57, 7.44)] and inconsistent associations with height. Among males, higher latent class was associated with increased adult height [pubic hair development class 3 compared with class 1: 2.43 cm (95% CI: 0.88, 4.00)] and increased risk of overweight/obesity [pubic hair development class 3 compared with class 1: OR, 3.44 (95% CI: 1.44, 8.20)]. In females, the association with adult height became inverse after adjusting for prepubertal height [pubic hair development class 3 compared with class 1: females, -1.31 cm (95% CI: -2.32, -0.31)]; in males, the association with height was attenuated with this adjustment [-0.56 cm (95% CI: -1.63, 0.52)]. Associations with adiposity were attenuated after adjusting for prepubertal adiposity. Progression through puberty modifies the relation between prepubertal and adult anthropometry. Screening for early or rapid progression of puberty might identify children at an increased risk of becoming overweight or obese adults.

A Latent Class Analysis of Maternal Responsiveness and Autonomy-Granting in Early Adolescence: Prediction to Later Adolescent Sexual Risk-Taking

PubMed Central

Lanza, H. Isabella; Huang, David Y. C.; Murphy, Debra A.; Hser, Yih-Ing

2013-01-01

The present study sought to extend empirical inquiry related to the role of parenting on adolescent sexual risk-taking by using latent class analysis (LCA) to identify patterns of adolescent-reported mother responsiveness and autonomy-granting in early adolescence and examine associations with sexual risk-taking in mid- and late-adolescence. Utilizing a sample of 12- to 14-year-old adolescents (N = 4,743) from the 1997 National Longitudinal Survey of Youth (NLSY97), results identified a four-class model of maternal responsiveness and autonomy-granting: low responsiveness/high autonomy-granting, moderate responsiveness/moderate autonomy-granting, high responsiveness/low autonomy-granting, high responsiveness/moderate autonomy-granting. Membership in the low responsiveness/high autonomy-granting class predicted greater sexual risk-taking in mid- and late-adolescence compared to all other classes, and membership in the high responsiveness/ moderate autonomy-granting class predicted lower sexual risk-taking. Gender and ethnic differences in responsiveness and autonomy-granting class membership were also found, potentially informing gender and ethnic disparities of adolescent sexual risk-taking. PMID:23828712

The Four U's: Latent Classes of Hookup Motivations Among College Students

PubMed Central

Uecker, Jeremy E.; Pearce, Lisa D.; Andercheck, Brita

2016-01-01

College students’ “hookups” have been the subject of a great deal of research in recent years. Motivations for hooking up have been linked to differences in well-being after the hookup, but studies detailing college students’ motivations for engaging in hookups focus on single motivations. Using data from the 2010 Duke Hookup Survey, we consider how motivations for hooking up cluster to produce different classes, or profiles, of students who hook up, and how these classes are related to hookup regret. Four distinct classes of motivations emerged from our latent class analysis: Utilitarians (50%), Uninhibiteds (27%), Uninspireds (19%), and Unreflectives (4%). We find a number of differences in hookup motivation classes across social characteristics, including gender, year in school, race-ethnicity, self-esteem, and attitudes about sexual behavior outside committed relationships. Additionally, Uninspireds regret hookups more frequently than members of the other classes, and Uninhibiteds report regret less frequently than Utilitarians and Uninspireds. These findings reveal the complexity of motivations for hooking up and the link between motivations and regret. PMID:27066516

Prolonged grief and post-traumatic growth after loss: Latent class analysis.

PubMed

Zhou, Ningning; Yu, Wei; Tang, Suqin; Wang, Jianping; Killikelly, Clare

2018-06-06

Bereavement may trigger different psychological outcomes, such as prolonged grief disorder or post-traumatic growth. The relationship between these two outcomes and potential precipitators remain unknown. The current study aimed to identify classes of Chinese bereaved individuals based on prolonged grief symptoms and post-traumatic growth and to examine predictors for these classes. We used data from 273 Chinese individuals who lost a relative due to disease (92.3%), accident (4.4%) and other reasons (1.8%). Latent class analysis revealed three classes: a resilient class, a growth class, and a combined grief/growth class. A higher level of functional impairment was found for the combined grief/growth class than for the other two classes. Membership in the combined grief/growth class was significantly predicted by the younger age of the deceased and the death of a parent, child or spouse. Subjective closeness with the deceased and gender were marginally significant predictors. When the four variables were included in the multinomial regression analysis, death of a parent, child or spouse significantly predicted the membership to the combined grief/growth class. These findings provide valuable information for the development of tailored interventions that may build on the bereaved individuals' personal strengths. Copyright © 2018. Published by Elsevier B.V.

Nonsuicidal Self-Injury and Suicidal Behavior: A Latent Class Analysis among Young Adults

PubMed Central

Hamza, Chloe A.; Willoughby, Teena

2013-01-01

Although there is a general consensus among researchers that engagement in nonsuicidal self-injury (NSSI) is associated with increased risk for suicidal behavior, little attention has been given to whether suicidal risk varies among individuals engaging in NSSI. To identify individuals with a history of NSSI who are most at risk for suicidal behavior, we examined individual variability in both NSSI and suicidal behavior among a sample of young adults with a history of NSSI (N = 439, Mage = 19.1). Participants completed self-report measures assessing NSSI, suicidal behavior, and psychosocial adjustment (e.g., depressive symptoms, daily hassles). We conducted a latent class analysis using several characteristics of NSSI and suicidal behaviors as class indicators. Three subgroups of individuals were identified: 1) an infrequent NSSI/not high risk for suicidal behavior group, 2) a frequent NSSI/not high risk for suicidal behavior group, and 3) a frequent NSSI/high risk for suicidal behavior group. Follow-up analyses indicated that individuals in the ‘frequent NSSI/high risk for suicidal behavior’ group met the clinical-cut off score for high suicidal risk and reported significantly greater levels of suicidal ideation, attempts, and risk for future suicidal behavior as compared to the other two classes. Thus, this study is the first to identity variability in suicidal risk among individuals engaging in frequent and multiple methods of NSSI. Class 3 was also differentiated by higher levels of psychosocial impairment relative to the other two classes, as well as a comparison group of non-injuring young adults. Results underscore the importance of assessing individual differences in NSSI characteristics, as well as psychosocial impairment, when assessing risk for suicidal behavior. PMID:23544113

Paternal Work Stress and Latent Profiles of Father-Infant Parenting Quality

ERIC Educational Resources Information Center

Goodman, W. Benjamin; Crouter, Ann C.; Lanza, Stephanie T.; Cox, Martha J.; Vernon-Feagans, Lynne

2011-01-01

The current study used latent profile analysis (LPA) to examine the implications of fathers' experiences of work stress for paternal behaviors with infants across multiple dimensions of parenting in a sample of fathers living in nonmetropolitan communities (N = 492). LPA revealed five classes of fathers based on levels of social-affective…

Development of Fraction Comparison Strategies: A Latent Transition Analysis

ERIC Educational Resources Information Center

Rinne, Luke F.; Ye, Ai; Jordan, Nancy C.

2017-01-01

The present study investigated the development of fraction comparison strategies through a longitudinal analysis of children's responses to a fraction comparison task in 4th through 6th grades (N = 394). Participants were asked to choose the larger value for 24 fraction pairs blocked by fraction type. Latent class analysis of performance over item…

Comparing Latent Structures of the Grade of Membership, Rasch, and Latent Class Models

ERIC Educational Resources Information Center

Erosheva, Elena A.

2005-01-01

This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other…

Heterogeneity in the Latent Structure of PTSD Symptoms among Canadian Veterans

ERIC Educational Resources Information Center

Naifeh, James A.; Richardson, J. Don; Del Ben, Kevin S.; Elhai, Jon D.

2010-01-01

The current study used factor mixture modeling to identify heterogeneity (i.e., latent classes) in 2 well-supported models of posttraumatic stress disorder's (PTSD) factor structure. Data were analyzed from a clinical sample of 405 Canadian veterans evaluated for PTSD. Results were consistent with our hypotheses. Each PTSD factor model was best…

Optimal Scaling of HIV-Related Sexual Risk Behaviors in Ethnically Diverse Homosexually Active Men.

ERIC Educational Resources Information Center

Cochran, Susan D.; And Others

1995-01-01

Used homogeneity analysis and latent class analysis to analyze sexual behavior patterns in two samples of homosexually active men. Results support the existence of a single, nonlinear, latent dimension underlying male homosexual behaviors consistent with HIV-related risk taking, providing an efficient means to scale sexual behavior patterns. (RJM)

Comparisons of polydrug use at national and inner city levels in England: associations with demographic and socioeconomic factors.

PubMed

Carter, Jennifer L; Strang, John; Frissa, Souci; Hayes, Richard D; Hatch, Stephani L; Hotopf, Matthew

2013-10-01

This study compares polydrug use in national and inner city samples to (1) examine patterns of use underlying different prevalence rates and (2) identify how inner city polydrug use needs targeting in ways not suggested by national research. Latent class analyses on indicators of illicit drug use in the last year, hazardous alcohol use, and cigarette smoking were compared between the inner city 2008-2010 South East London Community Health study (n = 1698) and the nationally representative 2007 Adult Psychiatric Morbidity Survey in England (n = 7403). Multinomial logistic regressions then examined latent class solutions with demographic and socioeconomic factors. Both samples revealed three notably similar classes of polydrug users: a "high-drug" group using multiple substances; a "moderate-drug" group using cannabis, alcohol, and cigarettes; and a "low-drug" group reporting minimal alcohol and cigarette use. However, South East London Community Health reported lower risks of polydrug use for ethnic minorities but not for more educated participants. Despite higher polydrug use prevalence in the inner city, latent classes of polydrug users were similar between samples. Some demographic and socioeconomic factors differed between the samples, suggesting the need for inner city services to use both local and national data for policy planning. Copyright © 2013 Elsevier Inc. All rights reserved.

Attachment in psychosis: A latent profile analysis of attachment styles and association with symptoms in a large psychosis cohort.

PubMed

Bucci, Sandra; Emsley, Richard; Berry, Katherine

2017-01-01

Attachment has been identified as one of various possible mechanisms involved in understanding models of psychosis, but measures that reliably and validly assess attachment styles in psychosis are limited. The aim of this study was to identify attachment patterns in psychosis and examine demographic and clinical correlates across attachment groups. Latent profile analysis on attachment data from 588 participants who met criteria for non-affective psychosis was used to classify people into attachment classes. Four latent classes of attachment were identified: secure, insecure-anxious, insecure-avoidant and disorganised. Secure attachment was the most common attachment style, suggesting that a significant number of clients with psychosis are inherently resilient. Disorganised attachment was associated with a higher proportion of sexual and physical abuse and more severe positive symptoms compared to other attachment classes. This is not only the largest study to examine attachment styles, their demographic and clinical profile, and the clinical profile of disorganised attachment more specifically, in psychosis, but also the first study to use a validated self-report measure of attachment in psychosis to identify four classes of attachment style. Findings advance developmental models of attachment and psychosis; participants with disorganised attachment report more frequent trauma history and more severe psychotic symptoms. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Demographic analysis from summaries of an age-structured population

USGS Publications Warehouse

Link, William A.; Royle, J. Andrew; Hatfield, Jeff S.

2003-01-01

Demographic analyses of age-structured populations typically rely on life history data for individuals, or when individual animals are not identified, on information about the numbers of individuals in each age class through time. While it is usually difficult to determine the age class of a randomly encountered individual, it is often the case that the individual can be readily and reliably assigned to one of a set of age classes. For example, it is often possible to distinguish first-year from older birds. In such cases, the population age structure can be regarded as a latent variable governed by a process prior, and the data as summaries of this latent structure. In this article, we consider the problem of uncovering the latent structure and estimating process parameters from summaries of age class information. We present a demographic analysis for the critically endangered migratory population of whooping cranes (Grus americana), based only on counts of first-year birds and of older birds. We estimate age and year-specific survival rates. We address the controversial issue of whether management action on the breeding grounds has influenced recruitment, relating recruitment rates to the number of seventh-year and older birds, and examining the pattern of variation through time in this rate.

Latent classes of resilience and psychological response among only-child loss parents in China.

PubMed

Wang, An-Ni; Zhang, Wen; Zhang, Jing-Ping; Huang, Fei-Fei; Ye, Man; Yao, Shu-Yu; Luo, Yuan-Hui; Li, Zhi-Hua; Zhang, Jie; Su, Pan

2017-10-01

Only-child loss parents in China recently gained extensive attention as a newly defined social group. Resilience could be a probable solution out of the psychological dilemma. Using a sample of 185 only-child loss people, this study employed latent class analysis (a) to explore whether different classes of resilience could be identified, (b) to determine socio-demographic characteristics of each class, and (c) to compare the depression and the subjective well-being of each class. The results supported a three-class solution, defined as 'high tenacity-strength but moderate optimism class', 'moderate resilience but low self-efficacy class' and 'low tenacity but moderate adaption-dependence class'. Parents with low income and medical insurance of low reimbursement type and without endowment insurance occupied more proportions in the latter two classes. The latter two classes also had a significant higher depression scores and lower subjective well-being scores than high tenacity-strength but moderate optimism class. Future work should care those socio-economically vulnerable bereaved parents, and an elastic economic assistance policy was needed. To develop targeted resilience interventions, the emphasis of high tenacity-strength but moderate optimism class should be the optimism. Moderate resilience but low self-efficacy class should be self-efficacy, and low tenacity but moderate adaption-dependence class should be tenacity. Copyright © 2016 John Wiley & Sons, Ltd.

A taxometric investigation of developmental dyslexia subtypes.

PubMed

O'Brien, Beth A; Wolf, Maryanne; Lovett, Maureen W

2012-02-01

Long-standing issues with the conceptualization, identification and subtyping of developmental dyslexia persist. This study takes an alternative approach to examine the heterogeneity of developmental dyslexia using taxometric classification techniques. These methods were used with a large sample of 671 children ages 6-8 who were diagnosed with severe reading disorders. Latent characteristics of the sample are assessed in regard to posited subtypes with phonological deficits and naming speed deficits, thus extending prior work by addressing whether these deficits embody separate classes of individuals. Findings support separate taxa of dyslexia with and without phonological deficits. Different latent structure for naming speed deficits was found depending on the definitional criterion used to define dyslexia. Non-phonologically based forms of dyslexia showed particular difficulty with naming speed and reading fluency. Copyright © 2012 John Wiley & Sons, Ltd.

Comparison of Suicide Attempters and Decedents in the U.S. Army: A Latent Class Analysis.

PubMed

Skopp, Nancy A; Smolenski, Derek J; Sheppard, Sean C; Bush, Nigel E; Luxton, David D

2016-08-01

A clearer understanding of risk factors for suicidal behavior among soldiers is of principal importance to military suicide prevention. It is unclear whether soldiers who attempt suicide and those who die by suicide have different patterns of risk factors. As such, preventive efforts aimed toward reducing suicide attempts and suicides, respectively, may require different strategies. We conducted a latent class analysis (LCA) to examine classes of risk factors among suicide attempters (n = 1,433) and decedents (n = 424). Both groups were represented by three classes: (1) External/Antisocial Risk Factors, (2) Mental Health Risk Factors, and (3) No Pattern. These findings support the conceptualization that military suicide attempters and decedents represent a single population. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Professional service use for a serious personal problem: Comparing older African Americans, Black Caribbeans, and non-Hispanic Whites using the National Survey of American Life

PubMed Central

Woodward, Amanda Toler; Chatters, Linda M.; Taylor, Harry Owen; Taylor, Robert Joseph

2014-01-01

Objectives Examines combinations of professionals visited for a serious personal problem. Methods The sample includes those aged 55 and older (n=862) from the National Survey of American Life (NSAL). Latent class analysis was used to identify groups of respondents based on types of professionals visited. Multinomial logistic regression was used to identify factors associated with group membership. Results Classes included health provider plus clergy, physician plus mental health provider, and limited provider use. Whites were more likely than African Americans to fall into the health provider plus clergy and physician plus mental health provider classes. Those with physical and emotional problems were more likely to be in the health provider plus clergy and physician plus mental health provider classes, respectively. Discussion Most respondents were in the limited provider use class suggesting that for many of problems, minimal professional help is utilized. Physicians and clergy were important across all three classes. PMID:25552527

CLASSIFICATION OF IRANIAN NURSES ACCORDING TO THEIR MENTAL HEALTH OUTCOMES USING GHQ-12 QUESTIONNAIRE: A COMPARISON BETWEEN LATENT CLASS ANALYSIS AND K-MEANS CLUSTERING WITH TRADITIONAL SCORING METHOD

PubMed Central

Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Background: Nurses constitute the most providers of health care systems. Their mental health can affect the quality of services and patients’ satisfaction. General Health Questionnaire (GHQ-12) is a general screening tool used to detect mental disorders. Scoring method and determining thresholds for this questionnaire are debatable and the cut-off points can vary from sample to sample. This study was conducted to estimate the prevalence of mental disorders among Iranian nurses using GHQ-12 and also compare Latent Class Analysis (LCA) and K-means clustering with traditional scoring method. Methodology: A cross-sectional study was carried out in Fars and Bushehr provinces of southern Iran in 2014. Participants were 771 Iranian nurses, who filled out the GHQ-12 questionnaire. Traditional scoring method, LCA and K-means were used to estimate the prevalence of mental disorder among Iranian nurses. Cohen’s kappa statistic was applied to assess the agreement between the LCA and K-means with traditional scoring method of GHQ-12. Results: The nurses with mental disorder by scoring method, LCA and K-mean were 36.3% (n=280), 32.2% (n=248), and 26.5% (n=204), respectively. LCA and logistic regression revealed that the prevalence of mental disorder in females was significantly higher than males. Conclusion: Mental disorder in nurses was in a medium level compared to other people living in Iran. There was a little difference between prevalence of mental disorder estimated by scoring method, K-means and LCA. According to the advantages of LCA than K-means and different results in scoring method, we suggest LCA for classification of Iranian nurses according to their mental health outcomes using GHQ-12 questionnaire PMID:26622202

CLASSIFICATION OF IRANIAN NURSES ACCORDING TO THEIR MENTAL HEALTH OUTCOMES USING GHQ-12 QUESTIONNAIRE: A COMPARISON BETWEEN LATENT CLASS ANALYSIS AND K-MEANS CLUSTERING WITH TRADITIONAL SCORING METHOD.

PubMed

Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi

2015-10-01

Nurses constitute the most providers of health care systems. Their mental health can affect the quality of services and patients' satisfaction. General Health Questionnaire (GHQ-12) is a general screening tool used to detect mental disorders. Scoring method and determining thresholds for this questionnaire are debatable and the cut-off points can vary from sample to sample. This study was conducted to estimate the prevalence of mental disorders among Iranian nurses using GHQ-12 and also compare Latent Class Analysis (LCA) and K-means clustering with traditional scoring method. A cross-sectional study was carried out in Fars and Bushehr provinces of southern Iran in 2014. Participants were 771 Iranian nurses, who filled out the GHQ-12 questionnaire. Traditional scoring method, LCA and K-means were used to estimate the prevalence of mental disorder among Iranian nurses. Cohen's kappa statistic was applied to assess the agreement between the LCA and K-means with traditional scoring method of GHQ-12. The nurses with mental disorder by scoring method, LCA and K-mean were 36.3% (n=280), 32.2% (n=248), and 26.5% (n=204), respectively. LCA and logistic regression revealed that the prevalence of mental disorder in females was significantly higher than males. Mental disorder in nurses was in a medium level compared to other people living in Iran. There was a little difference between prevalence of mental disorder estimated by scoring method, K-means and LCA. According to the advantages of LCA than K-means and different results in scoring method, we suggest LCA for classification of Iranian nurses according to their mental health outcomes using GHQ-12 questionnaire.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Adolescent stalking and risk of violence.

PubMed

Smith-Darden, Joanne P; Reidy, Dennis E; Kernsmith, Poco D

2016-10-01

Stalking perpetration and the associated risk for violence among adolescents has generally been neglected. In the present study, 1236 youth completed surveys assessing empirically established stalking indicators, threats and aggression toward stalking victims, dating violence, and violent delinquency. Latent Profile Analysis identified 3 latent classes of boys: non-perpetrators (NP), hyper-intimate pursuit (HIP), and comprehensive stalking perpetrators (CSP) and, and 2 classes for girls: NP and HIP. Boys in the CSP class were the most violent youth on nearly all indices with boys in the HIP class demonstrating an intermediate level of violence compared to NP boys. Girls in the HIP class were more violent than NP girls on all indices. These findings suggest stalking in adolescence merits attention by violence prevention experts. In particular, juvenile stalking may signify youth at risk for multiple forms of violence perpetrated against multiple types of victims, not just the object of their infatuation. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Identifying Gender-Specific Developmental Trajectories of Nonviolent and Violent Delinquency from Adolescence to Young Adulthood

PubMed Central

Zheng, Yao; Cleveland, H. Harrington

2013-01-01

Most research examining gender differences in developmental trajectories of antisocial behavior does not consider subtypes of antisocial behavior and is difficult to generalize due to small nonrepresentative samples. The current study investigated gender difference in developmental trajectories from adolescence to young adulthood while addressing those limitations. Analyses were limited to respondents ages 15 and 16 in wave 1 (16–17 in wave 2, and 21–22 in wave 3) of the National Longitudinal Study of Adolescent Health (n = 6244, 49.5% males). Self-report nonviolent and violent delinquencies were simultaneously entered into latent class analysis. Four latent classes were identified: low, desister, decliner, and chronic (male-only). In addition to finding a male-specific chronic class, gender differences included differences in levels of nonviolent and violent delinquency between synonymous classes of males and females, and differences in prevalence of classes across genders. Neighborhood disadvantage and family support predicted trajectories. PMID:23375843

Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers.

PubMed

Teipel, Stefan J; Cavedo, Enrica; Lista, Simone; Habert, Marie-Odile; Potier, Marie-Claude; Grothe, Michel J; Epelbaum, Stephane; Sambati, Luisa; Gagliardi, Geoffroy; Toschi, Nicola; Greicius, Michael; Dubois, Bruno; Hampel, Harald

2018-05-21

Cognitive change in people at risk of Alzheimer's disease (AD) such as subjective memory complainers is highly variable across individuals. We used latent class growth modeling to identify distinct classes of nonlinear trajectories of cognitive change over 2 years follow-up from 265 subjective memory complainers individuals (age 70 years and older) of the INSIGHT-preAD cohort. We determined the effect of cortical amyloid load, hippocampus and basal forebrain volumes, and education on the cognitive trajectory classes. Latent class growth modeling identified distinct nonlinear cognitive trajectories. Education was associated with higher performing trajectories, whereas global amyloid load and basal forebrain atrophy were associated with lower performing trajectories. Distinct classes of cognitive trajectories were associated with risk and protective factors of AD. These associations support the notion that the identified cognitive trajectories reflect different risk for AD that may be useful for selecting high-risk individuals for intervention trials. Copyright © 2018. Published by Elsevier Inc.

Replication-Competent Simian Immunodeficiency Virus (SIV) Gag Escape Mutations Archived in Latent Reservoirs during Antiretroviral Treatment of SIV-Infected Macaques▿

PubMed Central

Queen, Suzanne E.; Mears, Brian M.; Kelly, Kathleen M.; Dorsey, Jamie L.; Liao, Zhaohao; Dinoso, Jason B.; Gama, Lucio; Adams, Robert J.; Zink, M. Christine; Clements, Janice E.; Kent, Stephen J.; Mankowski, Joseph L.

2011-01-01

In response to pressure exerted by major histocompatibility complex (MHC) class I-mediated CD8+ T cell control, human immunodeficiency virus (HIV) escape mutations often arise in immunodominant epitopes recognized by MHC class I alleles. While the current standard of care for HIV-infected patients is treatment with highly active antiretroviral therapy (HAART), suppression of viral replication in these patients is not absolute and latently infected cells persist as lifelong reservoirs. To determine whether HIV escape from MHC class I-restricted CD8+ T cell control develops during HAART treatment and then enters latent reservoirs in the periphery and central nervous system (CNS), with the potential to emerge as replication-competent virus, we tracked the longitudinal development of the simian immunodeficiency virus (SIV) Gag escape mutation K165R in HAART-treated SIV-infected pigtailed macaques. Key findings of these studies included: (i) SIV Gag K165R escape mutations emerged in both plasma and cerebrospinal fluid (CSF) during the decaying phase of viremia after HAART initiation before suppression of viral replication, (ii) SIV K165R Gag escape mutations were archived in latent proviral DNA reservoirs, including the brain in animals receiving HAART that suppressed viral replication, and (iii) replication-competent SIV Gag K165R escape mutations were present in the resting CD4+ T cell reservoir in HAART-treated SIV-infected macaques. Despite early administration of aggressive antiretroviral treatment, HIV immune escape from CD8+ T cell control can still develop during the decaying phases of viremia and then persist in latent reservoirs, including the brain, with the potential to emerge if HAART therapy is interrupted. PMID:21715484

Typologies of sexually explicit media use among MSM: An application of latent class analysis

PubMed Central

Erickson, Darin J.; Galos, Dylan L; Smolenski, Derek, J.; Iantaffi, Alex; Rosser, B.R. Simon

2014-01-01

The viewing of sexually explicit media (SEM) is widespread, especially among men, and research linking SEM viewing and sexual behavior has shown a variety of results, some positive (e.g., sexuality education) and some negative (e.g., poorer body image). These results might be due to limitations in measuring SEM consumption, particularly around typology. The goal of the current study was to examine potential patterns of SEM viewing activities. Using data from an online survey of men who have sex with men (MSM), we conducted latent class analyses of 15 SEM activities. Results suggested a three-class solution. The most prevalent class included over 60% of men and was characterized by viewing primarily safer-sex or conventional behaviors. The second class included 32% of men and had a similar albeit amplified pattern of viewing. The final class included just 7% of men and was marked by high levels of viewing of all activities, including fetish and kink. Compared to the conventional or safer-sex class, the other classes had lower internalized homonegativity, lower condom use self-efficacy, and higher SEM consumption or dose. Implications for HIV prevention, sexuality research and the SEM industry are discussed. PMID:25642301

Clustering of adversity in young adults on disability pension due to mental disorders: a latent class analysis.

PubMed

Joensuu, Matti; Mattila-Holappa, Pauliina; Ahola, Kirsi; Ervasti, Jenni; Kivimäki, Mika; Kivekäs, Teija; Koskinen, Aki; Vahtera, Jussi; Virtanen, Marianna

2016-02-01

Mental disorders are the leading cause of work disability among young adults. This study examined whether distinct classes could be identified among young adults on the basis of medical history before receiving a disability pension due to a mental disorder. Medical history was obtained from pension applications and attached medical certificates for 1163 individuals aged 18-34 years who, in 2008, received a disability pension due to a mental disorder. Using latent class analysis, 10 clinical and individual adversities and their associations with sex, age and diagnostic category were examined. Three classes were identified: childhood adversity (prevalence, 33%), comorbidity (23%), and undefined (44%). The childhood adversity class was characterized by adverse events and symptoms reported during childhood and it associated with depressive disorders. The comorbidity class was characterized by comorbid mental disorders, suicide attempts and substance abuse and associated with younger age and bipolar disorder. The undefined class formed no distinct profile; individuals in this class had the lowest number of adversities and it associated with psychotic disorders. The identification of subgroups characterized by childhood circumstances and comorbidity may help planning of prevention and support practices for young adults with mental disorders and risk of work disability.

The impact of measurement differences on cross-country depression prevalence estimates: A latent transition analysis.

PubMed

Scorza, Pamela; Masyn, Katherine; Salomon, Joshua A; Betancourt, Theresa S

2018-01-01

Depression is currently the second largest contributor to non-fatal disease burden globally. For that reason, economic evaluations are increasingly being conducted using data from depression prevalence estimates to analyze return on investments for services that target mental health. Psychiatric epidemiology studies have reported large cross-national differences in the prevalence of depression. These differences may impact the cost-effectiveness assessments of mental health interventions, thereby affecting decisions regarding government and multi-lateral investment in mental health services. Some portion of the differences in prevalence estimates across countries may be due to true discrepancies in depression prevalence, resulting from differential levels of risk in environmental and demographic factors. However, some portion of those differences may reflect non-invariance in the way standard tools measure depression across countries. This paper attempts to discern the extent to which measurement differences are responsible for reported differences in the prevalence of depression across countries. This analysis uses data from the World Mental Health Surveys, a coordinated series of psychiatric epidemiology studies in 27 countries using multistage household probability samples to assess prevalence and correlates of mental disorders. Data in the current study include responses to the depression module of the World Mental Health Composite International Diagnostic Interview (CIDI) in four countries: Two high-income, western countries-the United States (n = 20, 015) and New Zealand (n = 12,992)-an upper-middle income sub-Saharan African country, South Africa (n = 4,351), and a lower-middle income sub-Saharan African country, Nigeria (n = 6,752). Latent class analysis, a type of finite mixture modeling, was used to categorize respondents into underlying categories based on the variation in their responses to questions in each of three sequential parts of the CIDI depression module: 1) The initial screening items, 2) Additional duration and severity exclusion criteria, and 3) The core symptom questions. After each of these parts, exclusion criteria expel respondents from the remainder of the diagnostic interview, rendering a diagnosis of "not depressed". Latent class models were fit to each of the three parts in each of the four countries, and model fit was assessed using overall chi-square values and Pearson standardized residuals. Latent transition analysis was then applied in order to model participants' progression through the CIDI depression module. Proportion of individuals falling into each latent class and probabilities of transitioning into subsequent classes were used to estimate the percentage in each country that ultimately fell into the more symptomatic class, i.e. classified as "depressed". This latent variable design allows for a non-zero probability that individuals were incorrectly excluded from or retained in the diagnostic interview at any of the three exclusion points and therefore incorrectly diagnosed. Prevalence estimates based on the latent transition model reversed the order of depression prevalence across countries. Based on the latent transition model in this analysis, Nigeria has the highest prevalence (21.6%), followed by New Zealand (17.4%), then South Africa (15.0%), and finally the US (12.5%). That is compared to the estimates in the World Mental Health Surveys that do not allow for measurement differences, in which Nigeria had by far the lowest prevalence (3.1%), followed by South Africa (9.8%), then the United States (13.5%) and finally New Zealand (17.8%). Individuals endorsing the screening questions in Nigeria and South Africa were more likely to endorse more severe depression symptomology later in the module (i.e. they had higher transition probabilities), suggesting that individuals in the two Western countries may be more likely to endorse screening questions even when they don't have as severe symptoms. These differences narrow the range of depression prevalence between countries 14 percentage points in the original estimates to 6 percentage points in the estimate taking account of measurement differences. These data suggest fewer differences in cross-national prevalence of depression than previous estimates. Given that prevalence data are used to support key decisions regarding resource-allocation for mental health services, more critical attention should be paid to differences in the functioning of measurement across contexts and the impact these differences have on prevalence estimates. Future research should include qualitative methods as well as external measures of disease severity, such as impairment, to assess how the latent classes predict these external variables, to better understand the way that standard tools estimate depression prevalence across contexts. Adjustments could then be made to prevalence estimates used in cost-effectiveness analyses.

Latent Growth Classes of Alcohol-Related Blackouts over the First Two Years of College

PubMed Central

Merrill, Jennifer E.; Treloar, Hayley; Fernandez, Anne C.; Monnig, Mollie A.; Jackson, Kristina M.; Barnett, Nancy P.

2016-01-01

Alcohol-related blackouts are common among college student drinkers. The present study extends prior work by examining latent growth classes of blackouts and several predictors of class membership. Participants (N=709 college drinkers) completed a baseline survey at college entry and biweekly online assessments throughout freshman and sophomore years. Results revealed five latent growth class trajectories, reflecting varying experiences of blackouts at the beginning of college and differential change in blackouts over time. The largest class represented a relatively low risk group (LOW DECR; 47.3%) characterized by endorsement of no or very low likelihood of blackouts, and decreasing likelihood of blackouts over time. Another decreasing risk group (HIGH DECR; 11.1%) initially reported a high proportion of blackouts and had the steepest decrease in blackout risk over time. A small percentage showed consistently high likelihood of blackouts over time (HIGH STABLE; 4.1%). The remaining two groups were distinguished by relatively moderate (MOD STABLE; 14.9%) and lower (LOW STABLE; 22.6%) likelihood of blackouts, which remained stable over time. Comparisons between classes revealed that students with greater perceived peer drinking, perceived peer approval of drinking, and enhancement motives upon entry to college tended to be in higher-risk groups with consistent experiences of blackouts over time, whereas blackout likelihood decreased over time for students with greater conformity motives. Findings suggest that pre-college preventive interventions may be strengthened by considering not only factors related to current risk for blackouts and other alcohol-related consequences, but also those factors related to persistence of these behaviors over time. PMID:27736145

Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study.

PubMed

Zammit, Andrea R; Hall, Charles B; Lipton, Richard B; Katz, Mindy J; Muniz-Terrera, Graciela

2018-05-01

The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup's characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer's disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511-523).

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing

PubMed Central

Wang, Guoli; Ebrahimi, Nader

2014-01-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data. PMID:25821345

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

PubMed

Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

2015-04-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H , such that V ∼ W H . It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H . In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

Reflected stochastic differential equation models for constrained animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Johnson, Devin S.; Hooten, Mevin B.

2017-01-01

Movement for many animal species is constrained in space by barriers such as rivers, shorelines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

Medical Complexity among Children with Special Health Care Needs: A Two-Dimensional View.

PubMed

Coller, Ryan J; Lerner, Carlos F; Eickhoff, Jens C; Klitzner, Thomas S; Sklansky, Daniel J; Ehlenbach, Mary; Chung, Paul J

2016-08-01

To identify subgroups of U.S. children with special health care needs (CSHCN) and characterize key outcomes. Secondary analysis of 2009-2010 National Survey of CSHCN. Latent class analysis grouped individuals into substantively meaningful classes empirically derived from measures of pediatric medical complexity. Outcomes were compared among latent classes with weighted logistic or negative binomial regression. LCA identified four unique CSHCN subgroups: broad functional impairment (physical, cognitive, and mental health) with extensive health care (Class 1), broad functional impairment alone (Class 2), predominant physical impairment requiring family-delivered care (Class 3), and physical impairment alone (Class 4). CSHCN from Class 1 had the highest ED visit rates (IRR 3.3, p < .001) and hospitalization odds (AOR: 12.0, p < .001) and lowest odds of a medical home (AOR: 0.17, p < .001). CSHCN in Class 3, despite experiencing more shared decision making and medical home attributes, had more ED visits and missed school than CSHCN in Class 2 (p < .001); the latter, however, experienced more cost-related difficulties, care delays, and parents having to stop work (p < .001). Recognizing distinct impacts of cognitive and mental health impairments and health care delivery needs on CSHCN outcomes may better direct future intervention efforts. © Health Research and Educational Trust.

A Latent Class Analysis of Weight-Related Health Behaviors among 2- and 4-year College Students, and Associated Risk of Obesity

PubMed Central

Mathur, C; Stigler, M; Lust, K; Laska, M

2016-01-01

Little is known about the complex patterning of weight-related health behaviors in 2- and 4-year college students. The objective of this study was to identify and describe unique classes of weight-related health behaviors among college youth. Latent class analysis was used to identify homogenous, mutually exclusive classes of nine health behaviors which represent multiple theoretically/clinically relevant dimensions of obesity risk among 2- versus 4-year college students using cross-sectional statewide surveillance data (n= 17,584). Additionally, differences in class membership on selected sociodemographic characteristics were examined using a model-based approach. Analysis was conducted separately for both college groups, and 5 and 4 classes were identified for 2-and 4-year college students, respectively. Four classes were similar across 2-and 4-year college groups and were characterized as “mostly healthy dietary habits, active”, “moderately high screen time, active”, “moderately healthy dietary habits, inactive”, and “moderately high screen time, inactive”. “Moderately healthy dietary habits, high screen time” was the additional class unique to 2-year college students. These classes differed on a number of sociodemographic characteristics, including the proportion in each class who were classified as obese. Implications for prevention scientists and future intervention programs are considered. PMID:24990599

A latent class analysis of weight-related health behaviors among 2- and 4-year college students and associated risk of obesity.

PubMed

Mathur, Charu; Stigler, Melissa; Lust, Katherine; Laska, Melissa

2014-12-01

Little is known about the complex patterning of weight-related health behaviors in 2- and 4-year college students. The objective of this study was to identify and describe unique classes of weight-related health behaviors among college students. Latent class analysis was used to identify homogenous, mutually exclusive classes of nine health behaviors that represent multiple theoretically/clinically relevant dimensions of obesity risk among 2- versus 4-year college students using cross-sectional statewide surveillance data (N = 17,584). Additionally, differences in class membership on selected sociodemographic characteristics were examined using a model-based approach. Analysis was conducted separately for both college groups, and five and four classes were identified for 2- and 4-year college students, respectively. Four classes were similar across 2- and 4-year college groups and were characterized as "mostly healthy dietary habits, active"; "moderately high screen time, active"; "moderately healthy dietary habits, inactive"; and "moderately high screen time, inactive." "Moderately healthy dietary habits, high screen time" was the additional class unique to 2-year college students. These classes differed on a number of sociodemographic characteristics, including the proportion in each class who were classified as obese. Implications for prevention scientists and future intervention programs are considered. © 2014 Society for Public Health Education.

Anxiety, depression, and the suicidal spectrum: a latent class analysis of overlapping and distinctive features.

PubMed

Podlogar, Matthew C; Rogers, Megan L; Stanley, Ian H; Hom, Melanie A; Chiurliza, Bruno; Joiner, Thomas E

2017-03-20

Anxiety and depression diagnoses are associated with suicidal thoughts and behaviours. However, a categorical understanding of these associations limits insight into identifying dimensional mechanisms of suicide risk. This study investigated anxious and depressive features through a lens of suicide risk, independent of diagnosis. Latent class analysis of 97 depression, anxiety, and suicidality-related items among 616 psychiatric outpatients indicated a 3-class solution, specifically: (1) a higher suicide-risk class uniquely differentiated from both other classes by high reported levels of depression and anxious arousal; (2) a lower suicide-risk class that reported levels of anxiety sensitivity and generalised worry comparable to Class 1, but lower levels of depression and anxious arousal; and (3) a low to non-suicidal class that reported relatively low levels across all depression and anxiety measures. Discriminants of the higher suicide-risk class included borderline personality disorder; report of worthlessness, crying, and sadness; higher levels of anxious arousal and negative affect; and lower levels of positive affect. Depression and anxiety diagnoses were not discriminant between higher and lower suicide risk classes. This transdiagnostic and dimensional approach to understanding the suicidal spectrum contrasts with treating it as a depressive symptom, and illustrates the advantages of a tripartite model for conceptualising suicide risk.

Two-Year Predictive Validity of Conduct Disorder Subtypes in Early Adolescence: A Latent Class Analysis of a Canadian Longitudinal Sample

ERIC Educational Resources Information Center

Lacourse, Eric; Baillargeon, Raymond; Dupere, Veronique; Vitaro, Frank; Romano, Elisa; Tremblay, Richard

2010-01-01

Background: Investigating the latent structure of conduct disorder (CD) can help clarify how symptoms related to aggression, property destruction, theft, and serious violations of rules cluster in individuals with this disorder. Discovering homogeneous subtypes can be useful for etiologic, treatment, and prevention purposes depending on the…

Refining the Classification of Children with Selective Mutism: A Latent Profile Analysis

ERIC Educational Resources Information Center

Cohan, Sharon L.; Chavira, Denise A.; Shipon-Blum, Elisa; Hitchcock, Carla; Roesch, Scott C.; Stein, Murray B.

2008-01-01

The goal of this study was to develop an empirically derived classification system for selective mutism (SM) using parent-report measures of social anxiety, behavior problems, and communication delays. The sample consisted of parents of 130 children (ages 5-12) with SM. Results from latent profile analysis supported a 3-class solution made up of…

When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT

ERIC Educational Resources Information Center

Cheng, Ying

2009-01-01

Computerized adaptive testing (CAT) is a mode of testing which enables more efficient and accurate recovery of one or more latent traits. Traditionally, CAT is built upon Item Response Theory (IRT) models that assume unidimensionality. However, the problem of how to build CAT upon latent class models (LCM) has not been investigated until recently,…

Validity of the Special Needs Education Assessment Tool (SNEAT), a Newly Developed Scale for Children with Disabilities.

PubMed

Kohara, Aiko; Han, ChangWan; Kwon, HaeJin; Kohzuki, Masahiro

2015-11-01

The improvement of the quality of life (QOL) of children with disabilities has been considered important. Therefore, the Special Needs Education Assessment Tool (SNEAT) was developed based on the concept of QOL to objectively evaluate the educational outcome of children with disabilities. SNEAT consists of 11 items in three domains: physical functioning, mental health, and social functioning. This study aimed to verify the reliability and construct validity of SNEAT using 93 children collected from the classes on independent activities of daily living for children with disabilities in Okinawa Prefecture between October and November 2014. Survey data were collected in a longitudinal prospective cohort study. The reliability of SNEAT was verified via the internal consistency method and the test-pretest method; both the coefficient of Cronbach's α and the intra-class correlation coefficient were over 0.7. The validity of SNEAT was also verified via one-way repeated-measures ANOVA and the latent growth curve model. The scores of all the items and domains and the total scores obtained from one-way repeated-measures ANOVA were the same as the predicted scores. SNEAT is valid based on its goodness-of-fit values obtained using the latent growth curve model, where the values of comparative fit index (0.983) and root mean square error of approximation (0.062) were within the goodness-of-fit range. These results indicate that SNEAT has high reliability and construct validity and may contribute to improve QOL of children with disabilities in the classes on independent activities of daily living for children with disabilities.

Are depression and frailty overlapping syndromes in mid- and late-life? A latent variable analysis.

PubMed

Mezuk, Briana; Lohman, Matthew; Dumenci, Levent; Lapane, Kate L

2013-06-01

Depression and frailty both predict disability and morbidity in later life. However, it is unclear to what extent these common geriatric syndromes represent overlapping constructs. To examine the joint relationship between the constructs of depression and frailty. Data come from 2004-2005 wave of the Baltimore Epidemiologic Catchment Area Study, and the analysis is limited to participants 40 years and older, with complete data on frailty and depression indicators (N = 683). Depression was measured using the Diagnostic Interview Schedule, and frailty was indexed by modified Fried criteria. A series of confirmatory latent class analyses were used to assess the degree to which depression and frailty syndromes identify the same populations. A latent kappa coefficient (κl) was also estimated between the constructs. Confirmatory latent class analyses indicated that depression and frailty represent distinct syndromes rather than a single construct. The joint modeling of the two constructs supported a three-class solution for depression and two-class solution for frailty, with 2.9% categorized as severely depressed, 19.4% as mildly depressed, and 77.7% as not depressed, and 21.1% categorized as frail and 78.9% as not frail. The chance-corrected agreement statistic indicated moderate correspondence between the depression and frailty constructs (κl: 66, 95% confidence interval: 0.58-0.74). Results suggest that depression and frailty are interrelated concepts, yet their operational criteria identify substantively overlapping subpopulations. These findings have implications for understanding factors that contribute to the etiology and prognosis of depression and frailty in later life. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Thoughts of Death and Suicide in Early Adolescence

ERIC Educational Resources Information Center

Vander Stoep, Ann; McCauley, Elizabeth; Flynn, Cynthia; Stone, Andrea

2009-01-01

The prevalence and persistence of thoughts of death and suicide during early adolescence were estimated in a community-based cohort. A latent class approach was used to identify distinct subgroups based on endorsements to depression items administered repeatedly over 24 months. Two classes emerged, with 75% in a low ideation class across four…

Predictors of Latent Trajectory Classes of Physical Dating Violence Victimization

ERIC Educational Resources Information Center

Brooks-Russell, Ashley; Foshee, Vangie A.; Ennett, Susan T.

2013-01-01

This study identified classes of developmental trajectories of physical dating violence victimization from grades 8 to 12 and examined theoretically-based risk factors that distinguished among trajectory classes. Data were from a multi-wave longitudinal study spanning 8th through 12th grade (n = 2,566; 51.9 % female). Growth mixture models were…

Cognitive Performance in Older Adults with Stable Heart Failure: Longitudinal Evidence for Stability and Improvement

PubMed Central

Alosco, Michael L.; Garcia, Sarah; Spitznagel, Mary Beth; van Dulmen, Manfred; Cohen, Ronald; Sweet, Lawrence H.; Josephson, Richard; Hughes, Joel; Rosneck, Jim; Gunstad, John

2013-01-01

Cognitive impairment is prevalent in heart failure (HF), though substantial variability in the pattern of cognitive impairment is found across studies. To clarify the nature of cognitive impairment in HF, we examined longitudinal trajectories across multiple domains of cognition in HF patients using latent growth class modeling. 115 HF patients completed a neuropsychological battery at baseline, 3-months and 12-months. Participants also completed the Beck Depression Inventory-II (BDI-II). Latent class growth analyses revealed a three-class model for attention/executive function, four-class model for memory, and a three-class model for language. The slope for attention/executive function and language remained stable, while improvements were noted in memory performance. Education and BDI-II significantly predicted the intercept for attention/executive function and language abilities. The BDI-II also predicted baseline memory. The current findings suggest that multiple performance-based classes of neuropsychological test performance exist within cognitive domains, though case-controlled prospective studies with extended follow-ups are needed to fully elucidate changes and predictors of cognitive function in HF. PMID:23906182

ADVERSE CHILDHOOD EXPERIENCES AMONG YOUTH AGING OUT OF FOSTER CARE: A LATENT CLASS ANALYSIS

PubMed Central

Rebbe, Rebecca; Nurius, Paula S.; Ahrens, Kym R.; Courtney, Mark E.

2017-01-01

Research has demonstrated that youth who age out, or emancipate, from foster care face deleterious outcomes across a variety of domains in early adulthood. This article builds on this knowledge base by investigating the role of adverse childhood experience accumulation and composition on these outcomes. A latent class analysis was performed to identify three subgroups: Complex Adversity, Environmental Adversity, and Lower Adversity. Differences are found amongst the classes in terms of young adult outcomes in terms of socio-economic outcomes, psychosocial problems, and criminal behaviors. The results indicate that not only does the accumulation of adversity matter, but so does the composition of the adversity. These results have implications for policymakers, the numerous service providers and systems that interact with foster youth, and for future research. PMID:28458409

Older Parent – Child Relationships in Six Developed Nations: Comparisons at the Intersection of Affection and Conflict

PubMed Central

Silverstein, Merril; Gans, Daphna; Lowenstein, Ariela; Giarrusso, Roseann; Bengtson, Vern L.

2014-01-01

Intergenerational solidarity and ambivalence paradigms suggest that emotional relationships between generations consist of both positive and negative sentiments. We applied latent class analysis to measures of affection and conflict in 2,698 older parent – child relationships in 6 developed nations: England, Germany, Israel, Norway, Spain, and the United States (Southern California). The best fitting model consisted of 4 latent classes distributed differently across nations but with a cross-nationally invariant measurement structure. After controlling for demographics, health, coresidence, contact, and support, the following classes were overrepresented in corresponding nations: amicable (England), detached (Germany and Spain), disharmonious (United States), ambivalent (Israel). We discuss policy and cultural differences across societies that may explain why the prevalence of particular emotional types varied by nation. PMID:26203197

Older Parent - Child Relationships in Six Developed Nations: Comparisons at the Intersection of Affection and Conflict.

PubMed

Silverstein, Merril; Gans, Daphna; Lowenstein, Ariela; Giarrusso, Roseann; Bengtson, Vern L

2010-08-01

Intergenerational solidarity and ambivalence paradigms suggest that emotional relationships between generations consist of both positive and negative sentiments. We applied latent class analysis to measures of affection and conflict in 2,698 older parent - child relationships in 6 developed nations: England, Germany, Israel, Norway, Spain, and the United States (Southern California). The best fitting model consisted of 4 latent classes distributed differently across nations but with a cross-nationally invariant measurement structure. After controlling for demographics, health, coresidence, contact, and support, the following classes were overrepresented in corresponding nations: amicable (England), detached (Germany and Spain), disharmonious (United States), ambivalent (Israel). We discuss policy and cultural differences across societies that may explain why the prevalence of particular emotional types varied by nation.

Everyday Discrimination and Mood and Substance Use Disorders: A Latent Profile Analysis with African Americans and Caribbean Blacks

PubMed Central

Clark, Trenette T.; Salas-Wright, Christopher P.; Vaughn, Michael G.; Whitfield, Keith E.

2016-01-01

Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N = 4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. PMID:25254321

Classes of Physical Activity and Sedentary Behavior in 5th Grade Children

PubMed Central

Dowda, Marsha; Dishman, Rod K; Pate, Russell R.

2016-01-01

Objectives To identify classes of physical activity (PA) and sedentary behaviors (SB) in 5th grade children, associated factors, and trajectories of change into 7th grade. Methods This study included n=495 children (221 boys, 274 girls) who participated in the Transitions and Activity Changes in Kids (TRACK) Study. PA was assessed objectively and via self-report. Children, parents, and school administrators completed surveys to assess related factors. Latent class analysis, growth modeling, and adjusted multinomial logistic regression procedures were used to classify children based on self-reported PA and SB and examine associated factors. Results Three classes of behavior were identified: Class 1: Low PA/Low SB, Class 2: Moderate PA/High SB, and Class 3: High PA/High SB (boys) or Class 3: High PA (girls). Class 3 children had higher levels of self-efficacy (boys), and enjoyment, parental support, and physical activity equipment at home (girls). Class 2 boys and Class 3 girls did not experience decline in PA (accelerometer) over time. Conclusions Self-efficacy (boys) and home environment (girls) may play a role in shaping patterns of PA in children. Findings may help to inform future interventions to encourage children to meet national PA guidelines. PMID:27103414

Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis

PubMed Central

Sipsma, Heather L; Falb, Kathryn L; Willie, Tiara; Bradley, Elizabeth H; Bienkowski, Lauren; Meerdink, Ned; Gupta, Jhumka

2015-01-01

Objective To examine patterns of conflict-related violence and intimate partner violence (IPV) and their associations with emotional distress among Congolese refugee women living in Rwanda. Design Cross-sectional study. Setting Two Congolese refugee camps in Rwanda. Participants 548 ever-married Congolese refugee women of reproductive age (15–49 years) residing in Rwanda. Primary outcome measure Our primary outcome was emotional distress as measured using the Self-Report Questionnaire-20 (SRQ-20). For analysis, we considered participants with scores greater than 10 to be experiencing emotional distress and participants with scores of 10 or less not to be experiencing emotional distress. Results Almost half of women (49%) reported experiencing physical, emotional or sexual violence during the conflict, and less than 10% of women reported experiencing of any type of violence after fleeing the conflict. Lifetime IPV was reported by approximately 22% of women. Latent class analysis derived four distinct classes of violence experiences, including the Low All Violence class, the High Violence During Conflict class, the High IPV class and the High Violence During and After Conflict class. In multivariate regression models, latent class was strongly associated with emotional distress. Compared with women in the Low All Violence class, women in the High Violence During and After Conflict class and women in the High Violence During Conflict had 2.7 times (95% CI 1.11 to 6.74) and 2.3 times (95% CI 1.30 to 4.07) the odds of experiencing emotional distress in the past 4 weeks, respectively. Furthermore, women in the High IPV class had a 4.7 times (95% CI 2.53 to 8.59) greater odds of experiencing emotional distress compared with women in the Low All Violence class. Conclusions Experiences of IPV do not consistently correlate with experiences of conflict-related violence, and women who experience high levels of IPV may have the greatest likelihood for poor mental health in conflict-affected settings. PMID:25908672

Latent homeless risk profiles of a national sample of homeless veterans and their relation to program referral and admission patterns.

PubMed

Tsai, Jack; Kasprow, Wesley J; Rosenheck, Robert A

2013-12-01

We identified risk and need profiles of homeless veterans and examined the relation between profiles and referrals and admissions to Department of Veterans Affairs (VA) homeless service programs. We examined data from the VA's new Homeless Operations Management and Evaluation System on 120,852 veterans from 142 sites nationally in 2011 and 2012 using latent class analyses based on 9 homeless risk factors. The final 4-class solution compared both referral and admission to VA homeless services. We identified 4 latent classes: relatively few problems, dual diagnosis, poverty-substance abuse-incarceration, and disabling medical problems. Homeless veterans in the first group were more likely to be admitted to the VA's permanent supportive housing program, whereas those in the second group were more likely to be admitted to more restrictive VA residential treatment. Homeless veterans in the third group were more likely to be admitted to the VA's prisoner re-entry program, and those in the fourth group were more likely to be directed to VA medical services. The heterogeneous risk and need profiles of homeless veterans supported the diversity of VA homeless services and encouraged the development of specialized services to meet their diverse needs.

Childhood personality types: vulnerability and adaptation over time.

PubMed

De Clercq, Barbara; Rettew, David; Althoff, Robert R; De Bolle, Marleen

2012-06-01

  Substantial evidence suggests that a Five-Factor Model personality assessment generates a valid description of childhood individual differences and relates to a range of psychological outcomes. Less is known, however, about naturally occurring profiles of personality and their links to psychopathology. The current study explores whether childhood personality characteristics tend to cluster in particular personality profiles that show unique associations with psychopathology and quality of life across time.   Latent class analysis was conducted on maternal rated general personality of a Flemish childhood community sample (N = 477; mean age 10.6 years). The associations of latent class membership probability with psychopathology and quality of life 2 years later were examined, using a multi-informant perspective.   Four distinguishable latent classes were found, representing a Moderate, a Protected, an Undercontrolled and a Vulnerable childhood personality type. Each of these types showed unique associations with childhood outcomes across raters.   Four different personality types can be delineated at young age and have a significant value in understanding vulnerability and adaptation over time. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

Longitudinal patterns of gambling activities and associated risk factors in college students

PubMed Central

Goudriaan, Anna E.; Slutske, Wendy S.; Krull, Jennifer L.; Sher, Kenneth J.

2009-01-01

Aims To investigate which clusters of gambling activities exist within a longitudinal study of college health, how membership in gambling clusters change over time and whether particular clusters of gambling are associated with unhealthy risk behaviour. Design Four-year longitudinal study (2002–2006). Setting Large, public university. Participants Undergraduate college students. Measurements Ten common gambling activities were measured during 4 consecutive college years (years 1–4). Clusters of gambling activities were examined using latent class analyses. Relations between gambling clusters and gender, Greek membership, alcohol use, drug use, personality indicators of behavioural undercontrol and psychological distress were examined. Findings Four latent gambling classes were identified: (1) a low-gambling class, (2) a card gambling class, (3) a casino/slots gambling class and (4) an extensive gambling class. Over the first college years a high probability of transitioning from the low-gambling class and the card gambling class into the casino/slots gambling class was present. Membership in the card, casino/slots and extensive gambling classes were associated with higher scores on alcohol/drug use, novelty seeking and self-identified gambling problems compared to the low-gambling class. The extensive gambling class scored higher than the other gambling classes on risk factors. Conclusions Extensive gamblers and card gamblers are at higher risk for problem gambling and other risky health behaviours. Prospective examinations of class membership suggested that being in the extensive and the low gambling classes was highly stable across the 4 years of college. PMID:19438422

RN jurisdiction over nursing care systems in nursing homes: application of latent class analysis

PubMed Central

Corazzini, Kirsten N.; Anderson, Ruth A.; Mueller, Christine; Thorpe, Joshua M.; McConnell, Eleanor S.

2015-01-01

Background In the context of declining registered nurse (RN) staffing levels in nursing homes, professional nursing jurisdiction over nursing care systems may erode. Objectives The purpose of this study is to develop a typology of professional nursing jurisdiction in nursing homes in relation to characteristics of RN staffing, drawing upon Abbott's (1988) tasks and jurisdictions framework. Method The study was a cross-sectional, observational study using the 2004 National Nursing Home Survey (N=1,120 nursing homes). Latent class analysis tested whether RN staffing indicators differentiated facilities in a typology of RN jurisdiction, and compared classes on key organizational environment characteristics. Multiple logistic regression analysis related the emergent classes to presence or absence of specialty care programs in 8 clinical areas. Results Three classes of capacity for jurisdiction were identified, including ‘low capacity’ (41% of homes) with low probabilities of having any indicators of RN jurisdiction, ‘mixed capacity’ (26% of homes) with moderate to high probabilities of having higher RN education and staffing levels, and ‘high capacity’ (32% of homes) with moderate to high probabilities of having almost all indicators of RN jurisdiction. ‘High capacity’ homes were more likely to have specialty care programs relative to ‘low capacity’ homes; such homes were less likely to be chain-owned, and more likely to be larger, provide higher technical levels of patient care, have unionized nursing assistants, have a lower ratio of LPNs to RNs, and a higher education level of the administrator. Discussion Findings provide preliminary support for the theoretical framework as a starting point to move beyond extensive reliance on staffing levels and mix as indicators of quality. Further, findings indicate the importance of RN specialty certification. PMID:22166907

Latent Culture as a Force for Change and the Change Process in Operation.

ERIC Educational Resources Information Center

Banfield, Beryle

The purpose of this study was to apply a theory of latent culture to describe the role of middle class black parents and students in effecting change in an elite educational organization and to use Schein's conceptual model of the Kurk Lewin paradigm of the change process (Unfreezing--Changing--Refreezing) to analyze this process over a three year…

An introduction to mixture item response theory models.

PubMed

De Ayala, R J; Santiago, S Y

2017-02-01

Mixture item response theory (IRT) allows one to address situations that involve a mixture of latent subpopulations that are qualitatively different but within which a measurement model based on a continuous latent variable holds. In this modeling framework, one can characterize students by both their location on a continuous latent variable as well as by their latent class membership. For example, in a study of risky youth behavior this approach would make it possible to estimate an individual's propensity to engage in risky youth behavior (i.e., on a continuous scale) and to use these estimates to identify youth who might be at the greatest risk given their class membership. Mixture IRT can be used with binary response data (e.g., true/false, agree/disagree, endorsement/not endorsement, correct/incorrect, presence/absence of a behavior), Likert response scales, partial correct scoring, nominal scales, or rating scales. In the following, we present mixture IRT modeling and two examples of its use. Data needed to reproduce analyses in this article are available as supplemental online materials at http://dx.doi.org/10.1016/j.jsp.2016.01.002. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Latent Class Analysis of Antisocial Behavior: Interaction of Serotonin Transporter Genotype and Maltreatment

PubMed Central

Li, James J.

2010-01-01

To improve understanding about genetic and environmental influences on antisocial behavior (ASB), we tested the association of the 44-base pair polymorphism of the serotonin transporter gene (5-HTTLPR) and maltreatment using latent class analysis in 2,488 boys and girls from Wave 1 of the National Longitudinal Study of Adolescent Health. In boys, ASB was defined by three classes (Exclusive Covert, Mixed Covert and Overt, and No Problems) whereas in girls, ASB was defined by two classes (Exclusive Covert, No Problems). In boys, 5-HTTLPR and maltreatment were not significantly related to ASB. However, in girls, maltreatment, but not 5-HTTLPR, was significantly associated with ASB. A significant interaction between 5-HTTLPR and maltreatment was also observed, where maltreated girls homozygous for the short allele were 12 times more likely to be classified in the Exclusive Covert group than in the No Problems group. Structural differences in the latent structure of ASB at Wave 2 and Wave 3 prevented repeat LCA modeling. However, using counts of ASB, 5-HTTLPR, maltreatment, and its interaction were unrelated to overt and covert ASB at Wave 2 and only maltreatment was related to covert ASB at Wave 3. We discuss these findings within the context of sex differences in ASB and relevant models of gene-environment interplay across developmental periods. PMID:20405199

Using latent class analysis to identify academic and behavioral risk status in elementary students.

PubMed

King, Kathleen R; Lembke, Erica S; Reinke, Wendy M

2016-03-01

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in the areas of reading, mathematics, and behavior were used as indicators of success on an end of year statewide achievement test. Results identified 3 subclasses of children, including a class with minimal academic and behavioral concerns (Tier 1; 32% of the sample), a class at-risk for academic problems and somewhat at-risk for behavior problems (Tier 2; 37% of the sample), and a class with significant academic and behavior problems (Tier 3; 31%). Each class was predictive of end of year performance on the statewide achievement test, with the Tier 1 class performing significantly higher on the test than the Tier 2 class, which in turn scored significantly higher than the Tier 3 class. The results of this study indicated that distinct classes of children can be determined through brief screening measures and are predictive of later academic success. Further implications are discussed for prevention and intervention for students at risk for academic failure and behavior problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Clinical classes of injured workers with chronic low back pain: a latent class analysis with relationship to working status.

PubMed

Carlesso, Lisa C; Raja Rampersaud, Y; Davis, Aileen M

2018-01-01

To determine (a) clinical classes of injured workers with chronic low back pain (CLBP), (b) predictors of class membership and (c) associations of classes with baseline work status. Patients with CLBP from a tertiary care outpatient clinic in Toronto, Canada were sampled. Latent class analysis was applied to determine class structure using physical, psychological and coping indicators. Classes were interpreted by class-specific means and analyzed for predictors of membership. Lastly, association of the classes with being off work was modeled. A 3-class model was chosen based on fit criteria, theoretical and clinical knowledge of this population. The resultant 3 classes represented low, moderate and high levels of clinical severity. Predictors of being in the high severity group compared to the low severity group were < high school education [odds ratio (OR) 3.06, 95% CI (1.47, 6.37)] and comorbidity total [OR 1.28, 95% CI (1.03, 1.59)]. High severity class membership was associated with four times increased risk of being off work at baseline compared to those in the low severity group [OR 3.98, 95% CI (1.61, 6.34)]. In a cohort of injured workers with CLBP, 3 clinical classes were identified with distinct psychological and physical profiles. These profiles are useful in aiding clinicians to identify patients of high clinical severity who may be potentially at risk for problematic return to work.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Simple construct evaluation with latent class analysis: An investigation of Facebook addiction and the development of a short form of the Facebook Addiction Test (F-AT).

PubMed

Dantlgraber, Michael; Wetzel, Eunike; Schützenberger, Petra; Stieger, Stefan; Reips, Ulf-Dietrich

2016-09-01

In psychological research, there is a growing interest in using latent class analysis (LCA) for the investigation of quantitative constructs. The aim of this study is to illustrate how LCA can be applied to gain insights on a construct and to select items during test development. We show the added benefits of LCA beyond factor-analytic methods, namely being able (1) to describe groups of participants that differ in their response patterns, (2) to determine appropriate cutoff values, (3) to evaluate items, and (4) to evaluate the relative importance of correlated factors. As an example, we investigated the construct of Facebook addiction using the Facebook Addiction Test (F-AT), an adapted version of the Internet Addiction Test (I-AT). Applying LCA facilitates the development of new tests and short forms of established tests. We present a short form of the F-AT based on the LCA results and validate the LCA approach and the short F-AT with several external criteria, such as chatting, reading newsfeeds, and posting status updates. Finally, we discuss the benefits of LCA for evaluating quantitative constructs in psychological research.

Thiopurine S-methyltransferase testing for averting drug toxicity: a meta-analysis of diagnostic test accuracy

PubMed Central

Zur, RM; Roy, LM; Ito, S; Beyene, J; Carew, C; Ungar, WJ

2016-01-01

Thiopurine S-methyltransferase (TPMT) deficiency increases the risk of serious adverse events in persons receiving thiopurines. The objective was to synthesize reported sensitivity and specificity of TPMT phenotyping and genotyping using a latent class hierarchical summary receiver operating characteristic meta-analysis. In 27 studies, pooled sensitivity and specificity of phenotyping for deficient individuals was 75.9% (95% credible interval (CrI), 58.3–87.0%) and 98.9% (96.3–100%), respectively. For genotype tests evaluating TPMT*2 and TPMT*3, sensitivity and specificity was 90.4% (79.1–99.4%) and 100.0% (99.9–100%), respectively. For individuals with deficient or intermediate activity, phenotype sensitivity and specificity was 91.3% (86.4–95.5%) and 92.6% (86.5–96.6%), respectively. For genotype tests evaluating TPMT*2 and TPMT*3, sensitivity and specificity was 88.9% (81.6–97.5%) and 99.2% (98.4–99.9%), respectively. Genotyping has higher sensitivity as long as TPMT*2 and TPMT*3 are tested. Both approaches display high specificity. Latent class meta-analysis is a useful method for synthesizing diagnostic test performance data for clinical practice guidelines. PMID:27217052

Reasons for Marijuana Use Among Young Adults and Long-Term Associations With Marijuana Use and Problems

PubMed Central

Patrick, Megan E.; Bray, Bethany C.; Berglund, Patricia A.

2016-01-01

Objective: This study examines reasons for marijuana use among young adults age 19/20 in the United States and the extent to which patterns of reasons are associated with marijuana use and problems 15 years later. Method: The national Monitoring the Future study provided data on marijuana users at age 19/20 who were also surveyed at age 35 (n = 2,288; 50% women; 83% White). Latent class analysis was used to identify distinct patterns of reasons for marijuana use, which were then used as predictors of later marijuana use and problems. Results: Five latent classes described the following patterns of reasons for marijuana use at age 19/20: Experimental, Get High + Relax, Typical, Typical + Escape, and Coping + Drug Use. Highest risk for later marijuana use and problems was found for people with Coping + Drug Use and Get High + Relax reasons in young adulthood; those with Experimental reasons were at lowest risk for later use or problems. Conclusions: Coping and getting high emerged as strong predictors of later marijuana use and problems. Results support the predictive value of self-reported reasons for using marijuana among young adults. PMID:27797689

Individual Differences in Toddlers' Prosociality: Experiences in Early Relationships Explain Variability in Prosocial Behavior.

PubMed

Newton, Emily K; Thompson, Ross A; Goodman, Miranda

2016-11-01

Latent class logistic regression analysis was used to investigate sources of individual differences in profiles of prosocial behavior. Eighty-seven 18-month-olds were observed in tasks assessing sharing with a neutral adult, instrumentally helping a neutral adult, and instrumentally helping a sad adult. Maternal mental state language (MSL) and maternal sensitivity were also assessed. Despite differing motivational demands across tasks, we found consistency in children's prosocial behavior with three latent classes: no prosocial behavior, moderate prosocial behavior, and frequent instrumental helping across emotional situations. Maternal sensitivity, MSL, and their interaction predicted toddlers' membership in the classes. These findings evidence moderate consistency in early prosocial behaviors and suggest that these capacities are motivated in early relationships with caregivers. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

HIV-related sexual risk behavior among African American adolescent girls.

PubMed

Danielson, Carla Kmett; Walsh, Kate; McCauley, Jenna; Ruggiero, Kenneth J; Brown, Jennifer L; Sales, Jessica M; Rose, Eve; Wingood, Gina M; Diclemente, Ralph J

2014-05-01

Latent class analysis (LCA) is a useful statistical tool that can be used to enhance understanding of how various patterns of combined sexual behavior risk factors may confer differential levels of HIV infection risk and to identify subtypes among African American adolescent girls. Data for this analysis is derived from baseline assessments completed prior to randomization in an HIV prevention trial. Participants were African American girls (n=701) aged 14-20 years presenting to sexual health clinics. Girls completed an audio computer-assisted self-interview, which assessed a range of variables regarding sexual history and current and past sexual behavior. Two latent classes were identified with the probability statistics for the two groups in this model being 0.89 and 0.88, respectively. In the final multivariate model, class 1 (the "higher risk" group; n=331) was distinguished by a higher likelihood of >5 lifetime sexual partners, having sex while high on alcohol/drugs, less frequent condom use, and history of sexually transmitted diseases (STDs), when compared with class 2 (the "lower risk" group; n=370). The derived model correctly classified 85.3% of participants into the two groups and accounted for 71% of the variance in the latent HIV-related sexual behavior risk variable. The higher risk class also had worse scores on all hypothesized correlates (e.g., self-esteem, history of sexual assault or physical abuse) relative to the lower risk class. Sexual health clinics represent a unique point of access for HIV-related sexual risk behavior intervention delivery by capitalizing on contact with adolescent girls when they present for services. Four empirically supported risk factors differentiated higher versus lower HIV risk. Replication of these findings is warranted and may offer an empirical basis for parsimonious screening recommendations for girls presenting for sexual healthcare services.

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

PubMed

Chen, Chi-Kan

2017-07-26

The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two-step algorithms can potentially incorporate with different nonlinear differential equation models to reconstruct the GRN.

Academic and Social Functioning Associated with Attention-Deficit/Hyperactivity Disorder: Latent Class Analyses of Trajectories from Kindergarten to Fifth Grade.

PubMed

DuPaul, George J; Morgan, Paul L; Farkas, George; Hillemeier, Marianne M; Maczuga, Steve

2016-10-01

Children with attention-deficit/hyperactivity disorder (ADHD) are known to exhibit significantly lower academic and social functioning than other children. Yet the field currently lacks knowledge about specific impairment trajectories experienced by children with ADHD, which may constrain early screening and intervention effectiveness. Data were analyzed from a nationally representative U.S. cohort in the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) for 590 children (72.7 % male) whose parents reported a formal diagnosis of ADHD. Children's math, reading, and interpersonal skills were assessed at 5 time points between kindergarten and fifth grade. Growth mixture model analyses indicated 4 latent trajectory classes for reading, 8 classes for math, and 4 classes for interpersonal skills. Membership in reading and math trajectory classes was strongly related; overlaps with interpersonal skills classes were weaker. Trajectory class membership was correlated with demographic characteristics and behavioral functioning. Children with ADHD display substantial heterogeneity in their reading, math, and interpersonal growth trajectories, with some groups of children especially likely to display relatively severe levels of academic and social impairment over time. Early screening and intervention to address impairment, particularly reading difficulties, among kindergarten students with ADHD is warranted.

Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.

PubMed

Klijn, Sven L; Weijenberg, Matty P; Lemmens, Paul; van den Brandt, Piet A; Lima Passos, Valéria

2017-10-01

Background and objective Group-based trajectory modelling is a model-based clustering technique applied for the identification of latent patterns of temporal changes. Despite its manifold applications in clinical and health sciences, potential problems of the model selection procedure are often overlooked. The choice of the number of latent trajectories (class-enumeration), for instance, is to a large degree based on statistical criteria that are not fail-safe. Moreover, the process as a whole is not transparent. To facilitate class enumeration, we introduce a graphical summary display of several fit and model adequacy criteria, the fit-criteria assessment plot. Methods An R-code that accepts universal data input is presented. The programme condenses relevant group-based trajectory modelling output information of model fit indices in automated graphical displays. Examples based on real and simulated data are provided to illustrate, assess and validate fit-criteria assessment plot's utility. Results Fit-criteria assessment plot provides an overview of fit criteria on a single page, placing users in an informed position to make a decision. Fit-criteria assessment plot does not automatically select the most appropriate model but eases the model assessment procedure. Conclusions Fit-criteria assessment plot is an exploratory, visualisation tool that can be employed to assist decisions in the initial and decisive phase of group-based trajectory modelling analysis. Considering group-based trajectory modelling's widespread resonance in medical and epidemiological sciences, a more comprehensive, easily interpretable and transparent display of the iterative process of class enumeration may foster group-based trajectory modelling's adequate use.

How 'core' are motor timing difficulties in ADHD? A latent class comparison of pure and comorbid ADHD classes.

PubMed

van der Meer, Jolanda M J; Hartman, Catharina A; Thissen, Andrieke J A M; Oerlemans, Anoek M; Luman, Marjolein; Buitelaar, Jan K; Rommelse, Nanda N J

2016-04-01

Children with attention-deficit/hyperactivity disorder (ADHD) have motor timing difficulties. This study examined whether affected motor timing accuracy and variability are specific for ADHD, or that comorbidity with autism spectrum disorders (ASD) contributes to these motor timing difficulties. An 80-trial motor timing task measuring accuracy (μ), variability (σ) and infrequent long response times (τ) in estimating a 1-s interval was administered to 283 children and adolescents (8-17 years) from both a clinic and population based sample. They were divided into four latent classes based on the SCQ and L data. These classes were: without behavioral problems 'Normal-class' (n = 154), with only ADHD symptoms 'ADHD-class' (n = 49), and two classes with both ASD and ADHD symptoms; ADHD(+ASD)-class (n = 39) and ASD(+ADHD)-class (n = 41). The pure ADHD-class did not deviate from the Normal class on any of the motor timing measures (mean RTs 916 and 925 ms, respectively). The comorbid ADHD(+ASD) and ASD(+ADHD) classes were significantly less accurate (more time underestimations) compared to the Normal class (mean RTs 847 and 870 ms, respectively). Variability in motor timing was reduced in the younger children in the ADHD(+ASD) class, which may reflect a tendency to rush the tedious task. Only patients with more severe behavioral symptoms show motor timing deficiencies. This cannot merely be explained by high ADHD severity with ASD playing no role, as ADHD symptom severity in the pure ADHD-class and the ASD(+ADHD) class was highly similar, with the former class showing no motor timing deficits.

The Effects of Cumulative Victimization on Mental Health Among Lesbian, Gay, Bisexual, and Transgender Adolescents and Young Adults

PubMed Central

Andrews, Rebecca; Puckett, Jae A.

2016-01-01

Objectives. To examine the effects of the cumulative victimization experienced by lesbian, gay, bisexual, and transgender youths on mental disorders. Methods. We recruited 248 participants from the Chicago, Illinois, area in 7 waves of data collected over 4 years, beginning in 2007 (83.1% retention rate). Mean age at enrollment was 18.7 years, and 54.7% were Black. We measured depression and posttraumatic stress disorder using structured psychiatric interviews. Results. Latent class analyses of victimization over time identified a 4-class solution. Class 1 (65.4%) had low, decreasing victimization. Class 2 (10.3%) had moderate, increasing victimization. Class 3 (5.1%) had high, steady victimization. Class 4 (19.2%) had high, decreasing victimization. Controlling for baseline diagnoses and birth sex, lesbian, gay, bisexual, and transgender youths in classes 2 and 3 were at higher risk for depression than were those in class 1; youths in classes 2, 3, and 4 were at elevated risk for posttraumatic stress disorder. Conclusions. Lesbian, gay, bisexual, and transgender youths with steadily high or increasing levels of victimization from adolescence to early adulthood are at higher risk for depression and posttraumatic stress disorder. PMID:26794175

Multiple Service System Involvement and Later Offending Behavior: Implications for Prevention and Early Intervention.

PubMed

Bright, Charlotte Lyn; Jonson-Reid, Melissa

2015-07-01

We investigated patterns of childhood and adolescent experiences that correspond to later justice system entry, including persistence into adulthood, and explored whether timing of potential supports to the child or onset of family poverty, according to developmental periods and gender, would distinguish among latent classes. We constructed a database containing records for 8587 youths from a Midwestern metropolitan region, born between 1982 and 1991, with outcomes. We used data from multiple publicly funded systems (child welfare, income maintenance, juvenile and criminal justice, mental health, Medicaid, vital statistics). We applied a latent class analysis and interpreted a 7-class model. Classes with higher rates of offending persisting into adulthood were characterized by involvement with multiple publicly funded systems in childhood and adolescence, with the exception of 1 less-urban, predominantly female class that had similarly high system involvement coupled with lower rates of offending. Poverty and maltreatment appear to play a critical role in offending trajectories. Identifying risk factors that cluster together may help program and intervention staff best target those most in need of more intensive intervention.

A Repeated Trajectory Class Model for Intensive Longitudinal Categorical Outcome

PubMed Central

Lin, Haiqun; Han, Ling; Peduzzi, Peter N.; Murphy, Terrence E.; Gill, Thomas M.; Allore, Heather G.

2014-01-01

This paper presents a novel repeated latent class model for a longitudinal response that is frequently measured as in our prospective study of older adults with monthly data on activities of daily living (ADL) for more than ten years. The proposed method is especially useful when the longitudinal response is measured much more frequently than other relevant covariates. The repeated trajectory classes represent distinct temporal patterns of the longitudinal response wherein an individual’s membership in the trajectory classes may renew or change over time. Within a trajectory class, the longitudinal response is modeled by a class-specific generalized linear mixed model. Effectively, an individual may remain in a trajectory class or switch to another as the class membership predictors are updated periodically over time. The identification of a common set of trajectory classes allows changes among the temporal patterns to be distinguished from local fluctuations in the response. An informative event such as death is jointly modeled by class-specific probability of the event through shared random effects. We do not impose the conditional independence assumption given the classes. The method is illustrated by analyzing the change over time in ADL trajectory class among 754 older adults with 70500 person-months of follow-up in the Precipitating Events Project. We also investigate the impact of jointly modeling the class-specific probability of the event on the parameter estimates in a simulation study. The primary contribution of our paper is the periodic updating of trajectory classes for a longitudinal categorical response without assuming conditional independence. PMID:24519416

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis.

PubMed

Terhune, Devin Blair

2015-05-01

High hypnotic suggestibility is a heterogeneous condition and there is accumulating evidence that highly suggestible individuals may be comprised of discrete subtypes with dissimilar cognitive and phenomenological profiles. This study applied latent profile analysis to response patterns on a diverse battery of difficult hypnotic suggestions in a sample of individuals in the upper range of hypnotic suggestibility. Comparisons among models indicated that a four-class model was optimal. One class was comprised of very highly suggestible (virtuoso) participants, two classes included highly suggestible participants who were alternately more responsive to inhibitory cognitive suggestions or posthypnotic amnesia suggestions, and the fourth class consisted primarily of medium suggestible participants. These results indicate that there are discrete response profiles in high hypnotic suggestibility. They further provide a number of insights regarding the optimization of hypnotic suggestibility measurement and have implications for the instrumental use of hypnosis for the modeling of different psychological conditions. Copyright © 2015 Elsevier Inc. All rights reserved.

Consequences of Misspecifying the Number of Latent Treatment Attendance Classes in Modeling Group Membership Turnover within Ecologically-Valid Behavioral Treatment Trials

PubMed Central

Morgan-Lopez, Antonio A.; Fals-Stewart, William

2015-01-01

Historically, difficulties in analyzing treatment outcome data from open enrollment groups have led to their avoidance in use in federally-funded treatment trials, despite the fact that 79% of treatment programs use open enrollment groups. Recently, latent class pattern mixture models (LCPMM) have shown promise as a defensible approach for making overall (and attendance class-specific) inferences from open enrollment groups with membership turnover. We present a statistical simulation study comparing LCPMMs to longitudinal growth models (LGM) to understand when both frameworks are likely to produce conflicting inferences concerning overall treatment efficacy. LCPMMs performed well under all conditions examined; meanwhile LGMs produced problematic levels of bias and Type I errors under two joint conditions: moderate-to-high dropout (30–50%) and treatment by attendance class interactions exceeding Cohen's d ≈.2. This study highlights key concerns about using LGM for open enrollment data: treatment effect overestimation and advocacy for treatments that may be ineffective in reality. PMID:18513917

Sleep schedules and school performance in Indigenous Australian children.

PubMed

Blunden, Sarah; Magee, Chris; Attard, Kelly; Clarkson, Larissa; Caputi, Peter; Skinner, Timothy

2018-04-01

Sleep duration and sleep schedule variability have been related to negative health and well-being outcomes in children, but little is known about Australian Indigenous children. Data for children aged 7-9 years came from the Australian Longitudinal Study of Indigenous Children and the National Assessment Program-Literacy and Numeracy (NAPLAN). Latent class analysis determined sleep classes taking into account sleep duration, bedtimes, waketimes, and variability in bedtimes from weekdays to weekends. Regression models tested whether the sleep classes were cross-sectionally associated with grade 3 NAPLAN scores. Latent change score modeling then examined whether the sleep classes predicted changes in NAPLAN performance from grades 3 to 5. Five sleep schedule classes were identified: normative sleep, early risers, long sleep, variable sleep, and short sleep. Overall, long sleepers performed best, with those with reduced sleep (short sleepers and early risers) performing the worse on grammar, numeracy, and writing performance. Latent change score results also showed that long sleepers performed best in spelling and writing and short sleepers and typical sleepers performed the worst over time. In this sample of Australian Indigenous children, short sleep was associated with poorer school performance compared with long sleep, with this performance worsening over time for some performance indicators. Other sleep schedules (eg, early wake times and variable sleep) also had some relationships with school performance. As sleep scheduling is modifiable, this offers opportunity for improvement in sleep and thus performance outcomes for these and potentially all children. Copyright © 2018 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Opioid withdrawal, craving, and use during and after outpatient buprenorphine stabilization and taper: A discrete survival and growth mixture model

PubMed Central

Stotts, Angela L.; Green, Charles; Potter, Jennifer S.; Marino, Elise N.; Walker, Robrina; Weiss, Roger D.; Trivedi, Madhukar

2014-01-01

Most patients relapse to opioids within one month of opioid agonist detoxification, making the antecedents and parallel processes of first use critical for investigation. Craving and withdrawal are often studied in relationship to opioid outcomes, and a novel analytic strategy applied to these two phenomena may indicate targeted intervention strategies. Specifically, this secondary data analysis of the Prescription Opioid Addiction Treatment Study used a discrete-time mixture analysis with time-to-first opioid use (survival) simultaneously predicted by craving and withdrawal growth trajectories. This analysis characterized heterogeneity among prescription opioid-dependent individuals (N=653) into latent classes (i.e., latent class analysis [LCA]) during and after buprenorphine/naloxone stabilization and taper. A 4-latent class solution was selected for overall model fit and clinical parsimony. In order of shortest to longest time-to-first use, the 4 classes were characterized as 1) high craving and withdrawal 2) intermediate craving and withdrawal 3) high initial craving with low craving and withdrawal trajectories and 4) a low initial craving with low craving and withdrawal trajectories. Odds ratio calculations showed statistically significant differences in time-to-first use across classes. Generally, participants with lower baseline levels and greater decreases in craving and withdrawal during stabilization combined with slower craving and withdrawal rebound during buprenorphine taper remained opioid-free longer. This exploratory work expanded on the importance of monitoring craving and withdrawal during buprenorphine induction, stabilization, and taper. Future research may allow individually tailored and timely interventions to be developed to extend time-to-first opioid use. PMID:25282598

Everyday discrimination and mood and substance use disorders: a latent profile analysis with African Americans and Caribbean Blacks.

PubMed

Clark, Trenette T; Salas-Wright, Christopher P; Vaughn, Michael G; Whitfield, Keith E

2015-01-01

Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N=4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. Copyright © 2014 Elsevier Ltd. All rights reserved.

Patterns of Dating Violence Perpetration and Victimization in U.S. Young Adult Males and Females.

PubMed

Spencer, Rachael A; Renner, Lynette M; Clark, Cari Jo

2016-09-01

Dating violence (DV) is frequently reported by young adults in intimate relationships in the United States, but little is known about patterns of DV perpetration and victimization. In this study, we examined sexual and physical violence perpetration and victimization reported by young adults to determine how the violence patterns differ by sex and race/ethnicity. Data from non-Hispanic White, non-Hispanic Black, and Hispanic participants in Wave 3 of the National Longitudinal Study of Adolescent to Adult Health were analyzed. DV was assessed using responses to four questions focused on perpetration and four questions focused on victimization. The information on DV was taken from the most violent relationship reported by participants prior to Wave 3. Latent class analysis was first conducted separately by sex, adjusting for age, race/ethnicity, and financial stress, then by race/ethnicity, adjusting for age and financial stress. Relative model fit was established by comparing Bayesian Information Criteria (BIC), adjusted BIC, entropy, interpretability of latent classes, and certainty of latent class assignment for covariate-adjusted models. The results indicate that patterns of violence differed by sex and for females, by race/ethnicity. A three-class model was the best fit for males. For females, separate four-class models were parsimonious for White, Black, and Hispanic females. Financial stress was a significant predictor of violence classification for males and females and age predicted membership in White and Black female models. Variations in DV patterns by sex and race/ethnicity suggest the need for a more nuanced understanding of differences in DV. © The Author(s) 2015.

Resveratrol Reactivates Latent HIV through Increasing Histone Acetylation and Activating Heat Shock Factor 1.

PubMed

Zeng, Xiaoyun; Pan, Xiaoyan; Xu, Xinfeng; Lin, Jian; Que, Fuchang; Tian, Yuanxin; Li, Lin; Liu, Shuwen

2017-06-07

The persistence of latent HIV reservoirs presents a significant challenge to viral eradication. Effective latency reversing agents (LRAs) based on "shock and kill" strategy are urgently needed. The natural phytoalexin resveratrol has been demonstrated to enhance HIV gene expression, although its mechanism remains unclear. In this study, we demonstrated that resveratrol was able to reactivate latent HIV without global T cell activation in vitro. Mode of action studies showed resveratrol-mediated reactivation from latency did not involve the activation of silent mating type information regulation 2 homologue 1 (SIRT1), which belonged to class-3 histone deacetylase (HDAC). However, latent HIV was reactivated by resveratrol mediated through increasing histone acetylation and activation of heat shock factor 1 (HSF1). Additionally, synergistic activation of the latent HIV reservoirs was observed under cotreatment with resveratrol and conventional LRAs. Collectively, this research reveals that resveratrol is a natural LRA and shows promise for HIV therapy.

Cognition and Literacy in English Language Learners at Risk for Reading Disabilities: A Latent Transition Analysis

ERIC Educational Resources Information Center

Swanson, H. Lee; Kudo, Milagros; Guzman-Orth, Danielle

2016-01-01

This study investigated the prevalence and stability of latent classes at risk for reading disabilities (RD) in elementary-aged children whose first language is Spanish. To this end, children (N = 489) in Grades 1, 2, and 3 at Wave 1 were administered a battery of reading, vocabulary, and cognitive measures (short-term memory [STM], working memory…

Dissecting and Targeting Latent Metastasis

DTIC Science & Technology

2015-09-01

distinct class of stem-like cancer cells , which primed to enter quiescence and evade innate immunity after infiltrating distant organs. LCC cells express...state and actively silencing WNT signaling, LCC cells can enter quiescence and evade innate immunity to remain latent for extended periods. These...mutation in Foxn1 renders the mice athymic, severely blunting the maturation of effector T cells but preserving innate immunity components including

On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2011-01-01

Cognitive diagnostic models (CDMs) attempt to uncover latent skills or attributes that examinees must possess in order to answer test items correctly. The DINA (deterministic input, noisy "and") model is a popular CDM that has been widely used. It is shown here that a logistic version of the model can easily be fit with standard software for…

What Matters for Excellence in PhD Programs? Latent Constructs of Doctoral Program Quality Used by Early Career Social Scientists

ERIC Educational Resources Information Center

Morrison, Emory; Rudd, Elizabeth; Zumeta, William; Nerad, Maresi

2011-01-01

This paper unpacks how social science doctorate-holders come to evaluate overall excellence in their PhD training programs based on their domain-specific assessments of aspects of their programs. Latent class analysis reveals that social scientists 6-10 years beyond their PhD evaluate the quality of their doctoral program with one of two…

Latent Profile Analysis and Conversion to Psychosis: Characterizing Subgroups to Enhance Risk Prediction.

PubMed

Healey, Kristin M; Penn, David L; Perkins, Diana; Woods, Scott W; Keefe, Richard S E; Addington, Jean

2018-02-15

Groups at clinical high risk (CHR) of developing psychosis are heterogeneous, composed of individuals with different clusters of symptoms. It is likely that there exist subgroups, each associated with different symptom constellations and probabilities of conversion. Present study used latent profile analysis (LPA) to ascertain subgroups in a combined sample of CHR (n = 171) and help-seeking controls (HSCs; n = 100; PREDICT study). Indicators in the LPA model included baseline Scale of Prodromal Symptoms (SOPS), Calgary Depression Scale for Schizophrenia (CDSS), and neurocognitive performance as measured by multiple instruments, including category instances (CAT). Subgroups were further characterized using covariates measuring demographic and clinical features. Three classes emerged: class 1 (mild, transition rate 5.6%), lowest SOPS and depression scores, intact neurocognitive performance; class 2 (paranoid-affective, transition rate 14.2%), highest suspiciousness, mild negative symptoms, moderate depression; and class 3 (negative-neurocognitive, transition rate 29.3%), highest negative symptoms, neurocognitive impairment, social cognitive impairment. Classes 2 and 3 evidenced poor social functioning. Results support a subgroup approach to research, assessment, and treatment of help-seeking individuals. Class 3 may be an early risk stage of developing schizophrenia.

School climate and bullying victimization: a latent class growth model analysis.

PubMed

Gage, Nicholas A; Prykanowski, Debra A; Larson, Alvin

2014-09-01

Researchers investigating school-level approaches for bullying prevention are beginning to discuss and target school climate as a construct that (a) may predict prevalence and (b) be an avenue for school-wide intervention efforts (i.e., increasing positive school climate). Although promising, research has not fully examined and established the social-ecological link between school climate factors and bullying/peer aggression. To address this gap, we examined the association between school climate factors and bullying victimization for 4,742 students in Grades 3-12 across 3 school years in a large, very diverse urban school district using latent class growth modeling. Across 3 different models (elementary, secondary, and transition to middle school), a 3-class model was identified, which included students at high-risk for bullying victimization. Results indicated that, for all students, respect for diversity and student differences (e.g., racial diversity) predicted within-class decreases in reports of bullying. High-risk elementary students reported that adult support in school was a significant predictor of within-class reduction of bullying, and high-risk secondary students report peer support as a significant predictor of within-class reduction of bullying. PsycINFO Database Record (c) 2014 APA, all rights reserved.

National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry.

PubMed

Evenson, Kelly R; Wen, Fang; Hales, Derek; Herring, Amy H

2016-05-03

Applying latent class analysis (LCA) to accelerometry can help elucidated underlying patterns. This study described the patterns of accelerometer-determined sedentary behavior and physical activity among youth by applying LCA to a nationally representative United States (US) sample. Using 2003-2006 National Health and Nutrition Examination Survey data, 3998 youths 6-17 years wore an ActiGraph 7164 accelerometer for one week, providing > =3 days of wear for > =8 h/day from 6:00 am-midnight. Cutpoints defined sedentary behavior (<100 counts/minute), light activity (100-2295 counts/minute), moderate to vigorous physical activity (MVPA; > = 2296 counts/minute), and vigorous activity (> = 4012 counts/minute). To account for wear time differences, outcomes were expressed as percent of day in a given intensity. LCA was used to classify daily (Monday through Sunday) patterns of average counts/minute, sedentary behavior, light activity, MVPA, and vigorous activity separately. The latent classes were explored overall and by age (6-11, 12-14, 15-17 years), gender, and whether or not youth attended school during measurement. Estimates were weighted to account for the sampling frame. For average counts/minute/day, four classes emerged from least to most active: 40.9% of population (mean 323.5 counts/minute/day), 40.3% (559.6 counts/minute/day), 16.5% (810.0 counts/minute/day), and 2.3% (1132.9 counts/minute/day). For percent of sedentary behavior, four classes emerged: 13.5% of population (mean 544.6 min/day), 30.1% (455.1 min/day), 38.5% (357.7 min/day), and 18.0% (259.2 min/day). For percent of light activity, four classes emerged: 12.3% of population (mean 222.6 min/day), 29.3% (301.7 min/day), 41.8% (384.0 min/day), and 16.6% (455.5 min/day). For percent of MVPA, four classes emerged: 59.9% of population (mean 25.0 min/day), 33.3% (60.9 min/day), 3.1% (89.0 min/day), and 3.6% (109.3 min/day). For percent of vigorous activity, three classes emerged: 76.8% of population (mean 7.1 min/day), 18.5% (23.9 min/day), and 4.7% (47.4 min/day). Classes were developed by age, gender, and school attendance since some patterns differed when stratifying by these factors. The models supported patterns for average intensity, sedentary behavior, light activity, MVPA, and vigorous activity. These latent class derived patterns can be used in other youth studies to explore correlates or outcomes and to target sedentary behavior or physical activity interventions.

Racial/Ethnic Differences in Patterns of Sexual Risk Behavior and Rates of Sexually Transmitted Infections Among Female Young Adults

PubMed Central

Cook, Emily C.; Niccolai, Linda M.; Connell, Christian M.

2013-01-01

Objectives. We examined patterns of sexual behavior and risk for sexually transmitted infections (STIs) in young adulthood for Black, Hispanic, and White females. Methods. We used a nationally representative sample of 7015 female young adults from wave III of the National Longitudinal Study of Adolescent Health. Sexual risk items assessed behaviors occurring in the previous 6 years and past year to determine classes of sexual risk and links to STIs in young adulthood. Results. Latent class analysis revealed 3 sexual risk classes for Black and Hispanic youths and 4 sexual risk classes for White youths. The moderate and high risk classes had the highest probabilities of risky sexual partners, inconsistent condom use, and early age of sexual initiation, which significantly increased odds for STIs compared with recent abstainers. Conclusions. We found different classes of sexual behavior by race/ethnicity, with Black and Hispanic young women most at risk for STIs in young adulthood. Preventive efforts should target younger adolescents and focus on sexual partner behavior. PMID:23488501

How do older adult drivers self-regulate? Characteristics of self-regulation classes defined by latent class analysis.

PubMed

Bergen, Gwen; West, Bethany A; Luo, Feijun; Bird, Donna C; Freund, Katherine; Fortinsky, Richard H; Staplin, Loren

2017-06-01

Motor-vehicle crashes were the second leading cause of injury death for adults aged 65-84years in 2014. Some older drivers choose to self-regulate their driving to maintain mobility while reducing driving risk, yet the process remains poorly understood. Data from 729 older adults (aged ≥60years) who joined an older adult ride service program between April 1, 2010 and November 8, 2013 were analyzed to define and describe classes of driving self-regulation. Latent class analysis was employed to characterize older adult driving self-regulation classes using driving frequency and avoidance of seven driving situations. Logistic regression was used to explore associations between characteristics affecting mobility and self-regulation class. Three classes were identified (low, medium, and high self-regulation). High self-regulating participants reported the highest proportion of always avoiding seven risky driving situations and the lowest driving frequency followed by medium and low self-regulators. Those who were female, aged 80years or older, visually impaired, assistive device users, and those with special health needs were more likely to be high self-regulating compared with low self-regulating. Avoidance of certain driving situations and weekly driving frequency are valid indicators for describing driving self-regulation classes in older adults. Understanding the unique characteristics and mobility limitations of each class can guide optimal transportation strategies for older adults. Published by Elsevier Ltd.

An Effective News Recommendation Method for Microblog User

PubMed Central

Gu, Wanrong; Dong, Shoubin; Zeng, Zhizhao; He, Jinchao

2014-01-01

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data. PMID:24983011