scholarly journals Variant type and position predict two distinct limb phenotypes in patients with GLI3-mediated polydactyly syndromes

2020 ◽  
pp. jmedgenet-2020-106948
Author(s):  
Martijn Baas ◽  
Elise Bette Burger ◽  
Ans MW van den Ouweland ◽  
Steven ER Hovius ◽  
Annelies de Klein ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Corpus Callosum ◽  
Latent Class Analysis ◽  
Family Member ◽  
Latent Class ◽  
Class Membership ◽  
Class Model ◽  
Limb Anomalies ◽  
Dna Variants ◽  
Greig Syndrome

IntroductionPathogenic DNA variants in the GLI-Kruppel family member 3 (GLI3) gene are known to cause multiple syndromes: for example, Greig syndrome, preaxial polydactyly-type 4 (PPD4) and Pallister-Hall syndrome. Out of these, Pallister-Hall is a different entity, but the distinction between Greig syndrome and PPD4 is less evident. Using latent class analysis (LCA), our study aimed to investigate the correlation between reported limb anomalies and the reported GLI3 variants in these GLI3-mediated polydactyly syndromes. We identified two subclasses of limb anomalies that relate to the underlying variant.MethodsBoth local and published cases were included for analysis. The presence of individual limb phenotypes was dichotomised and an exploratory LCA was performed. Distribution of phenotypes and genotypes over the classes were explored and subsequently the key predictors of latent class membership were correlated to the different clustered genotypes.Results297 cases were identified with 127 different variants in the GLI3 gene. A two-class model was fitted revealing two subgroups of patients with anterior versus posterior anomalies. Posterior anomalies were observed in cases with truncating variants in the activator domain (postaxial polydactyly; hand, OR: 12.7; foot, OR: 33.9). Multivariate analysis supports these results (Beta: 1.467, p=0.013 and Beta: 2.548, p<0.001, respectively). Corpus callosum agenesis was significantly correlated to these variants (OR: 8.8, p<0.001).ConclusionThere are two distinct phenotypes within the GLI3-mediated polydactyly population: anteriorly and posteriorly orientated. Variants that likely produce haploinsufficiency are associated with anterior phenotypes. Posterior phenotypes are associated with truncating variants in the activator domain. Patients with these truncating variants have a greater risk for corpus callosum anomalies.

Health Care Utilization, Transition Readiness, and Quality of Life: A Latent Class Analysis

2020 ◽  
Author(s):  
Katherine A Traino ◽  
Christina M Sharkey ◽  
Megan N Perez ◽  
Dana M Bakula ◽  
Caroline M Roberts ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Health Care Utilization ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Care Utilization ◽  
Class Membership ◽  
Transition Readiness ◽  
High Utilization

Abstract Objective To identify possible subgroups of health care utilization (HCU) patterns among adolescents and young adults (AYAs) with a chronic medical condition (CMC), and examine how these patterns relate to transition readiness and health-related quality of life (HRQoL). Methods Undergraduates (N = 359; Mage=19.51 years, SD = 1.31) with a self-reported CMC (e.g., asthma, allergies, irritable bowel syndrome) completed measures of demographics, HCU (e.g., presence of specialty or adult providers, recent medical visits), transition readiness, and mental HRQoL (MHC) and physical HRQoL (PHC). Latent class analysis identified four distinct patterns of HCU. The BCH procedure evaluated how these patterns related to transition readiness and HRQoL outcomes. Results Based on seven indicators of HCU, a four-class model was found to have optimal fit. Classes were termed High Utilization (n = 95), Adult Primary Care Physician (PCP)-Moderate Utilization (n = 107), Family PCP-Moderate Utilization (n = 81), and Low Utilization (n = 76). Age, family income, and illness controllability predicted class membership. Class membership predicted transition readiness and PHC, but not MHC. The High Utilization group reported the highest transition readiness and the lowest HRQoL, while the Low Utilization group reported the lowest transition readiness and highest HRQoL. Conclusions The present study characterizes the varying degrees to which AYAs with CMCs utilize health care. Our findings suggest poorer PHC may result in higher HCU, and that greater skills and health care engagement may not be sufficient for optimizing HRQoL. Future research should examine the High Utilization subgroup and their risk for poorer HRQoL.

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

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

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

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

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

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

Politieke voorkeur en antwoordgedrag van respondenten in politieke opiniepeilingen

Res Publica ◽  
1994 ◽  
Vol 36 (2) ◽  
pp. 143-152
Author(s):  
Geert Loosveldt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Response Category ◽  
The Political ◽  
Class Analysis ◽  
Background Characteristics ◽  
Class Model ◽  
Political Preference

In this article a typology of respondent's ability to participate in a survey interview is created by means of a latent class analysis. The indicators in the analysis are: the interviewer's evaluation of the respondent's ability, the use of the "don't know" response category and inconsistent answers. It was possible to fit a latent class model with three classes or types of respondents. The three types are clearly differentiated concerning ability. As expected, this typology is related to respondent's education and age. Ability to participate is higher for better educated and younger respondents. Given the fact that political preference is also related to these two background characteristics, there is a relationship between the respondent's typology and the political preference of the respondents.

scholarly journals A primer to latent profile and latent class analysis

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

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

Communal Coping Among Spanish-Speaking Mother–Child Dyads Engaging in Language Brokering: A Latent Class Analysis

2016 ◽  
Vol 44 (5) ◽  
pp. 743-769 ◽  
Author(s):  
Jennifer A. Kam ◽  
Erin D. Basinger ◽  
Lisa M. Guntzviller
Keyword(s):  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Low Income ◽  
Latent Class ◽  
Language Brokering ◽  
Class Membership ◽  
Communal Coping ◽  
Spanish Speaking ◽  
Class A ◽  
Different Types

Utilizing self-reported survey data from 120 low-income, Spanish-speaking mother–child dyads, this study examined different types of classes (i.e., subgroups) based on the ways in which mothers and adolescent children coped with language brokering, particularly when they found it stressful. Four classes emerged, listed from largest to smallest class: (a) communal coping mothers, (b) shared communal copers, (c) independent communal coping children, and (d) communal coping children. Mothers’ parent–child closeness predicted class membership, but adolescent children’s reported closeness was not a significant predictor. Nevertheless, adolescent children’s respect for family significantly predicted class membership, whereas mothers’ respect for family was not a significant predictor. Mothers who were members of the communal coping children class reported less frequent depressive symptoms, whereas children who were independent communal coping children reported more frequent depressive symptoms.

scholarly journals Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm

1996 ◽  
Vol 21 (3) ◽  
pp. 215-229 ◽  
Author(s):  
Peter G. M. van der Heijden ◽  
Jos Dessens ◽  
UIf Bockenholt
Keyword(s):  
Latent Class Analysis ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Estimation Procedure ◽  
Class Analysis ◽  
Class Model ◽  
Explanatory Variables ◽  
Concomitant Variable ◽  
The Em Algorithm

Latent class analysis assumes the existence of a categorical latent variable that explains the relations between a set of categorical manifest variables. Simultaneous latent class analysis deals with sets of multiway contingency tables simultaneously. In this way an explanatory categorical grouping variable is related to latent class results. In this article we discuss a tool called the concomitant-variable latent-class model, which generalizes this work to continuous explanatory variables. An EM estimation procedure to estimate the model is worked out in detail, and the model is applied to an example on juvenile delinquency.

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

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

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

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

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

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

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