scholarly journals PSYCHOMETRIC QUALITY OF FAMILY ADAPTABILITY AND COHESION EVALUATION SCALE IV BY LATENT CLASS ANALYSIS; QUANTITATIVE STUDY ON THE ELDERLY IN ROMANIA

2019 ◽  
Vol 9 (1) ◽  
pp. 100-115
Author(s):  
Cornelia RADA ◽  
◽  
◽  
Keyword(s):  
Latent Class Analysis ◽  
Quantitative Study ◽  
Latent Class ◽  
The Elderly ◽  
Class Analysis ◽  
Family Adaptability ◽  
Psychometric Quality

scholarly journals Psychological risk factors that characterize the trajectories of quality of life after a physical trauma: a longitudinal study using latent class analysis

2021 ◽  
Author(s):  
Eva Visser ◽  
Brenda Leontine Den Oudsten ◽  
Taco Gosens ◽  
Paul Lodder ◽  
Jolanda De Vries
Keyword(s):  
Quality Of Life ◽  
Latent Class Analysis ◽  
Health Care Providers ◽  
Psychological Health ◽  
Latent Class ◽  
Stress Disorder ◽  
Class Analysis ◽  
Care Providers ◽  
Physical Trauma

Abstract Background The course and corresponding characteristics of quality of life (QOL) domains in trauma population are unclear. Our aim was to identify longitudinal QOL trajectories and determine and predict the sociodemographic, clinical, and psychological characteristics of trajectory membership in physical trauma patients using a biopsychosocial approach. Methods Patients completed a questionnaire set after inclusion, and at 3, 6, 9, and 12 months follow-up. Trajectories were identified using repeated-measures latent class analysis. The trajectory characteristics were ranked using Cohen’s d effect size or phi coefficient. Results Altogether, 267 patients were included. The mean age was 54.1 (SD = 16.1), 62% were male, and the median injury severity score was 5.0 [2.0—9.0]. Four latent trajectories were found for psychological health and environment, five for physical health and social relationships, and seven trajectories were found for overall QOL and general health. The trajectories seemed to remain stable over time. For each QOL domain, the identified trajectories differed significantly in terms of anxiety, depressive symptoms, acute stress disorder, post-traumatic stress disorder, Neuroticism, trait anxiety, Extraversion, and Conscientiousness. Discussion Psychological factors characterized the trajectories during 12 months after trauma. Health care providers can use these findings to identify patients at risk for impaired QOL and offer patient-centered care to improve QOL.

Symptom profiles following combat injury and long-term quality of life: a latent class analysis

2021 ◽  
Author(s):  
Andrew J. MacGregor ◽  
Amber L. Dougherty ◽  
Edwin W. D’Souza ◽  
Cameron T. McCabe ◽  
Daniel J. Crouch ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Combat Injury ◽  
Symptom Profiles

scholarly journals Estimates and Determinants of SARS-Cov-2 Seroprevalence and Infection Fatality Ratio Using Latent Class Analysis: The Population-Based Tirschenreuth Study in the Hardest-Hit German County in Spring 2020

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1118
Author(s):  
Ralf Wagner ◽  
David Peterhoff ◽  
Stephanie Beileke ◽  
Felix Günther ◽  
Melanie Berr ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Care Home ◽  
Case Fatality ◽  
The Elderly ◽  
Population Based ◽  
Class Analysis ◽  
Population Based Study ◽  
Targeted Testing ◽  
June July

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the factor of underreported infections was 12.2 in the young versus 1.7 for ≥85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60–69, and 13.2% for age 70+. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.

Profiles of quality of life in opiate-dependent individuals after starting methadone treatment: A latent class analysis

2013 ◽  
Vol 24 (4) ◽  
pp. 342-350 ◽  
Author(s):  
Jessica De Maeyer ◽  
Chijs van Nieuwenhuizen ◽  
Ilja L. Bongers ◽  
Eric Broekaert ◽  
Wouter Vanderplasschen
Keyword(s):  
Quality Of Life ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Methadone Treatment ◽  
Class Analysis

Delirium superimposed on dementia: defining disease states and course from longitudinal measurements of a multivariate index using latent class analysis and hidden Markov chains

2011 ◽  
Vol 23 (10) ◽  
pp. 1659-1670 ◽  
Author(s):  
Antonio Ciampi ◽  
Alina Dyachenko ◽  
Martin Cole ◽  
Jane McCusker
Keyword(s):  
Mental Disorders ◽  
Markov Chains ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Hidden Markov ◽  
The Elderly ◽  
Population Heterogeneity ◽  
Class Analysis ◽  
Hidden Markov Chains ◽  
Time Point

ABSTRACTBackground: 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.Methods: 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.Results: 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.Conclusion: 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.

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