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.

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

2021 ◽  
Author(s):  
Ralf Wagner ◽  
David Peterhoff ◽  
Stephanie Beileke ◽  
Felix Guenther ◽  
Melanie Berr ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Error Control ◽  
Latent Class ◽  
Care Home ◽  
Case Fatality ◽  
Population Based ◽  
Misclassification Error ◽  
Class Analysis ◽  
June July ◽  
Dark Figure

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures, but the number of registered infections depends on testing strategies and deduced case fatality ratios (CFR) are poor proxies for IFR. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020 with particularly high CFR. To estimate seroprevalence, dark figure, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020 with misclassification error control, we conducted a population-based study, including home visits for elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests (64% of our random sample). Latent class analysis yielded 8.6% standardized county-wide seroprevalence, dark figure factor 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 dark figure 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, 13.2% for age 70+, confirming a previously reported age-model for IFR. 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.

Latent class analysis of the feared situations of social anxiety disorder: A population-based study

2016 ◽  
Vol 33 (12) ◽  
pp. 1178-1187 ◽  
Author(s):  
Hugo Peyre ◽  
Nicolas Hoertel ◽  
Fabrice Rivollier ◽  
Benjamin Landman ◽  
Kibby McMahon ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Latent Class Analysis ◽  
Social Anxiety Disorder ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis ◽  
Population Based Study

scholarly journals Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248992
Author(s):  
Marcus Heise ◽  
Astrid Fink ◽  
Jens Baumert ◽  
Christin Heidemann ◽  
Yong Du ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Population Based ◽  
Self Management ◽  
Marginal Effect ◽  
Type I ◽  
Class Analysis ◽  
Population Based Study ◽  
Percentage Points ◽  
Management Behaviour

Objective Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of SMB among persons with diabetes. Research design and methods The present analysis includes data of 1,466 persons (age 18 to 99 years; 44.0% female; 56.0% male) with diabetes (type I and II) from the population-based study German Health Update 2014/2015 (GEDA 2014/2015-EHIS). We used latent class analysis in order to distinguish different patterns of self-management behaviours among persons with diabetes. The assessment of SMB was based on seven self-reported activities by respondents (dietary plan, diabetes-diary, diabetes health pass, self-assessment of blood glucose, self-examination of feet, retinopathy-screenings and assessment of HbA1c). Subsequent multinomial latent variable regressions identified factors that were associated with self-management behaviour. Results Latent class analysis suggested a distinction between three patterns of SMB. Based on modal posterior probabilities 42.8% of respondents showed an adherent pattern of diabetes self-management with above-average frequency in all seven indicators of SMB. 32.1% showed a nonadherent pattern with a below-average commitment in all seven forms of SMB. Another 25.1% were assigned to an ambivalent type, which showed to be adherent with regard to retinopathy screenings, foot examinations, and the assessment of HbA1c, yet nonadherent with regard to all other forms of SMB. In multivariable regression analyses, participation in Diabetes Self-Management Education programs (DSME) was the most important predictor of good self-management behaviour (marginal effect = 51.7 percentage points), followed by attentiveness towards one’s personal health (31.0 percentage points). Respondents with a duration of illness of less than 10 years (19.5 percentage points), employed respondents (7.5 percentage points), as well as respondents with a high socioeconomic status (24.7 percentage points) were more likely to show suboptimal forms of diabetes self-management. Discussion In the present nationwide population-based study, a large proportion of persons with diabetes showed suboptimal self-management behaviour. Participation in a DSME program was the strongest predictor of good self-management. Results underline the need for continual and consistent health education for patients with diabetes.

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.

Latent class analysis of the Apathy Scale does not identify subtypes of apathy in general population-based older persons

2016 ◽  
Vol 31 (9) ◽  
pp. 1021-1028 ◽  
Author(s):  
Isis Groeneweg-Koolhoven ◽  
Lotte J. Huitema ◽  
Margot W. M. de Waal ◽  
Max L. Stek ◽  
Jacobijn Gussekloo ◽  
...  
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Older Persons ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis
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