Asset Ownership of the Elderly Across Europe: A Multilevel Latent Class Analysis to Segment Countries and Households

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.

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

International Psychogeriatrics ◽

10.1017/s1041610211000871 ◽

2011 ◽

Vol 23 (10) ◽

pp. 1659-1670 ◽

Cited By ~ 8

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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Competence Differences in Literacy, Numeracy, and Problem Solving According to Sex

Adult Education Quarterly ◽

10.1177/0741713619827386 ◽

2019 ◽

Vol 69 (2) ◽

pp. 101-119 ◽

Cited By ~ 2

Author(s):

Seher Yalcin

Keyword(s):

Problem Solving ◽

Latent Class Analysis ◽

Latent Class ◽

Latent Classes ◽

Class Analysis ◽

Country Level ◽

Successful Group ◽

Study Population ◽

Multilevel Latent Class Analysis ◽

International Assessment

This study aimed to determine individual- and country-level latent classes in literacy, numeracy and problem-solving competencies of individuals participating in the Programme for the International Assessment of Adult Competencies 2015. Specifically, it sought to distinguish these classes in relation to individuals’ sex and to identify the state of prediction of the determined latent classes by each person’s level of education. The study population consisted of 116,301 adults aged 16 to 65 years in 20 countries. Multilevel latent class analysis was conducted to consider the nested data structure and determine the number of latent classes. According to the results of the multilevel latent class analysis, Turkey and Chile were in the low achievement group in all skills, while Japan was in the most successful group. Moreover, the results revealed that sex and education level had a considerable influence on certain competence levels.

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