Comorbidity of metabolic syndrome components in a population-based screening program: A latent class analysis

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

BMC Psychiatry ◽

10.1186/s12888-021-03537-z ◽

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.

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

International Journal of Geriatric Psychiatry ◽

10.1002/gps.4413 ◽

2016 ◽

Vol 31 (9) ◽

pp. 1021-1028 ◽

Cited By ~ 2

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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Latent class analysis identifies three subtypes of aggressive end-of-life care: A population-based study in Taiwan

European Journal of Cancer ◽

10.1016/j.ejca.2013.05.005 ◽

2013 ◽

Vol 49 (15) ◽

pp. 3284-3291 ◽

Cited By ~ 4

Author(s):

Mei-Ling Chen ◽

Yun-Yi Chen ◽

Siew Tzuh Tang

Keyword(s):

Latent Class Analysis ◽

End Of Life ◽

End Of Life Care ◽

Latent Class ◽

Population Based ◽

Class Analysis ◽

Life Care ◽

Population Based Study

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Health Information-Seeking Behaviors of a German Speaking Minority in Italy: Latent Class Analysis of a Population-Based Cross-Sectional Survey

10.21203/rs.3.rs-483915/v2 ◽

2021 ◽

Author(s):

Dietmar Ausserhofer ◽

Wolfgang Wiedermann ◽

Christian J. Wiedermann ◽

Ulrich Becker ◽

Anna Vögele ◽

...

Keyword(s):

Latent Class Analysis ◽

Health Information ◽

Information Seeking ◽

Latent Class ◽

Population Based ◽

Health Information Seeking ◽

Class Analysis ◽

Online Class ◽

German Speaking ◽

Information Seeking Behaviors

Abstract Latent classes of health information-seeking behaviors of adults in a German-speaking minority of Italy were explored in a population-based, telephone survey on 10 health information sources conducted in South Tyrol, Italy. Data were collected from 504 adults (primary language German 68%, Italian 28%) and analyzed using latent class analysis and latent class multinomial logistic regression models. Three classes of health information-seeking behaviors emerged: “multidimensional” (23.3%), “interpersonal” (38.6%) and “technical/online” (38.1%). Compared to the “technical/online” class, “interpersonal” class members were older, had lower education than high school, and were less likely to be of Italian ethnicity. “Multidimensional” class members were more likely to be female, older, and of German ethnicity than those in the “technical/online” class. Linguistic ethnicity explains membership in classes of health-information-seeking behaviour. Policy makers and healthcare providers need to consider the health information-seeking behaviors of population subgroups to promote the health literacy skills of language minority groups.

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