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

Abstract

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.

Spectrum Bias and Individual Strengths of SARS-CoV-2 Serological Tests—A Population-Based Evaluation

“…That said, the impact of spectrum bias should definitely be considered in future population-based approaches, especially, when results are reliant on a single test. While in the past we applied latent class analysis comprising several different tests to determine the seroprevalence in our study population [10], we couldn't show in this analysis any advancement of predicting serostatus from our LCA models as compared to the best single test in this study, thus rendering the LCA approach obsolete in this situation. Nonetheless, in the absence of a golden standard and without prior information on the actual and/or relative performance of different available tests, LCA can still provide a good opportunity to define epidemiological seroprevalence.…”

“…While PCR testing is still the reference method to identify acute COVID-19 cases [26], serological testing is widely used in epidemiological studies to gather information on the dynamics of the pandemic [27], regarding the impact of political and health care measures, the number of undetected infections, the infection fatality ratio [10] or even identification of high antibody-titer convalescent sera for the treatment of severe COVID cases [3,28]. Manufacturers' information on test performance relies on the composition of the test population that was available at the time of development and validation of these tests.…”

“…For this retrospective analysis we used a subgroup of the well described populationbased TiKoCo-19 study cohort [10]. In brief, a representative sample of 6608 residents of Tirschenreuth aged 14 years and older, selected by means of a sex-and municipalitystratified random sample, were invited and 4203 (64.27%) fully participated in the study by filling out a questionnaire and giving blood samples.…”

“…In brief, a representative sample of 6608 residents of Tirschenreuth aged 14 years and older, selected by means of a sex-and municipalitystratified random sample, were invited and 4203 (64.27%) fully participated in the study by filling out a questionnaire and giving blood samples. Three independent antibody tests were applied (complete data from 4185 participants) and the "true" serostatus was predicted by latent class analysis, to finally estimate key epidemiological parameters like seroprevalence and infection fatality ratio [10].…”

“…The objective of this study therefore was to analyze and to compare different SARS-CoV-2 serological tests in a population-based setting following STARD (Studies of diagnostic accuracy) guidelines [10,11]. Furthermore, we opted to determine the quantitative agreement of the obtained test results with virus neutralization representing the currently best predictive immunological correlate for protection.…”