Socioeconomic Differences in SARS-CoV-2 Infection and Vaccination in Germany: A Seroepidemiological Study After One Year of COVID-19 Vaccination Campaign.

Objective: To evaluate the socioeconomic patterns of SARS-CoV-2 antigen contacts through infection, vaccination or both ("hybrid immunity") after 1 year of vaccination campaign. Methods: Data were derived from the German seroepidemiological Corona Monitoring Nationwide study (RKI-SOEP-2; n = 10,448; November 2021-February 2022). Combining serological and self-report data, we estimated adjusted prevalence ratios (PR) of SARS-CoV-2 infection, COVID-19 vaccination, basic immunization (at least two SARS-CoV-2 antigen contacts through vaccination and/or infection), and three antigen contacts by education and income. Results: Low-education groups had 1.35-times (95% CI 1.01-1.82) the risk of SARS-CoV-2 infection compared to high-education groups. COVID-19 vaccination (at least one dose) and basic immunization decreased with lower education and income. Low-education and low-income groups were less likely to have had at least three antigen contacts (PR low vs. high education: 0.74, 95% CI 0.65-0.84; PR low vs. high income: 0.66, 95% CI 0.57-0.77). Conclusion: The results suggest a lower level of protection against severe COVID-19 for individuals from low and medium socioeconomic groups. Pandemic response and vaccination campaigns should address the specific needs and barriers of these groups.

Family care during the first COVID-19 lockdown in Germany: longitudinal evidence on consequences for the well-being of caregivers.

We examine changes in the well-being of family caregivers during the early phase of the COVID-19 pandemic in Germany, using data from the German Socio-Economic Panel (SOEP) and the SOEP-CoV study. The COVID-19 pandemic posed an extraordinary challenge for family caregivers, as care recipients are a high-risk group requiring special protection, and professional care services were severely cut back. The specific situation of the COVID-19 pandemic allows us to re-examine the caregiver stress process model. Using first difference regression models, we analyse changes in general life satisfaction and depressive symptoms (PHQ-4 score) among family caregivers between 2019 and spring 2020, differentiating by care intensity and duration of the care episode. Caregivers show similar changes in well-being as non-caregivers: a simultaneous increase in depressive symptoms and life satisfaction between 2019 and 2020. However, our results reveal heterogeneity within the group of family caregivers as we find differences according to caregiving dynamics and intensity. Among the group of continuing caregivers, high-intensity caregivers experience a larger increase in life satisfaction, and low-intensity caregivers a smaller increase in life satisfaction, compared to non-caregivers. Our results therefore provide some support for the role enhancement hypothesis for continuing caregivers with high time commitment.

Nationally representative results on SARS-CoV-2 seroprevalence and testing in Germany at the end of 2020.

Pre-vaccine SARS-CoV-2 seroprevalence data from Germany are scarce outside hotspots, and socioeconomic disparities remained largely unexplored. The nationwide representative RKI-SOEP study (15,122 participants, 18-99 years, 54% women) investigated seroprevalence and testing in a supplementary wave of the Socio-Economic-Panel conducted predominantly in October-November 2020. Self-collected oral-nasal swabs were PCR-positive in 0.4% and Euroimmun anti-SARS-CoV-2-S1-IgG ELISA from dry-capillary-blood antibody-positive in 1.3% (95% CI 0.9-1.7%, population-weighted, corrected for sensitivity = 0.811, specificity = 0.997). Seroprevalence was 1.7% (95% CI 1.2-2.3%) when additionally correcting for antibody decay. Overall infection prevalence including self-reports was 2.1%. We estimate 45% (95% CI 21-60%) undetected cases and lower detection in socioeconomically deprived districts. Prior SARS-CoV-2 testing was reported by 18% from the lower educational group vs. 25% and 26% from the medium and high educational group (p < 0.001, global test over three categories). Symptom-triggered test frequency was similar across educational groups. Routine testing was more common in low-educated adults, whereas travel-related testing and testing after contact with infected persons was more common in highly educated groups. This countrywide very low pre-vaccine seroprevalence in Germany at the end of 2020 can serve to evaluate the containment strategy. Our findings on social disparities indicate improvement potential in pandemic planning for people in socially disadvantaged circumstances.

Socioeconomic position and SARS-CoV-2 infections: seroepidemiological findings from a German nationwide dynamic cohort.

BACKGROUND: Evidence on the relationship between socioeconomic position (SEP) and infections with SARS-CoV-2 is still limited as most of the available studies are ecological in nature. This is the first German nationwide study to examine differences in the risk of SARS-CoV-2 infections according to SEP at the individual level. METHODS: The 'CORONA-MONITORING bundesweit' (RKI-SOEP) study is a seroepidemiological survey among a dynamic cohort of the German adult population (n=15 122; October 2020-February 2021). Dried blood samples were tested for SARS-CoV-2 antibodies and oral-nasal swabs for viral RNA. SEP was measured by education and income. Robust logistic regression was used to examine adjusted associations of SARS-CoV-2 infections with SEP. RESULTS: 288 participants were seropositive, PCR positive or self-reported a previous laboratory-confirmed SARS-CoV-2 infection. The adjusted odds of SARS-CoV-2 infection were 1.87-fold (95% CI 1.06 to 3.29) higher among low-educated than highly educated adults. Evidence was weaker for income differences in infections (OR=1.65; 95% CI 0.89 to 3.05). Highly educated adults had lower odds of undetected infection. CONCLUSION: The results indicate an increased risk of SARS-CoV-2 infection in low-educated groups. To promote health equity in the pandemic and beyond, social determinants should be addressed more in infection protection and pandemic planning.

Time Spent on School-Related Activities at Home During the Pandemic: A Longitudinal Analysis of Social Group Inequality Among Secondary School Students.

Substantial educational inequalities have been documented in Germany for decades. In this article, we examine whether educational inequalities among children have increased or remained the same since the school closures of spring 2020 due to the COVID-19 pandemic. Our perspective is longitudinal: We compare the amount of time children in secondary schools spent on school-related activities at home before the pandemic, during school closures, and immediately after returning to in-person learning. We operationalize family socio-economic status using the highest parental educational attainment. Based on the theoretical assumption that the pandemic affected everyone equally, we formulate a hypothesis of equalization during the first period of school closures. For the period thereafter, however, we assume that parents with a low level of education had more difficulties bearing the additional burden of supervising and supporting their children's learning activities. Thus, for that period, we postulate an increase in educational inequality. To study our hypotheses, we use data from the 2019 wave of the SOEP and the SOEP-CoV study, both of which are probability samples. The SOEP-CoV study provides a unique database, as it was conducted during the lockdown of spring 2020 and in the following month. For statistical analysis, we use probit regressions at three measurement points (in 2019, in 2020 during the school closures, and in the month after closures). The comparison of these three time points makes our analysis and findings unique in the research on education during the COVID-19 pandemic, in particular with regard to Germany-wide comparisons. Our results confirm the hypothesis of equalization during the first school closures and the hypothesis of an increase in educational in the subsequent period. Our findings have direct policy implications regarding the need to further expand support systems for children.

[Parents' subjective burden of home schooling their children at times of the Corona-related lockdown in spring 2020]. / Subjektive Belastung der Eltern durch die Beschulung ihrer Kinder zu Hause zu Zeiten des Corona-bedingten Lockdowns im Frühjahr 2020.

The Corona-related school closures and the closure of childcare facilities in April and May 2020 meant an immense challenge for many parents. Suddenly, children had to be looked after and educated at home all day. In this paper, we address the question of the subjective burden that parents faced when schooling their children at home. In doing so, we pay special attention to the individual resources of the parents as well as to their family situation and their working life. In particular, we examine the subjective perception of stress of single parents. For our analyses we use data from the SOEP-CoV study, a special survey of the Socio-Economic Panel (SOEP) on the topic of Corona. Overall, we found moderate levels of stress from the demands of schooling their children at home among all parents surveyed (N = 1508, of which N = 243 were single parents). However, parents with a low level of education and single parents felt particularly burdened, especially if they were employed at the time of the school closures. Our analyses suggest that precisely these groups of parents had problems comprehensively meeting the demands of home schooling under the given circumstances.

Seroepidemiological study on the spread of SARS-CoV-2 in Germany: Study protocol of the CORONA-MONITORING bundesweit' study (RKI-SOEP study).

The SARS-CoV-2 coronavirus has spread rapidly across Germany. Infections are likely to be under-recorded in the notification data from local health authorities on laboratory-confirmed cases since SARS-CoV-2 infections can proceed with few symptoms and then often remain undetected. Seroepidemiological studies allow the estimation of the proportion in the population that has been infected with SARS-CoV-2 (seroprevalence) as well as the extent of undetected infections. The 'CORONA-MONITORING bundesweit' study (RKI-SOEP study) collects biospecimens and interview data in a nationwide population sample drawn from the German Socio-Economic Panel (SOEP). Participants are sent materials to self-collect a dry blood sample of capillary blood from their finger and a swab sample from their mouth and nose, as well as a questionnaire. The samples returned are tested for SARS-CoV-2 IgG antibodies and SARS-CoV-2 RNA to identify past or present infections. The methods applied enable the identification of SARS-CoV-2 infections, including those that previously went undetected. In addition, by linking the data collected with available SOEP data, the study has the potential to investigate social and health-related differences in infection status. Thus, the study contributes to an improved understanding of the extent of the epidemic in Germany, as well as identification of target groups for infection protection.

Web-based and mixed-mode cognitive large-scale assessments in higher education: An evaluation of selection bias, measurement bias, and prediction bias.

Educational large-scale studies typically adopt highly standardized settings to collect cognitive data on large samples of respondents. Increasing costs alongside dwindling response rates in these studies necessitate exploring alternative assessment strategies such as unsupervised web-based testing. Before respective assessment modes can be implemented on a broad scale, their impact on cognitive measurements needs to be quantified. Therefore, an experimental study on N = 17,473 university students from the German National Educational Panel Study has been conducted. Respondents were randomly assigned to a supervised paper-based, a supervised computerized, and an unsupervised web-based mode to work on a test of scientific literacy. Mode-specific effects on selection bias, measurement bias, and predictive bias were examined. The results showed a higher response rate in web-based testing as compared to the supervised modes, without introducing a pronounced mode-specific selection bias. Analyses of differential test functioning showed systematically larger test scores in paper-based testing, particularly among low to medium ability respondents. Prediction bias for web-based testing was observed for one out of four criteria on study-related success factors. Overall, the results indicate that unsupervised web-based testing is not strictly equivalent to other assessment modes. However, the respective bias introduced by web-based testing was generally small. Thus, unsupervised web-based assessments seem to be a feasible option in cognitive large-scale studies in higher education.

Modeling competence development in the presence of selection bias.

A major challenge for representative longitudinal studies is panel attrition, because some respondents refuse to continue participating across all measurement waves. Depending on the nature of this selection process, statistical inferences based on the observed sample can be biased. Therefore, statistical analyses need to consider a missing-data mechanism. Because each missing-data model hinges on frequently untestable assumptions, sensitivity analyses are indispensable to gauging the robustness of statistical inferences. This article highlights contemporary approaches for applied researchers to acknowledge missing data in longitudinal, multilevel modeling and shows how sensitivity analyses can guide their interpretation. Using a representative sample of N = 13,417 German students, the development of mathematical competence across three years was examined by contrasting seven missing-data models, including listwise deletion, full-information maximum likelihood estimation, inverse probability weighting, multiple imputation, selection models, and pattern mixture models. These analyses identified strong selection effects related to various individual and context factors. Comparative analyses revealed that inverse probability weighting performed rather poorly in growth curve modeling. Moreover, school-specific effects should be acknowledged in missing-data models for educational data. Finally, we demonstrated how sensitivity analyses can be used to gauge the robustness of the identified effects.

Emigration Rates From Sample Surveys: An Application to Senegal.

What is the emigration rate of a country, and how reliable is that figure? Answering these questions is not at all straightforward. Most data on international migration are census data on foreign-born population. These migrant stock data describe the immigrant population in destination countries but offer limited information on the rate at which people leave their country of origin. The emigration rate depends on the number leaving in a given period and the population at risk of leaving, weighted by the duration at risk. Emigration surveys provide a useful data source for estimating emigration rates, provided that the estimation method accounts for sample design. In this study, emigration rates and confidence intervals are estimated from a sample survey of households in the Dakar region in Senegal, which was part of the Migration between Africa and Europe survey. The sample was a stratified two-stage sample with oversampling of households with members abroad or return migrants. A combination of methods of survival analysis (time-to-event data) and replication variance estimation (bootstrapping) yields emigration rates and design-consistent confidence intervals that are representative for the study population.

Multistate modelling extended by behavioural rules: An application to migration.

We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age- and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.