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Socio-economic and Demographic Factors Influencing the Spatial Spread of COVID-19 in the USA

As the COVID-19 pandemic progressed in the USA, ‘hotspots’ shifted geographically over time to suburban and rural counties showing a high prevalence of the disease. We analyse population-adjusted confirmed case rates based on daily US county-level variations in COVID-19 confirmed case counts during the first several months of the pandemic (1 March 2020 through 23 May 2020) to evaluate the spatial dependence ...

Using Blood Test Parameters to Define Biological Age among Older Adults: Association with Morbidity and Mortality Independent of Chronological Age Validated in Two Separate Birth Cohorts

Biomarkers defining biological age are typically laborious or expensive to assess. Instead, in the current study, we identified parameters based on standard laboratory blood tests across metabolic, cardiovascular, inflammatory, and kidney functioning that had been assessed in the Berlin Aging Study (BASE) (n = 384) and Berlin Aging Study II (BASE-II) (n = 1517). We calculated biological age using those ...

Cohort Profile: Genetic data in the German Socio-Economic Panel Innovation Sample (Gene-SOEP)

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of private households in Germany. The sample provides a detailed life course perspective based on a rich collection of information about living conditions, socio-economic status, family relationships, personality, values, preferences, and health. We collected genetic data from ...

Germany’s Low Sars-Cov-2 Seroprevalence Confirms Effective Containment in 2020: Results of the Nationwide RKI-SOEP Study

Pre-vaccine SARS-CoV-2 seroprevalence data from Germany are scarce outside hotspots, andsocioeconomic disparities remained largely unexplored. The nationwide RKI-SOEP study with 15,122adult participants investigated seroprevalence and testing in a supplementary wave of the Socio-Economic-Panel conducted predominantly in October-November 2020. Self-collected oral-nasalswabs were PCR-positive in 0.4% ...

COVID-19 Lockdown Compliance, Financial Stress, and Acceleration in Technology Adoption in Rural Uganda

We examine the medium-term impact of COVID-19 for financial well-being and technology adoption in a low-income country. The analysis is based on regionally representative panel data consisting of 1,975 micro-entrepreneurs from rural Uganda. Using a LASSO approach, we first show that several business characteristics predict longer business shutdown due to COVID-19, including running a service business. ...

The Role of Pre-pandemic Depression for Changes in Depression, Anxiety, and Loneliness during the COVID-19 Pandemic: Results from a Longitudinal Probability Sample of Adults from Germany

Background: The present study aims to delineate the role of preexisting depression for changes in common mental health problems during the COVID-19 pandemic. Methods: Using mixed-effects linear regression models, we analyzed data on the course of depressive (Patient Health Questionnaire-2) and anxiety (Generalized Anxiety Disorder-2) symptoms as well as loneliness (three-item UCLA Loneliness Scale) ...

Attitudes Toward Mandatory COVID-19 Vaccination in Germany: A Representative Analysis of Data from the Socio-economic Panel for the Year 2021

Adequate immunity to COVID-19 apparently cannot be attained in Germany by voluntary vaccination alone, and therefore the introduction of mandatory COVID-19 vaccination is still under consideration. We present findings on the potential acceptance of such a requirement by the German population, and we report on the reasons given for accepting or rejecting it and how these reasons vary according to population ...

The Youth Mental Health Crisis during the COVID-19 Pandemic: The Role of School Closures

Lifestyle Behaviors and Wealth-Health Gaps in Germany

Assessing the Value of Data for Prediction Policies: The Case of Antibiotic Prescribing

We quantify the value of data for the prediction policy problem of reducing antibiotic prescribing to curb antibiotic resistance. Using varying combinations of administrative data, we evaluate machine learning predictions for diagnosing bacterial urinary tract infections and the outcomes of prescription rules based on these predictions. Simple patient demographics improve prediction quality substantially ...