Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). Results The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

BackgroundTimes of crisis such as the COVID-19 pandemic are expected to compromise mental health. Despite a large number of studies, evidence on the development of mental health in general populations during the pandemic is inconclusive. One reason may be that representative data spanning the whole pandemic and allowing for comparisons to pre-pandemic data are scarce.MethodsWe analyzed representative data from telephone surveys of Germany's adults. Three mental health indicators were observed in ~1,000 and later up to 3,000 randomly sampled participants monthly until June 2022: symptoms of depression (observed since April 2019, PHQ-2), symptoms of anxiety (GAD-2), and self-rated mental health (latter two observed since March 2021). We produced time series graphs including estimated three-month moving means and proportions of positive screens (PHQ/GAD-2 score ≥ 3) and reports of very good/excellent mental health, as well as smoothing curves. We also compared time periods between years. Analyses were stratified by sex, age, and level of education.ResultsWhile mean depressive symptom scores declined from the first wave of the pandemic to summer 2020, they increased from October 2020 and remained consistently elevated throughout 2021 with another increase between 2021 and 2022. Correspondingly, the proportion of positive screens first decreased from 11.1% in spring/summer 2019 to 9.3% in the same period in 2020 and then rose to 13.1% in 2021 and to 16.9% in 2022. While depressive symptoms increased in all subgroups at different times, developments among women (earlier increase), the youngest (notable increase in 2021) and eldest adults, as well as the high level of education group (both latter groups: early, continuous increases) stand out. However, the social gradient in symptom levels between education groups remained unchanged. Symptoms of anxiety also increased while self-rated mental health decreased between 2021 and 2022.ConclusionElevated symptom levels and reduced self-rated mental health at the end of our observation period in June 2022 call for further continuous mental health surveillance. Mental healthcare needs of the population should be monitored closely. Findings should serve to inform policymakers and clinicians of ongoing dynamics to guide health promotion, prevention, and care.

No abstract

In daycare centres, the close contact of children with other children and employees favours the transmission of infections. The majority of children <6 years attend daycare programmes in Germany, but the role of daycare centres in the SARS-CoV-2 pandemic is unclear. We investigated the transmission risk in daycare centres and the spread of SARS-CoV-2 to associated households. 30 daycare groups with at least one recent laboratory-confirmed SARS-CoV-2 case were enrolled in the study (10/2020-06/2021). Close contact persons within daycare and households were examined over a 12-day period (repeated SARS-CoV-2 PCR tests, genetic sequencing of viruses, symptom diary). Households were interviewed to gain comprehensive information on each outbreak. We determined primary cases for all daycare groups. The number of secondary cases varied considerably between daycare groups. The pooled secondary attack rate (SAR) across all 30 daycare centres was 9.6%. The SAR tended to be higher when the Alpha variant was detected (15.9% vs. 5.1% with evidence of wild type). The household SAR was 53.3%. Exposed daycare children were less likely to get infected with SARS-CoV-2 than employees (7.7% vs. 15.5%). Containment measures in daycare programmes are critical to reduce SARS-CoV-2 transmission, especially to avoid spread to associated households.

No abstract