Background: In general, the risk to develop Parkinson’s disease (PD) is higher in men compared to women. Besides male sex and genetics, research suggests diabetes mellitus (DM) is a risk factor for PD as well. Objective: In this population-level study, we aimed at investigating the sex-specific impact of DM on the risk of developing PD. Methods: Medical claims data were analyzed in a cross-sectional study in the Austrian population between 1997 and 2014. In the age group of 40–79 and 80+, 235,268 patients (46.6%females, 53.4%males) with DM were extracted and compared to 1,938,173 non-diabetic controls (51.9%females, 48.1%males) in terms of risk of developing PD. Results: Men with DM had a 1.46 times increased odds ratio (OR) to be diagnosed with PD compared to non-diabetic men (95%CI 1.38–1.54, p < 0.001). The association of DM with newly diagnosed PD was significantly greater in women (OR = 1.71, 95%CI 1.60–1.82, p < 0.001) resulting in a relative risk increase of 1.17 (95%CI 1.11–1.30) in the age group 40 to 79 years. In 80+-year-olds the relative risk increase is 1.09 (95%CI 1.01–1.18). Conclusion: Although men are more prone to develop PD, women see a higher risk increase in PD than men amongst DM patients.

Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.

Introduction Both diabetes mellitus and being female significantly increase the risk of being diagnosed with major depressive disorder (MDD). The diagnosis of MDD, combined with diabetes mellitus, can be detrimental in terms of mortality and morbidity. We aimed at investigating the impact of diabetes mellitus on the gender gap in MDD over the course of a human lifetime. Research design and methods In a cross-sectional study over the course of 17 years, medical claims data of the general Austrian population (n=8 996 916) between 1997 and 2014 was analyzed. Of these, 123 232 patients with diabetes mellitus were extracted and compared with non-diabetic controls. Results In a cohort of 123 232 patients with diabetes mellitus and 1 933 218 controls (52% females, 48% males), women with diabetes had 2.55 times increased ORs to be diagnosed with MDD compared with women without diabetes (95% CI 2.48 to 2.62, p<0.001) between the age of 30 and 69 years. The effect of diabetes mellitus on the prevalence of MDD was significantly smaller in men (OR=1.85, 95% CI 1.80 to 1.91, p<0.001). Between 0 and 30 years and after age 70 years, the gender gap of MDD was not different between patients with and without diabetes mellitus. The peak of the gender gap in MDD in patients with diabetes mellitus was around the age of 40–49 years. A sensitivity analysis identified overweight, obesity and alcohol dependence as the most potent influencing factors of the widening of the gender gap among patients with diabetes mellitus. Conclusions Diabetes mellitus is a stronger risk factor for MDD in women than in men, with the greatest width of the gender gap between 40 and 49 years. High-risk patients for MDD, such as overweight female patients with diabetes, should be more carefully assessed and monitored.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020. Measurement(s) time at medical intervention • medical intervention Technology Type(s) digital curation • content analysis strategy of existing information sources Factor Type(s) non-pharmaceutical intervention • date Sample Characteristic - Location global Measurement(s) time at medical intervention • medical intervention Technology Type(s) digital curation • content analysis strategy of existing information sources Factor Type(s) non-pharmaceutical intervention • date Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12668792