Objective Diabetes is a risk factor for dementia but little is known about the impact of diabetes duration on the risk of dementia. We investigated the effect of type 2 diabetes duration on the risk of dementia. Design Prospective cohort study using health claims data representative for the older German population. The data contain information about diagnoses and medical prescriptions from the in- and outpatient sector. Methods We performed piecewise exponential models with a linear and a quadratic term for time since first type 2 diabetes diagnosis to predict the dementia risk in a sample of 13,761 subjects (2,558 dementia cases) older than 65 years. We controlled for severity of diabetes using the Adopted Diabetes Complications Severity Index. Results We found a U-shaped dementia risk over time. After type 2 diabetes diagnosis the dementia risk decreased (26% after 1 year) and reached a minimum at 4.75 years, followed by an increase through the end of follow-up. The pattern was consistent over different treatment groups, with the strongest U-shape for insulin treatment and for those with diabetes complications at the time of diabetes diagnosis. Conclusions We identified a non-linear association of type 2 diabetes duration and the risk of dementia. Physicians should closely monitor cognitive function in diabetic patients beyond the first few years after diagnosis, because the later increase in dementia occurred in all treatment groups.

Data on the burden of disease in the last years of life are an important basis for health policy decisions and the allocation of health care resources. Since dementia is one of the most expensive diseases, we ask the question whether dementia will ever be the most common disease at the time of death in older people? While international cause-of-death statistics report the underlying cause of death, dementia patients generally die from complications or sequelae. Instead of using causes of death, we identified the five most prevalent disease categories at age 70 and older at the time of death using German health claims data from 2004 to 2007 and 2014–2017, and combined their prevalence rates with the estimated number of deaths at age 70 and older up to the year 2060. We developed two scenarios, first, to represent the impact of population aging and increasing life expectancy. Second, to additionally examine the impact of morbidity trends among those who died. We found that dementia was already the most prevalent disease at the time of death among German women aged 70 years and older in 2014–2017, while it was still in fifth place among German men. Population aging and increasing life expectancy will result in dementia ranking first among women and second among men if the morbidity profile at the time of death remains constant. Extrapolating the observed time trends into the projections, cancer will be the most prevalent disease at the time of death for both sexes. Dementia will be second for women, and third for men after IHD. In addition to projections of causes of death, we also need projections of diseases at the time of death to better prepare for the needs of people in their final stages of life.

BackgroundLittle is known about factors correlated with this geographic spread of the first wave of COVID-19 infections in Germany. Given the lack of individual-level socioeconomic information on COVID-19 cases, we resorted to an ecological study design, exploring regional correlates of COVID-19 diagnoses.Data and MethodWe used data from the Robert-Koch-Institute on COVID-19 diagnoses by sex, age (age groups: 0-4, 5-14, 15-34, 35-59, 60-79, 80+), county (NUTS3 region) differentiating five periods (initial phase: through 15 March; 1st lockdown period: 16 March to 31 March; 2nd lockdown period: from 1 April to 15 April; easing period: 16 April to 30 April; post-lockdown period: 1 May through 23 July). For each period we calculated age-standardized incidence of COVID-19 diagnoses on the county level, using the German age distribution from the year 2018. We characterized the regions by macro variables in nine domains: “Demography”, “Employment”, “Politics, religion, and education”, “Income”, “Settlement structure and environment”, “Health care”, “(structural) Poverty”, “Interrelationship with other regions”, and “Geography”. We trained gradient boosting models to predict the age-standardized incidence rates with the macro structures of the counties, and used SHAP values to characterize the 20 most prominent features in terms of negative/positive correlations with the outcome variable.ResultsThe change in the age-standardized incidence rates over time is reflected in the changing importance of features as indicated by the mean SHAP values for the five periods. The first COVID-19 wave started as a disease in wealthy rural counties in southern Germany, and ventured into poorer urban and agricultural counties during the course of the first wave. The negative social gradient became more pronounced from the 2nd lockdown period onwards, when wealthy counties appeared to be better protected. Population density per se does not appear to be a risk factor, and only in the post-lockdown period did connectedness become an important regional characteristic correlated with higher infections. Features related to economic and educational characteristics of the young population in a county played an important role at the beginning of the pandemic up to the 2nd lockdown phase, as did features related to the population living in nursing homes; those related to international migration and a large proportion of foreigners living in a county became important in the post-lockdown period.DiscussionIn the absence of individual level data, explainable machine learning methods based on regional data may help to better understand the changing nature of the drivers of the pandemic. High mobility of high SES groups may drive the pandemic at the beginning of waves, while mitigation measures and beliefs about the seriousness of the pandemic as well as the compliance with mitigation measures put lower SES groups at higher risks later on.

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Introduction We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are. Methods We analyzed data from the largest German health insurance company, including 117,895 dementia‐free people age 65+. Follow‐up was 10 years. Predictors were: 23 age‐related diseases, 212 medical prescriptions, 87 surgery codes, as well as age and sex. Statistical methods included logistic regression (LR), gradient boosting (GBM), and random forests (RFs). Results Discriminatory power was moderate for LR (C‐statistic = 0.714; 95% confidence interval [CI] = 0.708–0.720) and GBM (C‐statistic = 0.707; 95% CI  = 0.700–0.713) and lower for RF (C‐statistic = 0.636; 95% CI  = 0.628–0.643). GBM had the best model calibration. We identified antipsychotic medications and cerebrovascular disease but also a less‐established specific antibacterial medical prescription as important predictors. Discussion Our models from German claims data have acceptable accuracy and may provide cost‐effective decision support for early dementia screening.