How weather triggers the emergence of bats from their subterranean hibernacula.

Hibernation is one of the most important behaviours of bats of the temperate zone. During winter, when little food or liquid water is available, hibernation in torpor lowers metabolic costs. However, the timing of emergence from hibernation is crucial for the resumption of the reproductive process in spring. Here, we investigate the spring emergence of six bat species or pairs of bat species of the genera Myotis and Plecotus at five hibernation sites in Central Europe over 5 years. Using generalized additive Poisson models (GAPMs), we analyze the influence of weather conditions (air and soil temperature, atmospheric pressure, atmospheric pressure trend, rain, wind, and cloud cover) as predictors of bat activity and separate these extrinsic triggers from residual motivation to emerge from hibernation (extrinsic factors not studied; intrinsic motivation). Although bats in a subterranean hibernaculum are more or less cut off from the outside world, all species showed weather dependence, albeit to varying degrees, with air temperature outside the hibernaculum having a significant positive effect in all species. The residual, potentially intrinsic motivation of species to emerge from their hibernacula corresponds to their general ecological adaptation, such as trophic specialization and roosting preferences. It allows the definition of three functional groups (high, medium and low residual activity groups) according to the degree of weather dependence of spring activity. A better knowledge of the interplay of extrinsic triggers and residual motivation (e.g., internal zeitgebers) for spring emergence will help to understand the flexibility of a species to adapt to a changing world.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time.

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

Adjusting selection bias in German health insurance records for regional prevalence estimation.

BACKGROUND: Regional prevalence estimation requires epidemiologic data with substantial local detail. National health surveys may lack in sufficient local observations due to limited resources. Therefore, corresponding prevalence estimates may not capture regional morbidity patterns with the necessary accuracy. Health insurance records represent alternative data sources for this purpose. Fund-specific member populations have more local observations than surveys, which benefits regional prevalence estimation. However, due to national insurance market regulations, insurance membership can be informative for morbidity. Regional fund-specific prevalence proportions are selective in the sense that the morbidity structure of a fund's member population cannot be extrapolated to the national population. This implies a selection bias that marks a major obstacle for statistical inference. We provide a methodology to adjust fund-specific selectivity and perform regional prevalence estimation from health insurance records. The methodology is applied to estimate regional cohort-referenced diabetes mellitus type 2 prevalence in Germany. METHODS: Records of the German Public Health Insurance Company from 2014 and Diagnosis-Related Group Statistics data are combined within a benchmarked multi-level model. The fund-specific selectivity is adjusted in a two-step procedure. Firstly, the conditional expectation of the insurance company's regional prevalence given related inpatient diagnosis frequencies of its members is quantified. Secondly, the regional prevalence is estimated by extrapolating the conditional expectation using corresponding inpatient diagnosis frequencies of the Diagnosis-Related Group Statistics as benchmarks. Model assumptions are validated via Monte Carlo simulation. Variable selection is performed via multivariate methods. The optimal model fit is determined by analysis of variance. 95% confidence intervals for the estimates are constructed via semiparametric bootstrapping. RESULTS: The national diabetes mellitus type 2 prevalence is estimated at 8.70% with a 95% confidence interval of [8.48%, 9.35%]. This indicates an adjustment of the original fund-specific prevalence from - 32.79 to - 25.93%. The estimated disease distribution shows significant morbidity differences between regions, especially between eastern and western Germany. However, the cohort-referenced estimates suggest that these differences can be partially explained by regional demography. CONCLUSIONS: The proposed methodology allows regional prevalence estimation in remarkable detail despite fund-specific selectivity. This enhances and encourages the use of health insurance records for future epidemiologic studies.

Association between maternal occupational status and utilization of antenatal care Study based on the perinatal survey of Baden-Wuerttemberg 1998-2003.

OBJECTIVES: Adequate utilization of antenatal care reduces the morbidity of mother and child. How frequent a pregnant woman attends antenatal care is dependent on many factors. The aim of this study was to assess the current influence of educational level and occupational status on maternal utilization of antenatal care under the conditions of an industrialized country and provision of universal coverage. METHODS: The perinatal database 1998-2003 of the German state of Baden-Wuerttemberg (556.948 pregnancies) was studied comparing antenatal care utilization for the different occupational categories obtained in the survey. For statistical analysis descriptive statistics and t test on equity of proportions for independent samples were used. RESULTS: As occupational groups at risk of insufficient antenatal care unskilled workers, trainees, students, and housewives were identified. High rates of utilization were found for the categories "top management/executive position" and "skilled workers". Rate of one or less consultations per pregnancy has declined significantly compared to 1998, but has increased again since 2000. Low utilization (2-5 consultations per pregnancy) has not decreased, showing rather constant differences between the occupational categories throughout the observed 6-year period. Unskilled workers, trainees, students, and housewives avail less of prenatal care above standard (more than ten consultations per pregnancy). CONCLUSIONS: Differences in an individual woman's use of antenatal care is, besides many other factors, associated with occupational status. Antenatal care promotion should target trainees, students, and unskilled workers prone to insufficient utilization and its consequences, an increase in obstetrical risk. For these groups, the occupational physician may play a key role in reaching the pregnant women on time, as obstetric care reaches them insufficiently and too late. Although housewives are the most numerous group, inhomogeneity regarding their educational level and previous occupational status has to be assumed, calling for further clinical studies to design appropriate interventional concepts.