Dispensation Patterns of Glucose-Lowering Drugs in Newly Diagnosed Type 2 Diabetes: Routine Data Analysis of Insurance Claims in Germany.

AIMS: To describe dispensation patterns of glucose-lowering drugs in newly diagnosed type 2 diabetes in Germany. MATERIALS AND METHODS: Based on claims data from four statutory health insurances (German Pharmacoepidemiological Research Database,>25 million insurants), all individuals with newly diagnosed type 2 diabetes were identified. Eligible patients had a first diagnosis for type 2 diabetes between January 2012 and December 2016. We analyzed the dispensation patterns of first-line glucose-lowering therapies initiated in the year after diabetes diagnosis and patterns of second-line therapies dispensed one year after first-line treatment. RESULTS: A total of 356,647 individuals with newly diagnosed type 2 diabetes were included (average age [SD]: 63.5 [13.4] years; 49.3% males). Of the 31.6% of individuals who were pharmacologically treated in the year after diagnosis, metformin monotherapy was most frequently dispensed (73.1%), followed by dual therapy of metformin and dipeptidyl peptidase-4 inhibitors (DPP-4is) (6.4%), and monotherapy with DPP-4is (2.9%). From 2012 through 2016, sulfonylurea dispensations were reduced by more than 50%. Dispensations for combination therapies with DPP-4is increased up to 10.6%. Glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors contributed to 2% of all treatments. After a median of 5 months, 20.0% of individuals on pharmacological therapy initiated second-line glucose-lowering treatment. CONCLUSIONS: Data from German statutory health insurances (2012 to 2016) showed that most individuals with newly diagnosed type 2 diabetes were dispensed metformin monotherapy in line with diabetes care guidelines. A substantial decrease in the use of sulfonylureas was observed after the introduction of DPP-4i and GLP-1 receptor agonists.

[The investigation of health outcomes in the German National Cohort: the most relevant endpoints and their assessment]. / Erforschung von Erkrankungen in der NAKO Gesundheitsstudie. Die wichtigsten gesundheitlichen Endpunkte und ihre Erfassung.

The focus of the German National Cohort, the largest population-based cohort study in Germany to date, is the investigation of the most important widespread diseases, such as cardiovascular diseases, diabetes, cancer, neurological and psychiatric disorders, and frequent respiratory and infectious diseases. This cohort will answer questions on the development of these diseases and on the impact of genetic, environmental and lifestyle-related risk factors. Another focus is on the identification of early, subclinical markers of emerging diseases. To answer these questions, a comprehensive assessment of these health outcomes as well as of all potential determinants and precursors is mandatory.This paper describes the various health outcomes that are assessed in the German National Cohort, as well as the examination modules that are applied for deep phenotyping of study participants. Repeated collection of biosamples as well as functional measurements and application of modern imaging techniques at various time points allow for assessing the dynamics of physiological changes related to the individuals' health status. The prognostic value of these changes for disease development will be explored and translated to novel approaches for prevention and personalised medicine. Incident diseases are being assessed through self-reports by study participants and through record linkage with data from health insurances and cancer registries. Additional information about clinical diagnoses is obtained from the treating physicians to ensure the highest possible validity.

[The baseline assessment of the German National Cohort (NAKO Gesundheitsstudie): participation in the examination modules, quality assurance, and the use of secondary data]. / Die Basiserhebung der NAKO Gesundheitsstudie: Teilnahme an den Untersuchungsmodulen, Qualitätssicherung und Nutzung von Sekundärdaten.

BACKGROUND: The German National Cohort (NAKO) is an interdisciplinary health study aimed at elucidating causes for common chronic diseases and detecting their preclinical stages. This article provides an overview of design, methods, participation in the examinations, and their quality assurance based on the midterm baseline dataset (MBD) of the recruitment. METHODS: More than 200,000 women and men aged 20-69 years derived from random samples of the German general population were recruited in 18 study centers (2014-2019). The data collection comprised physical examinations, standardized interviews and questionnaires, and the collection of biomedical samples for all participants (level 1). At least 20% of all participants received additional in-depth examinations (level 2), and 30,000 received whole-body magnet resonance imaging (MRI). Additional information will be collected through secondary data sources such as medical registries, health insurances, and pension funds. This overview is based on the MBD, which included 101,839 participants, of whom 11,371 received an MRI. RESULTS: The mean response proportion was 18%. The participation in the examinations was high with most of the modules performed by over 95%. Among MRI participants, 96% completed all 12 MRI sequences. More than 90% of the participants agreed to the use of complementary secondary and registry data. DISCUSSION: Individuals selected for the NAKO were willing to participate in all examinations despite the time-consuming program. The NAKO provides a central resource for population-based epidemiologic research and will contribute to developing innovative strategies for prevention, screening and prediction of chronic diseases.

[Digital public health-an overview]. / Digital Public Health ­ ein Überblick.

The rapid development and proliferation of digital health technologies have not only changed the medical professions, but offer great potential for public health, particularly in health promotion and disease prevention.At the same time, this emerging field is also characterized by conceptual and terminological fuzziness, a marked lack of high-quality evidence, and an absence of an honest discussion of unintended consequences and side effects. Further challenges for digital public health lie in the fact that the development of new health technologies is mainly driven by technological progress and less by evidence-based needs and research in public health.In this overview paper, we aim at conceptually denoting the field of digital public health, using principal public health functions as guiding principles. We discuss some current applications of digital health technologies in fulfilling public health functions and propose a needs-based development of digital health technologies.We will further address specific challenges to digital public health, in particular socio-economic differences in the usage of and profiting from digital health technologies, data protection and privacy issues, as well as ethical issues.

Modeling physical activity data using L0 -penalized expectile regression.

In recent years accelerometers have become widely used to objectively assess physical activity. Usually intensity ranges are assigned to the measured accelerometer counts by simple cut points, disregarding the underlying activity pattern. Under the assumption that physical activity can be seen as distinct sequence of distinguishable activities, the use of hidden Markov models (HMM) has been proposed to improve the modeling of accelerometer data. As further improvement we propose to use expectile regression utilizing a Whittaker smoother with an L0 -penalty to better capture the intensity levels underlying the observed counts. Different expectile asymmetries beyond the mean allow the distinction of monotonous and more variable activities as expectiles effectively model the complete distribution of the counts. This new approach is investigated in a simulation study, where we simulated 1,000 days of accelerometer data with 1 and 5 s epochs, based on collected labeled data to resemble real-life data as closely as possible. The expectile regression is compared to HMMs and the commonly used cut point method with regard to misclassification rate, number of identified bouts and identified levels as well as the proportion of the estimate being in the range of ±10% of the true activity level. In summary, expectile regression utilizing a Whittaker smoother with an L0 -penalty outperforms HMMs and the cut point method and is hence a promising approach to model accelerometer data.

[Detection of drug risks after approval : Methods development for the use of routine statutory health insurance data]. / Aufdeckung von Arzneimittelrisiken nach der Zulassung : Methodenentwicklung zur Nutzung von Routinedaten der gesetzlichen Krankenversicherungen.

Adverse drug reactions are among the leading causes of death. Pharmacovigilance aims to monitor drugs after they have been released to the market in order to detect potential risks. Data sources commonly used to this end are spontaneous reports sent in by doctors or pharmaceutical companies. Reports alone are rather limited when it comes to detecting potential health risks. Routine statutory health insurance data, however, are a richer source since they not only provide a detailed picture of the patients' wellbeing over time, but also contain information on concomitant medication and comorbidities.To take advantage of their potential and to increase drug safety, we will further develop statistical methods that have shown their merit in other fields as a source of inspiration. A plethora of methods have been proposed over the years for spontaneous reporting data: a comprehensive comparison of these methods and their potential use for longitudinal data should be explored. In addition, we show how methods from machine learning could aid in identifying rare risks. We discuss these so-called enrichment analyses and how utilizing pharmaceutical similarities between drugs and similarities between comorbidities could help to construct risk profiles of the patients prone to experience an adverse drug event.Summarizing these methods will further push drug safety research based on healthcare claim data from German health insurances which form, due to their size, longitudinal coverage, and timeliness, an excellent basis for investigating adverse effects of drugs.

Familial Resemblance in Dietary Intakes of Children, Adolescents, and Parents: Does Dietary Quality Play a Role?

Information on familial resemblance is important for the design of effective family-based interventions. We aimed to quantify familial correlations and estimate the proportion of variation attributable to genetic and shared environmental effects (i.e., familiality) for dietary intake variables and determine whether they vary by generation, sex, dietary quality, or by the age of the children. The study sample consisted of 1435 families (1007 mothers, 438 fathers, 1035 daughters, and 1080 sons) from the multi-center I.Family study. Dietary intake was assessed in parents and their 2-19 years old children using repeated 24-h dietary recalls, from which the usual energy and food intakes were estimated with the U.S. National Cancer Institute Method. Food items were categorized as healthy or unhealthy based on their sugar, fat, and fiber content. Interclass and intraclass correlations were calculated for relative pairs. Familiality was estimated using variance component methods. Parent-offspring (r = 0.11-0.33), sibling (r = 0.21-0.43), and spouse (r = 0.15-0.33) correlations were modest. Parent-offspring correlations were stronger for the intake of healthy (r = 0.33) than unhealthy (r = 0.10) foods. Familiality estimates were 61% (95% CI: 54-68%) for the intake of fruit and vegetables and the sum of healthy foods and only 30% (95% CI: 23-38%) for the sum of unhealthy foods. Familial factors explained a larger proportion of the variance in healthy food intake (71%; 95% CI: 62-81%) in younger children below the age of 11 than in older children equal or above the age of 11 (48%; 95% CI: 38-58%). Factors shared by family members such as genetics and/or the shared home environment play a stronger role in shaping children's intake of healthy foods than unhealthy foods. This suggests that family-based interventions are likely to have greater effects when targeting healthy food choices and families with younger children, and that other sorts of intervention are needed to address the intake of unhealthy foods by children.

Dietary Patterns of European Children and Their Parents in Association with Family Food Environment: Results from the I.Family Study.

The aim of this study was to determine whether an association exists between children's  and  parental  dietary  patterns  (DP),  and  whether  the  number  of  shared  meals  or  soft  drink  availability  during  meals  strengthens  this  association.  In  2013/2014  the  I.Family  study  cross-sectionally assessed the dietary intakes of families from eight European countries using 24-h  dietary recalls. Usual energy and food intakes from six- to 16-year-old children and their parents  were estimated based on the NCI Method. A total of 1662 child-mother and 789 child-father dyads  were included; DP were derived using cluster analysis. We investigated the association between  children's and parental DP and whether the number of shared meals or soft drink availability  moderated this association using mixed effects logistic regression models. Three DP comparable in  children and parents were obtained: Sweet & Fat, Refined Cereals, and Animal Products. Children  were more likely to be allocated to the Sweet & Fat DP when their fathers were allocated to the  Sweet & Fat DP and when they shared at least one meal per day (OR 3.18; 95% CI 1.84; 5.47). Being  allocated to the Sweet & Fat DP increased when the mother or the father was allocated to the Sweet  & Fat DP and when soft drinks were available (OR 2.78; 95% CI 1.80; 4.28 or OR 4.26; 95% CI 2.16;  8.41, respectively). Availability of soft drinks and negative parental role modeling are important  predictors of children's dietary patterns.

Pester power and its consequences: do European children's food purchasing requests relate to diet and weight outcomes?

OBJECTIVE: Children may influence household spending through 'pester power'. The present study examined pestering through parent-child food shopping behaviours in relation to children's diet and weight status. DESIGN: Cross-sectional and prospective analyses drawn from the IDEFICS study, a cohort study of parents and their children. Children's height and weight were measured and their recent diets were reported by parental proxy based on the Children's Eating Habits Questionnaire-FFQ at baseline and 2-year follow-up. Parents also completed questionnaires at both time points about pestering, including whether the child goes grocery shopping with them, asks for items seen on television and is bought requested food items. SETTING: Participants were recruited from eight European countries for the IDEFICS study (non-nationally representative sample). SUBJECTS: Study participants were children aged 2-9 years at enrolment and their parents. A total of 13 217 parent-child dyads were included at baseline. Two years later, 7820 of the children were re-examined. RESULTS: Most parents (63 %) at baseline reported 'sometimes' acquiescing to their children's requests to purchase specific foods. Pestering was modestly associated with weight and diet. At baseline, children whose parents 'often' complied consumed more high-sugar and high-fat foods. Children who 'often' asked for items seen on television were likely to become overweight after 2 years (OR=1·31), whereas 'never' asking protected against overweight (OR=0·72). CONCLUSIONS: Pestering was modestly related to diet and weight in cross-sectional, but not longitudinal analyses. Asking for items seen on television had the most robust relationships across child outcomes and over time.

Prospective associations between socio-economic status and dietary patterns in European children: the Identification and Prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants (IDEFICS) Study.

Exploring changes in children's diet over time and the relationship between these changes and socio-economic status (SES) may help to understand the impact of social inequalities on dietary patterns. The aim of the present study was to describe dietary patterns by applying a cluster analysis to 9301 children participating in the baseline (2-9 years old) and follow-up (4-11 years old) surveys of the Identification and Prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants Study, and to describe the cluster memberships of these children over time and their association with SES. We applied the K-means clustering algorithm based on the similarities between the relative frequencies of consumption of forty-two food items. The following three consistent clusters were obtained at baseline and follow-up: processed (higher frequency of consumption of snacks and fast food); sweet (higher frequency of consumption of sweet foods and sweetened drinks); healthy (higher frequency of consumption of fruits, vegetables and wholemeal products). Children with higher-educated mothers and fathers and the highest household income were more likely to be allocated to the healthy cluster at baseline and follow-up and less likely to be allocated to the sweet cluster. Migrants were more likely to be allocated to the processed cluster at baseline and follow-up. Applying the cluster analysis to derive dietary patterns at the two time points allowed us to identify groups of children from a lower socio-economic background presenting persistently unhealthier dietary profiles. This finding reflects the need for healthy eating interventions specifically targeting children from lower socio-economic backgrounds.

Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.

INTRODUCTION: The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. METHODS: 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. RESULTS: The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). CONCLUSIONS: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.

Hospitalization rates of generic metoprolol compared with the original beta-blocker in an epidemiological database study.

PURPOSE: A less favourable galenic profile of generic formulations of the beta-blocker metoprolol raised the concern of a higher risk for serious cardiovascular (CV) events. We assessed hospital admission rates for CV diseases and prescription prevalences of various drugs using claims data of statutory health insurances (SHIs) to compare the incidence of serious CV events among users of original and generic metoprolol. Index events included hospitalization due to myocardial infarction, hypertensive crisis and stroke. METHODS: Data files of three SHIs were linked with dispensing data of drug prescriptions from each pharmacy's electronic data processing centre on an individual basis. Incidences of hospital admissions among patients receiving original metoprolol and among patients treated with the generic equivalent were compared by logistic regression, stratified for Bremen and the rest of Northern Germany. Risk estimates and confidence intervals were adjusted for confounders. RESULTS: A total of 49,673 patients receiving metoprolol were identified within a cohort of 3,649,285 insurance members. While the crude analysis revealed a higher risk for index events in patients receiving the generic drug (Bremen: RR 1.45; Northern Germany: RR 1.14), no elevated risk remained after confounder adjustment (Bremen: OR 1.06; Northern Germany: OR 1.04). Among co-morbid conditions considered as confounders, a previous CV event and an elevated thromboembolic risk exerted the strongest effect on index events. CONCLUSIONS: SHI data are a valuable source for pharmacoepidemiology and health services research in Germany. Incidence rates of serious CV events did not reveal any noticeable differences between the original and the generic group after confounder adjustment.

Undernutrition in Benin-an analysis based on graphical models.

Undernutrition is one of the most important health problems in developing countries. Examining its determinants implies the investigation of a complex association structure including a large number of potential influence variables and different types of influences. A recently developed statistical technique to cope with such situations are graphical chain models. In this paper, this approach is used to investigate the determinants of undernutrition in Benin (West Africa). Since this method also reveals indirect influences, interesting insight is gained into the association structure of all variables incorporated. The analysis identifies mother's education, socioeconomic status, and religion as three variables with particularly strong direct and indirect linkages to undernutrition.