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Zusammenfassung Hintergrund Diabetes mellitus ist eine Erkrankung von hoher Public-Health-Relevanz. Zur Schätzung der zeitlichen Entwicklung der Prävalenz kommen auch Routinedaten der gesetzlichen Krankenversicherung zum Einsatz. In diesen primär zu Abrechnungszwecken generierten Daten stellt die Definition von Erkrankungen eine besondere methodische Herausforderung dar. In der vorliegenden Studie wurde ein Algorithmus zur Unterscheidung von Typ-1- und Typ-2-Diabetes für die Analyse von Routinedaten entwickelt. Methodik Datengrundlage ist eine alters- und geschlechtsstratifizierte Zufallsstichprobe der Versicherten der Barmer mit einer durchgehenden Versicherungsdauer von 2010 bis 2018 in der Größenordnung von 1% der deutschen Bevölkerung. Ein Diabetes wurde im Berichtsjahr 2018 definiert als Dokumentation (1) einer gesicherten ICD-Diagnose E10.- bis E14.- im ambulanten Sektor in mindestens 2 Quartalen, (2) einer gesicherten ICD-Diagnose E10.- bis E14.- in einem Quartal mit einer zusätzlichen Antidiabetikaverordnung (ATC-Codes A10) oder (3) einer ICD-Diagnose E10.- bis E14.- im stationären Sektor, dem ambulanten Operieren oder der Arbeitsunfähigkeit. Anhand der ICD-Diagnosen E10.- bis E14.- und der verordneten Arzneimittel, unterschieden nach Insulin und anderen Antidiabetika, wurden die Personen einem Diabetestypen zugeordnet. Nicht eindeutige oder widersprüchliche Konstellationen wurden anhand des Alters, der Häufigkeit sowie überjährigen Betrachtung der Diagnosedokumentation zugeordnet. Die Einschreibung in ein Disease-Management-Programm wurde in einer Sensitivitätsanalyse berücksichtigt. Ergebnisse Die Prävalenz des dokumentierten Diabetes in der Stichprobe der Barmer betrug im Jahr 2018 8,8%. Anhand des Algorithmus konnten 98,5% der Personen mit Diabetes dem Typ-1-Diabetes (5,5%), dem Typ-2-Diabetes (92,6%) oder einer weiteren spezifischen Diabetesform (0,43%) zugeordnet werden. Somit ergaben sich für das Jahr 2018 Prävalenzen von 0,48% für den Typ-1-Diabetes und 8,1% für den Typ-2-Diabetes. Schlussfolgerung Bereits anhand weniger Merkmale wie Diagnosen, Arzneimittelverordnung und dem Alter lässt sich die große Mehrzahl der Personen mit Diabetes einem Typ zuordnen. In weiterführenden Studien sollte im Abgleich mit Primärdaten die externe Validität geprüft werden. So ermöglicht der Algorithmus die Auswertung wichtiger epidemiologischer Kennzahlen und der Häufigkeit von Folge- und Begleiterkrankungen auf Basis von Routinedaten differenziert nach Typ-1- und Typ-2-Diabetes, welche in der Surveillance des Diabetes zukünftig berücksichtigt werden sollen.

BACKGROUND: The German Diabetes Risk Score (GDRS) currently enables prediction of the individual risk of developing type 2 diabetes (T2D) within five years. The aim of this study is to extend the prediction period of the GDRS, including its non-clinical version and its HbA1c extension, to 10 years, and to perform external validation. METHODS: In data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study (n = 25 393), Cox proportional hazards regression was used to reweight the points that were used to calculate the five-year risk. Two population-based prospective cohorts (EPIC-Heidelberg n = 23 624, GNHIES98 cohort n = 3717) were used for external validation. Discrimination was represented by C-indices, and calibration by calibration plots and the expected-to-observed (E/O) ratio. RESULTS: Prediction performance in EPIC-Potsdam was very good (C-index for the non-clinical model: 0.834) and was confirmed in EPIC-Heidelberg (0.843) and in the GNHIES98 cohort (0.851). Among persons in the GNHIES98 cohort with a greater than 10% predicted probability of disease, 14.9% developed T2D within 10 years (positive predictive value). The models were very well calibrated in EPIC-Potsdam (E/O ratio for the non-clinical model: 1.08), slightly overestimated the risk in EPIC-Heidelberg (1.34), and predicted T2D very well in the GNHIES98 cohort after recalibration (1.06). CONCLUSION: The extended GDRS prediction period of 10 years, with a non-clinical version and an HbA1c extension that will soon be available in both German and English, enables the even longer-range, evidence-based identification of high-risk individuals with many different applications, including medical screening.

Diabetes mellitus is a disease of high public health relevance. To estimate the temporal development of prevalence, routine data of statutory health insurances (SHI) are being increasingly used. However, these data are primarily collected for billing purposes and the case definition of specific diseases remains challenging. In this study, we present an algorithm for differentiation of diabetes types analyzing SHI routine data.The basis for the analysis was an age and sex-stratified random sample of persons of the Barmer SHI with a continuous insurance duration from 2010 to 2018 in the magnitude of 1% of the German population. Diabetes was defined in the reporting year 2018, as documentation of (1) a "confirmed" ICD diagnosis E10.- to E14.- in at least two quarters, (2) a "confirmed" ICD diagnosis E10.- to E14.- in one quarter with an additional prescription of an antidiabetic drug (ATC codes A10), or (3) an ICD diagnosis E10.- to E14.- in the inpatient sector, outpatient surgery, or work disability. Individuals were assigned to a diabetes type based on the specific ICD diagnosis E10.- to E14.- and prescribed medications, differentiated by insulin and other antidiabetics. Still unclear or conflicting constellations were assigned on the basis of the persons' age or the frequency and observation of the diagnosis documentation over more than one year. The participation in a disease management program was considered in a sensitivity analysis.The prevalence of documented diabetes in the Barmer sample was 8.8% in 2018. Applying the algorithm, 98.5% of individuals with diabetes could be classified as having type 1 diabetes (5.5%), type 2 diabetes (92.6%), or another specific form of diabetes (0.43%). Thus, the prevalence was 0.48% for type 1 diabetes and 8.1% for type 2 diabetes in 2018.The vast majority of people with diabetes can be classified by their diabetes type on the basis of just a few characteristics, such as diagnoses, drug prescription, and age. Further studies should assess the external validity by comparing the results with primary data. The algorithm enables the analysis of important epidemiological indicators and the frequency of comorbidities based on routine data differentiated by type 1 and type 2 diabetes, which should be considered in the surveillance of diabetes in the future.Diabetes mellitus ist eine Erkrankung von hoher Public-Health-Relevanz. Zur Schätzung der zeitlichen Entwicklung der Prävalenz kommen auch Routinedaten der gesetzlichen Krankenversicherung zum Einsatz. In diesen primär zu Abrechnungszwecken generierten Daten stellt die Definition von Erkrankungen eine besondere methodische Herausforderung dar. In der vorliegenden Studie wurde ein Algorithmus zur Unterscheidung von Typ-1- und Typ-2-Diabetes für die Analyse von Routinedaten entwickelt.Datengrundlage ist eine alters- und geschlechtsstratifizierte Zufallsstichprobe der Versicherten der Barmer mit einer durchgehenden Versicherungsdauer von 2010 bis 2018 in der Größenordnung von 1% der deutschen Bevölkerung. Ein Diabetes wurde im Berichtsjahr 2018 definiert als Dokumentation (1) einer gesicherten ICD-Diagnose E10.- bis E14.- im ambulanten Sektor in mindestens 2 Quartalen, (2) einer gesicherten ICD-Diagnose E10.- bis E14.- in einem Quartal mit einer zusätzlichen Antidiabetikaverordnung (ATC-Codes A10) oder (3) einer ICD-Diagnose E10.- bis E14.- im stationären Sektor, dem ambulanten Operieren oder der Arbeitsunfähigkeit. Anhand der ICD-Diagnosen E10.- bis E14.- und der verordneten Arzneimittel, unterschieden nach Insulin und anderen Antidiabetika, wurden die Personen einem Diabetestypen zugeordnet. Nicht eindeutige oder widersprüchliche Konstellationen wurden anhand des Alters, der Häufigkeit sowie überjährigen Betrachtung der Diagnosedokumentation zugeordnet. Die Einschreibung in ein Disease-Management-Programm wurde in einer Sensitivitätsanalyse berücksichtigt.Die Prävalenz des dokumentierten Diabetes in der Stichprobe der Barmer betrug im Jahr 2018 8,8%. Anhand des Algorithmus konnten 98,5% der Personen mit Diabetes dem Typ-1-Diabetes (5,5%), dem Typ-2-Diabetes (92,6%) oder einer weiteren spezifischen Diabetesform (0,43%) zugeordnet werden. Somit ergaben sich für das Jahr 2018 Prävalenzen von 0,48% für den Typ-1-Diabetes und 8,1% für den Typ-2-Diabetes.Bereits anhand weniger Merkmale wie Diagnosen, Arzneimittelverordnung und dem Alter lässt sich die große Mehrzahl der Personen mit Diabetes einem Typ zuordnen. In weiterführenden Studien sollte im Abgleich mit Primärdaten die externe Validität geprüft werden. So ermöglicht der Algorithmus die Auswertung wichtiger epidemiologischer Kennzahlen und der Häufigkeit von Folge- und Begleiterkrankungen auf Basis von Routinedaten differenziert nach Typ-1- und Typ-2-Diabetes, welche in der Surveillance des Diabetes zukünftig berücksichtigt werden sollen.

There is evidence for an increased type 2 diabetes (T2D) risk associated with depression, but its role for diabetes prevention remains unclear. This study aimed to add insight by investigating the impact of major depressive disorder (MDD) on prospective glycaemic changes.The study was based on a cohort of n = 1,766 adults without diabetes (776 men, 990 women; 18-65 years of age) who participated in the mental health supplement of the German National Health Interview and Examination Survey (GNHIES98-MHS, 1997-1999) and in a follow-up survey (DEGS1, 2008-2011). Glycaemic status was defined as normoglycaemia [HbA1c39 mmol/mol (5.7%)], prediabetes [39 ≤ HbA1c 48 mmol/mol (5.7-6.4%)] and diabetes [HbA1c ≥ 48 mmol/mol (≥ 6.5%), diagnosed diabetes, or antidiabetic medication], and glycaemic changes categorized as 'remission', 'stability' and 'progression'. Baseline MDD was assessed via a modified German version of the WHO Composite International Diagnostic Interview. Multivariable logistic regressions were applied to analyse the association of MDD with glycaemic changes and incident T2D, adjusting for socio-demographics, lifestyle conditions, chronic diseases, antidepressant use and mental health care.MDD prevalence was 21.4% for women and 8.9% for men. Among women, MDD was associated with a lower chance for remission (RRR 0.43; 95% CI 0.23, 0.82). Among men, MDD was not significantly related to glycaemic changes. MDD had no significant effect on incident T2D (men: OR 1.58; 0.55, 4.52; women: OR 0.76; 0.37, 1.58).Findings of the current study highlight the role of depression in T2D prevention, particularly among women.

ObjectivesLittle is known about the age-specific excess mortality pattern of people with diagnosed diabetes in Germany. Thus, our goal was to determine the excess mortality in diagnosed diabetes overall and stratified by age and sex based on claims data.DesignRoutine data analysis using a claims dataset from all statutory health-insured persons in Germany in 2013, which accounts for about 90% of the population.ParticipantsWe included persons who lived in Germany, were insured at least 360 days, were not self-paying any health services and were aged 30 years or older leading to a total number of 47.3 million insured persons for analyses.ExposureDiabetes was determined by the International Classification of Diseases-10 codes E10–E14, which were documented in 2013 in at least two-quarters on an outpatient setting or at least once on an inpatient setting.Outcome measuresThe vital status in the study population was drawn from the claims dataset for the year 2014. We derived the excess mortality estimated as an age-adjusted mortality rate ratio (MRR) by sex and for age groups using a Poisson model.Main resultsWe found age-adjusted MRRs (95% CI) for diabetes of 1.52 (1.51 to 1.52) for women and 1.56 (1.56 to 1.56) for men. These figures declined with increasing age and were highest for ages 30–34 years with 6.76 (4.99 to 9.15) for women and 6.87 (5.46 to 8.64) for men, and lowest for age 95 years and older with 1.13 (1.10 to 1.15) for women and 1.11 (1.05 to 1.17) for men.ConclusionsWe derived deeply age-stratified figures on excess mortality in diabetes for Germany. Establishing a sustainable analysis of excess mortality is aimed at within the framework of diabetes surveillance.

People who have diabetes require regular medical care. The views of patients about the quality of their care are becoming increasingly relevant when it comes to chronic diseases such as diabetes. As part of the nationwide study Disease Knowledge and Information Needs - Diabetes mellitus (2017), data on self-assessed quality of care by people with diagnosed diabetes was collected using the Patient Assessment of Chronic Illness Care - DAWN short form (PACIC-DSF, scale 1 to 5) and analysed for respondents aged 45 years or above. The average score for quality of care was 2.47 and was lower for women than for men (2.33 vs 2.58). The respondents assessed the quality of their care as being worse with rising age and size of the population in their residential area. No significant differences were observed by education group. Overall, people with diabetes in Germany consider the quality of their care to be moderate, which indicates a need for improvement in care.

As a condition, diabetes mellitus is associated with risk factors and diseases such as obesity. At the same time, cardiovascular diseases are a frequent consequence of diabetes. There have yet to be any findings on the Germany-wide prevalence of diabetes and diabetes comorbidities based on statutory health insurance data. This study estimates the documented prevalence of diabetes in 2019 on the basis of all ambulatory physicians' claims data of German statutory health insurance. In addition, the prevalence of obesity, high blood pressure, coronary heart disease, heart failure, stroke and depression is calculated for diabetes and non-diabetes patients, and the prevalence ratio (PR) is determined as a quotient. The approach used was a case-control design, which assigns a control person without diabetes to each diabetes patient who is similar in terms of age, region and sex. In diabetes patients, a PR greater than 1 was observed for all examined diseases across all age groups, thus demonstrating a higher prevalence compared to persons without diabetes. The highest PR across all age groups for women (3.8) and men (3.7) was found for obesity. In a comparison over time, documented prevalence figures of diabetes in Germany stagnate. With the exception of depression, the documented prevalences of comorbidities correspond well with the prevalences found in population-wide examination surveys.

Background While life expectancy (LE) is rising, years lived with severe health impairments especially due to chronic diseases also increase. Diabetes is one of the major chronic diseases with high potential for co-morbidity and premature deaths needing life-long care. Therefore, information about loss in LE and healthy life years (HLY) in people with diabetes compared to people without diabetes is essential for assessing the burden of diabetes. Methods Data on all-cause mortality rates for the general population of Germany in 2014 was drawn from the Federal Statistical Office; mortality rate ratios for people with diabetes compared to people without diabetes were based on claims data from about 70 million people covered by statutory health insurances in 2014. Data of three nationwide health telephone surveys conducted among adults in Germany 2009-2012 (n ∼ 60,000) were used to assess severe health impairments defined as self-reported limitations in daily activities due to diseases for at least six months in people with and without diabetes. Based on these figures, estimates on LE and HLY could be calculated by sex and 5-year age-groups for people with and without diabetes aged ≥ 30 years. Results In both sexes and in all 5-year age-groups, LE and HLY were substantially lower for people with than for people without diabetes. For example, among women in the age group 30-34 years, LE and HLY estimates were 48.0 and 36.4 years for those with diabetes compared to 54.9 and 47.6 years for those without diabetes; in men, these figures were estimated as 42.6 and 32.4 years for those with diabetes compared to 50.3 and 44.1 years for those without diabetes. Differences in LE and HLY between people with and without diabetes attenuated with increasing age. Conclusions The present study revealed substantial reductions in LE and HLY related to diabetes and underlines the importance of integrating both figures in a national diabetes surveillance. Key messages There are substantial differences in life expectancy and healthy life years between people with and without diabetes. Specific intervention and prevention activities should be implemented to tackle disability in persons with diabetes.

Background Patient-assessed quality of chronic illness care is important to guide medical care for patients with diabetes and other complex chronic diseases, but information from epidemiological studies is scarce. Thus, we examined self-assessed quality of care among adults with type 2 diabetes (T2D) based on a population-based design. Methods The study population was drawn from a nationwide survey on diabetes-related knowledge and information needs conducted in Germany in 2017 and included participants aged ≥18 years with known type 2 diabetes (T2D) in the last 12 months (n = 1,328). A German short version of the “Patient assessment of chronic illness care (PACIC-DSF)” consisting of 9 items based on 5-point Likert scale was applied to assess self-reported quality of care in diabetes which was operationalized by a standardized PACIC sum score ranging from 1 to 5. Linear regression with different stages of adjustment was applied to assess the association of basic characteristics and diabetes-related factors with the PACIC score. Results Quality of care was assessed less favorably by women than by men (PACIC score: 2.38 vs. 2.47) overall and decreased along with age. The PACIC score significantly increased in participants with insulin use (β = 0.16, p = 0.024), ever participating in a diabetes education program (β = 0.33, p Conclusions Self-assessed quality of care by adults with known T2D from this population-based study is moderate and seems lower compared to findings from clinical studies. Key messages An active involvement of people with type 2 diabetes into the implementation of care is essential and may contribute to improved self-perceived quality of care. To identify and overcome obstacles in diabetes care based on the patient’s perspective remains a public health challenge.

Background Non-communicable diseases (NCD) are a major public health challenge in Germany and throughout the world. The epidemiology of diabetes, in particular type 2 diabetes, exemplifies the need for effective prevention and control. To support policy making with actionable evidence, the Robert Koch Institute (RKI) is developing a diabetes surveillance system for Germany serving as model for NCD surveillance. Methods First, we consented a conceptual framework and key indicators among experts and stakeholders based on an extensive literature review including national and international NCD surveillance systems. After prioritization of indicators applying a two-step Delphi method, we identified data sources for sustainable surveillance including data from nationwide RKI health surveys as well as external data such as claims or registry data. Lastly, in cooperation with stakeholders we developed first dissemination products. Results During concept phase, we identified 40 indicators ranging from risk factors, disease incidence and prevalence to quality of care, mortality and burden of disease. During implementation, suitable data sources and results on temporal trends have been obtained for a large part of the indicators. For dissemination we developed a website with interactive visualization of results supported by an explainer video on YouTube. Additionally, we prepared a printed diabetes report summarizing and interpreting key findings for a broader audience, including health politicians and public health researchers. Conclusions We demonstrated the feasibility of the systematic collection and analysis of health data to describe the disease and care situation of diabetes in Germany. The methodology and data sources of indicators can be transferred to further NCDs and shared risk factors are already depicted. Next steps are to close remaining data gaps and to advance dissemination products in collaboration with our stakeholder network tailored to their information needs. Key messages Considering available health data, we showed the feasibility of implementing a diabetes surveillance system for Germany providing reliable information on disease dynamics for various stakeholders. The knowledge on methodology and data sources gained establishing a diabetes surveillance system can be extended to other noncommunicable diseases.

Eine Kernaufgabe von Public Health ist die kontinuierliche Erfassung und Analyse von gesundheitsbezogenen Daten zu relevanten Krankheiten (Surveillance). Sie dient der zeitnahen Umsetzung von Maßnahmen zum Schutz der Gesundheit in der Bevölkerung. Dafür müssen relevante Informationen zur richtigen Zeit und in geeigneter Weise für die entscheidenden Zielgruppen bereitgestellt werden (Dissemination). Eine Disseminationsstrategie unterstützt die effektive Ergebniskommunikation und berücksichtigt 3 Kernfragen: (1) „Was sind die relevanten Inhalte der Surveillance?“, (2) „Wer benötigt welche Informationen?“ und (3) „Wie werden die Ergebnisse den Zielgruppen bereitgestellt?“ Die Digitalisierung eröffnet hierbei neue Möglichkeiten für die Gestaltung der Formate. Seit 2015 wird am Robert Koch-Institut die Diabetes-Surveillance aufgebaut. In einem strukturierten Konsensprozess wurden 4 gesundheitspolitisch relevante Handlungsfelder mit 40 Kennzahlen (Indikatoren) definiert. Anschließend wurden gemeinsam mit dem wissenschaftlichen Projektbeirat unter Berücksichtigung neuer Möglichkeiten durch die Digitalisierung erste Publikationsformate erarbeitet. Neben Artikeln in Fachzeitschriften stellen der Bericht „Diabetes in Deutschland“ und eine Webseite mit interaktiver Visualisierung der Ergebnisse die wichtigsten Formate der ersten Projektphase dar. Begleitend werden Twitter und Youtube für die Erhöhung der Reichweite genutzt. In der nächsten Projektphase steht neben der Weiterentwicklung des Indikatorensets der Ausbau der Dissemination hin zu einer nutzer- und handlungsorientierten Berichterstattung im Mittelpunkt. In engem Austausch mit dem wissenschaftlichen Beirat sollen Anforderungen der Zielgruppen erfasst und in der Entwicklung weiterer Formate berücksichtigt werden.

ZusammenfassungEine Kernaufgabe von Public Health ist die kontinuierliche Erfassung und Analyse von gesundheitsbezogenen Daten zu relevanten Krankheiten (Surveillance). Sie dient der zeitnahen Umsetzung von Maßnahmen zum Schutz der Gesundheit in der Bevölkerung. Dafür müssen relevante Informationen zur richtigen Zeit und in geeigneter Weise für die entscheidenden Zielgruppen bereitgestellt werden (Dissemination).Eine Disseminationsstrategie unterstützt die effektive Ergebniskommunikation und berücksichtigt 3 Kernfragen: (1) „Was sind die relevanten Inhalte der Surveillance?“, (2) „Wer benötigt welche Informationen?“ und (3) „Wie werden die Ergebnisse den Zielgruppen bereitgestellt?“ Die Digitalisierung eröffnet hierbei neue Möglichkeiten für die Gestaltung der Formate.Seit 2015 wird am Robert Koch-Institut die Diabetes-Surveillance aufgebaut. In einem strukturierten Konsensprozess wurden 4 gesundheitspolitisch relevante Handlungsfelder mit 40 Kennzahlen (Indikatoren) definiert. Anschließend wurden gemeinsam mit dem wissenschaftlichen Projektbeirat unter Berücksichtigung neuer Möglichkeiten durch die Digitalisierung erste Publikationsformate erarbeitet. Neben Artikeln in Fachzeitschriften stellen der Bericht „Diabetes in Deutschland“ und eine Webseite mit interaktiver Visualisierung der Ergebnisse die wichtigsten Formate der ersten Projektphase dar. Begleitend werden Twitter und Youtube für die Erhöhung der Reichweite genutzt.In der nächsten Projektphase steht neben der Weiterentwicklung des Indikatorensets der Ausbau der Dissemination hin zu einer nutzer- und handlungsorientierten Berichterstattung im Mittelpunkt. In engem Austausch mit dem wissenschaftlichen Beirat sollen Anforderungen der Zielgruppen erfasst und in der Entwicklung weiterer Formate berücksichtigt werden.

IntroductionAs a population-wide intervention, it has been proposed to raise taxes on unhealthy products to prevent diseases such as type 2 diabetes. In this study, we aimed to estimate the effect of tax policy interventions in 2020 on the projected prevalence and number of people with type 2 diabetes in the German adult population in 2040.Research design and methodsWe applied an illness-death model and the German Diabetes Risk Score (GDRS) to project the prevalence and number of adults with type 2 diabetes in Germany under a base case scenario and under a tax policy intervention scenario. For the base case scenario, we assumed constant age-specific incidence rates between 2020 and 2040. For the intervention scenario, we assumed a 50% price increase for sugar-sweetened beverages, tobacco and red meat products in the year 2020. Based on price elasticities, we estimated the impact on these risk factors alone and in combination, and calculated subsequent reductions in the age-specific and sex-specific GDRS. These reductions were used to determine reductions in the incidence rate and prevalence using a partial differential equation.ResultsCompared with the base case scenario, combined tax interventions in 2020 resulted in a 0.95 percentage point decrease in the prevalence of type 2 diabetes (16.2% vs 17.1%), which corresponds to 640 000 fewer prevalent cases of type 2 diabetes and a relative reduction by 6%.ConclusionsTaxation of sugar-sweetened beverages, tobacco products and red meat by 50% modestly lowered the projected number and prevalence of adults with type 2 diabetes in Germany in 2040. Raising taxes on unhealthy products as a stand-alone measure may not be enough to attenuate the future rise of type 2 diabetes.

The COVID-19 pandemic is posing major challenges to the health care sector. This scoping review compiles evidence concerning changes to health care service availability and utilisation as well as possible impacts on health for selected groups of chronically ill people in Germany. The focus is on cancer, cardiovascular diseases, diabetes mellitus and mental disorders. Most empirical data available concerned inpatient care and showed a clear decline in the utilisation of inpatient treatments in March and April 2020 in the areas of oncology and cardiology as well as in mental health. For cardiovascular emergencies such as heart attack and stroke, a decline was observed especially regarding less serious cases. Although there were indications of treatment delays, there was no evidence thus far that emergency care had been generally compromised due to adjustments to inpatient care capacities. In the outpatient setting, extensive adjustments to health care services availability were observed for all disease groups considered. Overall, very limited empirical data were available. In particular, hardly any data were available on how changes in care impacted population health. There is an urgent need for continuous surveillance and evaluation based on health care and epidemiological data.

IntroductionTo identify characteristics of people with undiagnosed type 2 diabetes (T2D) among adults in Germany.Research design and methodsThe study population comprised participants aged 40–79 years of the German Health Interview and Examination Survey for Adults 2008–2011. Glycemic status was categorized as undiagnosed T2D (glycated hemoglobin A1c (HbA1c) ≥48 mmol/mol (6.5%), n=135), diagnosed T2D (n=518) and normoglycemia (HbA1cResultsThe prevalence of undiagnosed T2D was 2.9% (95% CI 2.2% to 3.9%) at an overall prevalence of 12.3% (11.0% to 13.6%) of persons with undiagnosed or diagnosed T2D. In multivariable analyses, factors associated with undiagnosed as well as diagnosed T2D in comparison to normoglycemia were older age (OR 1.04, 95% CI 1.01 to 1.06, per year, for undiagnosed T2D; OR 1.08, 1.07 to 1.10 for diagnosed T2D), male sex (3.33, 2.18 to 5.07; 1.91, 1.43 to 2.56), obesity (3.47, 2.17 to 5.56; 2.68, 2.04 to 3.52), hypertension (1.66, 1.09 to 2.53; 2.04, 1.42 to 2.95) and parental history of diabetes (2.04, 1.24 to 3.35; 3.16, 2.30 to 4.34). Variables independently associated with undiagnosed T2D but not diagnosed T2D included living alone (2.20; 1.36 to 3.56) and not seeing a doctor within the past year (2.57; 1.34 to 4.93). People with undiagnosed T2D were further younger and more likely to be male sex and reside in the western part of Germany than people with diagnosed T2D.ConclusionApart from major known risk factors of diabetes, characteristics specific to undiagnosed diabetes among adults in Germany will serve to inform the national education and communication strategy on diabetes mellitus in Germany.

Diabetes mellitus ist eine nichtubertragbare Krankheit von hoher Public-Health-Relevanz. In Deutschland verfugbare Daten werden bislang nicht ausreichend fur eine zielorientierte Analyse und Bereitstellung von Ergebnissen genutzt. Ziel ist der Aufbau einer nationalen Diabetes-Surveillance am Robert Koch-Institut (RKI). Erarbeitet wird ein wissenschaftliches Rahmenkonzept mit 4 Handlungsfeldern und 40 Indikatoren auf Basis von Literaturreviews und strukturiertem Expertenkonsens. Es erfolgt eine Bestandsaufnahme zu verfugbaren Datenquellen und nachhaltiger Nutzbarkeit zur Abbildung der Indikatoren nach soziodemografischen Kriterien, zeitlichen Veranderungen und regionalen Unterschieden. Handlungsfeld 1 umfasst 10 Indikatoren zur Primarpravention von Diabetes. Die Handlungsfelder 2 bis 4 enthalten 30 Prozess- und Ergebnisindikatoren zur Sekundarpravention von Diabetes. Essenzielle Datenquellen sind bundesweite RKI-Gesundheitssurveys, Abrechnungs‑/Versorgungsdaten, amtliche Statistiken und Typ-1-Diabetes-Register. Erste Analysen zeigen eine Verbesserung in Prozessen und Ergebnissen. Bei patientennahen Indikatoren weisen die Ergebnisse auf Verbesserungspotenziale hin. Verbleibende Datenlucken und Nutzungsbarrieren bestehen bezuglich des Gestationsdiabetes und diabetesassoziierter Komplikationen. Analysen zu sozialen und regionalen Unterschieden sind bislang nicht uberall moglich. Die Diabetes-Surveillance ermoglicht systematische und fortlaufende Analysen zur Epidemiologie und Krankheitslast von Diabetes in Deutschland und kann als Entscheidungshilfe fur die Planung und Umsetzung von Masnahmen dienen. Datengrundlagen sowie Print- und Online-Produkte fur eine zeitnahe und adressatengerechte Ergebnisdarstellung mussen kontinuierlich weiterentwickelt werden.

Diabetes mellitus and other noncommunicable diseases (NCDs) represent an emerging global public health challenge. In Germany, about 6.7 million adults are affected by diabetes according to national health surveys, including 1.3 million with undiagnosed diabetes. Complications of diabetes result in an increasing burden for individuals and society as well as enormous costs for the health care system. In response, the Federal Ministry of Health commissioned the Robert Koch Institute (RKI) to implement a diabetes surveillance system and the Federal Center for Health Education (BZgA) to develop a diabetes prevention strategy. In a two-day workshop jointly organized by the RKI and the BZgA, representatives from public health institutes in seven countries shared their expertise and knowledge on diabetes prevention and surveillance. Day one focused on NCD surveillance systems and emphasized both the strengthening of sustainable data sources and the timely and targeted dissemination of results using innovative formats. The second day focused on diabetes prevention strategies and highlighted the importance of involving all relevant stakeholders in the development process to facilitate its acceptance and implementation. Furthermore, the effective translation of prevention measures into real-world settings requires data from surveillance systems to identify high-risk groups and evaluate the effect of measures at the population level based on analyses of time trends in risk factors and disease outcomes. Overall, the workshop highlighted the close link between diabetes prevention strategies and surveillance systems. It was generally stated that only robust data enables effective prevention measures to encounter the increasing burden from diabetes and other NCDs.

Unzureichend behandelt kann die Stoffwechselerkrankung Diabetes mellitus zu schwerwiegenden Folgeerkrankungen fuhren. Nationale und internationale Analysen zeigen einen kontinuierlichen Anstieg der Diabetespravalenz uber die letzten Jahre. Am Robert Koch-Institut (RKI) wird derzeit eine Nationale Diabetes-Surveillance aufgebaut, die das Diabetesgeschehen indikatorenbasiert unter Nutzung von Primar- und Sekundardaten kontinuierlich abbilden soll. Ziel des Bund-Lander-Gesprachs war es, die Bundeslander fruhzeitig in die Konzeption der Diabetes-Surveillance einzubinden und die Bedarfe der Gesundheitsberichterstattung (GBE) der Lander zu berucksichtigen. Das RKI lud alle Vertreter der Lander-GBE zu einem Gesprach nach Berlin ein. Dies war bereits das zweite Bund-Lander-Gesprach im Rahmen der Diabetes-Surveillance. Ein kontinuierlicher Austausch in Form regelmasiger Bund-Lander-Gesprache wird angestrebt.

Epidemiologische Daten belegen, dass Diabetes mellitus in Deutschland wie in vielen anderen Landern eine hohe Public-health-Relevanz hat. Am Robert Koch-Institut (RKI) wird derzeit eine Nationale Diabetes-Surveillance aufgebaut, die das Diabetesgeschehen indikatorenbasiert unter Nutzung von Primar- und Sekundardaten kontinuierlich abbilden soll. Ziel des Workshops war es, eine Bestandsaufnahme verfugbarer Sekundardatenquellen vorzunehmen und Dateninhalte, Datenzugange, Analysebeispiele sowie Moglichkeiten einer verstetigten Nutzung fur die Diabetes-Surveillance zu diskutieren.

Background Diabetes mellitus is a non-communicable disease with high public health relevance in Germany. Therefore, the Robert Koch Institute startetd to establish a national diabetes surveillance system in Germany in 2016. Essential part of a surveillance system is the dissemination of health information to the relevant stakeholders to facilitate its translation into action. Methods For the development of the dissemination strategy, different sources of information were used. First, an online-survey combined with structured web-research explored dissemination approaches in the international context. Secondly, a workshop with public health experts from seven countries highlighted best practice examples. Based on this information, a dissemination strategy was developed in close alignment with the scientific advisory board of the diabetes surveillance including experts from politics, prevention research, regional health reporting, physicians/diabetologists and public health scientists. Results Three main questions were identified to be considered in the dissemination of health information: Which audience group is addressed? Which content is relevant to this group? What is the preferred format to communicate the information? Therefore, a diabetes report will be generated focusing on health policy makers summarizing the key information on the disease dynamic. A website containing all information including a visualization tool should facilitate access for stakeholders. Additionally, awareness needs to be raised using social media, classical media and direct interactions with relevant stakeholders. Conclusions In alignment with different stakeholder groups, the diabetes surveillance developed a dissemination strategy considering international best practice examples and innovative ideas for health data visualization. The next steps are to refine the dissemination strategy based on feedback from the different stakeholder groups to ensure the use of the provided information. Key messages A dissemination strategy for the diabetes surveillance has been developed in a participatory approach including experts from various stakeholder groups. Next steps include the refinement of the dissemination strategy and its formats based on stakeholders feedback requirements.

The connection between social disadvantage and the presence of known diabetes and specific risk factors is well documented. This article summarises the results from the Robert Koch Institute examination surveys that were conducted between 1997 and 1999 as well as 2008 and 2011 to address social inequality – operationalised by level of education – with regard to prevalences of known and unknown diabetes, risk of diabetes and care of diabetes as well as their development over time. Both survey periods showed that the low education group has higher prevalences of known and unkown diabetes as well as a higher risk of developing diabetes within the next five years compared to the medium and high education group. Over time, prevalence tended to increase for known diabetes and to decrease for unknown diabetes for all education groups. For the 5-year diabetes risk, only the high education group showed a clear decrease over time. The chosen indicators of diabetes care indicated no clear differences between education groups and an improvement of diabetes care over time. For some indicators of care (foot examination, statins), improvements were only seen in the low education group. In conclusion, social inequalities in the prevalence of known and unknown diabetes as well as in diabetes risk remain in Germany; for the indicators of care, however, no clear education gradient is evident. Over time, inequality regarding the prevalence of diabetes has not increased further. However, with regard to diabetes risk, inequality has become slightly more evident. For individual care indicators, improvements are limited to specific education groups.

Ein Zusammenhang von sozialer Benachteiligung mit dem Vorliegen eines bekannten Diabetes und einzelner Risikofaktoren ist gut belegt. Der Artikel fasst Ergebnisse der Untersuchungssurveys des Robert Koch-Instituts der Zeiträume 1997 bis 1999 und 2008 bis 2011 zusammen, um die soziale Ungleichheit – operationalisiert über den Bildungsstatus – hinsichtlich der Prävalenzen des bekannten und unerkannten Diabetes, des Erkrankungsrisikos und der Versorgung des Diabetes sowie deren zeitlicher Entwicklung zu untersuchen. Für beide Surveyzeiträume zeigen sich in der unteren Bildungsgruppe höhere Prävalenzen des bekannten und unerkannten Diabetes sowie ein höheres 5-Jahres-Diabetesrisiko im Vergleich zur mittleren und oberen Bildungsgruppe. Im Zeitverlauf sind für alle Bildungsgruppen in der Tendenz ein Prävalenzanstieg des bekannten Diabetes und eine Prävalenzabnahme des unerkannten Diabetes zu beobachten. Für das 5-Jahres- Diabetesrisiko ist im Zeitverlauf nur für die obere Bildungsgruppe eine deutliche Abnahme ersichtlich. Für die ausgewählten Versorgungsindikatoren lassen sich insgesamt keine ausgeprägten Bildungsunterschiede und im Zeitverlauf eine Verbesserung erkennen. Für einige Versorgungsindikatoren (Fußuntersuchung, Statine) beschränkt sich die Verbesserung auf die untere Bildungsgruppe. Schlussfolgernd besteht in Deutschland weiterhin soziale Ungleichheit in der Prävalenz des bekannten und unerkannten Diabetes sowie im Diabetesrisiko, jedoch ist kein deutlicher Bildungsgradient für die Versorgungsindikatoren zu beobachten. Über die Zeit hat sich die Ungleichheit in der Diabetesprävalenz nicht weiter verschärft, hinsichtlich des Diabetesrisikos jedoch leicht vergrößert. Für einzelne Versorgungsindikatoren beschränken sich Verbesserungen auf bestimmte Bildungsgruppen.

BACKGROUND Evidence suggests that mobile health app use is beneficial for the prevention and management of type 2 diabetes (T2D) and its associated complications; however, population-based research on specific determinants of health app use in people with and without T2D is scarce. OBJECTIVE This cross-sectional study aimed to provide population-based evidence on rates and determinants of health app use among adults with and without T2D, thereby covering a prevention perspective and a diabetes management perspective, respectively. METHODS The study population included 2327 adults without a known diabetes diagnosis and 1149 adults with known T2D from a nationwide telephone survey in Germany conducted in 2017. Rates of smartphone ownership and health app use were estimated based on weighted sample proportions. Among smartphone owners, determinants of health app use were identified for both groups separately in multivariable logistic regression models. Sociodemographic factors, diabetes-related factors or indicators, psychological and health-related factors, and physician-provided information were selected as potential determinants. RESULTS Among participants without known diabetes, 74.72% (1690/2327) were smartphone owners. Of those, 49.27% (717/1690) used health apps, most often to improve regular physical activity. Among participants with T2D, 42.26% (481/1149) were smartphone owners. Of those, 41.1% (171/481) used health apps, most commonly to target a healthy diet. Among people without known diabetes, determinants significantly (all P values PP=.004, respectively), overweight or obesity (ORs 1.58; PPP=.045), former or current smoking (ORs 1.51; P=.002 and 1.58; PPP=.002), and having received health advice from a physician (OR 1.48; PP=.04 and 0.23; PP=.007), female gender (OR 1.61; P=.02), and using a glucose sensor in addition or instead of a glucose meter (OR 2.74; P=.04) were significantly positively associated with health app use. CONCLUSIONS In terms of T2D prevention, age, diabetes-related risk factors, psychological and health-related factors, and medical health advice may inform app development for specific target groups. In addition, health professionals may encourage health app use when giving advice on health behaviors. Concerning T2D management, only a few determinants seem relevant for explaining health app use among people with T2D, indicating a need for more future research on which people with T2D use health apps and why.

Background Random glucose is widely measured in epidemiological studies and in the clinical setting when standardized fasting protocols and oral glucose tolerance testing or HbA1c measuring are not feasible. The relationship between random glucose and all-cause mortality has hardly been studied so far and was examined in the present study. Methods We ascertained mortality status among 5955 persons aged 18–79 years and free of known diabetes when participating in the German National Health Interview and Examination Survey 1998 (mean observation time 11.7 years, 458 deaths). Cox regression was applied to analyze the association of random serum glucose with all-cause mortality taken potential confounders into account. Relative mortality risks were estimated as hazard ratios (HRs) with 95% confidence intervals (CIs) modeling random glucose as categorical or continuous variable. Results Compared to random glucose levels of 4.3

Mainly because of the large number of people affected and associated significant health policy implications, the Robert Koch Institute (RKI) is developing a public health surveillance system using diabetes as an example. In a first step to ensure long-term and comparable data collection and establish efficient surveillance structures, the RKI has defined a set of relevant indicators for diabetes surveillance. An extensive review of the available literature followed by a structured process of consensus provided the basis for a harmonised set of 30 core and 10 supplementary indicators. They correspond to the following four fields of activity: (1) reducing diabetes risk, (2) improving diabetes early detection and treatment, (3) reducing diabetes complications, (4) reducing the disease burden and overall costs of the disease. In future, in addition to the primary data provided by RKI health monitoring diabetes surveillance needs to also consider the results from secondary data sources. Currently, barriers to accessing this data remain, which will have to be overcome, and gaps in the data closed. The RKI intentends to continuously update this set of indicators and at some point apply it also to further chronic diseases with high public health relevance.

AIMS To examine the association between glycaemic status and depressive symptoms in a nationwide sample of the adult population in Germany. METHODS We conducted a cross-sectional analysis of data from 6385 participants aged 18-79 years in the nationwide German Health Interview and Examination Survey for Adults 2008-2011 (DEGS1). Glycaemic status was classified as follows: diagnosed diabetes (self-reported diagnosis or receiving antidiabetes medication); undiagnosed diabetes (HbA1c ≥48 mmol/mol [≥6.5%]); prediabetes (HbA1c 39-47 mmol/mol [5.7-6.4%]); or normoglycaemia (HbA1c

Aims Nationally representative data on temporal changes in the prevalence of diagnosed diabetes, as well as undiagnosed diabetes and prediabetes, have been lacking in Germany as in most other European countries. We aimed to fill this gap using data from nationwide examination surveys of German adults. Methods The study population comprised 18-79-year-old participants from the German Health Interview and Examination Surveys in 1997-1999 (GNHIES98, n = 6655) and 2008-2011 (DEGS1, n = 7017). Participants were classified as having diagnosed diabetes based on self-reported physician-diagnosed diabetes or the use of anti-diabetes agents. Among those without diagnosed diabetes, HbA1c measures were used to define undiagnosed diabetes [≥ 48 mmol/mol (≥ 6.5%)] or prediabetes [39-47 mmol/mol (5.7-6.4%)]. Results Although the age- and sex-standardized prevalence of total diabetes remained stable between 1997-1999 at 9.3% (95% CI 8.3-10.5%) and 2008-2011 at 9.2% (8.3-10.3%), the prevalence of diagnosed diabetes increased from 5.6% (4.9-6.3%) to 7.2% (6.5-8.0%), whereas the prevalence of undiagnosed diabetes decreased from 3.8% (3.1-4.6%) to 2.0% (1.5-2.7%), resulting in a decreased proportion of undiagnosed diabetes (40.9% vs. 21.7%). Over the same period, the prevalence of prediabetes decreased from 27.7% (25.6-29.8%) to 20.8% (18.2-23.7%). Observed temporal changes were not explained by changes in BMI, sport activity and educational level. Conclusions The two nationwide surveys indicate a shift from undiagnosed to diagnosed diabetes. However, the unchanged prevalence of total diabetes and the considerably high proportion of prediabetes strongly call for a continued and concerted effort in diabetes prevention among German adults.

Objective To estimate age-specific and sex-specific all-cause mortality among adults with and without type 2 diabetes (T2D) in Germany. Research design and methods The German National Health Interview and Examination Survey 1998 (GNHIES98) included a mortality follow-up (median follow-up time 12.0 years) of its nationwide sample representative of the population aged 18–79 years. After exclusion of participants with type 1 diabetes, age- and sex-stratified mortality rates (MR) were calculated for 330 GNHIES98 participants with diagnosed T2D (self-reported diagnosis or antidiabetic medication), 245 with undiagnosed T2D (no diagnosed T2D, glycated hemoglobin A1c ≥6.5% (≥48 mmol/mol)), and 5975 without T2D. Mortality rate ratios (MRR) comparing MR of persons with and without T2D were estimated. Age-/sex-standardized MR and MRR were calculated including persons aged 45 years or older. MRR were used to estimate the number of years of life lost (YLL) due to diagnosed diabetes in 2010. Results Over 75 994 person-years, 73 persons with undiagnosed T2D, 103 with diagnosed T2D, and 425 persons without T2D died. MRR were significantly higher in younger age groups, except for analyses limited to women or diagnosed T2D. Age- and sex-standardized MRR (95% CI) among persons aged 45 years or older were 1.96 (1.41 to 2.71) for undiagnosed, 1.68 (1.26 to 2.23) for diagnosed, and 1.82 (1.45 to 2.28) for total (undiagnosed or diagnosed) T2D. Sex-stratified analysis revealed similar age-standardized MRR for undiagnosed (1.56 (0.79 to 3.06)) and diagnosed T2D (1.56 (1.03 to 2.37)) among women, and a higher age-standardized MRR for undiagnosed (2.06 (1.43 to 2.97)) than diagnosed T2D (1.70 (1.10 to 2.63)) among men. YLL due to diagnosed diabetes in Germany in 2010 were 164 600 (35 000 to 279 300) among women and 169 900 (28 300 to 328 300) among men. Conclusions In Germany, age- and sex-standardized all-cause mortality is almost twice as high for adults with T2D as for adults without T2D. The T2D-associated excess risk of mortality appears to be most pronounced in younger adults and among men unaware of their T2D.

Insufficiently treated diabetes mellitus can lead to severe comorbidities. National and international analyses show a continuous increase in diabetes prevalence over the last decades. Currently, an indicator-based national diabetes surveillance system is implemented at the Robert Koch Institute (RKI) to monitor and report on diabetes development on the basis of available primary and secondary data. The aim of the meeting was to go into deeper discussions and to integrate expectations and expertise of the federal states into the design of the national surveillance system. A close collaboration between the RKI and the federal states is intended.