Excess mortality in adults with documented diabetes in Germany: routine data analysis of all insurance claims in Germany 2013-2014

Projecting the economic burden of type 1 and type 2 diabetes mellitus in Germany from 2010 until 2040

BACKGROUND

The aim is to estimate age- and sex-specific direct medical costs related to diagnosed type 1 and type 2 diabetes in Germany between 2010 and 2040.

METHODS

Based on nationwide representative epidemiological routine data from 2010 from the statutory health insurance in Germany (almost 80% of the population's insurance) we projected age- and sex-specific healthcare expenses for type 1 and 2 diabetes considering future demographic, disease-specific and cost trends. We combine per capita healthcare cost data (obtained from aggregated claims data from an almost 7% random sample of all German people with statutory health insurance) together with the demographic structure of the German population (obtained from the Federal Statictical Office), diabetes prevalence, incidence and mortality. Direct per capita costs, total annual costs, cost ratios for people with versus without diabetes and attributable costs were estimated. The source code for running the analysis is publicly available in the open-access repository Zenodo.

RESULTS

In 2010, total healthcare costs amounted to more than €1 billion for type 1 and €28 billion for type 2 diabetes. Depending on the scenario, total annual expenses were projected to rise remarkably until 2040 compared to 2010, by 1-281% for type 1 (€1 to €4 billion) and by 8-364% for type 2 diabetes (€30 to €131 billion). In a relatively probable scenario total costs amount to about €2 and €79 billion for type 1 and type 2 diabetes in 2040, respectively. Depending on annual cost growth (1% p.a. as realistic scenario vs. 5% p.a. as very extreme setting), we estimated annual per capita costs of €6,581 to €12,057 for type 1 and €5,245 to €8,999 for type 2 diabetes in 2040.

CONCLUSIONS

Diabetes imposes a large economic burden on Germany which is projected to increase substantially until 2040. Temporal trends in the incidence and cost growth are main drivers of this increase. This highlight the need for urgent action to prepare for the potential development and mitigate its consequences.

Using the illness-death model to estimate age- and sex-standardized incidence rates of diabetes in Mexico from 2003 to 2015

BACKGROUND

We aimed to estimate the age-specific and age-standardized incidence rate of diabetes for men and women in Mexico between 2003 and 2015, and to assess the relative change in incidence of diabetes between 2003 and 2015.

METHODS

We use a partial differential equation describing the illness-death model to estimate the incidence rate (IR) of diabetes for the years 2003, 2009 and 2015 based on prevalence data from National Health Surveys conducted in Mexico, the mortality rate of the Mexican general population and plausible input values for age-specific mortality rate ratios associated with diabetes.

RESULTS

The age-standardized IR of diabetes per 1000 person years (pryr) was similar among men (IRm) and women (IRw) in the year 2003 (IRm 6.1 vs. IRw 6.5 1000/pryr), 2009 (IRm: 7.0 vs. IRw: 8.4 1000/pryr), and in 2015 (IRm 8.0 vs. IRw 10.6 1000/pryr). The highest incident rates were observed among men and women in the 60-69 age group.

CONCLUSIONS

Overall, the incidence rate of diabetes in Mexico between the years 2003 and 2015 remained stable. However, rates were markedly higher among women in the age group 40-49 and 50-59 in the year 2015 compared with rates in 2003.

Mortality of type 2 diabetes in Germany: additional insights from Gompertz models

AIMS

The Gompertz law of mortality proclaims that human mortality rates in middle to old ages grow log-linearly with age and this law has been confirmed at multiple instances. We investigated if diabetes mortality in Germany also obeys to the Gompertz law and how this information helps to communicate diabetes mortality more intuitively.

METHODS

We analyzed all statutory health-insured persons in Germany in 2013 that were aged 30 years or older. Deaths in 2014 were recorded and given in 5-year age groups. We fitted weighted linear regression models (separately for females and males and for people with and without diabetes) and additionally computed the probability that a person with diabetes dies before a person of the same age and sex without diabetes, and the "diabetes age", that is, the additional years of mortality risk added to an individual's chronological age due to diabetes-related excess mortality.

RESULTS

We included N = 47,365,120 individuals, 6,541,181 of them with diabetes. In 2014, 763,228 deaths were recorded, among them 288,515 with diabetes. Diabetes mortality followed nearly perfectly Gompertz distributions. The probability that a person with diabetes dies before a person without diabetes was 61.9% for females and 63.3% for males.

CONCLUSIONS

Diabetes mortality for females and males aged 30 years or older in Germany in 2014 followed the Gompertz law of mortality. The survival information of the population with diabetes during a large part of the lifespan can thus be reduced to the two parameters of the Gompertz distribution.

Validation of Nutritional Approaches to Modulate Cardiovascular and Diabetic Risk Factors in Patients with Hypertriglyceridemia or Prediabetes-The MoKaRi II Randomized Controlled Study

Hypertriglyceridemia and diabetes mellitus type 2 are among the most important metabolic diseases globally. Diet plays a vital role in the development and progression of both clinical pictures. For the 10-week randomized, controlled, intervention study, 67 subjects with elevated plasma triglyceride (TG) concentrations (≥1.7 mmol/L) and 69 subjects with elevated fasting glucose concentrations (≥5.6 < 7.0 mmol/L) were recruited. The intervention groups received specially developed, individualized menu plans and regular counseling sessions to lower (A) TG or (B) fasting glucose and glycated hemoglobin A1c as well as other cardiovascular and diabetic risk factors. The hypertriglyceridemia intervention group was further supplemented with fish oil (3.5 g/d eicosapentaenoic acid + docosahexaenoic acid). The two control groups maintained a typical Western diet. Blood samples were taken every 2 weeks, and anthropometric data were collected. A follow-up examination was conducted after another 10 weeks. In both intervention groups, there were comparable significant reductions in blood lipids, glucose metabolism, and anthropometric parameters. These results were, with a few exceptions, significantly more pronounced in the intervention groups than in the corresponding control groups (comparison of percentage change from baseline). In particular, body weight was reduced by 7.4% (6.4 kg) and 7.5% (5.9 kg), low-density lipoprotein cholesterol concentrations by 19.8% (0.8 mmol/L) and 13.0% (0.5 mmol/L), TG concentrations by 18.2% (0.3 mmol/L) and 13.0% (0.2 mmol/L), and homeostatic model assessment for insulin resistance by 31.8% (1.1) and 26.4% (0.9) (p < 0.05) in the hypertriglyceridemia and prediabetes intervention groups, respectively. Some of these changes were maintained until follow-up. In patients with elevated TG or fasting glucose, implementing individualized menu plans in combination with regular counseling sessions over 10 weeks led to a significant improvement in cardiovascular and diabetic risk factors.

Future number of people with diagnosed type 1 diabetes in Germany until 2040: an analysis based on claims data

INTRODUCTION

We aim to project the number of people with diagnosed type 1 diabetes in Germany between 2010 and 2040.

RESEARCH DESIGN AND METHODS

We first estimate the age-specific and sex-specific incidence and prevalence of type 1 diabetes in Germany in 2010 using data from 65 million insurees of the German statutory health insurance. Then, we use the illness-death model to project the prevalence of type 1 diabetes until 2040. We alter the incidence and mortality underlying the illness-death model in several scenarios to explore the impact of possible temporal trends on the number of people with type 1 diabetes.

RESULTS

Applying the prevalence from 2010 to the official population projections of Germany's Federal Statistical Office yields a total number of 252 000 people with type 1 diabetes in Germany in 2040 (+1% compared with 2010). Incorporating different annual trends of the incidence and mortality in the projection model results in a future number of people with type 1 diabetes between 292 000 (+18%) and 327 000 (+32%).

CONCLUSIONS

For the first time in Germany, we provide estimates for the incidence, prevalence, and number of people with diagnosed type 1 diabetes for the whole German population between 2010 and 2040. The relative increase of the people with type 1 diabetes ranges from 1% to 32% in 2040 compared with 2010. The projected results are mainly influenced by temporal trends in the incidence. Ignoring these trends, that is, applying a constant prevalence to population projections, probably underestimates future chronic disease numbers.

[Algorithm for the Classification of Type 1 and Type 2 Diabetes Mellitus for the Analysis of Routine Data]

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Lifetime risk, life expectancy, and years of life lost to type 2 diabetes in 23 high-income jurisdictions: a multinational, population-based study

BACKGROUND

Diabetes is a major public health issue. Because lifetime risk, life expectancy, and years of life lost are meaningful metrics for clinical decision making, we aimed to estimate these measures for type 2 diabetes in the high-income setting.

METHODS

For this multinational, population-based study, we sourced data from 24 databases for 23 jurisdictions (either whole countries or regions of a country): Australia; Austria; Canada; Denmark; Finland; France; Germany; Hong Kong; Hungary; Israel; Italy; Japan; Latvia; Lithuania; the Netherlands; Norway; Scotland; Singapore; South Korea; Spain; Taiwan; the UK; and the USA. Our main outcomes were lifetime risk of type 2 diabetes, life expectancy in people with and without type 2 diabetes, and years of life lost to type 2 diabetes. We modelled the incidence and mortality of type 2 diabetes in people with and without type 2 diabetes in sex-stratified, age-adjusted, and calendar year-adjusted Poisson models for each jurisdiction. Using incidence and mortality, we constructed life tables for people of both sexes aged 20-100 years for each jurisdiction and at two timepoints 5 years apart in the period 2005-19 where possible. Life expectancy from a given age was computed as the area under the survival curves and lifetime lost was calculated as the difference between the expected lifetime of people with versus without type 2 diabetes at a given age. Lifetime risk was calculated as the proportion of each cohort who developed type 2 diabetes between the ages of 20 years and 100 years. We estimated 95% CIs using parametric bootstrapping.

FINDINGS

Across all study cohorts from the 23 jurisdictions (total person-years 1 577 234 194), there were 5 119 585 incident cases of type 2 diabetes, 4 007 064 deaths in those with type 2 diabetes, and 11 854 043 deaths in those without type 2 diabetes. The lifetime risk of type 2 diabetes ranged from 16·3% (95% CI 15·6-17·0) for Scottish women to 59·6% (58·5-60·8) for Singaporean men. Lifetime risk declined with time in 11 of the 15 jurisdictions for which two timepoints were studied. Among people with type 2 diabetes, the highest life expectancies were found for both sexes in Japan in 2017-18, where life expectancy at age 20 years was 59·2 years (95% CI 59·2-59·3) for men and 64·1 years (64·0-64·2) for women. The lowest life expectancy at age 20 years with type 2 diabetes was observed in 2013-14 in Lithuania (43·7 years [42·7-44·6]) for men and in 2010-11 in Latvia (54·2 years [53·4-54·9]) for women. Life expectancy in people with type 2 diabetes increased with time for both sexes in all jurisdictions, except for Spain and Scotland. The life expectancy gap between those with and without type 2 diabetes declined substantially in Latvia from 2010-11 to 2015-16 and in the USA from 2009-10 to 2014-15. Years of life lost to type 2 diabetes ranged from 2·5 years (Latvia; 2015-16) to 12·9 years (Israel Clalit Health Services; 2015-16) for 20-year-old men and from 3·1 years (Finland; 2011-12) to 11·2 years (Israel Clalit Health Services; 2010-11 and 2015-16) for 20-year-old women. With time, the expected number of years of life lost to type 2 diabetes decreased in some jurisdictions and increased in others. The greatest decrease in years of life lost to type 2 diabetes occurred in the USA between 2009-10 and 2014-15 for 20-year-old men (a decrease of 2·7 years).

INTERPRETATION

Despite declining lifetime risk and improvements in life expectancy for those with type 2 diabetes in many high-income jurisdictions, the burden of type 2 diabetes remains substantial. Public health strategies might benefit from tailored approaches to continue to improve health outcomes for people with diabetes.

FUNDING

US Centers for Disease Control and Prevention and Diabetes Australia.

Importance of Diagnostic Accuracy in Big Data: False-Positive Diagnoses of Type 2 Diabetes in Health Insurance Claims Data of 70 Million Germans

Large data sets comprising diagnoses of chronic conditions are becoming increasingly available for research purposes. In Germany, it is planned that aggregated claims data - including medical diagnoses from the statutory health insurance - with roughly 70 million insurants will be published regularly. The validity of the diagnoses in such big datasets can hardly be assessed. In case the dataset comprises prevalence, incidence, and mortality, it is possible to estimate the proportion of false-positive diagnoses using mathematical relations from the illness-death model. We apply the method to age-specific aggregated claims data from 70 million Germans about type 2 diabetes in Germany stratified by sex and report the findings in terms of the age-specific ratio of false-positive diagnoses of type 2 diabetes (FPR) in the dataset. The FPR for men and women changes with age. In men, the FPR increases linearly from 1 to 3 per 1,000 in the age group of 30-50 years. For age between 50 and 80 years, FPR remains below 4 per 1,000. After 80 years of age, we have an increase to approximately 5 per 1,000. In women, we find a steep increase from age 30 to 60 years, the peak FPR is reached at approximately 12 per 1,000 between 60 and 70 years of age. After age 70 years, the FPR of women drops tremendously. In all age groups, the FPR is higher in women than in men. In terms of absolute numbers, we find that there are 217,000 people with a false-positive diagnosis in the dataset (95% confidence interval, CI: 204-229), the vast majority being women (172,000, 95% CI: 162-180). Our work indicates that possible false-positive (and negative) diagnoses should appropriately be dealt with in claims data, for example, by the inclusion of age- and sex-specific error terms in statistical models, to avoid potentially biased or wrong conclusions.

Future prevalence of type 2 diabetes—A comparative analysis of chronic disease projection methods

Background

Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden.

Aim

To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040.

Methods

We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- and age-specific prevalence of T2D in 2010 with future population distributions projected by the German Federal Statistical Office (FSO). Methods 2) and 3) additionally account for the incidence of T2D and mortality rates using partial differential equations (PDEs). Method 2) models the prevalence of T2D employing a scalar PDE which incorporates incidence and mortality rates. Subsequently, the estimated prevalence is applied to the population projection of the FSO. Method 3) uses a two-dimensional system of PDEs and estimates future case numbers directly while future mortality of people with and without T2D is modelled independently from the projection of the FSO.

Results

Method 1) projects 3.6 million male people with diagnosed T2D in Germany in 2040. Compared to 2.8 million males in 2010, this equals an increase by 29%. Methods 2) and 3) project 5.9 million (+104% compared to 2010) and 6.0 million (+116%) male T2D patients, respectively.

Conclusions

The results of the three methods differ substantially. It appears that ignoring temporal trends in incidence and mortality may result in misleading projections of the future number of people with chronic diseases. Hence, it is essential to include these rates as is done by method 2) and 3).

Trends in the diabetes incidence and mortality in India from 1990 to 2019: a joinpoint and age-period-cohort analysis

Introduction

Globally, a metabolic disorder like Diabetes is considered as one of the largest global health issues, as it accounts for the majority of the disease burden and happens to be one of the leading causes of mortality as well as reduced life expectancy across the world. As in 2019, India is home to the second-largest number (77 million) of Diabetic adults and the number of people affected has been increasing rapidly over the years. Termed as "the diabetes capital of the world," with every fifth diabetic in the world being an Indian, there is an urgent need to address many critically significant challenges posed by Diabetes in India, like, increasing prevalence among young people in urban areas, less awareness among people, high cost of disease management, limited healthcare facilities, suboptimal diabetes control etc. In Indian context, not enough attempts have been made to observe and understand the long-term pattern of diabetes incidence and mortality. This study aims to provide deep insights into the recent trends of diabetes incidence and mortality in India from 1990 to 2019.

Materials and methods

This is an observational study based on the most recent data from the Global Burden of Disease (GBD) Study 2019. We extracted numbers, age-specific and age-standardized incidence and mortality rates of diabetes (from 1990 to 2019) from the Global Health Data Exchange. The average annual percentage changes in incidence and mortality were analysed by joinpoint regression analysis; the net age, period, and cohort effects on the incidence and mortality were estimated by age-period-cohort analysis.

Results

During the study period, age-standardized incidence and mortality rates of diabetes in India experienced an upsurge in numbers, the incidence rate increased from 199.14 to 317.02, and consequently, mortality increased from 22.30 to 27.35 per 100,000 population. The joinpoint regression analysis showed that the age-standardized incidence significantly rose by 1.63 % (95 % CI: 1.57 %, 1.69 %) in Indian males and 1.56 % in Indian females (95 % CI: 1.49 %, 1.63 %) from 1990 to 2019. On the other hand, the age-standardized mortality rates rose by 0.77 % (95 % CI: 0.24 %, 1.31 %) in Indian males and 0.57 % (95 % CI: -0.54 %, 1.70 %) in Indian females. For age-specific rates, incidence increased in most age groups, with exception of age groups 5-9, 70-74, 75-79 and 80-84 in male, and age groups 5-9, 75-79 and 80-84 in female. Mortality in male saw a decreasing trend till age group 20-24, whereas in female, the rate decreased till age group 35-39. The age effect on incidence showed no obvious changes with advancing age, but the mortality significantly increased with advancing age; period effect showed that both incidence and mortality increased with advancing time period; cohort effect on diabetes incidence and mortality decreased from earlier birth cohorts to more recent birth cohorts, while incidence showed no material changes from 1975 to 1979 to 2000-2004 birth cohort.

Conclusions

Mortality of diabetes decreased in younger age groups but increased in older age groups; however, Incidence increased in most age groups for both male and female. The net age or period effect showed an unfavourable trend while the net cohort effect presented a favourable trend. Aging was likely to drive a continued increase in the mortality of diabetes. Timely population-level interventions aiming for health education, lifestyle modification with special emphasis on the promotion of physical activity and healthy diet should be conducted, especially for male and earlier birth cohorts at high risk of diabetes.

Diabetes free life expectancy and years of life lost associated with type 2 diabetes: projected trends in Germany between 2015 and 2040

BACKGROUND

Type 2 diabetes (T2D) causes substantial disease burden and is projected to affect an increasing number of people in coming decades. This study provides projected estimates of life years free of type 2 diabetes (T2D) and years of life lost ([Formula: see text]) associated with T2D for Germany in the years 2015 and 2040.

METHODS

Based on an illness-death model and the associated mathematical relation between prevalence, incidence and mortality, we projected the prevalence of diagnosed T2D using currently available data on the incidence rate of diagnosed T2D and mortality rates of people with and without diagnosed T2D. Projection of prevalence was achieved by integration of a partial differential equation, which governs the illness-death model. These projected parameters were used as input values to calculate life years free of T2D and [Formula: see text] associated with T2D for the German population aged 40 to 100 years in the years 2015 and 2040, while accounting for different assumptions on future trends in T2D incidence and mortality.

RESULTS

Assuming a constant incidence rate, women and men at age 40 years in 2015 will live approximately 38 years and 33 years free of T2D, respectively. Up to the year 2040, these numbers are projected to increase by 1.0 years and 1.3 years. Assuming a decrease in T2D-associated excess mortality of 2% per year, women and men aged 40 years with T2D in 2015 will be expected to lose 1.6 and 2.7 years of life, respectively, compared to a same aged person without T2D. In 2040, these numbers would reduce by approximately 0.9 years and 1.6 years. This translates to 10.8 million and 6.4 million [Formula: see text] in the German population aged 40-100 years with prevalent T2D in 2015 and 2040, respectively.

CONCLUSIONS

Given expected trends in mortality and no increase in T2D incidence, the burden due to premature mortality associated with T2D will decrease on the individual as well as on the population level. In addition, the expected lifetime without T2D is likely to increase. However, these trends strongly depend on future improvements of excess mortality associated with T2D and future incidence of T2D, which should motivate increased efforts of primary and tertiary prevention.

Healthy life years among people with and without diabetes in Germany

In addition to life expectancy, the length of time a person can expect to remain free of health-related functional impairments is becoming increasingly important both for the individuals concerned and for society at large. The indicator healthy life years used for this purpose is a key figure for mapping mortality and morbidity. Diabetes is one of the most common chronic diseases and can be associated with health-related functional impairments. In 2014, women and men with diabetes could expect to have significantly fewer healthy life years than people without diabetes; this particularly applies to younger and middle-aged groups. Among 30- to 34-year-olds, for example, women and men with diabetes could expect eleven and twelve fewer healthy life years respectively than people without diabetes. These differences narrow with increasing age. Ensuring that people with and without diabetes have a similar length of lifetime free of health impairments is an important task for public health.