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

Objectives Little 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. Design Routine data analysis using a claims dataset from all statutory health-insured persons in Germany in 2013, which accounts for about 90% of the population. Participants We 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. Exposure Diabetes 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 measures The 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 results We 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. Conclusions We 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.

[Major] 1. Introduction: Mortality rate by 5 years of age in Caucasian country may have been already published. The researchers could introduce it and explain why they need to analyse it in Germany. Also, they could present the rates of representative Caucasians, Orientals and Africans as global epidemiologic data. 2. Analysis: If it is possible, instead of index of MMR, excess mortality rate may have more impact for readers in medical science. 3. The outcome is single as all-cause mortality. Having diabetes results in CVD and cancer. Isn't it possible to utilise the other major death causes? 4. Diabetes types: Generally, patients with type 1 and 2 diabetes have difference prognoses. Is it possible to analyse mortality rate by diabetes types? There also exists gestational diabetes and the other diabetes. 5. Table 1: If it is possible, instead of mortality rate, expected lifespan at each age group may have more impact; The index would be more useful. This may be difficult for researchers. I wonder that integration of the inverse of the presented mortality by 5 age may enable presenting expected life-span. 6. Figure 2: The exponential increase in mortality with advanced age may not be new finding in epidemiology of diabetes. This figure may ensure the data accuracy. However, I consider that a single figure could independently contain new finding. What is the new finding in the figure 2? Is it difference between datrav and destatis? I may be able to propose drawing the exponential curve of the two sexes in a single figure to compare the sex difference in Germany, or inserting the exponential curves from data of another nation. 7. Figure 3: Transposition of longitudinal and horizontal axes may increase understandability of the data.
[Minor] 8. Table 1 and 2: M and MMR need to be spelled out. Tables and  figures need to stand alone with understandability. Although I requested many points, I consider that this manuscript would contribute to epidemiology of diabetes, when it would be revised.

GENERAL COMMENTS
This is a very well conducted analysis on the mortality rate of patients with and without diabetes using the dataset of health insurance claims in Germany. Authors' conclusions is that the public system for diabetes surveillance should be improved, especially for the higher risk groups (younger people).
There are some, minor issues that need to be addressed: 1.In the abstract please avoid to use expressions such as "for the first..." 2.The discussion is quite long and should be more focused on the use of these results to improve diabetes surveillance.
3. No data are reported on the impact of therapy. It would be interesting to link mortality to type of therapy and adherence.

REVIEWER(S) COMMENTS:
General note to the reviewer and editorial team: We would like to thank Dr. Sicari and Dr. Yokomichi for their helpful comments and recommendations. As you can see, we have discussed every point and made changes were possible. We chose blue marking for discussion and red marking for change.
Reviewer: 1 Reviewer Name: Hiroshi Yokomichi Institution and Country: University of Yamanashi, Japan Competing interests: None declared.
Comments to the Author Paper by Dr. Schmidt C et al treated all-cause mortality in having and not having diabetes, using health insurance data of Germany. I would like to provide comments to increase impact of the manuscript.
[Major] 1. Introduction: Mortality rate by 5 years of age in Caucasian country may have been already published. The researchers could introduce it and explain why they need to analyse it in Germany. Also, they could present the rates of representative Caucasian people, Oriental people and African people as global epidemiologic data. Second, to emphasize the novelty and potential contribution of our study to public health, we clarify our objectives by underline the potential of the study data: Lines 111-113: Due to partly conflicting findings stated above, further research is needed to increase knowledge on diabetes-related excess mortality, especially with respect to differences in magnitude by age, sex, region and time trend. Lines 116-118: As almost 90% of the population is covered by statutory health insurance, this data source has enormous potential for public health research, including detailed analyses of mortality patterns. Lines 122-128: Up to now, diabetes-related MRRs from the age of 30 years in 5-years age bands have not been available for the German population. Against this background our main aim was to provide for the first time estimates of MRRs related to diabetes within strata of narrow age bands and sex for Germany and thus adds important knowledge in diabetes-related excess mortality. Deeply stratified mortality rates based on valid data are important for the surveillance of diabetes in Germany, as they allow a comparison over time and with other countries. Third, Dr. Yokomichi's last point concerns the presentation of mortality rates, particularly for Caucasian people. This is very important because we know that ethnicity is a potential factor in the development and progression of diabetes. Due to the missing information about the ethnic origin in the study data, this issue can not be addressed in our study. Clearly, it is a further limitation of routine data. But we take the ethnicity into consideration from line 402 onwards: In general, another limitation of routine data is that this data cannot identify the ethnicity of individuals. An ethnic risk profile is being discussed for diabetes in particular (https://doi.org/10.1136/bmj.k1497). This reference is a paper published in BMJ 2018 which estimated prevalence in the US population and categorized ethnicity into Hispanic, non-Hispanic white, non-Hispanic black, non-Hispanic Asian, and other. For example, type 2 diabetes was higher among non-Hispanic Asians. 2. Analysis: If it is possible, instead of index of MMR, excess mortality rate may have more impact for readers in medical science. Reply: One important application of the mortality rate ratios provided in our paper is to calculate excess deaths related to diabetes for Germany. We adapted the methods from a study conducted by Jacobs et al. 3. We analyzed all-cause mortality to derive excess mortality on the common assumption that diabetes is related to mortality via various paths. Certainly, CVD is one of the main death causes. In contrast to register data as used for example by Tancredi et al., our study data do not include the cause of death. In future work, we will process queries to address certain comorbidities together with diabetes to estimate all-cause mortality in diabetes associated with different comorbidities.
4. Diabetes types: Generally, patients with type 1 and 2 diabetes have difference prognoses. Is it possible to analyse mortality rate by diabetes types? There also exists gestational diabetes and the other diabetes. Reply: Type 1, type 2 and other forms may differ in terms of mortality. As we already stated out from line 422 onwards in our manuscript, there are difficulties in routine data (not exclusively there) to differentiate the types of diabetes in valid manner.
Because we see the reviewer's point we now emphasis this limitation as one of the five bullet points in the "strengths and limitations" just after the abstract. We have not distinguished the type of diabetes because routine data contain implausible double diagnoses of type 1 and type 2 diabetes in the same person. 5.  Figure 2: The exponential increase in mortality with advanced age may not be new finding in epidemiology of diabetes. This figure may ensure the data accuracy. However, I consider that a single figure could independently contain new finding. What is the new finding in the figure 2? Is it difference between datrav and destatis? I may be able to propose drawing the exponential curve of the two sexes in a single figure to compare the sex difference in Germany, or inserting the exponential curves from data of another nation. Reply: Since we use the concept of all-cause mortality, a good match of counted deaths in the study data compared to all counted deaths in the official statistics is very important for validity. This match is not natural. Otherwise, if the match were not as good as shown in Figure 2, our mortality rates would be biased and the derived mortality rates would not be valid for the German population. Thanks to the advice of Dr. Yokomichi given in point 2 above, we calculated absolute excess deaths for woman and men for Germany and compare them with a former study using mortality rates coming from a different country. Obviously, using valid mortality rate ratios change the excess deaths related to diabetes. This example demonstrates how important a good match between official death statistics and the study data is. 7. Figure 3: Transposition of longitudinal and horizontal axes may increase understandability of the data. Reply: We tried several options but in the end concluded that changing the axes didn't help, but we did change the design and style to hopefully make Figure 3 easier to understand.
[Minor] 8. Table 1 and 2: M and MMR need to be spelled out. Tables and figures need to stand alone with understandability. Reply: We changed the headings in N in million (delete M in columns) and Mortality rate ratios.
Although I requested many points, I consider that this manuscript would contribute to epidemiology of diabetes, when it would be revised.
Thank you!