In this paper the method of redistributing IDD to valid CoD, as carried out in the German BoD study BURDEN 2020, has been illustrated. Whereas some parts of the method like identification of IDD and target codes were adopted from the GBD study, the redistribution and the approach to calculate uncertainties were in large parts own developments. Our aim was to depict how CoD statistics can be made suitable for BoD estimations and beyond. Since 26% of all deaths in Germany in 2017 are defined as IDD, we observe considerable changes in cases numbers before and after redistribution. For the specific CoD stroke, diabetes, and respiratory infections the numbers more than double.
The method used for the redistribution is in part an adaptation of the GBD method, adjusted to the German data. The results show that there are some significant differences, when comparing the final CoD data. The GBD study (2017) was estimating about 15,000 more deaths in 2017 in Germany than the German national CoD statistics . This difference is most likely due to the additional correction steps that IHME applies when assessing the data quality and population coverage. The difference in the total number of deaths is reflected also in the cause specific number of deaths. However, the proportions of the main groups of CoD are very much comparable between the GBD and the German BURDEN 2020 study.
The development and implementation of methodologies for redistributing IDD is also done in other countries [21, 22, 27, 28]. The methods applied mainly reflect the available country specific data. Accordingly, the handling of IDD in national BoD studies differs significantly. In Scotland the CoD statistics include information on multiple CoD and in some cases deaths can be linked to individual clinical records. Thus, it is possible to develop a more precise, country specific method of identifying and redistributing IDD . Likewise, Australia has developed an own algorithm of redistributing IDD. It includes several methods such as data linkage for obtaining additional information, usage of multiple CoD statistics, and proportional redistribution . Other countries that lack multiple CoD statistics are forced to rely on alternative methods. For instance, Brazil has performed further research based on information from different health service providers or verbal autopsy. In cases where the actual CoD was not possible to be defined a proportional redistribution method was applied . In the Netherlands experts pursue a one-number-policy, they do not redistribute IDD but instead use the CoD statistics without adjustment .
The above described method of redistribution is applied on the federal state level. It must be considered that the subnational differences in mortality registration procedures influence the quality of the data and the regional amount of IDD [19, 31]. Thus, the uncertainty bands are of high importance additionally depicting the variation in quality of CoD registration between the regions.
Limitations and Strengths
The adopted method used the definition of IDD as developed by IHME as part of the GBD study. However, it must be considered that in many cases scientists and physicians may have different perspectives on which diseases should be defined as IDD. For instance, for some CoD there is no consensus whether the condition should be classified as underlying CoD or as a sequela of another disease, the second being an intermediate or secondary CoD. A critical example is septicemia, which is considered an IDD in the GBD study, with its own redistribution package. This assessment is controversially discussed, as some experts see septicemia, at least for a part of the reported deaths, as the underlying CoD .
Another limitation of the study is the lack of multiple CoD data. The redistribution methodology in Germany could be largely improved should this kind of data become available. Further research is underway to test possible redistribution methods using multiple CoD data for some regions in Germany. Related to this, a further limitation of the applied method is the assumption that the valid CoD present the true distribution of valid codes. To overcome this issue, in BURDEN 2020 uncertainty intervals supporting the interpretation of results are provided indicating the margin in which the actual death counts may vary.
Advantageously, the applied redistribution method is transparent and comprehensible. Another strength of the study is the high quality of the German mortality data, especially with regard to registering the correct number and the age and sex of the deceased. In Germany almost full coverage of all deaths can be assumed and hence no methods for correction of possible underreporting must be applied. For many other countries, where mortality data do not have the same quality, and consequently a lower coverage, the GBD study has developed methods for corrections . Another strength of the study, is the redistribution of IDD on a subnational level. As shown before  the quality of the CoD statistics in Germany differs strongly between the federal states. Additionally, we generally expect and observe differing mortality patterns across the federal states [33, 34], e.g. due to differences in age structure and socio-economic status [35, 36].
The method described here reflects the availability of data in Germany. It is the first comprehensive redistribution of IDD within the CoD statistics for Germany. Further methodological developments are possible. We have only analyzed data from one year (2017). Looking at trend data (3-year or 5-year period) might limit random variations in the CoD data. Other aspects of the improvement include the usage of multiple CoD data which will allow a better determination of the target codes and the redistribution proportions. Furthermore, the selection of the target codes needs a better documentation and possible a revision in the future. At the moment the selection of the target codes is based on current research and expert assessment, provided by IHME, with not always clear facts and description.
Performing a redistribution method on the CoD statistics is currently very important. Otherwise there would be an underreporting of certain CoD or large numbers of deaths coded to residual or unspecific codes. However, more efforts should be put into obtaining a better quality of death registries and hence CoD data. This encompasses defining the underlying CoD as well as providing information on the accompanying diseases . The nationwide implementation of the Iris/MUSE software, which improves the electronic processing and correction of CoD data, is a step in that direction and will contribute to better registration of the underlying CoD [8, 9]. From a public health perspective these successive improvements are of large importance as CoD data are an important information base for the identification of needs, the prioritization of actions required, and the development of targeted interventions.
BURDEN 2020 study group
Alexander Rommel, Elena von der Lippe, Annelene Wengler, Michael Porst, Aline Anton, Janko Leddin, Thomas Ziese (Robert Koch Institute), Helmut Schröder, Kathrin Schüssel, Gabriela Brückner, Jan Breitkreuz (WIdO – AOK Research Institute), Dietrich Plass, Heike Gruhl (German Environment Agency)