No abstract

No abstract

Background The cause of death statistics in Germany include a relatively high share (26% in 2017) of ill-defined deaths (IDD). To make use of the cause of death statistics for Burden of Disease calculations we redistribute those IDD to valid causes of death.Methods The process of proportional redistribution is described in detail. It makes use of the distribution of the valid ICD-codes in the data. We use examples of stroke, diabetes, and heart failure to illustrate how IDD are reallocated. ResultsThe largest increases for both women and men can be found for lower respiratory infections, diabetes mellitus, and stroke. The numbers of deaths for these causes more than double after redistribution. ConclusionThis is the first comprehensive redistribution of IDD within the German cause of death statistics. Performing a redistribution is necessary, otherwise there would be an underreporting of certain causes of death or large numbers of deaths coded to residual or unspecific codes.

No abstract

Background The cause of death statistics in Germany include a relatively high share (26% in 2017) of ill-defined deaths (IDD). To make use of the cause of death statistics for Burden of Disease calculations we redistribute those IDD to valid causes of death. Methods The process of proportional redistribution is described in detail. It makes use of the distribution of the valid ICD-codes in the cause of death data. We use examples of stroke, diabetes, and heart failure to illustrate how IDD are reallocated. Results The largest increases in the number of deaths for both women and men were found for lower respiratory infections, diabetes mellitus, and stroke. The numbers of deaths for these causes more than doubled after redistribution. Conclusion This is the first comprehensive redistribution of IDD using the German cause of death statistics. Performing a redistribution is necessary for burden of disease analyses, otherwise there would be an underreporting of certain causes of death or large numbers of deaths coded to residual or unspecific codes.

Background Within the BURDEN 2020 project (Burden of disease in Germany at the national and sub-national level) we calculate burden of disease indicators on a small-scale level for Germany. In order to provide a comprehensive view over diseases and their impact on population health, Years of Life Lost due to premature death (YLL) and Years Lived with Disability (YLD) are summed up to the measure Disability Adjusted Live Years (DALY). YLL, YLD and DALY will be attributed to a set of risk factors. Methods Object of the analysis is the indicator YLL across spatial planning regions in Germany. It is calculated by multiplying the number of deaths with the remaining life expectancy at the age of deaths. A share of YLL can be attributed to specific risk factors. Smoking and ambient particulate matter pollution are used as examples. The distribution of risk factors is based on German health surveys and local air pollution measurements. The analysis follows the Comparative Risk Assessment approach, which quantifies the possible change in burden by assuming a different exposure level in the past. Results Based on the absolute YLL estimates, the overall leading causes of deaths are ischemic heart diseases, tracheal, bronchus, and lung cancer and stroke. In addition, for women breast cancer is a leading cause of death. Causes vary by age groups and by region. We expect the attributable fraction to vary across local areas as well. Conclusions When it comes to health care intervention, the method of attributing risk factors to burden of disease outcomes reveals necessary fields of action, especially across regions. In order to allocate financial resources within the health care, the CRA can provide additional arguments for political decision making. Key messages Causes of death and the related years of life lost are an integral part of health monitoring. Our analysis reveals how YLL and risk factor attributable burden vary across German regions.