ICD-Codierung von Todesursachen: Herausforderungen bei der Berechnung der Krankheitslast in Deutschland

Wengler, Annelene; Rommel, Alexander; Plaß, Dietrich; Gruhl, Heike; Leddin, Janko; Porst, Michael; Anton, Aline; Lippe, Elena von der

doi:10.1007/s00103-019-03054-1

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…Another strength of the study, is the redistribution of IDD on a subnational level. As shown before [19] 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.…”

Section: Limitations and Strengthssupporting

confidence: 51%

“…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 in uence the quality of the data and the regional amount of IDD [19,31].…”

Section: Discussionmentioning

confidence: 99%

“…The GBD list of IDD contains more than 7,000 ICD-10-codes (3-and 4-digit codes) of which 859 actually occur in the German CoD statistics in 2017. Overall, 932,269 deaths were registered in 2017 and 26% of all deaths were coded as IDD [19]. The IDD are grouped in IDD packages.…”

Section: ) Idd De Nition and Grouping In Idd Packagesmentioning

confidence: 99%

“…In general, the methods used for the redistribution in the GBD study include proportional reassignment, xed proportions, regression models, experts' opinions, and fractional assignment of a death assigned to multiple causes [10]. The World Health Organization developed a different classi cation of IDD [18], still resulting in a relatively high share of deaths being classi ed as IDD in Germany [19].…”

Section: Introductionmentioning

confidence: 99%

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Redistributing Ill-defined Causes of Death – A Case Study from the Burden 2020-Project in Germany

Wengler

Gruhl²,

Plaß

et al. 2020

Preprint

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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.

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Section: Limitations and Strengthssupporting

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Section: ) Idd De Nition and Grouping In Idd Packagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Redistributing Ill-defined Causes of Death – A Case Study from the Burden 2020-Project in Germany

Wengler

Gruhl²,

Plaß

et al. 2020

Preprint

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“…The regional and time-related variations in the coding of causes of death could be another limitation of our study. Across regions, we see a different picture of how well causes of death are coded in Germany according to the WHO guidelines [30]. To counteract bias from miscoding, the most prominent ill-defined cause of death codes was redistributed proportionally to all valid underlying cause of death codes by age and sex for each year and district.…”

Section: Strength and Limitationsmentioning

confidence: 99%

The socioeconomic gap in life expectancy in Germany: a decomposition of age- and cause-specific mortality contributions, 2003–2021

Tetzlaff,

Sauerberg,

Grigoriev

et al. 2023

Preprint

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SummaryBackgroundEarlier death among people in socioeconomically deprived circumstances has been found internationally and for various causes of death, resulting in a considerable life expectancy gap between socioeconomic groups. This study examines how age- and cause-specific mortality contributions to the socioeconomic gap in life expectancy have changed in Germany over time.MethodsOfficial German population and cause-of-death statistics from 2003 to 2021 were linked to the district-level German Index of Socioeconomic Deprivation. Life-table and decomposition methods were applied to calculate life expectancy by deprivation quintile and decompose the life expectancy gap (ΔLE) between the most and least deprived quintiles into age- and cause-specific mortality contributions.FindingsFrom 2003 to 2019, the ΔLE between the most and least deprived quintiles of districts increased from 1.1 to 1.8 years among women and from 3.0 to 3.1 years among men. Thereafter, in the COVID-19 pandemic, it increased more rapidly to 2.2 and 3.5 years respectively in 2021. The causes of death contributing most to the ΔLE were cardiovascular diseases (CVD) and cancer, with declining contributions of CVD deaths at age 70 and above and increasing contributions of cancer deaths at ages 40–74 over time. COVID-19 mortality at ages 45+ was the strongest contributor to the increase in ΔLE after 2019.InterpretationTo reduce the socioeconomic gap in life expectancy, effective efforts are needed to prevent early deaths from CVD and cancer in deprived populations, with cancer prevention and control becoming an increasingly important field of action in this respect.

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Verlorene Lebensjahre: Bekanntes und Neues zur Methodik am Beispiel der häufigsten Todesursachen in Deutschland

Hübner

Mattutat

Katalinic

2021

Bundesgesundheitsbl

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Zusammenfassung Hintergrund Verlorene Lebensjahre (Years of Life Lost, YLL) sind ein aussagekräftiger, in Deutschland jedoch wenig verwendeter Indikator für die Relevanz von Todesursachen. Es existieren zahlreiche Methoden, mit denen YLL berechnet werden können. Ziel der Arbeit Es werden prototypische Methoden zur Berechnung von YLL vorgestellt und kritisch eingeordnet. Auf dieser Basis wird eine verbesserte Methode vorgeschlagen, die auf der Nutzung von todesursachenbereinigten Sterbetafeln (Cause-Elimination Life Tables, CELT) beruht. Methoden Etablierte Methoden und die hier vorgeschlagene Modifikation werden auf die Sterblichkeit in Deutschland 2018 angewandt. Veränderungen gegenüber 1998 werden anhand der modifizierten Methode dargestellt. Ergebnisse Während nach der Zahl der Sterbefälle Herz-Kreislauf-Erkrankungen im Jahr 2018 die bedeutendste Todesursache waren, war Krebs für die meisten YLL verantwortlich. Unterschiedliche Methoden zur Berechnung der YLL führen zu deutlich abweichenden Rängen bei den weniger bedeutsamen Todesursachen. YLL auf Basis von allgemeinen Sterbetafeln unterschätzen die YLL auf Basis der todesursachenbereinigten Sterbetafeln um bis zu 18,4 % (Herz-Kreislauf-Erkrankungen). Gemessen an den CELT-basierten YLL waren im Jahr 1998 Herz-Kreislauf-Erkrankungen die bedeutsamste Todesursache. Diskussion Die Berechnung von YLL auf der Basis von todesursachenbereinigten Sterbetafeln vermeidet Inkonsistenzen etablierter Methoden und führt zu relevant abweichenden Ergebnissen. Besonderheiten der vorgeschlagenen Methode (Verstoß gegen das Egalitätsprinzip, fehlende Additivität) beeinträchtigen ihren Nutzen als Instrument zur Steuerung der Gesundheitsversorgung nicht.

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ICD-Codierung von Todesursachen: Herausforderungen bei der Berechnung der Krankheitslast in Deutschland

Cited by 13 publications

References 20 publications

Redistributing Ill-defined Causes of Death – A Case Study from the Burden 2020-Project in Germany

Redistributing Ill-defined Causes of Death – A Case Study from the Burden 2020-Project in Germany

The socioeconomic gap in life expectancy in Germany: a decomposition of age- and cause-specific mortality contributions, 2003–2021

Verlorene Lebensjahre: Bekanntes und Neues zur Methodik am Beispiel der häufigsten Todesursachen in Deutschland

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