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[Coding problems in German mortality statistics as illustrated by ischaemic heart disease]
F Andersohn1, F Müller-Riemenschneider, S N Willich
1Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité-Universitätsmedizin Berlin. frank.andersohn@charite.de
Gesundheitswesen (Bundesverband Der Arzte Des Offentlichen Gesundheitsdienstes (Germany))
|June 23, 2010
Summary
German mortality statistics may contain errors due to inconsistent ICD-10 coding. This study found coding issues in several states, impacting heart disease mortality data and its link to poverty.
Area of Science:
- Public Health
- Epidemiology
- Health Statistics
Background:
- German mortality statistics are crucial for research and policy.
- Potential errors in ICD-10 coding by regional authorities can affect data accuracy.
- This study focuses on ischaemic heart disease (IHD) and myocardial infarction (MI) coding.
Purpose of the Study:
- To identify state-specific coding problems in German mortality data.
- To investigate coding issues related to ischaemic heart disease (IHD) and myocardial infarction (MI).
- To assess the impact of coding discrepancies on the correlation between poverty and heart disease mortality.
Main Methods:
- Age-standardised mortality rates (1998-2008) were analyzed.
- Outlier analyses identified German states with suspect mortality trends or proportional mortality from unspecific causes (ICD-10 R00-R99).
- Ecological correlation examined the impact of excluding outlier states on poverty-mortality association.
Main Results:
- Mortality rates for IHD and MI decreased by 38.1% and 38.7% respectively.
- Suspect coding issues were identified in Berlin, Bremen, Hamburg, Schleswig-Holstein, and Saarland.
- Excluding these states strengthened the association between poverty rates and heart disease mortality.
Conclusions:
- Regional ICD-10 coding practices appear to be a significant source of error in German mortality statistics.
- Standardization of coding procedures is essential for data reliability.
- Addressing coding inconsistencies is crucial for accurate health policy and research.
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