Abstract
BackgroundMortality data are affected by miscertification of the medical cause of death deaths and changes to cause of death classification systems. We present both mappings of ICD9 and ICD10 to a unified list of causes, and a new statistical model for reducing the impact of misclassification of cause of death.MethodsWe propose a Bayesian mixed-effects multinomial logistic model that can be run on individual record level death certificates to reclassify “garbage-coded” deaths onto causes that are more meaningful for public health purposes. The model uses information on the contributing causes of death and demographic characteristics of each decedent to make informed predictions of the underlying cause of death. We apply our method to death certificate data in the US from 1979 to 2011, creating more directly comparable series of cause-specific mortality for 25 major causes of death.ResultsWe find that many death certificates coded to garbage codes contain other information that provides strong clues about the valid underlying cause of death. In particular, a plausible underlying cause often appears in the contributing causes of death, implying that it may be incorrect ordering of the causal chain and not missed cause assignment that leads to many garbage-coded deaths. We present an example that redistributes 48 % of heart failure deaths to other cardiovascular diseases, 25 % to ischemic heart disease, and 15 % to chronic respiratory diseases.ConclusionsOur methods take advantage of more detailed micro-level data than is typically considered in garbage code redistribution algorithms, making it a useful tool in circumstances in which detailed death certificate data needs to be aggregated for public health purposes. We find that this method gives different redistribution results than commonly used methods that only consider population-level proportions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-016-0082-4) contains supplementary material, which is available to authorized users.
Highlights
Mortality data are affected by miscertification of the medical cause of death deaths and changes to cause of death classification systems
We have developed mappings of ICD9 and ICD10 to a mutually exclusive and collectively exhaustive set of 25 causes of death that are of public health importance in the US and other high-income countries
We used the coefficients estimated using this “training” dataset to predict a non-garbage underlying cause of death for those death certificates which have the garbage code listed as their underlying cause
Summary
Mortality data are affected by miscertification of the medical cause of death deaths and changes to cause of death classification systems. ICD10 contains many codes that are useful in classifying morbidity but are not themselves causes of mortality, such as those within Chapter 18 “Symptoms, Signs and Abnormal Clinical and Laboratory Findings, Not Elsewhere Classified.”. Other codes, such as heart failure or septicemia, describe intermediate causes of death that most likely have a different underlying cause that would be a better target for public health intervention [16]. As far back as 1948, heart disease classification has been described as a “convenient statistical ‘wastepaper basket’” [22]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
