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Cause of death affects racial classification on death certificates.

ABSTRACT: Recent research suggests racial classification is responsive to social stereotypes, but how this affects racial classification in national vital statistics is unknown. This study examines whether cause of death influences racial classification on death certificates. We analyze the racial classifications from a nationally representative sample of death certificates and subsequent interviews with the decedents' next of kin and find notable discrepancies between the two racial classifications by cause of death. Cirrhosis decedents are more likely to be recorded as American Indian on their death certificates, and homicide victims are more likely to be recorded as Black; these results remain net of controls for followback survey racial classification, indicating that the relationship we reveal is not simply a restatement of the fact that these causes of death are more prevalent among certain groups. Our findings suggest that seemingly non-racial characteristics, such as cause of death, affect how people are racially perceived by others and thus shape U.S. official statistics.

SUBMITTER: Noymer A

PROVIDER: S-EPMC3027630 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Cause of death affects racial classification on death certificates.

Noymer Andrew A Penner Andrew M AM Saperstein Aliya A

PloS one 20110126 1

Recent research suggests racial classification is responsive to social stereotypes, but how this affects racial classification in national vital statistics is unknown. This study examines whether cause of death influences racial classification on death certificates. We analyze the racial classifications from a nationally representative sample of death certificates and subsequent interviews with the decedents' next of kin and find notable discrepancies between the two racial classifications by ca ...[more]

PMID: 21298093

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Project description:BACKGROUND/AIMS:Randomized controlled trials frequently use death review committees to assign a cause of death rather than relying on cause of death information from death certificates. The National Lung Screening Trial, a randomized controlled trial of lung cancer screening with low-dose computed tomography versus chest X-ray for heavy and/or long-term smokers ages 55-74?years at enrollment, used a committee blinded to arm assignment for a subset of deaths to determine whether cause of death was due to lung cancer. METHODS:Deaths were selected for review using a pre-determined computerized algorithm. The algorithm, which considered cancers diagnosed during the trial, causes and significant conditions listed on the death certificate, and the underlying cause of death derived from death certificate information by trained nosologists, selected deaths that were most likely to represent a death due to lung cancer (either directly or indirectly) and deaths that might have been erroneously assigned lung cancer as the cause of death. The algorithm also selected deaths that might be due to adverse events of diagnostic evaluation for lung cancer. Using the review cause of death as the gold standard and lung cancer cause of death as the outcome of interest (dichotomized as lung cancer versus not lung cancer), we calculated performance measures of the death certificate cause of death. We also recalculated the trial primary endpoint using the death certificate cause of death. RESULTS:In all, 1642 deaths were reviewed and assigned a cause of death (42% of the 3877 National Lung Screening Trial deaths). Sensitivity of death certificate cause of death was 91%; specificity, 97%; positive predictive value, 98%; and negative predictive value, 89%. About 40% of the deaths reclassified to lung cancer cause of death had a death certificate cause of death of a neoplasm other than lung. Using the death certificate cause of death, the lung cancer mortality reduction was 18% (95% confidence interval: 4.2-25.0), as compared with the published finding of 20% (95% confidence interval: 6.7-26.7). CONCLUSION:Death review may not be necessary for primary-outcome analyses in lung cancer screening trials. If deemed necessary, researchers should strive to streamline the death review process as much as possible.

Project description:BACKGROUND:Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d'épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. OBJECTIVE:This article investigates the application of deep neural network methods to coding underlying causes of death. METHODS:The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject's age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject's underlying cause of death was then formulated as a predictive modelling problem. A deep neural network-based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach's superiority was assessed via bootstrap. RESULTS:The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. CONCLUSIONS:This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.

Dataset Information

Cause of death affects racial classification on death certificates.

Publications

Cause of death affects racial classification on death certificates.

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