Dataset Information

Accuracy of Death Certificates and Assessment of Factors for Misclassification of Underlying Cause of Death.

ABSTRACT:

Background

Cause of death (COD) information taken from death certificates is often inaccurate and incomplete. However, the accuracy of Underlying CODs (UCODs) recorded on death certificates has not been comprehensively described when multiple diseases are present.

Methods

A total of 450 consecutive autopsies performed at a geriatric hospital in Japan between February 2000 and August 2002 were studied. We evaluated the concordance rate, sensitivity, and specificity of major UCODs (cancer, heart disease, and pneumonia) reported on death certificates compared with a reference standard of pathologist assessment based on autopsy data and clinical records. Logistic regression analysis was performed to assess the effect of sex, age, comorbidity, and UCODs on misclassification.

Results

The concordance rate was relatively high for cancer (81%) but low for heart disease (55%) and pneumonia (9%). The overall concordance rate was 48%. Sex and comorbidity did not affect UCOD misclassification rates, which tended to increase with patient age, although the association with age was also not significant. The strongest factor for misclassification was UCODs (P < 0.0001). Sensitivity and specificity for cancer were very high (80% and 96%, respectively), but sensitivity for heart disease and pneumonia was 60% and 46%, respectively. Specificity for each UCOD was more than 85%.

Conclusions

Researchers should be aware of the accuracy of COD data from death certificates used as research resources, especially for cases of elderly patients with pneumonia.

SUBMITTER: Mieno MN

PROVIDER: S-EPMC4808686 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Accuracy of Death Certificates and Assessment of Factors for Misclassification of Underlying Cause of Death.

Mieno Makiko Naka MN Tanaka Noriko N Arai Tomio T Kawahara Takuya T Kuchiba Aya A Ishikawa Shizukiyo S Sawabe Motoji M

Journal of epidemiology 20151205 4

<h4>Background</h4>Cause of death (COD) information taken from death certificates is often inaccurate and incomplete. However, the accuracy of Underlying CODs (UCODs) recorded on death certificates has not been comprehensively described when multiple diseases are present.<h4>Methods</h4>A total of 450 consecutive autopsies performed at a geriatric hospital in Japan between February 2000 and August 2002 were studied. We evaluated the concordance rate, sensitivity, and specificity of major UCODs ( ...[more]

PMID: 26639750

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Project description:BACKGROUND:Although death certificates (DCs) provide valuable health information which may help to guide local health policies and priorities, there is little information concerning their validity in Thailand. First-year general practitioners (GPs) have a major role in DC completion, especially in provincial general hospitals. The aim of this study was to evaluate the accuracy and factors influencing the accuracy of DCs completed by first-year GPs in Thailand, compared with the cause of death (COD) derived from medical records by experts. METHODS:This retrospective study was conducted at 14 provincial general hospitals in Thailand during the June 2011 to May 2012 study period. Medical records and DCs completed by first-year GPs who graduated from 16 Thai medical schools were sampled. The cause of death recorded on the DCs was compared with the medical conditions and histories derived from patient medical records. A cross-sectional survey of the 210 GPs who completed the DCs reviewed in this study was also conducted. Respondent GPs' demographic characteristics, factors associated with COD, and COD coding system were evaluated. RESULTS:Five hundred and sixty-three medical records and corresponding DCs were included. Of those, 36.9% of DCs were found to be correct. Common mistakes included incorrect sequence of events leading to death (32.4%), and mode of death use (26.2%). Of the 210 GPs, 155 questionnaires were completed and returned. The mean time spent on recording COD and completing DCs in the medical school curriculum was 2.1?±?0.9 h and only 27.7% of participants had experience in completing DCs by themselves during medical school. Mean medical school GPA was significantly higher in the correctly completed DC GPs group than in the incorrectly completed DC GPs group (3.3?±?0.4 vs. 3.2?±?0.3; p?=?0.03). However, no significant difference was found for other factors associated with COD between groups. CONCLUSIONS:This is the first study documenting gaps and disparities in DC accuracy, and factors influencing completion of DCs among first-year GPs in Thailand, based on a clinical assessment of medical records. GPs made errors on 63.1% of DCs. This finding suggests that proven education, system-related support, and additional training interventions specific to DC completion are required.

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.

Project description:BackgroundCorrect certification of cause of death by physicians (i.e. completing the medical certificate of cause of death or MCCOD) and correct coding according to International Classification of Diseases (ICD) rules are essential to produce quality mortality statistics to inform health policy. Despite clear guidelines, errors in medical certification are common. This study objectively measures the impact of different medical certification errors upon the selection of the underlying cause of death.MethodsA sample of 1592 error-free MCCODs were selected from the 2017 United States multiple cause of death data. The ten most common types of errors in completing the MCCOD (according to published studies) were individually simulated on the error-free MCCODs. After each simulation, the MCCODs were coded using Iris automated mortality coding software. Chance-corrected concordance (CCC) was used to measure the impact of certification errors on the underlying cause of death. Weights for each error type and Socio-demographic Index (SDI) group (representing different mortality conditions) were calculated from the CCC and categorised (very high, high, medium and low) to describe their effect on cause of death accuracy.FindingsThe only very high impact error type was reporting an ill-defined condition as the underlying cause of death. High impact errors were found to be reporting competing causes in Part 1 [of the death certificate] and illegibility, with medium impact errors being reporting underlying cause in Part 2 [of the death certificate], incorrect or absent time intervals and reporting contributory causes in Part 1, and low impact errors comprising multiple causes per line and incorrect sequence. There was only small difference in error importance between SDI groups.ConclusionsReporting an ill-defined condition as the underlying cause of death can seriously affect the coding outcome, while other certification errors were mitigated through the correct application of mortality coding rules. Training of physicians in not reporting ill-defined conditions on the MCCOD and mortality coders in correct coding practices and using Iris should be important components of national strategies to improve cause of death data quality.

Project description:Background/aimsRandomized 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.MethodsDeaths 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.ResultsIn 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).ConclusionDeath 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.

Dataset Information

Accuracy of Death Certificates and Assessment of Factors for Misclassification of Underlying Cause of Death.

Background

Methods

Results

Conclusions

Publications

Accuracy of Death Certificates and Assessment of Factors for Misclassification of Underlying Cause of Death.

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