Whether to report diabetes as the underlying cause-of-death? a survey of internists of different sub-specialties

<p>Abstract</p> <p>Background</p> <p>Cause-specific mortality is a commonly used endpoint of clinical trials or prospective studies. However, it is sometimes difficult for physician to determine the underlying-cause-of-death (UCD), especially for diabetic patients coexisted with cardiovascular diseases (CVD). The aim of this survey was to examine whether internists with different specialties have different opinions on the reporting of diabetes as the UCD.</p> <p>Methods</p> <p>A total of 549 physicians completed the questionnaire in Taiwan, which comprised seven hypothetical case scenarios, each indicating a different level of contribution of diabetes in initiating the chain of events leading to death.</p> <p>Results</p> <p>As a whole, endocrinologists were more likely than cardiologists and nephrologists to report diabetes as the UCD. The differences were more prominent when the diabetic patient had a coexisting CVD. In scenario 3 (a diabetic patient with hypertension who died from acute myocardial infarction), the percentage was 56% in endocrinologists, which was significantly higher than in cardiologists (42%) and nephrologists (41%). In scenario 4 (a diabetic patient with hypertension who died from cerebrovascular infarction), the percentage was 45% in endocrinologists, and only 31% in cardiologists and 36% in nephrologists.</p> <p>Conclusions</p> <p>Internists of different sub-specialties do have different opinions on the reporting of diabetes as the UCD, especially when the diabetic patient has a coexisting CVD.</p

Discrepant comorbidity between minority and white suicides: a national multiple cause-of-death analysis

Abstract Background Clinician training deficits and a low and declining autopsy rate adversely impact the quality of death certificates in the United States. Self-report and records data for the general population indicate that proximate mental and physical health of minority suicides was at least as poor as that of white suicides. Methods This cross-sectional mortality study uses data from Multiple Cause-of-Death (MCOD) public use files for 1999–2003 to describe and evaluate comorbidity among black, Hispanic, and white suicides. Unintentional injury decedents are the referent for multivariate analyses. Results One or more mentions of comorbid psychopathology are documented on the death certificates of 8% of white male suicides compared to 4% and 3% of black and Hispanic counterparts, respectively. Corresponding female figures are 10%, 8%, and 6%. Racial-ethnic discrepancies in the prevalence of comorbid physical disease are more attenuated. Cross-validation with National Violent Death Reporting System data reveals high relative underenumeration of comorbid depression/mood disorders and high relative overenumeration of schizophrenia on the death certificates of both minorities. In all three racial-ethnic groups, suicide is positively associated with depression/mood disorders [whites: adjusted odds ratio (AOR) = 31.9, 95% CI = 29.80–34.13; blacks: AOR = 60.9, 95% CI = 42.80–86.63; Hispanics: AOR = 34.7, 95% CI = 23.36–51.62] and schizophrenia [whites: AOR = 2.4, 95% CI = 2.07–2.86; blacks: AOR = 4.2, 95% CI = 2.73–6.37; Hispanics: AOR = 4.1, 95% CI = 2.01–8.22]. Suicide is positively associated with cancer in whites [AOR = 1.8, 95% CI = 1.69–1.93] and blacks [AOR = 1.8, 95% CI = 1.36–2.48], but not with HIV or alcohol and other substance use disorders in any group under review. Conclusion The multivariate analyses indicate high consistency in predicting suicide-associated comorbidities across racial-ethnic groups using MCOD data. However, low prevalence of documented comorbid psychopathology in suicides, and concomitant racial-ethnic discrepancies underscore the need for training in death certification, and routinization and standardization of timely psychological autopsies in all cases of suicide, suspected suicide, and other traumatic deaths of equivocal cause

Accuracy of cause of death data routinely recorded in a population-based cancer registry: impact on cause-specific survival and validation using the Geneva Cancer Registry.

BACKGROUND: Information on the underlying cause of death of cancer patients is of interest because it can be used to estimate net survival. The population-based Geneva Cancer Registry is unique because registrars are able to review the official cause of death. This study aims to describe the difference between the official and revised cause-of-death variables and the impact on cancer survival estimates. METHODS: The recording process for each cause of death variable is summarised. We describe the differences between the two cause-of-death variables for the 5,065 deceased patients out of the 10,534 women diagnosed with breast cancer between 1970 and 2009. The Kappa statistic and logistic regression are applied to evaluate the degree of concordance. The impact of discordance on cause-specific survival is examined using the Kaplan Meier method. RESULTS: The overall agreement between the two variables was high. However, several subgroups presented a lower concordance, suggesting differences in calendar time and less attention given to older patients and more advanced diseases. Similarly, the impact of discordance on cause-specific survival was small on overall survival but larger for several subgroups. CONCLUSION: Estimation of cancer-specific survival could therefore be prone to bias when using the official cause of death. Breast cancer is not the more lethal cancer and our results can certainly not be generalised to more lethal tumours

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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