Differential mortality in Iran

Background: Among the available data provided by health information systems, data on mortality are commonly used not only as health indicators but also as socioeconomic development indices. Recognizing that in Iran accurate data on causes of death were not available, the Deputy of Health in the Ministry of Health and Medical Education (MOH&ME) established a new comprehensive system for death registration which started in one province (Bushehr) as a pilot in 1997, and was subsequently expanded to include all other provinces, except Tehran province. These data can be used to investigate the nature and extent of differences in mortality in Iran. The objective of this paper is to estimate provincial differences in the level of mortality using this death registration system. Methods: Data from the death registration system for 2004 for each province were evaluated for data completeness, and life tables were created for provinces after correction for under-enumeration of death registration. For those provinces where it was not possible to adjust the data on adult deaths by using the Brass Growth Balance method, adult mortality was predicted based on adult literacy using information from provinces with reliable data. Results: Child mortality (risk of a newborn dying before age 5, or q) in 2004 varied between 47 per 1000 live births for both sexes in Sistan and Baluchistan province, and 25 per 1000 live births in Tehran and Gilan provinces. For adults, provincial differences in mortality were much greater for males than females. Adult mortality (risk of dying between ages 15 and 60, or 45q15) for females varied between 0.133 in Kerman province and 0.117 in Tehran province; for males the range was from 0.218 in Kerman to 0.149 in Tehran province. Life expectancy for females was highest in Tehran province (73.8 years) and lowest in Sistan and Baluchistan (70.9 years). For males, life expectancy ranged from 65.7 years in Sistan and Baluchistan province to 70.9 years in Tehran. Conclusion: Substantial differences in survival exist among the provinces of Iran. While the completeness of the death registration system operated by the Iranian MOH&ME appears to be acceptable in the majority of provinces, further efforts are needed to improve the quality of data on mortality in Iran, and to expand death registration to Tehran province

Race/ethnicity and potential suicide misclassification: window on a minority suicide paradox?

<p>Abstract</p> <p>Background</p> <p>Suicide officially kills approximately 30,000 annually in the United States. Analysis of this leading public health problem is complicated by undercounting. Despite persisting socioeconomic and health disparities, non-Hispanic Blacks and Hispanics register suicide rates less than half that of non-Hispanic Whites.</p> <p>Methods</p> <p>This cross-sectional study uses multiple cause-of-death data from the US National Center for Health Statistics to assess whether race/ethnicity, psychiatric comorbidity documentation, and other decedent characteristics were associated with differential potential for suicide misclassification. Subjects were 105,946 White, Black, and Hispanic residents aged 15 years and older, dying in the US between 2003 and 2005, whose manner of death was recorded as suicide or injury of undetermined intent. The main outcome measure was the relative odds of potential suicide misclassification, a binary measure of manner of death: injury of undetermined intent (includes misclassified suicides) versus suicide.</p> <p>Results</p> <p>Blacks (adjusted odds ratio [AOR], 2.38; 95% confidence interval [CI], 2.22-2.57) and Hispanics (1.17, 1.07-1.28) manifested excess potential suicide misclassification relative to Whites. Decedents aged 35-54 (AOR, 0.88; 95% CI, 0.84-0.93), 55-74 (0.52, 0.49-0.57), and 75+ years (0.51, 0.46-0.57) showed diminished misclassification potential relative to decedents aged 15-34, while decedents with 0-8 years (1.82, 1.75-1.90) and 9-12 years of education (1.43, 1.40-1.46) showed excess potential relative to the most educated (13+ years). Excess potential suicide misclassification was also apparent for decedents without (AOR, 3.12; 95% CI, 2.78-3.51) versus those with psychiatric comorbidity documented on their death certificates, and for decedents whose mode of injury was "less active" (46.33; 43.32-49.55) versus "more active."</p> <p>Conclusions</p> <p>Data disparities might explain much of the Black-White suicide rate gap, if not the Hispanic-White gap. Ameliorative action would extend from training in death certification to routine use of psychological autopsies in equivocal-manner-of-death cases.</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

Using Verbal Autopsy to Measure Causes of Death: the Comparative Performance of Existing Methods.

Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices

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