Suicide rates in Maltese Islands (1955-2009) analysed in European context using WHO data

Aim: To calculate the suicide rates (for males and females) in Malta and other European countries with long series of suicide mortality as recorded in the WHO (World Health Organization) database, and compare the Maltese suicide rates with European rates. Method: Suicide rates were computed from the WHO database as rates (suicides per 100,000 persons) using the reported suicide and population counts in Malta and ten other European countries for a common period 1955-2009. Suicide rates were age standardized following the WHO recommendations. These calculations were carried out separately for both sexes. Results: Compared to other European counties, the suicide rates (both male and female) in Malta have remained at considerably low level as calculated over the full period. Maltese suicide rates have however multiplied since the 1980s. European data exhibit clear decrease in suicide rates towards the present consistently in several countries. Malta is the only European country showing its highest suicide rates during the 2000s. Conclusions: Although the suicide rates in Malta remain at considerably low level, they have exhibited a notable increase towards the present, whereas the European suicide rates are in decline. Becoming aware of this fact and the issue may help in building a suicide prevention programme to mitigate the situation.peer-reviewe

Mortality in Central Java: results from the indonesian mortality registration system strengthening project

Background. Mortality statistics from death registration systems are essential for health policy and development. Indonesia has recently mandated compulsory death registration across the entire country in December 2006. This article describes the methods and results from activities to ascertain causes of registered deaths in two pilot registration areas in Central Java during 2006-2007. The methods involved several steps, starting with adaptation of international standards for reporting causes of registered deaths for implementation in two sites, Surakarta (urban) and Pekalongan (rural). Causes for hospital deaths were certified by attending physicians. Verbal autopsies were used for home deaths. Underlying causes were coded using ICD-10. Completeness of registration was assessed in a sample of villages and urban wards by triangulating data from the health sector, the civil registration system, and an independent household survey. Finally, summary mortality indicators and cause of death rankings were developed for each site. Findings. A total of 10,038 deaths were registered in the two sites during 2006-2007; yielding annual crude death rates of 5.9 to 6.8 per 1000. Data completeness was higher in rural areas (72.5%) as compared to urban areas (52%). Adjusted life expectancies at birth were higher for both males and females in the urban population as compared to the rural population. Stroke, ischaemic heart disease and chronic respiratory disease are prominent causes in both populations. Other important causes are diabetes and cancer in urban areas; and tuberculosis and diarrhoeal diseases in rural areas. Conclusions. Non-communicable diseases cause a significant proportion of premature mortality in Central Java. Implementing cause of death reporting in conjunction with death registration appears feasible in Indonesia. Better collaboration between health and registration sectors is required to improve data quality. These are the first local mortality measures for health policy and monitoring in Indonesia. Strong demand for data from different stakeholders can stimulate further strengthening of mortality registration systems

Mortality Measurement Matters: Improving Data Collection and Estimation Methods for Child and Adult Mortality

Colin Mathers and Ties Boerma discuss three research articles in PLoS Medicine that address the measurement and analysis of child and adult mortality data collected through death registration, censuses, and household surveys

Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards

<p>Abstract</p> <p>Background</p> <p>Verbal autopsy (VA) is used to estimate the causes of death in areas with incomplete vital registration systems. The King and Lu method (KL) for direct estimation of cause-specific mortality fractions (CSMFs) from VA studies is an analysis technique that estimates CSMFs in a population without predicting individual-level cause of death as an intermediate step. In previous studies, KL has shown promise as an alternative to physician-certified verbal autopsy (PCVA). However, it has previously been impossible to validate KL with a large dataset of VAs for which the underlying cause of death is known to meet rigorous clinical diagnostic criteria.</p> <p>Methods</p> <p>We applied the KL method to adult, child, and neonatal VA datasets from the Population Health Metrics Research Consortium gold standard verbal autopsy validation study, a multisite sample of 12,542 VAs where gold standard cause of death was established using strict clinical diagnostic criteria. To emulate real-world populations with varying CSMFs, we evaluated the KL estimations for 500 different test datasets of varying cause distribution. We assessed the quality of these estimates in terms of CSMF accuracy as well as linear regression and compared this with the results of PCVA.</p> <p>Results</p> <p>KL performance is similar to PCVA in terms of CSMF accuracy, attaining values of 0.669, 0.698, and 0.795 for adult, child, and neonatal age groups, respectively, when health care experience (HCE) items were included. We found that the length of the cause list has a dramatic effect on KL estimation quality, with CSMF accuracy decreasing substantially as the length of the cause list increases. We found that KL is not reliant on HCE the way PCVA is, and without HCE, KL outperforms PCVA for all age groups.</p> <p>Conclusions</p> <p>Like all computer methods for VA analysis, KL is faster and cheaper than PCVA. Since it is a direct estimation technique, though, it does not produce individual-level predictions. KL estimates are of similar quality to PCVA and slightly better in most cases. Compared to other recently developed methods, however, KL would only be the preferred technique when the cause list is short and individual-level predictions are not needed.</p

Simplified Symptom Pattern Method for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards

Background: Verbal autopsy can be a useful tool for generating cause of death data in data-sparse regions around the world. The Symptom Pattern (SP) Method is one promising approach to analyzing verbal autopsy data, but it has not been tested rigorously with gold standard diagnostic criteria. We propose a simplified version of SP and evaluate its performance using verbal autopsy data with accompanying true cause of death.Methods: We investigated specific parameters in SP's Bayesian framework that allow for its optimal performance in both assigning individual cause of death and in determining cause-specific mortality fractions. We evaluated these outcomes of the method separately for adult, child, and neonatal verbal autopsies in 500 different population constructs of verbal autopsy data to analyze its ability in various settings.Results: We determined that a modified, simpler version of Symptom Pattern (termed Simplified Symptom Pattern, or SSP) performs better than the previously-developed approach. Across 500 samples of verbal autopsy testing data, SSP achieves a median cause-specific mortality fraction accuracy of 0.710 for adults, 0.739 for children, and 0.751 for neonates. In individual cause of death assignment in the same testing environment, SSP achieves 45.8% chance-corrected concordance for adults, 51.5% for children, and 32.5% for neonates.Conclusions: The Simplified Symptom Pattern Method for verbal autopsy can yield reliable and reasonably accurate results for both individual cause of death assignment and for determining cause-specific mortality fractions. The method demonstrates that verbal autopsies coupled with SSP can be a useful tool for analyzing mortality patterns and determining individual cause of death from verbal autopsy data

Risk factors of ischemic stroke and subsequent outcome in hemodialysis patients

Background and purpose: End stage renal disease (ESRD) requiring hemodialysis (HD) carries up to a 10-fold greater risk of stroke than normal renal function. Knowledge concerning risk factors and management strategies derived from the general population may not be applicable to those with ESRD. We studied a large ESRD population to identify risk factors and outcomes for stroke. Methods: All adult patients receiving HD for ESRD from 01/01/2007 to 31/12/2012 were extracted from the electronic patient record. Variables associated with stroke were identified by survival analysis; demographic, clinical, imaging and dialysis related variables were assessed and case-fatality determined. Follow-up was until 31/12/2013. Results: 1382 patients were identified (mean age 60.5 years, 58.5% male). The prevalence of AF was 21.2% and 59.4% were incident HD patients. 160 (11.6%) experienced a stroke during 3471 patient-years of follow-up (95% ischemic). Stroke incidence was 41.5/1000 patient-years in prevalent and 50.1/1000 patient-years in incident HD patients. Factors associated with stroke on regression analysis were prior stroke, diabetes and age at starting renal replacement therapy. AF was not significantly associated with stroke and warfarin did not affect stroke risk in warfarin treated patients. Fatality was 18.8% at 7, 26.9% at 28 and 56.3% 365 days after stroke.&lt;p&gt;&lt;/p&gt; Conclusions: Incidence of stroke is high in patients with ESRD on HD with high case-fatality. Incident HD patients had the highest stroke incidence. Many, but not all, important risk factors commonly associated with stroke in the general population were not associated with stroke in patients receiving HD

Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies

<p>Abstract</p> <p>Background</p> <p>Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff), which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs) from verbal autopsy data.</p> <p>Methods</p> <p>Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score) provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data.</p> <p>Results</p> <p>Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates.</p> <p>Conclusions</p> <p>Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science.</p

Robust metrics for assessing the performance of different verbal autopsy cause assignment methods in validation studies

<p>Abstract</p> <p>Background</p> <p>Verbal autopsy (VA) is an important method for obtaining cause of death information in settings without vital registration and medical certification of causes of death. An array of methods, including physician review and computer-automated methods, have been proposed and used. Choosing the best method for VA requires the appropriate metrics for assessing performance. Currently used metrics such as sensitivity, specificity, and cause-specific mortality fraction (CSMF) errors do not provide a robust basis for comparison.</p> <p>Methods</p> <p>We use simple simulations of populations with three causes of death to demonstrate that most metrics used in VA validation studies are extremely sensitive to the CSMF composition of the test dataset. Simulations also demonstrate that an inferior method can appear to have better performance than an alternative due strictly to the CSMF composition of the test set.</p> <p>Results</p> <p>VA methods need to be evaluated across a set of test datasets with widely varying CSMF compositions. We propose two metrics for assessing the performance of a proposed VA method. For assessing how well a method does at individual cause of death assignment, we recommend the average chance-corrected concordance across causes. This metric is insensitive to the CSMF composition of the test sets and corrects for the degree to which a method will get the cause correct due strictly to chance. For the evaluation of CSMF estimation, we propose CSMF accuracy. CSMF accuracy is defined as one minus the sum of all absolute CSMF errors across causes divided by the maximum total error. It is scaled from zero to one and can generalize a method's CSMF estimation capability regardless of the number of causes. Performance of a VA method for CSMF estimation by cause can be assessed by examining the relationship across test datasets between the estimated CSMF and the true CSMF.</p> <p>Conclusions</p> <p>With an increasing range of VA methods available, it will be critical to objectively assess their performance in assigning cause of death. Chance-corrected concordance and CSMF accuracy assessed across a large number of test datasets with widely varying CSMF composition provide a robust strategy for this assessment.</p