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Background: Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. However, a barrier to its use by national registration systems has been the amount of time and cost needed for data collection. Therefore, a short VA instrument (VAI) is needed. In this paper we describe a shortened version of the VAI developed for the Population Health Metrics Research Consortium (PHMRC) Gold Standard Verbal Autopsy Validation Study using a systematic approach.Methods: We used data from the PHMRC validation study. Using the Tariff 2.0 method, we first established a rank order of individual questions in the PHMRC VAI according to their importance in predicting causes of death. Second, we reduced the size of the instrument by dropping questions in reverse order of their importance. We assessed the predictive performance of the instrument as questions were removed at the individual level by calculating chance-corrected concordance and at the population level with cause-specific mortality fraction (CSMF) accuracy. Finally, the optimum size of the shortened instrument was determined using a first derivative analysis of the decline in performance as the size of the VA instrument decreased for adults, children, and neonates.Results: The full PHMRC VAI had 183, 127, and 149 questions for adult, child, and neonatal deaths, respectively. The shortened instrument developed had 109, 69, and 67 questions, respectively, representing a decrease in the total number of questions of 40-55%. The shortened instrument, with text, showed non-significant declines in CSMF accuracy from the full instrument with text of 0.4%, 0.0%, and 0.6% for the adult, child, and neonatal modules, respectively.Conclusions: We developed a shortened VAI using a systematic approach, and assessed its performance when administered using hand-held electronic tablets and analyzed using Tariff 2.0. The length of a VA questionnaire was shortened by almost 50% without a significant drop in performance. The shortened VAI developed reduces the burden of time and resources required for data collection and analysis of cause of death data in civil registration systems. [ABSTRACT FROM AUTHOR]

Background: Reliable data on the distribution of causes of death (COD) in a population are fundamental to good public health practice. In the absence of comprehensive medical certification of deaths, the only feasible way to collect essential mortality data is verbal autopsy (VA). The Tariff Method was developed by the Population Health Metrics Research Consortium (PHMRC) to ascertain COD from VA information. Given its potential for improving information about COD, there is interest in refining the method. We describe the further development of the Tariff Method.Methods: This study uses data from the PHMRC and the National Health and Medical Research Council (NHMRC) of Australia studies. Gold standard clinical diagnostic criteria for hospital deaths were specified for a target cause list. VAs were collected from families using the PHMRC verbal autopsy instrument including health care experience (HCE). The original Tariff Method (Tariff 1.0) was trained using the validated PHMRC database for which VAs had been collected for deaths with hospital records fulfilling the gold standard criteria (validated VAs). In this study, the performance of Tariff 1.0 was tested using VAs from household surveys (community VAs) collected for the PHMRC and NHMRC studies. We then corrected the model to account for the previous observed biases of the model, and Tariff 2.0 was developed. The performance of Tariff 2.0 was measured at individual and population levels using the validated PHMRC database.Results: For median chance-corrected concordance (CCC) and mean cause-specific mortality fraction (CSMF) accuracy, and for each of three modules with and without HCE, Tariff 2.0 performs significantly better than the Tariff 1.0, especially in children and neonates. Improvement in CSMF accuracy with HCE was 2.5%, 7.4%, and 14.9% for adults, children, and neonates, respectively, and for median CCC with HCE it was 6.0%, 13.5%, and 21.2%, respectively. Similar levels of improvement are seen in analyses without HCE.Conclusions: Tariff 2.0 addresses the main shortcomings of the application of the Tariff Method to analyze data from VAs in community settings. It provides an estimation of COD from VAs with better performance at the individual and population level than the previous version of this method, and it is publicly available for use. [ABSTRACT FROM AUTHOR]

Objective: This study assessed the agreement between medical physicians in their interpretation of verbal autopsy (VA) interview data for identifying causes of neonatal deaths in rural Bangladesh. Methods: The study was carried out in Matlab, a rural sub-district in eastern Bangladesh. Trained persons conducted the VA interview with the mother or another family member at the home of the deceased. Three physicians and a medical assistant independently reviewed the VA interviews to assign causes of death using the International Classification of Diseases - Tenth Revision (ICD-10) codes. A physician assigned cause was decided when at least two physicians agreed on a cause of death. Cause-specific mortality fraction (CSMF), kappa (k) statistic, sensitivity, specificity, and positive predictive values were applied to compare agreement between the reviewers. Results: Of the 365 neonatal deaths reviewed, agreement on a direct cause of death was reached by at least two physicians in 339 (93%) of cases. Physician and medical assistant reviews of causes of death demonstrated the following levels of diagnostic agreement for the main causes of deaths: for birth asphyxia the sensitivity was 84%, specificity 93%, and kappa 0.77. For prematurity/low birth weight, the sensitivity, specificity, and kappa statistics were, respectively, 53%, 96%, and 0.55, for sepsis/meningitis they were 48%, 98%, and 0.53, and for pneumonia they were 75%, 94%, and 0.51. Conclusion: This study revealed a moderate to strong agreement between physician- assigned and medical assistant- assigned major causes of neonatal death. A well-trained medical assistant could be considered an alternative for assigning major causes of neonatal deaths in rural Bangladesh and in similar settings where physicians are scarce and their time costs more. A validation study with medically confirmed diagnosis will improve the performance of VA for assigning cause of neonatal death. [ABSTRACT FROM AUTHOR]

Background: Poor neonatal health is a major contributor to under-five mortality in developing countries. A major constraint to effective neonatal survival programme has been the lack of population level data in developing countries. This study investigated the consultation patterns of caregivers during neonatal fatal illness episodes in the rural Matlab sub-district of eastern Bangladesh., Methods: Neonatal deaths were identified through a population-based demographic surveillance system in Matlab ICDDR,B maternal and child health (MCH) project area and an adjoining government service area. Trained project staff administered a structured questionnaire on care seeking to mothers at home who had experienced a neonatal death. Univariate, bivariate and binary multivariate logistic regressions were performed to describe care seeking during the fatal illness episode., Results: Of the 365 deaths recorded during 2003 and 2004, 84% died in the early (0-7 days) neonatal period, with the remaining deaths occurring over the subsequent 8 to 28 days. The first resort of care by parents was a qualified doctor or paramedic in 37% of cases, followed by traditional and unqualified health care providers in 25%, while 38% sought no care. Thus, almost two thirds (63%) of neonates who died received only traditional and unqualified care or no care at all during their final illness episode. About 22% sought care from more than one provider, including 6% from 3 or more providers. Such plurality in care seeking was more likely among male infants, in the late neonatal period, and in the MCH project area., Conclusions: The high proportion of neonatal deaths that had received traditional care or no medical care in a rural area of Bangladesh highlights the need to develop community awareness about prompt medical care seeking for neonatal illnesses and to improve access to effective health care. Integration of traditional care providers into mainstream health programs should also be considered.