BackgroundVerbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment.MethodsData collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths.ResultsOver 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions.ConclusionsThis unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.

BackgroundMonitoring 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.MethodsWe 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.ResultsThree 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.ConclusionsPhysician 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.

BackgroundReliable 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.MethodsThis 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.ResultsFor 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.ConclusionsTariff 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0527-9) contains supplementary material, which is available to authorized users.

"Social autopsy" refers to an interview process aimed at identifying social, behavioral, and health systems contributors to maternal and child deaths. It is often combined with a verbal autopsy interview to establish the biological cause of death. Two complementary purposes of social autopsy include providing population-level data to health care programmers and policymakers to utilize in developing more effective strategies for delivering maternal and child health care technologies, and increasing awareness of maternal and child death as preventable problems in order to empower communities to participate and engage health programs to increase their responsiveness and accountability.Through a comprehensive review of the literature, this paper examines the concept and development of social autopsy, focusing on the contributions of the Pathway Analysis format for child deaths and the Maternal and Perinatal Death Inquiry and Response program in India to social autopsy's success in meeting key objectives. The Pathway Analysis social autopsy format, based on the Pathway to Survival model designed to support the Integrated Management of Childhood Illness approach, was developed from 1995 to 2001 and has been utilized in studies in Asia, Africa, and Latin America. Adoption of the Pathway model has enriched the data gathered on care seeking for child illnesses and supported the development of demand- and supply-side interventions. The instrument has recently been updated to improve the assessment of neonatal deaths and is soon to be utilized in large-scale population-representative verbal/social autopsy studies in several African countries. Maternal death audit, starting with confidential inquiries into maternal deaths in Britain more than 50 years ago, is a long-accepted strategy for reducing maternal mortality. More recently, maternal social autopsy studies that supported health programming have been conducted in several developing countries. From 2005 to 2009, 10 high-mortality states in India conducted community-based maternal verbal/social autopsies with participatory data sharing with communities and health programs that resulted in the implementation of numerous data-driven maternal health interventions.Social autopsy is a powerful tool with the demonstrated ability to raise awareness, provide evidence in the form of actionable data and increase motivation at all levels to take appropriate and effective actions. Further development of the methodology along with standardized instruments and supporting tools are needed to promote its wide-scale adoption and use.

BackgroundVerbal 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0528-8) contains supplementary material, which is available to authorized users.

Abstract. Few studies have examined the influence of individual-, household-, and community-scale risk factors on carriage of resistant commensal bacteria. We determined children's medical, agricultural, and environmental exposures by household, pharmacy, and health facility surveys and Escherichia coli cultures of children, mothers' hands, household animals, and market chickens in Peru. Among 522 children with a positive stool culture, by log-binomial regression, using "any antibiotic" and 1-14 (versus 0) sulfa doses in the past 3 months increased children's risk, respectively, for ampicillin-and sulfamethoxazole-resistant E. coli carriage ( P = 0.01-0.02). Each household member taking "any antibiotic" increased children's risk for sulfamethoxazole-and multidrug-resistant E. coli carriage ( P < 0.0001). Residence in a zone where a larger proportion of households served home-raised chicken (as contrasted with intensively antibiotic-raised market chicken) protected against carrying E. coli resistant to all drugs ( P = 0.0004-0.04). Environmental contamination with drug-resistant bacteria appeared to significantly contribute to children's carriage of antibiotic-resistant E. coli .

In developing countries, diagnoses of diseases associated with deaths in children are frequently derived from retrospective maternal interviews. To determine the validity of this methodology, and to define sensitive and specific diagnostic algorithms, we compared symptoms and signs reported by mothers using structured questionnaires, with selected physician diagnoses for 164 deaths among hospitalized children on the Philippine island of Cebu. The 164 decreased children had 256 physician diagnoses of acute lower respiratory infections (ALRI) (100), diarrhoeas (92), measles (48), and neonatal tetanus cases (16). Forty-three per cent of children had multiple illnesses. An algorithm for tetanus (age at death less than or equal to 30 days with convulsion or spasm) was 100% sensitive, but specificity could not be estimated due to the small number of comparison neonatal deaths. An algorithm for measles (age greater than or equal to 120 days, with rash and fever for at least three days) had 98% sensitivity and 90% specificity. Diagnosis of ALRI was more difficult, cough and dyspnoea alone yielding 86% sensitivity but low specificity, whereas prolonged cough and dyspnoea provided 93% specificity but low sensitivity (41%). Diarrhoea diagnoses based on frequent loose or liquid stools had high sensitivity (78-84%) and specificity (79%), irrespective of whether the child died with diarrhoea alone or in combination with other illnesses. However, maternal reports of moderate/severe dehydration had low specificity. We conclude that, in this setting, verbal autopsies can diagnose major illnesses contributing to death in children with acceptable sensitivity and specificity.

Nigeria’s under-five mortality rate is the eighth highest in the world. Identifying the causes of under-five deaths is crucial to achieving Sustainable Development Goal 3 by 2030 and improving child survival. National and international bodies collaborated in this study to provide the first ever direct estimates of the causes of under-five mortality in Nigeria. Verbal autopsy interviews were conducted of a representative sample of 986 neonatal and 2,268 1–59 month old deaths from 2008 to 2013 identified by the 2013 Nigeria Demographic and Health Survey. Cause of death was assigned by physician coding and computerized expert algorithms arranged in a hierarchy. National and regional estimates of age distributions, mortality rates and cause proportions, and zonal- and age-specific mortality fractions and rates for leading causes of death were evaluated. More under-fives and 1–59 month olds in the South, respectively, died as neonates (N = 24.1%, S = 32.5%, p<0.001) and at younger ages (p<0.001) than in the North. The leading causes of neonatal and 1–59 month mortality, respectively, were sepsis, birth injury/asphyxia and neonatal pneumonia, and malaria, diarrhea and pneumonia. The preterm delivery (N = 1.2%, S = 3.7%, p = 0.042), pneumonia (N = 15.0%, S = 21.6%, p = 0.004) and malaria (N = 34.7%, S = 42.2%, p = 0.009) fractions were higher in the South, with pneumonia and malaria focused in the South East and South South; while the diarrhea fraction was elevated in the North (N = 24.8%, S = 13.2%, p<0.001). However, the diarrhea, pneumonia and malaria mortality rates were all higher in the North, respectively, by 222.9% (Z = -10.9, p = 0.000), 27.6% (Z = -2.3, p = 0.020) and 50.6% (Z = -5.7, p = 0.000), with the greatest excesses in older children. The findings support that there is an epidemiological transition ongoing in southern Nigeria, suggest the way forward to a similar transition in the North, and can help guide maternal, neonatal and child health programming and their regional and zonal foci within the country.