Search

Background: Millions of children worldwide suffer and die from conditions for which effective interventions exist. While there is ample evidence regarding these diseases, there is a dearth of information on the social factors associated with child mortality., Methods: The 2014 Verbal and Social Autopsy Study was conducted based on a nationally representative sample of 3,254 deaths that occurred in children under the age of five and were reported on the birth history component of the 2013 Nigerian Demographic and Health Survey. We conducted a descriptive analysis of the preventive and curative care sought and obtained for the 2,057 children aged 1-59 months who died in Nigeria and performed regional (North vs. South) comparisons., Results: A total of 1,616 children died in the northern region, while 441 children died in the South. The majority (72.5%) of deceased children in the northern region were born to mothers who had no education, married at a young age, and lived in the poorest two quintiles of households. When caregivers first noticed that their child was ill, a median of 2 days passed before they sought or attempted to seek healthcare for their children. The proportion of children who reached and departed from their first formal healthcare provider alive was greater in the North (30.6%) than in the South (17.9%) (p<0.001). A total of 548 children were moderately or severely sick at discharge from the first healthcare provider, yet only 3.9%-18.1% were referred to a second healthcare provider. Cost, lack of transportation, and distance from healthcare facilities were the most commonly reported barriers to formal care-seeking behavior., Conclusions: Maternal, household, and healthcare system factors contributed to child mortality in Nigeria. Information regarding modifiable social factors may be useful in planning intervention programs to promote child survival in Nigeria and other low-income countries in sub-Saharan Africa.

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.

Background: Physician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods, Tariff 2.0 and InterVA-4, which promise greater objectivity and lower cost. A disadvantage of the Tariff method is that it requires a training data set from a prior validation study, while InterVA relies on clinically specified conditional probabilities. We undertook to validate the hierarchical expert algorithm analysis of VA data, an automated, intuitive, deterministic method that does not require a training data set., Methods: Using Population Health Metrics Research Consortium study hospital source data, we compared the primary causes of 1629 neonatal and 1456 1-59 month-old child deaths from VA expert algorithms arranged in a hierarchy to their reference standard causes. The expert algorithms were held constant, while five prior and one new "compromise" neonatal hierarchy, and three former child hierarchies were tested. For each comparison, the reference standard data were resampled 1000 times within the range of cause-specific mortality fractions (CSMF) for one of three approximated community scenarios in the 2013 WHO global causes of death, plus one random mortality cause proportions scenario. We utilized CSMF accuracy to assess overall population-level validity, and the absolute difference between VA and reference standard CSMFs to examine particular causes. Chance-corrected concordance (CCC) and Cohen's kappa were used to evaluate individual-level cause assignment., Results: Overall CSMF accuracy for the best-performing expert algorithm hierarchy was 0.80 (range 0.57-0.96) for neonatal deaths and 0.76 (0.50-0.97) for child deaths. Performance for particular causes of death varied, with fairly flat estimated CSMF over a range of reference values for several causes. Performance at the individual diagnosis level was also less favorable than that for overall CSMF (neonatal: best CCC = 0.23, range 0.16-0.33; best kappa = 0.29, 0.23-0.35; child: best CCC = 0.40, 0.19-0.45; best kappa = 0.29, 0.07-0.35)., Conclusions: Expert algorithms in a hierarchy offer an accessible, automated method for assigning VA causes of death. Overall population-level accuracy is similar to that of more complex machine learning methods, but without need for a training data set from a prior validation study.

Background: This study was one of a set of verbal/social autopsy (VASA) investigations undertaken by the WHO/UNICEF-supported Child Health Epidemiology Reference Group to estimate the causes and determinants of neonatal and child deaths in high priority countries. The study objective was to help explain the lack of decrease in neonatal mortality in Niger from 2007 to 2010, a period during which child mortality was decreasing., Methods: VASA interviews were conducted of a random sample of 453 neonatal deaths identified by the 2010 Niger National Mortality Survey (NNMS). Causes of death were determined by expert algorithm analysis, and the prevalence of household, community and health system determinants were examined along the continuum of maternal and newborn care, the Pathway to Survival for newborn illnesses, and an extended pathway for maternal complications. The social autopsy findings were compared to available data for survivors from the same cohort collected by the NNMS and the 2012 Niger Demographic and Health Survey., Findings: Severe neonatal infection and birth asphyxia were the leading causes of early neonatal death in the community and facilities. Death in the community after delayed careseeking for severe infection predominated during the late neonatal period. The levels of nearly all demographic, antenatal and delivery care factors were in the direction of risk for the VASA study decedents. They more often resided rurally (P < 0.001) and their mothers were less educated (P = 0.03) and gave birth when younger (P = 0.03) than survivors' mothers. Their mothers also were less likely to receive quality antenatal care (P < 0.001), skilled attendance at birth (P = 0.03) or to deliver in an institution (P < 0.001). Nearly half suffered an obstetric complication, with more maternal infection (17.9% vs 0.2%), antepartum hemorrhage (12.5% vs 0.5%) and eclampsia/preeclampsia (9.5% vs 1.6%) than for all births in Niger. Their mothers also were unlikely to seek health care for their own complications (37% to 42%) as well as for the newborn's illness (30.6%)., Conclusions: Niger should scale up its recently implemented package of high-impact interventions to additional integrated health facilities and expand the package to provide antenatal care and management of labor and delivery, with support to reach a higher level facility when required. Community interventions are needed to improve illness recognition and careseeking for severe neonatal infection.

Background: Understanding the determinants of preventable deaths of children under the age of five is important for accelerated annual declines - even as countries achieve the UN's Millennium Development Goals and the target date of 2015 has been reached. While research has documented the extent and nature of the overall rapid decline in child mortality in Niger, there is less clear evidence to provide insight into the contributors to such deaths. This issue is the central focus of this paper., Methods: We analyzed a nationally representative cross-sectional sample of 620 child deaths from the 2012 Niger Verbal Autopsy/Social Autopsy (VASA) Survey. We conducted a descriptive analysis of the data on preventive and curative care, guided by the coverage of proven indicators along the continuum of well child care and illness recognition and care-seeking for child illnesses encompassed by the BASICS/CDC Pathway to Survival model., Results: Six hundred twenty deaths of children (1-59 months of age) were confirmed from the VASA survey. The majority of these children lived in households with precarious socio-economic conditions. Among the 414 children whose fatal illnesses began at age 0-23 months, just 24.4% were appropriately fed. About 24% of children aged 12-59 months were fully immunized. Of 601 children tracked through the Pathway to Survival, 62.4% could reach the first health care provider after about 67 minutes travel time. Of the 306 children who left the first health care provider alive, 161 (52.6%) were not referred for further care nor received any home care recommendations, and just 19% were referred to a second provider. About 113 of the caregivers reported cost (35%), distance (35%) and lack of transport (30%) as constraints to care-seeking at a health facility., Conclusion: Despite Niger's recent major achievements in reducing child mortality, the following determinants are crucial to continue building on the gains the country has made: improved socio-economic state of the poor in the country, investment in women's education, adoption of the a law to prevent marriage of young girls before 18 years of age, and implementation of health programs that encourage breastfeeding and complementary feeding, immunization, illness recognition, prompt and appropriate care-seeking, and improved referral rates.

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.

Background: 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., Methods: 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., Results: 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., Conclusions: 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.

Verbal autopsy is an interview-based technique to determine the cause distribution of death in a population. The use of verbal autopsy for understanding neurological diseases is crucial to burden of disease analyses in many countries, particularly in locations where civil registration systems are non-functioning or absent. We review the purposes, strengths, and weaknesses in the use of verbal autopsy for neurological diseases.

Abstract Background Verbal 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. Methods Data 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. Results Over 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. Conclusions This 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.