Accuracy of WHO verbal autopsy tool in determining major causes of neonatal deaths in India.
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ABSTRACT: Objectives
This study was conducted to evaluate the performance of World Health Organisation (WHO) verbal autopsy tool in determining major causes of neonatal deaths.Methods
From a tertiary care hospital and a government multispecialty hospital, the attending paediatricians ascertained a clinical cause of death for 371 neonatal deaths. Trained field workers conducted verbal autopsy (VA) interviews. Two independent paediatricians, who had no access to the clinical information, assigned cause of death as per verbal autopsy. Analysis was based on 313 cases in which both clinical diagnosis and VA diagnosis was obtained.Findings
As per the clinical diagnosis, four most common causes of neonatal deaths were sepsis (29.1%), preterm birth (27.8%), birth asphyxia (27.2%), and congenital anomalies (11.5%). Cause specific mortality fractions by VA diagnosis were statistically similar to those obtained by clinical diagnosis except for birth asphyxia (16.3%). Diagnostic accuracy of verbal autopsy diagnosis against clinical diagnosis ranged from 78% to 92% in ascertaining different underlying causes of death. Area under the Receiver-Operator Characteristics curve (95% confidence interval) was 0.75 (0.69-0.80) for sepsis, 0.74 (0.68-0.80) for preterm birth, 0.73 (0.65-0.82) for congenital anomaly and 0.70 (0.64-0.75) for birth asphyxia. Kappa for all four causes was moderate (0.46-0.55).Interpretation
The WHO verbal autopsy tools can provide reasonably good estimates of predominant causes of neonatal deaths in countries where neonatal mortality is high. Caution is required to interpret cause specific mortality fraction (CSMF) for birth asphyxia by VA because it is likely to be an underestimate.
SUBMITTER: Aggarwal AK
PROVIDER: S-EPMC3555991 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundRegistration of the fact of death is almost complete in the city of Chennai and not so in the rural Villupuram district in Tamilnadu, India. The cause of death is often inadequately recorded on the death certificate in developing countries like India. A special verbal autopsy (VA) study of 48,000 adult (aged >or= 25 yrs) deaths in the city of Chennai (urban) during 1995-97 and 32,000 in rural Villupuram during 1997-98 was conducted to arrive at the probable underlying cause of death to estimate cause specific mortality.MethodsA ten day training on writing verbal autopsy (VA) report for adult deaths was given to non-medical graduates with at least 15 years of formal education. They interviewed surviving spouse/close associates of the deceased to write a verbal autopsy report in local language (Tamil) on the complaints, symptoms, signs, duration and treatment details of illness prior to death. Each report was reviewed centrally by two physicians independently. Random re-interviewing of 5% of the VA reports was done to check the reliability and reproducibility of the VA report. The validity of VA diagnosis was assessed only for cancer deaths.ResultsVerbal autopsy reduced the proportion of deaths attributed to unspecified and unknown causes from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu for adult deaths (>or= 25). The sensitivity of VA to identify cancer was 95% in the age group 25-69.ConclusionA ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25-69 years) and less reliably for older ages (70+). Thus VA is practicable for deaths in early adult life or middle age and is of more limited value in old age.
| S-EPMC535898 | biostudies-literature
Project description:BackgroundThe most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.MethodsBetween March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.ResultsHCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.ConclusionBoth the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.
| S-EPMC3160942 | biostudies-literature
Project description:IntroductionEach year, more than 10 million children younger than five years of age die. The large majority of these deaths occur in the developing world. The verbal autopsy (VA) is a tool designed to ascertain cause of death in such settings. While VA has been validated against hospital diagnosed cause of death, there has been no research conducted to better understand the factors that may influence individual physicians in determining cause of death from VA.Methodology/principal findingsThis study uses data from over 27,000 neonatal and childhood deaths from The Million Death Study in which 6.3 million people in India were monitored for vital status between 1998 and 2003. The main outcome variable was physician agreement or disagreement of category of death and the variables were assessed for association using the kappa statistic, univariate and multivariate logistic regression using a conceptual hierarchical model, and a sensitivity and specificity analysis using the final VA category of mortality as the gold standard. The main variables found to be significantly associated with increased physician agreement included older ages and male gender of the deceased. When taking into account confounding factors in the multivariate analysis, we did not find consistent significant differences in physician agreement based on the death being in a rural or urban area, at home or in a health care facility, registered or not, or the respondent's gender, religion, relationship to the deceased, or whether or not the respondent lived with the deceased.Conclusions/significanceFactors influencing physician agreement/disagreement to the greatest degree are the gender and age of the deceased; specifically, physicians tend to be less likely to agree on a common category of death in female children and in younger ages, particularly neonates. Additional training of physician reviewers and continued adaptation of the VA itself, with a focus on gender and age of the deceased, may be useful in increasing rates of physician agreement in these groups.
| S-EPMC2833201 | biostudies-literature
Project description:BackgroundA verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone.MethodsAfter preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network.ResultsFor individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods.ConclusionsOur narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.
| S-EPMC6617656 | biostudies-literature
Project description:Mortality statistics by age, sex and cause are the foundation of basic health data required for health status assessment, epidemiological research and formation of health policy. Close to half the deaths in Malaysia occur outside a health facility, are not attended by medical personnel, and are given a lay opinion as to the cause of death, leading to poor quality of data from vital registration. Verbal autopsy (VA) is a very useful tool in diagnosing broad causes of deaths for events that occur outside health facilities. This article reports the development of the VA methods and our principal finding from a validation study.A cross sectional study on nationally representative sample deaths that occurred in Malaysia during 2013 was used. A VA questionnaire suitable for local use was developed. Trained field interviewers visited the family members of the deceased at their homes and conducted face to face interviews with the next of kin. Completed questionnaires were reviewed by trained physicians who assigned multiple and underlying causes. Reference diagnoses for validation were obtained from review of medical records (MR) available for a sample of the overall study deaths.Corresponding MR diagnosis with matched sample of the VA diagnosis were available in 2172 cases for the validation study. Sensitivity scores were good (>75%) for transport accidents and certain cancers. Moderate sensitivity (50% - 75%) was obtained for ischaemic heart disease (64%) and cerebrovascular disease (72%). The validation sample for deaths due to major causes such as ischaemic heart disease, pneumonia, breast cancer and transport accidents show low cause-specific mortality fraction (CSMF) changes. The scores obtained for the top 10 leading site-specific cancers ranged from average to good.We can conclude that VA is suitable for implementation for deaths outside the health facilities in Malaysia. This would reduce ill-defined mortality causes in vital registration data, and yield more accurate national mortality statistics.
| S-EPMC5553780 | biostudies-other
Project description:BackgroundIn Nepal, neonatal mortality fell substantially between 2000 and 2018, decreasing 50% from 40 to 20 deaths per 1,000 live births. Nepal's success has been attributed to a decreasing total fertility rate, improvements in female education, increases in coverage of skilled care at birth, and community-based child survival interventions.MethodsA verbal autopsy study, led by the Integrated Rural Health Development Training Centre (IRHDTC), conducted interviews for 338 neonatal deaths across six districts in Nepal between April 2012 and April 2013. We conducted a secondary analysis of verbal autopsy data to understand how cause and age of neonatal death are related to health behaviors, care seeking practices, and coverage of essential services in Nepal.ResultsSepsis was the leading cause of neonatal death (n=159/338, 47.0%), followed by birth asphyxia (n=56/338, 16.6%), preterm birth (n=45/338, 13.3%), and low birth weight (n=17/338, 5.0%). Neonatal deaths occurred primarily on the first day of life (27.2%) and between days 1 and 6 (64.8%) of life. Risk of death due birth asphyxia relative to sepsis was higher among mothers who were nulligravida, had <4 antenatal care visits, and had a multiple birth; risk of death due to prematurity relative to sepsis was lower for women who made ≥1 delivery preparation and higher for women with a multiple birth.ConclusionsOur findings suggest cause and age of death distributions typically associated with high mortality settings. Increased coverage of preventive antenatal care interventions and counseling are critically needed. Delays in care seeking for newborn illness and quality of care around the time of delivery and for sick newborns are important points of intervention with potential to reduce deaths, particularly for birth asphyxia and sepsis, which remain common in this population.
| S-EPMC8751254 | biostudies-literature
Project description:OBJECTIVE:To identify the medical causes of death and contribution of non-biological factors towards infant mortality by a retrospective analysis of routinely collected data using verbal and social autopsy tools. SETTING:The study site was Health and Demographic Surveillance System (HDSS), Ballabgarh, North India PARTICIPANTS: All infant deaths during the years 2008-2012 were included for verbal autopsy and infant deaths from July 2012 to December 2012 were included for social autopsy. OUTCOME MEASURES:Cause of death ascertained by a validated verbal autopsy tool and level of delay based on a three-delay model using the INDEPTH social autopsy tool were the main outcome measures. The level of delay was defined as follows: level 1, delay in identification of danger signs and decision making to seek care; level 2, delay in reaching a health facility from home; level 3, delay in getting healthcare at the health facility. RESULTS:The infant mortality rate during the study period was 46.5/1000 live births. Neonatal deaths contributed to 54.3% of infant deaths and 39% occurred on the first day of life. Birth asphyxia (31.5%) followed by low birth weight (LBW)/prematurity (26.5%) were the most common causes of neonatal death, while infection (57.8%) was the most common cause of post-neonatal death. Care-seeking was delayed among 50% of neonatal deaths and 41.2% of post-neonatal deaths. Delay at level 1 was most common and occurred in 32.4% of neonatal deaths and 29.4% of post-neonatal deaths. Deaths due to LBW/prematurity were mostly followed by delay at level 1. CONCLUSION:A high proportion of preventable infant mortality still exists in an area which is under continuous health and demographic surveillance. There is a need to enhance home-based preventive care to enable the mother to identify and respond to danger signs. Verbal autopsy and social autopsy could be routinely done to guide policy interventions aimed at reduction of infant mortality.
| S-EPMC5577880 | biostudies-literature
Project description:BackgroundDeveloping countries generally lack complete vital registration systems that can produce cause of death information for health planning in their populations. As an alternative, verbal autopsy (VA) - the process of interviewing family members or caregivers on the circumstances leading to death - is often used by Demographic Surveillance Systems to generate cause of death data. Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results. The aim of this paper is to explore how a computer-based probabilistic model, InterVA, performs in comparison with PR in interpreting VA data in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS).MethodsBetween August 2002 and December 2008, a total of 1,823 VA interviews were reviewed by physicians in the NUHDSS. Data on these interviews were entered into the InterVA model for interpretation. Cause-specific mortality fractions were then derived from the cause of death data generated by the physicians and by the model. We then estimated the level of agreement between both methods using Kappa statistics.ResultsThe level of agreement between individual causes of death assigned by both methods was only 35% (kappa = 0.27, 95% CI: 0.25 - 0.30). However, the patterns of mortality as determined by both methods showed a high burden of infectious diseases, including HIV/AIDS, tuberculosis, and pneumonia, in the study population. These mortality patterns are consistent with existing knowledge on the burden of disease in underdeveloped communities in Africa.ConclusionsThe InterVA model showed promising results as a community-level tool for generating cause of death data from VAs. We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.
| S-EPMC2902422 | biostudies-literature
Project description:BackgroundUnder-five mortality in Kenya has declined over the past two decades. However, the reduction in the neonatal mortality rate has remained stagnant. In a country with weak civil registration and vital statistics systems, there is an evident gap in documentation of mortality and its causes among low birth weight (LBW) and preterm neonates. We aimed to establish causes of neonatal LBW and preterm mortality in Migori County, among participants of the PTBI-K (Preterm Birth Initiative-Kenya) study.MethodsVerbal and social autopsy (VASA) interviews were conducted with caregivers of deceased LBW and preterm neonates delivered within selected 17 health facilities in Migori County, Kenya. The probable cause of death was assigned using the WHO International Classification of Diseases (ICD-10).ResultsBetween January 2017 to December 2018, 3175 babies were born preterm or LBW, and 164 (5.1%) died in the first 28 days of life. VASA was conducted among 88 (53.7%) of the neonatal deaths. Almost half (38, 43.2%) of the deaths occurred within the first 24 h of life. Birth asphyxia (45.5%), neonatal sepsis (26.1%), respiratory distress syndrome (12.5%) and hypothermia (11.0%) were the leading causes of death. In the early neonatal period, majority (54.3%) of the neonates succumbed to asphyxia while in the late neonatal period majority (66.7%) succumbed to sepsis. Delay in seeking medical care was reported for 4 (5.8%) of the neonatal deaths.ConclusionDeaths among LBW and preterm neonates occur early in life due to preventable causes. This calls for enhanced implementation of existing facility-based intrapartum and immediate postpartum care interventions, targeting asphyxia, sepsis, respiratory distress syndrome and hypothermia.
| S-EPMC8320164 | biostudies-literature
Project description:Decreasing trends in measles mortality have been reported in recent years. However, such estimates of measles mortality have depended heavily on assumed regional measles case fatality risks (CFRs) and made little use of mortality data from low- and middle-income countries in general and India, the country with the highest measles burden globally, in particular.We constructed a dynamic model of measles transmission in India with parameters that were empirically inferred using spectral analysis from a time series of measles mortality extracted from the Million Death Study, an ongoing longitudinal study recording deaths across 2.4 million Indian households and attributing causes of death using verbal autopsy. The model was then used to estimate the measles CFR, the number of measles deaths, and the impact of vaccination in 2000-2015 among under-five children in India and in the states of Bihar and Uttar Pradesh (UP), two states with large populations and the highest numbers of measles deaths in India.We obtained the following estimated CFRs among under-five children for the year 2005: 0.63% (95% confidence interval (CI): 0.40-1.00%) for India as a whole, 0.62% (0.38-1.00%) for Bihar, and 1.19% (0.80-1.75%) for UP. During 2000-2015, we estimated that 607,000 (95% CI: 383,000-958,000) under-five deaths attributed to measles occurred in India as a whole. If no routine vaccination or supplemental immunization activities had occurred from 2000 to 2015, an additional 1.6 (1.0-2.6) million deaths for under-five children would have occurred across India.We developed a data- and model-driven estimation of the historical measles dynamics, CFR, and vaccination impact in India, extracting the periodicity of epidemics using spectral and coherence analysis, which allowed us to infer key parameters driving measles transmission dynamics and mortality.
| S-EPMC5550950 | biostudies-other