Use of Verbal Autopsy to Determine Underlying Cause of Death during Treatment of Multidrug-Resistant Tuberculosis, India.
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ABSTRACT:
SUBMITTER: Naik PR
PROVIDER: S-EPMC5823351 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:BackgroundThe South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA).ObjectiveThe performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard).MethodsSeven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman's rank correlation assessed population level agreement.ResultsA total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2-51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (-0.05-0.49)and CCC (0.06-0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75-0.79).ConclusionsWhilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.
| S-EPMC10795603 | biostudies-literature
Project description:In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Post-mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense, and/or cultural norms. For this reason, Verbal Autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms, and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region, or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness-of-fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.
| S-EPMC8320757 | biostudies-literature
Project description:BackgroundComputer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.MethodsThe reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.ResultsThe 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.ConclusionsThere is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.
| S-EPMC3912516 | biostudies-literature
Project description:IntroductionUddanam is an agricultural area with a high burden of chronic kidney disease of unknown etiology (CKDu). Despite reports of many deaths due to CKD in the lay press, the exact contribution of CKD to deaths remains uncertain because most deaths occur outside medical care.MethodsWe used SmartVA automated verbal autopsy tool to ascertain the cause-specific mortality fractions among a 2419 subject-strong general population cohort of adult subjects in Uddanam between 2018 and 2022. Verbal autopsy interviews were conducted twice with the family members of the deceased.ResultsA total of 133 deaths were recorded, giving a crude death rate of 5.5%, 10 times higher than that recorded in national surveys. CKD was responsible for 45% of all deaths, followed by ischemic heart disease (15%) and respiratory disease (6%).ConclusionThis study confirms CKD as the leading cause of mortality in this high CKD burden area and provides crucial data for public health decision-making and resource allocation.
| S-EPMC10831392 | biostudies-literature
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:Multidrug-resistant tuberculosis (MDR-TB) is a serious life threatening condition affecting children as well as adults worldwide. Timely diagnosis and effective treatment, both of which are complex in children, are the prerogatives for a favorable outcome. Areas covered: This review covers epidemiology, treatment regimen and duration, newer drugs and adverse events in children with MDR-TB. Special note has been made of epidemiology and principles of treatment followed in Indian children. Expert opinion: High index of suspicion is essential for diagnosing childhood MDR-TB. If there is high probability, a child can be diagnosed as presumptive MDR-TB and started on empiric treatment in consultation with experts. However, every effort should be made to confirm the diagnosis. Backbone of an effective MDR-TB regimen consists of four 2nd line anti-TB drugs plus pyrazinamide; duration being 18-24 months. The newer drugs delamanid and bedaquiline can be used in younger children if no other alternatives are available after consultation with experts. Wider availability of these drugs should be ensured for benefit to all concerned. More research is required for development of new and repurposed drugs to combat MDR-TB. Children need to be included in clinical trials for such life-saving drugs, so that nobody is denied the benefits.
| S-EPMC5942143 | biostudies-other
Project description:Setting: India has one of the highest global rates of multidrug-resistant tuberculosis (MDR-TB), which is associated with poor treatment outcomes. A better understanding of the risk factors for unfavourable outcomes is needed. Objectives: To describe 1) the demographic and clinical characteristics of MDR-TB patients registered in three states of India during 2009-2011, 2) treatment outcomes, and 3) factors associated with unfavourable outcomes. Design: A retrospective cohort study involving a record review of registered MDR-TB patients. Results: Of 788 patients, 68% were male, 70% were aged 15-44 years, 90% had failed previous anti-tuberculosis treatment or were retreatment smear-positive, 60% had a body mass index < 18.5 kg/m2 and 72% had additional resistance to streptomycin and/or ethambutol. The median time from sputum collection to the start of MDR-TB treatment was 128 days (IQR 103-173). Unfavourable outcomes occurred in 40% of the patients, mostly from death or loss to follow-up. Factors significantly associated with unfavourable outcomes included male sex, age ⩾ 45 years, being underweight and infection with the human immunodeficiency virus. Adverse drug reactions were reported in 24% of patients, with gastrointestinal disturbance, psychiatric morbidity and ototoxicity the most common. Conclusion: Long delays from sputum collection to treatment initiation using conventional methods, along with poor treatment outcomes, suggest the need to scale up rapid diagnostic tests and shorter regimens for MDR-TB.
| S-EPMC5526484 | biostudies-other
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:BackgroundGood quality cause of death (COD) information is fundamental for formulating and evaluating public health policy; yet most deaths in developing countries, including the Solomon Islands, occur at home without medical certification of cause of death (MCCOD). As a result, COD data in such contexts are often of limited use for policy and planning. Verbal autopsies (VAs) are a cost-effective way of generating reliable COD information in populations lacking comprehensive MCCOD coverage, but this method has not previously been applied in the Solomon Islands. This study describes the establishment of a VA system to estimate the cause specific mortality fractions (CSMFs) for community deaths that are not medically certified in the Solomon Islands.MethodsAutomated VA methods (SmartVA) were introduced into the Solomon Islands in 2016. Trained data collectors (nurses) conducted VAs on eligible deaths to December 2020 using electronic tablet devices and VA responses were analysed using the Tariff 2.0 automated diagnostic algorithm. CSMFs were generated for both non-inpatient deaths in hospitals (i.e. 'dead on/by arrival') and community deaths.ResultsVA was applied to 914 adolescent-and-adult deaths with a median (IQR) age of 62 (45-75) years, 61% of whom were males. A specific COD could be diagnosed for more than 85% of deaths. The leading causes of death for both sexes combined were: ischemic heart disease (16.3%), stroke (13.5%), diabetes (8.1%), pneumonia (5.7%) and chronic-respiratory disease (4.8%). Stroke was the top-ranked cause for females, and ischaemic heart disease the leading cause for males. The CSMFs from the VAs were similar to Global Burden of Disease (GBD) estimates. Overall, non-communicable diseases (NCDs) accounted for 73% of adult deaths; communicable, maternal and nutritional conditions 15%, and injuries 12%. Six of the ten leading causes reported for facility deaths in the Solomon Islands were also identified as leading causes of community deaths based on the VA diagnoses.ConclusionsNCDs are the leading cause of adult deaths in the Solomon Islands. Automated VA methods are an effective means of generating reliable COD information for community deaths in the Solomon Islands and should be routinely incorporated into the national mortality surveillance system.
| S-EPMC8590305 | biostudies-literature