ArticleBMC medicine2015
Improving performance of the Tariff Method for assigning causes of death to verbal autopsies.
Article in BMC medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 109 citations in OpenAlex.
- Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India.BMC medicine · 2019Trial
- Mortality due to snakebite and other venomous animals in the Indian state of Bihar: Findings from a representative mortality study.PloS one · 2018Trial
- Trial
- Determining causes of death through an abridged verbal autopsy tool: a pilot test conducted among vulnerable communities in Kolkata, India.BMJ global health · 2026Article
- Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data.Statistics in medicine · 2026Article
- Late gestation stillbirth epidemiology: findings from a nationally representative mortality survey of one million population sample in India.BMC pregnancy and childbirth · 2026Article
- A comparison of verbal autopsy assignment methods to obtain adult cause-specific mortality in two longitudinal studies in Rakai and Kalungu districts of South Central, Uganda.PLOS global public health · 2026Article
- Private sector delivery of care for maternal and newborn health: trends over a decade in the Indian state of Bihar.BMC medicine · 2025Article
- BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS.The annals of applied statistics · 2024Article
- CKD is the Major Cause of Death in Uddanam: A Population-Representative Study Using Smart Verbal Autopsy.Kidney international reports · 2024Article
- Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa.Global health action · 2023Article
- Implementation research for developing Civil Registration and Vital Statistics (CRVS) Systems: lessons from Indonesia.BMJ global health · 2023Article
- Article
- Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique.The American journal of tropical medicine and hygiene · 2023Article
- Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.The American journal of tropical medicine and hygiene · 2023Review
- Estimating causes of community death of adults in Myanmar from a nationwide population sample: Application of verbal autopsy.PLOS global public health · 2023Article
- The openVA Toolkit for Verbal Autopsies.The R journal · 2022Article
- How advanced is the epidemiological transition in Papua New Guinea? New evidence from verbal autopsy.International journal of epidemiology · 2022Article
- Validation of SmartVA using conventional autopsy: A study of adult deaths in Brazil.Lancet regional health. Americas · 2022Article
- Causes of neonatal mortality using verbal autopsies in rural Southern Nepal, 2010-2017.PLOS global public health · 2022Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
55 authors at 20 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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