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dc.contributor.authorSerina, Peteren_US
dc.contributor.authorRiley, Ianen_US
dc.contributor.authorStewart, Andreaen_US
dc.contributor.authorJames, Spencer L.en_US
dc.contributor.authorFlaxman, Abraham D.en_US
dc.contributor.authorLozano, Rafaelen_US
dc.contributor.authorHernandez, Bernardoen_US
dc.contributor.authorMooney, Meghan D.en_US
dc.contributor.authorLuning, Richarden_US
dc.contributor.authorBlack, Roberten_US
dc.contributor.authorAhuja, Rameshen_US
dc.contributor.authorAlam, Nurulen_US
dc.contributor.authorAlam, Sayed Saidulen_US
dc.contributor.authorAli, Said Mohammeden_US
dc.contributor.authorAtkinson, Charlesen_US
dc.contributor.authorBaqui, Abdulla H.en_US
dc.contributor.authorChowdhury, Hafizur R.en_US
dc.contributor.authorDandona, Laliten_US
dc.contributor.authorDandona, Rakhien_US
dc.contributor.authorDantzer, Emilyen_US
dc.contributor.authorDarmstadt, Gary L.en_US
dc.contributor.authorDas, Vinitaen_US
dc.contributor.authorDhingra, Ushaen_US
dc.contributor.authorDutta, Arupen_US
dc.contributor.authorFawzi, Wafaieen_US
dc.contributor.authorFreeman, Michaelen_US
dc.contributor.authorGomez, Saraen_US
dc.contributor.authorGouda, Hebe N.en_US
dc.contributor.authorJoshi, Rohinaen_US
dc.contributor.authorKalter, Henry D.en_US
dc.contributor.authorKumar, Aartien_US
dc.contributor.authorKumar, Vishwajeeten_US
dc.contributor.authorLucero, Marillaen_US
dc.contributor.authorMaraga, Serien_US
dc.contributor.authorMehta, Saurabhen_US
dc.contributor.authorNeal, Bruceen_US
dc.contributor.authorOhno, Summer Locketten_US
dc.contributor.authorPhillips, Daviden_US
dc.contributor.authorPierce, Kelseyen_US
dc.contributor.authorPrasad, Rajendraen_US
dc.contributor.authorPraveen, Devarsateeen_US
dc.contributor.authorPremji, Zulen_US
dc.contributor.authorRamirez-Villalobos, Doloresen_US
dc.contributor.authorRarau, Patriciaen_US
dc.contributor.authorRemolador, Hazelen_US
dc.contributor.authorRomero, Minervaen_US
dc.contributor.authorSaid, Mwanaidien_US
dc.contributor.authorSanvictores, Diozeleen_US
dc.contributor.authorSazawal, Sunilen_US
dc.contributor.authorStreatfield, Peter K.en_US
dc.contributor.authorTallo, Veronicaen_US
dc.contributor.authorVadhatpour, Alirezaen_US
dc.contributor.authorVano, Miriamen_US
dc.contributor.authorMurray, Christopher J. L.en_US
dc.contributor.authorLopez, Alan D.en_US
dc.date.accessioned2016-01-04T19:25:19Z
dc.date.issued2015en_US
dc.identifier.citationSerina, P., I. Riley, A. Stewart, S. L. James, A. D. Flaxman, R. Lozano, B. Hernandez, et al. 2015. “Improving performance of the Tariff Method for assigning causes of death to verbal autopsies.” BMC Medicine 13 (1): 291. doi:10.1186/s12916-015-0527-9. http://dx.doi.org/10.1186/s12916-015-0527-9.en
dc.identifier.issn1741-7015en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:23993676
dc.description.abstractBackground: 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. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0527-9) contains supplementary material, which is available to authorized users.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.relation.isversionofdoi:10.1186/s12916-015-0527-9en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672473/pdf/en
dash.licenseLAAen_US
dc.subjectVerbal autopsy questionnaireen
dc.subjectMortality surveillanceen
dc.subjectCauses of deathen
dc.titleImproving performance of the Tariff Method for assigning causes of death to verbal autopsiesen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalBMC Medicineen
dash.depositing.authorFawzi, Wafaieen_US
dc.date.available2016-01-04T19:25:19Z
dc.identifier.doi10.1186/s12916-015-0527-9*
dash.authorsorderedfalse
dash.contributor.affiliatedFawzi, Wafaie
dc.identifier.orcid0000-0002-2908-600X


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