Improving performance of the Tariff Method for assigning causes of death to verbal autopsies

Peter Serina, Ian Riley, Andrea Stewart, Spencer L. James, Abraham D. Flaxman, Rafael Lozano, Bernardo Hernandez, Meghan D. Mooney, Richard Luning, Robert Black, Ramesh Ahuja, Nurul Alam, Sayed Saidul Alam, Said Mohammed Ali, Charles Atkinson, Abdulla H. Baqui, Hafizur R. Chowdhury, Lalit Dandona, Rakhi Dandona, Emily DantzerGary L. Darmstadt, Vinita Das, Usha Dhingra, Arup Dutta, Wafaie Fawzi, Michael Freeman, Sara Gomez, Hebe N. Gouda, Rohina Joshi, Henry D. Kalter, Aarti Kumar, Vishwajeet Kumar, Marilla Lucero, Seri Maraga, Saurabh Mehta, Bruce Neal, Summer Lockett Ohno, David Phillips, Kelsey Pierce, Rajendra Prasad, Devarsatee Praveen, Zul Premji, Dolores Ramirez-Villalobos, Patricia Rarau, Hazel Remolador, Minerva Romero, Mwanaidi Said, Diozele Sanvictores, Sunil Sazawal, Peter K. Streatfield, Veronica Tallo, Alireza Vadhatpour, Miriam Vano, Christopher J.L. Murray, Alan D. Lopez

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number291
JournalBMC medicine
Volume13
Issue number1
DOIs
StatePublished - Dec 8 2015

Keywords

  • Causes of death
  • Mortality surveillance
  • Verbal autopsy questionnaire

ASJC Scopus subject areas

  • Medicine(all)

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