@article{21881,
  author = {Joshi Rohina and Dandona R. and Dandona L. and Serina P. and Stewart A. and Riley I. and Hernandez B. and Freeman M. and Sanvictores D. and Tallo V. and Kumar V. and Murray C. and Lozano R. and Flaxman A. and Phillips D. and James S. and Atkinson C. and Ohno S. and Black R. and Ali S. and Baqui A. and Dantzer E. and Das V. and Dhingra U. and Dutta A. and Fawzi W. and Gómez S. and Mehta S. and Lopez A. and Alam S. and Gouda H. and Mooney M. and Kumar A. and Luning R. and Ahuja R. and Alam N. and Chowdhury H. and Darmstadt G. and Kalter H. and Lucero M. and Maraga S. and Pierce K. and Prasad R. and Premji Z. and Ramirez-Villalobos D. and Rarau P. and Remolador H. and Romero M. and Said M. and Sazawal S. and Streatfield P. and Vadhatpour A. and Vano M. and Praveen Devarsetty and Neal Bruce},
  title = {Improving performance of the Tariff Method for assigning causes of death to verbal autopsies},
  abstract = {<p>
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.
</p>
},
  year = {2015},
  journal = {BMC Medicine},
  volume = {13},
  edition = {2015/12/09},
  pages = {291},
  isbn = {1741-7015 (Electronic)<br/>1741-7015 (Linking)},
  note = {Serina, Peter<br/>Riley, Ian<br/>Stewart, Andrea<br/>James, Spencer L<br/>Flaxman, Abraham D<br/>Lozano, Rafael<br/>Hernandez, Bernardo<br/>Mooney, Meghan D<br/>Luning, Richard<br/>Black, Robert<br/>Ahuja, Ramesh<br/>Alam, Nurul<br/>Alam, Sayed Saidul<br/>Ali, Said Mohammed<br/>Atkinson, Charles<br/>Baqui, Abdulla H<br/>Chowdhury, Hafizur R<br/>Dandona, Lalit<br/>Dandona, Rakhi<br/>Dantzer, Emily<br/>Darmstadt, Gary L<br/>Das, Vinita<br/>Dhingra, Usha<br/>Dutta, Arup<br/>Fawzi, Wafaie<br/>Freeman, Michael<br/>Gomez, Sara<br/>Gouda, Hebe N<br/>Joshi, Rohina<br/>Kalter, Henry D<br/>Kumar, Aarti<br/>Kumar, Vishwajeet<br/>Lucero, Marilla<br/>Maraga, Seri<br/>Mehta, Saurabh<br/>Neal, Bruce<br/>Ohno, Summer Lockett<br/>Phillips, David<br/>Pierce, Kelsey<br/>Prasad, Rajendra<br/>Praveen, Devarsatee<br/>Premji, Zul<br/>Ramirez-Villalobos, Dolores<br/>Rarau, Patricia<br/>Remolador, Hazel<br/>Romero, Minerva<br/>Said, Mwanaidi<br/>Sanvictores, Diozele<br/>Sazawal, Sunil<br/>Streatfield, Peter K<br/>Tallo, Veronica<br/>Vadhatpour, Alireza<br/>Vano, Miriam<br/>Murray, Christopher J L<br/>Lopez, Alan D<br/>Research Support, Non-U.S. Gov't<br/>England<br/>BMC Med. 2015 Dec 8;13:291. doi: 10.1186/s12916-015-0527-9.},
  language = {eng},
}