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

Overview of attention for article published in BMC Medicine, December 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Improving performance of the Tariff Method for assigning causes of death to verbal autopsies
Published in
BMC Medicine, December 2015
DOI 10.1186/s12916-015-0527-9
Pubmed ID

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 Dantzer, Gary 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


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. 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. 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. 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.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 91 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 24%
Student > Master 12 13%
Student > Ph. D. Student 11 12%
Student > Postgraduate 5 5%
Student > Bachelor 5 5%
Other 14 15%
Unknown 22 24%
Readers by discipline Count As %
Medicine and Dentistry 32 35%
Nursing and Health Professions 7 8%
Computer Science 5 5%
Social Sciences 5 5%
Psychology 3 3%
Other 11 12%
Unknown 28 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2016.
All research outputs
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Outputs from BMC Medicine
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Outputs of similar age
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Outputs of similar age from BMC Medicine
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Altmetric has tracked 23,342,092 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.1. This one has gotten more attention than average, scoring higher than 57% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 391,361 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.