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 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

doi:10.1186/s12916-015-0527-9

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

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

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 language	English (US)
Article number	291
Journal	BMC medicine
Volume	13
Issue number	1
DOIs	https://doi.org/10.1186/s12916-015-0527-9
State	Published - Dec 8 2015

Keywords

Causes of death
Mortality surveillance
Verbal autopsy questionnaire

ASJC Scopus subject areas

General Medicine

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10.1186/s12916-015-0527-9

Cite this

Serina, P., Riley, I., Stewart, A., James, S. L., Flaxman, A. D., Lozano, R., Hernandez, B., Mooney, M. D., Luning, R., Black, R., Ahuja, R., Alam, N., Alam, S. S., Ali, S. M., Atkinson, C., Baqui, A. H., Chowdhury, H. R., Dandona, L., Dandona, R., ... Lopez, A. D. (2015). Improving performance of the Tariff Method for assigning causes of death to verbal autopsies. BMC medicine, 13(1), Article 291. https://doi.org/10.1186/s12916-015-0527-9

Serina, P, Riley, I, Stewart, A, James, SL, Flaxman, AD, Lozano, R, Hernandez, B, Mooney, MD, Luning, R, Black, R, Ahuja, R, Alam, N, Alam, SS, Ali, SM, Atkinson, C, Baqui, AH, Chowdhury, HR, Dandona, L, Dandona, R, Dantzer, E, Darmstadt, GL, Das, V, Dhingra, U, Dutta, A, Fawzi, W, Freeman, M, Gomez, S, Gouda, HN, Joshi, R, Kalter, HD, Kumar, A, Kumar, V, Lucero, M, Maraga, S, Mehta, S, Neal, B, Ohno, SL, Phillips, D, Pierce, K, Prasad, R, Praveen, D, Premji, Z, Ramirez-Villalobos, D, Rarau, P, Remolador, H, Romero, M, Said, M, Sanvictores, D, Sazawal, S, Streatfield, PK, Tallo, V, Vadhatpour, A, Vano, M, Murray, CJL & Lopez, AD 2015, 'Improving performance of the Tariff Method for assigning causes of death to verbal autopsies', BMC medicine, vol. 13, no. 1, 291. https://doi.org/10.1186/s12916-015-0527-9

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title = "Improving performance of the Tariff Method for assigning causes of death to verbal autopsies",

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.",

keywords = "Causes of death, Mortality surveillance, Verbal autopsy questionnaire",

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

note = "Publisher Copyright: {\textcopyright} 2015 Serina et al.",

year = "2015",

month = dec,

day = "8",

doi = "10.1186/s12916-015-0527-9",

language = "English (US)",

volume = "13",

journal = "BMC medicine",

issn = "1741-7015",

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TY - JOUR

T1 - Improving performance of the Tariff Method for assigning causes of death to verbal autopsies

AU - Serina, Peter

AU - Riley, Ian

AU - Stewart, Andrea

AU - James, Spencer L.

AU - Flaxman, Abraham D.

AU - Lozano, Rafael

AU - Hernandez, Bernardo

AU - Mooney, Meghan D.

AU - Luning, Richard

AU - Black, Robert

AU - Ahuja, Ramesh

AU - Alam, Nurul

AU - Alam, Sayed Saidul

AU - Ali, Said Mohammed

AU - Atkinson, Charles

AU - Baqui, Abdulla H.

AU - Chowdhury, Hafizur R.

AU - Dandona, Lalit

AU - Dandona, Rakhi

AU - Dantzer, Emily

AU - Darmstadt, Gary L.

AU - Das, Vinita

AU - Dhingra, Usha

AU - Dutta, Arup

AU - Fawzi, Wafaie

AU - Freeman, Michael

AU - Gomez, Sara

AU - Gouda, Hebe N.

AU - Joshi, Rohina

AU - Kalter, Henry D.

AU - Kumar, Aarti

AU - Kumar, Vishwajeet

AU - Lucero, Marilla

AU - Maraga, Seri

AU - Mehta, Saurabh

AU - Neal, Bruce

AU - Ohno, Summer Lockett

AU - Phillips, David

AU - Pierce, Kelsey

AU - Prasad, Rajendra

AU - Praveen, Devarsatee

AU - Premji, Zul

AU - Ramirez-Villalobos, Dolores

AU - Rarau, Patricia

AU - Remolador, Hazel

AU - Romero, Minerva

AU - Said, Mwanaidi

AU - Sanvictores, Diozele

AU - Sazawal, Sunil

AU - Streatfield, Peter K.

AU - Tallo, Veronica

AU - Vadhatpour, Alireza

AU - Vano, Miriam

AU - Murray, Christopher J.L.

AU - Lopez, Alan D.

PY - 2015/12/8

Y1 - 2015/12/8

N2 - 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.

AB - 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.

KW - Causes of death

KW - Mortality surveillance

KW - Verbal autopsy questionnaire

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

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