An integrated approach to processing WHO-2016 verbal autopsy data

the InterVA-5 model

Peter Byass (Corresponding Author), Laith Hussain-Alkhateeb, Lucia D'Ambruoso, Samuel Clark, Justine Davies, Edward Fottrell, Jon Bird, Chodziwadziwa Kabudula, Stephen Tollman, Kathleen Kahn, Linus Schiöler, Max Petzold

Research output: Contribution to journalArticle

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Background
Verbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts.

The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.

Results
The new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults.

The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.

Conclusions
Despite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.
Original languageEnglish
Article number102
Number of pages12
JournalBMC medicine
Volume17
DOIs
Publication statusPublished - 30 May 2019

Fingerprint

Autopsy
Mortality
Afghanistan
Cause of Death
Interviews
Health Planning
Knowledge Bases
Health
Research
Tertiary Care Centers
Computer Simulation
Population
Uncertainty
Datasets
Software

Keywords

  • verbal autopsy
  • mortality surveillance
  • civil registration
  • InterVA
  • cause of death
  • World Health Organization
  • Mortality surveillance
  • Civil registration
  • Verbal autopsy
  • Cause of death

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Byass, P., Hussain-Alkhateeb, L., D'Ambruoso, L., Clark, S., Davies, J., Fottrell, E., ... Petzold, M. (2019). An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model. BMC medicine , 17, [102]. https://doi.org/10.1186/s12916-019-1333-6

An integrated approach to processing WHO-2016 verbal autopsy data : the InterVA-5 model. / Byass, Peter (Corresponding Author); Hussain-Alkhateeb, Laith; D'Ambruoso, Lucia; Clark, Samuel; Davies, Justine; Fottrell, Edward ; Bird, Jon; Kabudula, Chodziwadziwa; Tollman, Stephen; Kahn, Kathleen ; Schiöler, Linus; Petzold, Max.

In: BMC medicine , Vol. 17, 102, 30.05.2019.

Research output: Contribution to journalArticle

Byass, P, Hussain-Alkhateeb, L, D'Ambruoso, L, Clark, S, Davies, J, Fottrell, E, Bird, J, Kabudula, C, Tollman, S, Kahn, K, Schiöler, L & Petzold, M 2019, 'An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model', BMC medicine , vol. 17, 102. https://doi.org/10.1186/s12916-019-1333-6
Byass, Peter ; Hussain-Alkhateeb, Laith ; D'Ambruoso, Lucia ; Clark, Samuel ; Davies, Justine ; Fottrell, Edward ; Bird, Jon ; Kabudula, Chodziwadziwa ; Tollman, Stephen ; Kahn, Kathleen ; Schiöler, Linus ; Petzold, Max. / An integrated approach to processing WHO-2016 verbal autopsy data : the InterVA-5 model. In: BMC medicine . 2019 ; Vol. 17.
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abstract = "BackgroundVerbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50{\%} of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts.The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.ResultsThe new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults.The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.ConclusionsDespite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.",
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note = "Acknowledgements We are grateful for technical discussions with Dr. Erin K. Nichols. Funding There was no specific funding, other than authors’ time at their institutions, for this work. Publication costs were funded by the Health Systems Research Initiative from the Department for International Development (DFID)/ Medical Research Council (MRC)/Wellcome Trust/Economic and Social Research Council (ESRC) (MR/P014844/1). Availability of data and materials The software, demonstration material, datasets and code supporting the conclusions of this article are freely available in the GitHub repository https://github.com/peterbyass/InterVA-5",
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T1 - An integrated approach to processing WHO-2016 verbal autopsy data

T2 - the InterVA-5 model

AU - Byass, Peter

AU - Hussain-Alkhateeb, Laith

AU - D'Ambruoso, Lucia

AU - Clark, Samuel

AU - Davies, Justine

AU - Fottrell, Edward

AU - Bird, Jon

AU - Kabudula, Chodziwadziwa

AU - Tollman, Stephen

AU - Kahn, Kathleen

AU - Schiöler, Linus

AU - Petzold, Max

N1 - Acknowledgements We are grateful for technical discussions with Dr. Erin K. Nichols. Funding There was no specific funding, other than authors’ time at their institutions, for this work. Publication costs were funded by the Health Systems Research Initiative from the Department for International Development (DFID)/ Medical Research Council (MRC)/Wellcome Trust/Economic and Social Research Council (ESRC) (MR/P014844/1). Availability of data and materials The software, demonstration material, datasets and code supporting the conclusions of this article are freely available in the GitHub repository https://github.com/peterbyass/InterVA-5

PY - 2019/5/30

Y1 - 2019/5/30

N2 - BackgroundVerbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts.The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.ResultsThe new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults.The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.ConclusionsDespite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.

AB - BackgroundVerbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts.The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.ResultsThe new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults.The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.ConclusionsDespite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.

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KW - mortality surveillance

KW - civil registration

KW - InterVA

KW - cause of death

KW - World Health Organization

KW - Mortality surveillance

KW - Civil registration

KW - Verbal autopsy

KW - Cause of death

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