Revealing the burden of maternal mortality

A probabilistic model for determining pregnancy-related causes of death from verbal autopsies

Edward Fottrell, Peter Byass, Thomas W. Ouedraogo, Cecile Tamini, Adjima Gbangou, Issiaka Sombié, Ulf Högberg, Karen H. Witten, Sohinee Bhattacharya, Teklay Desta, Sylvia Deganus, Janet Tornui, Ann E. Fitzmaurice, Nicolas Meda, Wendy Jane Graham

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death. Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output. Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference. Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.
Original languageEnglish
Article number1
JournalPopulation Health Metrics
Volume5
DOIs
Publication statusPublished - 8 Feb 2007

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Maternal Mortality
Statistical Models
Cause of Death
Autopsy
Pregnancy
Physicians
Interviews
Burkina Faso
Information Storage and Retrieval
Developing Countries
Malaria
Cardiovascular Diseases
Mortality
Infection
Research

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Revealing the burden of maternal mortality : A probabilistic model for determining pregnancy-related causes of death from verbal autopsies. / Fottrell, Edward ; Byass, Peter; Ouedraogo, Thomas W.; Tamini, Cecile ; Gbangou, Adjima; Sombié, Issiaka ; Högberg, Ulf ; Witten, Karen H.; Bhattacharya, Sohinee; Desta, Teklay ; Deganus, Sylvia ; Tornui, Janet ; Fitzmaurice, Ann E.; Meda, Nicolas ; Graham, Wendy Jane.

In: Population Health Metrics, Vol. 5, 1, 08.02.2007.

Research output: Contribution to journalArticle

Fottrell, E, Byass, P, Ouedraogo, TW, Tamini, C, Gbangou, A, Sombié, I, Högberg, U, Witten, KH, Bhattacharya, S, Desta, T, Deganus, S, Tornui, J, Fitzmaurice, AE, Meda, N & Graham, WJ 2007, 'Revealing the burden of maternal mortality: A probabilistic model for determining pregnancy-related causes of death from verbal autopsies', Population Health Metrics, vol. 5, 1. https://doi.org/10.1186/1478-7954-5-1
Fottrell, Edward ; Byass, Peter ; Ouedraogo, Thomas W. ; Tamini, Cecile ; Gbangou, Adjima ; Sombié, Issiaka ; Högberg, Ulf ; Witten, Karen H. ; Bhattacharya, Sohinee ; Desta, Teklay ; Deganus, Sylvia ; Tornui, Janet ; Fitzmaurice, Ann E. ; Meda, Nicolas ; Graham, Wendy Jane. / Revealing the burden of maternal mortality : A probabilistic model for determining pregnancy-related causes of death from verbal autopsies. In: Population Health Metrics. 2007 ; Vol. 5.
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title = "Revealing the burden of maternal mortality: A probabilistic model for determining pregnancy-related causes of death from verbal autopsies",
abstract = "Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death. Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output. Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60{\%}, rising to approximately 80{\%} when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30{\%} of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference. Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.",
author = "Edward Fottrell and Peter Byass and Ouedraogo, {Thomas W.} and Cecile Tamini and Adjima Gbangou and Issiaka Sombi{\'e} and Ulf H{\"o}gberg and Witten, {Karen H.} and Sohinee Bhattacharya and Teklay Desta and Sylvia Deganus and Janet Tornui and Fitzmaurice, {Ann E.} and Nicolas Meda and Graham, {Wendy Jane}",
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T1 - Revealing the burden of maternal mortality

T2 - A probabilistic model for determining pregnancy-related causes of death from verbal autopsies

AU - Fottrell, Edward

AU - Byass, Peter

AU - Ouedraogo, Thomas W.

AU - Tamini, Cecile

AU - Gbangou, Adjima

AU - Sombié, Issiaka

AU - Högberg, Ulf

AU - Witten, Karen H.

AU - Bhattacharya, Sohinee

AU - Desta, Teklay

AU - Deganus, Sylvia

AU - Tornui, Janet

AU - Fitzmaurice, Ann E.

AU - Meda, Nicolas

AU - Graham, Wendy Jane

PY - 2007/2/8

Y1 - 2007/2/8

N2 - Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death. Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output. Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference. Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.

AB - Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death. Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output. Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference. Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.

U2 - 10.1186/1478-7954-5-1

DO - 10.1186/1478-7954-5-1

M3 - Article

VL - 5

JO - Population Health Metrics

JF - Population Health Metrics

SN - 1478-7954

M1 - 1

ER -