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Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies

Fottrell, E; Byass, P; Ouedraogo, TW; Tamini, C; Gbangou, A; Sombié, I; Högberg, U; ... Graham, WJ; + view all (2007) Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies. Population Health Metrics , 5 10.1186/1478-7954-5-1. Green open access

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Abstract

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.

Type: Article
Title: Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/1478-7954-5-1
Publisher version: http://dx.doi.org/10.1186/1478-7954-5-1
Language: English
Additional information: © 2007 Fottrell et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
URI: https://discovery.ucl.ac.uk/id/eprint/1314718
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