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A probabilistic method to estimate the burden of maternal morbidity in resource-poor settings: preliminary development and evaluation
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health. Umeå Centre for Global Health Research and UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom.
Department of Women’s and Children’s Health, Uppsala University, Academic Hospital, 751 85 Uppsala.
London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom.
UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom.
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2014 (English)In: Emerging Themes in Epidemiology, ISSN 1742-7622, Vol. 11, no 1, 3- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.

RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.

CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

Place, publisher, year, edition, pages
BioMed Central, 2014. Vol. 11, no 1, 3- p.
Keyword [en]
Maternal health, Morbidity, Developing countries, Pregnancy, Childbirth, Bayesian analysis, Africa, Asia
National Category
Public Health, Global Health, Social Medicine and Epidemiology Obstetrics, Gynecology and Reproductive Medicine
URN: urn:nbn:se:umu:diva-90534DOI: 10.1186/1742-7622-11-3PubMedID: 24620784OAI: diva2:728349
Available from: 2014-06-24 Created: 2014-06-24 Last updated: 2015-04-29Bibliographically approved

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Fottrell, EdwardByass, Peter
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