A probabilistic method to estimate the burden of maternal morbidity in resource-poor settings: preliminary development and evaluation

Emerging Themes in Epidemiology

Veronique Filippi²  Peter Byass⁴  Sourou Goufodji⁷  Rasmane Ganaba³  Nicolas Meda³  Nirmala Nair¹  Kishwar Azad⁵  David Osrin⁶  Carine Ronsmans²  Ulf Högberg⁸  Edward Fottrell⁴ 

[1] Ekjut, Plot 556B, Potka, Chakradharpur Pin - 833102, West Singhbhum, Jharkhand, India;London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom;Centre MURAZ, Ministry of Health, 01 PO Box 390, Bobo-Dioulasso 01, Burkina Faso;Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden;Perinatal Care Project, Diabetic Association of Bangladesh (BADAS), BIRDEM 122 Kazi Nazrul Islam Avenue Shahbagh, Dhaka 1000, Bangladesh;UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom;Centre de Recherche en Reproduction Humaine et en Démographie, Cotonou, Benin;Department of Women’s and Children’s Health, Uppsala University, Academic Hospital, 751 85 Uppsala, Sweden

关键词: Asia;    Africa;    Bayesian analysis;    Childbirth;    Pregnancy;    Developing countries;    Morbidity;    Maternal health;   

Others  :  800886
DOI  :  10.1186/1742-7622-11-3

 received in 2013-07-12, accepted in 2014-02-18,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Fottrell et al.; licensee BioMed Central Ltd.

【 图 表 】

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