A prospective cause of death classification system for maternal deaths in low and middle‐income countries: results from the Global Network Maternal Newborn Health Registry

• Published in: BJOG : an international journal of obstetrics and gynaecology Vol. 125; no. 9; pp. 1137 - 1143
• Main Authors: Pasha, O, McClure, EM, Saleem, S, Tikmani, SS, Lokangaka, A, Tshefu, A, Bose, CL, Bauserman, M, Mwenechanya, M, Chomba, E, Carlo, WA, Garces, AL, Figueroa, L, Hambidge, KM, Krebs, NF, Goudar, S, Kodkany, BS, Dhaded, S, Derman, RJ, Patel, A, Hibberd, PL, Esamai, F, Tenge, C, Liechty, EA, Moore, JL, Wallace, DD, Koso‐Thomas, M, Miodovnik, M, Goldenberg, RL
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2018
• Subjects: Algorithms; Blacks - statistics & numerical data; Cause of Death; Classification; Data processing; Death; Democratic Republic of the Congo - epidemiology; Developing Countries; Female; Global Health - statistics & numerical data; Guatemala - epidemiology; Hemorrhage; Humans; Income; India - epidemiology; Infections; Kenya - epidemiology; low‐ and middle‐income countries; Maternal Death - classification; Maternal Death - etiology; Maternal Mortality; Mortality; Newborn babies; Pakistan - epidemiology; Population studies; Pregnancy; Pregnancy Complications - mortality; Prospective Studies; Public health; Registries; Trauma; Whites - statistics & numerical data; Womens health; Zambia - epidemiology; Guatemala; Zambia; Pakistan; Democratic Republic of the Congo; Kenya; India; classification; low- and middle-income countries; Cause of death; maternal mortality
• ISSN: 1470-0328; 1471-0528
• DOI: 10.1111/1471-0528.15011

Objective To describe the causes of maternal death in a population‐based cohort in six low‐ and middle‐income countries using a standardised, hierarchical, algorithmic cause of death (COD) methodology. Design A population‐based, prospective observational study. Setting Seven sites in six low‐ to middle‐income countries including the Democratic Republic of the Congo (DRC), Guatemala, India (two sites), Kenya, Pakistan and Zambia. Population All deaths among pregnant women resident in the study sites from 2014 to December 2016. Methods For women who died, we used a standardised questionnaire to collect clinical data regarding maternal conditions present during pregnancy and delivery. These data were analysed using a computer‐based algorithm to assign cause of maternal death based on the International Classification of Disease—Maternal Mortality system (trauma, termination of pregnancy‐related, eclampsia, haemorrhage, pregnancy‐related infection and medical conditions). We also compared the COD results to healthcare‐provider‐assigned maternal COD. Main outcome measures Assigned causes of maternal mortality. Results Among 158 205 women, there were 221 maternal deaths. The most common algorithm‐assigned maternal COD were obstetric haemorrhage (38.6%), pregnancy‐related infection (26.4%) and pre‐eclampsia/eclampsia (18.2%). Agreement between algorithm‐assigned COD and COD assigned by healthcare providers ranged from 75% for haemorrhage to 25% for medical causes coincident to pregnancy. Conclusions The major maternal COD in the Global Network sites were haemorrhage, pregnancy‐related infection and pre‐eclampsia/eclampsia. This system could allow public health programmes in low‐ and middle‐income countries to generate transparent and comparable data for maternal COD across time or regions. Tweetable An algorithmic system for determining maternal cause of death in low‐resource settings is described. Tweetable An algorithmic system for determining maternal cause of death in low‐resource settings is described.