A prospective key informant surveillance system to measure maternal mortality – findings from indigenous populations in Jharkhand and Orissa, India

Abstract Background In places with poor vital registration, measurement of maternal mortality and monitoring the impact of interventions on maternal mortality is difficult and seldom undertaken. Mortality ratios are often estimated and policy decisions made without robust evidence. This paper presents a prospective key informant system to measure maternal mortality and the initial findings from the system. Methods In a population of 228 186, key informants identified all births and deaths to women of reproductive age, prospectively, over a period of 110 weeks. After birth verification, interviewers visited households six to eight weeks after delivery to collect information on the ante-partum, intra-partum and post-partum periods, as well as birth outcomes. For all deaths to women of reproductive age they ascertained whether they could be classified as maternal, pregnancy related or late maternal and if so, verbal autopsies were conducted. Results 13 602 births were identified, with a crude birth rate of 28.2 per 1000 population (C.I. 27.7–28.6) and a maternal mortality ratio of 722 per 100 000 live births (C.I. 591–882) recorded. Maternal deaths comprised 29% of all deaths to women aged 15–49. Approximately a quarter of maternal deaths occurred ante-partum, a half intra-partum and a quarter post-partum. Haemorrhage was the commonest cause of all maternal deaths (25%), but causation varied between the ante-partum, intra-partum and post-partum periods. The cost of operating the surveillance system was US$386 a month, or US$0.02 per capita per year. Conclusion This low cost key informant surveillance system produced high, but plausible birth and death rates in this remote population in India. This method could be used to monitor trends in maternal mortality and to test the impact of interventions in large populations with poor vital registration and thus assist policy makers in making evidence-based decisions.