ABSTRACT
Stillbirth is a major public health problem across the world as well as in India. The programmatic interventions to tackle stillbirth require granular data upto local levels. The Health Management Information System (HMIS) in India is one of the best sources of granular data on stillbirth. This analysis was conducted using HMIS stillbirth data of three pre-pandemic years 2017-2020 to study the geo-spatial patterns of stillbirth at district level in nine states of India, forming a high burden cluster of four central Indian states and a low burden cluster of five southern states. Geo-spatial variation at sub-district level was studied for Maharashtra given the ready availability of sub-district shapefiles required for such analysis. The analysis also explores the seasonal variations in stillbirths at all-India level. A granular intra-cluster spatial pattern of stillbirth was observed in all states analyzed, with a clear hotspot across a few districts in Odisha and Chhattisgarh (>20 stillbirths/1,000 total births in 2019-20). Even in the southern cluster, the hotspots (8-20 stillbirths/1,000 total births) were found. Availability of sub-district level data in Maharashtra helped to identify intra-state regional variations in stillbirth with high prevalence in certain district clusters. In temporal terms, stillbirths exhibit a regular peak during August-October and a dip during February-April which is inclined with the birth seasonality patterns. This review and analysis underscore the need for more granular data availability, regular analysis of such data by expert and program managers, more decentralized and context specific programme intervention both in locational and seasonal terms.
