Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology Bombay, Mumbai, India.
Integrated Department of Health Policy, Epidemiology, Preventive Medicine and Pediatrics, Foundation for People-centric Health Systems, New Delhi, India.
Indian J Pediatr. 2023 Dec;90(Suppl 1):47-53. doi: 10.1007/s12098-023-04711-9. Epub 2023 Jul 25.
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.
死产是全球以及印度的一个重大公共卫生问题。解决死产问题的方案干预措施需要基层数据。印度的卫生管理信息系统（HMIS）是死产详细数据的最佳来源之一。本分析使用 HMIS 2017-2020 年三年大流行前的死产数据，研究印度九个邦的区县级死产的地理空间模式，形成了四个中央邦的高负担集群和五个南部邦的低负担集群。鉴于需要这种分析所需的现成的次区形状文件，对马哈拉施特拉邦进行了次区级的地理空间变化研究。该分析还探讨了全印水平的死产季节性变化。在所分析的所有邦都观察到了死产的颗粒内群空间模式，在奥里萨邦和恰蒂斯加尔邦的几个地区有明显的热点（2019-20 年每 1000 例总出生中仍有 20 多例死产）。即使在南部集群中，也发现了热点（每 1000 例总出生中有 8-20 例死产）。马哈拉施特拉邦次区级数据的可用性有助于确定某些地区集群中死产的州内区域差异，其患病率较高。从时间上看，死产在 8 月至 10 月期间呈定期高峰，在 2 月至 4 月期间呈下降趋势，这与生育季节性模式一致。本综述和分析强调了需要更详细的数据可用性，专家和项目管理人员定期对这些数据进行分析，以及在地理位置和季节性方面更加分散和具体情况的方案干预。
