https://arunmitra.com/categories/maternal-child-health/ Recent content in Maternal & Child Health on Arun Mitra Hugo -- gohugo.io en Mon, 30 Mar 2026 00:00:00 +0000 https://arunmitra.com/research/mch-data-ecosystem/ Mon, 30 Mar 2026 00:00:00 +0000 https://arunmitra.com/research/mch-data-ecosystem/ Background Despite heavy investment in digital health information systems, frontline facilities in resource-constrained settings remain paradoxically data-rich yet information-poor. Health information systems built with an upward gaze for accountability reporting often fail to support the decisions that medical officers, data entry operators, and ANMs need to make locally. This work, part of Arun Mitra’s PhD thesis on participatory data science in ITDA-Rampachodavaram, Andhra Pradesh, asks how the maternal and child health (MCH) data ecosystem at primary health centre (PHC) level is actually structured, used, and constrained. https://arunmitra.com/research/rop-maternal-nutrition/ Mon, 24 Mar 2025 00:00:00 +0000 https://arunmitra.com/research/rop-maternal-nutrition/ Background Retinopathy of prematurity (ROP) is a leading cause of preventable childhood visual impairment, with the burden shifting increasingly toward low- and middle-income countries. While prematurity and supplemental oxygen are the most established risk factors, upstream antenatal determinants such as maternal nutritional status may also shape fetal retinal vascular development and neonatal outcomes. This study examines whether maternal anthropometry is associated with the key neonatal determinants of ROP risk in an Indian tertiary NICU setting, where maternal undernutrition and high preterm birth rates coexist. https://arunmitra.com/research/participatory-data-science-mch/ Sat, 01 Jul 2023 00:00:00 +0000 https://arunmitra.com/research/participatory-data-science-mch/ Background Tribal primary health centres (PHCs) in India operate within data-rich routine health information systems (RHIS), yet local decision-making for maternal and child health (MCH) remains constrained: data is fragmented, misaligned with local needs, and rarely available in usable, actionable formats. The result is a wide gap between centrally oriented reporting and the ground-level realities of MCH service delivery. This PhD asks how a participatory data-science approach can re-orient routine systems to support PHC-level decisions, transforming under-used data into meaningful, context-specific, and actionable insight.