Study Identifies Neonatal and Infant Mortality Predictors
Study findings help identify early warning signs of child mortality that community health workers can use.
Researchers from the Indian Institute of Technology Jodhpur and Western Michigan University, USA, have identified significant neonatal and infant mortality predictors using multiple machine learning (ML) techniques. The study uses nationwide household survey data from India. Study findings help identify early warning signs of child mortality that community health workers can use.
Reducing child mortality is a specific goal under the Sustainable Development Goals 2030. Previous research in this direction has established that poor clinical knowledge among healthcare workers is one of the causes of child mortality in developing economies. The new joint study result sheds light on policy relevance and suggests new policy prescriptions such as close monitoring of at-risk babies, IIT, Jodhpur statement said.
Early-warning indicators include observable biological characteristics, demographic characteristics, and socio-economic factors of households, mothers and newborns. The early warning indicators identified in this study do not require advanced medical knowledge and can be easily used by community healthcare workers. The study uses a range of machine learning algorithms to assess the relative importance of being first-borns, being born in poorer households, and having a low birth weight.
The findings of the joint study by Dr Dweepobotee Brahma, Assistant Prof Centre for Mathematical and Computational Economics, School of AI and Data Science, IIT Jodhpur, and Dr Debasri Mukherjee, Professor, Department of Economics, Western Michigan University, USA, have been published in the Applied Economics journal.
“Early identification of risk factors through the help of community health workers can go a long way in helping India reach the sustainable development goals”, said Dr Dweepobotee Brahma.
The future goal is to extend and develop more streamlined screening criteria with the availability of more granular data with a combination of clinical and socio-economic characteristics. This research aims to train community health workers to use predictors as a screening mechanism to identify individuals at risk for mortality and refer them to qualified doctors for more rigorous evaluation. Early identification of risk factors will allow women and newborns to get timely medical care and reduce the child mortality rate in India. (India Science Wire)