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 leading causes of infant mortality in developing economies. Dr. Dwipoboti Brahma, Assistant Professor, Center for Mathematical and Computational Economics, School of AI and Data Science, IIT Jodhpur and Dr. Debashree Mukherjee, Professor, Department of Economics, University of Western Michigan, USA, worked together to develop early warning indicators for newborns. And infant mortality.
These early-warning indicators include:
- Observable biological characteristics
- Demographic characteristics
- 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 used a range of machine learning algorithms to assess the relative importance of first-born, poor-born and underweight symptoms. Talking about the purpose and future goal of the research, Dr. “Early identification of risk factors with the help of community health workers can help India reach the Sustainable Development Goals,” Brahma said.
The future goal is to expand and develop more streamlined screening criteria with the availability of more granular data with a combination of clinical and socio-economic characteristics. The aim of this research is to train community health workers to use predictors as a screening mechanism to identify individuals at risk of mortality and refer them to qualified doctors for a more rigorous assessment. Early identification of risk factors will enable women and newborns to receive timely medical care and reduce child mortality in India.
