Selective Intervention Planning using RMABs: Increasing Program Engagement to Improve Maternal and Child Health Outcomes

• URL: http://arxiv.org/abs/2103.09052v2
• Date: Thu, 18 Mar 2021 13:59:11 GMT
• Title: Selective Intervention Planning using RMABs: Increasing Program Engagement to Improve Maternal and Child Health Outcomes
• Authors: Siddharth Nishtala, Lovish Madaan, Harshavardhan Kamarthi, Anirudh Grama, Divy Thakkar, Dhyanesh Narayanan, Suresh Chaudhary, Neha Madhiwalla, Ramesh Padmanabhan, Aparna Hegde, Pradeep Varakantham, Balaraman Ravindran, Milind Tambe
• Abstract summary: We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs. We analyzed anonymized call-records of over 300,000 women registered in an awareness program. We built machine learning based models to predict the long term engagement pattern from call logs and beneficiaries' demographic information.
• Score: 34.38042786168279
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor for these deaths, especially in low-income households. We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs by early-on identifying women who might not engage with these programs that are proven to affect health parameters positively. We analyzed anonymized call-records of over 300,000 women registered in an awareness program created by ARMMAN that uses cellphone calls to regularly disseminate health related information. We built machine learning based models to predict the long term engagement pattern from call logs and beneficiaries' demographic information, and discuss the applicability of this method in the real world through a pilot validation. Through a randomized controlled trial, we show that using our model's predictions to make interventions boosts engagement metrics by 14.3%. We then formulate the intervention planning problem as restless multi-armed bandits (RMABs), and present preliminary results using this approach.

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