Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 18, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2025865118
Keywords
precision public policy; mapping; & nbsp; child undernutrition; & nbsp; local governance; India
Categories
Funding
- Bill & Melinda Gates Foundation [INV-002992]
- Korea University [K2008811]
Ask authors/readers for more resources
This study utilized data from the 2011 Indian Census and the 2016 Indian Demographic and Health Survey to predict child anthropometric failures for all villages in India using a bias-corrected semisupervised regression framework. The results showed that village-level factors had the largest impact on child anthropometric failures, followed by state-level factors.
There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available