Estimating the burden of child malnutrition across parliamentary constituencies in India: A methodological comparison

• Published in: SSM - population health 2019-04, Vol.7, p.100375-100375, Article 100375
• Main Authors: Kim, Rockli, Swaminathan, Akshay, Kumar, Rakesh, Xu, Yun, Blossom, Jeffrey C, Venkataramanan, R, Kumar, Alok, Joe, William, Subramanian, S V
• Format: Article
• Language: English
• ISSN: 2352-8273; 2352-8273
• DOI: 10.1016/j.ssmph.2019.100375

In India, data on key developmental indicators used to formulate policies and interventions are routinely available for the administrative unit of districts but not for the political unit of parliamentary constituencies (PC). Recently, Swaminathan et al. proposed two methodologies to generate PC estimates using randomly displaced GPS locations of the sampling clusters ('direct') and by building a crosswalk between districts and PCs using boundary shapefiles ('indirect'). We advance these methodologies by using precision-weighted estimations based on hierarchical logistic regression modeling to account for the complex survey design and sampling variability. We exemplify this application using the latest National Family Health Survey (NFHS, 2016) to generate PC-level estimates for two important indicators of child malnutrition - stunting and low birth weight - that are being monitored by the Government of India for the National Nutrition Mission targets. Overall, we found a substantial variation in child malnutrition across 543 PCs. The different methodologies yielded highly consistent estimates with correlation ranging r = 0.92-0.99 for stunting and r = 0.81-0.98 for low birth weight. For analyses involving data with comparable nature to the NFHS (i.e., complex data structure and possibility to identify a potential PC membership), modeling for precision-weighted estimates and direct methodology are preferable. Further field work and data collection at the PC level are necessary to accurately validate our estimates. An ideal solution to overcome this gap in data for PCs would be to make PC identifiers available in routinely collected surveys and the Census.