Undernutrition among children and its determinants across the parliamentary constituencies of India: a geospatial analysis.

In India, undernutrition among children has been extremely critical for the last few decades. Most analyses of undernutrition among Indian children have used the administrative boundaries of a state or a district level as a unit of analysis. This paper departs from such a practice and focuses instead on the political boundaries of a parliamentary constituency (PC) as the unit of analysis. The PC is a critical geopolitical unit where political parties and party candidates make election promises and implement programmes to improve the socio-economic condition of their electorate. A focus on child undernutrition at this level has the potential for greater policy and political traction and could lead to a paradigm shift in the strategy to tackle the problem by creating a demand for political accountability. Different dimensions and new approaches are also required to evaluate the socio-economic status and generate concrete evidence to find solutions to the problem. Given the significance of advanced analytical methods and models embedded into geographic information system (GIS), the current study, for the first time, uses GIS tools and techniques at the PC level, conducting in-depth analysis of undernutrition and its predictors. Hence, this paper examines the spatial heterogeneity in undernutrition across PCs by using geospatial techniques such as univariate and bivariate local indicator of spatial association and spatial regression models. The analysis highlights the high-low burden areas in terms of local hotspots and identifies the potential spatial risk factors of undernutrition across the constituencies. Striking variations in the prevalence of undernutrition across the constituencies were observed. Most of these constituencies that performed poorly both in terms of child nutrition and socio-economic indicators were located in the northern, western, and eastern parts of India. A statistically significant association of biological, socio-economic, and environmental factors such as women's body mass index, anaemia in children, poverty, household sanitation facilities, and institutional births was established. The results highlight the need to bring in a mechanism of political accountability that directly connects elected representatives to maternal and child health outcomes. The spatial variability and pattern of undernutrition indicators and their correlates indicate that priority setting in research may also be greatly influenced by the neighbourhood association.

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

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.