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Spatial modeling of child malnutrition attributable to drought in India

Author

Listed:
  • Subhojit Shaw

    (International Institute for Population Sciences)

  • Junaid Khan

    (International Institute for Population Sciences)

  • Balram Paswan

    (International Institute for Population Sciences)

Abstract

Objectives Indian agriculture is mostly dependent on monsoon. Poor and irregular rainfall may result in crop failure and food shortage among the vulnerable population. This study examined the variations in drought condition and its association with under age 5 child malnutrition across the districts of India. Methods Using remote sensing and National Family Health Survey (NFHS-4) data, univariate Moran’s I and bivariate local indicator of spatial autocorrelation (LISA) maps were generated to assess the spatial autocorrelation and clustering. To empirically check the association, we applied multivariate ordinary least square and spatial autoregressive models. Results The study identified highly significant spatial dependence of drought followed by underweight, stunting, and wasting. Bivariate LISA maps showed negative spatial autocorrelation between drought and child malnutrition. Regression results suggest agricultural drought is substantially associated with stunting. An increasing value of drought showed statistical association with the decreasing (β = − 8.251; p value

Suggested Citation

  • Subhojit Shaw & Junaid Khan & Balram Paswan, 2020. "Spatial modeling of child malnutrition attributable to drought in India," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(3), pages 281-290, April.
    • Handle: RePEc:spr:ijphth:v:65:y:2020:i:3:d:10.1007_s00038-020-01353-y
    DOI: 10.1007/s00038-020-01353-y
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    Cited by:

    1. Muhammad Usman & Katarzyna Kopczewska, 2022. "Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors," IJERPH, MDPI, vol. 19(17), pages 1-17, September.

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