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Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

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  • Mohammad Samsul Alam
  • Syed Shahadat Hossain
  • Farha Ferdous Sheela

Abstract

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500 g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.

Suggested Citation

  • Mohammad Samsul Alam & Syed Shahadat Hossain & Farha Ferdous Sheela, 2019. "Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(10), pages 1870-1885, July.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1870-1885
    DOI: 10.1080/02664763.2019.1572722
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    Cited by:

    1. Zemenu Tadesse Tessema & Getayeneh Antehunegn Tesema & Susannah Ahern & Arul Earnest, 2023. "A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research," IJERPH, MDPI, vol. 20(13), pages 1-24, July.

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