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Weighted Bayesian Poisson Regression for The Number of Children Ever Born per Woman in Bangladesh

Jabed H Tomal1, Jahidur Rahman Khan2, Abdus S Wahed3

  • 1Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, British Columbia Canada.

Journal of Statistical Theory and Applications : JSTA
|August 23, 2022
PubMed

Insights

This study analyzed factors influencing the number of children born to women of reproductive age in Bangladesh using a novel Bayesian model. Findings highlight areas for improving fertility reduction programs and policies.

Area of Science:

  • Demography
  • Public Health
  • Statistical Modeling

Background:

  • Fertility rates are crucial for understanding population dynamics.
  • Identifying factors influencing fertility is essential for policy development.

Purpose of the Study:

  • To identify individual, household, regional, and societal factors affecting the number of children ever born to married women of reproductive age in Bangladesh.
  • To apply a novel weighted Bayesian Poisson regression model for this analysis.

Main Methods:

  • Utilized data from the Bangladesh Multiple Indicator Cluster Survey 2019.
  • Employed a novel weighted Bayesian Poisson regression model.
  • Assessed model robustness with multiple prior distributions and the Metropolis algorithm.

Main Results:

  • Identified key multi-level factors influencing fertility in Bangladesh.
  • The novel Bayesian model proved robust and effective in identifying these factors.

Conclusions:

  • The identified factors necessitate a review and enhancement of current fertility reduction programs and policies in Bangladesh.
  • Statistical modeling provides valuable insights into demographic trends and policy effectiveness.

Keywords:
BangladeshBayesian methodFertility ratePoisson regressionWeighted likelihood

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