Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya

Dan Kipkosgei Kogei; Anthony Wanjoya; Joel Chelule; Lena Onyango

doi:doi:10.11648/j.ajtas.20231205.12

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Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya

Dan Kipkosgei Kogei, Anthony Wanjoya, Joel Chelule, Lena Onyango

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 5)

Received: 31 August 2023 Accepted: 14 September 2023 Published: 27 September 2023

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Abstract

Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.

Published in	American Journal of Theoretical and Applied Statistics (Volume 12, Issue 5)
DOI	10.11648/j.ajtas.20231205.12
Page(s)	110-116
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Injectable Contraceptives, Child Bearing Women, Bayesian Binary Quantile Regression, MCMC, Convergence Diagnostics

References

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[3]	Mochache, V., Lakhani, A., El-Busaidy, H., Timmerman, M., & Gichangi P. (2018). Pattern and determinants of contraceptive usage among women of reproductive age from digo community residing in kwale, Kenya: results from a cross sectional household survey. BMC Women’s Health, 18 (1), 1-11.
[4]	Akoth, C., Oguta, J. O., Gatimu, S. M. (2021). Prevalence and factors associated with covert contraceptive use in Kenya: a cross sectional study. BMC Women’s Health. (2021) 21: 1316. Doi: 10.1186/s12889-021-11375-7.
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[13]	Kirui, E. K., Mung’atu, J. K., Gichangi, P., Onguto, N. O., & Kamondo, D. K. (2021). Multiple logistic regression model for determinants of injectable contraceptive uptake among women of reproductive age in Kenya. Journal of Family & Reproductive Health, 15 (2), 82.
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APA Style

Dan Kipkosgei Kogei, Anthony Wanjoya, Joel Chelule, Lena Onyango. (2023). Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. American Journal of Theoretical and Applied Statistics, 12(5), 110-116. https://doi.org/10.11648/j.ajtas.20231205.12

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ACS Style

Dan Kipkosgei Kogei; Anthony Wanjoya; Joel Chelule; Lena Onyango. Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. Am. J. Theor. Appl. Stat. 2023, 12(5), 110-116. doi: 10.11648/j.ajtas.20231205.12

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AMA Style

Dan Kipkosgei Kogei, Anthony Wanjoya, Joel Chelule, Lena Onyango. Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. Am J Theor Appl Stat. 2023;12(5):110-116. doi: 10.11648/j.ajtas.20231205.12

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@article{10.11648/j.ajtas.20231205.12,
  author = {Dan Kipkosgei Kogei and Anthony Wanjoya and Joel Chelule and Lena Onyango},
  title = {Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {5},
  pages = {110-116},
  doi = {10.11648/j.ajtas.20231205.12},
  url = {https://doi.org/10.11648/j.ajtas.20231205.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231205.12},
  abstract = {Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.},
 year = {2023}
}

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TY  - JOUR
T1  - Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya
AU  - Dan Kipkosgei Kogei
AU  - Anthony Wanjoya
AU  - Joel Chelule
AU  - Lena Onyango
Y1  - 2023/09/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231205.12
DO  - 10.11648/j.ajtas.20231205.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 110
EP  - 116
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231205.12
AB  - Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.
VL  - 12
IS  - 5
ER  -

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Author Information

Dan Kipkosgei Kogei

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony Wanjoya

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Joel Chelule

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Lena Onyango

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Dan Kipkosgei Kogei, Anthony Wanjoya, Joel Chelule, Lena Onyango. (2023). Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. American Journal of Theoretical and Applied Statistics, 12(5), 110-116. https://doi.org/10.11648/j.ajtas.20231205.12

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ACS Style

Dan Kipkosgei Kogei; Anthony Wanjoya; Joel Chelule; Lena Onyango. Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. Am. J. Theor. Appl. Stat. 2023, 12(5), 110-116. doi: 10.11648/j.ajtas.20231205.12

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AMA Style

Dan Kipkosgei Kogei, Anthony Wanjoya, Joel Chelule, Lena Onyango. Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya. Am J Theor Appl Stat. 2023;12(5):110-116. doi: 10.11648/j.ajtas.20231205.12

Copy | Download

@article{10.11648/j.ajtas.20231205.12,
  author = {Dan Kipkosgei Kogei and Anthony Wanjoya and Joel Chelule and Lena Onyango},
  title = {Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {5},
  pages = {110-116},
  doi = {10.11648/j.ajtas.20231205.12},
  url = {https://doi.org/10.11648/j.ajtas.20231205.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231205.12},
  abstract = {Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya
AU  - Dan Kipkosgei Kogei
AU  - Anthony Wanjoya
AU  - Joel Chelule
AU  - Lena Onyango
Y1  - 2023/09/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231205.12
DO  - 10.11648/j.ajtas.20231205.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 110
EP  - 116
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231205.12
AB  - Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.
VL  - 12
IS  - 5
ER  -

Copy | Download