Bayesian zero-inflated regression model with application to under-five child mortality

被引:0
|
作者
Workie, Mekuanint Simeneh [1 ]
Azene, Abebaw Gedef [2 ]
机构
[1] Bahir Dar Univ, Dept Math & Stat Modeling Stat, Bahir Dar Inst Technol, Bahir Dar, Ethiopia
[2] Bahir Dar Univ, Dept Epidemiol & Biostat, Sch Publ Hlth, Coll Med & Hlth Sci, Bahir Dar, Ethiopia
关键词
Under-five death; Bayesian approach; Zero-inflated regression; MCMC; Ethiopia; INFANT;
D O I
10.1186/s40537-020-00389-4
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Under-five mortality is defined as the likelihood of a child born alive to die between birth and fifth birthday. Mortality of under the age of five has been the most targets of public health policies and may be a common indicator of mortality levels. Thus, this study aimed to assess the under-five child mortality and modeling Bayesian zero-inflated regression model of the determinants of under-five child mortality. A community-based cross-sectional study was conducted using the 2016 Ethiopia Demographic and Health Survey data. The sample was stratified and selected in a two-stage cluster sampling design. The Bayesian analytic approach was applied to model the mixture arrangement inherent in zero-inflated count data by using the negative Binomial-logit hurdle model. About 71.09% of the mothers had not faced any under-five deaths in their lifetime while 28.91% of the women experienced the death of their under-five children and the data were found to have excess zeros. From Bayesian Negative Binomial-logit hurdle model it was found that twin (OR = 1.56; HPD CrI 1.23, 1.94), Primary and Secondary education (OR = 0.68; HPD CrI 0.59, 0.79), mother's age at the first birth: 16-25 (OR = 0.83; HPD CrI 0.75, 0.92) and >= 26 (OR = 0.71; HPD CrI 0.52, 0.95), using contraceptive method (OR = 0.73; HPD CrI 0.64, 0.84) and antenatal visits during pregnancy (OR = 0.83; HPD CrI 0.75, 0.92) were statistically associated with the number of non-zero under-five deaths in Ethiopia. The finding from the Bayesian Negative Binomial-logit hurdle model is getting popular in data analysis than the Negative Binomial-logit hurdle model because the technique is more robust and precise. Furthermore, Using the Bayesian Negative Binomial-logit hurdle model helps in selecting the most significant factor: mother's education, Mothers age, Birth order, type of birth, mother's age at the first birth, using a contraceptive method, and antenatal visits during pregnancy were the most important determinants of under-five child mortality.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Bayesian zero-inflated regression model with application to under-five child mortality
    Mekuanint Simeneh Workie
    Abebaw Gedef Azene
    [J]. Journal of Big Data, 8
  • [2] Bayesian Analysis for the Zero-inflated Regression Models
    Jane, Hakjin
    Kang, Yunhee
    Lee, S.
    Kim, Seong W.
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2008, 21 (04) : 603 - 613
  • [3] Bayesian analysis of zero-inflated regression models
    Ghosh, SK
    Mukhopadhyay, P
    Lu, JC
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2006, 136 (04) : 1360 - 1375
  • [4] A Bayesian zero-inflated Poisson regression model with random effects with application to smoking behavior
    Kim, Yeon Kyoung
    Hwang, Beom Seuk
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2018, 31 (02) : 287 - 301
  • [5] Bayesian Approach to Zero-Inflated Bivariate Ordered Probit Regression Model, with an Application to Tobacco Use
    Gurmu, Shiferaw
    Dagne, Getachew A.
    [J]. JOURNAL OF PROBABILITY AND STATISTICS, 2012, 2012
  • [6] Bayesian Analysis of a Zero-inflated Poisson Regression Model: An Application to Korean Oral Hygienic Data
    Lim, Ah-Kyoung
    Oh, Man-Suk
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2006, 19 (03) : 505 - 519
  • [7] A Zero-Inflated Regression Model for Grouped Data
    Brown, Sarah
    Duncan, Alan
    Harris, Mark N.
    Roberts, Jennifer
    Taylor, Karl
    [J]. OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2015, 77 (06) : 822 - 831
  • [8] Bayesian inference and diagnostics in zero-inflated generalized power series regression model
    Barriga, Gladys D. Cacsire
    Dey, Dipak K.
    [J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2016, 45 (22) : 6553 - 6568
  • [9] Zero-inflated Poisson regression mixture model
    Lim, Hwa Kyung
    Li, Wai Keung
    Yu, Philip L. H.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2014, 71 : 151 - 158
  • [10] Zero-inflated Poisson regression analysis of factors associated with under-five mortality in Ethiopia using 2019 Ethiopian mini demographic and health survey data
    Argawu, Alemayehu Siffir
    Mekebo, Gizachew Gobebo
    [J]. PLOS ONE, 2023, 18 (11):