Bayesian zero-inflated regression model with application to under-five child mortality
نویسندگان
چکیده
Abstract Under-five mortality is defined as the likelihood of a child born alive to die between birth and fifth birthday. Mortality under age five has been most targets public health policies may be common indicator levels. Thus, this study aimed assess under-five modeling Bayesian zero-inflated regression model determinants mortality. A community-based cross-sectional was conducted using 2016 Ethiopia Demographic Health Survey data. The sample stratified selected in two-stage cluster sampling design. analytic approach applied mixture arrangement inherent count data by negative Binomial–logit hurdle model. About 71.09% mothers had not faced any deaths their lifetime while 28.91% women experienced death children were found have excess zeros. From Negative Binomial—logit it that twin (OR = 1.56; HPD CrI 1.23, 1.94), Primary Secondary education 0.68; 0.59, 0.79), mother’s at first birth: 16–25 0.83; 0.75, 0.92) ? 26 0.71; 0.52, 0.95), contraceptive method 0.73; 0.64, 0.84) antenatal visits during pregnancy statistically associated with number non-zero Ethiopia. finding from getting popular analysis than because technique more robust precise. Furthermore, Using helps selecting significant factor: education, Mothers age, Birth order, type birth, method, important
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ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2021
ISSN: ['2196-1115']
DOI: https://doi.org/10.1186/s40537-020-00389-4