Bayesian Binary Logistic Generalized Linear Mixed Models of Female Genital Mutilation

Abstract Background: Female genital mutilation could be a global public unhealthiness, and it's practiced by many communities in Africa, special Ethiopia. In Ethiopia, the factors related to FGM practices are poorly understood. Therefore, this study aimed to assess the prevalence of female genital mutilation and its associated factors with FGM among reproductive age women within the country. Method: A secondary data analysis was disbursed supported the Ethiopia Demographic and Health Survey 2016. Bayesian binary Logistic Regression GLMM, which allows taking into consideration both individual and population variability in model parameter estimate was employed. Results: The general prevalence of female genital mutilation among participants (15-49 years old) in Ethiopia was found to be 69.6%. From Bayesian random intercept binary logistic analysis it had been found that rural, Muslim, middle Wealth index, rich Wealth index people, Secondary and above were statistically significant with Female genital mutilation. Conclusion: Rural residence, Muslim religion, middle wealth index , rich wealth index, people 25-34 years old, the people 35-49 years old, ever heard of female genital mutilation, occupation of girls were positively related to female genital mutilation practice. On the opposite hand, husband/partner's primary education level, husband/partner's secondary and above educational level, husband/partner occupation (merchant and others) were negatively related to female genital mutilation. Despite the presence of various interventions, the prevalence of female genital mutilation continues to be very high within the country.