Bayesian Multilevel Analysis of Utilization of Antenatal Care Services in Ethiopia

Abstract

<jats:p>In sub-Saharan Africa, 72% of pregnant women received an antenatal care visit at least once in their pregnancy period. Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, this high maternal mortality levels remain a major public health problem. According to EDHS, 2016, the antenatal care (ANC), delivery care (DC), and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is in a low level. The main objective of this study was to examine the factors that affect the utilization of antenatal care services in Ethiopia using Bayesian multilevel logistic regression models. The data used for this study comes from the 2016 Ethiopian Demographic and Health Survey which was conducted by the Central Statistical Agency (CSA). The statistical method of data analysis used for this study is the Bayesian multilevel binary logistic regression model in general and the Bayesian multilevel logistic regression for the random coefficient model in particular. The convergences of parameters are estimated by using Markov chain Monte-Carlo (MCMC) using SPSS and MLwiN software. The descriptive result revealed that out of the 7171 women who are supposed to use ANC services, 2479 (34.6%) women were not receiving ANC services, while 4692 (65.4%) women were receiving ANC services. Moreover, women in the Somali and Afar regions are the least users of ANC. Using the Bayesian multilevel binary logistic regression of random coefficient model factors, place of residence, religion, educational attainment of women, husband educational level, employment status of husband, beat, household wealth index, and birth order were found to be the significant factors for usage of ANC. Regional variation in the usage of ANC was significant.</jats:p>

Bayesian Multilevel Analysis of Children Vaccination Coverage in Ethiopia

“…Multilevel models are used to account for the correlation of observations within a given group by incorporating group specific random effects. These random effects can be nested (individuals nested in regions, with random effects at the women and region levels) [7].The dependent variable must be examined at the lowest and highest level of analysis.…”

“…All experimental units are assumed to be independent which means that any variable which affects level of vaccination have the same effect in all regions, but these models are aiming at examining whether the effect of variables vary from region to region. The probability of 'success' or 'failure' is the same for all individuals in the group [7].…”

“…, 𝛽 𝑘 ) are regression parameter coefficients, and 𝑈 0𝑗 , 𝑈 1𝑗 , … … … … 𝑈 𝑘𝑗 are the random cluster effects of model parameter at higher levels with the assumption that follows normal distribution with mean zero and variance 𝜎 𝑢 2 . Therefore, conditional on 𝑈 0𝑗 , 𝑈 1𝑗 , … … … 𝑈 𝑘𝑗 , the 𝑌 𝑖𝑗 can be assumed to be independently distributed as Bernoulli random variables [7].…”

“…normally distributed with zero means and constant variances 𝜎 𝑢 2 . This model does not constitute a different parameter for the level-one variance, because the level-one error variance of the 𝑌 𝑖𝑗 follows Bernoulli distribution straight from the success probability, as indicated by Var (𝜖 𝑖𝑗 ) = 𝑝 𝑗 (1-𝑝 𝑗 ) [7].…”

“…For the parameter𝑠. It is written as; P (𝛽 0 ) ∝ 1 and P (𝜎 𝑢0 −2 ) ∝ gamma (α, β) where α and β are scale and shape parameters which are fixed constants [7].…”