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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.

Maternal health refers to the well-being of women through pregnancy, childbirth, and the postpartum period. In rich countries, where women have access to basic health care, giving birth is a positive and fulfilling experience. On the other hand, for many women that live in poor countries, it is associated with suffering, ill-health, and even death [

The recent World Health Organization (WHO) estimates on maternal mortality showed that developed countries had a consistent low maternal mortality ratio that averaged less than 10 deaths per 100,000 live births for over a decade [

In sub-Saharan Africa, there are still more maternal health care problems. It is approximated that about 800 girls and women died every day as a result of pregnancy and childbirth-related complications [

In sub-Saharan Africa, countries like Burundi, Cameroon, and Senegal registered about 740, 590, and 320 deaths per 100,000 live births, respectively. Variations in maternal health outcomes and any issues in sub-Saharan Africa have attributed to pregnancy and childbirth complications with unbalanced socioeconomic status [

The use of antenatal care services among women in sub-Saharan Africa has shown that 72% of pregnant women received an antenatal care visit once or more times. The very low maternal and infant morbidity and mortality rates reported for developed countries compared with the high figures in developing countries have been attributed to the higher utilization of modern maternal health services by the former [

Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, these high maternal mortality levels remain a major public health problem. The proportion of women reproductive age from 15 to 49 reports having problems in accessing health care services, i.e, the services are decreased from 96% in 2005, 94% in 2011, and 70% in 2016, despite the country’s quest to reduce maternal mortality of ratio to 199 per 100,000 live births by 2020 based on the suggestion made with the goal of the reproductive health program. According to the Ethiopian Demographic and Health Survey, the antenatal care (ANC), skilled delivery care, and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is low [

Considering the goal of reducing maternal mortality and improving maternal health care services, it is essential to understand the factors that affect antenatal care service utilization in order to find the possible solutions for improving maternal health care service in Ethiopia.

Studies conducted in Nigeria and Ethiopia tried their effort to establish correlates having an effect on antenatal care service utilization using binary logistic regression [

Despite the country’s mission to reduce maternal mortality ratio to 199 maternal deaths per 100,000 live births by 2020 on the basis of the recommendation made with the goal of the reproductive health program, the ratio is still very high [

There are a number of reasons for using multilevel models. The classical logistic regression analysis treats the units of analysis as independent observations. One consequence of not considering hierarchical structures is that standard errors of regression coefficients will be underestimated, leading to an overstatement of statistical significance and wrong conclusion. Standard errors for the coefficients of higher-level predictor variables will be the most affected by ignoring grouping.

In a situation where research questions concern the extent of grouping in individual outcomes, and the identification of “outlying” groups, multilevel analysis is highly advised. For evaluating health care intervention, obtaining the effect of antenatal care on maternal mortality can be examined. Additionally, for estimating group effects simultaneously with the effects of group-level predictors, the use of multilevel analysis is mandatory. Moreover, making an inference to a population of groups can be attained using the multilevel analysis [

The 2016 Ethiopian Demographic and Health Survey (EDHS) sample was a stratified cluster and selected in two stages [

The purpose of this study is to understand the current status of utilization of antenatal care services in Ethiopia and to elucidate the various explanatory variables having an effect on the use of ANC services in Ethiopia. Moreover, the study is aimed at comparing the predictive performance of the Bayesian multilevel and the classical binary logistic regression models. The results of the study would help the policymakers in understanding the determinants of maternal and child mortality in the country and serve as an important input for any possible intervention aimed at improving the low utilization of antenatal care services in the country. This study covered women in the reproductive age from 15 to 49 years who gave the outcome of antenatal care service in Ethiopia.

Ethiopia has 9 regional states and two city administrations with 611 districts and 15,000 lower administrations. According to the 2016 Ethiopia Demographic and Health Survey (EDHS), the total population of Ethiopia was around 102 million with 4.5 fertility rates. Secondary data was extracted from the 2016 Ethiopian Demographic and Health Survey (EDHS). It is the fourth Demographic and Health Survey conducted from January 18, 2016, to June 27, 2016, by the Central Statistical Agency (CSA). From a total of 18,008 households, 16,650 were successfully interviewed. In the interviewed households, 16,583 eligible women were identified for individual interviews [

On the basis of reviewed literatures, potential determinant factors that are expected to be correlated with antenatal care service utilization have been included. Variables considered in this study are categorized into dependent and independent variables.

With

When the data was collected in hierarchical or clustered structures, the suitable model is multilevel models. 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) [

The standard assumption is that

The likelihood function of the empty model is

For parameter

For the parameter

The model decomposes the total variance into two the region and women levels, representing between and within region variabilities in the utilization of antenatal care services [

We now assume that there are variables which are a potential explanation for observed success or failure. These variables are denoted by

The logistic random intercept model expresses the log-odds, i.e., the logit of

For the prior and posterior distribution, it is also the same as in the above equation.

Consider explanatory variables which are potential explanations for the observed outcomes. Denoting these variables by

The first part of the above model,

For Bayesian model selection, the deviance information criterion (DIC) that is a hierarchical modeling generalization of the AIC is used. The definition of deviance is a -2loglikelihood ratio of a reduced model compared to the full model. In Bayesian, the lowest expected deviance has the highest posterior probability. Assessing goodness of fit involves investigating how close the values are predicted by the model with that of observed values [

The results displayed in Table

Statistical measures depicting the relationship between ANC usage and some socioeconomic and demographic variables.

Variables | Category | Count | % | Usage of ANC | Df | chi-sq | ||
---|---|---|---|---|---|---|---|---|

No (%) | Yes (%) | |||||||

Educational attainment of women | No education | 4344 | 60.6 | 2018 (46.45) | 2326 (53.55%) | 3 | 742.1 | |

Primary | 1937 | 27.0 | 398 (20.55%) | 1539 (79.45%) | ||||

Secondary | 575 | 8.0 | 56 (9.74%) | 519 (90.26%) | ||||

Higher | 315 | 4.4 | 7 (2.22%) | 308 (97.78%) | ||||

Wealth index | Poor | 3602 | 50.2 | 1777 (49.33%) | 1825 (50.67%) | 2 | 784.69 | |

Medium | 1022 | 14.3 | 321 (31.41%) | 701 (68.59%) | ||||

Rich | 2547 | 35.5 | 381 (14.96%) | 2166 (85.04%) | ||||

Place of residence | Urban | 1505 | 21.0 | 117 (7.77%) | 1388 (92.23%) | 1 | 604.63 | |

Rural | 5666 | 79.0 | 2362 (41.69%) | 3304 (58.31%) | ||||

Women’s occupation | Not working | 4054 | 56.7 | 1593 (39.29%) | 2461 (60.71%) | 1 | 92.046 | |

Working | 3117 | 43.3 | 886 (28.42%) | 2231 (71.58%) | ||||

Husband occupation | Not working | 674 | 9.4 | 342 (50.74%) | 332 (49.26) | 1 | 85.21 | |

Working | 5969 | 83.2 | 1963 (32.89) | 4006 (67.11) | ||||

Missing | 528 | 7.4 | ||||||

Husband educational level | No education | 3124 | 43.6 | 1519 (48.94) | 1605 (51.38) | 3 | 568.08 | |

Primary | 2156 | 30.1 | 591 (27.41) | 1565 (72.59) | ||||

Secondary | 744 | 10.4 | 109 (14.65) | 635 (85.35) | ||||

Higher | 619 | 8.6 | 86 (13.89) | 533 (86.11) | ||||

Missing | 528 | 7.4 | ||||||

Age of respondents | 15-24 | 1850 | 25.8 | 573 (30.97%) | 1277 (69.03%) | 2 | 68.92 | |

25-34 | 3532 | 49.3 | 1144 (32.39%) | 2388 (67.61%) | ||||

35-49 | 1789 | 24.9 | 762 (42.59%) | 1027 (57.41%) | ||||

Birth order | 1-4 | 4547 | 63.4 | 1264 (27.81%) | 3283 (72.20%) | 2 | 262.53 | |

5-8 | 2212 | 30.8 | 996 (45.03%) | 1217 (55.02%) | ||||

>8 | 411 | 5.7 | 219 (53.28%) | 192 (46.72%) | ||||

Religion | Orthodox | 2360 | 32.9 | 511 (21.65%) | 1849 (78.35%) | 4 | 309.97 | |

Catholic | 49 | 0.7 | 11 (22.45%) | 28 (57.14%) | ||||

Protestant | 1337 | 18.6 | 484 (36.20%) | 853 (63.8%) | ||||

Muslim | 3313 | 46.2 | 1387 (41.87%) | 1926 (58.13%) | ||||

Other | 112 | 1.6 | 76 (67.86%) | 36 (32.14%) | ||||

Decision making | Women | 1179 | 16.4 | 382 (32.4%) | 797 (67.6%) | 1 | 66.04 | |

Both | 4120 | 58 | 1330 (32.18%) | 2790 (67.8%) | ||||

Husband | 1344 | 18.2 | 593 (44.82%) | 751 (55.18%) | ||||

Missing | 528 | 7.4 |

Statistical significance at 5%.

The descriptive measures in Table

In the multilevel analysis, a two-level structure is used with regions as the second-level units and women as the first-level units. The expectation is that there would be differences in the usage of ANC services among regions. The nesting structure is women within regions with a total of 7171 women.

As it is depicted in Table

Bayesian multilevel logistic regression model comparison.

Empty model | Random intercept | Random coefficient | |
---|---|---|---|

Deviance (MCMC) | 6889.59 | 6040.15 | 5979.97 |

Deviance based chi-square test | 906.42 | 298.02 | 290.47 |

Statistical significance at 5%.

From Table

Bayesian deviance information criteria.

pD | DIC | Model | ||
---|---|---|---|---|

6889.60 | 6459.95 | 429.65 | 7319.25 | Bayesian multilevel null model |

6040.15 | 5703.45 | 336.71 | 6376.86 | Random intercept model |

5979.98 | 5606.13 | 5591.61 | 6368.35 | Random coefficient model |

The null model contains no explanatory variables, and it can be focused on assessing the heterogeneity of utilization of antenatal care services among regions. As it is displayed in Table

Bayesian multilevel logistic regression empty model.

Fixed part | Estimate | S.E | ||

Intercept | 1.215 | 0.079 | 15.38 | 0.002 |

Random part | Estimate | S.E | ||

| 2.830 | 0.273 | ||

ICC | 0.4624 |

Statistical significance at 5%.

From the result presented in Table

From the result presented in Table

The variances

This model contains a random slope for the educational attainment of women, which means that it allows the effect of the coefficient of this variable to vary from region to region. This model is more appropriate than the random intercept model for the variables being used since it is intuitive to assume that the educational attainment of women varies from region to region.

As shown in Table

Results of the random coefficient Bayesian multilevel logistic regression model.

Fixed effect | S.E | ||||
---|---|---|---|---|---|

Constant | 1.696 | 5.452 | 0.212 | 8.00 | 0.002 |

Urban (ref) | |||||

Rural | -1.578 | 0.206 | 0.171 | 9.228 | |

No (ref) | |||||

Yes | -0.146 | 0.864 | 0.067 | 2.179 | 0.002 |

15-24 (ref) | |||||

25-34 | 0.124 | 1.132 | 0.105 | 1.181 | 0.167 |

35-49 | -0.083 | 0.92 | 0.133 | 0.624 | 0.273 |

1-4 (ref) | |||||

5-8 | -0.23 | 0.795 | 0.093 | 2.473 | 0.002 |

>8 | -0.33 | 0.719 | 0.159 | 2.075 | 0.026 |

Orthodox (ref) | |||||

Catholic | -1.094 | 0.335 | 0.451 | 2.426 | 0.034 |

Protestant | -0.707 | 0.493 | 0.147 | 4.809 | 0.009 |

Muslim | -0.473 | 0.623 | 0.134 | 3.529 | 0.020 |

Other | -1.375 | 0.252 | 0.313 | 4.393 | |

Not working (ref) | |||||

Working | 0.352 | 1.421 | 0.108 | 3.259 | |

Poor (ref) | |||||

Medium | 0.452 | 1.571 | 0.099 | 4.566 | |

Rich | 0.642 | 1.9 | 0.1 | 6.42 | |

No education (ref) | |||||

Primary | 0.436 | 1.547 | 0.083 | 5.253 | |

Secondary | 0.645 | 1.906 | 0.154 | 4.188 | |

Higher | 0.178 | 1.195 | 0.182 | 0.978 | 0.045 |

Statistical significance at 5%; ref = categorical reference.

For the categorical variable place of residence in Table

The birth order in Table

The effect of attitudes towards wives beating on the use of antenatal care services was the other concern of this study illustrated in Table

The household wealth index displayed in Table

The husband’s occupation in Table

The other concern of this study was to examine the effect of religion on the use of antenatal care services. As it is depicted in Table

From Table

Results for fixed and random effects of the Bayesian multilevel random coefficient model.

Covariates | S.E | ||||
---|---|---|---|---|---|

No education(ref) | |||||

Primary | 0.719 | 2.052 | 0.113 | 6.363 | |

Secondary | 1.226 | 3.408 | 0.224 | 5.473 | |

Higher | 2.362 | 10.612 | 0.502 | 4.705 | |

Estimated variance | S.E | ||||

| 1.361 | 0.173 | |||

| 0.440 | 0.181 | |||

| -0.168 | 0.173 |

Statistical significance at 5%; ref = reference category.

The estimated variance of intercept and slope of educational attainment varies significantly. This showed that the factor women educational level has brought a variation in the usage of antenatal care services across regions of the country. Some of the variances of the interaction terms between intercepts and slopes of explanatory variables are also found significant. Interpretation of significant covariance terms can be easily made in terms of the correlation coefficients displayed in Table

Binary logistic outcomes (dependent variables) are binary (dichotomous) and can be coded 0 (no use of ANC) and 1 (use of ANC).

The predictors can be continuous or dichotomous, as in regression analysis. The output of the binary logistic regression is depicted in Table

Results of the binary logistic regression model.

Covariates | S.E | Wald | Df | Sig. | Exp (B) | |
---|---|---|---|---|---|---|

15.635 | 2 | |||||

Urban (ref) | ||||||

Rural | -1.244 | 0.126 | 97.266 | 1 | 0.288 | |

2.713 | 2 | 0.258 | ||||

No (ref) | ||||||

Yes | -0.602 | 0.390 | 2.381 | 1 | 0.123 | 0.548 |

5.285 | 2 | 0.071 | ||||

15-24 (ref) | ||||||

25-34 | -0.344 | 0.075 | 20.883 | 1 | 0.709 | |

35-49 | -0.455 | 0.136 | 11.166 | 1 | 0.634 | |

23.048 | 2 | |||||

1-4 (ref) | ||||||

5-8 | 0.455 | 0.136 | 11.166 | 1 | 1.577 | |

>8 | 0.111 | 0.123 | 0.812 | 1 | 0.367 | 1.117 |

125.421 | 4 | |||||

Orthodox (ref) | ||||||

Catholic | -1.256 | 0.336 | 13.991 | 1 | 0.285 | |

Protestant | -0.761 | 0.089 | 73.755 | 1 | 0.467 | |

Muslim | -0.635 | 0.071 | 79.030 | 1 | 0.530 | |

Other | -1.552 | 0.237 | 42.982 | 1 | 0.212 | |

35.762 | 1 | |||||

No (ref) | ||||||

Yes | 0.427 | 0.092 | 21.622 | 1 | 1.533 | |

123.468 | 2 | |||||

Poor (ref) | ||||||

Medium | 0.568 | 0.082 | 48.128 | 1 | 1.764 | |

Rich | 0.808 | 0.080 | 101.434 | 1 | 2.244 | |

134.630 | 4 | |||||

No Edu. (ref) | ||||||

Primary | 1.054 | 0.125 | 71.367 | 1 | 2.868 | |

Secondary | 0.904 | 0.152 | 35.218 | 1 | 2.470 | |

Higher | 0.155 | 0.335 | 0.213 | 1 | 0.644 | 1.167 |

Constant | 1.367 | 0.175 | 60.958 | 1 | 3.925 |

Statistical significance at 5%; ref = categorical reference.

Among the covariates in the model, residence, birth order, religion, husband occupation, wealth index, and husband education have a significant effect on the odds of using antenatal care services. But the remaining covariate respondent’s opinion that a husband is justified in hitting or beating his wife and age of the respondents do not have a significant effect on the odds of using antenatal care services at a 5% level of significance.

The goodness of fit of various statistical models can be examined using various statistical measures. These measures are used to select a suitable model among the possible candidate models. In Table

Statistical measures used to compare the goodness of fit of the binary logistic and multilevel random coefficient model.

Model comparison measures | Binary logistic regression | Multilevel random coefficient model |
---|---|---|

-2 | -7593.75 | -5673.29 |

Deviance based on Chi-square | 1222.079 | 290.47 |

AIC (DIC) | 7631.749 | 6368.35 |

On the basis of the AIC and DIC values in Table

This study employed the Bayesian multilevel logistic regression analysis. Women are nested within the various regions in Ethiopia in order to explain regional variations in the usage of antenatal services. We employed three multilevel logistic regression models for the response variable on the use or not use of antenatal care services. The Bayesian multilevel logistic regression empty model, Bayesian multilevel logistic regression random intercept, and Bayesian multilevel logistic regression for the random coefficient model were applied to explain regional differences in the usage of antenatal care services among women.

The place of residence was significantly associated with the usage of antenatal care services. This shows that women who were living in rural areas were less likely to receive ANC services than living in urban areas. This difference might be due to the fact that urban women are more accessible to health services and have information and education about ANC service care than women in the rural area. The findings from this study and previous studies in Ethiopia showed that there exists an association between ANC utilization and residence of mothers [

In this study, it was found that women in primary, secondary, and higher education were more likely to receive antenatal care services than women who are not educated. The study that was conducted in Ghana also showed a similar finding with this study [

This study also concluded that the husband’s educational level were a significant contributing factor for receiving ANC services. This indicates that mothers whose husband’s educational levels were primary, secondary, and higher levels were more likely to receive ANC services than those not educated. The other studies conducted in Ethiopia also verified that women with partners having primary or higher education used ANC services more than those women who had partners/husbands with no education in Ethiopia [

The other factor of interest for this study was the household wealth index. It was found that the household wealth index was an important determinant factor on the usage of ANC service. This indicates that women who are from a household with medium and higher wealth quintile are more likely to utilize ANC service care than those who are from poor wealth households. This positive relationship between wealth index and antenatal care usage was also supported by a study conducted by various researchers [

The birth order was the other determinant factor that influences the usage of antenatal care services. The study conducted by Kamau and Habtom [

The husband’s occupation was a significant determinant variable on the usage of ANC services. This indicates that women whose husbands are working were more likely to receive ANC services than women whose husbands are not working. This finding is in agreement with the results of the previous study by Terfasa et al. [

The study conducted by Abosse et al. concluded that the attitude towards beating wives that argues with husband was a significant determinant factor on receiving ANC services. This study also approved that women who believed that the husband is justified in hitting or beating his wife are less likely to receive antenatal care services. Thus, women who have positive attitudes towards beating wives that argues with husband use less antenatal care services that is also confirmed by Abosse et al. [

The study identified some socioeconomic and demographic factors that affect the utilization of antenatal service cares. Based on the findings, the upgrading of women and their husband’s education is a very important factor on the utilization of antenatal care services. In this regard, all stakeholders particularly the government should act to raise women’s and their husband’s education level. Moreover, the government and other stakeholders working on health and related issues should strengthen the effort to improve the accessibility of health facilities, constructing roads and providing transportation services for pregnant women in rural areas. Finally, health extension workers should be given better training to identify and transfer high-risk women for better antenatal services in a health institution.

Akakie information criterion

Antenatal care

Bayesian information criterion

Central Statistical Agency

Delivery care

Deviance information criterion

Ethiopian Demographic and Health Survey

Interclass correlation

Markov chain Monte-Carlo

Postnatal care

Statistical Package for Social Sciences

World Health Organization.

In this study, we used the information from the fourth Demographic and Health Survey conducted of Ethiopia from January 18, 2016, to June 27, 2016 by Central Statistical Agency (CSA) focusing on all women who are in the reproductive age group (aged from 15-49 years).

The author declares no conflict of interest

The author is grateful to the Central Statistical Agency of Ethiopia for providing the 2016 DHS dataset of Ethiopia.