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Background: The maternal mortality rate in poor nations remains unacceptably high. The purpose of this study was to identify factors associated with institutional delivery usage. Methods: The data came from the Ethiopian mini demographic and health survey, which was conducted in 2019. This study comprised 3978 women of reproductive age who had given birth within the previous 5 years. To uncover significantly linked parameters associated with institutional delivery, we used a multilevel logistic regression model. Statistical significance was declared at p < 0.05, and we assessed the strength of association using adjusted odds ratios with 95% confidence intervals. Results: More than half of the women (53.67%) among 3978 women with last birth had their babies delivered in a health facility. In the multilevel logistic regression analysis, women in age group 45–49 (AOR = 2.43, 95% CI: 1.280, 4.591), primary educational level (AOR = 2.21, 95% CI: 1.864, 2.625, secondary and above education level (AOR = 6.37, 95% CI: 4.600, 8.837), being Muslim (AOR = 2.57, 95% CI: 1.245, 2.166), women who visited ANC service four up to seven times (AOR = 2.75, 95% CI: 2.175, 3.473), women visited ANC service eight times and above (AOR = 3.295% CI: 1.685, 6.050), women who reside in middle wealth index (AOR = 1.57, 95% CI: 1.273, 1.950), and rich wealth index (AOR = 3.43, 95% CI: 2.782, 4.225) were more likely to give birth at health institution compared to their counterparts. Furthermore, women being in rural area (AOR = 0.34, 95% CI:- 0.283, 0.474) and protestant women (AOR = 0.1.57, 95% CI: 0.479, 0.852) were less likely to deliver at health institution. Conclusions: Ethiopia still has a low level of institutionalized delivery. Institutional delivery in Ethiopia should be improved through context-specific and personalized programs, such as educating women and enhancing access to ANC services.
The data for this study came from the 2019 EMDHS, a cross-sectional survey that took place between March 21 and June 28, 2019. The EMDHS was created to give estimates of health and demographics in nine geographical regions and two administrative cities. The EMDHS 2019 followed a complex sampling design (i.e. combined stratified and cluster in two stages, with unequal probabilities of selection that result in the weighted sample to separate the sample components) and was designed to obtain representative estimates at the national, and regional levels (administratively, the country is divided into 9 geographical regions and 2 administrative cities). Among 8885 child-bearing mothers interviewed, only 3978 mothers who had given birth within the 5 years preceding the survey were considered to identify factors associated with the utilization of institutional delivery in Ethiopia. The whole report of the EMDHS 2019, which was the second inclusive survey and was implemented by the Ethiopian Public Health Institute (EPHI), includes detailed information on data management. The results are available online in the DHS database at https://www.dhsprogram.com/data/datasetadmin/loginmain.cfm. Being an Ethiopian national between the ages of 15 and 49, having given birth in the year preceding the interview, and living in Ethiopia during the pregnancy were the only requirements. Mothers with any mental illness and mothers who refused to participate were all excluded from this study. Based on the inclusion and exclusion criteria given above, only 3978 mothers were interviewed with a 100% response rate, and the rest 4907 mothers among 8885 reproductive-aged women were excluded from the study. Institutional delivery service utilization refers to mothers who had delivered their last baby in hospitals, health centers, private clinics, NGO health facilities, or Health Posts by skilled personnel. 21 The current study’s outcome variable was the use of institutional delivery. At the time of the survey, women were asked whether they were delivered to a health institution or not. We developed a binary dependent variable that was coded as 1 for institutional delivery and 0 for non-institutional delivery. The outcome variable was institutional delivery denoted by σue2 which is categorized as Where Thus, Yij takes on values 0, 1, 2 . . . Where Yij denotes the individual woman who gave birth and i is the region in which the mothers who gave birth are residing. The independent variables included in this study were chosen based on past research and extant literature.4,12 These include the age of the women at birth, place of residence, wealth index, religion, women’s educational level, current marital status, number of antenatal care visits, pregnancy complications, and husband/partner’s educational level. The data from the EMDHS 2019 for this study were cleaned, coded, and analyzed using the statistical tools SPSS version 20 and R version 4.1.2. The R packages used for the analysis of the multilevel model were packages “nlme,” “multilevel” and “glmmTMB.” The risk factors for non-institutional delivery were identified using descriptive statistics such as frequency and percentage, as well as a multilevel binary logistic regression model based on inferential statistics. In the multiple multilevel binary logistic regression analysis, the predictor variables that were significant at the 25% (value 0.25) level of significance in the univariable analysis were included.22–24 With a value, of less than 0.05, the estimated odds ratios and 95% confidence intervals in the multivariable analysis show that the variables are statistically significant, and adjusted odds ratios (AOR) with 95% confidence intervals were used to examine the statistical strength. 25 We fitted a multilevel model to account for the hierarchical nature of the data and to minimize possible parameter underestimation from a single-level model. 26 In this study, we use region of residence as a level-2 variable to group respondents. By integrating random effects in the model, this technique improves the single-level logistic regression model. Three models were estimated: the null model, the random intercept with fixed coefficient, and the random coefficient model. As a result, a two-level multilevel model was used to model the log of the chance of using institutional delivery as follows: Where j probability of utilization of institutional delivery is πij is the probability of home delivery (non-institutional delivery). The first part of the equation, 1−πij , is called the fixed part of the model, and the second part β0+Σk=1nβkxkij is called the random part. The distribution of u0j+Σp=1mupjxpij is normal with a mean 0 and variance u0j and also the distribution of regional effect variables σu02 is normal with a mean 0 and variance upj . 27 The intra-class correlation coefficient (ICC) measures the proportion of variance in the outcome explained by the grouping structure. It can be calculated as σup2 where, ICC=σu02σu02+σe2 is the variance of individual-level units which is constant as σe2 in logistic regression. 28
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