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Background In Ethiopia, despite the progress that has been made to improve maternal and child health, the proportion of births occurring at health institutions is still very low (26%), Which significantly contribute to a large number of maternal death 412 deaths/100,000 live births. Therefore, this study intended to determine spatial pattern and factors affecting institutional delivery among women who had live birth in Ethiopia within five years preceding survey. Method Data from 2019 Ethiopian demographic and health survey were used. Taking into account the nested structure of the data, multilevel logistic regression analysis has been employed to a nationally representative sample of 5753 women nested with in 305 communities/clusters. Result A significant heterogeneity was observed between clusters for institutional delivery which explains about 57% of the total variation. Individual-level variables: primary education (OR = 1.8: 95% CI: 1.44–2.26), secondary education (OR = 3.65: 95% CI: 2.19–6.1), diploma and higher (OR = 2.74: 95% CI: 1.02–7.34), women who had both Radio and Television were 4.6 times (OR = 4.6; 95% CI: 2.52, 8.45), four and above Antenatal visit (AOR = 2.72, 95% CI:2.2, 3.34), rich wealth index (OR = 2.22; 95% CI: 1.62–2.99), birth interval for 18 to 33 months (OR = 1.8; 95% CI: 1.19, 2.92), and women who space birth for 33 and above months (OR = 2.02; 95% CI: 1.3, 3.12) were associated with institutional delivery. Community level variables, community high proportion of antenatal visit (OR = 4.68; 95% CI: 4.13–5.30), and Region were associated with institutional delivery. Conclusion A clustered pattern of areas with low institutional delivery was observed in Ethiopia. Both individual and community level factors found significantly associated with institutional delivery theses showed the need for community women education through health extension programs and community health workers. And the effort to promote institutional delivery should pay special attention to antenatal care, less educated women and interventions considering awareness, access, and availability of the services are vital for regions. A preprint has previously been published.
Ethiopia is located in the Horn of Africa and shares a border with Eritrea, Djibouti, Somalia, Sudan, South Sudan, and Kenya. The country covers an area of 1.1 million km2 (square kilometer) with geographical diversity, ranging from 4,550 meters (m) above sea level down to the Afar depression 110m below sea level, which is comprised of over 80 ethnicities and speaking over 80 different languages [19]. Administratively, Ethiopia is divided into nine regional states and two city administrations subdivided into 68 zones, 817 districts, and 16,253 kebeles (lowest local administrative units of the country) in the administrative structure of the country [20]. Based on the 2018 world bank report Ethiopia had a total population of 109 million with a gross national income per capital of US$ 790 [21]. Ethiopia’s health system comprises three tiers: a primary health care unit, a general hospital, and a specialized hospital [22, 23]. The data came from EDHS 2019, specifically the under-five children’s file (KR) (http://www.measuredhs.com). We were able to download the datasets after the measurement program allowed us to do so. The unweighted sample consisted of 5753 women who had live births in the five years preceding the survey. The 2019 EDHS sample was stratified and selected in two stages, and interviews were conducted face-to-face with permanent residents and visitors who stayed in the residences the day before the survey. The 2019 EDHS sampling frame is a composite of all census enumeration areas (EAs) created for the upcoming 2019 Ethiopia Population and Housing Census (PHC) conducted by the Central Statistical Agency (CSA). The census frame includes the complete list of 149,093 EAs created for the 2019 PHC. An EA is a geographical area with an average of 131 households. The sampling frame includes data on the EA’s location, type of residence (urban or rural), and the estimated number of residential households [24] The outcome variable for this study was institutional delivery, which was coded as “0” if the women gave birth at home and “1” if the women gave birth at a health facility. Institutions/facilities delivery was stated as the births at health institution/facility within five years afore the survey. Individual level factors were women education level, household wealth index, birth interval, number of antenatal care visit, age of the women, media exposure and marital status. Community level factors were residence, region, community educational status, community ANC coverage and community poverty. The EDHS did not collect data that can directly describe the clusters’ characteristics except the place of residence and region. Therefore, other common community-level data were generated by aggregating the individual characteristics with our interest in a cluster. The aggregates were computed using the proportion of a given variables’ subcategory we were concerned on in a given cluster. Since the aggregate value for all generated variable was not normally distributed. It was categorized into groups based on the national median values. A frequency of listening to the radio and watching television were considered exposure to mass media in this study by excluding exposure to magazines and newspapers. So, women exposed to either television or radio at least once per week considered exposed, if not exposed at all, taken as not exposed [20]. Was defined as the proportion of mother’s who attended primary/secondary/ higher education within the cluster. The aggregate of individual mother’s primary/secondary/higher educational attainment can show the overall educational status of women within the cluster. There were two categories for this variable with reference to the national median value: higher proportion of mother’s who attended primary/secondary/higher education and lower proportion of mother’s who attended primary/secondary/higher education within the cluster. Was defined as mothers who had at least four antenatal care visit [25]. The proportion of women in the clusters who had four and above antenatal care (ANC) from a skilled provider during the pregnancy of last delivery. It is defined as the proportion of poor or poorest mothers within the cluster. Within the cluster proportion of poor or poorest were aggregated and show over all poverty status within the cluster. The 2019 EDHS data were pre-tested before the actual data collection. Data collectors had received training in interviewing techniques, field procedures, the content of the questionnaires, and how to administer both paper and electronic questionnaires; after all, questionnaires were finalized in English, then translated into Amarigna, Tigrigna, and Oromiffa [24]. Since this was secondary data, the data were maintained by processing, editing, raw coding data, and re-coding, checking its completeness, and cleaning the missing values by running frequencies based on the research’s interest. Sample weights were applied to compensate for the disproportional probability of sampling and non-response rate between the strata that have been geographically defined. A detailed explanation of the weighting procedure can be found in the EDHS final report [24]. Cross tabulations and summary statistics were used to describe the study population. The aggregated home and health facility delivery count data were joined to the geographic coordinates based on each cluster unique identification code. Global spatial autocorrelations were assessed with ArcGIS version 10.5 using the Global Moran’s I statistic (Moran’s I) to evaluate whether the pattern expressed is clustered, dispersed, or random across the study areas. Moran’s I values close to −1 indicated institutional delivery were dispersed, whereas I values close to +1 indicated institutional delivery were clustered, and distributed randomly if I value was zero. A statistically significant Moran’s I (p < 0.05) led to the rejection of the null hypothesis, and indicated the presence of spatial autocorrelation as well as it detect the existence of at least one cluster, but not the specific location of the cluster(s) [26]. For positive global spatial autocorrelation, local spatial association indicators were used to assess clusters and outliers by comparing the values in each specific location with values in neighboring locations. It allows for decomposing the pattern of spatial association into four categories (quadrants) called Hot spot analysis [27]. And this help us to identify the proportion of institutional delivery based on sampled enumeration area. Since geographic coordinates were collected at the cluster level, the unit of spatial analyses was 2019 EDHS clusters. Finally, we employed Kulldorff’s purely spatial scan statistic method using the Bernoulli probability model in SaTScan version 9.6 software to detect the local spatial clusters of areas with high home delivery. Its output presents the hotspot areas in circular windows, indicating the areas inside the windows are higher than expected distributions compared to the areas outside of the cluster windows [28]. We used a maximum 50% of the popul30+ation at risk for the spatial cluster size. A cluster was statistically significant if a p-value < 0.05. Interpolation- we run the empirical Kriging technique to predict values for areas where data points were not taken. First a descriptive analysis was conducted for all individual- and community-level variables in order to examine the characteristics of the sample. Considering this hierarchical nature of the data and the assumption of independence among individuals within the same community and the assumption of equal variance across the community is violated in nested data. Therefore, flat models could underestimate the effect sizes’ standard errors and lead to bias (loss of power or type I error), affecting the null hypothesis [29]. Hence, in order to account the hierarchical nature of the EDHS data and response variable multilevel logistic regression analysis was implemented to test the effect sizes of individual and community level factors on women’s decision to place of delivery. During analysis, the characteristics of women were taken as individual level (level-1) and characteristics of clusters were treated as community level (level-2). Model I (Empty model) was fitted without explanatory variables to test random variability in the intercept and to estimate the intra class correlation coefficient (ICC). Where σ2uo = variance due to group level error term (uoj) and π2/3 is level-1 variance. Model II examined the effects of individual level characteristics, Model III examined the effect of community level variables and Model IV examined the effects of both individual and community level characteristics simultaneously. The p value <0.05 was considered as statistically significant. For measurements of variation (random effects), intra-class correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV) statistics were computed. Model comparison was made based on Akakie Information Criteria (AIC) and Deviance Information Criteria (DIC). The model with the lowest information criterion was considered to be the best fit model [29]. For this study, The ethical clearance was obtained from Salale University ethics Committee.The data were obtained and used with the Central Statistical Agency of Ethiopia’s prior permission. We registered for dataset access and wrote the study’s title and significance on the website after completing a short registration form. Downloading of datasets was done using the accessed website at http://www.measuredhs.com on request with the help of ICF international. Downloading data were used only for this study. The dataset was not passed on to other researchers without the consent of EDHS. All EDHS data were treated as confidential, no need to identify any household or individual respondent interviewed in the survey.
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