Spatial Distribution and Associated Factors of Institutional Delivery Among Reproductive-Age Women (15-49 Years) in Ethiopia: The Case of Ethiopian Demographic and Health Survey 2016 Data

Background: Globally, there were an estimated 289,000 maternal deaths in 2013, yielding Maternal Mortality Rate (MMR) of 210 maternal deaths per 100,000 live births. Still, now maternal mortality in Ethiopia is the highest in the world. Methods: This study is a secondary data analysis of the 2016 EDHS. A total of 7590 women who gave birth in the last 5 years preceding the survey were included in the analysis was carried out by using Geographic information system and clusters with high and low hotspots with institutional delivery were identied using Sat Scan spatial statistical analysis. A multilevel multivariable mixed-effect logistic regression was used to identify factors associated with institutional delivery. Result: In this study, 33.25% of women who gave birth in the last 5 years preceding the survey were delivered at the health institution. The nding also indicated that the spatial distribution of institutional delivery was non-random in the country. At the individual level, the riches (AOR=2.18,95%CI:1.39-3.41), higher education(AOR=3.89,95%CI:1.51-10.01), four and above the number of antenatal care visits (AOR=6.57,95%CI:4.83-8.94) and parity more than two children(AOR=0.48,95%CI:0.34-0.68), and at the community level, higher education (AOR=1.70,95%CI:1.22-2.36), urban residence (AOR=5.30, 95%CI:3.10-9.06), were variables that had achieved statically signicant association for utilization of institutional delivery Conclusions: This study identied a spatial cluster of institutional delivery of Somali and Afar region have low utilization rates and Addis Ababa and Tigray regions have the highest utilization rate. The signicant associated factors of institution delivery were Individual factor of woman antenatal care visit, Household wealth index, Maternal education, Parity, and Community factor of Region, Place of residence, and Educational status, Therefore, to maximize health facility delivery in Ethiopia, the predictors of institutional delivery identied in this study should be given more attention by governmental and non-governmental

Despite the Ethiopian government's efforts to expand health service facilities and promote institutionbased delivery service in the country, an estimated 85% of births still take place at home [3][4].
Maternal death is one of the causes of personal and social distress in families, because women have a major responsibility in most family matters, including raising children and have a major role in society [5].
Still now maternal mortality in Ethiopia, the highest in the world from comparing to other countries [6]. In 2005 EDHS, the maternal mortality ratio was 673 per 100,000 live births [7]. in the 2011 EDHS report, the maternal mortality ratio was estimated at 676 per 100,000 live births [8]. and in 2016 maternal mortality ratio 412 per 100,000 live births.
One critical strategy for reducing maternal morbidity and mortality is to ensure that every baby is delivered in a health care facility with the assis tance of a skilled health care attendant. Therefore, to reduce maternal deaths, the most e cient strategy for lower-income countries is to promote childbirth at health care facilities with a referral capacity, as timely management and treatment can make the difference between life and death [9].
In Ethiopia, just like most developing countries, only 15% of births are delivered at a health facility despite more than 40 % of pregnant women having at least one ANC visit during pregnancy [16]. Further, as reported in the 2005 EDHS, the majority of births at home take place in poor hygienic conditions, while only 6 percent are in a health facility and are assisted by trained personnel [17]. Based on data in the 2011 Ethiopia Demographic and Health Survey (EDHS 2011), the MMR was estimated at 676 maternal deaths per 100,000 live births [18].

Study design and period
Cross-sectional study design was conducted using the EDHS data of 2016. The study was conducted from January 18 to June 27, 2016.

Study area
The study was conducted in Ethiopia (3o-14o N and 33o -48°E), situated at the eastern horn of Africa.
The country covers 1.1 million square kilometers and has a great geographical diversity, which ranges 4550 meters above sea level down to the Afar depression to 110 meters below sea level. There are 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.

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All reproductive women age 15-49 years old in Ethiopia who gave birth in the last 5 years preceding the survey.

Study population
All women aged 15 to 49 years in the selected enumeration areas who gave at least one birth in the last 5 years preceding the survey.

Inclusion and Exclusion Criteria Inclusion Criteria
All women who gave birth in the past 5 years before the survey in selected enumeration areas were included.

Exclusion Criteria
Women with unknown places of delivery out of the geographical positioning system were excluded.

Sample Size Estimation
A total of 15683 women aged 15-49 were interviewed in 2016 Ethiopian demographic health survey data (EDHS) 8490 women were excluded because they didn't have a birth in the ve years preceding the survey 7590 women's have at least one birth in ve years before the interview was included.

Sampling procedures
Administratively, regions in Ethiopia are divided into zones, and zones into administrative units called woreda. Each woreda is further subdivided into the lowest administrative unit, called kebeles. During the 2007 census, each kebele was subdivided into census enumeration areas (EA), which were convenient for the implementation of the census.
A strati ed two-stage cluster sampling procedure was employed where EA is the sampling unit for the rst stage and households for the second stage. In 2016 EDHS, a total of 645 EAs (202 in urban areas and 443 in rural areas) were selected with probability proportional to EA size (based on the 2007 housing and population census) and with independent selection in each sampling stratum. Of these 18,060 households were included; 7590 women. Community-de ned as based on the primary sample unit in the EDHS data. Community-level variables were driven by aggregating individual-level variables, the aggregated were computed using average values of the proportion of women in each category of a given variable based on national median values the aggregated values categorized into groups (42).
Community poverty: the proportion of women in the community those live households in the lower (poor) quintile of wealth index; categorized as high (proportion of women greater than median national value), and whereas low (proportion of women below the median national value).
Community education: the proportion of women in the community who primary and secondary, categorized as high (proportion of women greater than median national value) whereas low (proportion of women below-median national value).
Region: The 11 regions of Ethiopia, which are delineated for administrative purposes, were categorized into three contextual regions pastoralist(Afar, Gambella, Benishangulgumez and Somali) Agrarian(Amhara, Tigris, Oromia, and SNNP) and City (A.A, Diredewa, and Hariri) de ned based on the living conditions of their population (43) Data collection and quality assurance Data Source Data were obtained from the nationally representative 2016 Ethiopian Demographic and Health Survey (EDHS) which used a two-stage cluster sampling design with rural-urban and regions as strata. Approval letter for the use of this data was gained from the Measure DHS.

Methods of data analysis
First data Extracted and cleaning by Stata 14.1 version. A multi-level logistic regression analysis technique was employed in this study to account for the hierarchal structure of the DHS data and the binary response of the outcome variable Bivariate multilevel logistic regression analysis was performed to estimate the crude odds ratios at 95 % con dence interval and those variables which were statistically signi cant were considered in the multivariate analysis. Finally, multivariate multilevel logistic regression analysis was performed to estimate the adjusted odds ratios and to estimate the extent of random variations between communities. In the multilevel models, the xed effects (measures of association) estimate the association between the likelihood of institutional delivery and the individual and community level factors and were expressed as odds ratio with their 95 % con dence intervals. The random effects are the measures of variation in institutional delivery across communities expressed as Intracluster Correlation Coe cient (ICC) and Proportional Change in Variance (PCV).
Model comparison was conducted by using the Log-Likelihood ratio test and the model that maximum LLR was selected as a better-tted model. Geographical Information System (ArcGIS version 10.6) and spatial Sat scan was used to analyze spatial data.

Spatial Autocorrelation Analysis
The spatial autocorrelation (Global Moran's I) statistic measures were used to evaluate whether the institutional delivery patterns are dispersed, clustered, or randomly distributed in the study area. Calculated Moran's I values close to −1 indicate institutional delivery Is randomly distributed whereas Moran's I close to +1 indicate institutional delivery clustered distributed. A statistically signi cant Moran's I (p < 0.05) leads to rejection of the null hypothesis and indicates the presence of spatial autocorrelation. Local Moran's I will be used to investigate the local level cluster locations of institutional delivery. Local Moran's I measure whether there were positively correlated (high-high and low-low) clusters or negatively correlated (high-low and low-high) clusters of high values (High-High), and clusters of low values (Low-Low). It also measures outlier in which high value is surrounded primarily by low values, and an outlier in which a low value is surrounded primarily by high values. Value for 'I' indicated that a case is surrounded by cases with dissimilar values; this case is an outlier.
Hot spot analysis (Getis-OrdGi* statistic) Gettis-OrdGi* statistics were computed to measure how spatial autocorrelation varies over the study location by calculating Gi* statistic for each area. Z-score is computed to determine the statistical signi cance of clustering, and the p-value computed for the signi cance. The p-value associated with a 95% con dence level is 0.05. If the z-score is between −1.96 and +1.96, the p-value would be larger than 0.05, and could not reject the null hypothesis; the pattern exhibited could very likely be the result of random spatial processes. If the z-score falls outside the range, the observed spatial pattern is probably too unusual to be the result of random chance, and the p-value would be small to re ect this. In this case, it is possible to reject the null hypothesis and proceed with guring out what might be causing the statistically signi cant spatial pattern in the data. Statistical output with high Gi* indicates "hotspot" whereas low Gi* means a cold spot.

Incremental analysis
Incremental spatial autocorrelation measures spatial autocorrelation for a series of distances and optionally creates a line graph of those distances and their corresponding z-scores. Statistically signi cant peak z-scores indicate distances where spatial processes promoting clustering are most pronounced. These peak distances are often appropriate values to use for tools with a Distance Band or Distance Radius parameter.

Incremental spatial autocorrelation
The incremental spatial autocorrelation of institutional delivery utilization showed that the maximum peak, where the spatial clustering is highly signi cant at a distance of 151366.65 meters, with a corresponding z-score of 25.53 (p-value <,0.01).

Ethical consideration
Ethical clearance was obtained from the Institutional Review Board (IRB) of the Institute of Public Health, College of Medicine and Health Sciences, University of Gondar. The survey data was received from the Measure DHS International Program which authorized the data-sets. All the data used in this study are publicly available, aggregated secondary data with not having any personal identifying information that can be linked to particular individuals, communities, or study participants. Con dentiality of data was maintained anonymously (See Additional le 3).

Socio-demographic characteristics of study participants
In this study, a total of 7590 women with their most recent birth in the ve years preceding the 2016 EDHS survey were included in the analysis. Thirty-three percent of live births in the 5 years before the survey was delivered in a health facility. About one-fourths (24%) of participants were 15-24 years old age, and almost ninety percent 6620(87%) were rural residents and the majority of women who participated in the survey were orthodox Christians. Regarding the educational status of the participant's 4791(63%) has no education and 1654 (22%) were in the poorer wealth quintile (See Additional le 2).

Prevalence of institutional delivery across regions
The prevalence of institutional delivery utilization varies across the regions of the country. The highest and the lowest prevalence were observed in Addis Ababa (96%) and Afar (19.0%) regions respectively ( Figure 1).

Determinants of Institutional-Delivery
Effect of individual women characteristics on the place of delivery

Random effect and model comparison parameters
In the null model, about 63% of the total variation on institutional delivery was occurred at the community level and is attributable to the community-level factors. The highest MOR value (9.45) in the null model revealed there was a variation of institutional delivery between clusters. Furthermore, the highest (82.63%) PCV in the nal model (Model 4) indicates that 82.63% of the variation in institutional delivery across communities was explained by both individual and community level factors. The model tness was checked using the Loglikiehood ratio and the model with the lowest Loglikiehood ratio (Model 4) was the best-tted model (Table 3).

Discussion
This study aimed to explore the spatial distribution and associated factors of institutional delivery among women aged 15-49 years old: the case of EDHS 2016 data. In this study, the prevalence of institutional delivery utilization was 33.25%. This result is lower than the results from India 84.9% [44] and Tanzania 74.5% [45]. But higher than ndings from the southeast part of Ethiopia 12.3% [46]. North West of Ethiopia,12.1% [47].Our ndings show that the proportion of institutional delivery varies across the regions of the country. The highest and the lowest prevalence were observed in Addis Ababa (96%) and Afar (19.0%) regions respectively. The reason for the lowest utilization rate institutional delivery was lack of education and lack of access to maternal health delivery facility.
Regarding, the geographical information system (GIS) analysis indicates that the spatial distribution of institutional delivery was non-random in the country. The Global Moran's I values of our ndings are 0.42 (p-value <0.001) indicated that there was a statistically signi cant clustering of institutional delivery in the study area. And also cold spot shows areas of low institutional delivery were found in Afar, Somali and Bnshangulgumez regions. And Interpolation predicted a low proportion of institutional delivery regions are central and northern part of Afar, eastern Somali, and Beneshangulgumaz and Eastern part of Oromia. Generally, the above GIS spatial report shows that low institutional delivery found that region of Afar, Somali, Benishangulgumez, and eastern part of Oromia. This is due to a lack of access to media exposure and transport system in addition to having poor infrastructure for the utilization of institutional delivery service.
Regarding the determinants of Institutional delivery, the wealth index is a signi cant predictor of Institutional delivery. The odds of delivering at health institutional among mothers who had poorer and richest were 1.46 and 2.18 times respectively higher as compared to the poorest. This result similar to the study done in Bangladesh, Malawi Jacob, and Tigre northern part of Ethiopia [48][49][50]. This might be related to the costs needed to access health care services.
Women's education is positively associated with Institutional delivery in Ethiopia. The odds of delivering at health institutions among mothers who had primary education, secondary education, and higher educational level were 1.40, 3.25, and 3.9 times higher as compared to non-educated women. This result also similar to the study done in Ethiopia like Bahir Dar city, in Bangladesh and Pakistan. This might be due, educated women will have awareness about the risk of home delivery and the importance of maternal health services [24][25][26]. The number of antenatal care visits positively associated with health facility delivery. Mothers who had 1-3 and 4 and above ANC visits were 3.75 and 6.57 times more likely to deliver at health facility respectively, as compared to mothers with no ANC visit. This nding is consistent with the study done in North West Ethiopia, Somali regional states, and India. This might be due to those women attending antenatal care will be counseled about the importance of institutional delivery and birth preparedness plan [51][52][53].
Parity was another important predictor of institutional delivery. The odds of delivery at health facility among mothers who had the number of living children 2-4 were reduced by 52% as compared to those who had only one child. This nding is consistent with the study conducted in Pakistan and Rural Tanzania, This is due to that Women who deliver their rst child without any problem might think that they will not face the di culty of delivering their second baby and they may not also found a person who cares for their rst child to leave him at home while they go to the health institution for delivery [26][27][28][29][30][31][32][33][34][35][36][37][38].
Living in communities with a high proportion of educated women had a 70% higher chance of institutional delivery as compared to women living in communities with a low proportion of educated women. This study is consistent with a study done in six African countries [54]. This might be the reason In Community factor of Education could in uence women's overall empowerment enhancing their ability to access information and easily absorb health messages through the media and health professionals.
These could collectively in uence mothers' awareness to seek better medical services, including delivering in health facilities.
The odds of health facility delivery among women living in the Pastoralist community are reduced by 70 % and similarly, women living in the Agrarian community had a 55% reduction to deliver at the health facility as compared with women in city communities. This due to people are very hard to reach and mostly wander to distant areas to look for animal foods, in addition to having poor infrastructure. This nding is assured by its consistency with previous studies conducted in Ethiopia and other African countries [55][56][57].
Place of residence was also another community-level factor which determines the choice of place of delivery. The odds of delivery at institutions for women residing in urban areas were 5.3 times higher as compared to women living in a rural area. This nding supported by national survey India and urban Bangladesh, As explained by urban women tends to bene t from increased knowledge and access to maternal health services [49][50][51][52][53][54][55][56][57][58].

Conclusion
In this study both the individual and community level characteristics were found to have a signi cant in uence on institutional delivery and identi ed spatial clusters of health facility delivery in Afar and Somali Region with lowest utilization rates and Addis Ababa and Tigray with the highest utilization rate.
In multivariate multilevel logistic regression analysis were antenatal care visits, household wealth index, maternal education, Parity, community level of education, residence and region were variables that had achieved statistically signi cant association to institutional delivery. For this study ethical approval was not required since this is a secondary analysis of the 2016 EDHS data. But we registered and requested access to EDHS datasets from DHS on-line archive and received approval to access and download the data les.