Geographical variation in hotspots of antenatal care service utilization in Ethiopia: Geographic weighted regression and Multilevel analysis

Geographical variation in hotspots of antenatal care service utilization in Ethiopia: Geographic weighted regression and Multilevel analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geographical variation in hotspots of antenatal care service utilization in Ethiopia: Geographic weighted regression and Multilevel analysis Lewegneh Wegayehu Tessema, Tigabu Kidie Tesfie, Tsion Mulat Tebeje, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7220929/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Antenatal care refers to care given to pregnant women during pregnancy by skilled health professionals. Due to low coverage of antenatal care, women in many developing countries, including Ethiopia, are at risk for pregnancy-related complications. This research aims to explore geographical variation in hotspots of antenatal care service utilization in Ethiopia using data from the 2019 Ethiopian Mini Demographic Health Survey. Method: This study was conducted based on the 2019 Ethiopian Mini Demographic Health Survey (EMDHS) data. A total of 3,927 women were included in this study. To determine the factors associated with hotspots of antenatal care (ANC) utilization, we employ geographic weighted regression (GWR). Adjusted R 2 and AICc was used for model comparison. A multilevel analysis was fitted and variables with a p-value < 0.25 in the bi-variable analysis were considered for the multivariable analysis. The adjusted odd ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association. Result: Prevalence of ANC utilization in Ethiopia was 43% (95% CI: 41.5%, 44.6%). Tigray, SNNPR, Addis Ababa, and Benishangul Gmuz regions were most hotspot areas. Multilevel regression analysis revealed that Age 35-39 years, other religion followers, married women, higher educational status, rural residence, being rich in wealth status, and low community level poverty were associated with antenatal care service utilization. Conclusion and recommendation: In Ethiopia, the prevalence of ANC utilization in was low according to our study and there was a significant spatial variation of antenatal care utilization in Ethiopian regions. To improve ANC coverage, geographically targeted strategies are essential. These should focus on reducing regional disparities, improving women’s education and socioeconomic status, and enhancing accessibility and availability of reproductive health services, especially in rural areas of the country. Geographic weighted regression antenatal care multilevel regression analysis EDHS Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Antenatal Care (ANC) refers to the care provided to pregnant women by qualified and trained healthcare providers to ensure the health and well-being of women and their unborn child throughout pregnancy and delivery process 1 . Early identification and management of complications through accessible, high-quality care during pregnancy, labor, and after childbirth can significantly reduce maternal deaths 2 . over 90% of global maternal deaths occurred in low- and lower-middle-income countries in 2023, 3 . Approximately 86% of maternal deaths worldwide are estimated to occur in Southern Asia and Sub-Saharan Africa 4 . Adolescent girls under 15 years of age face the highest risk of maternal death, while those aged between 10–19 years are at greatest risk of pregnancy and delivery difficulties 5 . The lifetime risk of maternal mortality is significantly higher in low-income (1 in 45) countries compared to high-income countries (1 in 5400) 6 . Maternal death occurs during pregnancy and childbirth, and the majority of these deaths arise during pregnancy and can be avoided or managed 3 . Complications may exist before pregnancy but worsen during pregnancy, especially if not properly managed. Around 75% of cause of maternal deaths are bleeding, infections, pre-eclampsia and eclampsia, unsafe abortion and other indirect causes such as malaria or related to chronic conditions like cardiac diseases or diabetes 7 . According to (World Health Organization), in Ethiopia about 32% of women had at least four ANC visits during their last pregnancy, while 37% of them had no ANC visits. Only 20% of women had their first ANC during the first trimester, 26% during their fourth to fifth month of pregnancy, and 14% during their sixth to seventh month of pregnancy. Two percent of women did not receive any ANC until the eighth month of pregnancy or later. 44% of women in urban areas receive ANC within their first trimester of pregnancy, compared with 17% of those in rural areas 8 . Even though ANC service utilization is essential for further improvement of maternal and child health, little evidence is known about the current magnitude of ANC and factors hindering ANC service utilization in Ethiopia. In Ethiopia, maternal mortality and neonatal mortality is high. This problem could be reduced by provision of proper and quality ANC service and skilled delivery. Spatial analysis is important for improving quality ANC care by identification of geographic area with low ANC utilization with hotspots and investigate the driving force that women can’t utilize ANC service. Studies that have been done to explore the determinants of ANC service utilization in Ethiopia have shown that a variety of factors affect antenatal care service utilization. However, no studies have attempted to assess spatial distribution and Geographic Weighted Regression (GWR) analysis. Therefore, the aim of this study is to assess unveiling geographical variation in hotspots of antenatal care service utilization in Ethiopia using geographic weighted regression and multilevel analysis based on the recent 2019 Ethiopian Mini Demographic health survey data. Furthermore, this study helps as to an input for other researcher, policy makers, programmers and health care partners to develop appropriate strategy, interventions and prioritize risk areas aims to improve ANC service utilization and carry out a more extensive research on more factors in this particular area as well as to improve the quality of reproductive health services. Methods Study design and period A population-based cross-sectional study was conducted. A detailed analysis of the Ethiopian Mini Demographic and Health Survey (EMDHS) 2019 data conducted from March to June 2019 was used to assess the spatial distribution and factors associated with ANC visits. Study area Ethiopia is bordered by Sudan to the west, Eritrea and Djibouti to the northeast, Somalia to the east and southeast, and Kenya to the south. The 2019 EMDHS was a nationwide survey that was carried out over the entire country. Ethiopia is separated into two administrative cities and nine regions under its administrative structure. Regions are then further separated into zones, which are further divided into weredas, and finally into kebeles, which is the lowest administration level. Source and study population Source population All women in the reproductive aged (15–49 years) living in Ethiopia at time of survey. Study population All women in the reproductive age who had a live birth in the five years preceding the survey. Eligibility criteria Inclusion criteria All women age 15–49, who had a live birth in the five years preceding the survey, were included in our study. Exclusion criteria Participants with missing value of the outcome of interest and clusters with zero cases and controls were excluded from the analysis. Sampling procedures A stratified and two-stage cluster sampling technique was used based on 2019 EMDHS. In the first stage, 305 Enumeration Areas (EAs) (93 in urban areas and 212 in rural areas) were selected with probability proportional to EAs size (based on the 2019 PHC frame) and with independent selection in each sampling stratum. In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women aged 15–49, who were either permanent residents of the selected households or visitors who slept in the household the night before the survey, were eligible to be interviewed 9 . Study variables Our study area of interest has binary outcomes. Women having ANC visit during the five years preceding the survey were coded as “1” whereas women having below the ANC visit were coded as “0”. Individual factors include age, religion, marital status, birth order, woman education and sex of the household head. Community level factors include poverty, residence, region, and community media exposure and community level women education. Operational definition Antenatal care is defined as women having four and more antenatal care visits during the last pregnancy. Community level women education after assuring normality of the distribution the median value was considered as cutoff point. Community level women education considered as “low educational” if median value = 0.091. Community poverty after assuring normality of the distribution the median value was considered as cutoff point. Community poverty considered as “low poverty” if median value = 0.285. Enumeration Areas (EAs) a total of 305 (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 PHC frame) and with independent selection in each sampling stratum. Media exposure is defined as household who had radio and television were considered as exposed to media. Community media exposure If proportion of women who had radio and television was below the median were considered as “low media exposure” ( = 0.132). Hot spot Areas with high antenatal care utilization. Cold spot Areas with low antenatal care utilization. Data processing and analysis In this study, we utilized secondary data obtained from the Central Statistical Agency of Ethiopia, specifically the 2019 Mini Demographic and Health Survey data set, which was collected from March to June 2019, via www.dhsprogram.com. Data was managed using Microsoft Excel, ArcGIS 10.8, STATA 14, and SaTScan (software for spatial and space-time scan statistics). After obtaining the data from www.dhsprogram.com, we used the individual record (IR) STATA dataset. The variables were explored using their appropriate pictorial and summary descriptive statistics. For SaTScan analysis after making zero decimal place for cases and controls the data exported to STATA then clusters with zero cases and controls were dropped. Sat Scan version 9.6 was used to detect the more likely clusters of below the ANC visit. Spatial analysis The spatial autocorrelation (Global Moran’s I) statistics were applied to assess whether the utilization of ANC service distribution is random or not at the national level. Moran’s I value close to − 1 indicates that ANC service utilization is dispersed, whereas Moran’s I close to + 1 indicates ANC service utilization is clustered, and if Moran’s I is close to 0 reveals that ANC service utilization is randomly distributed. A statistically significant Moran’s I (p < 0.05) evidenced that ANC service utilization is non-random 10, 11 . In order to measure spatial autocorrelation, hot spot analysis was computed, which varies over the study location by calculating Getis-Ord Gi* statistic for each area. To appreciate the presence of significant clustering, Z-scores and p-values were calculated. Statistical values with high Getis-OrdGi* indicate “hotspot” whereas low Getis-OrdGi* means a “cold spot” 12 . Empirical Bayesian kriging interpolation was used to predict ANC visits in unsampled areas. The green areas were predicted to have high ANC visits in Ethiopia. SaTScan version 9.6 for Windows was used to carry out cluster analysis to detect the more likely clusters by computing the relative risk (RR) and testing the statistical significance. ANC controls, non-ANC (below four ANC visits) cases, and the coordinates of the study areas were used as input variables for the Bernoulli model, with the assumption that cases in each cluster follow a Bernoulli distribution with a known outcome, either recommended or non-recommended ANC visit. Due to cultural, socioeconomic and health infrastructure difference, practice in ANC services utilization varied across Ethiopian regions. Hence, spatial regression analysis was performed to overcome the limitation of logistic regression, to identify disparities and determinate factors of ANC service utilization at local level to explore spatial variations. Spatial regression analysis Ordinary least square and Geographical weighted regression analysis were used for exploring the spatial relationship between ANC utilization and predictor variables. The outcome variable for spatial regression is the proportion of ANC utilization among Ethiopian women at EAS level. A bandwidth or neighborhood is the distance band or the number of neighbors used for each regression equation; it is the most important parameter for spatial regression as it controls the degree of smoothening in the model. The complexity of the spatial regression model depends not only through variables numbers in the model but also on the band-width. There are three choices of band width methods, such as AICc, CV, and bandwidth parameter. In this study, we used adaptive kernel whose bandwidth was determined by minimizing the AICc value. Ordinary Least square (OLS) Ordinary Least Squares (OLS) regression; the spatial regression modeling was performed to identify predictors of the spatial heterogeneity of ANC service utilization. OLS is a global statistical model for testing and explaining the relationship between the outcome and explanatory variables 13 . It uses a single equation to estimate the relationship between the outcome and explanatory variables and assumes stationarity or consistent relationship across the study area. The OLS was used as a diagnostic tool and for selecting the appropriate predictors (concerning their relationship with ANC utilization) for the Geographic Weighted Regression (GWR) model 14 . Geographic weighted regression (GWR) Geographically Weighted Regression (GWR); A local spatial statistical technique that assumes the non-stationarity in relationships between the outcome and predictors across EAs 14 . The GWR analysis is employed when the Koenker test is statistically significant (p-value < 0.05), which means the relationships between the outcome and the predictors change from location to location. In the GWR analysis, the coefficients of the predictors take different values across the study area. Mapping the GWR coefficients associated with the predictors, which are produced using the GWR, provides insight for targeted interventions. The corrected Akaike Information Criteria (AICc) and adjusted R-squared for model comparison of OLS (global model) and GWR (local) model; A model with the lowest AICc value and a higher adjusted R squared value was considered as the best-fitted model for the data. Statistical analysis Both descriptive and analytical statistics were done accordingly. The socio-demographic characteristics and other explanatory categorical variables were described with weighted frequency tables. Both bi-variable and multivariable logistic regression models were fitted to identify factors associated with ANC visit. Variables with probability value (p-value) less than 0.2 in the bi-variable logistic regression were entered in to multivariable logistic regression model to measure the effect of explanatory variables after adjustment. Variables with p-value < 0.05 were considered statistically significant. Model selection was done using log likelihood statistics. Results Socio-demographic characteristics of participants A total of 3,927 women were included in the analysis with the mean age of respondents was 28.65 years (SD of ± 6.71). Near to one third (30%) of the Women age were between 25–29 years. The majority (93%) of respondents were married. About 51% of women were unable to read and write. Large number respondents were found in Oromia region which accounts around 38%. Almost three fourth of the participants 74% were rural residents. About 36% of women were Orthodox religion followers. Near to fifty percent 43% of respondents have ANC utilization (Table 1 & 2 ). Table 1 Socio-economic characteristics of women, 15–49 years, in Ethiopia, 2019 EMDHS Background characteristics Weighted Frequency Weighted percent Age 15–19 227 5.79 20–24 769 19.58 25–29 1193 30.37 30–34 799 20.36 35–39 591 15.04 40–44 259 6.59 45–49 89 2.27 Religion Orthodox 1441 36.69 Muslim 1340 34.12 Protestant 1082 27.57 *Other 64 1.62 Marital status Single 21 0.53 Married 3685 93.84 Widowed/divorced/separated 221 5.63 Education No education 2014 51.30 Primary 1415 36.03 Secondary 345 8.78 Higher 153 3.89 Wealth Poor 1647 41.95 Middle 762 19.39 Rich 1518 38.66 Birth order 1 825 21.02 2–4 1738 44.25 ≥ 5 1364 34.73 Sex of household head Male 3401 86.62 Female 526 13.38 Table 2 Community-level characteristics of women, 15–49 years in Ethiopia, 2019 EMDHS Variable Weighted frequency (n = 3927) Weighted percentage (%) Community level poverty High 2042 52.00 Low 1885 48.00 Media exposure Low 3244 82.61 High 683 17.39 Residence Urban 1027 26.14 Rural 2900 73.86 Community women education Low 2186 53.87 High 1793 46.13 Region Tigray 287 7.30 Afar 51 1.30 Amhara 840 21.38 Oromia 1519 38.69 Somali 218 5.55 Benishangul gumuz 47 1.20 SNNPR 787 20.05 Gambela 19 0.49 Harari 11 0.28 Addis Ababa 127 3.22 Dire Dawa 21 0.54 Prevalence of antenatal care utilization The prevalence of antenatal care utilization in Ethiopia was 43% (95% CI: 41.5%, 44.6%). The utilization of ANC was varied across the different regions and city administrations; the highest antenatal care utilization was reported in Addis Ababa (81.9%) followed by Tigray (38.8%) and Dire Dawa (61.9%) regions. The lowest prevalence of ANC utilization was observed in Somali (11%), Afar (31.4%), and Gambella (31.6%) regions (Fig. 1 ). Spatial distribution of ANC Visit A total of 305 clusters were considered for the spatial analysis of antenatal care visit in the country. Points on the map represent the clusters and their corresponding prevalence. The red color indicates areas with low prevalence whereas the green color represents areas with high proportion of ANC visit. Higher proportion was found Tigray, Addis Ababa, western Beneshangul Gumuz and some portions of Amhara (Fig. 2 ). Spatial autocorrelation of ANC Visit In this study the distribution pattern of ANC visit was clustered with highly significant p-value, large z-score and less than 1% likelihood of clustering due to chance (Fig. 3 ). Incremental spatial autocorrelation of ANC Visit Incremental spatial autocorrelation for a sequence of distance presented by line graph with corresponding z-score was done to determine the average nearest neighbor and minimum and maximum distance band. A total of 10 distance bands were detected by a beginning distance of 155190 meters and distance increment 24041 meter, and the first maximum peak (clustering) was observed at 299436 meter and z-score of 8.45 (Fig. 4 ). Hotspot analysis of ANC Visit This analysis was performed to identify hot areas of ANC visits in the country. The green color indicates hot areas and it is found in Tigray, north SNNPR, Addis Ababa and Southwestern of Benishangul Gmuz, whereas the red color indicates cold areas of ANC visits and is observed in Somali and SNNP (Fig. 5 ). Spatial interpolation of ANC Visit We were able to predict the spatial distribution of ANC visits for unsampled areas using spatial interpolation. As we travel from the red to the bright green areas, the possibility of having ANC visit was predicted to be increasing. Somali, Gambella, SNNP, and Afar were predicted to have less possibility of ANC visit. On the other hand, Dire Dawa, Addis Ababa, Beneshangul Gumuz, and some portions of Tigray were predicted to have a high possibility of ANC visit (Fig. 6 ). Spatial scan statistical analysis A total of 179 clusters with 56.1% of cases in the area were identified. The more likely clusters of ANC visit below the level located in Somali, Dire Dawa and western Oromia with LLR of 50.5, at p-value of < 0.000001. This indicates women within the primary spatial window had 1.34 times higher risk of not achieving ANC as compared to women outside the primary spatial window (Table 3 ), (Fig. 7 ). Table 3 SaTScan analysis of ANC visit below the focused level in Ethiopia, EMDHS 2019 Cluster detected Location ID Coordinate/radius RR LLR p-value 1 144, 125, 143, 114, 138, 137, 111, 89, 113, 123, 110, 183, 117, 134, 188, 186, 172, 181, 182, 115, 103, 187, 202, 184, 197, 116, 199, 178, 102, 190, 198, 104, 203, 189, 191, 180, 205, 105, 175, 88, 173, 90, 106, 177, 192, 179, 204 (4.028421 N, 41.180721 E) / 550.80 km 1.34 50.5 < 0.000001 Ordinary Least Square (OLS) regression analysis The Ordinary Least Square model explained that 51% (adjusted R 2 = 0.51) of the variation in ANC utilization among women with AICc =-96.80. The Joint F-statistics and Wald statistics were significant (p < 0.01), which implies that the model is statistically significant. OLS diagnoses multicollinearity among independent variables, which is < 7.5 for all variables. The spatial distribution of residuals was normally distributed since the Jarque-Bera statistics test is statistically significant (0.404). Spatial autocorrelation residuals were not normally distributed (Moran’s Index = 0.22) (p = 0.0000. The Koenker statistics were statistically significant, which shows the relationship between the predictor variables and the outcome variable was non-stationary or heterogeneous across the study areas. Since, Joint Wald statistics and joint F-statistics were significant robust probabilities to select significant predictors; proportion of women who had no education, proportion of poor women, proportion of women in Somali region, proportion of rural women, and proportion of women with other religion were significantly associated with the prevalence of ANC utilization among women in the OLS model (Table 3 ) and (Table 4 ). Table 3 : SaTScan analysis of ANC visit below the focused level in Ethiopia, EMDHS 2019 Cluster detected Location ID Coordinate/radius RR LLR p-value 1 144, 125, 143, 114, 138, 137, 111, 89, 113, 123, 110, 183, 117, 134, 188, 186, 172, 181, 182, 115, 103, 187, 202, 184, 197, 116, 199, 178, 102, 190, 198, 104, 203, 189, 191, 180, 205, 105, 175, 88, 173, 90, 106, 177, 192, 179, 204 (4.028421 N, 41.180721 E) / 550.80 km 1.34 50.5 < 0.000001 Table 4 Ordinary Least Square (OLS) regression analysis result Ordinary Least Square (OLS) regression analysis result Variable Coefficient Robust std-error Robust t-statistics Robust probability VIF Intercept 0.741 0.027 27.39 0.000000* Proportion of women aged 35–39 years 0.042 0.118 0.352 0.724795 1.08 Proportion of single women -0.087 0.466 -0.187 0.851857 1.06 Proportion of women who had no education -0.167 0.054 -3.124 0.001970* 2.12 Proportion of poor women -0.280 0.049 -5.675 0.000000* 2.21 Proportion of women in Somali region -0.209 0.030 -6.916 0.000000* 1.23 Proportion of rural women -0.100 0.036 -2.783 0.005730* 1.76 Proportion of women with other religion -0.610 0.151 -4.041 0.000074 1.06 Ordinary least square regression Diagnostics . Number of observations 305 Adjusted R-squared 0.509 Joint F-statistics 46.04 Prob(> F), (7,297) degrees of freedom 0.000000* Joint Wald statistics 638.55 Prob(> chi-squared), (7) degrees of freedom 0.000000* Koenker (BP) statistics 16.21 Prob(> chi-squared), (7) degrees of freedom 0.023241* Jarque–Bera 1.82 Prob(> chi-squared), (2) degrees of freedom 0.403511 VIF : Variance Inflation Factor Other religion : Muslim, Traditional, and others Geographically weighted regression (GWR) analysis In GWR analysis, there was a significant improvement over the global model (OLS). The AICc value decreased from − 96.80 to -131.41. This implies that GWR was best to explain the spatial heterogeneity of ANC service utilization among women, with a difference was 34.61. In addition, the model’s ability to explain ANC utilization has been improved by 6% by using GWR analysis. Since, the adjusted R 2 was 0.57 (Table 5 ). In the GWR analysis, the proportion of women who had no education, the proportion of poor women, the proportion of women in Somali region, the proportion of rural women, and proportion of women with other religions were significant predictors of hotspot areas of ANC utilization among women. The above five factors were considered as independent variables in the GWR analysis. Since it was significant in the OLS analysis (Table 5 ). Table 5 Shows Model comparison of OLS and GWR model Model comparison parameter OLS model GWR model AICc -96.80 -131.41 Adjusted R 2 0.51 0.57 Proportion of no education women There is negative relationship between proportion of no education women and hotspot areas of ANC service utilization in Somali, Tigray, Afar, Harari, Dire Dawa, Oromia, and Northern Amhara regions (Fig. 8 ). Proportion of women in other religion There is negative relationship between proportion of women in other religion and hotspot areas of ANC service utilization in Southern Afar, and Dire Dawa (Fig. 9 ). Proportion of rural women There is negative relationship between proportion of rural women and hotspot areas of ANC service utilization in Somali, Harari, Dire Dawa, Oromia, and Northern Eastern part of SNNP regions (Fig. 10 ). Proportion of poor women There is negative relationship between proportion of poor women and hotspot areas of ANC service utilization in Western Tigray, Amhara, Beneshangul Gumuz, Gambela, Addis Ababa, and SNNP regions (Fig. 11 ). Model comparison A model with highest log likelihood (-2175.546) or smallest deviance (4351.092) was used as the best fitted model of this analysis which was model IV (Table 4 ). Multilevel multivariable logistic regression model First binary logistic regression analysis was conducted to identify variables that were significant at p-value of 0.2. In the binary logistic regression analysis individual level factors: age, religion, marital status, women education, wealth status, birth order was found significantly associated with ANC visit. Community level factors: residence, region, community level poverty, community level women education and media exposure were statistically significant factors of ANC visit in the bi-variable analysis. The odds of having ANC visit among women whose age between 25–29 years old were increased by 88% as compared to women whose age between 15–19 years old (AOR = 1.88 CI: 1.27–2.76).The odds of having ANC visit among women whose age between 30–34 years were 2.01 times higher as compared to women whose age between 15–19 years old (AOR = 2.01,95% CI: 1.32–3.07).The odds of having ANC visit among women whose age between 35–39 years were 2.16 times higher as compared to women whose age between 15–19 years old (AOR = 2.16,95% CI : 1.37–3.40). The odds of having ANC visit among other religion groups (catholic and traditional religion) were decreased by 63% as compared to orthodox Christianity followers (AOR = 0.37, 95% CI: 0.16–0.83). The odds of having ANC visit among married and Widowed/divorced/separated women were 6.47and 4.58 times higher than single women (AOR = 6.47, 95% CI: 2.06–20.28) & (AOR = 4.58, 95% CI: 1.42–14.81) respectively. The odds of having ANC visit among women having primary, secondary and higher educational status were 1.89, 3.18 and 4.44 times higher than the odds of having ANC visit among women with no education (AOR = 1.89,95% CI:1.55–2.29), (AOR = 3.18,95% CI:2.3–4.4) and (AOR = 4.44,95% CI:2.90–6.79) respectively. The odds of having ANC visit among a women’s who was rich increased by 31% as compared to a women who was poor (AOR = 1.31, 95% CI: 1.003-1.70). The odds of having ANC visit among women who live in Afar, Oromia, Somali, SNNPR, Gambella and Harari regional states were decreased by 64%,48%,92%,70% ,79% and 72% as compared to women who live in Tigray region (AOR = 0.36,95% CI: 0.20–0.66),( AOR = 0.52,95% CI: 0.31–0.89),( AOR = 0.08,95% CI:0.04–0.16),( AOR = 0.3, 95% CI: 0.17–0.53),( AOR = 0.21,95% CI: 0.12–0.38) and (AOR = 0.28,95% CI: 0.14–0.53) respectively. Being a women who live in rural area decreases the odds of having ANC visit by 32% as compared to a women who live in urban areas (AOR = 0.68,95% CI: 0.46–0.99). The odds of having ANC visit among women who live in a community with low level of poverty were increased by 79% as compared to a women who live in a community with high level of poverty (AOR = 1.79,95% CI :1.28–2.51) (Table 6 ). Table 6 Multivariable multilevel logistic regression analysis results of both individual and community-level factors associated with ANC visits in Ethiopia, EMDHS 2019 Variables Null model AOR (95% CI) Model II AOR (95% CI) Model III AOR (95% CI) Model IV AOR (95% CI) Age 15–19 1 1 20–24 1.43 (0.98, 2.07) 1.33(0.92,1.92) 25–29 2.09(1.42,3.08) *** 1.88(1.27,2.76) *** 30–34 2.39(1.57,3.65) *** 2.01(1.32,3.07) *** 35–39 2.6(1.65, 4.09) *** 2.16(1.37,3.40) *** 40–44 2.12(1.26, 3.55) ** 1.67(0.99, 2.8) 45–49 1.89 (0.97, 3.71) 1.4(0.71,2.74) Religion Orthodox 1 1 Muslim 0.59(0.45,0.75) *** 0.91 (0.67,1.21) Protestant 0.56(0.41,0.75) *** 0.85 (0.61,1.18) Others* 0.26(0.11,0.58) *** 0.37(0.16,0.83) * Marital status Single 1 1 Married 6.35 (2.02,19.92)** 6.47(2.06,20.28) ** Widowed/divorced/separated 4.78 (1.48,15.47)** 4.58(1.42,14.81) * Education No education 1 1 Primary 2.01(1.65, 2.44) *** 1.89(1.55, 2.29) *** Secondary 3.48(2.53, 4.78) *** 3.18(2.3, 4.4) *** Higher 5.11(3.36, 7.78) *** 4.44(2.90, 6.79) *** Wealth Poor 1 1 Middle 1.26 (0.99,1.61) 1.09(0.86,1.39) Rich 2.17(1.72, 2.72) *** 1.31(1.003, 1.70)* Birth order 1 1 1 2–4 0.87 (0.69, 1.11) 0.94(0.74, 1.18) >=5 0.76 (0.56, 1.04) 0.9(0.66,1.23) Region Tigray 1 1 Afar 0.27(0.15, 0.46) *** 0.36(0.20, 0.66) *** Amhara 0.69(0.42, 1.13) 0.74(0.45, 1.21) Oromia 0.44(0.26, 0.72) *** 0.52(0.31, 0.89)* Somali 0.05(0.03, 0.11) *** 0.08(0.04 ,0.16) *** Benishangul 0.81(0.47, 1.37) 0.92(0.53, 1.58) SNNPR 0.27(0.16, 0.45) *** 0.3(0.17, 0.53) *** Gambela 0.20(0.12, 0.36) *** 0.21(0.12, 0.38) *** Harari 0.24(0.13, 0.45) *** 0.28(0.14, 0.53) *** Addis Abeba 1.06(0.53, 2.13) 1.09(0.54, 2.19) Dire Dawa 0.66(0.36, 1.20) 0.79(0.42,1.50) Residence Urban 1 1 Rural 0.58(0.40, 0.86) ** 0.68(0.46, 0 .99)* Community level poverty High 1 1 Low 2.06(1.50, 2.84) *** 1.79(1.28,2.51) *** Media exposure Low 1 1 High 1.12 (0.70, 1.78) 0.84(0.52,1.34) Community women education Low 1 1 High 1.54 (1.14, 2.07) ** 1.26(0.94,1.70) ICC 0.37(0.31,0.42) Model Diagnosis LL -2385.921 -2253.721 -2249.368 -2175.546 Deviance 4771.842 4507.442 4498.736 4351.092 AIC 4775.841 4547.442 4530.735 4419.091 Note: *p-value ≤ 0.05, **p-value ≤ 0.01, ***p-value ≤ 0.001 Others* catholic and traditional religion follower Discussion In this study, we investigated the individual and community-level predictor’s association with ANC utilization among reproductive age women in Ethiopia. In this study, the prevalence of ANC utilization among women reproductive age group in Ethiopia was 43% (95% CI: 41.5%, 44.6%). ranged from 11% in the Somali region to 81.9% in the Addis Ababa town. This was lower than a study conducted in Zimbabwe 15 and pooled prevalence in sub-Saharan African Countries 16 . Even though ANC is one of exempted service in all government health institution overall the country, ANC service utilization is very low. Also, the spatial distribution of ANC use in Ethiopia was non-random and the hotspot areas of ANC service utilization were identified in the Tigray, Addis Ababa, Benishangul Gmuz regions. From our multilevel analysis, we observed that seven variables were significantly associated with the use of ANC: individual factors age, religion, marital status, education, wealth, and community level factors region, residence, community level poverty were found statistically significant predictors for ANC service utilization. In the spatial regression analysis, education, religion, residence, and wealth was significant predictors of hotspot areas of ANC utilization service. An increased proportion of uneducated women decrease the odds of ANC service utilization in Somali, Tigray, Afar, Harari, Dire Dawa, Oromia, and Northern Amhara regions. This may be due to the fact that women with greater levels of education may be better able to notice danger signs and easily appreciate the consequences of forgoing critical prenatal care services more rapidly 17 . The result of this study also revealed that mothers whose age ≥ 25 more likely to receive ANC service than mothers in the ≤ 24-year-old age group. Studies conducted in Wonberma Woreda (Ethiopia) 18 and Nigeria 19 have provided support for this conclusion. This may be because older mothers may have more information, insight, and experience about pregnancy and issues associated with it. Studies revealed that teenage mothers are less likely to utilize ANC services and this is explained due to fear of social stigma 20 . But, other contradicting research revealed that relatively younger women were more likely to attend ANC services as compared to older. This might be due to low experience of younger women about their current pregnancy, which leads to fear of complications as a result of not attending ANC. As a result, they increase ANC visits. Being a follower of catholic and traditional religion decreased the ANC service utilization as we compared with Orthodox Christians. This finding is in line with a study conducted in Nigeria 21 and Ethiopia 22 . The effect of religion on ANC service utilization is because of that religion plays a significant role in shaping beliefs, norms, and values including those that relate to childbirth and health services use. Reproductive health issues may also be considered as a subject not to be discussed easily between husband and wife in some religions 23 . These studies showed that married women were more likely to utilize ANC service than the single. This finding is supported by studies done in sub- Saharan Africa 24 . This might be due to husband support and encouragement leads to more ANC service uptake in married mother, whereas single mother due to socioeconomic, cultural and religion influence may not utilize ANC service in addition to fear of social stigma 25 . Women education increases the ANC utilization as compared with women who are unable to read and write. This study is in line with a study conducted in sub-Saharan Africa 24 , Pakistan 26 , Bangladesh 20 and Ethiopia 22 . The possible reason might be women who are unable to read and write were more likely associated with inequalities in service delivery care. Other possible reason could be that educated women had much higher self-reliance on choosing reproductive health and have great potential to decide freely on where and when to seek medical services regardless of husband’s approval. It is known that most women are socioeconomically dependent on male partners who are decision makers in households, and influence on maternal health care services utilization 22 . Highly statistically significant associations were also obtained between rural resident and ANC service utilization. This finding is supported by studies done in Nigeria[21] and Ethiopia[7]. This is due to the socioeconomic inequalities and differences in health services access between urban and rural areas in the country 22 . Women living in Afar, Oromia, SNNPR, Somali, Gambella, and Harari regional states had lower ANC service utilization than women living in Tigray region. The utilization of ANC was a significant disparity across the country regions. Our study is supported by a previous study conducted in Ethiopia and Bangladesh[20]. The possible inhibiting factors could be lack of necessary medicines, service center as well as trained health worker staff, long waiting time, least access to information, absence of transportation and inability to pay for the ‘desired’ treatment, and instable life style of pastoral community results inaccessibility of maternal health care services including ANC service utilization 20 . The second community level factor in this study that affect ANC service utilization was low community poverty. Income affects health seeking behavior of the mother in which poor mothers had low health seeking behavior, because poor women had financial barrier to access ANC service utilization and difficulty of addressing wealth related inequalities 27 . Strength and limitation of the study Using a national representative data and large sample size might help us to have better estimation of parameters. The study also, applied a multilevel analysis to accommodate the hierarchical nature of the Ethiopian EDHS data. Similarly, applying spatial analysis and GWR was crucial to identify the geographic variation and predictors, respectively. Also, this study will help the policy makers to design or strengthen intervention strategies based on the identified geographic variations. Since we are using secondary data missing of necessary variables required for this study was missed. Crosse sectional study design didn’t show cause and effect relationship between associated factors and ANC visit. Conclusion and Recommendations In this study, the prevalence of ANC utilization significantly varied across regions in Ethiopia and predictors like; age, marital status, educational status, wealth, community level poverty, religion, rural residence, proportion of uneducated women, other religion followers, rural women, and proportion of poor women were identified as significant determinants of ANC service utilization. We recommend that policymakers give high priority and attention to improving socioeconomic status of women, education, and accessibility of ANC services in rural areas of the country to minimize disparities in health services utilization. As a result, it will reduce low birth weight and pregnancy complications, and contribute to the achievement of Sustainable Development Goal three. Abbreviations ANC Antenatal Care ANC Antenatal Care; AOR Adjusted Odds Ratio EA Enumeration Area EDHS Ethiopian Demographic and Health Survey; EMDHS Ethiopian mini demographic and health survey; PNC Postnatal Care; SBA, Skilled Birth Attendant; SNNPR Southern Nation and Nationality People Region; SSA, Sub-Saharan Africa; WHO World Health Organization. Declarations Ethics approval and consent to participate The data were obtained from Demographic and Health Survey (DHS), which is freely accessed in the program website https://dhsprogram.com. For this study, a brief description of proposal submitted to DHS program, which will access and analyze data, and following that, we obtained permission to access 2019 EDHS for statistical analysis and report. During data collection of EDHS, informed consent was obtained from each study participant, all identifiers were removed, and confidentiality was maintained. All methods were carried out under relevant guidelines and regulations of measures of DHS program. The data set was not shared with other bodies, and its confidentiality was not maintained. Consent for publication Not applicable Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests We declared that we had no computing interests. Funding This research received no external funding. Authors' contributions Conceptualization: LW, YM, TK, TM, NB, GS, WF. Data curation: LW, YM; Formal analysis: LW, YM, TM, NB; Methodology: YM, TK, TM, NB, GS, WF, LW; Software: LW, YM; Supervision: YM; Validation: LW, YM; Visualization: YM, LW; Writing – original draft: YM, TK, TM, NB, GS, WF, LW ; Writing – review & editing: YM, TK, TM, NB, GS, WF, LW Acknowledgement First of all, we would like to express our greatest gratitude to Mizan Aman College of Health Science for its internet and library service to search additional materials. References WHO. Antenatal care 2018 [Available from: https://platform.who.int/docs/default-source/mca-documents/policy-documents/policy-survey-reports/srmncah-policysurvey2018-fullreport-pt-3.pdf?sfvrsn=3465d5a7_4 WHO. Maternal mortality: Evidence brief 2019 [Available from: https://www.who.int/publications/i/item/WHO-RHR-19.20 WHO. Maternal mortality 2025 [Available from: https://www.who.int/news-room/fact-sheets/detail/maternal-mortality WHO U, UNFPA, World Bank,United Nations Population Division., Trends in maternal mortality: 2000 to 2017 Geneva: WHO; 2019 [Available from: https://www.who.int/publications/i/item/9789240068759 Ganchimeg T, Ota E, Morisaki N, Laopaiboon M, Lumbiganon P, Zhang J et al. Pregnancy and childbirth outcomes among adolescent mothers: a World Health Organization multicountry study. BJOG. 2014;121 Suppl 1:40 – 8. PMID. 24641534. Althabe FMJ, Gibbons L, Berrueta M, Goudar SS, Chomba E et al. Breaking the silence: violence against children with disabilities in Africa 2010 [Available from: https://asksource.info/resources/breaking-silence-violence-against-children-disabilities-africa Scherdel L, Martin A, Deivanayagam A, Adams E, Shanahan T. The search for international consensus on LGBT health. Lancet Glob Health. 2014;2(6):e321. PMID. 25103299. Maryland CSAIR, USA Ethiopia Demographic and Health Survey. 2016. Addis Ababa, Ethiopia 2016 [Available from: https://www.dhsprogram.com/pubs/pdf/FR328/FR328.pdf Ethiopian Public Health Institute I. Mini Demographic and Health Survey. 2019: ICF Rockville, Maryland, USA; 2019 [Available from: https://www.unicef.org/ethiopia/media/1721/file/The%202019%20Ethiopia%20Mini%20Demographic%20and%20Health%20Survey%20.pdf Amin S, Basu AM, Stephenson R. Spatial variation in contraceptive use in Bangladesh: looking beyond the borders. Demography. 2002;39(2):251–67. PMID. 12048951. Alemu K, Worku A, Berhane Y, Kumie A. Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia. Malar J. 2014;13:223. PMID. 24903061. ICPSR, Description C. ICPSR 2016 Regression Analysis for Spatial Data Elisabeth Dowling Root ( [email protected] ) The Ohio State University Department of Geography and Division of Epidemiology Columbus, OH 43210 Course Organization 2016 [Available from: https://www.icpsr.umich.edu/summerprog/biblio/2016/shortcourse/RegressionAnalysisforSpatialData2016ElizabethRoot.pdf Duong TN, Le MH, Beardsley J, Denning DW, Le NH, Nguyen BT. Updated estimation of the burden of fungal disease in Vietnam. Mycoses. 2023;66(4):346–53. PMID. 36564981. PMC10953305. Peprah MS, Mensah IO. Performance evaluation of the Ordinary Least Square (OLS) and Total Least Square (TLS) in adjusting field data: An empirical study on a DGPS data. South Afr J Geomatics. 2017;6(1):73–89. Raru TB, Ayana GM, Zakaria HF, Merga BT. Association of higher educational attainment on antenatal care utilization among pregnant women in east africa using Demographic and Health Surveys (DHS) from 2010 to 2018: a multilevel analysis. Int J women's health. 2022:67–77. Tessema ZT, Teshale AB, Tesema GA, Tamirat KS. Determinants of completing recommended antenatal care utilization in sub-Saharan from 2006 to 2018: evidence from 36 countries using Demographic and Health Surveys. BMC Pregnancy Childbirth. 2021;21:1–12. Ayalew TW, Nigatu AM. Focused antenatal care utilization and associated factors in Debre Tabor Town, northwest Ethiopia, 2017. BMC research notes . 2018;11:1–6. PMID. Getasew Mulat GM, Teketo Kassaw TK, Mekonnen Aychiluhim MA. Antenatal care service utilization and its associated factors among mothers who gave live birth in the past one year in Womberma Woreda, North West Ethiopia. 2015. Fagbamigbe AF, Idemudia ES. Assessment of quality of antenatal care services in Nigeria: evidence from a population-based survey. Reproductive health. 2015;12:1–9. PMID. Chanda SK, Ahammed B, Howlader MH, Ashikuzzaman M, Shovo T-E-A, Hossain MT. Factors associating different antenatal care contacts of women: A cross-sectional analysis of Bangladesh demographic and health survey 2014 data. PLoS ONE. 2020;15(4):e0232257. Adewuyi EO, Auta A, Khanal V, Bamidele OD, Akuoko CP, Adefemi K, et al. Prevalence and factors associated with underutilization of antenatal care services in Nigeria: A comparative study of rural and urban residences based on the 2013 Nigeria demographic and health survey. PLoS ONE. 2018;13(5):e0197324. Tessema ZT, Animut Y. Spatial distribution and determinants of an optimal ANC visit among pregnant women in Ethiopia: further analysis of 2016 Ethiopia demographic health survey. BMC Pregnancy Childbirth. 2020;20(1):137. PMID. 32131759. PMC7057476. Afulani PA, Buback L, Essandoh F, Kinyua J, Kirumbi L, Cohen CR. Quality of antenatal care and associated factors in a rural county in Kenya: an assessment of service provision and experience dimensions. BMC Health Serv Res. 2019;19:1–16. Okedo-Alex IN, Akamike IC, Ezeanosike OB, Uneke CJ. Determinants of antenatal care utilisation in sub-Saharan Africa: a systematic review. BMJ open. 2019;9(10):e031890. Woldegiorgis MA, Hiller J, Mekonnen W, Meyer D, Bhowmik J. Determinants of antenatal care and skilled birth attendance in sub-Saharan Africa: A multilevel analysis. Health Serv Res. 2019;54(5):1110–8. Ghaffar A, Pongponich S, Ghaffar N, Mehmood T. Factors associated with utilization of antenatal care services in Balochistan province of Pakistan: An analysis of the Multiple Indicator Cluster Survey (MICS) 2010. Pakistan J Med Sci. 2015;31(6):1447. Obse AG, Ataguba JE. Explaining socioeconomic disparities and gaps in the use of antenatal care services in 36 countries in sub-Saharan Africa. Health Policy Plann. 2021;36(5):651–61. Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":71958,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of ANC visit in Ethiopia, EMDHS 2019.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(Source: Shape file from Ethiopia Central Statistical Agency (CSA).\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/b2f703dd35297e7ca7b6c984.jpg"},{"id":89018264,"identity":"4467c4ee-9952-4d75-83b6-47e89ac8aad3","added_by":"auto","created_at":"2025-08-13 19:20:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61050,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial autocorrelation analysis of ANC visits in Ethiopia, EMDHS 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: Shape file from Ethiopia Central Statistical Agency 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5","display":"","copyAsset":false,"role":"figure","size":62951,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Hot spot analysis of ANC visit in Ethiopia, 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: Shape file from Ethiopia Central Statistical Agency (CSA).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/2d825bd9630cbca60cb7af20.jpg"},{"id":89018267,"identity":"20350ed2-c39d-4c14-bf37-719477627d42","added_by":"auto","created_at":"2025-08-13 19:20:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":195720,"visible":true,"origin":"","legend":"\u003cp\u003eInterpolation analysis ANC visit in Ethiopia 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: Shape file from Ethiopia Central Statistical Agency (CSA)).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/aa82a0614a6ce20e1aff5190.jpg"},{"id":89019236,"identity":"fb027414-729a-4417-979e-a32405095979","added_by":"auto","created_at":"2025-08-13 19:36:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43252,"visible":true,"origin":"","legend":"\u003cp\u003eSat Scan Statistics of ANC visit below the recommend level in Ethiopia 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: Shape file from Ethiopia Central Statistical Agency (CSA).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/ba312319998157d21b9ab203.jpg"},{"id":89018291,"identity":"c6abc43f-2b8a-4f7c-87c7-ffb992db098e","added_by":"auto","created_at":"2025-08-13 19:20:53","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":64091,"visible":true,"origin":"","legend":"\u003cp\u003eUneducated Women GWR coefficients for predicting ANC service utilization in Ethiopia, 2019.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/7b5701b6c89e769afd65c0b2.jpg"},{"id":89019004,"identity":"963f72ac-a5fe-4d99-9be3-86363f6269a2","added_by":"auto","created_at":"2025-08-13 19:28:52","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":64134,"visible":true,"origin":"","legend":"\u003cp\u003eWomen in other religion GWR coefficients for predicting ANC utilization in Ethiopia, 2019.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/42fffe54c3e64ed63d3478ec.jpg"},{"id":89019007,"identity":"92a8a160-9637-4d07-91b8-eff7daf7539e","added_by":"auto","created_at":"2025-08-13 19:28:52","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":64241,"visible":true,"origin":"","legend":"\u003cp\u003eRural women GWR coefficients for predicting ANC utilization in Ethiopia, 2019.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/e64e28097dc2925568ce1b00.jpg"},{"id":89018269,"identity":"15cba849-e42d-4628-9982-a9d14747b273","added_by":"auto","created_at":"2025-08-13 19:20:52","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":64913,"visible":true,"origin":"","legend":"\u003cp\u003ePoor women GWR coefficients for predicting ANC utilization in Ethiopia, 2019.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/d1132c7d0418c5a732b434c8.jpg"},{"id":90947485,"identity":"830ad1a2-91f6-4010-a3b9-ff9e003ea24d","added_by":"auto","created_at":"2025-09-09 21:16:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2779892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7220929/v1/4e9468ae-b758-4a64-a9df-5a12a7184b1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geographical variation in hotspots of antenatal care service utilization in Ethiopia: Geographic weighted regression and Multilevel analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntenatal Care (ANC) refers to the care provided to pregnant women by qualified and trained healthcare providers to ensure the health and well-being of women and their unborn child throughout pregnancy and delivery process\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early identification and management of complications through accessible, high-quality care during pregnancy, labor, and after childbirth can significantly reduce maternal deaths\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. over 90% of global maternal deaths occurred in low- and lower-middle-income countries in 2023, \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Approximately 86% of maternal deaths worldwide are estimated to occur in Southern Asia and Sub-Saharan Africa\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdolescent girls under 15 years of age face the highest risk of maternal death, while those aged between 10\u0026ndash;19 years are at greatest risk of pregnancy and delivery difficulties\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The lifetime risk of maternal mortality is significantly higher in low-income (1 in 45) countries compared to high-income countries (1 in 5400)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMaternal death occurs during pregnancy and childbirth, and the majority of these deaths arise during pregnancy and can be avoided or managed \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Complications may exist before pregnancy but worsen during pregnancy, especially if not properly managed. Around 75% of cause of maternal deaths are bleeding, infections, pre-eclampsia and eclampsia, unsafe abortion and other indirect causes such as malaria or related to chronic conditions like cardiac diseases or diabetes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to (World Health Organization), in Ethiopia about 32% of women had at least four ANC visits during their last pregnancy, while 37% of them had no ANC visits. Only 20% of women had their first ANC during the first trimester, 26% during their fourth to fifth month of pregnancy, and 14% during their sixth to seventh month of pregnancy. Two percent of women did not receive any ANC until the eighth month of pregnancy or later. 44% of women in urban areas receive ANC within their first trimester of pregnancy, compared with 17% of those in rural areas\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEven though ANC service utilization is essential for further improvement of maternal and child health, little evidence is known about the current magnitude of ANC and factors hindering ANC service utilization in Ethiopia.\u003c/p\u003e\u003cp\u003eIn Ethiopia, maternal mortality and neonatal mortality is high. This problem could be reduced by provision of proper and quality ANC service and skilled delivery. Spatial analysis is important for improving quality ANC care by identification of geographic area with low ANC utilization with hotspots and investigate the driving force that women can\u0026rsquo;t utilize ANC service. Studies that have been done to explore the determinants of ANC service utilization in Ethiopia have shown that a variety of factors affect antenatal care service utilization. However, no studies have attempted to assess spatial distribution and Geographic Weighted Regression (GWR) analysis. Therefore, the aim of this study is to assess unveiling geographical variation in hotspots of antenatal care service utilization in Ethiopia using geographic weighted regression and multilevel analysis based on the recent 2019 Ethiopian Mini Demographic health survey data.\u003c/p\u003e\u003cp\u003eFurthermore, this study helps as to an input for other researcher, policy makers, programmers and health care partners to develop appropriate strategy, interventions and prioritize risk areas aims to improve ANC service utilization and carry out a more extensive research on more factors in this particular area as well as to improve the quality of reproductive health services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA population-based cross-sectional study was conducted. A detailed analysis of the Ethiopian Mini Demographic and Health Survey (EMDHS) 2019 data conducted from March to June 2019 was used to assess the spatial distribution and factors associated with ANC visits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthiopia is bordered by Sudan to the west, Eritrea and Djibouti to the northeast, Somalia to the east and southeast, and Kenya to the south. The 2019 EMDHS was a nationwide survey that was carried out over the entire country. Ethiopia is separated into two administrative cities and nine regions under its administrative structure. Regions are then further separated into zones, which are further divided into weredas, and finally into kebeles, which is the lowest administration level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource and study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll women in the reproductive aged (15\u0026ndash;49 years) living in Ethiopia at time of survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll women in the reproductive age who had a live birth in the five years preceding the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll women age 15\u0026ndash;49, who had a live birth in the five years preceding the survey, were included in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with missing value of the outcome of interest and clusters with zero cases and controls were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA stratified and two-stage cluster sampling technique was used based on 2019 EMDHS. In the first stage, 305 Enumeration Areas (EAs) (93 in urban areas and 212 in rural areas) were selected with probability proportional to EAs size (based on the 2019 PHC frame) and with independent selection in each sampling stratum. In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women aged 15\u0026ndash;49, who were either permanent residents of the selected households or visitors who slept in the household the night before the survey, were eligible to be interviewed \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study area of interest has binary outcomes. Women having ANC visit during the five years preceding the survey were coded as \u0026ldquo;1\u0026rdquo; whereas women having below the ANC visit were coded as \u0026ldquo;0\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eIndividual factors include age, religion, marital status, birth order, woman education and sex of the household head.\u003c/p\u003e\n\u003cp\u003eCommunity level factors include poverty, residence, region, and community media exposure and community level women education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntenatal care\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eis defined as women having four and more antenatal care visits during the last pregnancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity level women education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eafter assuring normality of the distribution the median value was considered as cutoff point. Community level women education considered as \u0026ldquo;low educational\u0026rdquo; if median value\u0026thinsp;\u0026lt;\u0026thinsp;0.091 and \u0026ldquo;high education\u0026rdquo; if median value\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.091.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity poverty\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eafter assuring normality of the distribution the median value was considered as cutoff point. Community poverty considered as \u0026ldquo;low poverty\u0026rdquo; if median value\u0026thinsp;\u0026lt;\u0026thinsp;0.285 and \u0026ldquo;high poverty\u0026rdquo; if median value\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.285.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnumeration Areas (EAs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea total of 305 (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 PHC frame) and with independent selection in each sampling stratum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eis defined as household who had radio and television were considered as exposed to media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity media exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf proportion of women who had radio and television was below the median were considered as \u0026ldquo;low media exposure\u0026rdquo; (\u0026lt;\u0026thinsp;0.132) and above the median were considered as \u0026ldquo;high media exposure\u0026rdquo; (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.132).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHot spot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAreas with high antenatal care utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCold spot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAreas with low antenatal care utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized secondary data obtained from the Central Statistical Agency of Ethiopia, specifically the 2019 Mini Demographic and Health Survey data set, which was collected from March to June 2019, via www.dhsprogram.com. Data was managed using Microsoft Excel, ArcGIS 10.8, STATA 14, and SaTScan (software for spatial and space-time scan statistics).\u003c/p\u003e\n\u003cp\u003eAfter obtaining the data from www.dhsprogram.com, we used the individual record (IR) STATA dataset. The variables were explored using their appropriate pictorial and summary descriptive statistics.\u003c/p\u003e\n\u003cp\u003eFor SaTScan analysis after making zero decimal place for cases and controls the data exported to STATA then clusters with zero cases and controls were dropped. Sat Scan version 9.6 was used to detect the more likely clusters of below the ANC visit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial autocorrelation (Global Moran\u0026rsquo;s I) statistics were applied to assess whether the utilization of ANC service distribution is random or not at the national level. Moran\u0026rsquo;s I value close to \u0026minus;\u0026thinsp;1 indicates that ANC service utilization is dispersed, whereas Moran\u0026rsquo;s I close to +\u0026thinsp;1 indicates ANC service utilization is clustered, and if Moran\u0026rsquo;s I is close to 0 reveals that ANC service utilization is randomly distributed. A statistically significant Moran\u0026rsquo;s I (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) evidenced that ANC service utilization is non-random \u003csup\u003e10, 11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to measure spatial autocorrelation, hot spot analysis was computed, which varies over the study location by calculating Getis-Ord Gi* statistic for each area. To appreciate the presence of significant clustering, Z-scores and p-values were calculated. Statistical values with high Getis-OrdGi* indicate \u0026ldquo;hotspot\u0026rdquo; whereas low Getis-OrdGi* means a \u0026ldquo;cold spot\u0026rdquo;\u003csup\u003e12\u003c/sup\u003e. Empirical Bayesian kriging interpolation was used to predict ANC visits in unsampled areas. The green areas were predicted to have high ANC visits in Ethiopia.\u003c/p\u003e\n\u003cp\u003eSaTScan version 9.6 for Windows was used to carry out cluster analysis to detect the more likely clusters by computing the relative risk (RR) and testing the statistical significance. ANC controls, non-ANC (below four ANC visits) cases, and the coordinates of the study areas were used as input variables for the Bernoulli model, with the assumption that cases in each cluster follow a Bernoulli distribution with a known outcome, either recommended or non-recommended ANC visit.\u003c/p\u003e\n\u003cp\u003eDue to cultural, socioeconomic and health infrastructure difference, practice in ANC services utilization varied across Ethiopian regions. Hence, spatial regression analysis was performed to overcome the limitation of logistic regression, to identify disparities and determinate factors of ANC service utilization at local level to explore spatial variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrdinary least square and Geographical weighted regression analysis were used for exploring the spatial relationship between ANC utilization and predictor variables. The outcome variable for spatial regression is the proportion of ANC utilization among Ethiopian women at EAS level. A bandwidth or neighborhood is the distance band or the number of neighbors used for each regression equation; it is the most important parameter for spatial regression as it controls the degree of smoothening in the model. The complexity of the spatial regression model depends not only through variables numbers in the model but also on the band-width. There are three choices of band width methods, such as AICc, CV, and bandwidth parameter. In this study, we used adaptive kernel whose bandwidth was determined by minimizing the AICc value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrdinary Least square (OLS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrdinary Least Squares (OLS) regression; the spatial regression modeling was performed to identify predictors of the spatial heterogeneity of ANC service utilization. OLS is a global statistical model for testing and explaining the relationship between the outcome and explanatory variables\u003csup\u003e13\u003c/sup\u003e. It uses a single equation to estimate the relationship between the outcome and explanatory variables and assumes stationarity or consistent relationship across the study area. The OLS was used as a diagnostic tool and for selecting the appropriate predictors (concerning their relationship with ANC utilization) for the Geographic Weighted Regression (GWR) model\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic weighted regression (GWR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographically Weighted Regression (GWR); A local spatial statistical technique that assumes the non-stationarity in relationships between the outcome and predictors across EAs \u003csup\u003e14\u003c/sup\u003e. The GWR analysis is employed when the Koenker test is statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which means the relationships between the outcome and the predictors change from location to location. In the GWR analysis, the coefficients of the predictors take different values across the study area. Mapping the GWR coefficients associated with the predictors, which are produced using the GWR, provides insight for targeted interventions. The corrected Akaike Information Criteria (AICc) and adjusted R-squared for model comparison of OLS (global model) and GWR (local) model; A model with the lowest AICc value and a higher adjusted R squared value was considered as the best-fitted model for the data.\u003c/p\u003e\n\u003cdiv id=\"Sec2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eBoth descriptive and analytical statistics were done accordingly. The socio-demographic characteristics and other explanatory categorical variables were described with weighted frequency tables. Both bi-variable and multivariable logistic regression models were fitted to identify factors associated with ANC visit. Variables with probability value (p-value) less than 0.2 in the bi-variable logistic regression were entered in to multivariable logistic regression model to measure the effect of explanatory variables after adjustment. Variables with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Model selection was done using log likelihood statistics.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSocio-demographic characteristics of participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 3,927 women were included in the analysis with the mean age of respondents was 28.65 years (SD of \u0026plusmn;\u0026thinsp;6.71). Near to one third (30%) of the Women age were between 25\u0026ndash;29 years. The majority (93%) of respondents were married. About 51% of women were unable to read and write. Large number respondents were found in Oromia region which accounts around 38%. Almost three fourth of the participants 74% were rural residents. About 36% of women were Orthodox religion followers. Near to fifty percent 43% of respondents have ANC utilization (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026amp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-economic characteristics of women, 15\u0026ndash;49 years, in Ethiopia, 2019 EMDHS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBackground characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeighted Frequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrthodox\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtestant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e*Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBirth order\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex of household head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCommunity-level characteristics of women, 15\u0026ndash;49 years in Ethiopia, 2019 EMDHS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeighted frequency (n\u0026thinsp;=\u0026thinsp;3927)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted percentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCommunity level poverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMedia exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCommunity women education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTigray\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAfar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmhara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOromia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenishangul gumuz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNNPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGambela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHarari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAddis Ababa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDire Dawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence of antenatal care utilization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe prevalence of antenatal care utilization in Ethiopia was 43% (95% CI: 41.5%, 44.6%). The utilization of ANC was varied across the different regions and city administrations; the highest antenatal care utilization was reported in Addis Ababa (81.9%) followed by Tigray (38.8%) and Dire Dawa (61.9%) regions. The lowest prevalence of ANC utilization was observed in Somali (11%), Afar (31.4%), and Gambella (31.6%) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial distribution of ANC Visit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 305 clusters were considered for the spatial analysis of antenatal care visit in the country. Points on the map represent the clusters and their corresponding prevalence. The red color indicates areas with low prevalence whereas the green color represents areas with high proportion of ANC visit. Higher proportion was found Tigray, Addis Ababa, western Beneshangul Gumuz and some portions of Amhara (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial autocorrelation of ANC Visit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study the distribution pattern of ANC visit was clustered with highly significant p-value, large z-score and less than 1% likelihood of clustering due to chance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncremental spatial autocorrelation of ANC Visit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIncremental spatial autocorrelation for a sequence of distance presented by line graph with corresponding z-score was done to determine the average nearest neighbor and minimum and maximum distance band. A total of 10 distance bands were detected by a beginning distance of 155190 meters and distance increment 24041 meter, and the first maximum peak (clustering) was observed at 299436 meter and z-score of 8.45 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHotspot analysis of ANC Visit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis analysis was performed to identify hot areas of ANC visits in the country. The green color indicates hot areas and it is found in Tigray, north SNNPR, Addis Ababa and Southwestern of Benishangul Gmuz, whereas the red color indicates cold areas of ANC visits and is observed in Somali and SNNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial interpolation of ANC Visit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe were able to predict the spatial distribution of ANC visits for unsampled areas using spatial interpolation. As we travel from the red to the bright green areas, the possibility of having ANC visit was predicted to be increasing. Somali, Gambella, SNNP, and Afar were predicted to have less possibility of ANC visit. On the other hand, Dire Dawa, Addis Ababa, Beneshangul Gumuz, and some portions of Tigray were predicted to have a high possibility of ANC visit (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial scan statistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 179 clusters with 56.1% of cases in the area were identified. The more likely clusters of ANC visit below the level located in Somali, Dire Dawa and western Oromia with LLR of 50.5, at p-value of \u0026lt;\u0026thinsp;0.000001. This indicates women within the primary spatial window had 1.34 times higher risk of not achieving ANC as compared to women outside the primary spatial window (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSaTScan analysis of ANC visit below the focused level in Ethiopia, EMDHS 2019\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster detected\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoordinate/radius\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144, 125, 143, 114, 138, 137, 111, 89, 113, 123, 110, 183, 117, 134, 188, 186, 172, 181, 182, 115, 103, 187, 202, 184, 197, 116, 199, 178, 102, 190, 198, 104, 203, 189, 191, 180, 205, 105, 175, 88, 173, 90, 106, 177, 192, 179, 204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.028421 N, 41.180721 E) / 550.80 km\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOrdinary Least Square (OLS) regression analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Ordinary Least Square model explained that 51% (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51) of the variation in ANC utilization among women with AICc =-96.80. The Joint F-statistics and Wald statistics were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which implies that the model is statistically significant. OLS diagnoses multicollinearity among independent variables, which is \u0026lt;\u0026thinsp;7.5 for all variables. The spatial distribution of residuals was normally distributed since the Jarque-Bera statistics test is statistically significant (0.404). Spatial autocorrelation residuals were not normally distributed (Moran\u0026rsquo;s Index\u0026thinsp;=\u0026thinsp;0.22) (p\u0026thinsp;=\u0026thinsp;0.0000. The Koenker statistics were statistically significant, which shows the relationship between the predictor variables and the outcome variable was non-stationary or heterogeneous across the study areas. Since, Joint Wald statistics and joint F-statistics were significant robust probabilities to select significant predictors; proportion of women who had no education, proportion of poor women, proportion of women in Somali region, proportion of rural women, and proportion of women with other religion were significantly associated with the prevalence of ANC utilization among women in the OLS model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: SaTScan analysis of ANC visit below the focused level in Ethiopia, EMDHS 2019\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster detected\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoordinate/radius\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144, 125, 143, 114, 138, 137, 111, 89, 113, 123, 110, 183, 117, 134, 188, 186, 172, 181, 182, 115, 103, 187, 202, 184, 197, 116, 199, 178, 102, 190, 198, 104, 203, 189, 191, 180, 205, 105, 175, 88, 173, 90, 106, 177, 192, 179, 204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.028421 N, 41.180721 E) / 550.80 km\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOrdinary Least Square (OLS) regression analysis result\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eOrdinary Least Square (OLS) regression analysis result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust std-error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRobust t-statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRobust probability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e27.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of women aged 35\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.724795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of single women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.851857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of women who had no education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-3.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001970*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of poor women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-5.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of women in Somali region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-6.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of rural women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-2.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005730*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of women with other religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-4.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOrdinary least square regression Diagnostics\u003c/b\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoint F-statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eProb(\u0026gt;\u0026thinsp;F), (7,297) degrees of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoint Wald statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e638.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eProb(\u0026gt;\u0026thinsp;chi-squared), (7) degrees of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKoenker (BP) statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eProb(\u0026gt;\u0026thinsp;chi-squared), (7) degrees of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.023241*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJarque\u0026ndash;Bera\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eProb(\u0026gt;\u0026thinsp;chi-squared), (2) degrees of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.403511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e: Variance Inflation Factor\u003c/p\u003e\u003cp\u003e\u003cb\u003eOther religion\u003c/b\u003e: Muslim, Traditional, and others\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeographically weighted regression (GWR) analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn GWR analysis, there was a significant improvement over the global model (OLS). The AICc value decreased from \u0026minus;\u0026thinsp;96.80 to -131.41. This implies that GWR was best to explain the spatial heterogeneity of ANC service utilization among women, with a difference was 34.61. In addition, the model\u0026rsquo;s ability to explain ANC utilization has been improved by 6% by using GWR analysis. Since, the adjusted R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e was 0.57 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the GWR analysis, the proportion of women who had no education, the proportion of poor women, the proportion of women in Somali region, the proportion of rural women, and proportion of women with other religions were significant predictors of hotspot areas of ANC utilization among women. The above five factors were considered as independent variables in the GWR analysis. Since it was significant in the OLS analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShows Model comparison of OLS and GWR model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel comparison parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOLS model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGWR model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAICc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-96.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-131.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProportion of no education women\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is negative relationship between proportion of no education women and hotspot areas of ANC service utilization in Somali, Tigray, Afar, Harari, Dire Dawa, Oromia, and Northern Amhara regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProportion of women in other religion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is negative relationship between proportion of women in other religion and hotspot areas of ANC service utilization in Southern Afar, and Dire Dawa (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProportion of rural women\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is negative relationship between proportion of rural women and hotspot areas of ANC service utilization in Somali, Harari, Dire Dawa, Oromia, and Northern Eastern part of SNNP regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProportion of poor women\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is negative relationship between proportion of poor women and hotspot areas of ANC service utilization in Western Tigray, Amhara, Beneshangul Gumuz, Gambela, Addis Ababa, and SNNP regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel comparison\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA model with highest log likelihood (-2175.546) or smallest deviance (4351.092) was used as the best fitted model of this analysis which was model IV (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultilevel multivariable logistic regression model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst binary logistic regression analysis was conducted to identify variables that were significant at p-value of 0.2. In the binary logistic regression analysis individual level factors: age, religion, marital status, women education, wealth status, birth order was found significantly associated with ANC visit. Community level factors: residence, region, community level poverty, community level women education and media exposure were statistically significant factors of ANC visit in the bi-variable analysis.\u003c/p\u003e\u003cp\u003eThe odds of having ANC visit among women whose age between 25\u0026ndash;29 years old were increased by 88% as compared to women whose age between 15\u0026ndash;19 years old (AOR\u0026thinsp;=\u0026thinsp;1.88 CI: 1.27\u0026ndash;2.76).The odds of having ANC visit among women whose age between 30\u0026ndash;34 years were 2.01 times higher as compared to women whose age between 15\u0026ndash;19 years old (AOR\u0026thinsp;=\u0026thinsp;2.01,95% CI: 1.32\u0026ndash;3.07).The odds of having ANC visit among women whose age between 35\u0026ndash;39 years were 2.16 times higher as compared to women whose age between 15\u0026ndash;19 years old (AOR\u0026thinsp;=\u0026thinsp;2.16,95% CI : 1.37\u0026ndash;3.40).\u003c/p\u003e\u003cp\u003eThe odds of having ANC visit among other religion groups (catholic and traditional religion) were decreased by 63% as compared to orthodox Christianity followers (AOR\u0026thinsp;=\u0026thinsp;0.37, 95% CI: 0.16\u0026ndash;0.83).\u003c/p\u003e\u003cp\u003eThe odds of having ANC visit among married and Widowed/divorced/separated women were 6.47and 4.58 times higher than single women (AOR\u0026thinsp;=\u0026thinsp;6.47, 95% CI: 2.06\u0026ndash;20.28) \u0026amp; (AOR\u0026thinsp;=\u0026thinsp;4.58, 95% CI: 1.42\u0026ndash;14.81) respectively.\u003c/p\u003e\u003cp\u003eThe odds of having ANC visit among women having primary, secondary and higher educational status were 1.89, 3.18 and 4.44 times higher than the odds of having ANC visit among women with no education (AOR\u0026thinsp;=\u0026thinsp;1.89,95% CI:1.55\u0026ndash;2.29), (AOR\u0026thinsp;=\u0026thinsp;3.18,95% CI:2.3\u0026ndash;4.4) and (AOR\u0026thinsp;=\u0026thinsp;4.44,95% CI:2.90\u0026ndash;6.79) respectively. The odds of having ANC visit among a women\u0026rsquo;s who was rich increased by 31% as compared to a women who was poor (AOR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.003-1.70).\u003c/p\u003e\u003cp\u003eThe odds of having ANC visit among women who live in Afar, Oromia, Somali, SNNPR, Gambella and Harari regional states were decreased by 64%,48%,92%,70% ,79% and 72% as compared to women who live in Tigray region (AOR\u0026thinsp;=\u0026thinsp;0.36,95% CI: 0.20\u0026ndash;0.66),( AOR\u0026thinsp;=\u0026thinsp;0.52,95% CI: 0.31\u0026ndash;0.89),( AOR\u0026thinsp;=\u0026thinsp;0.08,95% CI:0.04\u0026ndash;0.16),( AOR\u0026thinsp;=\u0026thinsp;0.3, 95% CI: 0.17\u0026ndash;0.53),( AOR\u0026thinsp;=\u0026thinsp;0.21,95% CI: 0.12\u0026ndash;0.38) and (AOR\u0026thinsp;=\u0026thinsp;0.28,95% CI: 0.14\u0026ndash;0.53) respectively.\u003c/p\u003e\u003cp\u003eBeing a women who live in rural area decreases the odds of having ANC visit by 32% as compared to a women who live in urban areas (AOR\u0026thinsp;=\u0026thinsp;0.68,95% CI: 0.46\u0026ndash;0.99). The odds of having ANC visit among women who live in a community with low level of poverty were increased by 79% as compared to a women who live in a community with high level of poverty (AOR\u0026thinsp;=\u0026thinsp;1.79,95% CI :1.28\u0026ndash;2.51) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable multilevel logistic regression analysis results of both individual and community-level factors associated with ANC visits in Ethiopia, EMDHS 2019\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNull model AOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel III\u003c/p\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel IV\u003c/p\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43 (0.98, 2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33(0.92,1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09(1.42,3.08) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.88(1.27,2.76) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.39(1.57,3.65) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.01(1.32,3.07) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.6(1.65, 4.09) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.16(1.37,3.40) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.12(1.26, 3.55) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.67(0.99, 2.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89 (0.97, 3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4(0.71,2.74)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrthodox\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59(0.45,0.75) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.91 (0.67,1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtestant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56(0.41,0.75) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85 (0.61,1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26(0.11,0.58) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.37(0.16,0.83) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.35 (2.02,19.92)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.47(2.06,20.28) **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.78 (1.48,15.47)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.58(1.42,14.81) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01(1.65, 2.44) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.89(1.55, 2.29) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.48(2.53, 4.78) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.18(2.3, 4.4) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.11(3.36, 7.78) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.44(2.90, 6.79) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26 (0.99,1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09(0.86,1.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17(1.72, 2.72) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31(1.003, 1.70)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBirth order\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.69, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94(0.74, 1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;=5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76 (0.56, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9(0.66,1.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTigray\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAfar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27(0.15, 0.46) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.36(0.20, 0.66) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmhara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69(0.42, 1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74(0.45, 1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOromia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44(0.26, 0.72) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.52(0.31, 0.89)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05(0.03, 0.11) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08(0.04 ,0.16) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenishangul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81(0.47, 1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92(0.53, 1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNNPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27(0.16, 0.45) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3(0.17, 0.53) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGambela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20(0.12, 0.36) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21(0.12, 0.38) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHarari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24(0.13, 0.45) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28(0.14, 0.53) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAddis Abeba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06(0.53, 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09(0.54, 2.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDire Dawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66(0.36, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79(0.42,1.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58(0.40, 0.86) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68(0.46, 0 .99)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCommunity level poverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.06(1.50, 2.84) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79(1.28,2.51) ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMedia exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12 (0.70, 1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84(0.52,1.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCommunity women education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.54 (1.14, 2.07) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.26(0.94,1.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37(0.31,0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eModel Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2385.921\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2253.721\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2249.368\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2175.546\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4771.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4507.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4498.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4351.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4775.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4547.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4530.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4419.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, **p-value\u0026thinsp;\u0026le;\u0026thinsp;0.01, ***p-value\u0026thinsp;\u0026le;\u0026thinsp;0.001 Others* catholic and traditional religion follower\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the individual and community-level predictor\u0026rsquo;s association with ANC utilization among reproductive age women in Ethiopia. In this study, the prevalence of ANC utilization among women reproductive age group in Ethiopia was 43% (95% CI: 41.5%, 44.6%). ranged from 11% in the Somali region to 81.9% in the Addis Ababa town. This was lower than a study conducted in Zimbabwe\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and pooled prevalence in sub-Saharan African Countries\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Even though ANC is one of exempted service in all government health institution overall the country, ANC service utilization is very low.\u003c/p\u003e\u003cp\u003eAlso, the spatial distribution of ANC use in Ethiopia was non-random and the hotspot areas of ANC service utilization were identified in the Tigray, Addis Ababa, Benishangul Gmuz regions. From our multilevel analysis, we observed that seven variables were significantly associated with the use of ANC: individual factors age, religion, marital status, education, wealth, and community level factors region, residence, community level poverty were found statistically significant predictors for ANC service utilization.\u003c/p\u003e\u003cp\u003eIn the spatial regression analysis, education, religion, residence, and wealth was significant predictors of hotspot areas of ANC utilization service. An increased proportion of uneducated women decrease the odds of ANC service utilization in Somali, Tigray, Afar, Harari, Dire Dawa, Oromia, and Northern Amhara regions. This may be due to the fact that women with greater levels of education may be better able to notice danger signs and easily appreciate the consequences of forgoing critical prenatal care services more rapidly\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe result of this study also revealed that mothers whose age\u0026thinsp;\u0026ge;\u0026thinsp;25 more likely to receive ANC service than mothers in the \u0026le;\u0026thinsp;24-year-old age group. Studies conducted in Wonberma Woreda (Ethiopia)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and Nigeria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e have provided support for this conclusion. This may be because older mothers may have more information, insight, and experience about pregnancy and issues associated with it. Studies revealed that teenage mothers are less likely to utilize ANC services and this is explained due to fear of social stigma\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBut, other contradicting research revealed that relatively younger women were more likely to attend ANC services as compared to older. This might be due to low experience of younger women about their current pregnancy, which leads to fear of complications as a result of not attending ANC. As a result, they increase ANC visits. Being a follower of catholic and traditional religion decreased the ANC service utilization as we compared with Orthodox Christians. This finding is in line with a study conducted in Nigeria\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and Ethiopia \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The effect of religion on ANC service utilization is because of that religion plays a significant role in shaping beliefs, norms, and values including those that relate to childbirth and health services use. Reproductive health issues may also be considered as a subject not to be discussed easily between husband and wife in some religions\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese studies showed that married women were more likely to utilize ANC service than the single. This finding is supported by studies done in sub- Saharan Africa\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This might be due to husband support and encouragement leads to more ANC service uptake in married mother, whereas single mother due to socioeconomic, cultural and religion influence may not utilize ANC service in addition to fear of social stigma\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWomen education increases the ANC utilization as compared with women who are unable to read and write. This study is in line with a study conducted in sub-Saharan Africa\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, Pakistan\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, Bangladesh\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and Ethiopia\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The possible reason might be women who are unable to read and write were more likely associated with inequalities in service delivery care. Other possible reason could be that educated women had much higher self-reliance on choosing reproductive health and have great potential to decide freely on where and when to seek medical services regardless of husband\u0026rsquo;s approval. It is known that most women are socioeconomically dependent on male partners who are decision makers in households, and influence on maternal health care services utilization\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHighly statistically significant associations were also obtained between rural resident and ANC service utilization. This finding is supported by studies done in Nigeria[21] and Ethiopia[7]. This is due to the socioeconomic inequalities and differences in health services access between urban and rural areas in the country\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWomen living in Afar, Oromia, SNNPR, Somali, Gambella, and Harari regional states had lower ANC service utilization than women living in Tigray region. The utilization of ANC was a significant disparity across the country regions. Our study is supported by a previous study conducted in Ethiopia and Bangladesh[20]. The possible inhibiting factors could be lack of necessary medicines, service center as well as trained health worker staff, long waiting time, least access to information, absence of transportation and inability to pay for the \u0026lsquo;desired\u0026rsquo; treatment, and instable life style of pastoral community results inaccessibility of maternal health care services including ANC service utilization\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe second community level factor in this study that affect ANC service utilization was low community poverty. Income affects health seeking behavior of the mother in which poor mothers had low health seeking behavior, because poor women had financial barrier to access ANC service utilization and difficulty of addressing wealth related inequalities\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrength and limitation of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing a national representative data and large sample size might help us to have better estimation of parameters. The study also, applied a multilevel analysis to accommodate the hierarchical nature of the Ethiopian EDHS data. Similarly, applying spatial analysis and GWR was crucial to identify the geographic variation and predictors, respectively. Also, this study will help the policy makers to design or strengthen intervention strategies based on the identified geographic variations.\u003c/p\u003e\u003cp\u003eSince we are using secondary data missing of necessary variables required for this study was missed. Crosse sectional study design didn\u0026rsquo;t show cause and effect relationship between associated factors and ANC visit.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eIn this study, the prevalence of ANC utilization significantly varied across regions in Ethiopia and predictors like; age, marital status, educational status, wealth, community level poverty, religion, rural residence, proportion of uneducated women, other religion followers, rural women, and proportion of poor women were identified as significant determinants of ANC service utilization.\u003c/p\u003e\u003cp\u003eWe recommend that policymakers give high priority and attention to improving socioeconomic status of women, education, and accessibility of ANC services in rural areas of the country to minimize disparities in health services utilization. As a result, it will reduce low birth weight and pregnancy complications, and contribute to the achievement of Sustainable Development Goal three.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eANC\u003c/strong\u003e Antenatal Care\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANC\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e Antenatal Care;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAOR\u003c/strong\u003e Adjusted Odds Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEA\u003c/strong\u003e Enumeration Area\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEDHS\u0026nbsp;\u003c/strong\u003e Ethiopian Demographic and Health Survey;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEMDHS\u003c/strong\u003e Ethiopian mini demographic and health survey;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePNC\u003c/strong\u003e Postnatal Care; SBA, Skilled Birth Attendant;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNNPR\u003c/strong\u003e Southern Nation and Nationality People Region; SSA, Sub-Saharan Africa;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003eWorld Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were obtained from Demographic and Health Survey (DHS), which is freely accessed in the program website https://dhsprogram.com. For this study, a brief description of proposal submitted to DHS program, which will access and analyze data, and following that, we obtained permission to access 2019 EDHS for statistical analysis and report.\u003c/p\u003e\n\u003cp\u003eDuring data collection of EDHS, informed consent was obtained from each study participant, all identifiers were removed, and confidentiality was maintained. All methods were carried out under relevant guidelines and regulations of measures of DHS program. The data set was not shared with other bodies, and its confidentiality was not maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declared that we had no computing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc74548367\"\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e LW, YM, TK, TM, NB, GS, WF. \u003cstrong\u003eData curation:\u003c/strong\u003e LW, YM; \u003cstrong\u003eFormal analysis:\u003c/strong\u003e LW, YM, TM, NB; \u003cstrong\u003eMethodology:\u003c/strong\u003e YM, TK, TM, NB, GS, WF, LW; \u003cstrong\u003eSoftware:\u003c/strong\u003e LW, YM; \u003cstrong\u003eSupervision:\u003c/strong\u003e YM; \u003cstrong\u003eValidation:\u003c/strong\u003e LW, YM; \u003cstrong\u003eVisualization:\u003c/strong\u003e YM, LW; \u003cstrong\u003eWriting \u0026ndash; original draft:\u003c/strong\u003e YM, TK, TM, NB, GS, WF, LW\u003cstrong\u003e; Writing \u0026ndash; review \u0026amp; editing:\u003c/strong\u003e YM, TK, TM, NB, GS, WF, LW\u003c/p\u003e\n\u003cp id=\"_Toc74548364\"\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst of all, we would like to express our greatest gratitude to Mizan Aman College of Health Science for its internet and library service to search additional materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Antenatal care 2018 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://platform.who.int/docs/default-source/mca-documents/policy-documents/policy-survey-reports/srmncah-policysurvey2018-fullreport-pt-3.pdf?sfvrsn=3465d5a7_4\u003c/span\u003e\u003cspan address=\"https://platform.who.int/docs/default-source/mca-documents/policy-documents/policy-survey-reports/srmncah-policysurvey2018-fullreport-pt-3.pdf?sfvrsn=3465d5a7_4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. Maternal mortality: Evidence brief 2019 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/WHO-RHR-19.20\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/WHO-RHR-19.20\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. Maternal mortality 2025 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/maternal-mortality\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/maternal-mortality\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO U, UNFPA, World Bank,United Nations Population Division., Trends in maternal mortality: 2000 to 2017 Geneva: WHO; 2019 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240068759\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240068759\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGanchimeg T, Ota E, Morisaki N, Laopaiboon M, Lumbiganon P, Zhang J et al. Pregnancy and childbirth outcomes among adolescent mothers: a World Health Organization multicountry study. BJOG. 2014;121 Suppl 1:40\u0026thinsp;\u0026ndash;\u0026thinsp;8. PMID. 24641534.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlthabe FMJ, Gibbons L, Berrueta M, Goudar SS, Chomba E et al. Breaking the silence: violence against children with disabilities in Africa 2010 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asksource.info/resources/breaking-silence-violence-against-children-disabilities-africa\u003c/span\u003e\u003cspan address=\"https://asksource.info/resources/breaking-silence-violence-against-children-disabilities-africa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScherdel L, Martin A, Deivanayagam A, Adams E, Shanahan T. The search for international consensus on LGBT health. Lancet Glob Health. 2014;2(6):e321. PMID. 25103299.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaryland CSAIR, USA Ethiopia Demographic and Health Survey. 2016. Addis Ababa, Ethiopia 2016 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dhsprogram.com/pubs/pdf/FR328/FR328.pdf\u003c/span\u003e\u003cspan address=\"https://www.dhsprogram.com/pubs/pdf/FR328/FR328.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEthiopian Public Health Institute I. Mini Demographic and Health Survey. 2019: ICF Rockville, Maryland, USA; 2019 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unicef.org/ethiopia/media/1721/file/The%202019%20Ethiopia%20Mini%20Demographic%20and%20Health%20Survey%20.pdf\u003c/span\u003e\u003cspan address=\"https://www.unicef.org/ethiopia/media/1721/file/The%202019%20Ethiopia%20Mini%20Demographic%20and%20Health%20Survey%20.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin S, Basu AM, Stephenson R. Spatial variation in contraceptive use in Bangladesh: looking beyond the borders. Demography. 2002;39(2):251\u0026ndash;67. PMID. 12048951.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlemu K, Worku A, Berhane Y, Kumie A. Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia. Malar J. 2014;13:223. PMID. 24903061.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eICPSR, Description C. ICPSR 2016 Regression Analysis for Spatial Data Elisabeth Dowling Root ([email protected]) The Ohio State University Department of Geography and Division of Epidemiology Columbus, OH 43210 Course Organization 2016 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icpsr.umich.edu/summerprog/biblio/2016/shortcourse/RegressionAnalysisforSpatialData2016ElizabethRoot.pdf\u003c/span\u003e\u003cspan address=\"https://www.icpsr.umich.edu/summerprog/biblio/2016/shortcourse/RegressionAnalysisforSpatialData2016ElizabethRoot.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuong TN, Le MH, Beardsley J, Denning DW, Le NH, Nguyen BT. Updated estimation of the burden of fungal disease in Vietnam. Mycoses. 2023;66(4):346\u0026ndash;53. PMID. 36564981. PMC10953305.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeprah MS, Mensah IO. Performance evaluation of the Ordinary Least Square (OLS) and Total Least Square (TLS) in adjusting field data: An empirical study on a DGPS data. South Afr J Geomatics. 2017;6(1):73\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaru TB, Ayana GM, Zakaria HF, Merga BT. Association of higher educational attainment on antenatal care utilization among pregnant women in east africa using Demographic and Health Surveys (DHS) from 2010 to 2018: a multilevel analysis. Int J women's health. 2022:67\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTessema ZT, Teshale AB, Tesema GA, Tamirat KS. Determinants of completing recommended antenatal care utilization in sub-Saharan from 2006 to 2018: evidence from 36 countries using Demographic and Health Surveys. BMC Pregnancy Childbirth. 2021;21:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyalew TW, Nigatu AM. Focused antenatal care utilization and associated factors in Debre Tabor Town, northwest Ethiopia, 2017. \u003cem\u003eBMC research notes\u003c/em\u003e. 2018;11:1\u0026ndash;6. PMID.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGetasew Mulat GM, Teketo Kassaw TK, Mekonnen Aychiluhim MA. Antenatal care service utilization and its associated factors among mothers who gave live birth in the past one year in Womberma Woreda, North West Ethiopia. 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFagbamigbe AF, Idemudia ES. Assessment of quality of antenatal care services in Nigeria: evidence from a population-based survey. Reproductive health. 2015;12:1\u0026ndash;9. PMID.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChanda SK, Ahammed B, Howlader MH, Ashikuzzaman M, Shovo T-E-A, Hossain MT. Factors associating different antenatal care contacts of women: A cross-sectional analysis of Bangladesh demographic and health survey 2014 data. PLoS ONE. 2020;15(4):e0232257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdewuyi EO, Auta A, Khanal V, Bamidele OD, Akuoko CP, Adefemi K, et al. Prevalence and factors associated with underutilization of antenatal care services in Nigeria: A comparative study of rural and urban residences based on the 2013 Nigeria demographic and health survey. PLoS ONE. 2018;13(5):e0197324.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTessema ZT, Animut Y. Spatial distribution and determinants of an optimal ANC visit among pregnant women in Ethiopia: further analysis of 2016 Ethiopia demographic health survey. BMC Pregnancy Childbirth. 2020;20(1):137. PMID. 32131759. PMC7057476.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAfulani PA, Buback L, Essandoh F, Kinyua J, Kirumbi L, Cohen CR. Quality of antenatal care and associated factors in a rural county in Kenya: an assessment of service provision and experience dimensions. BMC Health Serv Res. 2019;19:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkedo-Alex IN, Akamike IC, Ezeanosike OB, Uneke CJ. Determinants of antenatal care utilisation in sub-Saharan Africa: a systematic review. BMJ open. 2019;9(10):e031890.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoldegiorgis MA, Hiller J, Mekonnen W, Meyer D, Bhowmik J. Determinants of antenatal care and skilled birth attendance in sub-Saharan Africa: A multilevel analysis. Health Serv Res. 2019;54(5):1110\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhaffar A, Pongponich S, Ghaffar N, Mehmood T. Factors associated with utilization of antenatal care services in Balochistan province of Pakistan: An analysis of the Multiple Indicator Cluster Survey (MICS) 2010. Pakistan J Med Sci. 2015;31(6):1447.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObse AG, Ataguba JE. Explaining socioeconomic disparities and gaps in the use of antenatal care services in 36 countries in sub-Saharan Africa. Health Policy Plann. 2021;36(5):651\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geographic weighted regression, antenatal care, multilevel regression analysis, EDHS, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-7220929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7220929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAntenatal care refers to care given to pregnant women during pregnancy by skilled health professionals. Due to low coverage of antenatal care, women in many developing countries, including Ethiopia, are at risk for pregnancy-related complications. This research aims to explore geographical variation in hotspots of antenatal care service utilization in Ethiopia using data from the 2019 Ethiopian Mini Demographic Health Survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This study was conducted based on the 2019 Ethiopian Mini Demographic Health Survey (EMDHS) data. A total of 3,927 women were included in this study. To determine the factors associated with hotspots of antenatal care (ANC) utilization, we employ geographic weighted regression (GWR). Adjusted R\u003csup\u003e2\u003c/sup\u003e and AICc was used for model comparison. A multilevel analysis was fitted and variables with a p-value \u0026lt; 0.25 in the bi-variable analysis were considered for the multivariable analysis. The adjusted odd ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e Prevalence of ANC utilization in Ethiopia was 43% (95% CI: 41.5%, 44.6%). Tigray, SNNPR, Addis Ababa, and Benishangul Gmuz regions were most hotspot areas. Multilevel regression analysis revealed that Age 35-39 years, other religion followers, married women, higher educational status, rural residence, being rich in wealth status, and low community level poverty were associated with antenatal care service utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion and recommendation:\u003c/strong\u003e In Ethiopia, the prevalence of ANC utilization in was low according to our study and there was a significant spatial variation of antenatal care utilization in Ethiopian regions. To improve ANC coverage, geographically targeted strategies are essential. These should focus on reducing regional disparities, improving women’s education and socioeconomic status, and enhancing accessibility and availability of reproductive health services, especially in rural areas of the country.\u003c/p\u003e","manuscriptTitle":"Geographical variation in hotspots of antenatal care service utilization in Ethiopia: Geographic weighted regression and Multilevel analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 19:20:45","doi":"10.21203/rs.3.rs-7220929/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ff1ed3d-1076-4f2e-8e56-2afb807d0f6d","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T21:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 19:20:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7220929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7220929","identity":"rs-7220929","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}