Abstract
Access to safely managed sanitation services is a major public health concern, particularly in low- and middle-income countries like Ethiopia. Addressing the sanitation problem requires a comprehensive understanding of the spatial variation and determinant factors. This study aims to estimate the prevalence of unimproved sanitation services, identify hotspot areas, predict high-risk zones, detect spatial cluster and determinant factors in Ethiopia. A community-based cross-sectional study was conducted using the 2019 Mini-Ethiopian Demographic Health Survey. A total of 8,663 households were sampled using a stratified two-stage cluster sampling method. Arc-GIS version 10.7.1 software was used to visualize the spatial distribution of unimproved sanitation services. SaTScan version 10.2.5 software was used for detecting and analyzing clusters. To identify determinant factors of unimproved sanitation services, a multilevel logistic regression model was employed. In the multilevel analysis, those variables with a p-value < 0.05 were considered to be significant factors for the use of unimproved sanitation services. Approximately 80.23% [95% CI 79.4–81.1] of the population in Ethiopia uses unimproved sanitation services, with significant spatial differences across regions. Determinant factors for the use of unimproved sanitation services includes, households owning a television [AOR = 0.47; 95% CI 0.38, 0.58], households with a middle wealth index [AOR = 0.50; 95% CI 0.37, 0.67], households with a rich wealth index [AOR = 0.27; 95% CI 0.2, 0.36], being in a metropolitan region [AOR = 0.21; 95% CI 0.12, 0.37], living in rural areas [AOR = 5.52; 95% CI 3.28, 9.3], and households with water access within > 30 min [AOR = 2.13; 95% CI 1.56, 2.9] and ≤ 30 min [AOR = 2.67; 95% CI 2.02, 3.51]. Hotspot areas for the use of unimproved sanitation services were observed in the regions of Benishangul-Gumuz, SNNP, Amhara, Gambela, and some parts of Oromia. The primary cluster with low relative risks was found in Addis Ababa, Dire Dawa, and Harari. More than three-fourths of the Ethiopian population relies on unimproved sanitation services, with significant regional variations. Interventions such as media campaigns, investing in sanitation infrastructure, and establishing a monitoring system are highly recommended to address these issues.
Keywords: Safely managed sanitation services, Ethiopia, Multilevel analysis, Spatial analysis, Unimproved sanitation services
Subject terms: Diseases, Health care
Introduction
Access to safely managed sanitation services is a crucial public health issue and a fundamental human right1. Improved Water, Sanitation, and Hygiene (WASH) services play a significant role in preventing mortality and morbidity among children under five2. Furthermore, access to safely managed, affordable, and acceptable water and sanitation services significantly reduces the incidence of health problems, including waterborne diseases, water-washed diseases, and neglected tropical diseases3,4. The United Nations supports the Sustainable Development Goal (SDG) 6, Targets 1 and 2, which aim to address global inequality in access to safe water and improved sanitation services for all by 20305.
Globally, the lack of safely managed sanitation facilities causes significant public health risks, particularly in low- and middle-income countries. Only 4.6 billion people, or 57% of the global population, used a safely managed sanitation service. Around 2 out of 5 people still lacked safely managed sanitation services6. Two-thirds of people who still lacked even basic services lived in rural areas, with nearly half of this population living in sub-Saharan Africa, such as Ethiopia6.
In Ethiopia, sanitation issues remain a major public health concern. The problem is multifaceted. The majority of the population still relies on unimproved sanitation services. While there has been significant progress in reducing open defecation from 79% in 2000 to 17% in 20206,7. Evidence suggested that only 10.2% of households have access to safely managed sanitation services8. Additionally, the 2019 Ethiopian Mini-Demographic and Health Survey (EMDHS) revealed that around 80.5% of households had access to unimproved sanitation facilities, with significant regional disparities9. To improve sanitation services, the Ethiopian government has implemented various strategies, including the Community-Led Total Sanitation (CLTS) approach and the health extension programs. However, these efforts have primarily increased the availability of sanitation facilities rather than ensuring their sustainability and hygienic quality7.
The implications of unimproved sanitation services are substantial, contributing to the transmission of waterborne diseases and presenting a major public health challenge. Unimproved sanitation is closely linked to diarrheal diseases, which are a leading cause of morbidity and mortality among children under-five8. In addition, the lack of access to safely managed sanitation facilities is not only a health problem but also a significant barrier to economic development.
Addressing the sanitation problem in Ethiopia requires a comprehensive understanding of the contributing factors affecting access to safely managed sanitation services. Factors such as lack of awareness, poverty, and inadequate infrastructure play a significant role in the prevalence of unimproved sanitation. Additionally, disparities between urban and rural households and regional variation in access to sanitation services are great challenges10. Being a rural resident, having water available on premises, media exposure, and the wealth index of households are the identified factors for access to sanitation services10–13.
Access to safe water remains a significant challenge, but various studies indicate that Ethiopia has made some progress in coverage, although further efforts are still required13,14. In contrast, sanitation presents a much larger issue, and it requires comprehensive evidence to address it effectively. There is limited evidence regarding geographical disparities of unimproved sanitation services and determinant factors at the individual and community levels in Ethiopia. This gap highlights the need for further studies and evidence in Ethiopia.
Therefore, this study aims to address the following research questions: (i) What is the magnitude of unimproved sanitation services in Ethiopia? (ii) What are the determinant factors to unimproved sanitation service in Ethiopia? (iii) What are the hotspot areas of unimproved sanitation services in Ethiopia? (iv) What are high-risk areas through prediction of unimproved sanitation services? (v) What are the identified clusters of unimproved sanitation services?
The study will provide comprehensive evidence that will support the achievement of SDG target 6.2. Additionally, it will inform effective strategies to improve access to safely managed sanitation in Ethiopia, thereby enhancing the quality of life and improving public health.
Methods
Study setting, design and period
The study was conducted across nine regions of Ethiopia, including the city administrations of Addis Ababa and Dire Dawa (Fig. 1). Ethiopia is the second most populous country in Africa after Nigeria. It is bordering in the north by Eritrea, in the east by Somalia and Djibouti, in the west by Sudan and South Sudan, and in the south by Kenya. The data were collected using a community based cross-sectional study design. The 2019 EMDHS data were collected between March 21, 2019, and June 28, 2019. The survey was conducted through a collaboration between the Central Statistical Agency (CSA) and the Ethiopian Public Health Institute (EPHI).
Fig. 1.
Study area map.
Data source
This study used the 2019 EMDHS dataset. The data were accessed from the Demographic and Health Survey (DHS) program website (https://dhsprogram.com) after justifying the research purpose and receiving a confirmation letter to obtain access. The variables for this study were extracted from the household record datasets.
Sampling procedures
The 2019 EMDHS employed a two-stage stratified cluster sampling method. Each region and city administration was stratified into urban and rural areas. In the first stage, a total of 305 Enumeration Areas (EA) were randomly selected, with 93 in urban areas and 212 in rural areas, using a probability proportional to the size of each EA. In the second stage, from an updated household listing, 30 households per cluster were chosen through systematic selection. For detailed sampling procedures, it is available in the 2019 EMDHS report on the DHS website (https://dhsprogram.com)9. This study included a total weighted sample of 8,663 households extracted from the household record dataset. Weighting ensures the survey sample accurately represents the target population by adjusting for unequal selection probabilities and non-response.
Study variables
Dependent variable
The outcome variable of this study was the safely managed sanitation services, categorized into unimproved and improved sanitation services. Unimproved sanitation services included such options as flush toilets with unknown disposal locations, pit latrines without slabs/open pits, no facilities/bush/field, bucket toilets, hanging toilets/latrines, and others. Improved sanitation systems included flush to piped sewer systems, flush to septic tanks, flush to pit latrines, flush to somewhere else, Ventilated Improved Pit latrines (VIP), pit latrines with slabs, and composting toilets9. During analysis, the unimproved sanitation services were labeled as “1”, and the improved sanitation services were labeled as “0”.
Independent Variables
The predictor variables were categorized into individual-level and community-level predictors. Individual-level predictors included the sex and age of household heads, household size, ownership of radio and television, and the wealth index. Community-level predictors include residency, region, source of drinking water, and time to access drinking water.
In this study, the region variable was categorized based on geopolitical features into three levels: large central region (Tigray, Amhara, Oromia, and SNNP Region), metropolitan areas (Dire Dawa, Harari, and Addis Ababa), and small peripheral regions (Somalia, Benishangul-Gumuz, Gambela, and Afar)14,15. The source of drinking water variable was also categorized as improved source (piped into dwelling, piped to yard/plot, piped to neighbor, public tap/standpipe, tube well or borehole, protected well, protected spring, rainwater, tanker truck, cart with small tank, bottled water) and unimproved source (unprotected well, unprotected spring, surface water, and others)16.
Operational definitions
Safely managed sanitation
Use of improved facilities that are not shared with other households and where excreta are safely disposed of in situ or removed and treated offsite17.
Basic sanitation
Use of improved facilities which are not shared with other households17.
Limited sanitation
Use of improved facilities shared between two or more households17.
Unimproved sanitation
Use of pit latrines without a slab or platform, hanging latrines or bucket latrines17.
Open defecation
Disposal of human faces in fields, forests, bushes, open bodies of water, beaches, and other open spaces or with solid waste17.
Data management and analysis
Before conducting any statistical analysis, the data were cleaned, coded, labeled, and weighted to ensure representativeness using sampling weights. The analysis was carried out using STATA version 17. This study employed spatial autocorrelation, interpolation, and SaTscan analysis to assess spatial distribution. Additionally, to identify factors associated with unimproved sanitation services, a multilevel analysis was performed. Multilevel analysis is appropriate for this study because it accounts for the nested structure of DHS data, allowing us to identify both individual- and community-level factors influencing the outcome. For the bivariable analysis, variables with p-values less than 0.2 were considered eligible for inclusion in the multivariable model18. Detailed procedures are outlined as follows.
Spatial analysis
Global spatial autocorrelation and hotspot analysis
ArcGIS version 10.7.1 was used for assessing spatial autocorrelation, distribution, hotspot detection, and interpolation related to unimproved sanitation services. The proportion of unimproved sanitation services in each survey cluster was calculated and linked to the coordinates of latitude and longitude of the selected EAs.
The Global Moran’s I statistic was used to evaluate whether the distribution of unimproved sanitation services was dispersed, clustered, or random in Ethiopia. Moran’s I value is close to − 1, + 1, and 0, indicating dispersed, clustered, and random distribution, respectively. A p-value of Moran’s I less than 0.05 indicated the presence of spatial autocorrelation. To identify the hotspot areas of unimproved sanitation services and how spatial autocorrelation varies across study locations, Getis-Ord Gi* statistics were applied. A high GI* value implies a “hotspot”, whereas a low GI* value denotes a “cold spot”19.
Spatial interpolation
The spatial interpolation technique was performed to predict unimproved sanitation services for unmeasured or unsampled areas based on data from sampled clusters. The geostatistical ordinary Kriging interpolation technique was used for spatial prediction, as it provides unbiased estimates with minimal variance compared to other interpolation techniques. A semi-variogram model was constructed and used to define the weight that further determines the prediction of new values in unsampled areas, based on the input data at each location20,21. Using the findings from a simulated semivariogram, an interpolation model (map) was generated to visualize the predictions.
Spatial SaTScan analysis
SaTScan version 10.2.5 software was used for local cluster detection analysis. A purely spatial Bernoulli-based model was used to identify statistically significant clusters of unimproved sanitation services among households. In this model, households with unimproved sanitation services were used as cases, while those with access to improved sanitation systems were used as controls. The most likely clusters were determined using p-values and likelihood ratio tests, with results based on 999 Monte Carlo replications.
Multilevel logistic regression analysis
In this study, a multilevel mixed-effect logistic regression analysis was performed. Participants were nested within households, and households were nested within EAs. This hierarchical structure means that participants from the same cluster share more similarities regarding the outcome interest than those from different clusters, potentially leading to data dependency. To address this in the multilevel analysis, four models were fitted. The first model was a null model (Model 0) containing no predictor variable, which was used to check the variability of the use of unimproved sanitation services within the community. The second model (Model I) contains individual-level variables. The third (Model II) contains community-level variables. The fourth model (Model III) contains both individual and community-level variables.
To measure variance through random effects, the Interclass Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV) were calculated. The ICC indicated the variation of unimproved sanitation services between clusters, calculated as follows:
 |
where Vc = cluster level variance
The MOR is the median value of the odds ratio between the area of the lowest and highest risk when two clusters are chosen at random.
 |
The PCV measures the proportion of total observed individual variation that can be attributed to differences between clusters.
 |
where Vnull = variance of the null model, and VC = cluster-level variance of the next model15,22.
To compare the model, a deviance test was conducted, and the lowest deviance revealed the best-fitted model15,22. In the multivariable analysis, the association between the outcome and predictor variables was reported using adjusted odds ratios with a 95% CI and a p-value of 0.05.
Ethical consideration
In this study, ethical approval and participant consent were not required, as the data are publicly available on the DHS program website (https://dhsprogram.com). Upon submitting the purpose for requesting data, approval was obtained to download the datasets. All methods were carried out in accordance with relevant guidelines and regulations.
Results
In the random effect analysis, the ICC from the null model indicated that 76% of the total variability in the use of unimproved sanitation services was attributed to differences between clusters. When randomly selecting households from two distinct clusters, those in the cluster with a higher risk of using an unimproved sanitation service had odds 21.5 times higher of using such systems compared to households in a lower-risk cluster.
The PCV increased from 53% in Model I to 79% in Model III, which included both individual-and community-level variables. This suggested that Model III provides the most comprehensive explanation for the variability in the use of unimproved sanitation services. Additionally, Model III had the lowest deviance (5312), indicating that it is the best-fitting model. All variables had VIF values below 10; for instance, for model III, the mean of VIF was 4.17, confirming the absence of multicollinearity (Table 1).
Table 1.
Community-level clustering (random effect) and model selection in the use of unimproved sanitation services among households in Ethiopia, 2019 EMDHS.
| Variable | Null model | Model I | Model II | Model III |
|---|---|---|---|---|
| Random effect | ||||
| Variance | 10.44 [8.19, 13.3] | 4.95 [3.8, 6.43] | 2.33 [1.79, 3.02] | 2.2 [1.69, 2.85] |
| ICC | 0.76 [0.71, 0.80] | 0.6 [0.54, 0.66] | 0.41 [0.35, 0.48] | 0.4 [0.34, 0.46] |
| MOR | 21.5 | 8.24 | 4.35 | 4.05 |
| PCV | Ref | 0.53 | 0.78 | 0.79 |
| Model comparison | ||||
| Log-likelihood | − 2914.6 | − 2768.7 | − 2741.9 | − 2656.5 |
| Deviance | 5829.2 | 5537.4 | 5483.8 | 5312 |
| Mean VIF | – | 2.07 | 3.96 | 4.17 |
N.B: ICC = Inter Cluster Correlation coefficient, MOR = Medians Odds Ratio, PCV = Proportional Change in Variance, and VIF = Variance Inflation Factor.
In this study, a total of 8,663 weighted households were included. The majority of these households, 80.23% [95% CI 79.4–81.1] used unimproved sanitation services. Around 78% of the household heads were men, and about one-third (35.5%) were aged between 31 and 46 years. Approximately 45% of the households had a family size of 4 to 6 members.
The majority of households had no media exposure, with 72.2% lacking access to television and 83.2% lacking access to radio. Around 69.2% of participants resided in rural areas. More than one-third (36.2%) of households were living at a poor economic level. Additionally, only one-fifth (20.3%) of the included households had access to drinking water on their premises (Table 2).
Table 2.
Socio-demographic characteristics and magnitude of unimproved sanitation services in Ethiopia, 2019 EMDHS.
| Variables | Categories | Safely managed sanitation services | Total weighted frequency (%) | |
|---|---|---|---|---|
| Unimproved (%) n = 6,950.6(80.23) |
Improved (%) n = 1,712.4(19.77) |
|||
| Sex of household head | Male | 5546.9 (82.2) | 1204.5 (17.8) | 6751.4 (78) |
| Female | 1403.8 (73.4) | 507.9 (26.6) | 1911.7 (22) | |
| Age of household head (years) | 15–30 | 1772.4 (78.2) | 493.22 (21.8) | 2265.62 (26.2) |
| 31–46 | 2481.9 (80.8) | 589.83 (19.2) | 3071.73 (35.5) | |
| 47–62 | 1623.7 (80.4) | 395.8 (19.6) | 2019.5 (23.3) | |
| ≥ 63 | 1072.7 (82.1) | 233.53 (17.8) | 1306.23 (15.1) | |
| Household family size | 1–3 | 2,139.1 (75.7) | 688.5 (24.3) | 2,827.6 (32.6) |
| 4–6 | 3,141.4 (81.2) | 728 (18.8) | 3,869.6 (44.7) | |
| ≥ 7 | 1,670.1 (85) | 295 (15) | 1,965.8 (22.7) | |
| Have radio | Yes | 1770.1 (73.4) | 640.6 (26.6) | 2410.7 (27.8) |
| No | 5180.5 (82.8) | 1071.8 (17.2) | 6252.3 (72.2) | |
| Have television | Yes | 588.1 (40.3) | 869.6 (59.7) | 1457.7 (16.8) |
| No | 6362.5 (88.3) | 842.7 (11.7) | 7205.2 (83.2) | |
| Wealth index | Poor | 2931 (93.6) | 201.8 (6.4) | 3132.8 (36.2) |
| Middle | 1516.3 (90.5) | 158.9 (9.5) | 1675.2 (19.3) | |
| Rich | 2503.3 (64.9) | 1351.7 (35.1) | 3855 (44.5) | |
| Community level variables | ||||
| Residency | Urban | 1533.2 (57.6) | 1130.8 (42.4) | 2664 (30.8) |
| Rural | 5417 (90.3) | 581.6 (9.7) | 5998.6 (69.2) | |
| Region | Small peripherals | 487.8 (76.7) | 147.9 (23.3) | 635.7 (7.3) |
| Larger central | 6373.8 (84.2) | 1195.7 (15.8) | 7569.5 (87.4) | |
| Metropolis | 89.0 (19.4) | 368.8 (80.6) | 457.8 (5.3) | |
| Source of drinking water | Improved | 4485.5 (75.3) | 1467.7 (24.7) | 5953.2 (68.7) |
| Unimproved | 2465.1 (91) | 244.7 (9) | 2709.8 (31.3) | |
| Time to get drinking water | On premises | 773.3 (43.9) | 988.7 (56.1) | 1762 (20.3) |
| ≤ 30 min | 4310.9 (90.4) | 458.9 (9.6) | 4769.8 (55) | |
| > 30 min | 1866.4 (87.6) | 264.7 (12.4) | 2131.1 (24.6) | |
Factors associated with unimproved sanitation services
Based on the findings from the final model, several factors were significantly associated with the use of unimproved sanitation services. These factors included ownership of television, household wealth index, type of residence, region, and the time required to obtain drinking water.
Households having television were 53% less likely to use unimproved sanitation services compared to those that did not have [AOR = 0.47; 95% CI 0.38, 0.58]. Similarly, households categorized as middle and rich wealth index were 50% [AOR = 0.50; 95% CI 0.37, 0.67] and 73% [AOR = 0.27; 95% CI 0.2, 0.36], respectively, less likely to use unimproved sanitation services than those with a poor wealth index.
Additionally, households residing in rural areas were 5.52 times more likely to use unimproved sanitation services compared to the urban areas [AOR = 5.52; 95% CI 3.28, 9.3]. In metropolitan regions, the odds of using unimproved sanitation services were decreased by 79% [AOR = 0.21; 95% CI 0.12, 0.37] compared to those in small peripheral regions. Furthermore, the odds of using an unimproved sanitation service for the household that took drinking water > 30 min were 2.13 times higher [AOR = 2.13; 95% CI 1.56, 2.9], while those taking within 30 min had 2.67 times higher odds [AOR = 2.67; 95% CI 2.02, 3.51] compared to households with water available on their premises (Table 3).
Table 3.
Multilevel analysis of factors associated with unimproved sanitation services among households in Ethiopia, 2019 EMDHS.
| Variables | Categories | Null model | Model I | Model II | Model III |
|---|---|---|---|---|---|
| AOR [95%] | AOR [95%] | AOR [95%] | |||
| Sex of household head | Male | 1 | 1 | ||
| Female | 1.12 [0.94, 1.34] | 1.15 [0.97, 1.38] | |||
| Age of household head (years) | 15–30 | 1 | 1 | ||
| 31–46 | 1.21 [0.99, 1.47] | 1.2 [0.99, 1.46] | |||
| 47–62 | 1.13 [0.90, 1.41] | 1.10 [0.88, 1.37] | |||
| ≥ 63 | 1.03 [0.79, 1.33] | 0.99 [0.76, 1.3] | |||
| Household family size | 1–3 | 1 | 1 | ||
| 4–6 | 1.13 [0.94, 1.34] | 1.07 [0.89, 1.27] | |||
| 7 & above | 0.97 [0.77, 1.23] | 0.9 [0.71, 1.13] | |||
| Have radio | No | 1 | 1 | ||
| Yes | 0.88 [0.774, 1.05] | 0.89 [0.75, 1.05] | |||
| Have television | No | 1 | 1 | ||
| Yes | 0.36 [0.29, 0.45] *** | 0.47 [0.38, 0.58] *** | |||
| Wealth index | Poor | 1 | 1 | ||
| Middle | 0.44 [0.33, 0.6] *** | 0.5 [0.37, 0.67] *** | |||
| Rich | 0.17 [0.13, 0.22] *** | 0.27 [0.2, 0.36] *** | |||
| Community level variables | |||||
| Residency | Urban | 1 | 1 | ||
| Rural | 11.9 [7.1, 20] *** | 5.52 [3.28, 9.3] *** | |||
| Region | Small peripherals | 1 | 1 | ||
| Larger central | 1.32 [0.82, 2.13] | 0.97 [0.61, 1.56] | |||
| Metropolitan | 0.13 [0.07, 0.23] *** | 0.21 [0.12, 0.37] *** | |||
| Source of drinking water | Unimproved | 1 | 1 | ||
| Improved | 0.7 [0.54, 0.9] ** | 0.89 [0.68, 1.15] | |||
| Time to get drinking water | On premises | 1 | 1 | ||
| ≤ 30 min | 3.18 [2.42, 4.17] *** | 2.67 [2.02, 3.51] *** | |||
| > 30 min | 2.70 [2.0, 3.68] *** | 2.13 [1.56, 2.9] *** | |||
AOR = adjusted odds ratio; CI = confidence interval; ** = p-value < 0.01; *** = p-value < 0.001.
Spatial analysis results
Spatial autocorrelation of unimproved sanitation services
The analysis using Global Moran’s Index revealed a strong spatial clustering of unimproved sanitation services across Ethiopia, with a Moran’s I value of 1.59, indicating high positive spatial autocorrelation. This means regions with poor sanitation tend to be near other regions with similarly poor access, while areas with better sanitation also cluster together. The extremely low p-value (< 0.001) and high z-score (34.7) confirm that this pattern is not random and is statistically significant with over 99% confidence (Fig. 2). These findings highlight clear regional disparities, suggesting that unimproved sanitation is concentrated in specific geographic areas, likely due to shared socioeconomic, infrastructural, or environmental challenges.
Fig. 2.
Spatial autocorrelation of unimproved sanitation systems in Ethiopia, 2019 EMDHS.
Spatial distribution of unimproved sanitation services
The highest proportions of unimproved sanitation services in Ethiopia were detected in SNNPR, Gambela, Benishangul-Gumuz, Amhara, and some parts of Oromia. In this study, areas marked in red indicated the highest prevalence of unimproved sanitation services, with proportions ranging from 26 to 30%. While areas marked with blue indicated the lowest prevalence of unimproved sanitation services, with proportions ranging from 0 to 5% (Fig. 3).
Fig. 3.
Spatial distribution of unimproved sanitation systems in Ethiopia, 2091 EMDHS.
Hotspot (Getis-Ord Gi*) analysis of unimproved sanitation services
Significant hotspot areas with a high proportion of unimproved sanitation services were indicated in red and were observed in the SNNP, Benishangul-Gumuz, Amhara, Gambela, and some parts of the Oromia regions of Ethiopia. In contrast, cold spot areas with a low proportion of unimproved sanitation services were found in Addis Ababa, Dire Dawa, and Harari, as indicated in blue (Fig. 4).
Fig. 4.
Hotspot analysis of unimproved sanitation services in Ethiopia, 2019 EMDHS.
Kriging interpolation of unimproved sanitation services
The proportion of high-risk areas predicted for unimproved sanitation services was extremely high, ranging from 24.28% to 28.91%. These areas included SNNP, Benishangul-Gumuz, Gambela, Amhara, Oromia, and some parts of Somalia, which were indicated in red. In contrast, lower predicted levels of unimproved sanitation services were observed in Addis Ababa, Dire Dawa, and Harari, represented by green (Fig. 5).
Fig. 5.
Kriging interpolation of unimproved sanitation services in Ethiopia, 2019 EMDHS.
SaTScan analysis of unimproved sanitation services
The primary clusters were found in Addis Ababa, Dire Dawa, and Harari, centered at 9.013835 N, 38.678364 E, with a 20.73 km radius, and had a relative risk of 0.16 (LLR = 565.49, p-value < 0.001). Households in this cluster had 84% less likelihood of using unimproved sanitation services compared to households outside the spatial window. The second, mostly clustered, was detected in regions of SNNP, Benishangul-Gumuz, Oromia, and Somalia, centered at 6.672214 N, 38.974495 E with a 180.47 km radius, and had a relative risk of 1.34 (LLR = 145.75, p-value < 0.001). Households in this cluster were 34% more likely to use unimproved sanitation services compared to households outside the spatial window (Fig. 6).
Fig. 6.
SaTScan analysis of unimproved sanitation services in Ethiopia, 2019 EMDHS.
Discussions
Unimproved sanitation services in Ethiopia display considerable spatial disparities, influenced by regional, socio-economic, and environmental factors7,23. Despite existing efforts, critical gaps remain, including insufficient comprehensive evidence to guide targeted interventions. Addressing these challenges necessitates geospatial analysis, context-specific strategies, and enhanced community participation to meet SDG 6 targets. Therefore, this study aims to assess the spatial variations and identify the determinants of unimproved sanitation services in Ethiopia.
More than three-fourths of the Ethiopian population are utilizing unimproved sanitation services. This finding was consistent with a study conducted in Ghana24 and Ethiopia10. The present finding was higher than in Somalia11, Liberia25, and India26. Conversely, it was lower than a study conducted in Benin, West Africa27. The variation might be due to differences in the study period, the level of infrastructure, culture, policy conditions, and socioeconomic contexts across countries. Therefore, the present finding implies that access to sanitation services in Ethiopia is significantly limited, which may pose a significant health risk.
The spatial distribution of unimproved sanitation services in Ethiopia varied across regions. Hotspot areas include SNNP, Benishangul-Gumuz, Amhara, Gambela, and some parts of the Oromia regions of Ethiopia. It might be due to several factors, including socioeconomic conditions, geographic challenges, cultural practices, and climate-related factors. The finding is consistent with a study conducted in Ethiopia10. Furthermore, the predicted proportion of high-risk areas for unimproved sanitation services is extremely high in SNNP, Benishangul-Gumuz, Gambela, Amhara, Oromia, and some parts of Somalia. This finding is supported by Demsash et al.10. The presence of unimproved sanitation services in these regions might be due to a lack of education, environmental factors, and economic constraints.
In this study, households with televisions were found to be less likely to use unimproved sanitation services compared to those without televisions. This finding is supported by Azanaw et al.12 and Demsash et al.10. TV ownership enhances sanitation practices through multiple pathways. By providing access to health education programs and public service campaigns, television exposes households to critical sanitation and hygiene messages. This increased awareness can stimulate demand for improved sanitation facilities while promoting positive behavior change. Furthermore, television programming often portrays modern lifestyles that include proper sanitation, creating aspirational motivations for households to adopt better hygienic practices.
Households in the middle and rich wealth index categories were found to utilize unimproved sanitation services at lower levels than those in the low wealth index category. This finding is supported by studies conducted in Ethiopia10,28, Zambia29, and Somalia11. The possible explanation for this might be that wealth directly enables improved sanitation access by providing households with the financial capacity to construct, maintain, and connect to proper sanitation infrastructure. Higher-income families can afford the upfront costs of quality facilities and sustain long-term maintenance while also accessing better water and sewage systems. In contrast, those households with a low wealth index are often dependent on unimproved sanitation services due to financial constraints, which are significantly exposing them to public health risks12,13. Therefore, it is highly recommended to address economic disparities to improve access to safe sanitation services.
Households residing in rural areas are more likely to utilize unimproved sanitation facilities as compared to households living in urban areas. This finding is supported by studies conducted in sub-Saharan Africa30, Somalia11, and Ethiopia12. The possible reason for the present finding might be that rural households are more likely to limited access to sanitation facilities, weaker policy enforcement, and lower income. In addition, lack of sanitation supply chains, limited access to water, and cultural practice further hinder the progress. Closing this gap demands targeted investments, public awareness campaigns, and inclusive policies.
In this study, metropolitan regions were found to be less likely to utilize unimproved sanitation services compared to small peripheral regions. This finding is supported by studies12,31. Possible reasons for this might be that small peripheral regions are often pastoral communities; they are mobile and may not prioritize constructing improved sanitation services. Furthermore, they are significantly affected by socioeconomic conditions, with higher levels of drought and shortage of water, which contribute to low coverage of sanitation facilities32.
Households with water on the premises were found to be less likely to use unimproved sanitation services compared to those households that travel more than 30 min or up to 30 min to obtain water. This finding is supported by Andualem et al.13, as physical distance is one of the major factors in accessing WASH services. Additionally, the present finding is also supported by Demsash et al.10, as households that obtained water on their premises are more likely to access appropriate sanitation services. Therefore, the findings implied that the time required to fetch water is a major factor for influencing access to sanitation services.
The strength of this study is the use of a large sample size, which is a weighted national representative dataset, and the use of a mixed-effect model that accounts for the hierarchical structure of the data. Additionally, spatial analysis was conducted to explore the distribution and identify significant hotspot areas of unimproved sanitation service. However, there are limitations to consider. The data were collected through a cross-sectional study design that limited establishing cause-and-effect relationships. Furthermore, the study is limited by reliance on secondary data.
Conclusion
More than three-fourths of the Ethiopian population utilizes unimproved sanitation services, with significant variation across regions. Factors such as households’ media exposure, wealth index, type of residency, regional disparity, and time taken to get water were found to be significantly associated with the use of unimproved sanitation services.
Therefore, relevant stakeholders, including local health authorities, regional governments, and NGOs, must prioritize improving access to safely managed sanitation services, particularly for vulnerable populations such as low-income households, rural communities, residents of sanitation hotspots, and those with limited water access. Key interventions should include targeted media campaigns to promote behavior change, investments in context-appropriate sanitation infrastructure, and robust monitoring systems to track progress and evaluate intervention effectiveness. A coordinated, equity-focused approach is essential to address these systemic challenges and achieve sustainable sanitation improvements.
Acknowledgements
The authors of this study would like to thank DHS for allowing us to use the data.
Author contributions
BD: Conceptualization, Data Cleaning, Analysis, and Writing Original Draft, AKG: Data Curation, Analysis, and Reviewing, CD: Methodology, Analysis, and Reviewing, AEB: Writing Original Draft and Analysis, SR: Data cleaning and Reviewing, TS: Analysis and Reviewing, MG: Analysis and Reviewing, and LB: Writing Original Draft and Reviewing. All authors have read and approved the manuscript.
Funding
In this study, funding was not obtained.
Data availability
The datasets used and analyzed in this study are publicly available from the DHS program website (https://dhsprogram.com).
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and analyzed in this study are publicly available from the DHS program website (https://dhsprogram.com).