Background

Water and sanitation facilities together with hygiene account for considerable health impact in the world. In the previous years, the world is working and can bring substantial development in access to improved water sources internationally1. Accessibility and affordability of safe and acceptable water and sanitation facilities decrease the number of health problems like diarrhea, water-washed, water-borne, and neglected tropical diseases2,3. The World Health Organization’s 2022 report reveals that close to two billion people consume water tainted with fecal matter, posing a significant health threat to those affected4,5. This contamination is responsible for around 80% of infectious and parasitic gastrointestinal diseases and contributes to one-third of related deaths. Each year, approximately 829,000 people die due to diarrheal diseases linked to unsafe water source, inadequate sanitation facilities, and poor hand hygiene practices4.

To tackle such public health problems from the unimproved water source and sanitation facilities concerned bodies like United Nations (UN), the World Health Organization (WHO), United Nations Children’s Fund (UNICEF) and others are working on it6. As evidence, United Nations is standing for Sustainable Development Goal (SDG) 6 sets the agenda to address worldwide inequality in accessing safe water and improved sanitation facilities for all by 20307. This is due to providing facilities for adequate WASH in a sustainable way is a cost-effective measure for the prevention and control of communicable diseases8. However, governments in Africa are unable to address the issue water and of sanitation facilities, since there are problems like increasing costs of sustaining existing water sources and the requirement to deliver new facilities ahead of time9. As well economic problems, unplanned urbanization, and higher population growth in sub-Saharan Africa (SSA) countries including Ethiopia account for the largest share of experiencing unimproved water and sanitation facilities in the world10.

Therefore, even though there are such advancements in the area, there are substantial inequalities in access to improved water sources and sanitation facilities among the nations, especially among the developing countries in the world11,12,13. Subnational disparities in access to water sources and sanitation facilities, measured as the difference between the unit with the highest access level and the unit with the lowest14. Studies conducted in India indicated that the existence of persistently high levels of disparity across different regions and social classes pose a serious obstacle to India’s human development and sound socioeconomic growth15,16,17. Other study done in Ghana revealed that there was inequality in accessing improved water sources due to geographical variation6. As well as study done in Ethiopia using EDHS 2016 dataset indicated that there was significant variation in accessing improved water sources and sanitation facilities18. This is means despite previous efforts; significant disparities in access to improved water and sanitation facilities remain unaddressed, particularly in specific regions of Ethiopia. Due to economic constraints, difficult geography, urbanization, climate change and insufficient infrastructure that impede effective resource distribution and facility development, Ethiopia, like many other developing nations, continues to have disparity in access to water sources and sanitation facilities. While earlier studies identified some disparities, there is a lack of comprehensive coexistence spatial analysis of water sources and sanitation facilities inequality in the post-2019 context. The fifth demographic health survey in Ethiopia, the EMDHS 2019, allows for revealing the distribution of sanitation facilities and water sources across the county. Therefore, this study can show the progress and the extent of inequalities in water sources and sanitation facilities over the country. Because monitoring development on the approach to SDG 6 targets for water and sanitation (6.1 and 6.2) needs vigorous data systems19.

Hence, this study objective is investigating unimproved water sources and sanitation facilities geographical variation in Ethiopia using EDHS 2019 datasets. Such consistent and repeated studies are used as evidence for the existence of inequality of water sources and sanitation facilities to concerned bodies like health-related policymakers, UN, WHO, and UNICEF for further actions to achieve no one behind. The other significance of this study will provide essential figures into geographic inequalities, enabling evidence-based planning and allocation of resources to improve public health infrastructure in underserved areas.

Methods

Study area and data source

A community-based cross-sectional study was done to identify the extent of unimproved water sources and sanitation facilities due to demographic variability using the recent demographic health survey (EDHS 2019) in Ethiopia. The country is divided into nine geographical regions and two administrative cities (Fig. 1). The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a representative, for urban and rural areas separately, and for each of the nine regions and the two administrative cities. Each region was stratified into urban and rural areas, yielding 21 sampling strata. The country is known by extreme sociodemographic charactestics (pastoralist, semi pastoralist, agrarian, city dwellers), geographical (high and low lands) and climatic variations (hot arid, warm semi-arid, cool and humid, cool sub-humid, cool and moist) that might have contribution in water sources and sanitation facilities variation.

Fig. 1
figure 1

The stud area, regions of Ethiopia with distribution of enumeration areas.

Full size image

A total of 149,093 enumeration areas (EAs) were used as census frames created for the 2019 EPHC. Individual EA is covering 131 households on average. The sampling frame contains information about EA location, and type of residence (urban or rural). From the total EAs, the 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size based on the 2019 EPHC frame and with independent selection in each sampling stratum. The selected sample size of 305 enumeration areas (EAs) and 30 households per cluster is justified as it provides a statistically representative and sufficient sample to appropriately assess the relationship between water quality, sanitation practices, and health outcomes, thereby enabling the study to achieve its objectives effectively. Then a fixed number of 30 households per cluster were selected with an equal probability of systematic selection from the recently created household report (HR) list. The sample size of 305 enumeration areas (EAs) and 30 households per cluster is statistically robust, providing sufficient power for detecting variations in water quality, sanitation practices, and health outcomes, while accounting for spatial and demographic variability, as recommended by sample size calculations for multi-level spatial epidemiological studies20,21. A systematic random sampling design was employed to select study participants. Then, we found the data at previous research work link22.Then 8,663 (2,645 urban and 6,018 rural) households were included. Like other DHS data, 2019 EMDHS is a comprehensive in incorporating socio-demographic, health and environmental variables including water sources and sanitation facilities.

Study variable and data processing

Unimproved water sources and sanitation facilities are the study variables. Component variables in the table below were transformed to make them convenient for analysis based on the kinds of literature23,24,25,26. These variables were categorized into two “unimproved” labeled by 0 or “improved” water sources and sanitation facilities labeled by 1 (Table 1).

Table 1 Category of water sources and sanitation facilities27,28,29.
Full size table

After accessing the data from the mini demography health survey of Ethiopian (EMDHS-2019) at previous study22, then data processing take place. Excel Microsoft 2016, STATA 14, ArcGIS 10.7, and SaTScan 10.1 software were used in doing this data processing. Weighted by sampling weight was performed to do a trustworthy statistical analysis.

Spatial autocorrelation

To explore the geographical distribution, both global and local indicators of spatial relationship are the best imperative tools for unimproved water sources and sanitation facilities within the specified period. In order to determine whether similar values are clustered, dispersed, or randomly distributed within a geographic area, spatial autocorrelation measures how much adjacent or close spatial units resemble one another in terms of water source and sanitation facilities30.

Global autocorrelations

Finding and measuring the spatial clustering of unimproved water sources and sanitation facilities is the aim of global autocorrelation analysis. To proceed with geographical variation identification of unimproved water and sanitation facilities, Global autocorrelations analysis was done. Global Moran’s I index was used to detect whether the difference is due to the clustering effect or non-random/dispersion. The statistically significant autocorrelation was assessed using z-scores and p-values within a 95% CI. A positive autocorrelation, indicated by a positive z-score, reflects clustering of similar values on the map—such as high rates surrounded by other high rates or low rates surrounded by nearby low rates. On the other hand, negative autocorrelation, indicated by a negative z-score, reflects clustering of dissimilar values, where high rates are near low rates or vice versa31. The critical z-score thresholds are as follows: a positive z-score greater than 2.58 at a 0.01 significance level, between 1.96 and 2.58 at a 0.05 significance level, and between 1.65 and 1.96 at a 0.10 significance level, indicating high clustering of unimproved water source and sanitation facilities values. A negative z-score below − 2.58 (0.01 significance level), between − 1.96 and − 2.58 (0.05 level), or between − 1.65 and − 1.96 (0.10 level) signifies lower-than-expected clustering, suggesting a concentration of low values. If the z-score lies between − 1.65 and 1.65, it indicates no significant autocorrelation. It is relevant to this study as it quantifies the degree of spatial clustering, helping to identify patterns that may influence resource allocation in addressing water source and sanitation facility inequalities. Such a deeper understanding of these patterns allows for more effective targeted interventions and resource allocation, directly supporting efforts to meet Sustainable Development Goal (SDG) 6—ensuring availability and sustainable management of water sources and sanitation facilities for all.

Local statistical analysis

Global Moran’s I provides an overall measure of spatial clustering, but local tools such as Getis-Ord Gi* and Anselin’s Local Moran’s I refine this understanding by identifying specific clusters or hot/cold spots. This complementary approach is essential for designing targeted interventions to address regional disparities in unimproved water sources and sanitation facilities. Global Moran’s I gives a picture of spatial autocorrelation throughout the entire study area while local approaches identify particular clusters or outliers and provide comprehensive insights into regional variations within the larger pattern,.

Hence, further investigation using figures and maps is needed since Global autocorrelations indicate a clustering effect (positive spatial autocorrelation) on unimproved water and sanitation facilities over the country. The Getis-Ord Gi* statistic is a spatial analysis tool used to identify statistically significant hot and cold spots32. Therefore, hot spot analysis (Gettis-Ord Gi*) was performed to identify patterns of spatial variation and emphasize the previously stated using global autocorrelations on unimproved water source and sanitation facilities. Anselin’s Local Moran’s I offers a thorough understanding of spatial patterns within a dataset by assisting in the identification of outliers (such as high values surrounded by low values) and clusters of similar values (such as high-high or low-low clusters)33. Cluster and outlier analysis (Anselin local Moran’s I) was used to describe the spatial patterns of the dependent variables (unimproved water source and sanitation facilities). This cluster and outlier analysis was used to confirm and accompaniment to show extreme (the hot spot and cold spot) since it permits the identification of groupings and areas where the differences happen34.

Spatial scan statistical analysis

Local Likelihood Ratio (LLR) is a statistical measure used to detect significant clusters within spatial data, indicating areas where observed values deviate significantly from the expected pattern35. The Bernoulli model, a probability model often used in spatial clustering, assumes binary outcomes (presence or absence of the outcome variable) and helps identify clusters based on these binary events. Cluster size refers to the number of spatial units (e.g., regions or points) included in each detected cluster, which influences the statistical significance and practical interpretation of spatial patterns. While primary clusters indicate the main areas of high prevalence of unimproved water sources and sanitation facilities, often highlighting critical regions of focus, while secondary clusters reveal additional, potentially smaller areas of the prevalence that may support or expand the understanding of the primary pattern. Kuldorff’s SaTScan software v.10.1 was used in spatial scan statistical analysis to find the most likely clusters (primary cluster) and another secondary cluster. A spatial cluster size < 50% that is the default of the population was used as a higher boundary. This value (< 50%) cluster size threshold is chosen to ensure that identified clusters represent localized patterns rather than overly large. A threshold helps maintain the focus on actionable insights and ensures that disproportionately large populations do not bias detected clusters. The primary and secondary clusters were identified and assigned p-values and ranked based on their log-likelihood ratio. Secondary clusters, such as those identified in Amhara and SNNPR, highlight areas requiring additional resources or targeted policy efforts, complementing interventions in the primary clusters. That means Spatial scan statistical analysis able to show parts of the country with high unimproved water sources and sanitation facilities using high LLR and p-value to associate to clusters outside of the window.in doing this purely spatial analysis scanning for clusters with high rates Bernoulli model was used.

Ethics approval

Ethical permission was not directly obtained from study participants, but primary was collected with written consent that found from the Institutional Review Board (IRB) of the Demographic and Health Surveys (DHS) Program, which approved the datasets for scientific research.

Results

Spatial variation of unimproved water source and sanitation facilities

Global autocorrelations and local statistical analysis

The result of simple descriptive analysis indicated that there is a spatial variation of unimproved water source and sanitation facilities overall in Ethiopia. Different colors on the map represent the distribution variation of unimproved water source and sanitation facilities among regions in Ethiopia (Figs. 2 and 3).

Fig. 2
figure 2

Spatial distribution of unimproved water sources in Ethiopia using the EMDHS 2019 dataset.

Full size image
Fig. 3
figure 3

Spatial distribution of unimproved sanitation facilities in Ethiopia using the EMDHS 2019 dataset.

Full size image

The term “clustering effect” refers to the tendency of similar data points to group together rather than being distributed evenly across a space, “non-random” indicates a selection process that is influenced by specific criteria rather than occurring by chance, and “dispersion” describes the extent to which data points are spread out or scattered across a given area, reflecting the variability in their distribution. A clustering effect refers to the grouping of regions with similar values, such as areas with high rates of unimproved water sources being located near each other. Non-random patterns highlight how regional factors influence spatial disparities, rather than patterns occurring by chance. Dispersion refers to scattered values, contrasting with clustering, where no clear spatial pattern is observed. Global Moran’s I index is 0.399182 with a z-score of 8.735338 and a p-value of 0.000000, revealed that spatial variation of unimproved water source in Ethiopia was a highly significant clustered spatial pattern rather than non-random or dispersion. The likelihood that this highly concentrated pattern was the product of random chance was less than 1% (Fig. 4).

Fig. 4
figure 4

Spatial patterns of unimproved water sources in Ethiopia using the EMDHS 2019 dataset.

Full size image

Global Moran’s I index = 0.610757, z-score = 13.313517, P-value < 0.0001 in the spatial analysis of unimproved sanitation facilities indicated that the clustered pattern’s statistical significance than random or dispersed Given the z score of 13.313517, there was less than a 1% chance that this high-clustered pattern of sanitation facilities was the result of random chance (Fig. 5). The significant z-scores and p-values provide statistical confirmation of spatial clustering, highlighting regions where inequalities in water sources and sanitation facilities access are concentrated. This evidence is critical for guiding targeted interventions to address these disparities and achieve SDG 6.

Fig. 5
figure 5

Spatial patterns of unimproved sanitation facilities in Ethiopia using the EMDHS 2019 dataset.

Full size image

Hot spot analysis (Getis-Ord-Gi*)

The hot spot and cold spot analysis of unimproved water sources in Ethiopia were indicated using the figure below. Clusters found in the Easter part of the country (Dire Dawa and Harari), had significant variation with z-score of 2.85 and p-value < 0.01 of unimproved water sources (hot spots). Whereas clusters located in Tigray and some in Afar regions showed that there was a statistically substantial cold spot a z-score of -2.58 and p-value < 0.01.in unimproved water sources (Fig. 6).

Fig. 6
figure 6

Hot and cold spot results of unimproved water sources in Ethiopia using the EMDHS 2019 dataset.

Full size image

Whereas the hot spot and cold spot analysis of unimproved sanitation facilities in the country were shown using Fig. 7. Clusters found in the western part of Ethiopia (Benishangul Gumuz region), and southern part (SNNPR) had statistically significant unimproved sanitation facilities (hot spots) with a z-score of > 2.58 and p-value < 0.01, and the cold spot clusters located in Dire Dawa city administrative and Harari region at a z-score of < -2.58 and p-value < 0.01 (Fig. 7).

Fig. 7
figure 7

Hot and cold spot results of unimproved sanitation facilities in Ethiopia using the EMDHS 2019 dataset.

Full size image

These results highlight critical areas requiring targeted intervention to improve access to water and sanitation facilities. Addressing these hotspots is essential for advancing public health in Ethiopia and achieving the SDG 6 targets.

Cluster and outlier spatial analysis

The map below revealed different colors that are used to show the hot and cold spot of unimproved water and sanitation facilities proportions among regions in Ethiopia. The yellow color on the map represents areas classified as “HH” (High-High), indicating a high prevalence unimproved water sources in regions like Gambela, Dire Dawa, some parts of Oromia, and SNNPR. On the contrary, the red line represents “LL” (Low-Low), indicating that all parts of Tigray, some parts of Oromia, and most parts of Amhara and Afar regions of Ethiopia are clusters with lower unimproved water sources (Fig. 8).

Fig. 8
figure 8

Cluster and outlier analysis of unimproved water sources in Ethiopia using the EMDHS 2019 dataset.

Full size image

Different colors were used to demonstrate the hot spot of unimproved sanitation facilities proportion in parts of Ethiopia. The green color represents “HH” (High-High), indicating that the Benishangul Gumuz region, some parts of Tigray, and SNNPR are part of the country where unimproved sanitation facilities is located. On the other hand, the red line represents “LL” (Low-Low), indicating that Addis Ababa city administration, Dire Dawa, Afar, and some parts of Somalia regions of the country are cold spot areas in unimproved sanitation facilities (Fig. 9).

Fig. 9
figure 9

Cluster and outlier analysis of unimproved sanitation facilities in Ethiopia using the EMDHS 2019 dataset.

Full size image

Kriging interpolation

Such identification of high-risk areas, such as Somalia and some parts of Oromia regions in water sources, and Benishangul Gumuz and the western part of Amhara regions in sanitation facilities can guide policymakers in prioritizing interventions and allocating resources more effectively. This targeted approach could significantly improve access to water and sanitation facilities, contributing to public health improvements and the realization of SDG 6. Kriging interpolation is a statistical method that predicts values for unsampled locations based on spatial trends from sampled points, helping to identify high-risk areas. This technique is particularly valuable for understanding regional disparities in water sources and sanitation facilities36,37.

The spatial analysis of Kriging interpolation was used to indicate high and low38 unimproved water source and sanitation facilities proportions in different parts of Ethiopia. The red color indicated the high-risk area whereas the green part of the country is a low-risk area. Dire Dawa, Harari, the southern part of Amhara, and the northern part of Oromia are parts of Ethiopia where an unimproved water source is more experienced (Fig. 10). By facilitating targeted resource allocation and customized strategies to improve access to water and sanitation, Kriging interpolation can identify high-risk areas and have a significant impact on public health policies and interventions. This, in turn, can reduce health disparities and improve community well-being.

Fig. 10
figure 10

Kriging analysis of unimproved water sources in Ethiopia using the EMDHS 2019 dataset.

Full size image

The figure below showed the spatial analysis of Kriging interpolation indicating high and low parts of Ethiopia in unimproved sanitation facilities. The red color showed that the high-risk western (Benishangul Gumuz) and southern (SNNPR) were part of the country with unimproved sanitation facilities. However, Dire Dawa, Afar, southern Somalia and the northern part of Oromia are parts of Ethiopia where unimproved sanitation facilities is lower (Fig. 11).

Fig. 11
figure 11

Kriging interpolation analysis of unimproved sanitation facilities in Ethiopia EMDHS 2019 dataset.

Full size image

Kriging interpolation is a statistical method that predicts values for unsampled locations based on spatial trends from sampled points, helping to identify high-risk areas. This technique is particularly valuable for understanding regional disparities in water and sanitation access.

Spatial scan statistical analysis of unimproved water source and sanitation facilities

The spatial scan statistic showed that five scanning windows were generated in unimproved water source geographic variation analysis. The SaTScan analysis of unimproved water sources indicated that the primary cluster (LLR = 292.89) is found southeast part (most of Somalia and some parts of Oromia regions) of Ethiopia which centered at 11.267438 N, 35.292873 E)/152.50 km radius. Whereas secondary clusters windows were, include some parts of Amhara, Afar, Dire Dawa, and Harari. This indicated that households inside the primary spatial window had a 2.34 times more likelihood of accessing unimproved water sources than households outside this spatial window (Table 2; Figure 12).

Fig. 12
figure 12

SaTScan analysis of unimproved water sources in Ethiopia EMDHS 2019 dataset.

Full size image
Table 2 Spatial scan analysis of areas with a significant unimproved water source in Ethiopia (EMDHS 2019).
Full size table

The spatial scan statistic indicated that five scanning windows were generated in unimproved sanitation facilities on a geographic variation analysis. The SaTScan analysis of unimproved sanitation facilities that the primary cluster window (LLR = 212.50) was found in Benishangul Gumuz and the western part of Amhara regions which placed at (5.479641 N, 42.196835 E)/387.4 km radius. Whereas secondary cluster windows were, include some parts of Amhara, SNNPR, and some parts of Oromia, Tigray. This indicated that households inside the primary cluster window had a 2.66 times more likelihood inexperienced with unimproved sanitation facilities than households outside the primary cluster window (Table 3; Fig. 13).

Fig. 13
figure 13

SaTScan analysis of unimproved water sources in Ethiopia EMDHS 2019 dataset.

Full size image
Table 3 Spatial scan analysis of areas with significant unimproved sanitation facilities in Ethiopia (EMDHS 2019).
Full size table

Discussion

This study aimed to investigate the distribution disparity of water source and sanitation facilities in Ethiopia using EMDHS 2019. The analysis reveals significant regional differences in the prevalence of unimproved water sources and unimproved sanitation facilities across Ethiopia. Sustainable Development Goal (SDG targets 6.1 and 6.2) focused on ensuring safe water and sanitation facilities services for all, fairly and sustainably over the world39. However, inequalities in accessing health-related infrastructures including water sources and sanitation facilities are obvious in developing countries. This finding could be used to evaluate the United Nations’ SDG 6 for 2016–2030 progress on achieving adequate and equitable universal access to sanitation and hygiene by 203040.Spatial autocorrelation is used as preliminaries intended for recognizing the presence of real clustering effect, hot and cold spots (high and low clusters) utilizing diagnostic methods being applied in a diversity of fields41,42. Global Moran’s z-score was 8.735338 for unimproved water sources and 13.313517 for unimproved sanitation facilities with p-values < 0.0001. Such a small p-value and higher z-score confirmed that the witnessed spatial pattern in unimproved water sources and sanitation facilities is less likely to result from random chance or reject the null hypothesis43. As well as these results (positive z scores) indicated that comparable values found together spatially.

After confirming the degree of clustering feature values and summarizing unimproved water sources and sanitation facilities- into a single value using Moran’s z scores and p-values over the country, local autocorrelation was performed. This could be due to Ethiopia’s geographical and climatic diversity, socio-demographic characteristics variation. Therefore, in Ethiopia, there was a clear high and low cluster with unimproved water sources and sanitation facilities surrounded by neighboring clusters. The spatial interpolation analysis indicated that Dire Dawa, Harari, the southern part of Amhara, and the northern part of Oromia are parts of Ethiopia where unimproved water sources are more experienced. Whereas Benishangul Gumuz and SNNPR regions of the country with a high risk of unimproved sanitation facilities. The possible explanation for this geographical variation could be the existence of a marginalized population, uneven distribution of water sources, urbanization differences, technological coverage, and attitude variation in the community toward sanitation facilities. As well as SaTScan analyses also identified a substantial primary cluster (LLR = 292.89) for unimproved water sources which is found in most parts of Somalia and some parts of Oromia regions and for unimproved sanitation facilities that the primary cluster window (LLR = 212.50) found in Benishangul Gumuz and western part of Amhara regions.

All the above evidence indicated sub-regional inequalities in accessing improved water source and sanitation facilities. This showed that some parts of the country accessed the highest level of improved water source and sanitation facilities, at the same time other parts of the country are reduced in having improved water source and sanitation facilities. This finding was in line with the studies done previously in Ethiopia based on EHDS 201618,44, India17and Peruvian45. These results revealed that geographical disparities persist in accessing improved water source and sanitation facilities in Ethiopia. This funding is supported by previous studies done in different parts of the world46,47.

Therefore, to achieve improved water source and sanitation facilities (SDG 6.1 and 6.2 targets) for all by 2030, policymakers, WHO, UNICEF, and other non-governmental organizations needed to work on increasing accessibility across the country. Overall, according to the current study, GIS and SaTScan statistical analysis revealed that unimproved water sources, and sanitation facilities were inequality exist. The necessity for focused WASH interventions to reduce inequalities in access to better water and sanitation facilities services is highlighted by these significance variances. In order to improve public health outcomes and align with national goals for sustainable development, effective policy adjustments could concentrate on giving priority to high-need areas, distributing resources fairly, and putting region-specific strategies to improve water quality and sanitation facilities infrastructure into practice. Especially, findings suggest, guide modifications to existing WASH policies in the countries, implementing community-led sanitation facilities initiatives and enhancing water quality monitoring systems, to improve water source and sanitation conditions in the countries and support the achievement of Sustainable Development Goal 6 (SDG 6.

This study had a number of limitations that could affect how its results are interpreted. First, our capacity to demonstrate causal links between health outcomes and inadequate water and sanitation facilities may be limited by our reliance on cross-sectional data from the 2019 Demographic Health Survey. Furthermore, the accuracy of our findings may be impacted by inherent biases in self-reported data about access to water and sanitation facilities. Last but not least, although while spatial analysis offers insightful information, it might not fully reflect the complexities of socioeconomic and cultural issues affecting access to water and sanitation facilities, indicating the need for additional qualitative research to support these conclusions.

Conclusion

This study contribute to the understanding of the status of water source and sanitation facilities based on recent EHDS data, which can inform policymakers in designing effective strategies that address specific regional challenges. Policymakers should prioritize investments in sanitation infrastructure, particularly in underserved rural and economically disadvantaged regions. Community-based programs promoting hygienic practices, combined with efforts to increase access to clean water, can significantly reduce diarrheal disease burden. This evidence supports aligning public health strategies with equity-driven frameworks, ensuring resources are allocated to the most vulnerable populations. Hence, our result recommends health related policy makers, WASH concerned NGOs and any other stakeholders in reducing inequalities on safe water sources and sanitation facilities (SDG 6.1 and 6.2) in Ethiopia. In addition to providing insight into Ethiopia’s water and sanitation facilities situation, this study highlights important areas for development that can be applied to other nations dealing with comparable problems, especially those in Sub-Saharan Africa. Conducting longitudinal studies could provide insights into the long-term effectiveness of specific interventions in reducing diarrheal diseases. Investigating the role of climate change in exacerbating WASH-related health disparities can inform adaptive strategies.