Exploring the determinants of water, sanitation, and hygiene (WASH) status in Somalia: a multilevel mixed-effects modeling approach using a nationwide survey Open Access

Access to water, sanitation, and hygiene (WASH) services is a fundamental human right, important for promoting health, human growth, and development (Prüss-Ustün et al. 2019). However, millions worldwide still lack access to these essential services, particularly in low- and middle-income countries (LMICs). Sanitation is the maintenance of sanitary conditions through safe feces disposal, wastewater disposal to separate human excreta from human touch at all sanitation facilities in sequence, and wastewater disposal (Yaya et al. 2018). Hygiene is an old concept related to medicine, as well as to personal and professional care practices (Anthonj et al. 2018). Personal hygiene in a straight line aids in disease prevention and health promotion (Levanova et al. 2015; Campbell-Lendrum & Prüss-Ustün 2018). Regular hygienic practices may be considered good habits by society, while the neglect of hygiene can be considered disgusting, disrespectful, or even threatening (Lima & Cordeiro 2017; Yadav et al. 2017).

Definitions relating to hygiene are also somewhat removed from household handwashing with soap. As such, it is important to note that handwashing with soap is a critical hygiene practice that significantly reduces the spread of infectious diseases. However, access to handwashing facilities with soap and water remains limited in many parts of the world. According to the WHO/UNICEF (2015) report, 344 million out of the estimated 768 million people (globally) without access to improved drinking water live in Africa (WHO/UNICEF Joint Water Supply & Sanitation Monitoring Programme 2015).

Somalia faces significant challenges in providing universal WASH access. The country is located in the Horn of Africa and has experienced prolonged periods of conflict, drought, and instability, which have severely impacted its infrastructure and access to basic services.

Somalia faces significant challenges in providing universal access to WASH. The country is located in the Horn of Africa, with an estimated surface area of 637,657 km², characterized by a predominantly plateaued and hilly terrain and a tropical hot climate with little seasonal variation (Farih et al. 2024). It has the longest coastline in Africa, stretching over 3,333 km along the Gulf of Aden to the north and the Indian Ocean to the east and south. It shares borders with Ethiopia to the west, Kenya to the southwest, and Djibouti to the northwest. Somalia has experienced prolonged periods of conflict, drought, and instability, which have severely impacted its infrastructure and access to basic services. The economy runs largely on agriculture and livestock, which accounts for 65% of both the Gross Domestic Product and employment (Hassan et al. 2024b; Ismail et al. 2024; Dahir et al. 2025).

Unsafe drinking water sources and sanitation facilities cause skin diseases, acute respiratory infections, diarrheal diseases, Guinea worm disease, typhoid, cholera, schistosomiasis, trachoma, dysentery, mortality, and other illnesses (Bain et al. 2014; Patunru 2015; Cronk & Bartram 2018; Ali et al. 2025). Estimates suggest that one in four people worldwide do not have access to a handwashing facility with soap and water on premises and that only 26% of potential fecal contacts are followed by handwashing with soap (Wolf et al. 2019). Diarrhoeal diseases due to poor sanitation, poor hygiene, or unsafe drinking water were responsible for 9% of the deaths of children under five (484,000) (UNICEF 2006; WHO/UNICEF Joint Water Supply & Sanitation Monitoring Programme 2015).

Globally, 2 billion people lack access to safely managed drinking water, 3.6 billion people do not have access to safely managed sanitation, and 2.3 billion people lack basic hygiene services (Organization & Fund 2021). WASH assessment results are not composite in LMICs where only 2% of the healthcare facilities provide WASH and waste management services (Bartram & Cairncross 2010). In Ethiopia, 60% of the communicable disease burden is related to poor WASH, and more than 250,000 children die every year from WASH-related diseases. Thus, they are considered major causes of illness, death, and disability in Ethiopia (Shehmolo et al. 2021).

Without access to clean water, toilets, and good hygiene practices, the risk of contracting easily preventable diseases, such as diarrhea, acute watery diarrhea, cholera, and respiratory infections, is high. In the past 3 years, more than 900 people in Somalia, the majority of them children under the age of five, have died from cholera (Bartram & Cairncross 2010; Günther & Fink 2010). Approximately 6.6 million people in Somalia will require lifesaving WASH assistance in 2024, down from 8.0 million in 2023, representing an 18% decrease in the number of people in need (Mafuta et al. 2021).

The United Nations adopted the 2030 Agenda for Sustainable Development Goals (SDGs) 6, which aims to ‘ensure the availability and sustainable management of water and sanitation for all’ (Nkiaka et al. 2021). SDG 6 Target 6.1 also aims to achieve universal and equitable access to safe and affordable drinking water for all (Twinoburyo et al. 2019; Sharma et al. 2025). So, this study aimed to explore the magnitude and determinants of WASH status in Somalia: a multilevel analysis using nationwide survey.

Somalia, covering an estimated 637,657 km², is a country in the Horn of Africa characterized by a predominantly plateaued and hilly terrain. With a coastline of 3,333 km along the Gulf of Aden in the north and the Indian Ocean in the east and south, it has the longest coastline in Africa. It shares borders with Ethiopia to the west, Kenya to the southwest, and Djibouti to the northwest. Home to approximately 17 million people, the country is predominantly inhabited by ethnic Somalis. Mogadishu serves as the capital and acts as Somalia's political and economic hub. The country's arid and semi-arid climate, combined with seasonal rainfall, shapes its agriculture- and livestock-based economy. As a result, Somalia faces substantial health challenges, including high child mortality rates and widespread malnutrition. In Somalia, the healthcare system heavily depends on humanitarian organizations and non-governmental groups to provide critical services (Hassan et al. 2024a; Yousuf et al. 2024; Farih et al. 2025).

This study used a cross-sectional design using a secondary analysis of data obtained from the Somali Demographic and Health Survey (SHDS), which is the first nationally representative survey carried out by the Somalia National Bureau of Statistics from January 2018 to February 2019 (Hassan et al. 2024b). In addition to providing vital indicators for the entire nation, the study also targeted specific urban, rural, and nomadic areas as well as each of the 18 pre-war geographical districts. There were 47 sampling strata in all since some areas, including Lower Shabelle, Middle Juba, and portions of the Bay, were not included in the survey because of security concerns. In order to guarantee survey precision uniformity across regions, the sampling strategy comprised a three-stage stratified cluster sample technique in both urban and rural areas, with proportionate sampling of primary sampling units (PSU) and secondary sampling units (SSU) at different phases (22). In the first phase, a total of 1,433 enumeration areas (EAs) in urban, rural, and nomadic areas were sampled within each stratum using digitally constructed dwellings. In a few chosen urban and rural EAs, household listings were completed, and births and deaths were noted. Then, in the second phase, 30 families were chosen from each of the 10 EAs that were sampled from the original group of 35. In the third stage, a total of 16,360 households from 538 EAs were covered, with a specific focus on interviewing ever-married women aged 12–49 and never-married women aged 15–49, alongside administering household and maternal mortality questionnaires. However, in this study, after data preprocessing, a final sample of 14,314 households is used to study the WASH status in Somalia.

The survey instrument was administered through face-to-face interviews using structured questionnaires (Ahmed & Ali 2024). Trained enumerators from the Somalia National Bureau of Statistics were responsible for data collection and its organization. The enumerators were trained in household listing concepts (identification of structures, dwelling units, and EA boundaries), interview techniques, interviewers' and supervisors' roles, age probing techniques, fieldwork procedures, sampling techniques, the importance of data on births and deaths, recognizing and handling age inconsistencies, identification of maternal deaths, and the CSPro mobile data collection application. The questionnaires were further tested and refined in the field to ensure that culturally and religiously sensitive questions were appropriately worded.

The regional sampling was proportional to the population of each region (Mohamud et al. 2023). The sampling rationale was to guarantee survey precision uniformity across regions, and the sampling strategy comprised a three-stage stratified cluster sample technique in both urban and rural areas, with a proportionate sampling of PSU and SSU at different phases. The survey managed to capture information on nomadic populations through nomadic link workers (NLWs) and community gatekeepers (clan elders) (Yousuf et al. 2024). The NLWs are associated with nomads through clan affiliation and have linkages with clan elders who reside in rural villages that are frequented by nomads to buy essential commodities and to sell their livestock and livestock products. IDPs were included in the survey, but further analysis on Internally Displaced Persons (IDP) was not included due to time and resource constraints.

The dependent variables (DVs) in this study represented household access to basic WASH services. Following the WHO/UNICEF Joint Monitoring Programme (JMP) guidelines, access to drinking WASH was categorized by service levels: ‘basic,’ ‘limited,’ ‘unimproved,’ and ‘no service’ (as shown in Table 1 and Table 2 [1]). To simplify the analysis, the variables were dichotomized, with 1 indicating ‘yes’ for households with a basic service level and 0 for all other levels (individual basic WASH services). Additionally, a binary variable was created, referred to as combined basic WASH services, to represent households with yes if they have access to all three basic facilities and no if not.

Instead of creating a combined WASH index, the analysis was conducted separately for each of the following DVs.

This study included several independent variables (IVs), categorized into three groups based on the literature [2]. Geographical factors consisted of region and type of place of residence (urban, rural, and nomadic). Characteristics of the household head were represented by age (<25, 26–35, 36–45, 46–55, and 56+), sex (male and female), and school attendance (yes and no). Household composition and economic status were indicated by the wealth index (poor, middle, and rich) and household size (1–3 members, 4–7 members, and 8 + members).

Wealth was measured by a composite measure of a household's cumulative living standard, known as the wealth index (Directorate of National Statistics, Federal Government of Somalia 2020). The wealth index is calculated using easy-to-collect data on a household's ownership of selected assets, such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities. Each household asset for which information is collected is assigned a weight or factor score generated through principal components analysis. The resulting asset scores are standardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one. These standardized scores are then used to create the breakpoints that define wealth quintiles as lowest, second, middle, fourth, and highest. Each household is assigned a standardized score for each asset, where the score differs depending on whether or not the household owned that asset (or, in the case of sleeping arrangements, the number of people per room). These scores are summed by household, and individuals are ranked according to the total score of the household in which they reside. The sample is then divided into population quintiles – five groups with the same number of individuals in each. A single asset index is developed on the basis of data from the entire country sample and used in all the tabulations presented. Separate asset indices are not prepared for rural and urban population groups on the basis of rural or urban data, respectively. Wealth quintiles are expressed in terms of quintiles of individuals in the population, rather than quintiles of individuals at risk for any one health or population indicator. This approach to defining wealth quintiles has the advantage of producing information directly relevant to the principal question of interest, for example, the health status or access to services for the poor in the population as a whole. This choice also facilitates comparisons across indicators for the same quintile, since the quintile denominators remain unchanged across indicators. However, some types of analysis may require data for quintiles of individuals at risk.

Statistical analysis was conducted using Stata 17. Univariate analysis was performed to summarize the study variables, with results presented as frequencies and percentages. Bivariate analysis examined the distribution of each IV across the levels of the four DVs: Water, Sanitation, Hygiene, and Combined WASH. Relationships between the IVs and each DV were assessed using chi-square tests (χ²) with a significance threshold of <0.05. For the multivariate analysis, the variance inflation factor (VIF) is also used to test multicollinearity among IVs. In addition, a multilevel mixed-effects modeling approach with binary logistic regression was applied. Four models (Models 0, I, II, and III) were compared to identify the best-fitting model for each DV. Model selection was guided by evaluation metrics, including the akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood, intraclass correlation coefficient (ICC), and variance. The final results were presented by interpreting the adjusted odds ratios (AOR) and confidence intervals (CI) derived from the selected models.

Table 3 presents the distribution of household characteristics across regions, residence types, wealth indices, and demographic variables in Somalia. The highest proportion of households is in Banadir (11.58%), followed by Woqooyi Galbeed (8.75%), and Sanaag (8.44%), with Bay having the lowest representation (2.00%). Urban households constitute 41.79%, while rural and nomadic households make up 27.94% and 30.26%, respectively. Most households belong to the poorest wealth quintile (54.71%), whereas 27.72% are classified as rich. Regarding household head characteristics, the majority are male (66.87%) and fall within the age group of 26–35 years (26.63%), followed by 36–45 years (22.96%) and 56 years or above (22.82%). Household sizes primarily range between 4 and 7 members (53.57%), with smaller households (1–3 members) accounting for 19.84%.

The findings indicate that 62.45% of Somalia's population has access to basic water services, while 10.47% rely on limited services, 18.88% use unimproved sources, and 8.19% depend on surface water, which means that over a third of the population lacks safe water, exposing them to significant health risks. As shown in Table 3, geographical disparities in water access are evident (Region: χ² = 2029.5; Place of residence: χ² = 4,281.5). The highest access rates are found in Banadir (97.65%) and Galgaduud (82.05%), while Bakool (26.16%) and Sool (44.49%) have the lowest. Urban residents (89.27%) have better access than rural (61.75%) and nomadic populations (26.06%). Socioeconomic factors also play a significant role. Wealthier households enjoy significantly better access (χ² = 3302.6), with 91.00% of the richest group having basic services compared to only 41.43% among the poorest. The sex of the household head also influences access (χ² = 11.5), with female-headed households (64.40%) having slightly better access than male-headed ones (61.48%). Education level is strongly associated with access to water (χ² = 408.2), as households led by educated individuals (73.52%) fare better than those without formal education (56.41%). Additionally, the age of the household head is significantly associated with access (χ² = 168.6), with younger household heads struggling more to secure water services. Household size also plays a role (χ² = 34.9), as smaller households report lower access compared to larger ones.

The prevalence of basic sanitation services in Somalia is 27.72%, while 15.46% of the population relies on limited services, 18.40% uses unimproved sanitation, and 38.42% lacks access to any form of sanitation. As shown in Table 3, significant regional disparities in sanitation exist (χ² = 1,136.4). The highest coverage is observed in Banadir (52.05%), followed by Mudug (45.63%) and Middle Shabelle (37.43%). In contrast, the lowest access is found in Bakool (10.55%), Gedo (10.38%), and Bay (14.34%), highlighting stark regional inequalities. Place of residence also plays a significant role in sanitation access (χ² = 2,591.4). Urban areas have significantly higher sanitation access (45.92%) compared to rural areas (29.90%) and nomadic populations (0.58%). Wealth status is another crucial determinant (χ² = 3,882.5). While 60.03% of the richest households have access to basic sanitation, this drops to 40.16% among middle-income households and just 7.36% among the poorest. Household characteristics also influence sanitation access. Female-headed households (28.53%) report slightly better coverage than male-headed ones (27.32%) (χ² = 2.3), though this difference is not statistically significant. The age of the household head is significantly associated with sanitation access (χ² = 74.6), with the highest coverage observed among those aged 46–55 (30.11%) and 36–45 (30.06%), while the lowest is among those aged 25 or younger (18.67%). Education level plays a strong role (χ² = 392.8), as households with educated heads have significantly higher access (37.76%) compared to those without formal schooling (22.24%). Additionally, household size is a key determinant (χ² = 402.9), with larger households (8 or more members) having the highest access (39.39%), while the smallest households (1–3 members) report the lowest (18.38%).

The availability of basic hygiene services in Somalia remains extremely low, with only 8.44% of the population having access. A majority, 67.71%, depend on limited services, while 23.85% have no access at all. Significant regional variations exist (χ² = 540.7), as shown in Table 3, with Galgaduud (18.49%), Banadir (15.44%), and Woqooyi Galbeed (15.34%) reporting the highest coverage. Conversely, access is severely limited in Bakool (0.27%), Sool (1.96%), and Middle Shabelle (2.79%), underscoring disparities across regions. Hygiene access also differs substantially by place of residence (χ² = 982.8). Urban populations (16.77%) are significantly better off than their rural counterparts (4.92%) and nomadic groups (0.18%), emphasizing the rural-urban divide in hygiene infrastructure. Economic status is another key determinant (χ² = 1,445.5). Access is highest among the wealthiest households (21.85%), while it declines to 9.54% for middle-income groups and just 1.29% among the poorest, indicating a strong correlation between affluence and hygiene services. Household characteristics also play a role, though some factors are less influential than others. The sex of the household head does not show a statistically significant impact (χ² = 0.5), with male-headed households (8.56%) and female-headed ones (8.20%) reporting nearly identical access levels. In contrast, the age of the household head is significantly associated with hygiene access (χ² = 12.9), with the highest rates found among those aged 26–35 (9.39%) and 36–45 (8.85%), while the youngest group (25 or younger) has the lowest (6.81%). Education strongly influences hygiene accessibility (χ² = 384.3). Households led by individuals with formal education benefit from notably higher access (14.61%) compared to those without schooling (5.08%). Household size is another significant factor (χ² = 53.2). Larger families (8 or more members) experience the highest access rates (11.06%), whereas smaller households (1–3 members) report the lowest (6.30%).

The prevalence of combined WASH access in Somalia is 4.88%, with significant regional disparities (χ² = 272.5). The highest access is observed in Banadir (9.41%), Woqooyi Galbeed (9.03%), and Urban Nugaal (7.47%), while the lowest is in Bakool (0.14%), Sool (1.21%), and Gedo (1.90%), reflecting an uneven distribution of services. Urban households have significantly higher access (10.06%) compared to rural (2.25%) and nomadic populations (0.09%) (χ² = 621.9), highlighting geographic inequalities. Economic status also plays a crucial role (χ² = 1,245), with 14.94% of the richest households having access, compared to 3.34% among middle-income households and only 0.24% among the poorest. Household characteristics further influence WASH access. The sex of the household head is not statistically significant (χ² = 1.3), with male-headed households reporting 5.00% access and female-headed ones 4.58%. Age of the household head shows a significant association (χ² = 11.9), with the highest access among those aged 26–35 (5.38%) and 36–45 (5.17%), while the lowest is among those 25 or younger (3.37%).

Education is strongly associated with WASH access (χ² = 252.2), as households with educated heads have higher access (8.73%) compared to those without formal schooling (2.75%). Household size also shows a significant association (χ² = 64.8), with the highest access among larger households (8 or more members) at 7.15%, while smaller households (1–3 members) have the lowest access (3.17%).

Table 4 presents the VIF values and their reciprocals (1/VIF) for the variables included in the analysis. The VIF values indicate low collinearity, with the region (1.636), type of place of residence (1.067), wealth index (1.606), sex of household head (1.074), household head age (1.049), school attendance of household head (1.161), and household size (1.084). The corresponding 1/VIF values are 0.611, 0.937, 0.623, 0.931, 0.953, 0.861, and 0.923, respectively. The mean VIF of 1.24 confirms that multicollinearity is not a concern, as all values remain well below the conventional threshold of 5.

To examine the factors associated with water, sanitation, hygiene, and the combined WASH status in Somalia, separate multilevel analyses were conducted for each component. Across all analyses, Model III consistently emerged as the superior model based on multiple evaluation metrics, including the lowest AIC, the lowest BIC, and the highest log-likelihood values. This indicates that Model III provided the best overall fit compared to alternative models. Furthermore, substantial reductions in the ICC across all analyses highlighted the explanatory power of the included predictors in accounting for cluster-level variance. For water, Model III exhibited the lowest AIC (11,882.65) and BIC (12,102.15) values, along with an improved log-likelihood of −5,912.32. The ICC decreased from 70.19% in the null model (Model 0) to 34.75%, indicating that a considerable proportion of variance was explained by the predictors. In the analysis of sanitation, Model III demonstrated superior performance with an AIC of 11,114.82, a BIC of 11,326.75, and a log-likelihood of −5,529.41. The ICC dropped from 78.83% in Model 0 to 9.92%, underscoring the substantial explanatory power of the model. For hygiene, Model III achieved the lowest AIC (6,141.17) and BIC (6,353.10) and the highest log-likelihood value of −3,042.58. The ICC significantly decreased from 60.30% in the null model to 3.18%, reflecting a marked reduction in unexplained cluster variance. Finally, for the combined WASH variable, Model III showed the lowest AIC (4,134.57) and BIC (4,346.50), along with an improved log-likelihood of −2,039.29. The ICC reduced from 52.34% in Model 0 to 5.24%, further affirming the model's superior fit and robustness. These findings consistently highlight Model III as the most appropriate for examining the factors associated with WASH status in Somalia, offering robust explanatory power and a significant reduction in cluster-level variance across all components.

As shown in Table 5, several key factors were significantly associated with access to basic water services. Regional disparities were pronounced, with households in Galgaduud (AOR = 13.35, 95% CI: 7.55–23.62), Mudug (AOR = 5.07, 95% CI: 3.43–7.48), and Banadir (AOR = 5.73, 95% CI: 3.35–9.80) being significantly more likely to have basic water access compared to those in Awdal. Conversely, households in Bakool (AOR = 0.30, 95% CI: 0.22–0.42), Hiraan (AOR = 0.47, 95% CI: 0.34–0.65), and Lower Juba (AOR = 0.52, 95% CI: 0.36–0.74) faced substantially lower odds of accessing basic water services. Place of residence also played a crucial role, with rural (AOR = 0.47, 95% CI: 0.38–0.57) and nomadic (AOR = 0.03, 95% CI: 0.02–0.04) households significantly less likely to have basic water access compared to urban households. Wealth status exhibited a strong positive association, as middle-income (AOR = 2.07, 95% CI: 1.78–2.40) and wealthy households (AOR = 3.14, 95% CI: 2.64–3.72) had significantly higher odds of having basic water access than poor households. Among demographic factors, the age of the household head was a significant determinant of water access. Households led by individuals aged 26–35 (AOR = 1.22, 95% CI: 1.03–1.46) and 46–55 (AOR = 1.24, 95% CI: 1.02–1.50) had significantly greater odds of accessing basic water compared to those headed by individuals aged 25 or younger. However, neither the sex of the household head nor household size showed significant associations in Model II.

In a multilevel analysis of sanitation, regional disparities were significant in sanitation access, as shown in Table 6. Households in Mudug (AOR = 9.42, 95% CI: 6.64–13.36), Middle Shabelle (AOR = 9.10, 95% CI: 6.43–12.88), and Bari (AOR = 3.80, 95% CI: 2.67–5.40) were significantly more likely to have basic sanitation access compared to Awdal. Conversely, Bay (AOR = 0.69, 95% CI: 0.37–1.28) and Gedo (AOR = 1.15, 95% CI: 0.76–1.75) had no significant association. Place of residence had a notable impact, with nomadic households (AOR = 0.02, 95% CI: 0.01–0.03) being significantly less likely to have access to basic sanitation compared to urban households. However, rural households (AOR = 1.17, 95% CI: 0.98–1.38) showed no significant difference. Wealth status exhibited a strong positive association, with middle-income (AOR = 3.62, 95% CI: 3.15–4.17) and rich households (AOR = 8.64, 95% CI: 7.45–10.02) having significantly higher odds of basic sanitation access compared to poor households. Demographic factors also played a role. Households led by individuals aged 36–45 (AOR = 1.43, 95% CI: 1.17–1.74), 46–55 (AOR = 1.54, 95% CI: 1.26–1.89), and 56 or above (AOR = 1.55, 95% CI: 1.27–1.88) had significantly higher odds of basic sanitation compared to those headed by individuals aged 25 or younger. Education and household size influenced sanitation access. Households, where the head did not attend school (AOR = 0.86, 95% CI: 0.78–0.95), were significantly less likely to have basic sanitation access. Larger households (8 or more members) had higher odds (AOR = 1.85, 95% CI: 1.59–2.15) compared to smaller households (1–3 members).

Based on the results presented in Table 7, the multilevel binary logistic regression analysis reveals significant regional, residential, and socioeconomic disparities in access to basic hygiene services in Somalia. Compared to Awdal (reference region), regions such as Sool (AOR = 0.17, 95% CI: 0.10–0.30), Sanaag (AOR = 0.22, 95% CI: 0.14–0.34), and Bari (AOR = 0.41, 95% CI: 0.27–0.64) exhibit significantly lower odds of hygiene access. Conversely, Galgaduud (AOR = 2.30, 95% CI: 1.58–3.34) demonstrates higher odds, suggesting better hygiene access than the reference region. Place of residence plays a pivotal role, with individuals in rural (AOR = 0.39, 95% CI: 0.31–0.49) and nomadic (AOR = 0.03, 95% CI: 0.015–0.07) areas significantly less likely to access basic hygiene compared to urban residents. Similarly, wealth status is a strong determinant: households in the middle (AOR = 2.86, 95% CI: 2.21–3.72) and rich (AOR = 6.12, 95% CI: 4.77–7.84) categories are more likely to report hygiene access compared to poor households. Household head education significantly affects hygiene access; households, where the head did not attend school (AOR = 0.55, 95% CI: 0.48–0.64), show lower odds compared to those with educated heads. The age of the household head and household size, however, do not exhibit significant associations.

For the combined WASH variable, as shown in Model III presented in Table 8, significant findings for the region variable show that the odds of combined WASH outcomes are significantly lower in Sool (AOR = 0.28, 95% CI: 0.14–0.56), Sanaag (AOR = 0.43, 95% CI: 0.25–0.74), and Bakool (AOR = 0.09, 95% CI: 0.01–0.70), compared to Awdal. The place of residence variable also indicates that rural (AOR = 0.48, 95% CI: 0.36–0.66) and nomadic (AOR = 0.07, 95% CI: 0.02–0.20) populations are significantly less likely to have combined basic WASH services access compared to urban residents. In terms of wealth, individuals in middle (AOR = 5.47, 95% CI: 3.23–9.27) and rich (AOR = 21.25, 95% CI: 12.89–35.03) households are more likely to report WASH services access compared to poor households. Household head school attendance reveals that households where the head did not attend school (AOR = 0.58, 95% CI: 0.48–0.69) have lower odds of access to basic combined WASH services compared to those where the head attended school. In contrast, household size and household head age show no statistical significance in household access to basic WASH services.

This study provides a critical assessment of household WASH access in Somalia, which paints a concerning picture where only a small fraction of households (4.88%) have combined basic access to WASH. The disaggregation of the WASH index into its constituent components – WASH – reveals a more nuanced understanding of the challenges. While a larger percentage of households have basic access to water (62.45%), sanitation (27.72%), and especially hygiene (8.44%), they lag significantly. This underscores the urgent need for targeted interventions tailored to each component, rather than a one-size-fits-all approach. The multilevel mixed-effects modeling approach allows for a more nuanced understanding of the complex interplay of various factors influencing WASH access, including individual-level and contextual variables. This finding resonates with previous research (Adams et al. 2016; Ahmed et al. 2021) that emphasizes the importance of socioeconomic factors in shaping WASH outcomes. Furthermore, the analysis moves beyond simply identifying associations to exploring potential causal links, supported by relevant theory. This strengthens the study's conclusions and provides a more robust basis for policy recommendations.

The strong association between wealth and WASH status, confirming previous findings (Ayesu et al. 2015), highlights persistent socioeconomic disparities as a fundamental barrier to equitable WASH access. Households in the richest wealth quintile are significantly more likely to have access to basic WASH compared to the poorest, and this pattern holds true for each individual component: WASH. This necessitates interventions specifically designed to address these socioeconomic inequalities, ensuring that the benefits of WASH programs reach the most vulnerable populations. The study also reveals a significant association between education level and improved water and sanitation access. As such, in order to address inequities and provide effective solutions, it is recommended that the Somalia WASH Sector Strategic Plan have ‘a reliable sector baseline' that includes ‘qualitative data generated through periodic knowledge, attitude and practice (KAP) studies, indicative of local contexts in different parts of the country, particularly vulnerable areas’ (Mafuta et al. 2021; Dahir et al. 2025).

Significant regional disparities are also a factor in accessing WASH (Abebaw et al. 2010; Hassan et al. 2024a). The highest overall access is found in the Banadir region, while the lowest access is found in the Galgaduud, Sool, and Bakool regions, which have lower access to basic hygiene. As such, it is suggested that tailored interventions should be carried out on the specific challenges and the need to implement them in the different regions. Interventions should recognize the diverse contexts and challenges within each region, moving beyond broad generalizations to address the unique needs of specific communities.

The significant difference in WASH status between urban and rural areas is consistent with findings from other studies (Dhital et al. 2024), indicating that a divide is persistent. While the survey has managed to capture the information of the nomadic populations and adjustments done, it is important to add a note on how IDPs were included and any limitations this could impose. Regardless, it must be reiterated that all interventions should take into account the different water sources, sanitation, and hygiene behaviors between rural and urban regions. This is especially given that rural areas often lack the resources and infrastructure that are abundant in urban regions. Finally, the analysis confirms the critical importance of water access in influencing overall WASH status (Prüss-Ustün et al. 2019; Wolf et al. 2019).

In addition, the study's findings align with some of the broader trends revealed by the WHO/UNICEF JMP data for Somalia (WHO/UNICEF Joint Water Supply & Sanitation Monitoring Programme 2015). Specifically, the low level of overall basic sanitation coverage and persistent disparities are echoed in the JMP's service ladders. However, given the more detailed service ladder definitions in the JMP for each component of WASH and our own use of those definitions to create our DVs, this study further elucidates the specific challenges faced by communities in accessing each component (water, sanitation, hygiene). Our analysis, therefore, provides a more granular picture of the WASH situation in Somalia, highlighting the need for more targeted interventions. As the Strategic Plan (Federal Government of Somalia, 2019) noted, most of the existing infrastructure is concentrated in urban areas, with rural areas lagging behind.

Overall, it must be recognized that it is imperative that there be additional studies and research to improve and promote WASH access to those in need and vulnerable, in order to address and resolve this public health crisis.

This study, employing a multilevel mixed-effects modeling approach and binary logistic regressions, provides a detailed analysis of household WASH access in Somalia using data from the 2020 Somali Demographic and Health Survey. This study underscores the critical importance of addressing both individual and community-level factors to effectively improve WASH access in Somalia. While most households in Somalia face significant challenges in accessing basic WASH services, this burden is exacerbated by factors such as poverty, regional disparities, urban-rural divides, and access to drinking water.

The re-analysis using JMP-aligned service levels and separate DVs for each component of WASH has enabled the study to demonstrate the specific determinants of water access, sanitation access, and hygiene practices. The analysis revealed that the factors impacting access to each component differed, requiring a multi-pronged approach that prioritizes the implementation of interventions specifically targeted to address the distinct challenges to each service. It is imperative that this study's results, alongside those already highlighted by the JMP (WHO/UNICEF Joint Water Supply & Sanitation Monitoring Programme 2015), inform the design of targeted interventions and policies to achieve SDG 6.

The findings of this study offer several valuable insights for policymakers and stakeholders aiming to improve WASH access in Somalia, moving beyond general statements to provide concrete recommendations informed by the study's findings.

• Targeted investments: Prioritize investments in rural areas and regions with low WASH access, particularly in Galgaduud, Bakool, and Sool. Given the lower levels of sanitation and hygiene access in these regions, focus resources on improving sanitation facilities, such as constructing latrines and promoting handwashing stations, while at the same time, expanding water infrastructure. This aligns with the Strategic Plan (Federal Government of Somalia 2019) which highlights the need to ‘prioritize interventions in rural areas, which often lack the infrastructure and resources available in urban settings’ (p. 11). Furthermore, these investments should be informed by a detailed understanding of the specific needs and challenges within each region, as identified through qualitative research and community consultations.

• Address socioeconomic disparities: Implement policies and programs that address the underlying socioeconomic factors that contribute to disparities in WASH access. To address the wealth gap in WASH access, prioritize interventions specifically targeting low-income communities. This includes considering subsidies for latrine construction, promoting affordable water filter technologies, and implementing social protection programs, as well as creating access for those with educational and other disadvantages. These policies should also consider the role of education in promoting WASH practices and should aim to improve access to education for girls and women, particularly in rural areas.

• Prioritize hygiene education: Invest in comprehensive hygiene education programs, including education on handwashing, safe water management, and sanitation practices. Given the low rates of basic hygiene practices, these programs should be targeted at both children and adults, emphasizing the importance of hygiene for preventing disease and promoting overall health. Community health workers can play a critical role in disseminating hygiene information and promoting behavior change at the household level. These programs should be culturally sensitive and tailored to the specific needs and beliefs of different communities.

• Community led interventions: Empower communities to participate in the planning, implementation, and management of WASH interventions to foster local ownership and to ensure interventions are culturally appropriate and sustainable. This aligns with the Strategic Plan's emphasis on ‘community-based sanitation approaches’ (p. 13), where communities are active agents in ensuring effective service provision and use. This includes providing communities with the resources and training they need to maintain and repair WASH infrastructure.

• Data-driven decision-making: Regularly monitor WASH indicators and conduct assessments to track progress and inform policy decisions using high-quality and up-to-date data from sources such as the Demographic Health Survey. In addition, consider expanding the use of data in national planning, by creating a framework and standards to allow for regular WASH data collection, analysis, reporting, dissemination and use. This data should be disaggregated by region, wealth quintile, and other relevant factors to identify areas where interventions are most needed.

• Policy advocacy: Advocate for policies that prioritize WASH infrastructure development, promote equitable access to safe WASH services, and address the needs of marginalized communities and vulnerable groups, as noted in the Somalia WASH Sector Policy (Federal Government of Somalia 2019) (p. 11). Additionally, provide explanations about wealth or other factors and their impact on WASH status. This advocacy should also focus on promoting the integration of WASH into other development sectors, such as health, education, and agriculture.

Future research should build upon the findings of this study by exploring causal links between various determinants and WASH outcomes to strengthen the evidence base for effective policy-making. In addition, future studies should incorporate qualitative research methods to explore and analyze the lived experiences of households and communities to better understand their perceptions, behaviors, and challenges related to WASH access. Furthermore, longitudinal studies should be conducted to track WASH trends over time and assess the effectiveness of interventions. An in-depth analysis should be conducted to further analyze other factors that contribute to WASH outcomes, such as the sex and age of the household head, to see if they may be mediating factors. Finally, data triangulation should be used to triangulate these findings with other data sources, such as the JMP datasets, to understand the inconsistencies or consistencies of different findings, and what these different datasets and analytical approaches tell us. Future research should also consider the spatial distribution of access to basic hygiene, as measured by the Somali Health and Demographic Survey (SHDS) 2020, to identify areas where targeted interventions are most needed and to understand the factors contributing to disparities in access.

Authors got permission from Demographic and Health Surveys (DHS) Program and downloaded data from this link (https://microdata.nbs.gov.so/index.php/catalog/50). As this data is publicly available and has no personal identifiers, Ethical approval was not necessary.

The study did not receive funding.

All relevant data are available from https://microdata.nbs.gov.so/index.php/catalog/50.

The authors declare there is no conflict.