Access to Water, Sanitation, and Hygiene (WASH) practices and their associations with waterborne diseases and malnutrition in Ibrahim Kodbuur District, Hargeisa, Somaliland: a cross-sectional study

Abstract

Background

In low-resource environments like Somaliland, where insufficient infrastructure leads to high rates of hunger and waterborne illnesses, access to water, sanitation, and hygiene (WASH) is crucial for health. WASH practices in Ibrahim Kodbuur District, Hargeisa, were evaluated in this study in order to find gaps, guide solutions that support Sustainable Development Goal 6, and their links to health outcomes, including child malnutrition.

Methods

Ninety-seven households chosen through multi-stage cluster sampling participated in a cross-sectional community-based survey that was carried out between June and August 2025. Data on sociodemographic, WASH access, and health outcomes were collected using WHO-adapted questionnaires and visual assessments. Nutritional status was evaluated via mid-upper arm circumference (MUAC) for rapid screening of acute malnutrition in under-fives and BMI z-scores, as per WHO standards, to capture WASH-related vulnerabilities. Descriptive statistics, principal component analysis (PCA), and multiple linear regression (MLR) analysed associations with waterborne diseases and malnutrition.

Results

Among 38.10% of families reported round-trip water collection times exceeding 15 min (indicating moderate to high access burden), 22.70% stored it exposed, and only 13.40% of households had piped water access, with 61.90% depending on communal taps and 24.70% on unprotected wells. Although 60.80% of people used boiling, sanitation was inadequate, with 77.30% disposing of liquid waste in open pits, 14.40% using septic tanks, and 15.50% using dry latrines. Handwashing facilities were available in 32%. Waterborne illnesses affected 39.20% in the past two weeks, primarily diarrhoea. Among under-fives, 53.60% had moderate wasting (MUAC yellow), 12.50% severe (red), 35.70% wasting, and 17.90% severe malnutrition (BMI z-scores). PCA revealed a dominant socioeconomic-WASH deprivation factor (eigenvalue > 1). For malnutrition, MLR found that education (p = 0.002) and water sources (p = 0.003) were predictors, while income (p < 0.001), handwashing (p < 0.001), and distance to source (p = 0.006) were predictors of illnesses. Hence, WASH practices were substantially linked to health outcomes (malnutrition and waterborne illnesses), regardless of sociodemographic but in a multifactorial setting.

Conclusion

Due to socioeconomic obstacles, Ibrahim Kodbuur’s subpar WASH feeds the cycles of illness and malnutrition. Evidence from analogous low-resource settings indicates that targeted interventions—such as Biosand filters (reducing diarrheal risks by 25–58%), subsidized infrastructure, and hygiene education—could substantially mitigate these cycles, fostering equitable health gains in comparable peri-urban Somaliland environments, while more extensive multi-district research is required for wider generalizability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-26022-8.

Background

The general health of people and communities is closely related to access to clean water and sanitary services, as a fundamental right. Good health depends on basic sanitation, hygiene, and a safe supply of drinking water. Improved basic sanitation and usable facilities are necessary to promote better health and prevent environmental contamination by fecal-transmitted diseases [1–3]. Through goals like achieving universal and equitable access to clean and inexpensive drinking water, achieving adequate, equitable sanitation, and hygiene for all, and ending open defecation, the Sustainable Development Goals (SDG) 3 and 6 highlight the significance of WASH [4].

Across the globe, around 30% of the population lack access to safe drinking water, whereas 40% lack essential services such as handwashing services (soap and water). More than 673 million people still use open-pit latrines due to poverty, inequality, mismanagement of public funds, and insufficient funding. The World Health Organization claims the WASH systems are destroyed during hostilities and waterborne illnesses frequently occur. Poor sanitation, a lack of handwashing stations, and contaminated drinking water are to blame for more than 1.3 million fatalities in low- and middle-income regions worldwide. In Africa, 1.8 billion people lack basic WASH facilities, such as toilets and latrines, and over 660 million lack access to better water sources [5–8]. Four hundred million people in Sub-Saharan Africa still do not have access to basic drinking water, and 709 million do not have access to basic sanitation facilities. With 16 countries having fewer than 40% of basic sanitation facilities and 17 countries having less than 40% of basic drinking water facilities [9–12].

Nigeria exemplifies this, with 28% lack access to basic drinking water, 75% basic sanitation, two-thirds do not have a hand washing facility comprising soap and water, while 20% practice open defecation [4, 7]. In Ethiopia who defecate in the open was 33%, only 38% of families have handwashing facilities (13% using soap and water), and 27.5% of houses have improved toilet facilities, with stark urban-rural water quality gaps [13–16]. Uganda’s urban remains low, with 12.6% practicing open defecation, 39.6% hand-washing facilities, and more than 78% of slum households share sanitary facilities [17, 18]. Kenyan urban slums revealed: 38% unimproved sanitation facility, 10% open defecation, 32% lack an improved drinking water supply at home, and 75% of homes pose health hazards [19, 20]. Of the 46 million Sudanese, 17.3 million lacking clean drinking water, exacerbated by open defecation [21]. Rural Zimbabwe sees over 30% of households accessing drinking water from unimproved sources [10], while 40–46% of the rural population in Tanzania access declined to clean and safe water [22].

The millions of Africans who drink polluted water and live in unhealthy environment are exposed to the risk of diarrhea, cholera, dysentery, typhoid, and parasitic diseases that cause illnesses, death, and stunted lives (78% of all cases are children and adults) [7, 23–26]. 88% of newborn deaths in Ethiopia are caused by unsafe water supplies, poor sanitation, and poor hygiene habits [27, 28]. In Mozambique, cholera outbreaks related with people live in conditions of inadequate water supply, poor sanitation, high population density, and a severe lack of resources [29]. The 2019 Ethiopian Mini Demography and Health Surveys (EDHS) study found that 21% of children under five were underweight, 7% were wasting, and 37% were stunted. Denial of safe WASH access is an essential cause of maternal and child health and nutrition problems [30–32]. Women of reproductive age (from 15 to 49 years) have an additional potential vulnerability as they must manage menstrual periods [17]. Unhealthy sanitation and hygiene practices have also been associated with inadequate knowledge and negative attitude [33]. Inadequate WASH, also as a stressor of common mental symptoms, such as fear of infection risks, chronic stress, anxiety, and depression [34].

Sanitation and hygiene target will require by 2030, with requiring a transition from open-pit latrines to fixed (modern) hygienic toilets and container-based management, shared sanitation coupled with safety management [5]. Improved sanitation, such as chlorine-threated water reduces risk of diarrheal by 25–58% and hand-washing promotion by 42–47% [7, 35]. The Biosand filter (BSF) is one such option, as simple and low-cost household water treatment technology in removing both particles and pathogens in the water [36]. Furthermore, access to safe drinking water, proper sanitation facilities, and consistent hygiene habits, improving overall well-being and strengthening health policy implementation [37, 38]. Community settings should become spotlights for water, sanitation, and hygiene (WASH) programming in low- and middle-income countries (LMICs) include reducing poverty, promoting equality, and supporting socioeconomic development [39, 40].

WASH deficiencies remain unaddressed in Ibrahim Kodbuur District, a densely populated peri-urban area in Hargeisa, Somaliland, marked by overcrowding and informal settlements. According to the 2020 Somaliland Health and Demographic Survey (SLHDS), open defecation affects 20% of Hargeisa households, 25% rely on inadequate sanitation, and 59% lack better water sources. These factors contribute to a prevalence of 39.2% diarrhea and 53.6% under-five wasting in comparable slums [41]. This study aims to comprehensively assess the status of water, sanitation, and hygiene (WASH) practices in Ibrahim Kodbuur District, Hargeisa, Somaliland, with a focus on identifying gaps in access, associated health risks (waterborne disease and malnutrition), and potential interventions, informing targeted interventions for SDG 6.

Methods

Study design and setting

In the Ibrahim Kodbuur District, Hargeisa, Somaliland—which is marked by a mixed socioeconomic environment, intermittent water supply, and reliance on communal sanitation facilities—a cross-sectional community-based design was employed to capture a contemporaneous overview of WASH practices and their associations with malnutrition and waterborne illnesses. In order to capture common water access issues in the area, the study was conducted from June to August 2025, which corresponds with the dry season. To give an overview of WASH status and find associations with malnutrition and waterborne illnesses, a cross-sectional design was selected.

Sampling technique

To guarantee representativeness within Ibrahim Kodbuur District, a two-stage cluster sampling technique was utilized. Using administrative maps from the Hargeisa Municipal Council, the district was first divided into geographic clusters, or neighbourhoods; five clusters were then chosen at random using probability proportional to size (PPS) to represent differences in population density. In the second stage, in order to create a de facto sampling frame, enumerators conducted a preliminary transect walk within each chosen cluster to sequentially count all visible residences (~ 60–80 per cluster). Households were then systematically sampled from this frame (every third household found along a random starting point produced by a random number table) until the goal allocation (~ 19–20 households per cluster) was reached, resulting in a total of 97 households.

Study participants

Participants were household heads or primary caregivers (aged 18 years or older) from selected households.

Inclusion criteria

Inclusion criteria included residency in the district for at least 6 months and willingness to provide informed consent.

Exclusion criteria

Exclusion criteria were households unavailable during data collection or those declining participation.

Sample size determination

The sample size was 97 households, calculated using a formula for prevalence studies: n = Z² * P(1-P)/d², where Z = 1.96 (95% confidence), P = 0.5 (based on SLHDS 2020 statistics indicating 59.1% unimproved water sources nationwide and ~ 55% in urban Hargeisa, it is estimated that 50–59% of people have inadequate WASH), and d = 0.1 (10% margin of error, chosen for this exploratory study’s viability in a peri-urban area with limited resources, yielding n = (1.96)² × 0.5(0.5) / (0.1)² = 3.8416 × 0.25 / 0.01 = 96.04 ≈ 96 [42, 43]), adjusted for a finite population and 10% non-response rate (using the correction factor n_adj = n / (1 + (n-1)/N), resulting in the final target of 97 households), and a 1.2 design effect (based on low ICC ≈ 0.03 for urban WASH/health outcomes) [44]. Despite having sufficient power for exploratory connections (post-hoc power > 0.80 at α = 0.05 for MLR models), the sample size of 97 households restricts the potential to discover small effect sizes in subgroups and to generalize beyond similar peri-urban Somaliland environments. In order to maintain data integrity, decliners were eliminated; based on preliminary screening, there were no discernible differences between responders and non-responders in cluster-level demographics (such as household size). For the purpose of assessing health outcomes, vulnerable groups—pregnant women, elderly people (≥ 65 years), and children under five—were identified within families, were identified within families, with a primary focus on child malnutrition via standardized anthropometry and self-reported morbidity across all subgroups.

Data collection

Data were collected by trained local enumerators fluent in Somali, using a structured questionnaire adapted from WHO JMP tools and Ethiopian Demographic and Health Survey modules. The questionnaire was pre-tested on 10 non-study households (Cakara IDP camp, ~ 10% of final sample size n = 97) for clarity and cultural appropriateness, with minor revisions for local terminology (e.g., local terms for waste disposal). A hybrid approach was used to collect WAS variables: direct observations for objective indicators (e.g., water storage, handwashing facilities) and structured household questionnaires for self-reported metrics (e.g., water source distance, treatment methods). To mitigate recollection bias, sanitation and hygiene observations were given priority. The corresponding author has supplied the supplemental material for this work, which can be obtained upon reasonable request. Because of the modular design, reliability was verified by expert review (95% content agreement) and pilot inter-rater evaluation (κ = 0.82 for key observations), guaranteeing good consistency without full-scale Cronbach’s α.

Quality control

At several phases, quality control procedures were implemented to ensure the validity, reliability, and completeness of the data collection tools. The questionnaire (adapted from WHO/UNICEF JMP tools and Ethiopian DHS modules) has been discussed by experts in the field of public health at Edna Adan University (attaining 95% consensus regarding the cultural appropriateness and relevance of the item). In order to enhance understanding and lessen response bias, it was subsequently pre-tested on ten houses that were not part of the study. Interviews lasted 30–45 min, conducted privately and were entered daily to minimize errors, includes regular field monitoring by the lead investigator to verify the accuracy and completeness of about 20% of the forms. For the WASH knowledge subscale, internal consistency was evaluated using Cronbach’s α = 0.81, suggesting good reliability. Anonymized data was kept on password-protected devices, and all procedures followed ethical guidelines.

Variables

Sociodemographic

Gender, marital status, education level, occupation, household size, monthly income, presence of vulnerable group, healthcare utilization, and contraceptive use were created to gather general information about participants.

Household environment

Enumerators visually assessed house cleanliness, ventilation, cooking fuel, lighting sources, insect eradication practices, and handwashing facilities to validate self-reports.

WASH practices

A mixed-methods approach was used to evaluate water supply which are water sources (e.g., piped shared, piped public stand post, and un-protected source), treatment methods (e.g., boiling, chemicals used, water purification tablets), and storage (e.g., jerrycan, pot, tank), with travel time to source (round-trip duration from home to source and back, including queuing); sanitation (was assessed through facility type and condition) and handwashing facilities, liquid and solid waste management (assessed via storage stage, collection, and disposal): expert-guided household observations for structural indicators and self-administered questionnaires for behavioural reports. Trained enumerators were supervised to reduce bias.

Health outcomes

Health outcomes included self-reported waterborne diseases (diarrhoea) in the household within the last 2 weeks, anthropometric measurements using calibrated mid-upper arm circumference (MUAC) for children under-five dan body mass index (BMI) z-scores. MUAC was measured using non-stretchable tapes following WHO standards: green (≥ 12.5 cm, normal), yellow (11.5–12.5 cm, moderate acute malnutrition), and red (< 11.5 cm, severe acute malnutrition), complemented by BMI z-scores for confirmatory classification (e.g., ≤-2 SD wasted malnutrition, ≤-3 SD severe malnutrition). Malnutrition was unique to children under five, with denominators specifically indicated to accommodate for heterogeneity. Disease outcomes (waterborne diseases) were household-aggregated (any member impacted).

Statistical analysis

Data were analysed using IBM SPSS version 26.0. Descriptive statistics (frequencies, percentages) summarized sociodemographic, WASH characteristics, and health outcomes. Underlying components from variables influencing malnutrition and waterborne illnesses were recovered using principal component analysis (PCA) with varimax rotation, keeping components with eigenvalues greater than one. After adjusting for confounders such as age and gender, multiple linear regression (MLR) models investigated relationships between independent variables (household size, income, and WASH indicators) and dependent outcomes (waterborne illness presence and malnutrition via BMI z-scores), estimating adjusted effects while acknowledging cross-sectional limits in causation by accounting for potential confounders (household size, income, education, occupation, marital status, and healthcare utilization). Kolmogorov Smirnov tests were used to verify the assumption (normality). 95% confidence intervals were provided, and significance was defined at p < 0.05.

Results

Sociodemographic and household characteristics

Of the 97 families that were presented, the majority (41.20%) included six to ten persons, including vulnerable groups (3 households with elderly people and 21 households with under five children). A poor socioeconomic status is shown by the fact that 53.60% of households reported earning between $50 and $100 each month. The majority of these households were housewives without formal education. In the past month, 59 households used healthcare facilities, however 81.40% cited the distance to the facility as a hindrance. While contraceptive use was low, 81.40% of households were potentially exacerbating maternal and child WASH vulnerabilities. Mostly moderate, cleanliness received a rating of 48.40%. While ventilation was sufficient, respiratory hazards were increased because 69.10% of cooking fuels were charcoal. Vector-borne disease overlap with inadequate WASH was increased by the fact that 46.60% of households had electric lights and only 10 families practiced insect control (Table 1).

Table 1.

Distribution of sociodemographic characteristics of respondents

Characteristics	Frequency (Percentage), n = 97
Gender
Male	14 (14.40)
Female	83 (85.60)
Marital status
Married	60 (61.90)
Divorced	17 (17.50)
Widowed	10 (10.30)
Single	10 (10.30)
Education level
No formal education	60 (61.90)
Primary education	23 (23.70)
Secondary education	11 (11.30)
Higher education	3 (3.10)
Occupation
Housewife	40 (41.30)
Daily laborer	20 (20.60)
Merchant	7 (7.20)
Government worker	7 (7.20)
Student	3 (3.10)
Unemployed	20 (20.60)
Household size
≤ 2 people	5 (5.20)
3–5 people	30 (30.90)
6–10 people	40 (41.20)
> 11 people	22 (22.70)
Monthly income
Less than $50	41 (42.30)
$50-$100	52 (53.60)
$101-$150	4 (4.10)
Cleanliness of the house
Good	35 (36.10)
Moderate	47 (48.40)
Poor	15 (15.50)
Home ventilation
Good	45 (46.40)
Moderate	41 (42.30)
Poor	11 (11.30)
Cooking fuel
Firewood	30 (30.90)
Charcoal	67 (69.10)
Home lighting sources
Candle	17 (17.50)
Solar lamp	24 (24.70)
Electricity	45 (46.40)
Other	11 (11.30)
Insect eradication
Yes	10 (10.30)
No	87 (89.70)
Household with vulnerable groups
Children under 5 years old	56 (57.70)
Pregnant women	31 (32.00)
Elderly	4 (4.10)
Healthcare utilization
Yes	59 (60.80)
No	38 (39.20)
Overview of healthcare facilities
Not satisfied with previous care	12 (12.40)
Long distance to facility	79 (81.40)
Believed it was unnecessary	6 (6.20)
Contraceptive use
Yes	18 (18.60)
No	79 (81.40)

WASH practices

Only 13.40% of families had piped water, with unprotected wells (24.70%) and community taps (61.90%) serving as the main supplies. While being kept in open containers (pot: 17.50%, tank: 12.40%) and jerrycans: 47.40%, 22.70% are left exposed, and 38.10% require more than 15 min (round-trip) to reach the water source. Boiling is used as a treatment method in 60.80% of houses. The majority of households—15.50%—use dry latrines, and 14.40% use septic tanks, which require significant repairs and are not kept clean. There are just 32% of households with handwashing facilities. Additionally, 77.30% of houses discharged their liquid waste into open pits, indicating insufficient disposal. Municipal services that are mostly used are involved in 76.30% of open dumping (Table 2). In the past two weeks, 39.20% of families reported having waterborne illnesses, such as diarrhoea. Under-five children (MUAC) reported a moderate wasting rate of 53.60% and a severe wasting rate of 12.50% due to malnutrition. The BMI z-score indicated 17.90% severe malnutrition and 35.70% wasting (Table 3).

Table 2.

Characteristics of household water, sanitation, and hygiene

Characteristics	Frequency (Percentage), n = 97
Water sources
Piped shared	13 (13.40)
Piped public stand post	60 (61.90)
Un-protected source	24 (24.70)
Water treatment
Boiling	59 (60.80)
Chemicals used	11 (11.30)
Water purification tablets	20 (20.60)
None	7 (7.20)
Water storage
Jerrycan	46 (47.40)
Pot	17 (17.50)
Tank	12 (12.40)
Other	22 (22.70)
Travel time to water source (round-trip)
Less than 5 min	11 (11.30)
5–9 min	23 (23.70)
10–15 min	26 (26.80)
More than 15 min	37 (38.10)
Dry latrine
Yes	15 (15.50)
No	82 (84.50)
Availability of septic tank
Yes	14 (14.40)
No	83 (85.60)
Condition of latrine
Needs minor repair	17 (17.50)
Needs major repair	78 (80.40)
No need to repair	2 (2.10)
Cleanliness of latrine
Clean and usable	21 (21.60)
Dirty and not usable	76 (78.40)
Handwashing facility
Yes	31 (32.00)
No	66 (68.00)
Liquid waste management
Septic tank	22 (22.70)
Open ditch	75 (77.30)
Final disposal of solid waste
Burning	20 (20.60)
Refuse pit burial	2 (2.10)
Municipal service	73 (75.30)
None	2 (2.10)
Storage system for solid waste
Yes	23 (23.70)
No	74 (76.30)

Table 3.

Findings on waterborne disease and malnutrition

Characteristics	Frequency (Percentage)
Waterborne disease within last 2 weeks (97 households)
Yes	38 (39.20)
No	59 (60.80)
MUAC score of children under-fives (56 households)
Green	19 (33.90)
Yellow	30 (53.60)
Red	7 (12.50)
BMI z-score of children under-fives (56 households)
Normal status	26 (46.40)
Wasted malnutrition	20 (35.70)
Severe malnutrition	10 (17.90)

Underlying factors in WASH and association with health outcomes

Table 4 shows the components that PCA retrieved from the environment, sociodemographic, and WASH variables (eigenvalues > 1, varimax rotation):

Table 4.

Component matrix of variables affecting waterborne disease and malnutrition

	Components
	1	2	3
Marital status	0.824	-0.345	0.348
Education level	0.727	-0.568	0.215
Occupation	0.786	-0.502	-0.101
Household size	0.922	-0.202	-0.059
Monthly income	0.869	-0.066	0.011
Cleanliness of the house	0.877	-0.263	-0.099
Home ventilation	0.817	-0.380	0.030
Insect eradication	0.663	0.451	0.312
Healthcare utilization	0.642	0.257	-0.665
Water sources	0.777	-0.446	0.193
Water treatment	0.753	-0.600	0.150
Water storage	0.821	-0.522	-0.027
Distance	0.952	-0.079	-0.103
Dry latrine	0.774	0.502	0.174
Availability of septic tank	0.757	0.500	0.215
Condition of latrine	0.812	0.398	0.195
Cleanliness of latrine	0.832	0.418	-0.105
Handwashing facility	0.856	0.135	-0.410
Liquid waste management	-0.227	0.550	0.360
Final disposal of solid waste	0.859	0.385	-0.043
Storage system	0.838	0.365	-0.182

1. Component 1: High loadings on distance (0.952), household size (0.922), cleanliness of the house (0.877), income (0.869), final disposal of solid waste (0.859), handwashing facility (0.856), storage system (0.838), cleanliness of the latrine (0.832), marital status (0.824), water storage (0.821), ventilation (0.817), latrine condition (0.812), occupation (0.786), water sources (0.777), dry latrine (0.774), septic tank (0.757), water treatment (0.753), education level (0.727), insect eradication (0.663), and healthcare utilization (0.642).

2. Component 2: Liquid waste management loadings (0.550).

The factor loading matrix from a PCA performed on the study’s variables to find underlying patterns or “components” (latent factors to explain variance in the data) influencing malnutrition and waterborne illnesses is shown in this table. High loadings (absolute value >|0.5|) have been bolded to show strong contributions to a component.

Malnutrition and the occurrence of waterborne diseases are two outcomes predicted by MLR models. The results of a MLR model examining the associations between independent variables (sociodemographic, household environment, and WASH practices) and the dependent variable: waterborne disease and malnutrition (p-value < 0.05). Monthly income, household hygiene, handwashing facilities, occupation, water storage, distance to water source, education level, healthcare utilization, water treatment, and marital status are among the key predictors of waterborne diseases that are highlighted by the model. Likewise, occupation, handwashing facilities, education level, water sources, cleanliness of the house, water storage, storage system, healthcare utilization, marital status, water treatment, and distance to water source are highlighted for the malnutrition model (Tables 5 and 6).

Table 5.

MLR model analysis between variables and waterborne disease

	Waterborne disease
Characteristic of variables	T-table	T	Sig.
Marital status	1.985	2.047	0.044
Education level	1.985	2.776	0.007
Occupation	1.985	3.521	0.001
Household size	1.985	-0.456	0.650
Monthly income	1.985	5.233	0.000
Cleanliness of the house	1.985	4.109	0.000
Home ventilation	1.985	1.683	0.096
Insect eradication	1.985	0.030	0.977
Healthcare utilization	1.985	2.696	0.008
Water sources	1.985	1.239	0.219
Water treatment	1.985	2.067	0.042
Water storage	1.985	3.194	0.002
Distance	1.985	2.823	0.006
Dry latrine	1.985	-0.092	0.927
Availability of septic tank	1.985	-0.567	0.572
Condition of latrine	1.985	0.165	0.869
Cleanliness of latrine	1.985	-0.178	0.859
Handwashing facility	1.985	3.889	0.000
Liquid waste management	1.985	0.281	0.779
Final disposal of solid waste	1.985	0.165	0.869
Storage system	1.985	-0.073	0.942

Table 6.

MLR model analysis between variables and malnutrition

	Waterborne disease
Characteristic of variables	T-table	T	Sig.
Marital status	1.985	2.601	0.011
Education level	1.985	3.226	0.002
Occupation	1.985	3.998	0.000
Household size	1.985	1.345	0.182
Monthly income	1.985	1.299	0.197
Cleanliness of the house	1.985	2.946	0.004
Home ventilation	1.985	1.788	0.077
Insect eradication	1.985	0.190	0.850
Healthcare utilization	1.985	2.628	0.010
Water sources	1.985	3.041	0.003
Water treatment	1.985	2.528	0.013
Water storage	1.985	2.891	0.005
Distance	1.985	2.403	0.018
Dry latrine	1.985	0.131	0.896
Availability of septic tank	1.985	-0.099	0.921
Condition of latrine	1.985	-0.236	0.814
Cleanliness of latrine	1.985	0.254	0.800
Handwashing facility	1.985	3.989	0.000
Liquid waste management	1.985	-0.778	0.439
Final disposal of solid waste	1.985	-0.236	0.814
Storage system	1.985	2.721	0.008

Discussion

A thorough evaluation of water, sanitation, and hygiene (WASH) practices in Ibrahim Kodbuur District, Hargeisa, Somaliland, is given in this study. It identifies notable gaps in access and utilization that raise the risk of malnutrition and waterborne illnesses. Water supply is still below ideal, with only 13.40% of families having access to piped water and depending on unprotected wells (24.70%) and communal taps (61.90%). This is made worse by unsuitable storage (22.70% uncovered) and lengthy collection periods (> 15 min round-trip cut-off for 38.10%). According to regional trends in sub-Saharan Africa, where unimproved sources affect approximately 400 million people, despite 60.8% boiling adherence, the 39.2% frequency of diarrhoea highlights the continued dangers of faecal-oral contamination, which are primarily bacterial in regional profiles, underscoring the necessity for full treatment chains [9, 10]. Sanitation facilities were equally inadequate, with 77.30% of households disposing of liquid waste in open pits and dry latrines (15.50%) and septic tanks (14.40%) frequently in poor condition. These practices facilitate faecal-oral transmission and are similar to issues in Ethiopian slums, where 33% of households still practice open defecation [13, 16]. Vulnerabilities associated to hygiene were exacerbated by the fact that only 32% of houses had handwashing facilities, well below the global objective of 62% [4].

The implementation of WASH is critical among most of the population living to hard-to-reach settlements in LMICs [45]. Our study indicates unimproved water sources/quality among the study population, which implies unprotected wells and 39.2% of the 61.9% of homes that use communal taps reported not boiling water before drinking or using it for other purposes, increasing the risk of contamination from shared sources that can contain harmful microorganisms. Boiling alone only reduces 60–80% of bacterial loads in comparable situations, highlighting the necessity for point-of-use treatment. Community taps contamination paths include human handling, pipe biofilms, and environmental exposure [2]. Likewise, high malnutrition rates among under-fives: 53.60% moderate wasting (MUAC yellow: 11.5-<12.5 cm) and 12.50% severe (red: <11.5 cm), alongside 35.70% wasting (≤-2 SD) and 17.90% severe malnutrition (≤-3 SD) via BMI z-scores directly correlated with adverse health outcomes. WASH condition in hard-to-reach communities of LMICs are frequently unsatisfactory due to low budgets, a lack of capability (distance), and a lack of realistic options. However, user-maintained portable sanitation facilities and communal water taps, which are common in 61.9% of households, present maintenance issues that exacerbate child malnutrition through recurring infections and facilitate the fecal-oral transfer of diarrheagenic bacteria [32].

Multiple linear regression further elucidated predictors: for waterborne diseases, monthly income, household cleanliness, and handwashing facilities (p = 0.000), occupation (p = 0.001), water storage (p = 0.002), distance (p = 0.006), education level (p = 0.007), healthcare utilization (p = 0.008), water treatment (p = 0.042), and marital status (p = 0.044) were significant; for malnutrition, occupation and handwashing facilities (p = 0.000), education level (p = 0.002), water sources (p = 0.003), household cleanliness (p = 0.004), water storage (p = 0.005), waste storage system (p = 0.008), healthcare utilization (p = 0.010), marital status (p = 0.011), water treatment (p = 0.013), and distance (p = 0.018) emerged as key drivers. These associations highlight how low socioeconomic status—evident in 53.60% of households earning $50–100 monthly and 61.90% lacking formal education—constrains adaptive behaviors, consistent with findings in Nigeria where 75% sanitation deprivation links to economic instability [4, 7]. Unstable basic economic status significantly impacts disease outbreaks’ incidence and interventions, because the unstable basic economic status of a population tends to have a concomitant influence on bad water quality, likewise, occupation significantly influences household water sources and along with inaccessibility to proper nutritional diet and healthcare services [32, 46].

Unimproved latrines and open-pit latrine users were still increasing justified by the percentage of unemployment. Parents with no formal education were also more likely to suffer from stunting [5, 13]. Important caregiving practices such as timely weaning, good hygiene habits and appropriate child feeding may all benefit from higher levels of education [28]. Children who had at least one basic service were more affluent, healthier, and less food insecure, and this was significantly influenced by the mother’s level of education. The best environmental and nutritional conditions for the homes are provided by educated parents, who may also be linked to improved health-care practices and increased nutritional knowledge. Children receiving at least one WASH service also had a larger proportion of moms who had completed elementary school, which is the best indicator of a lower risk of diarrhea [47–49].

Marital status was significantly correlated with the double burden of malnutrition in mother-child pairs. According to the findings, divorced women are more likely to bear this burden than their married counterparts. Married mothers can adopt healthier child feeding practices and lower their children’s risk of malnutrition since they often have better access to food resources. On the other hand, divorced mothers could find it difficult to feed children enough, either as a result of economical limitations or loneliness. Children from divorced and never-married families are at high risk of suffering illness and experiencing more health problems than children from two-partner families [50, 51]. Married mothers usually get support from their spouse, family, or community, resulting in adequate practices regarding feeding their children. Unlike, single parents often experience higher levels of stress and have less time and resources available to devote to their children compared to two-parent households. They cannot invest in the child’s welfare adequately and cannot provide a balanced diet for their children [52–54]. Widowed household, potentially protected by support from extended relatives, yet sorrow may worsen vulnerabilities indirectly through diminished ability to provide care. In general, unmarried statuses limit adaptive actions, highlighting the necessity of focused assistance in unstable family structures to end cycles of hunger and WASH [55].

Comparatively, our findings are consistent with larger sub-Saharan trends: in Ethiopia, diarrhoea caused by unsafe WASH accounts for 88% of mortality among children under five [27, 28], whereas in Ugandan slums, 78% of facilities are shared, similar to our 77.30% open-pit disposal [17, 18]. Unimproved sanitation (water quality for bacteriological and primary chemical), unhygienic handling and storage of drinking water, no treating water, unsafe disposing waste, lack of access to clean and safe water, and limited access to health care may have contributed to the high prevalence of acute diarrhoea. Improperly disposed waste is accessed by flies that then contaminate food and water by pathogenic organisms and independently associated with diarrhoea. This is due to the fact that spread of pathogens and contamination can lead to morbidity and mortality [24, 56–59]. Likewise, a higher rate of malnutrition associated with unimproved water facilities, caused by pathogen transmission networks. Stunting was associated with latrine facilities, sources of drinking water, waste disposal, and household flooring. Bacterial and viral diarrhoeal illness such as typhoid, cholera, hepatitis, and are among childhood infectious conditions that are associated with undernutrition. Diarrheal illnesses contributes to undernutrition by reducing food intake, nutrient absorption, and increasing the catabolism of nutrient stores [3, 30].

Though Hargeisa’s sporadic supply (reflected in 38.10% trip burdens) surpasses Zimbabwe’s 30% rural unimproved access, Somaliland’s urban-rural differences could increase our estimations [10]. Scalability is suggested by the fact that interventions such as Biosand filters (requires future randomized evaluations tailored to Somaliland’s urban slums), which reduce diarrhoea by 25–58% via point-of-use therapy [35, 36], proven practicable in Tanzanian pastoralists [36]. Though our low use of contraception (18.60%) and healthcare constraints (81.40% distance-related) suggest compounded maternal-child hazards, as observed in Sudanese war zones [21], combined WASH interventions could reduce diarrhoea by up to 57% [7]. Higher fertility and family crowding may exacerbate maternal-child WASH vulnerabilities, as seen by regional statistics showing a 25% greater risk of diarrhoea with poor uptake [60]. This suggests that public health authorities, sanitation departments, and respective governments may need to provide specific programs targeting increasing access to improved water sources, providing information on hygiene practices, and providing safe water—tailored for high-fertility households in both rural and urban centres. Improved water access will also enhance the effectiveness of nutrition initiatives, such as promoting vegetable gardening and utilization of child nutrient supplements [56, 61].

These results highlight the need for focused interventions in remote LMIC communities like Ibrahim Kodbuur, where occupation and economic instability affect WASH adherence [32]. Prioritizing community chlorination, subsidized piped infrastructure, and hygiene education through health extension programs—possibly incorporating IBM-WASH models for behavioural change—should be a top priority for policymakers [39]. Reducing the risks of anaemia and stunting may be achieved by increasing latrine repairs and waste management [3, 30]. In order to overcome our cross-sectional constraints and self-report biases, microbiological water testing should be incorporated into future longitudinal research in order to prove causation. To sum up, long-standing WASH disparities in Ibrahim Kodbuur feed a vicious cycle of illness and malnutrition, necessitating multisectoral action to promote SDG 6 and lessen health burdens on Somaliland’s most vulnerable citizens.

Limitations

Longitudinal designs are required for temporal insights because this cross-sectional study is unable to demonstrate a causal relationship between WASH behaviours and health outcomes. Estimates may be impacted by residual confounding from unmeasured factors (such as nutritional intake) due to the cross-sectional design. With a sample size of 97, subgroup power is limited and variability is at risk; remote places may not be adequately represented by cluster sampling. It is important to exercise caution when extrapolating to rural/national Somaliland or other LMICs, prioritizing hypothesis testing and future scaled surveys over firm findings. Even while the 10% non-response rate is low, it might introduce a small selection bias in favour of more stable households; to further reduce this, future research should apply refusal conversion techniques or weighting changes. For wider applicability, larger, multi-district investigations are required. While observer-dependent evaluations lack inter-rater reliability, self-reports include recall and desirability biases even though observations reduced recollection for infrastructure factors (the 2-week timeframe is in line with established techniques to restrict this); objective measures such as microbiological testing are advised. It was not possible to confirm the pathogen; in order to distinguish between bacterial and other aetiologies, future research should incorporate microbiological assays. Dry-season statistics (June–August 2025) may overlook dangers during the rainy season, and nutritional concentration on children under five ignores wider family consequences. The lack of comprehensive metrics for pregnant and elderly participants highlights the necessity for integrated WASH-vulnerability frameworks in future research, even if under-five children got priority nutritional screening. Future mixed-methods research is advised to examine these dynamics because the quantitative focus prevents qualitative insights on sociocultural variables (such as gender time poverty or sanitation stigma), which could improve generalizability to various urban-peri-urban subgroups.

Abbreviations

BMI

Body Mass Index

BSF

Biosand Filter

EDHS

Ethiopian Demographic and Health Survey

IBM-WASH

Integrated Behavioral Model for Water, Sanitation, and Hygiene

JMP

WHO/UNICEF Joint Monitoring Progamme

LMICs

Low- and Middle-Income Countries

MLR

Multiple Linear Regression

MUAC

Mid-Upper Arm Circumference

PCA

Principal Component Analysis

SDG

Sustainable Development Goal

SLHDS

Somaliland Health and Demographic Survey

WASH

Water, Sanitation, and Hygiene

WHO

World Health Organization

Authors’ contributions

LA conceptualized the study under supervision, designed the methodology, collected and analysed data, and drafted the initial manuscript. PG provided expert guidance on study design, supervised data analysis (including PCA and MLR), contributed to interpretation of results, and critically revised the manuscript for intellectual content and publication readiness. Both authors approved the final version and agree to be accountable for all aspects of the work.

Funding

This work has not received any funding to support this work.

Data availability

The dataset used and analysed during the current study is available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of the Edna Adan University and administration of Ibrahim Kodbuur District, Hargeisa, Somaliland. All participants provided written or thumbprint informed consent, with minors providing assent. With the option to discontinue participation at any moment, participation was entirely optional. Unique codes were utilized to anonymize the data, which were then safely stored on password-protected devices and used only for study. In the event that malnutrition or disease instances were detected, referrals to nearby health facilities were provided, and potential dangers (discomfort from health queries) were reduced. The Declaration of Helsinki’s guidelines were followed in this investigation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.