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Abstract

The absence of improved sanitation facilities presents a major global health and environmental issue, particularly affecting impoverished communities. Globally, around 2.4 billion people face inadequate sanitation, a crisis significantly reflected in Ethiopia, where over 48 million individuals lack access to improved sanitation facilities. This study tries to identify the determinant of access to improved sanitation facilities among households in Ethiopia. The study utilizes cross-sectional data from the 2016 EDHS, and a total of 9,036 households were included. Multilevel binary logistic regression model was utilized to identify significant determinant factors of access to improved sanitation. Statistical significance was confirmed at p < .05, and adjusted odds ratios (AOR) with 95% confidence intervals were used to assess the strength of association. Approximately 33% of households had access to improved sanitation facilities, with notable variations observed across enumeration areas. This study showed significant association between access to improved sanitation facilities and factors like place of residence, education status, and wealth status. Likewise, modern housing status (AOR: 2.087), access to water within 30 min (AOR: 1.291), older age (56 and above; AOR: 1.336) and administrative region were significantly associated with access to improved sanitation facilities. The prevalence of access to improved sanitation facilities is still low in Ethiopia; priority should be given to households who are rural residents, less-educated, and low-income groups. The study stresses the need for government and NGOs to adopt a multifaceted approach, including infrastructure development, income generation, hygiene education, and community engagement to enhance sanitation.

Keywords

multilevel model improved sanitation household head Ethiopia logistic regression model

Background

Sanitation is the provision of facilities and services for the safe disposal of human waste and the promotion of hygiene practices to prevent the spread of diseases (WHO, 2011, 2018). It is a critical public health concern, primarily focusing on the proper management of human excreta and waste (Mara et al., 2010; World Health Organization and UNICEF, 2010). Achieving universal access to safe and affordable sanitation is essential for global sustainable development and the protection of public health (Friedman & Gostin, 2016; Leigland et al., 2016).

Unsafe sanitation poses substantial health and environmental risks, particularly affecting the world’s poorest populations, and contributing to 775,000 deaths in 2017 alone (Ritchie & Roser, 2019). In low-income countries, 5% of deaths are attributed to inadequate sanitation, with 2.4 billion people (approximately one-third of the world) lack access to improved sanitation facilities (Behera et al., 2020). The burden of poor sanitation is particularly sever in low- and middle-income countries, where access to improved sanitation facilities remains limited (Bisung & Elliott, 2017; Chirgwin et al., 2021; Sinharoy et al., 2019). Then, ensuring adequate and efficient sanitation, along with improved hygienic practices and access to clean water, are imperative for societal and economic progress and for maintaining good health (Bartram & Cairncross, 2010; Matta & Kumar, 2017).

Poor sanitation and a lack of adequate sanitation facilities have far-reaching impacts on health of the society, the economy, and the environment (Amanabo-Arome & Abbas, 2021; Hennegan et al., 2016; Hulton, 2012). Poor sanitation practice contributes to the spread of diseases such as cholera, diarrhea, dysentery, typhoid, and polio, with children being particularly affected (Arya et al., 2019; Connolly, 2005; Meki et al., 2021; Prüss-Ustün et al., 2014; World Health Organization, 2008, 2019; World Health Organization and UNICEF, 2017). Millions of children are at risk due to poor sanitation, as infections related to inadequate sanitation are among the leading causes of death for children under five (Bartlett, 2003; Cyprian En, 2011; Pokhrel & Viraraghavan, 2004). Tragically, more than 800 children succumb daily from preventable diseases that could be mitigated through access to clean water, proper sanitation, and improved hygiene practices (UNICEF, 2019; UN Water, 2018).

Inadequate sanitation not only diminishes human well-being but also hampers social and economic development (Delle, 2010; Hossain, 2014). It fosters anxiety, increases the risk of sexual violence, and limits educational opportunities (Behera et al., 2020). Socially, poor sanitation compromises dignity and safety, particularly for women and girls, leading to stigma and gender-based violence (Sommer et al., 2015). In education, it hampers attendance and performance, especially for girls lacking menstrual hygiene facilities (Hennegan et al., 2016). Economically, it leads to significant healthcare costs and productivity losses, hindering labor productivity and economic growth due to illness-related absenteeism and healthcare expenses (Hulton, 2012; Spears et al., 2013). Similarly, improper waste disposal harms ecosystems and biodiversity, with open defecation and untreated wastewater worsening water pollution and antibiotic resistance, affecting both terrestrial and aquatic environments (Amanabo-Arome & Abbas, 2021; Jarma et al., 2021).

Previously, numerous initiatives aimed at improving WASH programs were undertaken globally. The drive for enhancement was fueled by initiatives like the UN’s sustainable development goals, striving for universal access to clean water and sanitation by 2030 (McMichael, 2019; Weber et al., 2018; Weststrate et al., 2019). Despite significant strides in improving water and sanitation access over the past 40 years, approximately 2.4 billion people worldwide still lack access to proper toilets and improved sanitation (Giné Garriga & Pérez Foguet, 2016; Mosello & Matoso, 2017). According to WHO’s 2022 statistics, only 57% of the global population, roughly 4.6 billion people, had access to safely managed sanitation services, while the remainder relied on unimproved sanitation facilities (World Health Organization and UNICEF, 2021). Annually, inadequate drinking-water, sanitation, and hygiene lead to 1.4 million deaths worldwide (2.5% of all deaths), with the majority occurring in low- and middle-income nations (World Health Organization, 2023). Unsafe sanitation, primarily attributed to diarrheal disease, accounts for 564,000 fatalities annually and significantly contributes to various neglected tropical diseases (World Health Organization, 2023).

In sub-Saharan Africa, the progress made in improving sanitation access over recent decades suggests the possibility for achieving basic sanitation coverage by 2030 (Roche et al., 2017). Despite this, Sub-Saharan Africa (SSA) faces the lowest level of access to clean water and proper sanitation, with approximately 695 million people still relying on unimproved facilities (Roche et al., 2017). Besides, the access to WASH services in Sub-Saharan Africa varies widely based on geographic location (sub regions within SSA) and socioeconomic status, with some areas having significantly lower access compared to others (Roche et al., 2017). Currently, in Ethiopia, a sub-Saharan African nation, nearly 48 million people lack access to basic sanitation, and around 39 million individuals, primarily residing in rural areas, lack access to safe water sources (World Vision, 2018).

Despite various initiatives over the years, including government programs, international aid projects, and community-based interventions, efforts to enhance access to adequate sanitation in Ethiopia have led to only modest progress in improved sanitation practices (Baye, 2021; Kwami et al., 2019). As a consequence, it was found that 90% of cases of diarrheal illness stemmed from inadequate personal hygiene, limited access to clean water, and poor sanitation practices (Gebru et al., 2014). Similarly, studies conducted in Ethiopia indicates that people’s practices related to water, sanitation, and hygiene have been greatly influenced by both health education and economic factors (Donacho et al., 2022). This result highlights the necessity of tackling inadequate sanitation facilities to enhance public health and well-being in Ethiopia. To achieve the targets set forth in Sustainable Development Goal (SDG) 6 “ensuring availability and sustainable management and sanitation for all” (UN Water, 2018), the Ministry of Health in Ethiopia, in collaboration with the international community, must vigorously strive toward attaining universal access to improved sanitation facilities by 2030.

Numerous studies have been undertaken in developing countries to explore household access to sanitation facilities and the associated factors of unimproved sanitation (Abubakar, 2017; Gebru et al., 2014; Johnson et al., 2015; Njuguna, 2019; Nyanza et al., 2018). The prevalence of open defecation or inadequate sanitation facilities was reported as 87% in Jimma town, Ethiopia (Donacho et al., 2022), 50.3% in rural areas of northern Tanzania (Nyanza et al., 2018), and a pooled prevalence of open defecation was found to be 22.5% across in sub- Saharan Africa countries (belay). The gender of household head and household size (Abubakar, 2017; Nyanza et al., 2018; Osumanu et al., 2019), educational level of the household head (Njuguna, 2019; Nyanza et al., 2018), wealth or financial status of the household (Abebe & Tucho, 2020; Osumanu et al., 2019), region or residence (Njuguna, 2019; UNICEF, 2017), having under five children in the household, and received education on sanitation (Nyanza et al., 2018), water source, number of rooms and housing type (Johnson et al., 2015; Nyanza et al., 2018), and improved hygiene practices (Johnson et al., 2015) had a significant association with poor sanitation.

Ethiopia, situated in sub-Saharan Africa, faces considerable hurdles in accomplishing Sustainable Development Goal (SDG) 6, which focuses on “ensuring availability and sustainable management and sanitation for all.” To reach this goal, it is crucial to identify the factors associated with sanitation practices. However, based on my understanding, there is limited evidence on the variations in sanitation practices across different communities, socioeconomic groups, and demographic characteristics in Ethiopia. Additionally, there is scant information regarding the disparities in access to sanitation facilities across various geographical or administrative units within Ethiopia, such as regions and place of residence (rural vs. urban). Furthermore, there is a lack of comprehensive understanding about the associated factors on the access to improved sanitation facilities among households at the national level, as well as differences in the adoption of improved sanitation practices between enumeration areas (EAs) in Ethiopia. Therefore, this manuscript tried to assess the associated factors of access to improve sanitation facilities among households, as well as tries to examine the variation across EAs in Ethiopia via multilevel binary logistic regression model. This study will offer essential insights into the factors associated with sanitation practices; and it will serve as evidence in shaping future cross-cutting initiatives in Ethiopia and beyond, guiding efforts to improve sanitation globally.

Methods

Study Area and Data Source

This study was carried out in Ethiopia, a developing country in East Africa with an economy based on agricultural economy. At the time of the survey, Ethiopia was administratively divided into two administrative cities and nine regional regions (states; Alene et al., 2019). In 2016, the country’s population was expected to be 102.3 million, of which 83% lived in rural areas (Birhanu et al., 2019). This study used secondary data from the 2016 Ethiopian Demographic and Health Survey (EDHS). The 2016 EDHS was the fourth Demographic and Health Survey conducted in Ethiopia, and the sample was developed to offer latest estimates of the country’s main demographic and health indicators as well as to support program managers and policy makers in evaluating and creating activities and policies aimed at enhancing the general public’s health (EDHS, 2016). The data used in this analysis comes from the Demographic and Health Surveys (DHS) Program, accessible at https://dhsprogram.com with a reasonable request.

Study Design and Setting

A community-based cross-sectional study was conducted in all regions of Ethiopia from January 18 to June 27, 2016; and it was carried out by the Central Statistical Agency (CSA) by the appeal of the Federal Ministry of Health (FMoH). The sampling framework used for the 2016 EDHS was the Ethiopian Population and Housing Census (PHC) carried out in 2007 by the Ethiopian Central Statistical Agency. The census framework includes a full list of 84,915 enumeration areas (EAs) adopted by the Ethiopian PHC in 2007 (CSA, 2016).

The 2016 EDHS employed the stratified random sampling technique through regions and selected in two stages (urban and rural areas) yielding 21 sampling strata. Samples of EAs were chosen separately from each stratum, a total of 645 EAs (202 in urban areas and 443 in rural areas) had been considered. Implicit stratification and proportional allocation to the size of the EA (adopted from the 2007 PHC) and independent selection in each sampling stratum were accomplished at each of the lower administrative levels. Lastly, a fixed number of 28 households per cluster were selected with an equal probability systematic selection were considered (CSA, 2016). The survey questions in the EDHS survey assessed the type of sanitation facility in which the household members used. A total of 18,008 households were considered for the survey, 17,067 of which were engaged, and 16,650 of which were effectively interrogated (EDHS, 2016).

Data Extraction and Processing

After the data has been coded, households who had missed information were excluded to extract relevant ideas, and only 9,036 household heads (HHs) were included in this study. The data was exported to Stata version 14 for the analysis purpose from SPSS version 20. Since the average likelihood of selection of each household head was not consistent across the stratum weighting had been performed. The 2016 EDHS dataset has a hierarchical structure as households are nested within enumeration areas. The hierarchy for this study follows households as level-1 and EAs as level-2.

Variables Under the Study

The response variable for this study was the access to sanitation facility (improved, unimproved) represented as dichotomous variables, coded with “1” representing “improved” and “0” representing “unimproved.” In this study, improved sanitation refers to facilities that hygienically separate human excreta from human contact. The households who have access for pit latrine with slab, VIP latrine, composting toilet, flush or pour-flush to piped sewer system, septic tank, pit and latrine are deemed improved; on the other hand, those households that use their latrines publicly or in shared spaces with multiple households are classified as unimproved (WHO/UNICEF, 2006).

In this study, the independent variables were selected based on available literature and their theoretical relevance to influence the access to sanitation facility among households (Abubakar, 2017; Akpakli et al., 2018; Nyanza et al., 2018; Tumwebaze et al., 2013). The predictors (independent) variables considered in this study were: family size (1–3, 4–5, 6+), Place of residence (rural, urban), Having under-five children (No, yes), sex of the HHs (male, female), educational achievement of the head of households (No education, Incomplete primary, complete primary, Incomplete secondary, complete secondary, and higher), age of the HHs (below 30, 31–40, 41–56, and 56+), wealth index status (Poorest, Poorer, Middle, Richer, and Richest), Media exposure (not exposed, exposed), housing status (not modern, modern), accessibility of water (within 30 min, more than 30 min), Administrative unit/region (Tigray, Afar, Amhara, Oromia, Somali, Benishangul, SNNPR, Gambella, Harari, Addis Ababa, and Dire Dawa).

Statistical Data Analysis

In this study, the descriptive statistics were performed to describe and summarize the data, while inferential statistics were employed to extract relevant information. Specifically, Multivariable single-level and multilevel binary logistic regression models were employed to identify the determinants factors of access to improved sanitation facility among households. The chi-square test was used to assess the heterogeneity of proportions between the groups (EAs), to determine whether systematic differences exist between the groups in order to properly use multilevel analysis (Hox et al., 2010). The multilevel binary logistic regression model had been used to predict a binary response variable from a set of independent variables (Hox et al., 2017). This analysis was conducted using three different scenarios: the empty model, which is the most basic scenario without any explanatory variables; the random intercept model, which involves only the intercept term as an explanatory variable; and the random coefficient model, which takes into account the random variation of both the intercept and regression coefficients (Hox et al., 2017).

The best-fitting model was chosen based on Akaike’s Information Criterion (AIC), with the model having the lowest AIC value being chosen (Portet, 2020; Snipes & Taylor, 2014). Variables with a p-value of less than .20 were included in the multivariable analysis. In the final multivariable model, variables with a p-value < .05 were considered statistically significant, and the results were reported by the adjusted odds ratio (AOR) with 95% confidence interval (CI).

Results

Characteristics of Study Participants

In this study, a weighted sample of 9,036 household heads was included. Among them, 6,022 (66.64%) had access to unimproved sanitation facilities, while the remaining 3,014 (33.36%) had access to improved sanitation facilities (Table 1). The widespread use of unimproved sanitation practices may lead to a variety of diseases within households and, in particular, a high spread of infectious diseases (Thompson & Khan, 2003). This finding serves as a critical alarm for the government, non-governmental organizations, and the relevant bodies to prioritize the improvement of sanitation, particularly for the federal ministry of health (FMoH) of Ethiopia.

Table 1.

Magnitude of Household Sanitation Facilities Status in Ethiopia (Evidence From EDHS [2016], n = 9,036).

Sanitation status	n	% (95% CI)
Improved facilities	3,014	33.36 [31.676, 35.043]
Unimproved facilities	6,022	66.64 [65.449, 67.831]

Table 2 provides descriptive information about the socio-demographic attributes of the 9,036 households (HHs) who were included in the study. The majority of rural the HHs (91.76%) had access to unimproved sanitation facilities, whereas 67.7% of urban households had access to improved sanitation facilities. Among the 3,974 HHs with no formal education, 82.03% relied on unimproved sanitation facilities. In contrast, households who had the highest (richest) wealth index were 4,124 in total, with 64.94% had access to improved sanitation facilities (Table 2). In this study, household exposed to media had relatively higher proportion for access to improved sanitation facilities (53.44%), compared to those without media access (13.41%). Similarly, households who had modern housing status (71.42%) had a better tendency for access to enhanced sanitation facilities than those HHs with non-modern housing (13.35%). Households who could access water within 30 min were more likely to use enhanced/improved sanitation services (48.5%) than the counterpart. Regionally, Dire Dawa had the highest proportion of households with improved sanitation facilities (85.39%), followed by Addis Ababa (81.78%), while Benishangul region had the lowest access to improved sanitation facility (5.24%; Table 2). The bivariate analysis of independence, conducted using the chi-square test for all the independent variables considered in this study, and revealed a significant association with access to enhanced /improved sanitation facilities (p ≤ .001) among households (Table 2).

Table 2.

Characteristics of Study Participants (Households) and Results of Pearson Chi-Square Tests for Independence (n = 9,036).

Variables	Sanitation status (n = 9,036)					Chi-square test
	Total	Unimproved facilities		Improved facilities		Chi.	p-Value
	Total	n	%	n	%	Chi.	p-Value
Family size
1–3	3,614	2,136	59.1	1,478	40.9
4–5	2,820	1946	69.01	874	30.99	172.84	<.001
6+	2,602	1940	74.56	662	25.44
Place of residence
Urban	3,817	1,233	32.3	2,584	67.7	3506.1	<.001
Rural	5,219	4,789	91.76	430	8.24	3506.1	<.001
U5C (Do you have children 5 years old and under?)
No	5,029	3,032	60.29	1997	39.71	205.983	.001
Yes	4,007	2,990	74.62	1,017	25.38	205.983	.001
Sex of head of household
Male	6,177	4,336	70.2	1,841	29.8	110.763	<.001
Female	2,859	1,686	58.97	1,173	41.03	110.763	<.001
Educational attainment of households heads
No education	3,974	3,260	82.03	714	17.97		<.001
Incomplete primary	2,324	1,635	70.35	689	29.65	1370.7
Complete primary	360	180	50	180	50
Incomplete secondary	868	460	53	408	47
Complete secondary	291	69	23.71	222	76.29
Higher	1,219	418	34.29	801	65.71
Wealth index
Poorest	1,389	1,333	95.97	56	4.03		<.001
Poorer	1,092	1,027	94.05	65	5.95	3415.75
Middle	1,167	1,081	92.63	86	7.37
Richer	1,264	1,135	89.79	129	10.21
Richest	4,124	1,446	35.06	2,678	64.94
Media exposure
Not exposed	4,534	3,926	86.59	608	13.41	1628.6	<.001
Exposed	4,502	2,096	46.56	2,406	53.44	1628.6	<.001
Housing status
Not modern	5,726	5,076	88.65	650	11.35	3404.6	<.001
Modern	3,310	946	28.58	2,364	71.42	3404.6	<.001
Time for accessing water (min)
30+ min	3,595	3,220	89.57	375	10.43	1,400	<.001
≤30 min	5,441	2,802	51.5	2,639	48.5	1,400	<.001
Age of household head
Below 30	2,343	1,490	63.59	853	36.41
31–40	2,222	1,478	66.52	744	33.48	16.271	.001
41–56	2,369	1,633	68.93	736	31.07
56+	2,102	1,421	67.6	681	32.4
Region
Tigray	886	638	72.01	248	27.99
Afar	365	247	67.67	118	32.33
Amhara	1,505	1,397	92.82	108	7.18	3,300	<.001
Oromia	1,023	868	84.85	155	15.15
Somali	502	316	62.95	186	37.05
Benishangul-Gumuz	783	742	94.76	41	5.24
SNNPR	1,004	821	81.77	183	18.23
Gambela	663	517	77.98	146	22.02
Harari	365	143	39.18	222	60.82
Addis Ababa	1,372	250	18.22	1,122	81.78
Dire Dawa	568	83	14.61	485	85.39

Results of Multilevel Logistic Regression Analysis

In the null model, the Intra-class Correlation Coefficient (ICC) value was found to be .741. This indicates that approximately 74% of the total variation in the access to improved sanitation could be explained by grouping the household heads respect to the EAs. The null model’s existence of an ICC greater than zero suggested that multilevel modeling performed better for us than the conventional single-level regression model (Krull & MacKinnon, 2001; Scherbaum & Ferreter, 2009). The level −2 variability was lowered to 34.2% for the random intercept model and 24.5% for the random coefficient model after accounting for both individual and higher-level predictors. This demonstrated that individual and higher-level predictors accounted for the largest proportional change in variance (Table 4). In addition, a chi-square test statistic was used to evaluate any heterogeneity in the proportion of the households’ access to enhanced/improved sanitation facilities among the EAs. The test yield χ² = 3,910.19 (p-value ≤ .001), which was evidence for heterogeneity between EAs concerning the access to enhanced/improved sanitation facility of households.

Among the models considered, the random coefficient mode1 (model 4) exhibited the smallest AIC value, indicating it provided the better fit (Table 3). This also suggested that the addition of the higher-level factors improved the ability of the multilevel model in explaining the variation in sanitation practice between the EAs. Among the set of explanatory variables considered the place of residence, educational attainment of HHs, wealth index, housing status, accessibility of water, age of HHs, and region were found to be significantly determinant factors for access to improved sanitation facilities (Table 4).

Table 3.

Description of Candidate Statistical Models, Selection Criteria, and Comparison.

Model	Log-likelihood	Df	AIC
Binary logistic regression model	−2,995.93	31	6,053.84
Empty model	−2,956.05	2	5,916.09
Random intercept model	−2,558.63	32	5,181.26
Random coefficient model	−2,556.83	33	5,179.66

Table 4.

Comparison of Multivariable Single-level Binary Logistic Regression Model and Multilevel Logistic Regression Model for Identifying Associated Factors With Household Improved Sanitation Facilities From the EDHS (2016) (n = 9,036).

Variable with their categories	Models under comparison
	Model 1	Model 2	Model 3	Model 4
	AOR (95% CI)	AOR (95%CI)	AOR (95% CI)	AOR (95% CI)
Family size
1–3 (Ref.)
4–5	0.967 [0.810, 1.153]		0.908 [0.736, 1.120]	0.908 [0.736, 1.120]
6+	0.969 [0.784, 1.195]		0.999[0.780, 1.279]	0.998 [0.779, 1.277]
Residence
Urban (Ref.)
Rural	0.334 [0.268, 0.416)		0.183 (0.111, 0.302)	0.164 [0.989, 0.272)
U5C (Do you have children 5 years old and under?)
No
Yes	1.062 [0.901, 1.251]		1.128 [0.932, 1.365]	1.122 [0.927, 1.359]
Sex of household heads
Male (Ref.)
Female	1.127 [0.964, 1.316]		1.041 [0.865, 1.253]	1.044 [0.868, 1.255]
Educational attainment of HHs
No education (Ref.)
Incomplete primary	0.966 [0.798, 1.169]		0.867 [0.696, 1.079]	0.867 [0.696, 1.079]
Complete primary	1.450 [1.032, 2.037]		1.215 [0.812, 1.816]	1.220 [0.816, 1.825]
Incomplete secondary	1.100 [0.855, 1.416]		1.032 [0.771, 1.382]	1.036 [0.774, 1.387]
Complete secondary	1.887 [1.263, 2.819]		2.160 [1.339, 3.483]	2.146 [1.333, 3.454]
Higher	2.142 [1.695, 2.706]		2.252 [1.694, 2.994]	2.252 [1.695, 2.993]
Wealth index
Poorest (Ref)
Poorer	1.726 [1.121, 2.657]		1.605 [0.985, 2.615]	1.593 [0.974, 2.607]
Middle	2.549 [1.697, 3.828]		2.275 [1.428, 3.623]	2.251 [1.408, 3.599]
Richer	3.187 [2.145, 4.734]		3.013 [1.891, 4.797]	2.977 [1.862, 4.762]
Richest	5.316 [3.498, 8.078]		5.681 [3.412, 9.458]	5.699 [3.408, 9.531]
Media exposure
Not exposed (Ref)
Exposed	1.200 [1.020, 1.411]		1.068 [0.881, 1.296]	1.075 [0.886, 1.305]
Housing status
Not modern (Ref.)
Modern	2.329 [1.966, 2.759]		2.071 [1.667, 2.573]	2.087 [1.682, 2.592]
Time for accessing water (min)
30+ min (Ref.)
≤30	1.561 [1.327, 1.835]		1.268 [1.02, 1.576]	1.291 [1.036, 1.608]
Age of household heads
≤30 (Ref.)
31–40	0.871 [0.717, 1.056]		0.883 [0.705, 1.106]	0.885 [0.707, 1.108]
41–56	0.902 [0.734, 1.109]		0.912 [0.713, 1.165]	0.914 [0.715, 1.167]
56+	1.348 [1.076, 1.688]		1.331 [1.019, 1.737]	1.336 [1.023, 1.744]
Region
Addis Ababa (Ref.)
Tigray	0.559 [0.412, 0.759]		0.343 [0.173, 0.681]	0.361 [0.188, 0.691]
Afar	0.900 [0.421, 1.926]		0.591 [0.195, 1.795]	0.628 [0.215, 1.829]
Amhara	0.144 [0.112, 0.184]		0.038 [0.019, 0.077]	0.043 [0.022, 0.086]
Oromia	0.264 [0.208, 0.336]		0.142 [0.073, 0.277]	0.156 [0.082, 0.298]
Somali	3.330 [2.083, 5.322]		1.733 [0.759, 3.955]	1.765 [0.783, 3.979]
Benishangul-Gumuz	0.070 [0.027, 0.175]		0.044 [0.013, 0.158]	0.045 [0.013, 0.152]
SNNPR	0.513 [0.392, 0.671]		0.319 [0.162, 0.628]	0.335 [0.174, 0.642]
Gambela	0.271 [0.099, 0.736]		0.160 [0.044, 0.583]	0.168 [0.048, 0.584]
Harari	0.545 [0.163, 1.819]		0.474 [0.109, 2.049]	0.482 [0.115, 2.013]
Dire Dawa	1.899 [0.763, 4.72]		2.118 [0.668, 6.708]	2.067 [0.675, 6.328]
Constant		0.095 [0.071, 0.126]	0.326 [0.160, 0.662]	0.301 [0.150, 0.606]
Random effect (Estimate (95% Wald type approximate CI)
Variance between EAs	-	9.42 [7.62, 1.65]***	1.71 [1.335, 2.18]***	1.07 [0.562, 2.021]**
Variance between Residence	-			0.272 [0.090, 0.821]
Intra-class correlation coefficient	-	0.741	0.342	0.245
AIC	6,053.84	5,916.09	5,181.26	5,179.66

Note. Model 1: Binary Logit model, model 2: Empty model, model 3: Random intercept Model, model 4: Random coefficient model, CI in parentheses.

p < .05. **p < .01. ***p < .001.

In this study, households found in a rural area were 83.6% (AOR: 0.164, 95% CI [0.989, 0.272], p < .001) times less likely to access improved sanitation facility as compared to those found in the urban area. Household head with complete secondary, and higher education status are more likely (AOR: 2.14, 95% CI [1.333, 3.454], p < .001; AOR: 2.25, 95% CI [1.695, 2.993], p < .001 respectively) to access improved sanitation facility as compared to household heads who had incomplete primary educational status. Households who had middle, richer and richest socioeconomic status are more likely (AOR: 2.25, 95% CI [1.408, 3.599], p = .001; AOR: 2.977, 95% CI [1.862, 4.762], p < .001; AOR: 5.699, 95% CI [3.408, 9.531], p < .001, respectively) to access improved sanitation facility as compared to household who had the poorest socioeconomic status. Households who had modern housing status are 2.087 times more likely (AOR: 2.087, 95% CI [1.682, 2.593], p < .001) to access improved sanitation facility as compared to households who had not the modern house (Table 4).

Households who access water within 30 min is 1.29 times (AOR: 1.291, 95%CI [1.036, 1.608], p< .05) more likely to access improved sanitation facility as compared to HHs who doesn’t access water within 30 min (30+). Similarly, HHs who had higher age (56+) were 1.336 times more likely (AOR: 1.336, 95%CI [1.024, 1.744], p< .05) to access improved sanitation facility as compared to HHs who had lower age (less than 31). Households reside in Tigray, Amhara, Oromia, Benishangul-Gumuz, Southern Nations Nationalities and People Region (SNNPR), and Gambella are less likely (AOR: 0.361, 95% CI [0.188, 0.691], p < .01; AOR: 0.043, 95% CI [0.022, 0.086], p < .001; AOR: 0.156, 95% CI [0.082, 0.298], p < .001; AOR: 0.045, 95% CI [0.013, 0.152], p < .001; AOR: 0.335, 95% CI [0.174, 0.642], p < .01; and AOR: 0.168), 95% CI [0.048, 0.584], p < .01 respectively) to access to improved sanitation facility as compared to Addis Ababa City administration (Table 4).

Discussion

The finding of this study revealed that a significant portion of households lacked access to improved sanitation facilities, with approximately 67% relying on unimproved options (Table 1). This situation poses series health risks, potentially increasing the transmission and prevalence of diseases associated with poor sanitation (Nansereko, 2010; Ritchie & Roser, 2019). It also undermines human well-being, social cohesion, and economic development. However, there has been a slight improvement compared to earlier reports in Ethiopia (CSA, 2014; WHO/UNICEF, 2014). The 2014 Joint Monitoring Programme (JMP) report indicated that 73% of the urban population and 77% of the rural population in Ethiopia utilized unimproved sanitation facilities, with 8% and 43% respectively practicing open defecation (WHO/UNICEF, 2014). Additionally, the 2014 Ethiopian mini DHS report revealed that 82.5% of urban and 97.5% of rural inhabitants lacked access to improved sanitation, while 8.7% and 37.5% respectively engaged in open defecation (CSA, 2014).

This slight improvement might be attributed to the Ethiopian government’s endeavors to enhance sanitation infrastructure, especially in rural areas. Additionally, promoting hygiene awareness and engaging communities in sustainable sanitation solutions have helped address poor sanitation practices nationwide. To further mitigate these issues, both governmental and non-governmental organizations should prioritize infrastructure development, awareness campaigns, and hygiene education programs to promote improved sanitation practices and encourage behavioral change. The place of residence of households was found to be a statistically significant associated factor for the sanitation practice of households. HHs reside in a rural area was 83.6% less likely to access improved sanitation facility as compared to those reside in the urban area (Table 4). This showed that households who reside in urban stood a better chance of having access to improved sanitation facilities. This finding can be attributed to the fact that most households in rural areas of Ethiopia, particularly in Somali, Afar, and some parts of the Oromia region, are nomadic pastoralist communities. Those communities often remain distant from areas with sanitation infrastructure due to their mobility. Besides, the high prevalence of poverty in rural areas, combined with limited awareness of the health risks associated with unimproved sanitation, contributed to the widespread use of unimproved sanitation facilities. This result align with a study conducted in Nigeria (Abubakar, 2017),which reported similar findings. However, it contrasts with the study conducted in Ghana by Agbadi et al. (2019), which found that rural households were more likely to access to improved toilet facilities.

The household head’s highest educational attainment was found to be a statistically significant associated factor for the household’s sanitation practice. Household head with complete secondary, and higher education status were more likely to access to improved sanitation facility as compared to household heads that had incomplete primary educational status (Table 4). This result showed that the higher the level of schooling of HHs, the more likely they were to use better sanitation. This can be attributed to education serving as a key tool for accessing information on effective strategies to prevent environmental pollution and understanding of disease transmission due to poor sanitation. This result aligns with studies done in Ghana (Akpakli et al., 2018) and Kenya (Koskei et al., 2013), which highlighted a strong link between the educational level of HHs and access to sanitation facilities. Therefore, education plays an active part in in combating unimproved sanitation by fostering active awareness and promoting the use of proper sanitation practices.

The wealth index (socio-economic status) of households was found to be a statistically significant factor for sanitation practice. Households in the middle, richer and richest socioeconomic status were more likely to access to improved sanitation facilities compared to those in the poorest socioeconomic status (Table 4). This indicates that households with higher wealth level were more likely to use improved sanitation services than their lower wealth counterparts. This might be due to the fact that those having better wealth had better financial capacity enabling households’ to afford the construction of improved sanitation facilities—such as flush/pour flush systems connected to sewer networks, septic tanks, or pit latrines; ventilated improved pit latrines; compositing toilets or pit latrines with slabs (Adams et al., 2016; Okurut et al., 2015), and the ability to pay for municipal services for access to water. These findings align with studies conducted in selected sub-Saharan Africa countries (Armah et al., 2018), Zambia (Mulenga et al., 2017), and Ghana (Agbadi et al., 2019). Another significant determinant of access to improved sanitation facilities in this study was housing status.

The other predictor that found significantly associated with access to improved sanitation facility was housing status. Households who had modern housing status were 2.087 times more likely to access improved sanitation facilities compared to those who had not modern house (Table 4). This could be attributed to the fact that households in improved housing conditions are typically wealthier and more capable of affording the infrastructure needed for improve sanitation facilities. This finding aligns with a study by Johnson conducted in Benin, West Africa, which also reported a positive association between housing type and access to improved sanitation (Johnson et al., 2015). The accessibility of water was identified as a statistically significant associated factor of access to improved sanitation facilities among households. Households with water access within 30 min were 1.29 times more likely to have improved sanitation facilities compared to those requiring more than 30 minutes (30+) to access water (Table 4).

This finding showed that having nearby for access to water increases the likelihood of households utilizing improved sanitation facilities. The bodies concerned, particularly the Ethiopian government, should prioritize enhancing water accessibility and supply to households to reduce the prevalence of unimproved sanitation. This finding was consistent with the study carried by Ritchie and Roser, which highlighted a strong correlation between unsafe sanitation practice and reliance on unsafe water sources (Ritchie & Roser, 2019).

Similarly, households with older heads (aged 56 and above) were 1.336 times more likely to access improved sanitation facilities compared to those with younger heads (aged less than 31; Table 4). This could be attributed to the higher awareness and understanding of the health benefits of improved sanitation among older household heads, as well as their potential ability to afford the associated costs. This finding was in agreement with the studies done in Ghana, where households with heads aged 35 years and older was found to have greater likelihood of accessing improved toilet facilities compared to the counterpart (Agbadi et al., 2019). Similarly, this result was consistent with the study done in rural districts of southern Ghana (Akpakli et al., 2018).

The administrative region, where a household is located, was found to be a statistically significant associated factor for the household’s sanitation practice. Households reside in Tigray, Amhara, Oromia, Benishangul-Gumuz, Southern Nations Nationalities and People Region (SNNPR), and Gambella are less likely to access improved sanitation facility as compared to Addis Ababa City administration (Table 4). The likely reason for this might be economic disparities and access to resource that urban areas (like Addis Ababa) may offer significant opportunities for more integrated and sustainable water use and waste management than rural areas (regions). In addition, households in the regions of Ethiopia were less aware of their health and might not want to make use of facilities that improve their quality of life, and open defecation such as forests, ditches, streets, and canals are highly accessible than the city of Addis Ababa. This disparity indicated that there was a difference in the prevalence of improved sanitation in the various administrative regions within the country. This study, therefore, suggested that due attention be paid to the regions of Ethiopia, in particular, Tigray, Amhara, Oromia, Benishangul-Gumuz, SNNPR, and Gambella in order to achieve the Sustainable Development Goal, which calls for adequate and fair sanitation for all.

Strength and Limitations of the Study

The use of nationally representative DHS data is an important strength of this study, ensuring reliability, comparability, and comprehensive coverage to support robust analysis and evidence-based policy and program development. This study has several important limitations. The DHS surveys in Ethiopia are typically conducted every 5 to 6 years. However, a recent survey has not been conducted, likely due to ongoing civil unrest in various regions of the country that have disrupted survey operations. This study used older EDHS data (EDHS, 2016), which may not fully reflect current trends or recent changes in access to improved sanitation facilities in Ethiopia. However, this study offers important insights into baseline tendencies and persistent challenges. The data’s nature limits the inclusion of all potential factors that could affect improved sanitation facilities. Additionally, missing household characteristics led to the exclusion of some cases, which may affect the representativeness of the findings for sanitation conditions nationwide. The investigator made a thorough effort to identify the determinants of access to improved sanitation facilities; however, the findings may not be entirely free from limitations.

Conclusions and Recommendations

Adequate sanitation, coupled with improved hygiene and access to clean water, is crucial for enhancing public health and driving socioeconomic development. Conversely, poor sanitation and inadequate facilities profoundly affect health, society well-being, the economy, and the environment. This study revealed that approximately 67% of households utilized unimproved sanitation facilities. This study identified various factors that are significantly associated with sanitation practices, including place of residence, education level of household head, wealth status, housing conditions, water accessibility, age of household heads, and geographic region. Priority should be given to improve sanitation practices among rural residents (communities), individuals with lower education attainment, and households with lower socioeconomic status. This serves as a call to action for concerned bodies, particularly the Federal Ministry of Health (FMoH) of Ethiopia, to intensify efforts toward achieving the Sustainable Development Goal of ensuring universal access to sufficient and equitable sanitation for all. Stakeholders should embrace a holistic approach to improve sanitation practices, which includes upgrading infrastructure, engaging communities through educational programs and workshops to address misconceptions and establish hygiene standards. Collaboration with community and spiritual leaders is crucial for fostering trust and leveraging their influence to advocate for improved sanitation practices within communities.

Footnotes

The author would like to express his gratitude to the Demographic and Health Surveys (DHS) Program,supported by the U.S. Agency for International Development,for providing the data utilized in this study.

ORCID iD

Amare Wubishet Ayele

Abbreviations

AIC: Akaike information criterion;CSA: Central statistics agency;EAs: Enumeration areas;EDHS: Ethiopia Demographic and Health Survey;FMoH: Federal Ministry of Health;HHs: household heads;ICC: Intra-class Correlation Coefficient;PHC: Population and Housing Census;SNNPR: Southern Nations Nationalities and People Region;SSA: Sub-Saharan Africa

Statements and Declarations

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Prevalence and Associated Factors of Access to Improve Sanitation Facilities Among Households in Ethiopia: Evidence From the 2016 Ethiopian Demographic and Health survey

Abstract

Keywords

Background

Methods

Study Area and Data Source

Study Design and Setting

Data Extraction and Processing

Variables Under the Study

Statistical Data Analysis

Results

Characteristics of Study Participants

Results of Multilevel Logistic Regression Analysis

Discussion

Strength and Limitations of the Study

Conclusions and Recommendations

Footnotes

ORCID iD

Abbreviations

Statements and Declarations

References