1 Background

Access to clean, safe drinking water, sanitation, and hygiene is an important human right and essential to public health [1,2,3,4]. Safe water, sanitation, and cleanliness are vital for survival, development, health, and well-being [5, 6]. Access to improve the upper ladders of water supply and sanitation services aimed at eradicating open defecation and providing everyone with access to sanitation and clean water. Five categories make up the potable water supply ladder: safely managed, basic, limited, unimproved, and surface water [7, 8]. Similar to this, there are five categories within the sanitation service ladders: safely managed, basic, limited, unimproved, and open defecation [9].

Epidemics linked to inadequate and improper usage of water supply and sanitation services have been a major public health problem [10]. Diarrhea occurs world-wide and causes 4% of all deaths and 5% of health loss to disability [11]. In 2015, about 10% of individuals in Sub-Saharan Africa utilized surface water, a substantial source of waterborne illnesses [12]. Diarrhea is the leading cause of WASH-attributable mortality. Every year, nearly 1.37 million children (0–14 years) die from diarrheal diseases caused by contaminated water and poor sanitation, making diarrhea the leading cause of death in children [13]. Furthermore, limited access to clean water, sanitation, and hygiene is responsible for roughly 88% of all diarrheal deaths in developing countries, including Ethiopia [14, 15]. Improving sanitation is recognized to have a significant positive influence on health both in households and throughout communities. Lack of access to clean water, proper sanitation, and adequate hygiene contributes to disease worldwide [16]. The provision of water, sanitation, and hygiene is crucial for preserving human health and promoting a country's rapid development [17,18,19]. Despite the country's improvement, around 40% of its citizens lack access to a better water source [20] and 37% still defecate in the open [21]. As a result, many people have suffered from water and sanitation-related health problems [22].

Credible scientific literatures have examined sanitation-related health issues, emphasizing the importance and relevance of water supply and sanitation assessment [23,24,25,26,27,28]. Despite this, further research on trends and demographic disparities simultaneously in using JMP ladders for water supply and sanitation services is required. Thus, the main objectives of this study are to examine the impact of demographic parameters on the use of JMP ladders for drinking water and sanitation services and to determine trends in JMP ladder utilization. The current study is conducted based on the five rounds of data from the Ethiopian Demographic and Health Survey (EDHS) [29,30,31,32,33]. The study was conducted to investigate trends and the demographic factors associated with Ethiopia's basic drinking water and sanitation services. These surveys are consistent from round to round and collect cross-sectional data on households, residents, sex, education, age, wealth index, and family size, which are nationally and regionally representative.

The remainder of this document is organized as follows: The research methodology of this study, including the study area, data quality control, logistic regression, data collection and processing, and data analysis tools, are described in Sect. 2. The findings are presented in Sect. 3, with a brief discussion, and the study's general conclusions are presented in Sect. 4.

2 Methods

Descriptive statistics and multivariate logistic regression analysis were employed to examine the drinking water and sanitation trends, the significant level and to assess the impact of demographic disparity in the research area. The study's streamlined methodological framework is shown in Fig. 1.

Fig. 1
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General methodological framework

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2.1 Description of the study area

Ethiopia is located in the orn of Africa. Ethiopia's population has increased from 52,743,917 in 1994 to 99,873,000 in 2015 [34]. According to Worldometer's elaboration of the most recent United Nations data, Ethiopia had a population of 120,990,881 in 2022 [35]. There are two levels of government hierarchy in Ethiopia: city government and regional government. The country has nine regional states and two city administrations. A map of the study area and the EDHS survey region is displayed in Fig. 2.

Fig. 2
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Map of the study area and the EDHS survey region

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2.2 Quality control of data

The data quality was checked using statistical and visual techniques. Visually data cleaning was done to check errors from data compilation and entry. Pearson goodness-of-fit tests were used to evaluate the model's statistical appropriateness.

2.3 Logistic regression model

A logistic model was used to analyze EDHS data for 2019 to identify the variables affecting basic sanitation and drinking water services. It is a predictive model for categorical dependent variables and the predictors that might be continuous or dichotomous. Binary (dichotomous) logistic outcomes (dependent variables) had a code of 0 (failure) or 1 (success). Multivariate analysis did not export variables from bivariate analysis with P-values higher than 0.2.

In general, it is appropriate to use binary logistic regression when the dependent variable is dichotomous (such as presence or absence, success or failure) [37]. Therefore, the logistic regression model is used to investigate the effect of predictors on the probability of having basic drinking water or sanitation services. It also defined as dependent variable and the dependent variable is given as follows:

Let π denote the proportion of success (basic drinking water /sanitation services):\(\mathrm{P}({\mathrm{Y}}_{\mathrm{ij}}=1)={\uppi }_{\mathrm{ij}}\mathrm{P}({\mathrm{Y}}_{\mathrm{ij}}=0)=1-{\uppi }_{\mathrm{ij}}\), And \({\mathrm{Y}}_{\mathrm{i}} \sim \mathrm{ Bernoulli }{\uppi }_{\mathrm{i}}\)

Then, the logistic regression function is given as

$$\pi_{i} = \frac{{{\text{e}}^{{\beta_{0} + \beta 1X_{i1} + \beta 2Xi2 + ... + \beta kXik}} }}{{1 + {\text{e}}^{\beta 0 + \beta 1Xi1 + \beta 2Xi2 + ... + \beta kXik} }} = \frac{{{\text{e}}^{Xi\beta } }}{{1 + {\text{e}}^{Xi\beta } }}$$
(1)

where: \({\pi }_{i}^{(i=1, 2, ..., n)}\) is the probability of ith household having basic drinking water/sanitation services given the vector of predictors (xi).

The advantage of logistic regression is that, unlike other types of analysis, it requires fewer assumptions. The outcome variables, for instance, are dichotomous, have no enormously influential outliers, and required a sufficient sample size [38, 39]. Additionally, as outlined in Hosmer [31], other assumptions, such as multicollinearity, were considered for the practical use of logistic regression. Therefore, this study was conducted to ensure that all the hypothesized conditions were true.

Likewise, the study uses the odds ratio to predict the odds of having a basic drinking water/sanitation service for a given category of the predictor variable against the reference category. The odds ratio (OR) is the ratio of the odds for x = 1 to the odds for x = 0 and is given by the equation [40].

$${\text{log }}\left(\frac{{{\text{Pi}}}}{{1 - {\text{Pi}}}}\right) = {\text{ a }} + {\text{ b}}_{{1}} {\text{x}}_{{{\text{1i}}}} + {\text{ b}}_{{2}} {\text{x}}_{{{\text{2i}}}} + \, ... \, + {\text{ b}}_{{\text{p}}} {\text{x}}_{{{\text{pi}}}}$$
(2)

where \(\frac{\mathrm{Pi}}{(1-\mathrm{Pi})}\) is the odds that the response variable takes the value of 1,

The model’s overall goodness of fit was evaluated as the last step in the evaluation process. Finally, Pearson goodness-of-fit tests were used to check model adequacy [41]. The sanitation and drinking water ladder models were found to suit the data, and the authors approved and validated this.

2.4 Data collection and processing

The five rounds of data from the Ethiopian Demographic and Health Survey (EDHS) were used (i.e., 2000, 2005, 2011, 2016, and 2019). These surveys are uniform between rounds and gather cross-sectional data on households, residents, sex, education, age, wealth index, and family size that is nationally and regionally representative. EDHS household data was used for this investigation. As shown in Table 1, the ladders for the water and sanitation services were created according to the JMP definition (specification) [36].

Table 1 JMP ladders for drinking water and sanitation services in households are defined below [36]
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Generally, a DHS sample is typically clustered and stratified. From the 1994 Population and Housing Census sampling frame, a representative sample of 14072 and 13721 households from 540 clusters was selected for the 2000 and 2005 EDHS, respectively [31, 32]. The EDHS surveys carried out in 2011, 2016, and 2019 also contained a representative sample of 16702, 16650, and 8663 households from 624, 635, and 304 clusters from the 2007 Population and Housing Census sampling frame [30, 33, 42].

2.5 Data analysis tools and software

To examine the trend of water and sanitation services, and the effects of demographic disparity in the research area, descriptive statistics and multivariate logistic regression (i.e., P-value, AOR) analysis were used. Origin 2022 software was employed to create the trend graph, and STATA Version 16 was used to analysis data. Throughout the analysis process, STATA commands with the svy package were utilized to handle the intricate sample design. Using the weighted factor included in the EDHS data, all estimates provided in this publication were weighted for sampling probability and nonresponse. The methodology of the EDHS final reports [18, 30, 33, 42, 43] contains a comprehensive explanation of the weighting process. The trend of water supply and sanitation, plus the effects of demographic variations, were discovered for each service ladder using the data that had been evaluated. In all analysis, a P-value of 0.05 or below was considered statistically significant.

3 Results and discussion

A time trend analysis was done on the drinking water and sanitation service ladder data using data from the five rounds of the Ethiopian Demographic and Health Survey. The results showed that while basic drinking water service ladders have increased, surface water consumption and unimproved drinking water services have slightly decreased (Figs. 3 and 4). Moreover, unimproved sanitation services increased while the open defecation ladder was reduced. On the other hand, the trend of basic and limited sanitation services increased over time. Many demographic factors also affect the coverage of waters supply and sanitation. Despite monitoring of access to all JMP ladders for water and sanitation services, it also essential and stipulated in Sustainable Development Goals (SDG) 6.1 and 6.2, respectively [44, 45], safely managed drinking water and sanitation service ladders were not calculated in this study since EDHS household data had not included essential variables.

Fig. 3
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Trends of JMP ladders for drinking water services in different survey years

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Fig. 4
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Trends of JMP ladders for sanitation services in different survey years

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3.1 Trends of drinking water and sanitation services based on JMP ladders in different DHS survey years

The trend of drinking water services, such as basic, limited, unimproved, and surface water, was computed from 2000 to 2019. Basic drinking water services coverage was increased from 18% in 2000 to 57% in 2019. This result corresponds to about a 217% increase in basic drinking water at the national level within 19 years. Having access to safe drinking water is a fundamental requirement for human health and development [46]. Although access to clean drinking water is woefully inadequate [47]. Hence, an estimated 10–25% of neonatal and maternal deaths are attributed to poor WASH access [45]. Therefore, having such access and trend is essential to prevent many infectious diseases and to improve public health.

On the other hand, unimproved drinking water services were reduced by 57% from 44% in 2000 to 19% in 2019. However, the rate of progress remained lower, from 24% in 2016 to 19% in 2019. This outcome corresponds to about 21% of unimproved drinking water services. Likewise, surface water use declined from 31% in 2000 to 12% in 2019. This result relates to about a 61% decrement in surface water use at the national level within 19 years. This surface water result (12%) in 2019 agrees with other study findings [43]. The 2019 EDHS data showed that 5,232 (57%) and 983 (12%) of the households used basic and limited drinking-water services, respectively (Fig. 3).

Trends for open defecation, limited sanitation, and basic sanitation services were calculated from 2000 to 2019. The coverage of basic sanitation services decreased from 8% in 2011 to 6% in 2016, corresponding to about a 25% decrease. Nonetheless, the overall coverage of basic sanitation service increased from 1% in 2000 to 10% in 2019. This result resembles about a 900% (or nine times) increase in basic sanitation service at the national level within 19 years. The sanitation coverage in 2019 agrees with the finding of WHO [48]. In contrast, the practice of open defecation decreased three times, from 81% in 2000 to 27% in 2019. This result corresponds to about a 67% decrease in open defecation practice, and the authors suggested that one reason for this is that households shifted to use unimproved sanitation services. Hence, the coverage of unimproved sanitation services increased from 17% in 2000 to 54% in 2019. The result revealed that at the national level there was a 218% increment in unimproved sanitation services within 19 years (Fig. 4). Access to basic sanitation facilities is critical to the health and well-being of individuals and communities [49].

3.2 Demographic factors associated with basic water and sanitation services in different survey years

One of the foundational views of the Sustainable Development Goal is addressing inequality in all of its manifestations. The 2030 SDG Agenda, however, needs strong commitment to address disparities in access to JMP ladders for water supply and sanitation services. Equitable water supply and sanitation access continue to be one of the most significant issues facing nations, especially low and middle-income countries like Ethiopia. The study revealed that the main demographic factors are gender, residence, age, education, family size, and wealth index. Another issue is the need for more specific information on how demographic factors affect the provision of water supply and sanitation services. Therefore, evaluating water supply, sanitation services, and related demographic aspects is crucial to improving public health concerns. Compared to global estimates [50] household use of basic drinking water and sanitation services was quite low in this study.

According to this finding, the study region may need to be on schedule to provide basic drinking water and sanitation services by 2030 as specified by the SDGs. The current study found that Households (HHs) with rural residents were 0.64 times less likely to use basic drinking water services than HHs with an urban population (AOR = 0.64 with 95% CI [0.22–1.89]) (Table 2). The computed p-value (0.41) is higher than the significant level (alpha = 0.05); hence the change is insignificant at the 5% significance level. This result was in line with that of Aderajew et al. [6], who found that households in urban areas were 21.7 times more likely than those in rural areas to use basic drinking-water services (AOR = 21.7 with 95% CI 16–30). Similarly, this finding was consistent with WHO data [51]. Moreover, the likelihood that households headed by men would use the JMP ladders to access basic drinking water services was 1.29 times lower than that of households headed by a female (AOR = 1.29 with 95% CI [1.00–1.67]). There may be a cultural perception of collecting water by women and girls.

Table 2 Multivariate logistic regression analysis for basic drinking water service ladder with selected explanatory variables among respondents for 2019 EDHS data
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Additionally, HHs with the richest heads were 19.61 times more likely than HHs with the poorest heads to use basic drinking water services (AOR = 19.61 with 95% CI [10.70–35.94]). There may be economic differences to influence the basic drinking water services. HH respondents with ages between 62 to 72 heads were 1.47 times more likely to use basic drinking water services (AOR = 1.25 with 95% CI [1.06–1.48]) than those with HH respondents with ages between 18 to 28 heads. This result showed that basic drinking water services also depended on different ages of household heads (Table 2). This study's findings concur with those of Gaffan et al. [24], who discovered that the wealthiest homes and few were more likely to have access to both individual and combined basic WASH services, as were households headed by people who were female, older, and more educated.

According to this study, the JMP ladder for basic sanitation services was highly correlated with wealth index status. In particular, HHs headed by the richest heads used basic sanitation services 9.76 times more frequently (AOR = 9.76 with 95% CI [5.26–18.13) than HHs headed by the poorest heads. This study finding is supported by other studies [6, 52]. Moreover, the age of the household was strongly correlated with access to basic sanitation facilities in the current study. In particular, households aged between 51 and 61 were 5.75 times more likely to use the JMP ladder of basic sanitation services than households aged between 18 and 28 AOR = 5.75 with 95% CI [3.16–10.47]). Likewise, this study showed that households in rural areas were 0.74 times less likely than those in urban areas to use basic sanitation services (AOR = 0.74 with 95% CI [0.50–1.09]). However, the computed p-value (0.41) is higher than the significant level (alpha = 0.05); hence the change is not significant at the 5% significance level. Besides, the current study found a strong correlation between basic sanitation services and living norms (urban–rural). This study finding agrees with Quispe-Coica and Pérez-Foguet [53], who discovered that urban households are more likely than rural ones to have access to basic hygiene services. With regard to tracking family access to drinking water and sanitation services globally, the traditional urban–rural difference is frequently utilized.

Besides, households with female heads were 0.76 times less likely than households with male heads to use basic sanitation services (AOR = 0.76 with 95% CI [0.58–0.99]). Higher education levels were 1.52 times more likely to use basic sanitation services than households with heads with no education (AOR = 1.52 with 95% CI [1.04–2.21]) (Table 3). This finding demonstrated a direct relationship between using basic sanitation services and education level. Numerous studies supported this conclusion [54,55,56].

Table 3 Multivariate logistic regression analysis for basic sanitation service ladder with selected explanatory variables among respondents
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Furthermore, households with female heads were 1.29 times more likely than households with male heads to use basic drinking water services (AOR = 1.29 with 95% CI [1.00–1.67]). This indicated that female household heads had a higher sense of responsibility in collecting water than male heads. Rural women and girls are particularly in charge of finding water for household use and spent more time searching for water (Table 2). In contrast, households with female heads were 0.76 times less likely than households with male heads to use basic sanitation services (AOR = 0.76 with 95% CI [0.58–0.99]). This finding showed that female household heads had poor attitudes toward basic sanitation services, possibly due to cultural and educational status, especially in rural areas (Table 3). The authors suggested that a gender segregation study for WASH is crucial. Thus, it can be used to monitor male–female heads in the JMP service ladders on drinking water, sanitation, and hygiene (WASH).

4 Conclusion

This study revealed that the trend of basic water and sanitation services of Ethiopia is significantly increased. However, the trend of using unimproved sanitation facilities was also increased. On the other hand, the trend of using an unimproved water source, surface water, and open defecation decreased. Despite having increasing trend on access to water and sanitation services, there are a number of households without these services. Many demographic factors affecting drinking water and sanitation services were identified. The wealth quantile was used as an example since it has a major impact on the water and sanitation service ladders. We found that many households had lower utilization of basic drinking water and sanitation services.

Therefore, to reduce water and sanitation related health problems, the households should take practical measures to improve access to water and sanitation services. Furthermore, to reduce health risks caused by poor sanitation, the households should replace the unimproved sanitation facility with improved sanitation facilities and encourage hand washing habits. Furthermore, the effect of demographic factors should be considered in the development of strategies to increase access to drinking water and sanitation services.