Despite the Sustainable Development Goal (SDG6) of achieving universal access to clean water and sanitation by 2030, many developing countries still face water, sanitation, and hygiene (WASH)-related health issues such as child mortality caused by diarrhea. This study investigated the factors contributing to diarrhea prevalence in rural children, utilizing a cross-sectional survey (n = 517) of smallholder household representatives from a Risk, Attitudes, Norms, Abilities, and Self-Regulation (RANAS) perspective. Using binary logistic regression, the study found that a high prevalence of diarrhea among children was associated with unsafe/open disposal of child feces, living in the poorest households, poor self-rated health, and residing in the Wa East district. Conversely, children from the Brifo ethnicity and those from larger households were less likely to have a high prevalence of diarrhea. These findings underscore the influence of behavioral, socio-cultural, and socioeconomic factors on the prevalence of diarrhea in rural areas. To achieve SDG6, child-friendly sanitation infrastructure, behavior change communication strategies, and incentivizing WASH infrastructure in Ghana and other regions in Sub-Saharan Africa facing similar conditions are recommended.

  • The world is not meeting the SDG6 that aims to provide universal access to clean water and sanitation for all.

  • Diarrhea is a significant cause of mortality among children under five.

  • Unsafe disposal of child feces contributes significantly to rural children's diarrhea.

  • Child-friendly infrastructure is needed in WASH design systems.

  • WASH behavioral change strategies are required to address child diarrhea prevalence.

Access to clean drinking water, proper sanitation facilities, and good hygiene practices (safe water, sanitation, and hygiene (WASH)) is crucial for global health (World Health Organization and United Nations Children's Fund [WHO/UNICEF/JMP] 2021). They help reduce and prevent waterborne (e.g., cholera, typhoid, and dysentery) and sanitation-related (e.g., hepatitis A) disease burden and promote overall public health (WHO/UNICEF/JMP 2021). Despite some progress toward safe WASH in 2021, a staggering 2 billion people lack access to clean drinking water, 3.6 billion lack proper sanitation facilities, and 2.3 billion lack good hygiene services globally (WHO/UNICEF/JMP 2021). This lack of safe WASH has resulted in 1.7 billion cases of diarrhea each year, causing approximately 446,000 deaths among children under five (WHO/UNICEF/JMP 2021). Unfortunately, these cases are primarily found in low- and middle-income countries, highlighting the urgent need for safe WASH (UNICEF 2023). Access to safe WASH is essential in preventing child deaths from diarrhea (UNICEF 2023). For example, access to clean water and proper sanitation, along with the promotion of good hygiene practices, such as handwashing, prevents diarrheal diseases (Troeger et al. 2018).

In Sub-Saharan Africa (SSA), access to safe WASH remains a significant challenge for many people, including Ghana (Akanzum & Pienaah 2023; UNICEF 2023). Access to safe WASH refers to the availability and use of facilities and services for WASH that are safe, hygienic, and culturally acceptable and that provide privacy and dignity for all members of a community (WHO/UNICEF/JMP 2021). In Ghana, access to safe WASH is limited. For example, less than 18% of the Ghanaian population (31.8 million) has access to improved sanitation (Ghana Statistical Service 2021). This has resulted in children's diarrhea persistence in the country (Kombat et al. 2024). Previous studies show that unsafe WASH practices (e.g., not washing hands before handling food, using contaminated water for drinking or cooking, and improper disposal of waste in water sources) are a significant contributor to this issue, with poor households and children from female-headed households being disproportionately affected (Kumi-Kyereme & Amo-Adjei 2016; Anyorikeya et al. 2016). Also, unhygienic stool disposal and open defecation practices are linked to an increased risk of diarrhea among children (Anyorikeya et al. 2016; Tetteh et al. 2022). These issues have resulted in thousands of deaths each year due to diarrhea in 195 countries (Troeger et al. 2018).

While these studies underscore the importance of safe access to WASH, they have primarily focused on the availability of improved sanitation facilities to reduce diarrhea, with little attention to the persistent challenges surrounding the disposal of child feces and the associated behavioral and psychosocial factors. The disposal practice of children's feces remains a challenge in the WASH sector, particularly in rural areas of Ghana's Upper West Region (UWR). Despite empirical evidence showing the pathogenic load of child feces, many people in these areas still hold the common misconception that the feces of children under 5 years old are not harmful. This misconception underscores the urgent need for research and interventions in this area (Bawankule et al. 2017; Tetteh et al. 2022). This study sets out to investigate the factors influencing the prevalence of diarrhea, explicitly focusing on the disposal of child feces. Guided by the Risk, Attitudes, Norms, Abilities, and Self-Regulation (RANAS) model, the study hypothesizes that unsafe/open disposal of children's feces is a significant contributor to the high prevalence of diarrhea among children under five. With the global community's increasing commitment to achieving Sustainable Development Goal 6 (SDG6) by 2030, this research aims to provide valuable insights for shaping policies and interventions in the UWR and SSA, where child deaths from diarrheal diseases due to unsafe WASH are a pressing issue.

Therefore, this study is anchored on the RANAS approach to understanding human behavior in terms of factors influencing childhood diarrhea prevalence (Mosler 2012). The RANAS model provides a systematic approach to understanding behavior change factors, making it valuable for analyzing childhood diarrhea determinants in a specific study context. It recognizes that creating lasting changes in behavior entails more than just raising awareness (Mosler 2012). Therefore, by exploring the complex dynamics of psychosocial factors in various settings, RANAS offers valuable insights into forming sanitation behaviors (Harter et al. 2020). This, therefore, informs the basis for categorizing the study variables from the RANAS perspective. Risk factors are attributed to an individual's perceived susceptibility and the health risk associated with a particular behavior. RANAS argues that understanding and addressing people's perceived risks is crucial, as doing so may change sanitation behavior (Mosler 2012). In this RANAS context, exposure to diarrhea (the study outcome variable) is considered as a risk factor for water insecurity. The attitude factors revolve around how an individual feels about behavior, including their beliefs about the outcomes and how they evaluate them. In this study context, people's attitudes toward child feces disposal (the study's focal independent variable) and handwashing practices, as conceptualized in the RANAS model, have implications that can lead to positive or negative outcomes. Sclar et al. (2022) claimed that people are motivated to change their behavior by understanding the advantages associated with it. Normative factors from the RANAS perspective in this study encompass what is commonly regarded as socially acceptable behavior within a social setting (e.g., community). Gender, age, education, marital status, ethnicity, religion, and household size are considered normative factors in this context and are perceived to shape sanitation practices in rural communities significantly. Mosler (2012) and Akanzum & Pienaah (2023) claimed that societal expectations and peer behaviors influence norms and cultural and spiritual practices. Skills and resources play a vital role in an individual's ability to adopt behavior and their confidence or self-efficacy in their capability to perform that behavior (the ability factor) (Mosler 2012; Sclar et al. 2022). As conceptualized from the RANAS model in this study, access to WASH infrastructure, household wealth, government and community support systems, and WASH training and education shape people's behavior. Osumanu et al. (2019) claimed that economic disparities can affect a household's ability to invest in sanitation facilities. Self-regulation factors center around individuals' ability to control their thoughts, emotions, and actions per their objectives, goals, and values (Harter et al. 2020; Sclar et al. 2022). It involves planning, executing, and maintaining positive behaviors over time, even when facing challenges or setbacks (Mulopo et al. 2020). In this study, RANAS highlights that an individual's access to resources, such as WASH infrastructure, healthcare services, and other amenities, can be influenced by the district in which they reside. Self-rated health is another crucial self-regulation factor that influences behavior change. Nevertheless, the RANAS model may not apply to specific cultural contexts, overlook certain external factors like climate change impacts, and oversimplify complex behaviors related to understanding the causes of diarrhea in children. We argue that the factors influencing the prevalence of diarrhea can be better understood from behavioral and psychosocial perspectives through the RANAS framework.

Study context

The study was conducted in Ghana's UWR, which covers 18,476 km² and has a population of 901,502 (Figure 1) (Ghana Statistical Service 2021). The UWR is semi-arid, characterized by multiple climatic stressors (Pienaah & Luginaah 2024), perennial bushfires, drought-tolerant scattered, and harmattan (Pienaah et al. 2024a). Smallholder farming is the main economic activity (80%), with many residents confronted with food, nutrition, and water insecurities (Pienaah et al. 2024d). The UWR also faces poor health and WASH-related insecurity, such as open defecation and lack of infrastructure (Osumanu et al. 2019; Pienaah et al. 2024c). Many households lack child-friendly, age-friendly, and disability-friendly toilets and waste disposal mechanisms. The region's socio-cultural norms also influence further WASH practices. The UWR has the third-highest Multidimensional Poverty Index (MPI) of 0.348, surpassing the national MPI (0.112), which suggests that 65.5% of the region's population experiences multidimensional poverty (Ghana Statistical Service 2020). Women in the UWR depend on the shea tree for livelihood (Pienaah et al. 2024b). Nevertheless, the UWR has recorded several interventions, such as UNICEF's Community-Led Total Sanitation and the Community Water and Sanitation Agency's Sustainable Rural Water Supply and Sanitation Project, which aim to provide safe WASH. Yet, diarrhea is a significant challenge in the UWR.
Figure 1

Map of the UWR showing the study area (prepared by authors).

Figure 1

Map of the UWR showing the study area (prepared by authors).

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Research design, sample size, and sampling method

This study is part of a broader research focusing on investigating the impact of Community Resource Management Areas (CREMAs) on livelihoods and climate change resilience in Ghana's UWR. The study is designed to involve a two-phase, cross-sectional data collection process. In the first phase, three out of four municipals/districts in Ghana's UWR were purposively selected for the study because of the implementation of the CREMA approach. These districts included Wa East, Wa West, and Nadowli-Kaleo, while the Sissala East Municipality was excluded due to its municipality status. Within these districts, 18 CREMA and 18 non-CREMA communities were randomly selected, totaling 36 communities. The study compiled a list of 2,604 households in these communities. In the second phase, we used Raosoft's sample calculation at a confidence level of 95%, which yielded a sample size of 335 as the minimum sample threshold for the unbiased findings of our study. However, to strengthen the predictive power of our analysis, we increased the sample size to 517 participants. The sampling process involved selecting every fifth household from the study population until the entire list was exhausted. This systematic and random approach ensured that the sample was representative of the study area's household population, providing an equal opportunity for both female- and male-headed households to participate. The study focused on adult household representatives who responded to the survey questionnaire.

Data collection method

Trained field enumerators with bachelor's degrees visited the selected households and administered survey questionnaires in the respondent's preferred language, such as Sissale, Dagaare, Brifo, and Waale. Before data collection, the questionnaire was pretested in two communities in the Sissala East municipal to identify and resolve any discrepancies and anomalies. The data collected covered various topics, including demographics, socioeconomic and socio-cultural characteristics, food–nutrition–water insecurities, and climate change. The study ensured data accuracy and reliability through quality checks like validation, cross-referencing, peer review, and internal audits. The data were collected between 10 November 2022 and 31 January 2023.

Ethical clearance

The Non-Medical Research Ethics Board (NMREB) at the University of Western Ontario, Canada, ethically approved this study.

Measurement

Following the broader WASH literature, we categorize the variables from the RANAS perspective (Harter et al. 2020; Tetteh et al. 2022). The dependent variable, ‘prevalence of diarrhea,’ among children under 5 years in the household is coded as low (0) and high (1). This risk variable reflects the perceived vulnerability and severity related to poor WASH. Household water insecurity was studied as one of the explanatory independent variables under risk factors within the RANAS model perspective. We assessed water insecurity as a risk factor using the household water insecurity experiences (HWISE) scale proposed measurement by Young et al. (2019). It was coded as 0 = water secure and 1 = water-insecure households affecting hygiene and sanitation (Pienaah et al. 2024c).

The focal independent variable was ‘child feces disposal practice,’ coded as safe/closed disposal (0) or unsafe/open disposal (1), which was characterized as an attitude factor. Attitudes toward proper feces disposal can affect adherence to best practices. Households that dispose of feces properly, such as by flushing through a water closet, using an improved latrine, or using safe waste bins, are classified as close/safe disposal. In contrast, households that dispose of feces in the open field or use unimproved sanitary facilities without proper sanitation are classified as having open/unsafe disposal. Another attitudinal factor studied is household hygiene/handwashing facility usage, coded as (0 = nonfunctional) or (1 = functional), to study the impact of access and attitudes toward hygiene behaviors.

Other independent variables studied include normative factors such as gender, education, age, marital status, religion, ethnicity, and household size, which are coded as gender (0 = male, 1 = female), education (0 = no formal education, 1 = primary education, 2 = secondary education and above), age (continuous), marital status (0 = married, 1 = single, 2 = divorced/widowed), religion (0 = Christian, 1 = Muslim, 2 = African tradition), ethnicity (0 = Dagaaba, 1 = Sissala, 2 = Brifo, 3 = Waala), and household size (0 = 1–4, 1 = 5–8, 2 = 9+). These socio-demographic factors shape societal norms, peer behavior, and expectations for sanitation and hygiene practices. Ability factors include access to health and WASH infrastructure (0 = no, 1 = yes), household wealth (0 = poorest, 1 = poorer, 2 = middle, 3 = richer, 4 = richest), access to government support systems (0 = no, 1 = yes), community support systems (0 = no, 1 = yes), and WASH training and education (0 = no, 1 = yes). These factors can impact households' physical ability to access WASH facilities, their capacity to invest in WASH solutions, and the acquisition of necessary skills and knowledge to practice safe WASH. Self-regulation factors being studied include the district of residence (0 = Nadowli-Kaleo, 1 = Wa East, 2 = Wa West) and the self-rated household health. The district might have specific regulations, programs, or campaigns that promote sanitation and hygiene. Self-rated household health was based on the perceived health status of the household. It can impact the perceived vulnerability to diseases. Responses for self-rated household health were categorized as poor health (‘Fair’ and ‘Poor’) and good health (‘Excellent,’ ‘Very Good,’ and ‘Good’) and then coded as 1 and 0, respectively.

Data analysis

The study used descriptive and inferential methods to investigate factors influencing diarrhea prevalence. These factors were categorized from the RANAS perspective. Based on the dichotomous nature of the dependent variable (diarrhea prevalence), binary logistic regression was utilized (Harris 2021). We conducted the analysis using three stages. First, we used univariate analysis to understand the sample's characteristics. Next, we employed binary logistic regression at the bivariate level to examine the relationship between each independent variable and diarrhea prevalence. Finally, we utilize multiple binary logistic regression to explore the independent variables' combined impact on diarrhea prevalence. The formula for the binary logistic regression model is as follows:
(1)

In this formula, the symbol π represents the probability that an observation falls into the category of the dichotomous Y value (i.e., 1 for high diarrhea prevalence). The term ‘exp’ refers to the exponential function. β0 is the intercept, β1 is the coefficient of the first predictor variable, and βk is the coefficient of the last predictor variable. The binary regression coefficients are shown as odds ratios (ORs). An OR greater than one suggests a higher probability of high diarrhea prevalence, while an OR less than one indicates a lower probability. We checked for multicollinearity using the variable inflation factor (VIF), and all VIF values were less than 2.0, with an average value of 1.22, indicating minimal multicollinearity. The final model's reliability was assessed with R2 (38%), Akaike Information Criteria (AIC) (489.21), and Bayesian Information Criteria (BIC) (608.05), suggesting a good fit for our model. The data analysis was conducted using Stata version 18.

This section describes the risk factors for diarrhea within households (univariate), their interaction with the occurrence of diarrhea (bivariate), and their collective influence on diarrhea prevalence using binary logistic regression analysis (multivariate). The combined impact of these factor variables is examined in the multivariate results of the multiple binary logistic regression, discussing the factors influencing diarrhea prevalence at the multivariate level.

Table 1 describes diarrhea risk factors among households and shows the sample characteristics categorized according to the RANAS factors. The results reveal several descriptives, such as that 55.90% of households reported high diarrhea prevalence, while 36.750% openly disposed of their children's feces. The mean score for the Household Water Insecurity Experiences (HWISE) scale was 7.541.

Table 1

Socio-demographic characteristics and diarrhea risk factors among households in the UWR

RANAS modelVariablePercentage (%)/mean ± SDFrequency
Risk factors Dependent variable Diarrhea prevalence   
 Low 44.10 228 
 High 55.90 289 
 Household water insecurity scale 7.54 ± 9.44 Min. = 0, Max. = 36 
  Water secure 67.70 350 
  Water-insecure 32.30 167 
Attitude factors Focal independent variable Child feces disposal practice   
 Close disposal 63.25 327 
 Open disposal 36.75 190 
 Hygiene/handwashing facility   
  Nonfunctional 91.88 475 
  Functional 8.12 42 
Normative factors Gender   
 Male 62.86 325 
 Female 37.14 192 
Education   
 No formal education 71.95 372 
 Primary 18.57 96 
 Secondary or above 9.48 49 
Age (continuous) 44.37 ± 14.08 Min. = 18, Max. = 91 
Marital status   
 Married 77.37 400 
 Single 10.83 56 
 Divorced/widowed/separated 11.80 61 
Religion   
 Christian 55.51 287 
 Muslim 29.79 154 
 African tradition 14.70 76 
Ethnicity   
 Dagaaba 60.54 313 
 Sissala 15.28 79 
 Brifo 12.38 64 
 Waala 11.80 61 
Household size   
 1–4 26.89 139 
 5–8 43.71 226 
 9 + 29.40 152 
Ability factors Access to health and WASH infrastructure   
 No 32.11 166 
 Yes 67.89 351 
Wealth   
 Richest 20.89 108 
 Richer 17.60 91 
 Middle 19.92 103 
 Poorer 16.63 86 
 Poorest 24.95 129 
Access to government support systems   
 No 55.71 288 
 Yes 44.29 229 
Access to community support systems   
 No 90.72 469 
 Yes 9.28 48 
WASH training and education   
 No 50.10 259 
 Yes 49.90 258 
Self-regulation factors Self-rated household health   
 Good 62.48 323 
 Poor 37.52 194 
District of residence   
 Nadowli-Kaleo 23.40 121 
 Wa East 32.30 167 
 Wa West 44.29 229 
RANAS modelVariablePercentage (%)/mean ± SDFrequency
Risk factors Dependent variable Diarrhea prevalence   
 Low 44.10 228 
 High 55.90 289 
 Household water insecurity scale 7.54 ± 9.44 Min. = 0, Max. = 36 
  Water secure 67.70 350 
  Water-insecure 32.30 167 
Attitude factors Focal independent variable Child feces disposal practice   
 Close disposal 63.25 327 
 Open disposal 36.75 190 
 Hygiene/handwashing facility   
  Nonfunctional 91.88 475 
  Functional 8.12 42 
Normative factors Gender   
 Male 62.86 325 
 Female 37.14 192 
Education   
 No formal education 71.95 372 
 Primary 18.57 96 
 Secondary or above 9.48 49 
Age (continuous) 44.37 ± 14.08 Min. = 18, Max. = 91 
Marital status   
 Married 77.37 400 
 Single 10.83 56 
 Divorced/widowed/separated 11.80 61 
Religion   
 Christian 55.51 287 
 Muslim 29.79 154 
 African tradition 14.70 76 
Ethnicity   
 Dagaaba 60.54 313 
 Sissala 15.28 79 
 Brifo 12.38 64 
 Waala 11.80 61 
Household size   
 1–4 26.89 139 
 5–8 43.71 226 
 9 + 29.40 152 
Ability factors Access to health and WASH infrastructure   
 No 32.11 166 
 Yes 67.89 351 
Wealth   
 Richest 20.89 108 
 Richer 17.60 91 
 Middle 19.92 103 
 Poorer 16.63 86 
 Poorest 24.95 129 
Access to government support systems   
 No 55.71 288 
 Yes 44.29 229 
Access to community support systems   
 No 90.72 469 
 Yes 9.28 48 
WASH training and education   
 No 50.10 259 
 Yes 49.90 258 
Self-regulation factors Self-rated household health   
 Good 62.48 323 
 Poor 37.52 194 
District of residence   
 Nadowli-Kaleo 23.40 121 
 Wa East 32.30 167 
 Wa West 44.29 229 

Max., maximum; Min., minimum.

Furthermore, the findings of the bivariate analysis are presented in Table 2. The results show that open/unsafe disposal of child feces (OR = 12.200, p < 0.001) and water-insecure (OR = 1.585, p < 0.001) households were more likely to face high diarrhea prevalence. Households with secondary or higher educated persons were less likely to experience high diarrhea cases (OR = 0.514, p < 0.01) compared to those without formal education. Muslim households were more likely to experience high diarrhea prevalence (OR = 3.140, p < 0.001) compared to those of Christians, whereas households practicing African tradition (OR = 0.537, p < 0.01) were less likely to experience high diarrhea cases. Regarding ethnicity, Sissala households were more likely to experience high diarrhea cases (OR = 24.225, p < 0.001), whereas Brifo households were less likely to report high diarrhea cases (OR = 0.434, p < 0.001) compared to Dagaaba. Households that received WASH training and education (OR = 2.509, p < 0.001), those with access to health and WASH infrastructure (OR = 1.472, p < 0.01), and government support systems (OR = 1.726, p < 0.001) were more likely to report high diarrhea cases compared to those that did not. Households that rated their health as poor were more likely to have high diarrhea (OR = 3.145, p < 0.001) compared to those who rated it as good. Geographically, Wa East households were more likely to face high diarrhea cases (OR = 9.664, p < 0.001) compared to the Nadowli-Kaleo district.

Table 2

Binary logistic regression analysis predicting high diarrhea prevalence from the RANAS perspective

RANAS factorsVariableBivariate logistics regression
Multiple logistics regression
OR (SE)95% CIOR (SE)95% CI
Risk Household water insecurity (ref: water secure)     
  Water-insecure 1.585 (0.306)*** 1.086–2.315 1.254 (0.353) 0.721–2.178 
Attitude Child feces disposal (ref: close disposal)     
  Open disposal 12.200 (3.051)*** 7.473–19.918 13.357 (4.205)*** 7.207–24.757 
 Hygiene/handwashing facility (ref: nonfunctional)     
  Functional 1.844 (0.638) 0.935–3.636 0.566 (0.309) 0.194–1.654 
Normative Gender (ref: male)     
  Female 1.131 (0.207) 0.789–1.622 1.160 (0.341) 0.651–2.067 
 Education (ref: no formal education)     
  Primary 1.045 (0.242) 0.663–1.646 0.871 (0.314) 0.429–1.768 
  Secondary or above 0.514 (0.159)** 0.280–0.943 0.358 (0.191) 0.126–1.020 
 Age (continuous) 0.003 (0.006) −0.008 to 0.016 1.019 (0.011) 0.997–1.042 
 Marital status (ref: married)     
  Single 0.925 (0.264) 0.527–1.621 3.774 (1.780)*** 1.497–9.516 
  Divorced/widowed/separated 1.236 (0.347) 0.712–2.143 1.877 (0.789) 0.823–4.280 
 Religion (ref: Christian)     
  Muslim 3.140 (0.698)*** 2.030–4.856 3.846 (1.564)*** 1.732–8.535 
  African tradition 0.537 (0.145)** 0.316–0.914 0.634 (0.230) 0.311–1.293 
 Ethnicity (ref: Dagaaba)     
  Sissala 24.225 (14.520)*** 7.482–78.428 2.169 (1.682) 0.474–9.921 
  Brifo 0.434 (0.127)*** 0.245–0.771 0.374 (0.180)** 0.145–0.963 
  Waala 1.127 (0.316) 0.650–1.953 0.412 (0.207) 0.154–1.106 
 Household size (ref: 1–4)     
  5–8 1.216 (0.265) 0.793–1.865 0.812 (0.255) 0.439–1.503 
  9 + 0.805 (0.189) 0.507–1.277 0.481 (0.177)** 0.233–0.992 
Ability Access to health and WASH infrastructure (ref: no)     
  Yes 1.472 (0.278)** 1.015–2.134 1.065 (0.322) 0.588–1.928 
 Wealth (ref: richest)     
  Richer 0.776 (0.224) 0.440–1.366 1.327 (0.5476) 0.591–2.979 
  Middle 0.600 (0.167) 0.347–1.037 1.266 (0.511) 0.574–2.794 
  Poorer 0.579 (0.169) 0.326–1.028 1.019 (0.441) 0.436–2.380 
  Poorest 1.1102 (0.298) 0.655–1.881 2.622 (1.135)*** 1.122–6.128 
 Access to government support systems (ref: no)     
  Yes 1.726 (0.312)*** 1.211–2.460 1.261 (0.331) 0.753–2.111 
 Access to community support systems(ref: no)     
  Yes 1.226 (0.379) 0.669–2.250 1.245 (0.525) 0.544–2.849 
 WASH training and education (ref: no)     
  Yes 2.509 (0.456)*** 1.755–3.585 0.751 (0.223) 0.419–1.344 
Self-regulation Self-rated household health (ref: good)     
  Poor 3.145 (0.616)*** 2.142–4.619 1.976 (0.640)** 1.047–3.730 
 District of residence (ref: Nadowli-Kaleo)     
  Wa East 9.664 (2.940)*** 5.319–17.560 5.776 (2.627)*** 2.368–14.087 
  Wa West 0.760 (0.173) 0.486–1.188 1.122 (0.367) 0.590–2.134 
 Log-likelihood   −216.608  
 Pseudo R2   0.387  
 AIC   489.216  
 BIC   608.053  
RANAS factorsVariableBivariate logistics regression
Multiple logistics regression
OR (SE)95% CIOR (SE)95% CI
Risk Household water insecurity (ref: water secure)     
  Water-insecure 1.585 (0.306)*** 1.086–2.315 1.254 (0.353) 0.721–2.178 
Attitude Child feces disposal (ref: close disposal)     
  Open disposal 12.200 (3.051)*** 7.473–19.918 13.357 (4.205)*** 7.207–24.757 
 Hygiene/handwashing facility (ref: nonfunctional)     
  Functional 1.844 (0.638) 0.935–3.636 0.566 (0.309) 0.194–1.654 
Normative Gender (ref: male)     
  Female 1.131 (0.207) 0.789–1.622 1.160 (0.341) 0.651–2.067 
 Education (ref: no formal education)     
  Primary 1.045 (0.242) 0.663–1.646 0.871 (0.314) 0.429–1.768 
  Secondary or above 0.514 (0.159)** 0.280–0.943 0.358 (0.191) 0.126–1.020 
 Age (continuous) 0.003 (0.006) −0.008 to 0.016 1.019 (0.011) 0.997–1.042 
 Marital status (ref: married)     
  Single 0.925 (0.264) 0.527–1.621 3.774 (1.780)*** 1.497–9.516 
  Divorced/widowed/separated 1.236 (0.347) 0.712–2.143 1.877 (0.789) 0.823–4.280 
 Religion (ref: Christian)     
  Muslim 3.140 (0.698)*** 2.030–4.856 3.846 (1.564)*** 1.732–8.535 
  African tradition 0.537 (0.145)** 0.316–0.914 0.634 (0.230) 0.311–1.293 
 Ethnicity (ref: Dagaaba)     
  Sissala 24.225 (14.520)*** 7.482–78.428 2.169 (1.682) 0.474–9.921 
  Brifo 0.434 (0.127)*** 0.245–0.771 0.374 (0.180)** 0.145–0.963 
  Waala 1.127 (0.316) 0.650–1.953 0.412 (0.207) 0.154–1.106 
 Household size (ref: 1–4)     
  5–8 1.216 (0.265) 0.793–1.865 0.812 (0.255) 0.439–1.503 
  9 + 0.805 (0.189) 0.507–1.277 0.481 (0.177)** 0.233–0.992 
Ability Access to health and WASH infrastructure (ref: no)     
  Yes 1.472 (0.278)** 1.015–2.134 1.065 (0.322) 0.588–1.928 
 Wealth (ref: richest)     
  Richer 0.776 (0.224) 0.440–1.366 1.327 (0.5476) 0.591–2.979 
  Middle 0.600 (0.167) 0.347–1.037 1.266 (0.511) 0.574–2.794 
  Poorer 0.579 (0.169) 0.326–1.028 1.019 (0.441) 0.436–2.380 
  Poorest 1.1102 (0.298) 0.655–1.881 2.622 (1.135)*** 1.122–6.128 
 Access to government support systems (ref: no)     
  Yes 1.726 (0.312)*** 1.211–2.460 1.261 (0.331) 0.753–2.111 
 Access to community support systems(ref: no)     
  Yes 1.226 (0.379) 0.669–2.250 1.245 (0.525) 0.544–2.849 
 WASH training and education (ref: no)     
  Yes 2.509 (0.456)*** 1.755–3.585 0.751 (0.223) 0.419–1.344 
Self-regulation Self-rated household health (ref: good)     
  Poor 3.145 (0.616)*** 2.142–4.619 1.976 (0.640)** 1.047–3.730 
 District of residence (ref: Nadowli-Kaleo)     
  Wa East 9.664 (2.940)*** 5.319–17.560 5.776 (2.627)*** 2.368–14.087 
  Wa West 0.760 (0.173) 0.486–1.188 1.122 (0.367) 0.590–2.134 
 Log-likelihood   −216.608  
 Pseudo R2   0.387  
 AIC   489.216  
 BIC   608.053  

***p < 0.001, **p < 0.01, *p < 0.05, p < 0.1.

OR, odds ratio; SE, standard error; CI, confidence interval; dependent variable, diarrhea prevalence.

The multivariate results from the multiple binary logistic regression analysis are presented in Table 2. Consistent with our hypothesis, the results show that households that dispose of child feces openly/unsafely were more likely to experience a high diarrhea prevalence (OR = 13.357, p < 0.001) compared to those who dispose of it closely/safely. These findings reinforce the RANAS model's premise on attitude factor influence and that sanitation practices perceived as a health risk can profoundly impact health outcomes (Mosler 2012). Similar studies conducted in Ghana (Kumi-Kyereme & Amo-Adjei 2016), Nigeria (Hussein 2017), India (Bawankule et al. 2017), and SSA (Essuman et al. 2023) have found a positive association between open feces disposal and increased diarrhea prevalence. Linking to the RANAS conceptualization, an individual's attitude toward a behavior could result in either a positive or negative outcome. As observed in our findings, this negative attitude toward the open disposal of children's feces could be addressed through behavioral change awareness campaigns and community education. Ellis et al.’s (2020) work in Western Kanya claimed that by changing people's attitudes, individuals could be encouraged to adopt good sanitary practices and safe disposal practices like using latrines or toilets and sanitizing them. As noted by Curtis et al.’s (2009) work on a country review, people can be motivated to change their behavior by recognizing the benefits of a particular sanitation behavior perceived as unfavorable. These findings highlight the importance of effectively managing fecal matter to prevent diarrhea outbreaks.

The study also found normative factors such as marital status, religion, ethnicity, and household size associated with high diarrhea prevalence. Specifically, we also found that single individuals living with children were more likely to experience high diarrhea compared to married individuals (OR = 3.774, p < 0.001). This can be attributed to single individuals who may have specific hygiene habits or limited resources and tend to experience an increased prevalence of diarrhea. Bamlaku Golla et al. (2023) argue that effective health improvement is about having sanitation facilities and their proper use. Similarly, religious disparities within the study context exist, as Muslim households (OR = 3.846, p < 0.001) were more likely to experience high diarrhea prevalence compared to Christians. It is possible that certain beliefs and practices, rather than religious teachings, could be contributing to differences in diarrhea prevalence between Muslim and Christian households. For example, Christian households may prioritize investments in healthcare, healthy food, clean water, and WASH facilities differently than Muslim households. Differences in health education and awareness about WASH and disease prevention could also play a role. Additionally, these households' geographical location (place factor) and the associated infrastructure could contribute to the differences in health outcomes. This resonates with the work of Hussein (2017) in Nigeria, who found heightened diarrhea cases among Muslim children and mothers, respectively. Additionally, normative dimensions manifest vividly in terms of ethnicity. Brifo households (OR = 0.374, p < 0.01) reported a lower likelihood of diarrhea cases compared to Dagaaba households. This suggests varying cultural norms, habits, or community structures that might influence WASH practices. According to the study, the Dagaaba people have a longstanding tradition of open disposal of feces and defecation, which they believe was passed down by their ancestors. Some believe this practice has persisted because their ancestors lived long lives even before the advent of modern sanitation facilities. Interestingly, this finding echoes a similar study conducted by O'Connell (2014) in Peru, where respondents similarly described open disposal of feces as a routine practice. Culturally, men are considered the heads of households among the Dagaaba and are responsible for providing toilet facilities for the household, as women do not own lands. Failure to provide such facilities can lead household members to defecate outside. Furthermore, the study highlighted that larger household sizes (OR = 0.481, p < 0.01) were associated with a lower risk of experiencing diarrhea compared to smaller households. This may be due to the shared responsibilities and increased access to information and resources from having a larger family unit. These findings are consistent with the work of Al-Mazrou et al. (1995) conducted in Saudi Arabia, which found that large households are less prone to diarrhea.

The effectiveness of sanitation behaviors was subject to socioeconomic disparity perceived from an ability factor perspective of the RANAS model. The study found that the poorest households (OR = 2.622, p < 0.001) were at a higher risk of experiencing diarrhea compared with the wealthiest households, indicating possible disparities in access to sanitation resources or facilities (UNICEF 2023). The findings align with He et al.’s (2023) work in 36 SSA countries, which found that the diarrhea rate decreased progressively as the wealth quintiles increased. Like Ghosh et al.'s work in India, they found poorer households more prone to diarrhea than the richest (Ghosh et al. 2021).

The RANAS model emphasizes the importance of self-regulation in promoting better WASH practices and reducing the risk of illnesses such as diarrhea. The study found a positive association between households that rated their health as poor (OR = 1.976, p < 0.01) and an increased prevalence of diarrhea compared to those who rated their health as good. This could be characterized by poor healthcare access, insufficient sanitation facilities, and limited health literacy, which play roles within the context of the UWR (Akanzum & Pienaah 2023). Also, many rural populations often face challenges in managing diarrhea due to water quality challenges, hygienic practices, and unfavorable living conditions such as dirty surroundings and broken or no household toilet facilities (Ghana Statistical Service 2021). This finding is consistent with the work of Birhan et al. (2023), which posits that households that regularly maintain a clean compound and have access to sanitary facilities have a lower likelihood of their under-five children contracting diarrheal diseases. Geographically, households in the Wa East district were more likely to experience high diarrhea cases compared to those in Nadowli-Kaleo (OR = 5.776, p < 0.001). The following section discusses these findings. Localized health initiatives, infrastructural disparities, improvements in education, and access to safe water and sanitation facilities are crucial, as district-specific differences, such as higher diarrhea propensities in Wa East than in Nadowli-Kaleo, demonstrate. This could be due to the difference in poverty in these districts (Ghana Statistical Service 2020), as many poor households prioritize food and other basic needs over sanitation.

The study has some limitations, including the fact that the cross-sectional design does not allow for causal inference, so more longitudinal research is needed, as well as potential recall bias and social desirability bias. Despite these limitations, the study provides significant policy and intervention recommendations.

In the rural areas of the UWR of Ghana, there is a significant prevalence of diarrhea among children under five. To address this issue, we propose developing community-wide awareness campaigns and culturally specific educational programs focusing on the safe disposal of child feces and providing child-friendly WASH facilities. Collaboration with local leaders and organizations is essential to address normative factors such as marital status, religion, and ethnicity through culturally sensitive approaches. Targeted economic initiatives, such as microfinance programs and agricultural cooperatives, should be implemented to enhance wealth and resource access within rural communities. Additionally, establishing healthcare facilities and resources, including mobile health clinics, is crucial for providing essential healthcare services and education on diarrhea prevention. Moreover, investing in WASH infrastructure and incentives, such as clean water supply, toilets, and handwashing facilities, in marginalized areas is vital. Finally, a multi-stakeholder approach involving non-governmental organizations (NGOs) and community organizations is recommended to effectively address WASH challenges in Ghana's UWR and similar countries in SSA.

We acknowledge the participants' and community leadership's support and the research assistants' contribution to the research.

This research did not receive any funding.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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