Introduction

Public health is a fundamental pillar of sustainable development, particularly in regions where health infrastructure remains inadequate. In West and North Africa, water, sanitation, hygiene and health challenges are considerable due to factors such as rapid population growth, uncontrolled urbanization and inequalities in access to basic resources such as safe drinking water and adequate sanitation facilities. These challenges exacerbate the vulnerability of populations to communicable diseases, particularly water-borne diseases that are caused by consumption of or contact with contaminated water, including: Diarrhea, Cholera, Dysentery, Malaria and Giardiasis. WASH (Water, Sanitation and Hygiene) is an integrated framework aimed at addressing these shortcomings and ensuring access to quality drinking water and adequate sanitation facilities, supported by awareness campaigns, public policies and the involvement of international organizations. In West and North Africa, this Approach has been adopted. Actions and initiatives are undertaken to promote WASH practices and its activity packages. All these various related interventions seem necessary to analyze their impacts on the well-being of populations and the protection of the environment. As highlighted by the World Health Organization (2017); insufficient WASH practices are responsible for millions of cases of diarrhea, cholera, and other infectious diseases each year. Improving these practices can not only reduce the burden of diseases, but also promote general well-being and socio-economic development in these regions. However, what are the main factors related to access to water, sanitation and hygiene (WASH) that contribute to the prevalence of waterborne diseases in West and North Africa? To what extent are public policies and local initiatives effective in improving WASH services and reducing the incidence of waterborne diseases? In view of all this, it would be important to deepen knowledge on the real effectiveness of WASH; hence the interest of our study.

I. Documentary reviews

West and North African countries face significant health challenges related to WASH and especially to inadequate drinking water supply infrastructure. These deficits in access to quality drinking water, sanitation and good hygiene practices are directly linked to a range of health problems, including waterborne diseases. According to a report by the World Health Organization (WHO, 2019), approximately 785 million people worldwide did not have access to an improved drinking water source, with a substantial number living in West and North Africa. Unfortunately, the few sources of drinking water that exist are often affected by anthropogenic pollution. “Poor wastewater management and water pollution contaminate water resources, exacerbating health risks” (Bach et al. 2020). Therefore, access to clean drinking water is fundamental to preventing waterborne diseases. Reliable water infrastructure limits people’s exposure to pathogens. For example, in Bangladesh, the study by Clasen et al. (2015) found that household water filtration reduced the incidence of diarrhea by 35%. WASH has a significant impact on waterborne diseases. WASH interventions play a key role in preventing waterborne diseases by ensuring access to safe drinking water, improving sanitation facilities, and promoting hygiene behaviors. According to WHO (2017), diarrheal diseases are responsible for 1.6 million deaths per year. Up to 58% of these deaths could be prevented through WASH improvements. In sub-Saharan Africa, Prüss-Ustün et al. (2019) show that access to safe drinking water reduces the incidence of diarrheal diseases in children under five by 40%. Community-based approaches such as education programs on the use of safe water sources have improved the health of millions of people in sub-Saharan Africa. In addition, WASH practice is essential in protecting public health, particularly in developing countries. In West and North Africa, health challenges are multiple, including infectious diseases and episodes of health crises. The evaluation of hygiene practices is essential to understand their impacts on the health of populations. Several scientific studies have demonstrated that targeted hygiene interventions, such as improving the supply of drinking water and hand washing, can significantly reduce waterborne diseases. For example, the study conducted by Fewtrell et al. (2005) revealed that improved hygiene practices reduce the incidence of diarrheal diseases by 30% to 50% in children. In addition, it should be noted that Initiatives such as water treatment and improved sanitation facilities significantly reduce cases of cholera and diarrhea. As the article published in the Journal of Water and Health states, improving WASH infrastructure can reduce the incidence of water-related diseases by 84% (Fewtrell et al., 2005) As for Sanitation, it remains a pillar against waterborne diseases. Poor sanitation promotes the spread of pathogens in the environment by increasing the risk of waterborne diseases. Thus, improved latrines, according to JMP (2020), reduce the risk of intestinal infections by 25%. Programmes such as CLTS (Community-Led Total Sanitation) in Ghana and Tanzania have reduced open defecation and led to a significant drop in the incidence of cholera. Also, community awareness campaigns have led to significant improvements in handwashing in several West African countries. As pointed out by Cairncross and Curtis (2003), who estimate that handwashing can reduce the incidence of diarrhoea by 47%. A study by The Lancet showed that access to effective WASH infrastructure reduces child mortality from diarrheal diseases. Children under five (05) years are particularly vulnerable, and WASH improvements can save lives (Wolf et al., 2014). In addition, WASH plays an indirect role in preventing malnutrition. An article in the American Journal of Tropical Medicine and Hygiene found that infections linked to inadequate WASH infrastructure can interfere with nutrient absorption, exacerbating malnutrition (Kosek et al., 2003). WASH practice is essential in pandemics. Effective water, sanitation, and hygiene systems and infrastructure play a critical role in public health, as observed during the COVID-19 crisis. Studies show that access to water and handwashing facilities are crucial in preventing the transmission of infectious diseases (WHO, 2020). In epidemic prevention as well, WASH is crucial to prevent the spread of diseases, as observed during the cholera outbreak in Haiti (Pope et al., 2017, “Emerging Infectious Diseases”). To ensure access to safe water, sanitation and hygiene services, and safely managed, WASH practice is not only essential for health but also contributes to the dignity of people by helping to create a resilient community living in healthy environments. As highlighted by a research conducted by the Water Supply and Sanitation Collaborative Council (WSSCC, 2016) which highlights that the absence of these infrastructures mainly affects women and girls, increasing the risks of violence and psychological distress Access. Similarly, the study conducted by Jones et al. (2019) in ‘’The Lancet Global Health’’ which highlights that access to safe sanitation facilities contributes to human dignity and security, especially for women and girls, who are often victims of violence and stigmatization due to the lack of such infrastructures. Thus, access to safe and private sanitation facilities is fundamental to human dignity and has positive impacts on psychological well-being, especially for women and girls, who are often the most affected by the lack of these infrastructures. Excluding the impacts on health, access to sustainable water, sanitation and hygiene services is a key criterion of equity, an element of Universal Health Coverage (UHC), and is recognized by the United Nations as a fundamental human right. These fundamental human rights not only contribute to individual well-being, but also significantly influence health outcomes at the community level. WASH has an economic impact that is not perceived by the world and sees it as a budget-consuming element. Thus, a study by the ‘’World Bank’’ highlights that for every dollar invested, for example, in WASH infrastructure, there is an economic return of 4 dollars thanks to reduced health costs and increased productivity (Hutton & Bartram, 2008). Also, some hygienic behaviors, such as handwashing with soap, are low-cost, high-impact interventions to reduce waterborne diseases. In view of this, we can say that improving WASH practice not only represents an essential intervention for public health, but it is also linked to economic benefits. As this study, which, in support, confirms the previous results: “each dollar invested in WASH can generate more than 4 dollars of economic return thanks to reduced health costs and increased productivity” (Hutton and Varughese, 2016). The scientific literature highlights the direct impact of WASH on public health. For example, an article by Fewtrell et al. (2005) highlights that improving access to drinking water and sanitation can reduce cases of diarrheal diseases by 30%. In West Africa, this relationship is particularly marked, where the prevalence of water-related diseases is among the highest in the world (Mara et al., 2010). In addition, the relationship between access to water, sanitary conditions and child health is alarming; as confirmed by the study conducted by Guiron et al. (2018) ‘’Inadequate WASH infrastructure was a determining factor in child mortality’’. Some environmental factors influence WASH and create inequality between different areas and exacerbate public health problems. According to a UNICEF report (2021), rural populations in West Africa are twice as likely to be without access to improved water services than those in urban areas. This raises concerns about health equity and sustainable development in the region. The relationship between WASH and public health is particularly visible but continues to weigh heavily on developing countries, with high mortality rates. In this regard, in West and North Africa, many communities continue to face major challenges in accessing these essential services. These inadequacies directly impact the health of populations, contributing to a high prevalence of waterborne diseases and increased morbidity linked to poor hygiene. Thus, according to the World Health Organization (WHO), insufficient WASH practices are responsible for millions of cases of diarrhea, cholera, and other infectious diseases each year. Given these compelling realities, research on the link between WASH and the reduction of waterborne diseases in West and North Africa remains relatively limited. Several studies have begun to explore this complex relationship at the occurrence, that made by Béné et al. (2021) on infrastructure inadequacies; which highlighted that populations living in informal areas have limited access to sanitation facilities, which impacts public health. Also, in many regions, WASH infrastructure is inadequate or missing, which limits access to safe drinking water and adequate sanitation facilities. Research shows that some practices or behaviors remain negative with respect to WASH. Even with WASH infrastructure in place, if hygiene practices are not adopted by the community (e.g. handwashing), the impact on disease reduction may be limited. For example, local beliefs and customs can influence attitudes toward hygiene practices. The research by Tchokonté et al. (2019) illustrates this and found that some traditional hygiene practices are deeply rooted and sometimes conflict with modern health recommendations. Hence, education and awareness raising are needed. A lack of health education and awareness raising programs within communities can reduce the effectiveness of WASH interventions. For example, the study by KMR et al. (2020) on handwashing demonstrated that awareness campaigns had a significant impact on reducing waterborne diseases. Furthermore, research published in the “Journal of Global Health” highlights that awareness and community education are fundamental elements in promoting sustainable hygiene behaviors, thus promoting better collective health (Campbell et al., 2018). Some environmental factors are not left out. Environmental problems such as water pollution or extreme weather conditions can also reduce the effectiveness of WASH interventions.
In addition, socio-economic factors such as poverty can limit access to resources. In this state of affairs, many regions suffer from limited access to drinking water, which hinders hygiene measures. According to a report by the World Health Organization (WHO), water quality is essential for the implementation of hygiene practices. In addition to this scourge, climate change is added. The impact of climate change on water availability and the spread of diseases is another issue. Research, such as that of Sarr et al. (2020), shows that extreme weather events worsen hygiene conditions, especially in vulnerable communities. At the level of Cooperation and public policies, the existence of weak or poorly implemented public policies can also be an obstacle. Authors such as Yaka et al. (2021) recommend increased collaboration between governments and NGOs to promote sustainable hygiene initiatives. Therefore, the systematic review of the multiple dimensions of WASH and its effects remains somewhat ambiguous especially at the level of health indicators and the availability of technical and financial resources as well as cultural practices. This analysis will shed light not only on insufficient policies or gaps in the implementation of WASH initiatives, but also on interventions that have met with success as well as the promotion of integrating WASH into public health frameworks for sustainable development and better health resilience. Despite the significant progress expected, it is clear that several obstacles still hamper the effectiveness of WASH. Future research could further explore the combined impact of the different WASH (water, sanitation and hygiene) components to maximize their benefits.

I. 1 Presentation of the study area

The study area includes ten (10) countries in Africa. Including five (05) countries in West Africa (Burkina Faso, Ghana, Niger, Nigeria, Mali) and five (05) countries in North Africa (Algeria, Egypt, Libya, Morocco, Tunisia).

Country Data: Population, Area, and Climate
Country Population Area_km2 Climate
Burkina Faso 20 million 274200 Tropical
Ghana 34 million 238533 Tropical
Mali 20 million 1241238 Tropical
Niger 22 million 1267000 Tropical
Nigeria 223 million 923768 Tropical
Algeria 45 million 2381741 Mediterranean
Egypt 110 million 1001450 Mediterranean
Libya 6 million 1759541 Mediterranean
Morocco 37 million 446550 Mediterranean
Tunisia 12 million 163610 Mediterranean

I.2 Definitions of key concepts

Access to water: it is the ability of individuals or communities to obtain sufficient and sustainable drinking water to meet their daily needs; Access to sanitation:It is the availability and use of adequate sanitation services for the disposal of wastewater and excreta. Hygiene practices:These are behaviors and actions that aim to maintain cleanliness and prevent the spread of disease; Waterborne diseases:These are diseases directly linked to water, often caused by the consumption of contaminated water or by poor management of water resources.

II. Objectives

  1. Assess access to WASH infrastructure: Study access to drinking water facilities, improved sanitation facilities and hygiene equipment in target populations
  2. Analyze hygiene practices: Observe people’s habits regarding hand washing, waste management and toilet use, and their link with the reduction of waterborne diseases
  3.  Measuring the prevalence of waterborne diseases: Assess the occurrence of diseases such as diarrhea, cholera and malaria in areas with and without WASH interventions

III.Methodology

III.1 Tools/Software

  • Zotero: who helped to produce the bibliographies review;

  • Microsoft Excel: who helped with data collection and data processing to extract the data matrix for analysis;

  • RStudio: which enabled the analysis and classification of data to be carried out;

  • QGIS : which made it possible to create maps by theme (variable);

  • Microsoft Word for writing the report.

  • Microsoft Powerpoint : for Slide Presentation

  • Kobotoolbox: Implementation of data collection tools

III.2. Choice of variables

PCA is performed on a given population. In our case, the individuals constituting the population are 10 African countries (Algeria, Burkina Faso, Egypt, Libya, Mali, Morocco, Niger, Nigeria, Ghana, Tunisia). The first essential step was to choose variables from which the PCA will be carried out. Following our subject which is Analysis of the impacts of WASH on the reduction of waterborne diseases, the following variables were retained: Infant mortality rate, rate of diarrheal diseases, mortality linked to WASH, Open defecation rate, rate of access to drinking water, access to improved sanitation, malaria, sustainable development goals linked to drinking water, financing linked to water and sanitation, population density, rate of the population without access to handwashing facilities.

• Explanation of study variables

The variables that our study focused on are as follows: Infant mortality Infant mortality is the number of deaths of children under 5 years of age per 1,000 live births. Infant mortality is directly influenced by diseases related to contaminated water, such as diarrhea, which is a major cause of death among young children. Diarrheal diseases Diarrheal diseases are the incidence of acute diarrhea per year, expressed as a percentage of the population or per 1,000 inhabitants. These diseases are strongly linked to the lack of drinking water, sanitation systems and hygiene practices. WASH-related mortality WASH-related mortality is the number of deaths attributable to inadequate sanitation conditions, including unsafe water, open defecation and lack of hygiene practices. This metric includes the direct and indirect impacts of WASH deficits on public health. Open defecation Open defecation practices is the proportion of the population still practicing open defecation, expressed as a percentage. This practice promotes the contamination of the environment and water sources, increasing the risks of waterborne diseases. Access to drinking water Access to safe drinking water is the proportion of the population with access to a safe and improved source of drinking water (tap, protected well, etc.). Access to safe drinking water significantly reduces water-borne diseases and improves quality of life. Access to improved sanitation Access to sanitation is the proportion of the population using sanitation facilities that do not allow human contact with excreta (improved latrines, flush toilets, etc.). Access to sanitation infrastructure reduces the transmission of waterborne diseases and cross-contamination. Sustainable Development Goals related to sanitation SDG 6.2 aims to “ensure access to adequate and equitable sanitation and hygiene for all and end open defecation by 2030. SDG 6.2 assesses the proportion of the population with access to improved sanitation infrastructure, the open defecation rate, the coverage of sanitation infrastructure in rural and urban areas, public policies put in place to achieve SDG 6.2. Sustainable Development Goals related to drinking water SDG 6.1 aims to”ensure universal and equitable access to safe and affordable drinking water for all by 2030 SDG 6.1 assesses the proportion of the population with access to improved drinking water (protected sources), average distance travelled to access a drinking water source, frequency of interruptions in the drinking water supply, microbiological and chemical quality of the water distributed. Water and sanitation financing International and local funding directly influences the implementation of WASH infrastructure and its effectiveness. These are: the total amount of investments dedicated to the WASH sector (by country or region), Sources of financing (governments, international donors, NGOs, private sector).

III.3. Target population

The population targeted by the study is that aged at least 18 years, who play a direct or indirect role in the management of water, hygiene and sanitation. This population includes several categories of actors, including household members, health actors, education authorities, authorities of the Ministry of Water and Sanitation, authorities of the Ministry of Health as well as NGOs working in the field of WASH. Regarding the actors living in households, particular attention will be paid to mothers or main heads of households, who, in many regions, play a key role in the management of drinking water, meal preparation and the management of waterborne diseases.

III.4. Sampling method

To determine the survey areas, we stratified the countries of the African continent, which allowed us to form two (2) groups based on their level of economic development, using the Our world in data database. The first group consists of five (5) West African countries (Burkina Faso, Ghana, Mali, Niger, Nigeria) and the second group consists of five (5) North African countries (Algeria, Egypt, Libya, Morocco, Tunisia). Stratification allows us to better take into account the heterogeneity of the situation of hygiene practices in the different countries. It also limits the geographical dispersion of the sample and therefore reduces the cost of the collection operation. From the 2010 to 2020 data on water, hygiene, sanitation available on Our world in data, 13 variables were considered: Infant mortality rate, malaria, diarrheal disease rate, WASH-related mortality, Open defecation rate, rate of access to drinking water, access to sanitationimproved, the objectives ofsustainable development related to drinking water, financing related to water and sanitation, population density, rate of population without access to hand washing facilities.

III.5. Data Collection

Data collection method The data collection phase consisted of extracting data through the exploitation of documents during documentary research. These searches were carried out on several scientific research sites such as Google Scholar, ERUDI and the Our World In Data database. The quantitative data obtained could have been collected using data collection tools such as questionnaires and interview guides. • Data collection tools

The questionnaire The questionnaire is a reliable, fast and simple collection tool to have quantifiable data. It will allow us to interview a set of households, in order to collect various statistically exploitable information to have information on water, hygiene, sanitation and water-borne diseases. The questionnaireis essentially composed of five (05) parts:

Table: General Information Categories

Categories of Information in WASH Analysis
Category
General information
Information on access to water and water works
Hygiene practices
Sanitation
Health

The questionnaire we propose for data collection in the appendix.

 The Maintenance Guide

Six (6) individual interview guides will be used for data collection.

List of Individual Interview Guides
Number Individual.Interview.Guides
1 Ministry of Water and Sanitation Authorities
3 Ministry of Health Authorities
4 Non-governmental organizations
5 Community leaders (Mayor, village chief, etc.)
6 Beneficiaries of WASH programs

The individual interview guides were organized around the following themes:

Background and Policy Themes in WASH Analysis
No Background.and.Policy
1 Implementation and coordination
2 Monitoring and evaluation
3 The challenges encountered
4 WASH program implementation objectives
5 Hygiene practices and awareness
6 Impact assessment
7 Collaboration between authorities and communities
8 Access to WASH resource management
9 Perceived health impacts
10 Hygiene practices
11 Community challenges
12 Access to water and sanitation
13 Hygiene practices and impacts on health
14 Awareness and education

Ethical considerations When conducting household surveys and interviews with key informants, several ethical considerations must be taken into account: • Informed consent: Before beginning, it is crucial to inform participants about the objectives of the study, the nature of the questions to be asked, the length of the interview or questionnaire, and how the responses will be used. They must give their consent to participate. • Confidentiality: The information collected must be treated confidentially. The personal data of the participants is protected. • Non-discrimination: All participants must be treated fairly. Selection criteria must be fair and transparent. Data processing

  • Data cleaning Data cleaning is the first essential step in the data analysis process. It consisted of preparing and correcting the raw data to ensure its quality and relevance. Cleaning also consisted of identifying missing data, correcting spelling errors, removing outliers and redundancies. Data normalization Data normalization consisted of adjusting the values of the variables so that they fell within a common scale.
  1. Presentation of the Analysis method In order to understand and interpret the results of the surveys and interviews, we will use the principal component analysis (PCA) method. Principal component analysis is a statistical method of data analysis. It consists of processing several quantitative random variables to retrieve new information. These variables are contained in a data matrix. They are then transformed into new variables called principal components or principal axes. PCA thus makes it possible to move from a point cloud of ‘’k’’ variables contained in a ‘’k’’-dimensional space to a two-dimensional representation. PCA also makes it possible to study multidimensional data sets with quantitative variables. This analysis method is used for several purposes, including: -The study of the visualization of the correlation between variables; -Obtaining uncorrelated factors which are linear combinations of the starting variables, in order to use these factors in modeling methods such as linear regression and logistic regression; -Visualization of observations in a two- or three-dimensional space, in order to identify homogeneous or atypical groups. Performing PCA requires software like RStudio. RStudio is a free software that offers a flexible environment with packages that allow complex analyses to be performed efficiently.

IV. Results and discussions

Country typology based on spatialized data on study variables

- Thematic maps

Figure1:Study area

Figure2: Population density

This map shows the population density in our study area. We can see that the majority of countries have a population that is quite less dense than Nigeria which has a high density, so the bright red color shows this, and countries like Morocco, Egypt, Ghana have a denser population than the other countries in the study.

Figure3: Wash-related mortality

This map shows the death rate directly related to WASH. Countries like Mali, Niger, and Nigeria have high mortality related to lack of WASH infrastructure. Then there is Burkina Faso with an average rate. And the other countries have a fairly low rate.

.

Figure4: Open Defecation

This map shows the practice of open defecation. We see that the practice remains high in Niger, then in Burkina Faso, then Ghana and Nigeria. In the other countries of the study, the practice remains very low.

Figure 5: SGD on drinking water

This map shows the evolution of the achievement of the SDGs in terms of access to drinking water in the countries of the study. The evolution remains quite significant in the different countries except Niger

.

Figure 6: Diarrheal disease

The map shows the rate of diarrheal disease in the study countries. The rate is largely high in Nigeria than other countries. In which, the rate remains uniform.

Figure 7: Improved Sanitation

This map shows the countries’ access rate to improved sanitation. Access to improved sanitation is high in North African countries and Nigeria. Access remains low in countries such as Mali, Burkina Faso, Niger and Ghana.

Figure 8: infant mortality

This map illustrates the infant mortality rate. This value remains very high in Mali, Niger, Burkina Faso and Nigeria.

Figure 9: Drinking Water Consumption

The map shows the population’s access to drinking water. High access in North African countries with the exception of Morocco. In West Africa, only Nigeria has a high rate.

Figure 10: Hand Washing Device

This figure illustrates the population’s access to handwashing facilities. Algeria, Egypt and Ghana have a high rate of access.

Figure 11: SGD On Sanitation

This map shows the evolution of the achievement of the SDGs in terms of sanitation in the countries of the study. The evolution remains quite significant in the different countries except Niger and Burkina Faso.

Figure 12: Water and sanitation Financing

This map illustrates the financing of different countries in access to drinking water and sanitation. We note that with the exception of Algeria and Libya, the other countries inject a large amount into access to drinking water and sanitation.

Figure 13: Malaria

This map shows malaria rates in different countries. The most affected countries are: Mali, Niger, Burkina Faso, Ghana then Nigeria and the other countries are less affected.

Data Visualization

The data analysis was carried out with 10 individuals and 12 variables including 3 complementary variables.

Charger les bibliothèques nécessaires

library(knitr)

Créer un tableau de données avec les données fournies

data <- data.frame( Country = c(“Algeria”, “Burkina Faso”, “Egypt”, “Ghana”, “Libya”, “Mali”, “Morocco”, “Niger”, “Nigeria”, “Tunisia”), Child_mortality = c(2.528743418, 10.12614795, 2.358870618, 5.619475436, 1.382464491, 11.59180268, 2.407081718, 12.73356745, 12.54266245, 1.693287373), Malaria = c(0.002695807, 440.0944009, 0, 272.8733073, 0, 396.2329209, 0, 383.1918827, 315.0641282, 0), Diarrheal_diseases = c(1139.87909, 11097.74818, 2406.11909, 7019.69909, 77.26909, 14893.13727, 720.77455, 22914.70818, 193243.0945, 100.32), Mortality_WASH = c(4.05, 60.91, 4.81, 25.16, 2.15, 66.12, 4.55, 70.26, 71.73, 3.13), Open_Defecation = c(1.204341017, 45.773195, 0.320034789, 18.305391, 0.749271426, 8.799540591, 5.176359773, 70.94560736, 20.500146, 1.74261624), Population = c(16.82337782, 68.72051609, 99.89093036, 126.0866662, 3.745839473, 15.32345482, 77.49363727, 15.80426318, 209.2096118, 73.30390409), Drinking_Water = c(93.39068827, 50.94078109, 98.67865945, 80.95869282, 97.04435218, 73.255171, 80.27913227, 45.74745791, 97.89891672, 94.17035645), Access_Sanitation = c(18219690.8, 653306.962, 29250827.64, 120015869.3, 0, 510150.184, 7114072.571, 584033.015, 26931338.55, 6629255.227), Improved_Sanitation = c(86.66650236, 20.58041555, 95.93658264, 20.47399445, 91.90626927, 37.96791118, 82.60192436, 12.85928105, 77.70596359, 92.12394309), Hand_Washing_Device = c(83.95678136, 8.780607318, 87.00093773, 41.1993227, 0, 16.603444, 0, 17.99968818, 30.2672872, 85.54538236), SDG_Sanitation = c(95.66868927, 45.843942, 98.66505691, 81.11400471, 99.03692655, 55.62677545, 86.31715709, 20.78230791, 59.27325836, 95.66983591), SDG_Drinking_Water = c(98.54868436, 75.51928936, 98.97676045, 88.61925491, 97.04435218, 78.01188227, 96.88813428, 60.09941564, 74.73670582, 97.00130273), Water_Sanitation_Financing = c(7532727.27, 126661818.2, 220087272.7, 91481818.18, 708571.43, 135273636.4, 228805454.5, 81362727.27, 162126363.6, 141851818.2) )

Créer le tableau formaté avec une légende

kable(data, format = “markdown”, caption = “Country Data on Child Mortality, Sanitation, and Related Indicators”)

###Matrice de corréltion

Studyfrom the correlation matrix table The correlation matrix is a matrix grouping a set of values called correlation coefficient. Indeed, these values show the relationship that exists between the variables taken two by two. They vary between -1 and 1 depending on whether the relationship that exists between the two variables is strong or weak. Indeed, there are three types of correlation namely:
Positive Correlation:It allows us to say that two variables evolve in the same direction, that is to say that the increase of one leads to that of the other and vice versa. The closer the value is to +1, the greater the relationship. The most remarkable values are in red and greater than 0.5 Negative correlations:It tends to show that two variables move in opposite directions. That is to say, an increase in one leads to a decrease in the other and vice versa. The closer the value is to -1, the greater the opposition relationship. Dark blue values are more remarkable and less than -0.5; Zero correlation:This shows that increases or decreases in one variable have no influence on the other. Values in light colors, white or pale blue, indicate no or weak linear relationship between the variables.

Correlation between variables Strong positive correlation •The SDGSanitation and SDGDrinkingWater variables have a very high correlation (0.98). This indicates that progress in sanitation and access to drinking water go hand in hand, probably due to joint efforts to achieve the Sustainable Development Goals. •ImprovedSanitationand HandWashingDevice also show a high correlation (0.85), suggesting that improved sanitation facilities promote the installation of handwashing equipment. Strong negative correlation •OpenDefecation is strongly negatively correlated with DrinkingWater (-0.87) and ImprovedSanitation (-0.79). This indicates that in areas where access to drinking water and improved sanitation infrastructure is high, the practice of open defecation decreases significantly. • MortalityWASH shows a significant negative correlation with DrinkingWater (-0.63) and ImprovedSanitation (-0.70), suggesting that improved WASH services reduce deaths from WASH-related diseases. Weak or no correlations Variables such as Pop and AccessSanitation show relatively low correlations with other variables, indicating a limited or indirect relationship with them. This matrix reveals relationships consistent with expected hypotheses in WASH (water, sanitation, and hygiene) analyses. Efforts to improve access to safe drinking water, sanitation, and handwashing facilities contribute significantly to reducing mortality and waterborne diseases. Negative relationships highlight persistent challenges in areas where open defecation is still widespread.

###Study of eigenvalues # Charger les bibliothèques nécessaires library(knitr)

Créer le tableau de données

dimension_data <- data.frame( Dimension = c(“Sun 1”, “Sun 2”, “Sun 3”, “Sun 4”, “Sun 5”, “Sun 6”, “Sun 7”, “Sun 8”, “Sun 9”), Own Value = c(5.32439167, 2.24041325, 1.2599755, 0.66481231, 0.34104138, 0.13702812, 0.01946232, 0.01152247, 0.00135297), Percentage of Variance = c(53.24391675, 22.4041325, 12.599755, 6.648123079, 3.410413801, 1.370281205, 0.194623235, 0.115224747, 0.013529677), Cumulative Percentage of Variance = c(53.24391675, 75.64804925, 88.24780426, 94.89592733, 98.30634114, 99.67662234, 99.87124558, 99.98647032, 100) )

Afficher le tableau au format markdown

kable(dimension_data, format = “markdown”, caption = “Tableau des Dimensions Principales”)

This table presents the eigenvalues with their percentage of variance on each axis as well as the cumulative percentage of variance which is equal to 100% at the ninth (9th) variable. The number of eigenvalues is equal to 09 because according to PCA the number of eigenvalues must be equal to the number of variables. These eigenvalues also correspond to the variance of the cloud of individuals.
From these eigenvalues we can determine a priori the number of axes that we can retain using the method called the Kaiser criterion. Thus from the Kaiser criterion which says that we must retain the axes associated with eigenvalues greater than 1, then we can retain axis 1 and 2 which group together 75.65% of the information.

So looking at this figure, we see that the first two axes of the analysis express 75.65% of the total inertia of the dataset which means that 75.65% of the total v ariability of the cloud of individuals or variables is represented in this plane. This is a high percentage, and the first plane therefore represents well the variability contained in a very large part of the active dataset. This is a high percentage, and the first plane therefore represents well the variability contained in a very large part of the active dataset. This value is higher than the reference value of 60.63%, the variability explained by this plane is therefore significant (this reference inertia is the 0.95-quantile of the distribution of inertia percentages obtained by simulating 10913 random datasets of comparable dimensions on the basis of a normal distribution). Because of these observations, we can say that only the first two axes carry real information. Consequently, we will keep only these two axes for the description of the analysis. Thus, we reaffirm our choice of axes based on Kaizer’s criterion.

###Analysis of the contributions of individuals and variables on the two axes

The correlation circle results from the PCA of the variables. It takes into account the correlations of the variables between them, of the variables with the two axes as well as the quality of representation of the variables. Indeed, the variables whose vectors are close to each other are correlated with each other in a positive way, on the other hand, those whose vectors are opposite are correlated in a negative way. Furthermore, the closer they are to an axis, the more they are correlated with this axis (positively or negatively). Also, the closer the vector of a variable is to the circumference of the circle, the better it is represented, otherwise it is less represented.

·       Dimension 1

Variables strongly associated with this axis (long arrows close to Dim 1) are Child mortality, WASH mortality, Malaria, and Open Defecation, which seem to indicate poor sanitary conditions and their impacts on health.

This axis differentiates countries based on their sanitation infrastructure and hygiene practices. Countries on the right are associated with better practices (e.g. Ghana), while those on the left reflect deficiencies.

·       Dimension 2

The variables Diarrhea Diseases, Pop, and Access Sanitation contribute mainly to this axis, indicating potential links between population density and access to WASH infrastructure.

This axis captures demographic variations and access to resources. Countries higher up (e.g. Nigeria) appear to face demographic or infrastructure challenges, while those lower down have less pressure on these aspects.

However, countries like Mali, Burkina Faso and Niger have moderate positions or those close to the centre, which indicates a low contribution to the structuring of dimensions.

Charger les bibliothèques nécessaires

library(knitr)

Créer le tableau de données

individuals_sun2 <- data.frame( Individuals = c(“Nigeria”, “Tunisia”, “Algeria”, “Ghana”, “Egypt”), Contribution (%) on Sun 2 = c(22.1, 10.4, 9.3, 14.2, 12.8) )

Afficher le tableau au format markdown

kable(individuals_sun2, format = “markdown”, caption = “Tableau des Contributions des Individus à Sun 2”)

After the data obtained during the analysis of the different graphs, the correlation circle, the factorial plane and the contribution graph, now we will classify the individuals

Hierarchical Ascending Classification (HAC) Classification is a method that aims to group individuals together. having characteristics or some characteristics in common. It can be done in a supervised way (Knowing the number of desired classes, we seek to know to which class an individual can belong) and unsupervised (We do not know in advance the number of classes). In our case, we carried out an unsupervised classification (a hierarchical ascending classification).

Result obtained:

Charger les bibliothèques nécessaires

library(knitr)

Créer le tableau de données

individuals_clusters <- data.frame( N = 1:10, Individuals = c( “Algeria”, “Burkina Faso”, “Egypt”, “Ghana”, “Libya”, “Mali”, “Morocco”, “Niger”, “Nigeria”, “Tunisia” ), Cluster = c(1, 5, 1, 3, 2, 5, 2, 5, 4, 1) )

Afficher le tableau au format markdown

kable(individuals_clusters, format = “markdown”, caption = “Tableau des Individus et leurs Clusters”)

As a result, we obtained five (05) clusters which are presented as follows: • Cluster 1: Egypt, Tunisia, Algeria, • Cluster 2: Libya, Morocco, • Cluster 3: Mali, Burkina Faso, Niger • Cluster 4: Ghana, • Cluster 5: Nigeria.

Cluster 1 countries are mainly North African countries. This group is characterized by: high values for the Improved Sanitation variable. Cluster 3 countries which are Sub-Saharan African countries marked by similar challenges in economic and social development and characterized by high values for the Open Defecation variable. And low values for the Drinking Water, SDG Sanitation, SDG Drinking Water and Access Sanitation variables. Ghana in Cluster 4 and Nigeria, Cluster 5 stand out as isolated cases. Due to variables like funding related to Wash for Ghana and variables diarrheal diseases and Pop for Nigeria.

This segmentation highlights structural differences and similarities between countries. The resulting clusters can guide in the development of targeted policies, adapted to the specific needs of each group of countries.

Since we obtained isolated or atypical clusters, we will do a supervised classification by limiting the group to 3.

So, as a result obtained, it is the following:

Charger les bibliothèques nécessaires

library(knitr)

Créer le tableau de données

individuals_clusters <- data.frame( Individuals = c( “Algeria”, “Burkina Faso”, “Egypt”, “Ghana”, “Libya”, “Mali”, “Morocco”, “Niger”, “Nigeria”, “Tunisia” ), Cluster = c(1, 3, 1, 2, 1, 3, 1, 3, 2, 1) )

Afficher le tableau au format markdown

kable(individuals_clusters, format = “markdown”, caption = “Tableau des individus et leurs clusters”)

We have 03 clusters as follows: Cluster 1: Algeria, Egypt, Libya, Morocco, Tunisia, Cluster 2: Ghana, Nigeria, Cluster 3: Burkina Faso, Mali, Niger

Cluster 1

Individuals in this cluster are strongly associated with variables related to access to drinking water and improved sanitation: • Relevant variables: DrinkingWater, ImprovedSanitation, HandWashingDevice, and SDGSanitation. • Interpretation: These individuals perform well in indicators of access to water and hygiene. Cluster 2 This cluster is correlated with variables representing population and financing problems: • Relevant variables: Population, Diarrheal Diseases, and Water Sanitation Financing.

• Interpretation: Individuals in this cluster are in contexts where structural challenges such as financing of health infrastructure and population density predominate.

Cluster 3

This cluster is aassociated with negative variables related to infant mortality and public health problems such as: • Relevant variables: Child Mortality, WASH Mortality, Malaria, and Open Defecation • Interpretation: These individuals have poor health conditions, with high rates of open defecation and diseases related to poor hygiene. And finally, we will perform the linear regression. Study of linear regression The aim of this regression is to explain child mortality as a function of variables related to health and environmental conditions, such as access to drinking water and improved sanitation facilities. Context of the analysis

• Dependent variable: Childmortality. • Explanatory variables: Scores of the first two main dimensions from the PCA.

Charger les bibliothèques nécessaires

library(kableExtra)

Créer le tableau de données

estimation_results <- data.frame( Term = c(“(Intercept)”, “DrinkingWater”, “AccessSanitation”, “Malaria”, “OpenDefecation”, “diarrheal diseases”), Estimate = c(2.276422e+00, 4.310056e-05, -1.899672e-08, 1.928444e-02, 2.041566e-02, 2.276761e-05), Std. Error = c(1.034261e+01, 1.114478e-01, 1.527704e-08, 6.247755e-03, 6.271100e-02, 1.790948e-05), t value = c(0.2201013069, 0.0003867332, -1.2434818491, 3.0866185925, 0.3255515095, 1.2712600762), Pr(>|t|) = c(0.83656915, 0.99970995, 0.28159119, 0.03669468, 0.76108165, 0.27253070) )

Afficher le tableau avec une mise en forme

kable(estimation_results, format = “html”, col.names = c(“Term”, “Estimate”, “Std. Error”, “t value”, “Pr(>|t|)”), caption = “Tableau des résultats de l’estimation”) %>% kable_styling(full_width = FALSE, bootstrap_options = c(“striped”, “hover”))

Table of model coefficients: • (Intercept) = 2.276422: The intercept is 2.28, which means that if all independent variables are equal to zero, the value of the dependent variable (Childmortality) would be 2.28 (depending on the measurement scale of the variable). • DrinkingWater = 4.310056e-05 (p-value = 0.9997): The coefficient for DrinkingWater is 0.0000431, suggesting a very weak association between drinking water and child mortality. However, the p-value is 0.9997, which is extremely high, indicating that the effect of this variable on Childmortality is not statistically significant. It can therefore be concluded that access to drinking water has no significant impact on child mortality in this model. • AccessSanitation = -1.899672e-08 (p-value = 0.2816): The coefficient for AccessSanitation is close to zero, suggesting a very weak relationship with infant mortality. The p-value is 0.2816, which is well above the 0.05 threshold, indicating that this variable is not significantly associated with infant mortality in the model. • Malaria = 0.01928 (p-value = 0.0367): The coefficient for Malaria is 0.01928, which suggests that increasing malaria increases child mortality. The p-value of 0.0367 is less than 0.05, which means that this variable has a statistically significant effect on child mortality. It can be concluded that malaria has a positive impact on child mortality in this model. • OpenDefecation = 0.02042 (p-value = 0.7611): The coefficient for OpenDefecation is 0.02042, but with a p-value of 0.7611, this indicates that this variable is not significantly related to infant mortality. The impact of open defecation on infant mortality is therefore not demonstrated by this model. • Diarrheal diseases = 0.00002277 (p-value = 0.2725): The coefficient for diarrhealdiseases is very low, showing a minimal relationship between diarrheal diseases and infant mortality. Furthermore, the p-value of 0.2725 is much higher than 0.05, suggesting that this variable is not statistically significant in the model.

Recommendations

In terms of recommendations, we can cite: Strengthening access to drinking water • Expanding access to drinking water infrastructure: Prioritize rural and peri-urban areas where access to drinking water is often limited. • Establish water quality monitoring programs: Using affordable technologies to test water quality and prevent waterborne diseases. Improving health infrastructure • Build more public and family toilets in low-access areas, especially schools and hospitals. • Introduce policies for the safe management of human waste, particularly in disadvantaged communities. Hygiene education and awareness • Strengthen awareness campaigns on hygiene practices, particularly on handwashing, through school programmes, media campaigns, and the use of community health workers.

Support for community initiatives • Encourage communities to take part in the management of water and sanitation infrastructure to ensure their sustainability. Strengthening resilience to health crises • Establish emergency plans to ensure the continuity of drinking water and sanitation services during crises (epidemics, floods, droughts). • Prevention of epidemics: Strengthen early detection systems for hygiene-related communicable diseases and implement prevention strategies. These recommendations aim to strengthen hygiene policies in a systematic manner by involving all stakeholders and taking into account local and national specificities. They would improve health conditions and reduce preventable diseases in the West African countries studied.

Conclusion The analysis of the impacts of WASH (water, sanitation and hygiene) interventions on the reduction of waterborne diseases in West and North Africa reveals significant conclusions for public health. The principal component analysis revealed that variables such as infant and WASH-related mortality, open defecation and hygienic hand washing are strongly correlated with the first axis, reflecting the importance of these factors in understanding the dynamics of WASH implementation on the general well-being of individuals. Furthermore, the second axis of the analysis highlights a strong correlation between populations and diarrheal diseases. These results highlight the significant impact of poor hygiene among populations in West and North Africa, which is affecting public health. WASH initiatives have proven crucial in reducing the incidence of water-related diseases, such as diarrhea, cholera, and typhoid, by improving access to quality drinking water and adequate sanitation infrastructure. However, the impacts of these interventions are often limited by persistent challenges such as lack of appropriate infrastructure, low adoption of hygiene practices, and socio-economic and environmental barriers. It is essential to strengthen efforts in these regions through increased investments in infrastructure, health education programs, and effective intersectoral collaboration. To maximize the impact of WASH initiatives, governments, international partners, and local communities must work together to overcome these challenges. With integrated and sustainable approaches, significant progress can be made in combating waterborne diseases and improving the living conditions of populations in West and North Africa. Bibliography

[1] L. Fewtrell, RB Kaufmann, D. Kay, W. Enanoria, L. Haller, and JM Colford, “Water, sanitation, and hygiene interventions to reduce diarrhea in less developed countries: a systematic review and meta-analysis” , Lancet Infect Dis, vol. 5, no. 1, p. 4252, Jan. 2005, doi:10.1016/S1473-3099(04)01253-8.

[2] Guillaume, “Principal Component Analysis in R”, Louernos Nature. Accessed: December 9, 2024. [Online]. Available at:https://louernos-nature.fr/analyse-comisantes-principales-logiciel-r/

[3] K. Hassfurter, “Progress on Drinking Water, Sanitation and Hygiene: 2017 update and SDG baselines,” UNICEF DATA. Accessed: December 9, 2024. [Online]. Available at:https://data.unicef.org/resources/progress-drinking-water-sanitation-hygiene-2017-update-sdg-baselines/

[4] “Egypt: population - LAROUSSE”. Accessed: December 9, 2024. [Online]. Available at:https://www.larousse.fr/encyclopedie/divers/%C3%89gypte_population/187007

[5] “Home | JMP”. Accessed: December 9, 2024. [Online]. Available at:https://washdata.org/

[6] “Interventions to improve water quality for preventing diarrhoea - Clasen, TF - 2015 | Cochrane Library”. Accessed: 9 December 2024. [Online]. Available at:https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD004794.pub3/full

[7] “WASH in Health Care Facilities: Global Baseline Report 2019”. Accessed: 9 December 2024. [Online]. Available at:https://www.who.int/en/publications/i/item/9789241515504

[8] “Water, sanitation, hygiene, and waste management for SARS-CoV-2, the virus that causes COVID-19”. Accessed: December 9, 2024. [Online]. Available at:https://www.who.int/publications/i/item/WHO-2019-nCoV-IPC-WASH-2020.4

[9] “WSSCC 2016 Annual Report - World | ReliefWeb”. Accessed: 9 December 2024. [Online]. Available at:https://reliefweb.int/report/world/wsscc-2016-annual-report

[10] “National Institute of Statistics of Mali|INSTAT”. Accessed: December 9, 2024. [Online]. Available on:https://www.instat-mali.org/fr/actualites-et%20evenements/resultats-du-rgph5

[11] “Sanitation and Health | PLOS Medicine”. Accessed: December 9, 2024. [Online]. Available at:https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000363

[12] “Mara, D., Lane, J., Scott, B. and Trouba, D. (2010) Sanitation and Health. PLoS Med, 7, Article ID e1000363. - References - Scientific Research Publishing”. Accessed: 9 December 2024. [Online]. Available at:https://www.scirp.org/reference/referencespapers?referenceid=2145477 [13] “WHO CED PHE WSH 18.03 Free 002 | PDF | World Health Organization | Sanitation”. Accessed: 9 December 2024. [Online]. Available at:https://fr.scribd.com/document/603044199/WHO-CED-PHE-WSH-18-03-free-002

[14] C. Tf and R. Ig, “Interventions to improve water quality for preventing diarrhea”, 2009.

[15] A. Prüss-Ustün et al., “Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: An updated analysis with a focus on low- and middle-income countries”, Int J Hyg Environ Health , flight. 222, no. 5, p. 765777, June 2019, doi:10.1016/j.ijheh.2019.05.004.

[16] G. Hutton and J. Bartram, “Global costs of achieving the Millennium Development Goal for water supply and sanitation”, Bull World Health Organ, vol. 86, no. 1, p. 1319, Jan. 2008, doi:10.2471/BLT.07.046045.

[17] TF Clasen et al., “Interventions to improve water quality for preventing diarrhea,” Cochrane Database Syst Rev, vol. 2015, no. 10, p. CD004794, Oct. 2015, doi:10.1002/14651858.CD004794.pub3.

APPENDIX

Questionnaire: WASH (Water, Hygiene and Sanitation) and health survey among households I. Questionnaire details Date :

Country Name:

Region Name:

Province Name:

Locality name (City/Village):

GPS coordinates:

Team ID:

Household number:

  1. Quiz
  1. General household information Has the household given consent to conduct the interview? Yes No

Gender of respondent Female Male

Age of respondent

How many people live in the household?

Respondent status Spouse Child How many children under 5 years old live in the household?

Are there any elderly people or people living with disabilities? Yes No B. Information on access to water and water works What is the main source of drinking water?

Public tap/water fountain Branch connected to the house or the neighbor’s house Water bottles or water bags Kiosk/Vendor Tanker truck Drilled well/hand pump Unimproved dug well Improved source Unimproved source Surface water (river, dam, lake, etc.) Rainwater harvesting Aure What containers do you use to store drinking water?

Can you show us all your containers? If yes, Container type Number of containers Total volume of containers

If not, move on to the next question.

How long does it take to walk to the water point? Minute : Water is available on site Don’t know

How many trips were made yesterday with containers for collecting water used for domestic purposes?

How often do you have the drinking water storage containers? Daily Once a week Once a month Once a year Don’t know Never

  1. Hygiene practices When do you find it important to wash your hands? Before eating Before cooking Before breastfeeding After the latrines Other Don’t know What do you wash your hands with? Water only Water plus soap Ash Other What types of equipment are available for hand washing? Bucket basin Kettle Payment device How do you store food to avoid contamination? Covered dish or pot Uncovered dish or pot Other : Where do you collect garbage? Open ground Garbage bin at home/on the street Open pit Buried Other :

Is the living environment clean? Yes No Does your community have awareness about hygiene practices? Yes No Does your locality have a waste management center? Yes No How do you wash drinking water storage containers? Rinse with water Use of specific product (detergent) Other I don’t know D. Sanitation Does the household have sanitation facilities? Yes No If yes, what types of sanitation facilities does the household have? Latrines Sumps Washhouse-sump Hand Washing Devices Showers Flush toilet Septic tanks Others Do the latrines and toilets you have provide sufficient privacy? Yes No If not, why? Is the latrine full? Yes No How often do you use the sanitation facilities? Daily Weekly Monthly Rarely Are the sanitation facilities shared with other households? Yes No If not, what are the main obstacles to accessing sanitation facilities? The cost Lack of information Others If no, where do family members usually relieve themselves? Private installation At the neighbor’s Community work Open defecation Others E. HEALTH Have you ever suffered from a water-related illness? Yes No If so, what disease is it? Cholera Dysentery Acute diarrhea Typhoid Malaria Other : How many times have you been affected by these waterborne diseases in recent years?

Are you bothered by insects? Mosquito Flies Cockroaches If someone in your household becomes ill, where do you go for care? At the hospital At a healer’s At the pharmacy (self-medication) Has anyone in your household ever lost their life to a waterborne disease? Yes No Do you have any suggestions for improving access to sanitation facilities?

Maintenance guide Introduction Good morning Madam/Sir. We are ………………………………………………… and we work on behalf of………………………, which is conducting a study on the relationship between water, hygiene and sanitation (commonly called WASH) on the reduction of water-borne diseases in West Africa and North Africa. Thank you for receiving us and taking the time to answer our questions.

The objective of the study is to “take stock of access to water, hygiene and sanitation services in countries such as Algeria, Burkina Faso, Algeria, Egypt, Libya, Mali, Morocco, Niger, Nigeria, Ghana, Tunisia public with a view to adequate intervention to ensure better living conditions for the population. It aims to analyze the reduction of waterborne diseases in these different countries. We assure you that the information that will be collected will remain strictly confidential. Stakeholder: Ministry of Water and Sanitation Authority Authority of the Ministry of Public Health Community Manager

Interviewee ID

  1. Specific questions to government authorities (Ministry of Water and Sanitation and Ministry of Public Health) To begin our interview, what do you think is the importance of water, hygiene and sanitation services? What are the main challenges in ensuring adequate access to water and sanitation infrastructure in your locality?
  1. Background and policy What are the national policies on water, sanitation and hygiene management? What are the government’s priorities for the WASH programme in your country? How are these policies integrated into public health strategies, particularly with regard to waterborne diseases?
  2. Implementation and coordination of programs What are the main WASH programmes implemented in your country and how are they managed? What is the coordination between the different ministries (water, sanitation, health) in the implementation of the programs? How are local authorities involved in the management of WASH infrastructure?
  3. Monitoring and evaluation What mechanisms are in place to monitor the impact of WASH programs on waterborne diseases? Have you observed a reduction in waterborne diseases in the intervention areas? What health indicators are monitored? What evaluation tools do you use to measure the impact of these programs?
  4. Challenges encountered What are the main challenges encountered in maintaining the sustainability of WASH projects (financing, infrastructure, training of beneficiaries)? What obstacles have you identified regarding the management of water resources and sanitation facilities in rural areas?
  1. Questions for non-governmental organizations (NGOs) and international partners
  1. Objectives and implementation of WASH programs What types of WASH programs have you implemented in the targeted communities? How do you assess the results of WASH programs on public health, particularly on the reduction of waterborne diseases?
  2. Hygiene practices and awareness What actions have you taken to promote hygiene practices in communities, such as hand washing? How do you raise awareness among communities about preventing water-related diseases?
  3. Impact assessment What impact have you observed in terms of reduction of waterborne diseases thanks to your programs? What monitoring data do you use to assess the effectiveness of WASH initiatives?
  4. Collaboration with authorities and communities How do you work with local governments and health authorities to strengthen the impact of programs? What initiatives have you put in place to strengthen community participation in water and sanitation management?
  1. Questions for community leaders (village chiefs, local leaders)
  1. Access and management of WASH resources What is the current situation regarding access to clean water and sanitation facilities in your community? What actions have been taken to improve access to safe water and sanitation in your community?
  2. Perceived health impacts Have you observed a reduction in waterborne diseases since the implementation of WASH projects in your community? How do waterborne diseases affect your community? What are the most visible impacts?
  3. Hygiene practices What are the main hygiene habits in your community? Is hand washing practiced regularly? What efforts are being made to improve hygiene behaviors in the community?
  4. Community Challenges What are the specific challenges your community faces in maintaining access to clean water and sanitation? How do you manage sanitation issues in rural areas or informal urban areas?
  1. Questions for WASH program beneficiaries (community residents))
  1. Access to water and sanitation Do you have access to clean water in your community? If so, is it easily accessible? Do you have adequate sanitation? If not, what difficulties do you encounter?
  2. Hygiene practices and impact on health What are your hygiene habits (hand washing, waste management, storage of drinking water)? Have you noticed a decrease in cases of waterborne diseases since the implementation of WASH initiatives in your community?
  3. Awareness and education Have you received any training or information on hygiene and water management? How important are these trainings for the prevention of water-related diseases in your daily life? Conclusion of the interview These questions aim to collect a broad range of information on perceptions and outcomes related to WASH initiatives and their impact on reducing waterborne diseases. By collecting this data, it will be possible to assess the effectiveness of ongoing programs and propose solutions to improve access to WASH resources in targeted communities. EInvestigator, complete the following sections:

Observations on the conduct of the interview: