Link between access to sanitation and diarrheal mortality
DEGTOUMDA Christelle Graciala
KIMBA Yélognissè Ulrich Tarcius
MOUMOUNI Hassane Malika
SOULAMA Gnarbaya Jonathan
2025-10-31
ENSEIGNANTS :
➢ Dr. ZOROM Malicki
➢ Dr. Maïmouna BOLOGO/TRAORE
➢ Dr. RICHARDSON Yohan
➢ Mr. THIAM Sina
ABSTRACT
Water is an essential resource for human life, yet when it is of poor quality or inadequately treated, it becomes a major vector for waterborne diseases. In West Africa, access to safe drinking water remains a major challenge, particularly in rural and peri-urban areas. According to the World Health Organization (WHO) and UNICEF (2021), safely managed water must be accessible, available when needed, and free from contamination. Limited access to clean water contributes directly to illnesses such as cholera, typhoid fever, and acute diarrhea, which continue to affect millions of people annually, creating heavy burdens on public health systems and socio-economic development. Understanding the factors behind these disparities is crucial to mitigate disease prevalence. Access to drinking water is influenced by environmental, socio-economic, and security-related factors, including water stress, resource management, urbanization, poverty, population growth, and the quality of infrastructure. To capture the complexity of these interactions, this study combines statistical and machine learning approaches. Principal Component Analysis (PCA) is first applied to identify key determinants of unequal water distribution, while Multiple Factor Analysis for Mixed Data (FAMD) integrates both quantitative and qualitative variables, including security level, geographical region (Sahelian vs. coastal countries), and climate-related factors such as precipitation.
Introduction
Water is an essential resource for life, but when it is of poor quality or insufficiently treated, it becomes a major vector for waterborne diseases. In West Africa, access to safe drinking water and adequate sanitation infrastructure remains a challenge for many populations, particularly in rural and peri-urban areas. Illnesses such as cholera, typhoid fever, and acute diarrhea continue to affect millions of people each year, leading to significant impacts on public health and socio-economic development.In this context, it is essential to analyze the links between access to drinking water and the prevalence of waterborne diseases in order to better understand the aggravating factors and guide public policies toward effective solutions. This work is based on the use of existing epidemiological and environmental data at the country level in West Africa. The objective is to highlight the correlations between water infrastructure, sanitation, and disease incidence using statistical analysis and machine learning methods in R.This study is structured around the following points: 1. A literature review on waterborne diseases and their relationship with access to drinking water. 2. A statistical and exploratory data analysis, including factorial analysis and classification and regression models.
Bibliographie rewiew
Overview of Water Quality Water quality is a critical aspect of public health, particularly in rural communities where access to safe drinking water can be challenging. Poor water quality often results from a combination of outdated infrastructure, limited treatment capacity, and the influence of agricultural and industrial activities that can contaminate water supplies. This situation can lead to the spread of waterborne diseases such as cholera, dysentery, and typhoid fever, which pose significant health risks, especially to vulnerable populations including children and the elderly 1 [2]. Factors Affecting Water Quality Contaminants Water in rural areas may contain various contaminants that compromise its safety. Microbial contaminants, such as coliform bacteria and protozoa like Cryptosporidium, can lead to gastrointestinal illnesses [3]. Chemical contaminants may arise from both natural sources and human activities. Naturally occurring substances, including arsenic and chromium, can leach into groundwater, while nitrates from fertilizers and industrial pollutants, such as per- and polyfluoroalkyl substances (PFAS), frequently enter water systems through runoff [3][4]. Agricultural Impact Agricultural practices significantly contribute to water quality degradation in rural areas. Farm runoff, which carries fertilizers, pesticides, and animal waste into nearby waterways, is a leading cause of water contamination. High levels of nitrates from fertilizers can be particularly harmful, posing health risks to infants and pregnant women when present in drinking water [4]. Moreover, pesticide residues can affect aquatic ecosystems and pose risks to wildlife and human health [4].
#Wastewater Management Effective management of wastewater is another critical factor in maintaining water quality. The lack of regulation over private drinking water sources, such as well water, complicates efforts to ensure safe drinking water in rural communities. Innovative methods, such as wastewater-based surveillance (WBS), are being proposed to address data gaps in health assessments related to water quality in these areas [5]. Government and Community Roles Governments play a vital role in establishing and enforcing water quality standards. Through funding and regulatory oversight, state agencies work to improve infrastructure, develop treatment facilities, and promote public awareness about water safety 1. Community engagement and education are essential for ensuring that residents are informed about the importance of water quality and the steps they can take to protect their water sources.
#Overview of Sanitation Sanitation is a critical component of public health, particularly in rural communities where access to basic sanitation services can significantly affect health outcomes. In 2022, it was reported that 57% of the global population (approximately 4.6 billion people) utilized a safely managed sanitation service. However, over 1.5 billion people still lacked basic sanitation services, with 419 million practicing open defecation, such as defecating in street gutters or open bodies of water [6]. The consequences of poor sanitation extend beyond individual health, impacting social and economic development through increased anxiety, the risk of sexual assault, and lost educational and employment opportunities [6].
#Health Impacts of Poor Sanitation Inadequate sanitation is closely linked to the transmission of waterborne diseases, including cholera, dysentery, typhoid, and intestinal worm infections, as well as exacerbating conditions like stunting and antimicrobial resistance [6] [7]. Poor sanitation can lead to significant health issues, particularly in children, as evidenced by updated meta-analyses that demonstrate the profound impact of improved sanitation and hygiene practices on reducing childhood diarrheal diseases [11]. The World Health Organization (WHO) emphasizes the need for effective risk assessment and management practices in sanitation to prevent disease and promote health[7]. Barriers to Effective Sanitation Several factors contribute to the sustainability of sanitation interventions, including household socioeconomic status, education, and infrastructure [12]. Rural areas are particularly vulnerable, with 91% of those who practice open defecation residing in such regions [8]. Additionally, communities often face barriers related to the inclusivity of sanitation solutions, where marginalized groups may lack access to necessary services. Addressing these barriers requires targeted strategies that engage local populations and leverage existing resources to improve sanitation systems in schools, health centers, and public spaces [9]. Sanitation and Community Well-being The WHO has highlighted that improved sanitation facilities contribute to enhanced human dignity, privacy, and safety, while also reducing the risk of waterborne infections [10]. Community-led sanitation initiatives, which focus on safe feces disposal and the use of latrines, are crucial for achieving Sustainable Development Goal 6, which aims to ensure availability and sustainable management of water and sanitation for all [10]. Comprehensive sanitation programming that encompasses entire communi- ties is essential for optimizing health outcomes and ensuring equitable access to sanitation services [8].
#Impact on Public Health Access to clean water and adequate sanitation plays a crucial role in determining public health outcomes in rural communities. The absence of these essential services can lead to a range of health issues, primarily waterborne diseases, which significantly affect morbidity and mortality rates among populations.
#Waterborne Diseases Waterborne diseases are caused by pathogenic microorganisms, including bacteria, viruses, and protozoa, which are transmitted through contaminated water sources. Poor sanitation practices and inadequate hygiene contribute to the transmission of these pathogens, leading to widespread outbreaks, particularly in rural areas where access to safe drinking water is limited [9]. According to the World Bank, approximately 2.6 billion people worldwide lack access to basic sanitation facilities, exacerbating health risks associated with poor hygiene and contaminated water [9]. Infections transmitted through water can result in severe health complications, including disability and death. For example, diseases like cholera, typhoid fever, and hepatitis A are prevalent in regions with poor sanitation and contaminated water sources [9]. The transmission of these diseases often occurs via the fecal-oral route, where ingestion of contaminated water leads to infection, highlighting the critical need for effective sewage management and sanitation systems [9]. Health Disparities in Rural Areas Rural communities frequently experience significant health disparities related to access to clean water and sanitation. Factors such as poverty, limited infrastructure, and inadequate health services exacerbate these disparities. Rural residents often face barriers in accessing healthcare, further complicating their ability to respond to waterborne diseases [13]. For instance, in many rural areas, individuals must travel long distances to obtain clean water, leading to increased exposure to contaminated sources [13]. The lack of public health resources and education on hygiene practices also contributes to higher rates of waterborne infections in these communities. Effective public health interventions, such as education campaigns and improved water infrastructure, are necessary to reduce the incidence of these diseases and promote overall health equity [13] [9]. Strategies for Improvement Addressing the public health impact of water quality and sanitation in rural communities requires a multifaceted approach. Strategies include enhancing water supply systems, promoting safe sanitation practices, and implementing community educa- tion programs focused on hygiene and disease prevention. The Federal Communications Commission’s initiatives, such as the Lifeline Program and the Connect America Fund, aim to improve infrastructure and access to necessary services in underserved areas, which could also support efforts in public health and sanitation improvement [13][9].
#Case Studies Exploratory Case Studies in Rural Tribal Villages An exploratory case study design was employed to investigate the impact of water, sanitation, and hygiene (WASH) on public health in rural tribal villages in India. This design, while limited in generalizability due to its context-specific findings, provides valuable insights applicable to similar communities. The study aimed for an in-depth understanding of the factors affecting domestic and childcare practices rather than broad generalizations. Authenticity of findings was enhanced through data triangulation, ensuring theoretical saturation was achieved regarding diverse practices and beliefs around hygiene and childcare [14]. Interlinkages of Socio-Economic Factors The study highlighted how broader socio-economic challenges, including caste inequalities and corruption, directly affect community perceptions of hygiene and self-efficacy in improving their environments. This conceptual framework illustrates the intricate connections between individual, household, and community-level factors, emphasizing the necessity for coordinated efforts in addressing these issues as outlined in the Sustainable Development Goals (SDGs) [14]. Specifically, efforts to improve WASH have been linked to benefits across multiple SDGs, demonstrating the interdependence of health, nutrition, and socio-economic stability. Community-Level Factors and Hygiene Practices Despite communities recognizing that poor hygiene practices contribute to enteric infections among children, many fail to implement necessary changes due to perceived lack of control over their circumstances. The study found that while caregivers were aware of risks such as inadequate hand hygiene and unsafe food and water practices, barriers stemming from socio-economic limitations often hindered action. The socio-cultural dynamics present in these communities, alongside political corruption affecting public service delivery, significantly influence caregivers’ motivation and capability to enhance hygiene conditions, which in turn exacerbates health risks for children [14]. Integrating WASH and Nutrition The compilation of 12 case studies aimed at integrating WASH into nutrition policies and programs showcases the measurable impact of such integration on child health outcomes. These case studies, developed by UNICEF, USAID, and the World Health Organization, focus on activities that yield substantial nutrition-related benefits, highlighting diverse contexts where WASH and nutrition can be effectively combined. This integrative approach emphasizes the importance of understanding the interplay between water quality, sanitation, and nutrition in improving public health [15]. Community Organizing and Public Health Further studies indicate the significant role of community organizing in enhancing public health outcomes. A narrative synthesis of qualitative outcomes from various studies reveals that partnerships between public health and community organizing groups can lead to increased effectiveness and improved community capacities. However, challenges such as administrative barriers and power dynamics need to be addressed to optimize these collaborations. The evidence suggests that community engagement through organizing methods can effectively tackle health inequities and enhance the overall efficacy of public health interventions [16]. A Complex Intervention Model Research on a complex intervention that combines new institution creation, infrastructure funding, and behavior change campaigns indicates a positive impact on public health in rural settings. This model, implemented on a large scale, seeks to pro vide realistic estimates of effectiveness for policymakers. The longitudinal follow-up of 3.6 years offers valuable insights into the sustainability of health improvements driven by coordinated WASH and health interventions [17]. These case studies collectively underscore the critical nature of integrated approaches to WASH and nutrition, emphasizing the need for tailored interventions that consider local socio-cultural contexts and engage communities in sustainable health improvements. Policy and Management Strategies Governance and Stakeholder Engagement Effective governance and stakeholder alignment are crucial for the successful implementation of water quality and sanitation (WASH) initiatives in rural communities. Government leadership is essential, as national and local authorities define priorities, co-invest resources, and provide ongoing administrative support and oversight [8]. Engaging all relevant stakeholders, including civil society and local organizations, fosters a collaborative environment where strategies can be tailored to local contexts and needs. This requires the facilitation of government-led coordination and cross-sectoral dialogue to build consensus and synergies [8].
#Policy Development Policies play a significant role in enhancing access to safe drinking water and sanitation. Various approaches can be adopted to increase public water access, particularly in rural areas. For instance, state-level policies may expand definitions of water systems to ensure comprehensive coverage, or prioritize funding for infrastructure projects in regions facing acute water access challenges[18]. Additionally, a participatory approach involving community members in the policy-making process increases trust and likelihood of community buy-in, further reinforcing sustainable initiatives [19]. Monitoring and Evaluation Establishing a robust monitoring and evaluation framework is critical for measuring the impact of WASH interventions. Key performance indicators should include the number of households with access to clean water, the percentage using improved sanitation facilities, and changes in hygiene practices among community members- [20]. This data collection, coupled with community feedback mechanisms, ensures that programs can adapt and evolve based on real-time needs and challenges [21]. Funding and Resource Allocation Securing adequate funding and ensuring efficient resource allocation are vital components of effective WASH management. Development programs often rely on social action funds, which support local governments and communities in building necessary infrastructure [22]. Organizations seeking funding for WASH projects should provide detailed proposals that articulate community needs and anticipated outcomes, alongside letters of support from local authorities [21]. This comprehensive approach not only addresses immediate infrastructure needs but also invests in community capacity-building to promote long-term sustainability [20]. Community Empowerment and Capacity Building Empowering communities to take responsibility for their health and welfare is a foundational element of successful WASH initiatives. By building local capacity and fostering community participation, initiatives can create a sense of ownership and responsibility among residents, enhancing the likelihood of sustainable outcomes- [16]. This approach aligns with public health strategies that prioritize community engagement and participatory practices as key drivers of change [23].
#Metrics for Evaluating Success Introduction Evaluating the success of water quality and sanitation interventions in rural communities involves assessing various metrics that indicate improvements in public health outcomes, community engagement, and sustainability. The integration of these met- rics ensures a comprehensive understanding of the project’s impact on community well-being. Health Outcomes One of the primary metrics for evaluating success is the reduction in the prevalence of waterborne diseases. This can be measured by tracking the incidence of conditions such as diarrhea, gastroenteritis, and typhoid among community members over time. The outcome variable concerning waterborne diseases is typically binary, indicating whether individuals have been diagnosed by health professionals within a specified period [24]. Significant improvements in health are expected to correlate with increased access to clean water and sanitation facilities, which should also lead to enhanced school attendance rates among children who previously missed classes due to illness [20].
#Community Engagement and Education Another essential metric is the level of community engagement and education regarding hygiene practices. The adoption of improved sanitation technologies, such as point-of-use water treatment solutions, can be assessed by evaluating how many households implement these practices and maintain them over time. Educational campaigns play a critical role in this metric, as they help raise awareness of the importance of sanitation and water management [9]. The perception of public health effectiveness among community members can also serve as an indirect measure of success, indicating increased trust and responsiveness to health initiatives [16].
#Sustainability Indicators For interventions to be considered successful, sustainability metrics must also be evaluated. This includes assessing the maturity of local markets and supply chains for sanitation products, as well as governance structures that support ongoing maintenance of sanitation facilities [12]. Factors such as baseline coverage levels and community capacity to manage WASH (Water, Sanitation, and Hygiene) needs sustainably are indicative of the program’s long-term viability [25]. Moreover, the interplay between increased health equity and community empowerment as a result of improved WASH conditions highlights the broader social impacts of these interventions. # 3. Variables and Hypotheses Following a preliminary review of various scholarly articles and research reports, we identified a set of representative variables for our study entitled The Impact of Waterborne Diseases on Public Health: An Analysis of Epidemiological Data Related to Drinking Water in West Africa. These variables were selected based on their relevance to the studied phenomena and are outlined as follows: 1. Mortality rate due to waterborne diseases: This is a direct indicator of the impact of waterborne illnesses on public health. It enables the assessment of the severity of the issue and the identification of the most vulnerable populations. 2.Urbanization rate: Urbanization influences access to drinking water and sanitation services. While it can facilitate improved infrastructure in some areas, it may also lead to overcrowding and increased pressure on existing resources in others, thereby exacerbating the risk of waterborne diseases. 3. Exposure to unsafe water sources: Contaminated water is the primary vector for many waterborne illnesses such as cholera, typhoid, and various diarrheal diseases. This variable is therefore essential for understanding population-level exposure to waterborne pathogens. 4. Child mortality due to waterborne diseases: Children are particularly susceptible to water-related illnesses. A high rate of child mortality linked to such diseases may reflect limited access to potable water and essential healthcare services. 5. Mortality rate from diarrheal diseases: Diarrheal illnesses remain among the most prevalent and fatal conditions associated with unsafe water. This variable allows for quantifying their burden and informing prevention strategies. 6. Access to improved sanitation facilities: Enhanced access to sanitation infrastructure contributes to the reduction of water contamination and disease transmission. This variable is critical for evaluating hygiene and sanitation initiatives. 7. Public and private expenditure on water and sanitation: Investment in water and sanitation infrastructure has a direct effect on the reduction of waterborne disease prevalence. This variable reflects both governmental and household efforts to address these challenges. 8. Ratio of child mortality to diarrheal disease mortality: This ratio serves as an indicator of disease severity relative to mortality rates, offering further insight into the impact of waterborne diseases on vulnerable age groups. 9. Domestic water use : is crucial as it reflects the actual level of access to water in people’s daily lives, and it directly determines households’ ability to prevent waterborne diseases. Together, these variables provide a framework for analyzing the relationships among drinking water access, sanitation, and public health. They highlight key risk factors and potential levers for intervention to mitigate the effects of waterborne diseases. From this analysis, we propose the following hypotheses: i) Countries with similar living conditions are likely to adopt comparable water treatment and public health policies. ii) The current management of sanitation infrastructure has not produced a significant measurable impact on reducing mortality related to waterborne diseases. iii) Infectious diseases and neonatal mortality account for a substantial portion of cases, warranting particular attention across all countries included in the study.
4. Problem Statement:
Despite the efforts of governments and development partners to improve access to safe drinking water in West Africa, waterborne diseases continue to pose a major burden on public health. What are the actual links between access to quality water and the prevalence of these diseases in the region, and how can epidemiological data help to better understand and target public health interventions?
5. General Objective:
To analyze the impact of waterborne diseases on public health in West Africa using epidemiological data and indicators related to access to safe drinking water, in order to identify the main health determinants and key intervention levers. Specific Objectives: 1. Identify the main prevalent waterborne diseases in West Africa and understand their transmission mechanisms. 2. Assess the status of access to drinking water and sanitation in the region through statistical data. 3. Analyze the correlation between water quality and the incidence of waterborne diseases using epidemiological and statistical models. 4. Highlight the socio-economic and environmental factors that exacerbate the prevalence of waterborne diseases. 5. Propose action-oriented recommendations based on the results, aligned with public policies and the Sustainable Development Goals (SDGs).
6. Materials and Methods
This study involved the use of multiple tools and the application of specific analytical methods tailored to our research objectives. Data Sources and Tools: Our primary data source was the OurWorldInData.org platform, recommended as part of this research project. Most of the relevant data were extracted from this database, while missing values were supplemented using official sources specific to each country in the study. The literature review was conducted via the Google Scholar search engine, which provided access to peer-reviewed articles and reports relevant to our topic. We utilized R-Studio, equipped with a range of statistical packages, to analyze our dataset according to a unified set of variables across all studied countries. To spatially represent the data, we developed thematic maps using the QGIS software. Analytical Approach: OurWorldInData.org is a scientific database curated primarily by researchers from the University of Oxford. It offers thematically organized data on major global issues, which facilitated access to the variables required for our study. Additional data—considered essential for the analysis but unavailable through the main database—were retrieved from official government portals corresponding to the countries of interest. The literature review allowed us to identify a range of studies aligned with our research question. These studies employed various analytical frameworks and data collection strategies. A common technique observed was the use of verbal autopsies to gather mortality data. While this method has inherent subjectivity, it appears to be particularly suited to the rural and semi-urban contexts of the countries under study, as evidenced by its frequent use. To synthesize and interpret our dataset, we conducted a Principal Component Analysis (PCA) using R-Studio. PCA, which belongs to the broader family of multivariate component analysis methods, enables a descriptive exploration of quantitative variables while reducing dimensional complexity. This approach provided a comprehensive overview of the relationships among our selected indicators.
7. Study Area Presentation
The map above shows the countries included in our study area, which consists of 14 countries. These countries are all located in West Africa and include Benin, Burkina Faso, Côte d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, and Sierra Leone.
#commende
library(ggrepel)## Le chargement a nécessité le package : ggplot2library(FactoMineR)
library(Factoshiny)## Le chargement a nécessité le package : shiny## Le chargement a nécessité le package : FactoInvestigatelibrary(prettyR)
library(FactoInvestigate)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(GPArotation)
library(rsconnect)##
## Attachement du package : 'rsconnect'## L'objet suivant est masqué depuis 'package:shiny':
##
## serverInfolibrary(ggplot2)
library(corrplot)## corrplot 0.95 loadedVE<-read.csv(file="C:/Users/hp14p/Desktop/BASE DE DONNE.csv", header = TRUE, sep = ";",
dec = ".",row.names = 1)
VE## TMH TU TDI TDD TDEI ISA DEA TDI.TDD IDH
## Bénin 42.8 48 948 56.6 3362 39.46 28.10 16.75 0.504
## BurkinaFaso 53.2 31 1842 68.3 9164 58.08 67.26 26.97 0.438
## Côted'Ivoire 24.8 52 908 34.5 7230 59.40 7.60 26.32 0.534
## Gambie 26.5 63 110 39.0 432 73.13 51.34 2.82 0.495
## Ghana 24.4 57 1653 38.5 5242 60.58 30.66 42.94 0.602
## Guinée 47.1 37 1311 80.1 4359 45.59 5.59 16.37 0.471
## Guinée-Bissau 46.3 44 102 75.7 835 48.98 13.37 1.35 0.483
## Libéria 38.3 52 256 61.1 1985 61.17 68.60 4.19 0.487
## Mali 51.2 44 1789 62.9 7821 26.44 147.80 28.44 0.410
## Mauritanie 8.2 67 22 11.5 36 64.18 25.72 1.91 0.540
## Sénégal 21.6 48 898 32.0 1924 57.20 21.75 28.06 0.517
## SierraLeone 49.0 43 759 61.1 2545 46.75 41.50 12.42 0.458
## mortalité_lie.a.M.d.acc.EP Stress.hidrique
## Bénin 2.9 0.98
## BurkinaFaso 3.2 7.82
## Côted'Ivoire 1.6 5.09
## Gambie 1.6 2.21
## Ghana 1.7 6.31
## Guinée 2.7 1.37
## Guinée-Bissau 2.2 1.50
## Libéria 2.5 0.26
## Mali 2.8 8.00
## Mauritanie 1.7 13.25
## Sénégal 1.5 16.28
## SierraLeone 3.7 0.50summary(VE)## TMH TU TDI TDD
## Min. : 8.20 Min. :31.00 Min. : 22.0 Min. :11.50
## 1st Qu.:24.70 1st Qu.:43.75 1st Qu.: 219.5 1st Qu.:37.50
## Median :40.55 Median :48.00 Median : 903.0 Median :58.85
## Mean :36.12 Mean :48.83 Mean : 883.2 Mean :51.77
## 3rd Qu.:47.58 3rd Qu.:53.25 3rd Qu.:1396.5 3rd Qu.:64.25
## Max. :53.20 Max. :67.00 Max. :1842.0 Max. :80.10
## TDEI ISA DEA TDI.TDD
## Min. : 36 Min. :26.44 Min. : 5.59 Min. : 1.350
## 1st Qu.:1652 1st Qu.:46.46 1st Qu.: 19.66 1st Qu.: 3.848
## Median :2954 Median :57.64 Median : 29.38 Median :16.560
## Mean :3745 Mean :53.41 Mean : 42.44 Mean :17.378
## 3rd Qu.:5739 3rd Qu.:60.73 3rd Qu.: 55.32 3rd Qu.:27.242
## Max. :9164 Max. :73.13 Max. :147.80 Max. :42.940
## IDH mortalité_lie.a.M.d.acc.EP Stress.hidrique
## Min. :0.4100 Min. :1.500 Min. : 0.260
## 1st Qu.:0.4677 1st Qu.:1.675 1st Qu.: 1.272
## Median :0.4910 Median :2.350 Median : 3.650
## Mean :0.4949 Mean :2.342 Mean : 5.298
## 3rd Qu.:0.5212 3rd Qu.:2.825 3rd Qu.: 7.865
## Max. :0.6020 Max. :3.700 Max. :16.280VE_scaled <- scale(VE)
res.pca <- PCA(VE_scaled, graph = FALSE)
RESULTATS AND DISCUSIONS
Thématic maps
1-Mortality rate due to waterborne diseases:
2-Urbanization rate: 
3-Exposure to unsafe water sources: 
4-Child mortality due to waterborne diseases: 
5-Mortality rate from diarrheal diseases: 
6-Access to improved sanitation facilities:
7-Public and private expenditure on water and sanitation: 
8-Population Growth: 
9-Water Stress : 
DATA COLLECTION QUESTIONNAIRE
Follow this link to access the form on Kobotoolbox: https://ee-eu.kobotoolbox.org/x/183YJ1qD Section 1: Household identification 1. Gender of respondent: ☐ Male ☐ Female 2. Age: ____ years 3. Education level: ☐ None ☐ Primary ☐ Secondary ☐ Higher 4. Number of people living in the household: ____ Section 2: Access to drinking water 5. What is your main source of drinking water? ☐ Well ☐ Borehole ☐ River ☐ Rainwater ☐ Public network ☐ Other: _____ 6. How far away is your main water source? ☐ Less than 500 m ☐ Between 500 m and 1 km ☐ More than 1 km 7. Do you have regular access to drinking water? ☐ Yes ☐ No Section 3: Water storage and treatment 8. How do you store water for consumption? ☐ Closed cans ☐ Buckets ☐ Barrels ☐ Futs ☐ Tank ☐ Other : ____ 9. Do you treat water before consumption? ☐ Yes ☐ No 10. If yes, what method do you use? ☐ Filtration ☐ Boiling ☐ Chlorination ☐ Other : ____ Section 4: Health and waterborne diseases 11. Have you observed any cases of water-related illness in your household in the last 6 months? ☐ Yes ☐ No 12. If yes, what type of disease? ☐ Diarrhea ☐ Cholera ☐ Typhoid fever ☐ Other : ____ 13. Do you have access to medical care in the event of water-related illness? ☐ Yes ☐ No Section 5: Perception and recommendations 14. In your opinion, what are the main difficulties related to access to drinking water in your community? 15. What solutions would you suggest to improve the situation?
RESULTS AND INTERPRETATIONS
Eigenvalues Call: PCA(X = VE, graph = FALSE)
Eigenvalues Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Variance 5.522 2.622 1.172 0.608 0.510 0.296 0.175 0.045 0.041 0.008 0.001 % of var. 50.201 23.839 10.653 5.528 4.632 2.687 1.592 0.413 0.370 0.077 0.010 Cumulative % of var. 50.201 74.040 84.693 90.221 94.853 97.540 99.131 99.544 99.913 99.990 100.000
Individuals (the 10 first) Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
ctr cos2
Bénin | 1.795 | 0.944 1.345 0.276 | -0.616 1.206 0.118 | -0.571 2.318
0.101 | BurkinaFaso | 3.939 | 3.239 15.832 0.676 | 1.331 5.626 0.114 |
-0.138 0.135 0.001 | Côted’Ivoire | 2.518 | -1.353 2.762 0.289 | 1.423
6.433 0.319 | -0.998 7.077 0.157 | Gambie | 3.358 | -2.566 9.933 0.584 |
-1.505 7.199 0.201 | 0.698 3.466 0.043 | Ghana | 3.712 | -1.563 3.687
0.177 | 2.751 24.044 0.549 | -1.337 12.711 0.130 | Guinée | 2.676 |
1.990 5.973 0.553 | -0.661 1.390 0.061 | -1.429 14.525 0.285 |
Guinée-Bissau | 2.781 | 0.265 0.106 0.009 | -2.429 18.745 0.763 | -0.623
2.762 0.050 | Libéria | 2.173 | -0.194 0.057 0.008 | -1.824 10.579 0.705
| 0.429 1.311 0.039 | Mali | 4.810 | 3.725 20.944 0.600 | 1.538 7.518
0.102 | 2.438 42.286 0.257 | Mauritanie | 4.667 | -4.348 28.531 0.868 |
-0.169 0.091 0.001 | 1.326 12.501 0.081 |
Variables (the 10 first) Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
cos2
TMH | 0.956 16.536 0.913 | -0.254 2.459 0.064 | -0.076 0.499 0.006 | TU
| -0.879 13.983 0.772 | -0.035 0.046 0.001 | 0.257 5.642 0.066 | TDI |
0.642 7.463 0.412 | 0.732 20.430 0.536 | -0.129 1.420 0.017 | TDD |
0.846 12.969 0.716 | -0.371 5.245 0.138 | -0.253 5.453 0.064 | TDEI |
0.634 7.284 0.402 | 0.653 16.286 0.427 | -0.094 0.758 0.009 | ISA |
-0.746 10.084 0.557 | -0.048 0.088 0.002 | -0.170 2.467 0.029 | DEA |
0.498 4.487 0.248 | 0.170 1.107 0.029 | 0.761 49.406 0.579 | TDI.TDD |
0.251 1.138 0.063 | 0.914 31.847 0.835 | -0.227 4.398 0.052 | IDH |
-0.782 11.069 0.611 | 0.303 3.500 0.092 | -0.439 16.445 0.193 |
mortalité_lie.a.M.d.acc.EP | 0.833 12.571 0.694 | -0.282 3.030 0.079 |
0.011 0.010 0.000 |
mat_cor<-cor(VE[,1:7], y = NULL, use = "everything",
method = c("pearson"))
mat_cor## TMH TU TDI TDD TDEI ISA
## TMH 1.0000000 -0.8526095 0.4355733 0.9382083 0.4416089 -0.6582711
## TU -0.8526095 1.0000000 -0.5897781 -0.8161608 -0.5744086 0.5541116
## TDI 0.4355733 -0.5897781 1.0000000 0.3082393 0.8797168 -0.4735631
## TDD 0.9382083 -0.8161608 0.3082393 1.0000000 0.2926768 -0.5532800
## TDEI 0.4416089 -0.5744086 0.8797168 0.2926768 1.0000000 -0.3742539
## ISA -0.6582711 0.5541116 -0.4735631 -0.5532800 -0.3742539 1.0000000
## DEA 0.3958573 -0.1449249 0.3694235 0.1961893 0.3937928 -0.4195314
## DEA
## TMH 0.3958573
## TU -0.1449249
## TDI 0.3694235
## TDD 0.1961893
## TDEI 0.3937928
## ISA -0.4195314
## DEA 1.0000000corrplot(
mat_cor,
method ="color",
type = "upper",
addCoef.col ="black")
The correlation analysis of all variables in our study allows us to
identify those that are most strongly correlated—that is, variables that
tend to evolve in the same direction and reflect similar outcomes. In
the context of our research, which focuses on the impact of waterborne
diseases on public health through the analysis of epidemiological data
related to drinking water, the correlation between variables highlights
those that have a similar and simultaneous impact on public health
across the studied countries.
fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 60), title = "Variance expliquée par chaque axe")
Water stress (top): Projects mainly on Axis 2, not on Axis 1 → it
provides a different dimension, independent of the development-mortality
link. Water stress does not directly influence diarrheal mortality, but
it characterizes another aspect of vulnerability (water resource
availability).
TDI/TDD (ratio of infant mortality / diarrheal deaths): also correlated with Axis 2. This means that the relative structure of causes of death (more diarrheal vs. more general infant deaths) explains a variability of its own, distinct from the simple development gradient.
fviz_pca_var(res.pca, col.var = "cos2", repel = TRUE, title = "Corrélations des variables")
This figure, called the correlation circle, results from the PCA of the
variables. It takes into account the correlations of the variables with
each other, 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 positively correlated with each other, whereas
those whose vectors are opposite are negatively correlated. Furthermore,
the closer they are to an axis, the more they are correlated to 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. Axis 1 (Dim 1 = 50.20% of
variance) explains the largest share of variability → it represents a
major contrast between the variables. Axis 2 (Dim 2 = 23.84% of
variance) provides a complementary dimension.
Together, these two axes explain about 74% of the total information, which is very satisfactory.
fviz_pca_biplot(res.pca, repel = TRUE, col.var = "green", col.ind = "red", title = "Plan Factoriel ACP")
The representation of individuals in the factorial plan was done according to the previously chosen axes. The projection of individuals on to the factorial plane defined by Dim1 and Dim2 makes it possible to obtain this graph bringing together as much information as possible to be visualized on a plane from the initial data. The inertia explained by the factorial plan is 50.20% for Dim1 and 23.84% for Dim2, 74.04% of the information retained. Individuals far from the origin are better represented while those close to the origin are poorly represented. In our case, Nigeria and Cape Verde are better represented compared to other individuals. Analyse of Factorial Axes Inertia Variables factor map (PCA) the labeled variables are those the best shown on the plane. The dimension 1 opposes individuals such as BurkinaFaso and Mali (to the right of the graph, characterized by a strongly positive coordinate on the axis) to individuals such as Mauritanie and Gambie (to the left of the graph, characterized by a strongly negative coordinate on the axis). The group in which the individuals BurkinaFaso and Mali stand (characterized by a positive coordinate on the axis) is sharing: • High values for the variables DEA, TDEI and TDI (variables are sorted from the strongest). • Low values for the variable IDH. The group in which the individuals Mauritanie and Gambie stand (characterized by a negative coordinate on the axis) is sharing: • High values for the variable TU. • Low values for the variables TMH and TDD (variables are sorted from the weakest). Note that the variable TMH is highly correlated with this dimension (correlation of 0.91). This variable could therefore summarize itself the dimension 1. The dimension 2 opposes individuals such as Ghana and Sénégal (to the top of the graph, characterized by a strongly positive coordinate on the axis) to individuals such as SierraLeone, Guinée-Bissau and Libéria (to the bottom of the graph, characterized by a strongly negative coordinate on the axis). The group in which the individuals Ghana and Sénégal stand (characterized by a positive coordinate on the axis) is sharing: • High values for the variables TDI.TDD and IDH (variables are sorted from the strongest). • Low values for the variable mortalité_lie.a.M.d.acc.EP. The group in which the individuals SierraLeone, Guinée-Bissau and Libéria stand (characterized by a negative coordinate on the axis) is sharing: • High values for the variable TDD. • Low values for the variable Stress.hidrique.
By observing this table we can highlight the different variables correlated to the different axes (Dim1 and Dim2) both positively and negatively and thus see if we have a size effect. In our case, we have 6 variables correlated to axis 1 (these are the variables whose data are colored in yellow) and 3 variables correlated to axis 2 (these are the variables whose data are colored in red). In addition, the contribution and the cos2 which shows the contribution that each variable makes and the quality of representation on the axes. However, the contribution and the cos2 of the variables with respect to the dimension evolve in the same direction as that of the variables with respect to the coordinates. To better observe the variables contributing the most to the different axes, we will use the following graphs:
# ACP sur la base VE
res.pca <- PCA(VE, scale.unit = TRUE, ncp = 5, graph = FALSE)
# Graphique des contributions des individus sur Dim 2
fviz_contrib(res.pca,
choice = "ind", # individus (pays)
axes = 1, # ici Dim 2
top = nrow(VE), # afficher tous les individus
fill = "tomato", # couleur des barres
color = "black") +
theme_minimal()
1. OVERVIEW OF DIMENSION 1 SIGNIFICANCE Dim 1 explains 50.20% of total
variance (dominant axis) Represents the primary gradient in your WASH
(Water, Sanitation, Hygiene) dataset Clear bipolar structure evident
from contribution patterns 2. HIGH CONTRIBUTION VARIABLES (>10%) Top
Contributors: WMR (Water-related Mortality Rate): ~14% contribution UR
(Urbanization Rate): ~13% contribution DDR (Diarrheal Disease Mortality
Rate): ~12% contribution Mortality related to lack of access to safe
water: ~11% contribution Interpretation: These four variables are the
primary drivers of the socio-economic development gradient captured by
Dim 1. 3. MEDIUM CONTRIBUTION VARIABLES (5-10%) Significant
Contributors: HDI (Human Development Index): ~8% contribution AUS
(Access to Unsafe Water Sources): ~7% contribution CDR (Child Mortality
Rate): ~6% contribution MRUSW (Mortality Rate from Unsafe Water): ~6%
contribution Interpretation: These variables provide important secondary
reinforcement to the main gradient. 4. LOW CONTRIBUTION VARIABLES
(<5%) Minor Contributors: WSE (Water and Sanitation Expenditures):
~4% contribution Water Stress: ~3% contribution CDR/DDR (Ratio): ~2%
contribution
fviz_pca_contrib(res.pca, choice = "var", axes = 1:2, title = "Contribution des variables aux axes")## Warning in fviz_pca_contrib(res.pca, choice = "var", axes = 1:2, title =
## "Contribution des variables aux axes"): The function fviz_pca_contrib() is
## deprecated. Please use the function fviz_contrib() which can handle outputs of
## PCA, CA and MCA functions.
fviz_contrib(res.pca, choice = "var", axes = 1, top = 12, title = "Contribution des variables - Axe 1")
fviz_contrib(res.pca, choice = "var", axes = 2, top = 12, title = "Contribution des variables - Axe 2")
1. OVERVIEW OF DIMENSION 2 SIGNIFICANCE Dim 2 explains 23.84% of total
variance (substantial secondary axis) Represents complementary patterns
not captured by the main development gradient Reveals specific mortality
and environmental factors 2. HIGH CONTRIBUTION VARIABLES Top
Contributors: TDI (Taux de décès maladies infantiles / Child Mortality
Rate): Highest contribution TDEI (Taux de décès liés aux eaux insalubres
/ Mortality from Unsafe Water): Very high contribution Stress hydrique
(Water Stress): Significant contribution TDD (Taux de décès maladies
diarrhéiques / Diarrheal Mortality Rate): Important contribution
Interpretation: Dim 2 primarily capturent child health vulnerability and
water resource constraints. 3. MEDIUM CONTRIBUTION VARIABLES Secondary
Contributors: IDH (Indice de développement humain / HDI): Moderate
contribution Mortalité liée au non-accès à l’eau potable: Moderate
contribution TMH (Taux de mortalité maladies hydriques / Water-related
Mortality): Moderate contribution 4. LOW CONTRIBUTION VARIABLES Minor
Contributors: DEA (Dépenses eau et assainissement / WASH Expenditures):
Low contribution ISA (Accès à une source d’eau insalubre / Unsafe Water
Access): Low contribution TU (Taux d’urbanisation / Urbanization Rate):
Lowest contribution
# Réaliser l'ACP sans graphique (base de la classification)
res.pca <- PCA(VE, scale.unit = TRUE, ncp = 5, graph = FALSE)
# Extraire les coordonnées des individus
ind_coord <- res.pca$ind$coord
# Calculer la Classification Hiérarchique Ascendante (CHA)
res.hcpc <- HCPC(res.pca, graph = FALSE)
# Afficher le dendrogramme
fviz_dend(res.hcpc, rect = TRUE, show_labels = TRUE, main = "Dendrogramme de la classification")## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Overall Structure and Meaning This dendrogram results from a
hierarchical cluster analysis applied to your dataset of West African
countries. The analysis groups countries based on similarities across
your 11 variables related to water access, sanitation, and diarrheal
mortality. The vertical axis represents the distance or dissimilarity
between countries and clusters—countries that merge at lower heights are
more similar. Key Cluster Interpretation Primary Division: Two Distinct
Country Profiles The first major split separates the countries into two
main groups, revealing a fundamental divide in their
water-sanitation-health profiles:
Group 1 (Left Branch): Mauritania, Gambia, Senegal, Ghana, Côte d’Ivoire, Guinea, and Benin Group 2 (Right Branch): Sierra Leone, Liberia, Guinea-Bissau, Burkina Faso, and Mali This primary division likely corresponds to the Dim 1 gradient identified in your PCA, where Group 1 represents countries with relatively better outcomes (right side of PCA) and Group 2 represents countries facing greater challenges (left side of PCA). Country Similarities and Groupings High-Similarity Pairings (merge at low height): Burkina Faso & Mali: Very similar profiles, likely sharing challenges as landlocked Sahelian nations with water stress and higher mortality rates. Sierra Leone, Liberia, and Guinea-Bissau: Form a tight cluster, possibly indicating similar post-conflict challenges, infrastructure deficits, and high diarrheal disease burdens. Moderate-Similarity Groupings: Coastal West Africa Cluster: Senegal, Ghana, Côte d’Ivoire, Benin group together, suggesting shared characteristics of more developed water infrastructure and better health indicators. Guinea’s positioning between the two main groups may indicate a transitional or mixed profile. Notable Observations Mauritania’s Unique Position: Merges with the main cluster at a relatively high distance, suggesting it has a distinct profile compared to other countries. This could reflect its Saharan environment, unique water challenges, or different development trajectory. Geographic Patterns: Clear clustering of coastal nations (except Guinea-Bissau) versus inland nations. Guinea-Bissau clusters with inland nations despite being coastal, possibly indicating it shares more developmental challenges with Burkina Faso and Mali than with its coastal neighbors. Implications for Your Research Policy and Intervention Planning:
Countries within the same cluster likely respond to similar intervention strategies. Group 2 countries (right branch) may require more intensive, targeted investments in water and sanitation infrastructure. Regional Patterns: The clustering suggests that regional approaches could be effective for countries with similar profiles. The clear grouping indicates that geographic and economic factors strongly influence water-sanitation-health outcomes. Validation of PCA Results: This dendrogram likely corroborates the patterns seen in your correlation circle, showing that countries naturally group according to the developmental gradient captured by Dim 1. The dendrogram provides a valuable country-level perspective that complements the variable-level relationships shown in your correlation circle, offering practical insights for targeted public health interventions.
#la regression
# Importation de la base
VE <- read.csv("C:/Users/hp14p/Desktop/BASE DE DONNE.csv",
header = TRUE, sep = ";", dec = ".", row.names = 1)
# Régression simple : mortalité expliquée par l'IDH
modele_simple <- lm(mortalité_lie.a.M.d.acc.EP ~ IDH, data = VE)
# Résultats de la régression
summary(modele_simple)##
## Call:
## lm(formula = mortalité_lie.a.M.d.acc.EP ~ IDH, data = VE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.74082 -0.35957 -0.05294 0.32169 0.98342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.368 1.623 4.540 0.00107 **
## IDH -10.156 3.263 -3.112 0.01102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5479 on 10 degrees of freedom
## Multiple R-squared: 0.492, Adjusted R-squared: 0.4412
## F-statistic: 9.684 on 1 and 10 DF, p-value: 0.01102# Nuage de points + droite de régression
plot(VE$IDH, VE$mortalité_lie.a.M.d.acc.EP,
xlab = "IDH",
ylab = "Mortalité liée aux maladies/accidents EP",
pch = 19, col = "steelblue")
# Droite de régression
abline(modele_simple, col = "red", lwd = 2)
# Extraire les coefficients
coef_reg <- coef(modele_simple)
eq <- paste0("y = ", round(coef_reg[1], 3), " + ", round(coef_reg[2], 3), "x")
# Ajouter l’équation sur le graphe
text(x = min(VE$IDH),
y = max(VE$mortalité_lie.a.M.d.acc.EP),
labels = eq,
pos = 4, col = "red")
DISCUSSION & RECOMMENDATIONS
The analysis of country classifications based on indicators related to water, sanitation, and mortality reveals significant disparities. Each group of countries exhibits distinct characteristics that influence their performance in terms of public health and access to essential services. This discussion aims to interpret the observed trends and propose tailored recommendations for each group. Class 1 — Coastal Countries with Better Performance (Ghana, Senegal, Côte d’Ivoire, Benin)
This class includes countries with relatively high urbanization and Human Development Index (HDI) levels. In the PCA, they are positioned on the “development” side of the main axis (Dim 1) and show lower values for waterborne and diarrheal mortality indicators (TMH, TDD). Public and private expenditures on water and sanitation (DEA) are generally well managed, and urban infrastructure coverage (drinking water networks, public fountains, treatment plants) is denser. These countries benefit from stronger institutions and greater investment capacity, but disparities remain between urban and rural areas. Urban growth places increasing pressure on existing networks. Short-term actions (0–2 years): - Conduct preventive maintenance of urban networks and repair leakages. - Expand access in peri-urban areas with secure water points. Medium-term actions (2–5 years): - Strengthen Integrated Water Resources Management (IWRM) for better water allocation. - Develop public-private partnerships for modernization and extension of infrastructure. Long-term actions (>5 years): - Diversify water sources (reuse, rainwater harvesting) and build climate-resilient urban planning. - Implement sustainable financial mechanisms to guarantee maintenance and operation.
Class 2 — Transitional or Mixed Profile Countries (Mauritania, Gambia, Guinea)
This class groups countries with mixed profiles: some urban centers perform well, but rural or arid areas remain vulnerable. In the PCA, they show a notable influence from water stress (Dim 2). Mauritania, in particular, faces severe aridity and salinization challenges. Vulnerability here is largely due to physical resource availability and territorial heterogeneity. Infrastructure may exist but is often poorly adapted to climatic conditions, and maintenance governance remains weak. Short-term actions: - Conduct hydrological assessments to identify sustainable groundwater sources. - Launch pilot projects using adapted technologies (deep solar-powered boreholes, small networks, rainwater collection). Medium-term actions: - Implement local resilience plans and create community-based water management committees. - Build technical capacity among local authorities for sustainable maintenance. Long-term actions: - Integrate WASH planning with watershed and agricultural management. - Develop financing mechanisms focused on climate resilience and adaptation.
Class 3 — Highly Vulnerable and Institutionally Fragile Countries (Sierra Leone, Liberia, Guinea-Bissau)
These countries face post-conflict fragility and severe WASH deficiencies. They occupy the “mortality” pole of Dim 1 and contribute strongly to Dim 2 (sanitary vulnerability). Local institutions and technical capacities are weak, with high dependency on international aid. Their main challenge is twofold: structural deficits in basic infrastructure (protected wells, improved latrines, sludge management) and weak public health systems. Interventions must combine emergency measures with long-term capacity building. Short-term actions: - Deploy emergency WASH programs (safe water supply, sanitation kits, emergency chlorination). - Reinforce epidemiological surveillance and community health programs. Medium-term actions: - Rehabilitate and construct community WASH facilities with local management contracts. - Promote hygiene education through schools and community awareness campaigns. Long-term actions: - Strengthen local governance frameworks to reduce dependence on external aid. - Diversify funding sources (multilateral, NGO, social investment) and foster regional cooperation.
Class 4 — Sahelian Countries with Severe Structural Challenges (Burkina Faso, Mali)
This class includes Sahelian countries with high water stress, low urbanization, and high waterborne disease mortality. Despite significant expenditures in the WASH sector, effectiveness remains low due to governance and maintenance issues. Their challenges are structural: resource scarcity, dispersed populations, and limited institutional capacity. Centralized infrastructure models show diminishing returns unless complemented by decentralized and adaptive approaches. Short-term actions: - Audit past WASH projects to identify inefficiencies and improve targeting. - Prioritize low-cost, decentralized solutions (improved wells, ecological latrines, household filters). Medium-term actions: - Reform planning and monitoring frameworks with independent performance indicators. - Strengthen local capacity (technicians, social enterprises) for long-term maintenance. Long-term actions: - Promote regional cooperation for transboundary water management and measurable results-based funding. - Integrate water security into agricultural and territorial planning. Improving the situation requires expanding effective access to infrastructure in rural settings, using solutions suited to local contexts such as boreholes, eco-latrines, and decentralized maintenance services. Awareness campaigns must also be carried out to educate populations on proper usage of these facilities, ensuring long-term effectiveness. Finally, developing health prevention strategies—including vaccination programs and improved access to treatment—will help reduce mortality and improve public health outcomes.
Conclusion
This classification-based analysis highlights considerable disparities in water and sanitation access across the studied countries. While some nations enjoy relatively favorable conditions, others continue to face persistent challenges that require targeted intervention. To address these issues, it is crucial to strengthen infrastructure and improve management to ensure sustainable access to clean water and sanitation services. Reducing disparities between urban and rural areas should also be a top priority for ensuring equitable access. Lastly, public education and awareness campaigns must be implemented to promote health-positive behaviors. By adopting measures tailored to the specific needs of each country, significant improvements in public health and reductions in waterborne disease-related mortality can be achieved
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