JURY MEMBERS :

Dr. Maïmouna Bologo/Traoré, Dr. Malicki Zorom, Dr. Yohan Richardson and Mr. Sina Thiam

“RECOMMANDATIONS -Classification results

-Source (BNDT, incorrect source)

-Legend (Include the intervals)

-Add the link to the KOBOTOOLBOX

-Remove the first question from the questionnaire about monthly income

-Assign a name to each of our groups after the classification

-Explain the tables and graphs

-Use ZOTERO for the literature review (bibliographic references)”

ABSTRACT

This research project aims to analyze the impact of waterborne diseases on public health in West Africa, based on existing data and statistical analysis methods. Waterborne diseases such as cholera, typhoid fever, and acute diarrhea remain widespread in this region due to limited access to safe drinking water, sanitation, and hygiene.The project began with a structured literature review, which made it possible to:Define the main waterborne diseases and their transmission mechanisms,Provide an overview of access to drinking water in West Africa,Examine the correlations between waterborne diseases and water quality,Identify aggravating factors and mitigation policies.The study then relied on existing epidemiological data at the country level in West Africa. Using a set of variables such as access to safe drinking water, sanitation coverage, frequency of waterborne diseases, and socio-economic indicators, several quantitative methods were implemented, including:A factorial analysis, which showed that the first two axes explained 65.6 of the variance,A classification of household profiles based on sanitary conditions,Regression models to identify predictive variables of disease occurrence.Hypothetical data collection tools were also designed, such as a household questionnaire focusing on water sources, hygiene practices, and the frequency of illnesses within the household. The chosen approach takes into account field realities, including linguistic diversity, education levels, and geographical accessibility.Special attention was paid to the management of missing data, which was addressed using statistical imputation techniques in R to ensure the robustness of the analyses.In conclusion, this work highlights the close links between water infrastructure, public policy, and population health. It emphasizes the urgent need to strengthen investments in water, sanitation, and hygiene to sustainably reduce the burden of waterborne diseases in West Africa.

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:

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.
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.
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.
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.
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.
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.
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.
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.
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:

Countries with similar living conditions are likely to adopt comparable water treatment and public health policies.
The current management of sanitation infrastructure has not produced a significant measurable impact on reducing mortality related to waterborne diseases.
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: Our World In Data.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.

Link to join our kobotoolbox:https://kf.kobotoolbox.org/#/forms/aPkibovDyDE4GTRMpFGdHL

7. Study Area Presentation

The map above shows the countries included in our study area, which consists of 12 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, Senegal and Sierra Leone.

8. Variables and Hypotheses

Resultats and interpresentation

Eigenvalues Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Variance 4.553 2.006 1.230 0.835 0.699 0.435 0.117 % of var. 45.527 20.059 12.304 8.346 6.994 4.348 1.165 Cumulative % of var. 45.527 65.585 77.889 86.236 93.230 97.578 98.743 Dim.8 Dim.9 Dim.10 Variance 0.095 0.023 0.009 % of var. 0.945 0.226 0.086 Cumulative % of var. 99.688 99.914 100.000

Individuals (the 10 first) Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 Bénin | 1.941 | 0.408 0.304 0.044 | -0.267 0.297 0.019 | -0.996 BurkinaFaso | 3.736 | 2.719 13.528 0.530 | 1.086 4.898 0.084 | -0.899 Côted’Ivoire | 2.637 | -0.746 1.018 0.080 | 1.790 13.317 0.461 | 1.369 Gambie | 3.181 | -2.797 14.319 0.773 | -0.578 1.386 0.033 | 0.039 Ghana | 2.940 | -0.181 0.060 0.004 | 2.620 28.529 0.795 | 0.972 Guinée | 3.265 | 2.103 8.096 0.415 | -1.399 8.136 0.184 | 0.355 Guinée-Bissau | 2.971 | -0.157 0.045 0.003 | -2.470 25.346 0.691 | 1.325 Libéria | 2.266 | -0.689 0.868 0.092 | -1.428 8.473 0.397 | -1.244 Mali | 4.507 | 3.692 24.950 0.671 | 0.634 1.668 0.020 | -1.294 Mauritanie | 4.761 | -4.360 34.789 0.839 | 0.481 0.962 0.010 | -1.698 ctr cos2
Bénin 6.720 0.263 | BurkinaFaso 5.469 0.058 | Côted’Ivoire 12.686 0.269 | Gambie 0.010 0.000 | Ghana 6.396 0.109 | Guinée 0.855 0.012 | Guinée-Bissau 11.894 0.199 | Libéria 10.475 0.301 | Mali 11.341 0.082 | Mauritanie 19.533 0.127 |

Variables Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr TMH | 0.857 16.145 0.735 | -0.425 8.991 0.180 | -0.033 0.088 TU | -0.872 16.714 0.761 | 0.212 2.242 0.045 | -0.128 1.340 DASEI | 0.501 5.511 0.251 | -0.373 6.929 0.139 | 0.392 12.489 TDI | 0.802 14.114 0.643 | 0.560 15.625 0.313 | -0.013 0.014 TDD | 0.767 12.936 0.589 | -0.568 16.086 0.323 | 0.073 0.434 TDEI | 0.745 12.192 0.555 | 0.562 15.769 0.316 | -0.049 0.194 EUD | -0.060 0.080 0.004 | 0.102 0.520 0.010 | 0.817 54.315 ISA | -0.745 12.180 0.555 | 0.175 1.526 0.031 | 0.126 1.281 DEA | 0.468 4.811 0.219 | 0.101 0.511 0.010 | -0.559 25.396 TDI.TDD | 0.492 5.317 0.242 | 0.799 31.800 0.638 | 0.234 4.449 cos2
TMH 0.001 | TU 0.016 | DASEI 0.154 | TDI 0.000 | TDD 0.005 | TDEI 0.002 | EUD 0.668 | ISA 0.016 | DEA 0.312 | TDI.TDD 0.055 |

#Packages uses

library(FactoMineR)

## Warning: le package 'FactoMineR' a été compilé avec la version R 4.4.3

library(Factoshiny)

## Warning: le package 'Factoshiny' a été compilé avec la version R 4.4.3

## Le chargement a nécessité le package : shiny

## Warning: le package 'shiny' a été compilé avec la version R 4.4.3

## Le chargement a nécessité le package : FactoInvestigate

## Warning: le package 'FactoInvestigate' a été compilé avec la version R 4.4.3

## Le chargement a nécessité le package : ggplot2

## Warning: le package 'ggplot2' a été compilé avec la version R 4.4.3

library(prettyR)
library(FactoInvestigate)
library(factoextra)

## Warning: le package 'factoextra' a été compilé avec la version R 4.4.3

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(GPArotation) 
library(rsconnect)

## 
## Attachement du package : 'rsconnect'

## L'objet suivant est masqué depuis 'package:shiny':
## 
##     serverInfo

library(ggplot2)
library(corrplot)

## Warning: le package 'corrplot' a été compilé avec la version R 4.4.3

## corrplot 0.95 loaded

library(RColorBrewer)
library(ggrepel)

## Warning: le package 'ggrepel' a été compilé avec la version R 4.4.3

Matrice de corr”lation

Synthetic Interpretation of the Correlation Matrix

The chart shows the correlations between the various variables studied. We observe several noteworthy relationships:

-Strong positive correlations between certain variables, such as:
-TU and TDI (r = 0.82): This indicates that an increase in TU is generally associated with an increase in TDI.
-TDI and TDD (r = 0.88): These two variables move almost in parallel, suggesting they measure closely related or interdependent dimensions.
-ISA and EUD (r = 1.00): A perfect correlation, likely due to a structural link or redundancy between these two variables.

-Moderate to weak correlations:
- For example,DASEI and TDI (r = 0.30) indicate a positive but fairly weak relationship.
- Some variables like DE show more moderate or inconsistent links with the others.

Negative correlations:
- TDI.TDD and DE (r = –1.00) exhibit a perfect negative correlation, which may signal a direct opposition in how these two variables evolve.

Overall, the matrix highlights significant relationships among certain variables, which could guide variable selection in more advanced analyses (regression, PCA, etc.) or support hypotheses about interactions among the factors studied.

AZ<-read.csv(file="C:/Users/JC NEGOCE/Desktop/RTI PROJET GROUPE 11 GEAAH/pacifique.csv", header = TRUE, sep = ";", 
             dec = ".",row.names = 1)
AZ

##                TMH TU DASEI  TDI  TDD TDEI   EUD   ISA    DEA TDI.TDD
## Bénin         42.8 48   3.5  948 56.6 3362  1.41 39.46  28.10   16.75
## BurkinaFaso   53.2 31   3.5 1842 68.3 9164  2.78 58.08  67.26   26.97
## Côted'Ivoire  24.8 52   4.6  908 34.5 7230 15.81 59.40   7.60   26.32
## Gambie        26.5 63   5.8  110 39.0  432 10.55 73.13  51.34    2.82
## Ghana         24.4 57   5.7 1653 38.5 5242 11.82 60.58  30.66   42.94
## Guinée        47.1 37  11.3 1311 80.1 4359  0.00 45.59   5.59   16.37
## Guinée-Bissau 46.3 44   7.3  102 75.7  835 15.00 48.98  13.37    1.35
## Libéria       38.3 52   7.1  256 61.1 1985  0.00 61.17  68.60    4.19
## Mali          51.2 44   9.0 1789 62.9 7821  6.12 26.44 147.80   28.44
## Mauritanie     8.2 67   1.0   22 11.5   36  0.00 64.18  25.72    1.91
## Sénégal       21.6 48   9.4  898 32.0 1924  7.80 57.20  21.75   28.06
## SierraLeone   49.0 43   7.7  759 61.1 2545 15.33 46.75  41.50   12.42

mat_cor<-cor(AZ[,1:10], y = NULL, use = "everything",
             method = c("pearson"))
mat_cor

##                 TMH          TU       DASEI         TDI         TDD
## TMH      1.00000000 -0.85260950  0.41740853  0.43557328  0.93820828
## TU      -0.85260950  1.00000000 -0.44751138 -0.58977815 -0.81616083
## DASEI    0.41740853 -0.44751138  1.00000000  0.22349003  0.53801217
## TDI      0.43557328 -0.58977815  0.22349003  1.00000000  0.30823929
## TDD      0.93820828 -0.81616083  0.53801217  0.30823929  1.00000000
## TDEI     0.44160889 -0.57440856  0.02902906  0.87971679  0.29267680
## EUD     -0.03223684  0.06598954  0.11841438 -0.08316151 -0.06732566
## ISA     -0.65827113  0.55411162 -0.40662268 -0.47356313 -0.55327999
## DEA      0.39585727 -0.14492492  0.11782418  0.36942348  0.19618930
## TDI.TDD  0.03546873 -0.26919389  0.13578429  0.86487130 -0.06589532
##                 TDEI          EUD        ISA        DEA     TDI.TDD
## TMH      0.441608886 -0.032236836 -0.6582711  0.3958573  0.03546873
## TU      -0.574408564  0.065989537  0.5541116 -0.1449249 -0.26919389
## DASEI    0.029029064  0.118414381 -0.4066227  0.1178242  0.13578429
## TDI      0.879716791 -0.083161514 -0.4735631  0.3694235  0.86487130
## TDD      0.292676801 -0.067325661 -0.5532800  0.1961893 -0.06589532
## TDEI     1.000000000  0.005155155 -0.3742539  0.3937928  0.73209711
## EUD      0.005155155  1.000000000  0.0871461 -0.1876000  0.16073121
## ISA     -0.374253891  0.087146099  1.0000000 -0.4195314 -0.22258126
## DEA      0.393792771 -0.187599975 -0.4195314  1.0000000  0.15068350
## TDI.TDD  0.732097106  0.160731208 -0.2225813  0.1506835  1.00000000

corrplot(
  mat_cor,
  method ="color",
  type = "upper",
  addCoef.col ="black")

INTERPRETATION OF correlation circle:

Dimension 1 contrasts individuals such as Mali and Burkina-Faso (on the right side of the plot, characterized by a strongly positive coordinate on this axis) with individuals like Mauritania and The Gambia (on the left side of the plot, characterized by a strongly negative coordinate on this axis).

The group to which Mali and Burkina-Faso belong (positive coordinates on this axis) shares:
- High values for DEA, TDEI, and TDI (from most to least extreme).
The group to which Mauritania and The Gambia belong (negative coordinates on this axis) shares:
- High values for TU.
- Low values for TMH and TDD (from most to least extreme).

RTI<-read.csv(file="C:/Users/JC NEGOCE/Desktop/RTI PROJET GROUPE 11 GEAAH/pacifique.csv", header = TRUE, sep = ";", 
             dec = ".",row.names = 1)
RTI_scaled <- scale(RTI)
res.pca <- PCA(RTI_scaled, graph = FALSE)

fviz_pca_var(res.pca, col.var = "cos2", repel = TRUE, title = "Corrélations des variables")

# Inertia Distribution The inertia of the factorial axes both indicates whether the variables are structured and suggests the appropriate number of principal components to study.

The first two axes of the analysis capture 65.59 of the total inertia of the dataset; this means that 65.59 of the overall variability of the cloud of individuals (or of the variables) is represented in this plane. This is a fairly large percentage, so the first plane adequately reflects a substantial portion of the variability contained in the active dataset. This value exceeds the reference threshold of 56.56%, making the explained variability on this plane statistically significant (this reference inertia is the 0.95–quantile of the distribution of inertia‐percentages obtained by simulating 5,377 random datasets of comparable dimensions under a normal distribution).

Given these observations, it would nonetheless be advisable to consider in the analysis the dimensions from the third component onward as well.

fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 50), title = "Variance expliquée par chaque axe")

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.

Individuals PCA

res.PCA <- PCA(RTI[, -c(8)], graph=FALSE)

plot.PCA(res.PCA, invisible=c('ind.sup'), habillage='cos2', 
         title="Graphe des individus de l'ACP", label=c('ind','quali'))

fviz_pca_ind(res.PCA, repel=TRUE, col.ind="cos2", gradient.cols=c("blue", "purple", "red"))

library(RColorBrewer)
dist_matrix <- dist(RTI_scaled)
hc <- hclust(dist_matrix, method = "ward.D2")
nb_clusters <- 3
colors <- brewer.pal(nb_clusters, "Dark2")
fviz_dend(hc, k = nb_clusters, cex = 0.8,
          k_colors = colors, rect = TRUE, rect_fill = TRUE,
          rect_border = "black", lwd = 1.2,
          main = "Dendrogramme des pays - Classification hiérarchique")

## 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.

Correlation and P-Value

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. dimdesc(res, axes = 1:1)

Annexes dimdesc(res, axes = 1:1) $Dim.1

Link between the variable and the continuous variables (R-square)

  correlation      p.value

TMH 0.9187859 9.157950e-06 TDD 0.8596445 1.650097e-04 TDI 0.8429176 2.963337e-04 DASEI 0.7094983 6.603028e-03 DEA 0.6804847 1.047119e-02 TU -0.9197324 8.602135e-06 Figure 5 - Liste des variables caractéristiques des dimensions de l’analyse. res.hcpc$desc.var

Link between the cluster variable and the quantitative variables

         Eta2     P-value

TMH 0.8683285 0.001344611 TU 0.8414615 0.002758085 TDI 0.8271102 0.003849444 TDI.TDD 0.8213406 0.004366069 TDD 0.7544826 0.014599272 EUD 0.7151271 0.025424314 ISA 0.6673192 0.044946048

Description of each cluster by quantitative variables

$1 v.test Mean in category Overall mean sd in category Overall sd TU 2.135605 60.666667 46.38462 6.3420992 12.68881 TDI.TDD -2.266782 2.973333 17.76769 0.9370995 12.38334 TDI -2.278758 129.333333 990.92308 96.5033102 717.38719 p.value TU 0.03271163 TDI.TDD 0.02340355 TDI 0.02268147

$2 v.test Mean in category Overall mean sd in category Overall sd TDI.TDD 2.248081 32.44 17.76769 7.458525 12.38334 p.value TDI.TDD 0.024571

$3 v.test Mean in category Overall mean sd in category Overall sd EUD 1.977555 15.165 7.326923 0.165 5.854507 p.value EUD 0.04797898

$4 v.test Mean in category Overall mean sd in category Overall sd TDEI 2.113379 6176.5000 3467.17538 2390.30610 2960.62170 ISA -2.294351 42.3925 54.14462 11.39521 11.82921 p.value TDEI 0.03456830 ISA 0.02177037

$5 v.test Mean in category Overall mean sd in category Overall sd TMH 2.273416 79.6 39.46154 0 17.65557 DEA 2.179148 152.1 50.87615 0 46.45111 TU -2.315790 17.0 46.38462 0 12.68881 p.value TMH 0.02300113 DEA 0.02932068 TU 0.02056976

Descriptions of Thematic Maps

The classification performed on the individuals reveals three distinct classes.

Class 1: Highly Water‑Vulnerable States

Countries: Guinea‑Bissau, Sierra-Leone, Benin, Guinea, Burkina-Faso, Mali These countries exhibit the highest rates of water‑related mortality (TMH and DASEI) and alarmingly high infant and diarrheal death rates (TDI, TDET). Urbanization (TU) is low, while access to improved sanitation (ISA) and water‑sanitation expenditures (DEA) remain very limited. This combination reflects rural populations’ heavy exposure to waterborne pathogens, with insufficient infrastructure and funding to control health risks.

Class 2:Coastal Economies in Transition

Countries: Senegal, Côte d’Ivoire, Ghana

Detailed Profile
- Water-related and infant mortality:Intermediate levels – there is improvement compared to Group 1, but pockets of vulnerability remain.
- WASH coverage (Drinking Water, Sanitation, Hygiene): Moderate levels – these countries have already launched significant urban and rural infrastructure projects.
- Demographic transition and rapid urbanization:Increasing pressure on existing networks.

Class 3:Coastal States with Reinforced Infrastructure

Countries: Mauritania,Gambia,Liberia

Detailed Profile - Waterborne disease and child mortality indicators (TMH, DASEI, TDI/TDET):Among the lowest in the region, indicating strong control over waterborne diseases.
- WASH coverage indicators (Drinking Water, Sanitation, Hygiene):Among the highest – these countries have invested significantly in water and sanitation coverage as well as urban infrastructure.
- Presence of pilot initiatives (NGO-government partnerships) and structured maintenance programs.

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.

Group 1:Highly Water‑Vulnerable States

Countries: Guinea‑Bissau, Sierra-Leone, Benin, Guinea, Burkina-Faso, Mali

Summary Profile: These countries exhibit the highest rates of water‑related mortality (TMH and DASEI) and alarmingly high infant and diarrheal death rates (TDI, TDET). Urbanization (TU) is low, while access to improved sanitation (ISA) and water‑sanitation expenditures (DEA) remain very limited. This combination reflects rural populations’ heavy exposure to waterborne pathogens, with insufficient infrastructure and funding to control health risks.

Key Recommendations: 1.Strengthen Access to Safe Drinking Water
- Install community wells and boreholes equipped with manual or solar pumps.
- Establish a community‑based maintenance plan to ensure long‑term functionality.

2.Expand Basic Sanitation Facilities
- Build ventilated pit latrines in villages and train local committees in their upkeep.
- Provide financial incentives (subsidies or micro‑credits) to households for latrine construction or rehabilitation.

3.Increase WASH Financing and Governance - Create a dedicated national WASH fund co‑financed by the government, technical and financial partners, and the private sector.
- Strengthen interministerial coordination (water, health, education) so investments prioritize schools and health centers.

4.Conduct Hygiene Education Campaigns - Promote domestic water chlorination and hand‑washing practices.
- Train and deploy local “WASH agents” to champion and disseminate good hygiene behaviors.

This action plan—focusing on potable water supply, sanitation infrastructure, and institutional capacity—aims to significantly reduce the burden of waterborne diseases in these most vulnerable countries..

Class 2:Coastal Economies in Transition

Countries: Senegal, Côte d’Ivoire, Ghana

Recommendations

1.Consolidate and expand infrastructure - Modernize water treatment plants in major cities and extend networks to peri-urban areas.
- Implement wastewater collection and treatment systems suitable for medium-sized towns.

2.Strengthen water quality monitoring - Deploy a network of control points (mobile labs) to regularly test for potability.
- Publish monthly water quality bulletins to inform users and adjust treatment processes accordingly.

3.Support local innovations - Encourage WASH startups (low-cost filters, IoT sensors for treatment stations).
- Organize “Water & Health” hackathons to stimulate context-specific solutions adapted to West Africa.

4.Integrate emerging rural areas
- Include growing rural municipalities in sanitary urban planning.
- Schedule targeted allocations (school latrines, village water points) in anticipation of the influx of new residents.

Class 3:Coastal States with Reinforced Infrastructure

Countries: Mauritania,Gambia,Liberia

Recommendations

1.Capitalize on best practices - Document maintenance and monitoring procedures to serve as guides for other countries in the region.
- Organize peer learning exchange visits with other West African states.

2.Ensure infrastructure sustainability
- Implement a social water pricing system to self-finance maintenance without excluding the poorest populations.
- Train a corps of WASH technicians within municipalities, with continuous training and certification.

3.Address internal disparities
- Map urban neighborhoods with low coverage (slums, islands, mangroves) and deploy mini-networks in those areas.
- Strengthen access to sanitation in rapidly growing peri-urban zones.

4.Secure long-term mixed financing
- Strengthen public-private partnerships to raise long-term funding (e.g., green bonds – “WASH Bonds”).
- Support village-based water supply cooperatives to co-finance and manage infrastructure locally.

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.

BIBLIOGRAPHIE

[2]: Mabel Gomez, Jordi Perdiguero et Alex Sanz-Socioeconomic Factors Affecting Water Access in Rural Areas of … https://www.academia.edu/47409212/Socioeconomic_Factors_Affecting_Water_Access_in_Rural_Areas_of_Low_and_Middle_Income_Countries [3]: Aftab A. Khand, Mushtaque Ahmed Memon-Common Water Contaminants and Their Health Impacts https://www.academia.edu/47409212/Socioeconomic_Factors_Affecting_Water_Access_in_Rural_Areas_of_Low_and_Middle_Income_Countries [4]: Aftab A. Khand, Mushtaque Ahmed Memon-Common Contaminants in Water from Agricultural Processes https://intownplumbingtx.com/articles/common-water-contaminants/ [5]: Hao Liang et Qipeng Yuan-Advancing Rural Public Health: From Drinking Water Quality and Health … [6]: Amjad Hussain Memon, Yongqin Wei-Sanitation - World Health Organization (WHO) https://www.who.int/news-room/fact-sheets/detail/sanitation [7]: Iffat Mahmud et Nkosinathi Mbuya-‘You feel how you look’: Exploring the impacts of unmet water … https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000019 [8]: Ballard AM, Cooper HLF, Young AM et Caruso BA-Effectiveness of community and school-based sanitation interventions in … https://www.worldbank.org/en/news/feature/2019/10/07/rural-sanitation-matters [9]: Assessing the Sustainability of an Integrated Rural Sanitation and … https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-1015 [10]: Iffat Mahmud et Nkosinathi Mbuya-Equitable and Sustainable Rural Sanitation at Scale: A Call to Action https://www.ghspjournal.org/content/10/4/e2100564 [11]:Ballard AM, Cooper HLF, Young AM et Caruso BA-Severity of waterborne diseases in developing countries and the … https://bnrc.springeropen.com/articles/10.1186/s42269-023-01088-9[12]: Frontiers | Implications of sanitation for rural resident health … https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1060558/full[13]: Social Determinants of Health for Rural People - Rural Health Info https://www.ruralhealthinfo.org/topics/social-determinants-of-health[14]:Ballard AM, Cooper HLF, Young AM et Caruso BA-Towards transformative WASH: an integrated case … - BMC Public Health https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-11353-z [15]:Iffat Mahmud et Nkosinathi Mbuya-WASH Nutrition Case Studies - WASHplus https://www.washplus.org/wash-nutrition/casestudies.html [16]: Amjad Hussain Memon, Yongqin Wei-Community organizing and public health: a rapid review https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-025-21303-8 [17]: Effects of a community-driven water, sanitation, and hygiene.. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004524[18]: How Poor Water Access Dilutes Quality of Life in Rural Areas https://www.changelabsolutions.org/blog/how-poor-water-access-dilutes-quality-life-rural-communities [19]: Iffat Mahmud et Nkosinathi Mbuya-How do I present a proposal for improving access to safe drinking water … https://www.fundsforngos.org/all-questions-answered/how-do-i-present-a-proposal-for-improving-access-to-safe-drinking-water-in-rural-areas [20]:Amjad Hussain Memon, Yongqin Wei- A Sample Proposal on “Water, Sanitation, and Hygiene (WASH) Project for … https://www.fundsforngos.org/all-proposals/a-sample-proposal-on-water-sanitation-and-hygiene-wash-project-for-rural-communities 21]: Water, Sanitation, and Hygiene (WASH) Grants for Poor Communities https://www2.fundsforngos.org/articles-searching-grants-and-donors/water-sanitation-and-hygiene-wash-grants-for-poor-communities [22]: Increasing Water Access in Rural and Urban Communities http://12.000.scripts.mit.edu/mission2017/solutions/engineering-solutions/increasing-water-access-in-rural-and-urban-communities/ [23]: Allah Bux Ghanghro, Taj Mohammad Jahangir, Aftab A. Khand-Smart Growth in Small Towns and Rural Communities US EPA https://www.epa.gov/smartgrowth/smart-growth-small-towns-and-rural-communities [24]: Allah Bux Ghanghro, Taj Mohammad Jahangir, Aftab A. Khand-Prevalence and predictors of water-borne diseases among elderly people … https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13376-6 [25]: Iffat Mahmud et Nkosinathi Mbuya-The Integrated Behavioural Model for Water … - BMC Public Health https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-1015

Impact of water quality and sanitation on public heath

BESSANH Pacifique Charles Comlan BOURGOU Dahandi Sandra Rosalie KAFANDO Ferdinand SANI Dédami Inass S7 GEAAH GROUPE 11 2IE

2025-04-09

Dr. Maïmouna Bologo/Traoré, Dr. Malicki Zorom, Dr. Yohan Richardson and Mr. Sina Thiam

ABSTRACT

Introduction

Bibliographie rewiew

Wastewater Management

Government and Community Roles

Overview of Sanitation

Health Impacts of Poor Sanitation

Sanitation and Community Well-being

Impact on Public Health

Waterborne Diseases

Case Studies

Interlinkages of Socio-Economic Factors

Integrating WASH and Nutrition

Community Organizing and Public Health

Policy Development

Community Empowerment and Capacity Building

Metrics for Evaluating Success Introduction

Community Engagement and Education

Sustainability Indicators

3.Variables and Hypotheses

4.Problem Statement:

5.General Objective:

6.Materials and Methods

Link to join our kobotoolbox:https://kf.kobotoolbox.org/#/forms/aPkibovDyDE4GTRMpFGdHL

7. Study Area Presentation

8. Variables and Hypotheses

Resultats and interpresentation

Matrice de corr”lation

INTERPRETATION OF correlation circle:

Individuals PCA

Correlation and P-Value

Link between the variable and the continuous variables (R-square)

Link between the cluster variable and the quantitative variables

Description of each cluster by quantitative variables

Descriptions of Thematic Maps

Class 1: Highly Water‑Vulnerable States

Class 2:Coastal Economies in Transition

Class 3:Coastal States with Reinforced Infrastructure

DISCUSSION & RECOMMENDATIONS

Group 1:Highly Water‑Vulnerable States

Class 2:Coastal Economies in Transition

Class 3:Coastal States with Reinforced Infrastructure

Conclusion

BIBLIOGRAPHIE