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TABLE OF CONTENTS

TABLE OF CONTENTS 0

LIST OF TABLES 1

LIST OF FIGURES 0

Introduction 0

CHAPTER I : 2 LITERATURE REVIEW 2

I. Identification of the Main Diseases Responsible for Mortality 2

  1. Analysis of Risk Factors (Socioeconomic, Environmental, and Behavioral) 3

II.1. Socioeconomic Risk Factors 3

II.2. Environmental Risk Factors 3

II.3. Behavioral Risk Factors 4

  1. Evaluation of the Effectiveness of Existing Healthcare Systems 4

III.1. Analysis of Healthcare Structures (Hospitals, Health Centers, Medical Personnel) 4

III.2. Examination of Prevention Programs and Public Health Policies 5

  1. Proposals for Strengthening Prevention and Awareness 5

V.Research Questions 6

CHAPTER II : 1 MATERIALS AD METHODS 1

II.1. MATERIALS 1

II.1.1. Population-Sample 1

II.1.2. Data source 1

II..1.3. Presentation of Data 1

II.1.4. Tools Used 1

II.2. Methodology 2

II.2.1.Research Approach 2

II.2.2. Choice of method 2

II.2.3. Types of Analysis Conducted 2

CHAPTER III: 3 RESULTS AND DISCUSSIOn 3

III.1. RESULTS 1 III.1.1. Principal Component Analysis (PCA) 1

III.1.2. Correlation Analysis 1 III.1.3. Linear regression Analysis 3

III.1.4. Data Visualization and Spatial Analysis 3

III.1.5. Spatial Distribution of Mortality Data 4

III.2. DISSCUSION 5 III.2.1. Principal Component Analysis and Factorial Maps 5

III.2.2. Hierarchical Clustering and Mortality Clusters 6

III.2.3. Correlation Analysis and Disease Interrelationships 7

III.2.4. Implications for Public Health 7

III.2.5. Limitations and Future Research 8

CONCLUSION 8

RECOMMENDATIONS 9

ANNEX 1

Interview Guide 1

LIST OF TABLES

Table 1: Study data 16

Table 2: Eiegenvalues 20

LIST OF FIGURES

Figure 1 :The study area 14

Figure 2 :Scree plot of the dimensions 20

Figure 3:PCA variables graphe 21

Figure 4:PCA graph of individual 22

Figure 5: Classification Diagram 23

Figure 6: Correction graph 24

Figure 7: Correlation Diagram 25

Figure 8: Linear regression between malaria and COVID-19 26

Figure 9: Prevalence graph of COVID-19 27

Figure 10: Prevalence graph of malaria 27

Figure 11: Dimensions map 29

Figure 12: Contributions map 30

Figure 13: Map of cosine square 31

INTRODUCTION

Since the end of the Second World War, most regions of the world have experienced rapid progress. On a global scale, in recent years, life expectancy at birth has seen a notable increase, rising from 46 to 68 years for men and from 48 to 72 years for women (United Nations, 2015).[1] Despite relatively significant advancements, Africa remains somewhat behind in terms of health outcomes. The continent is characterized by highly disparate developments from one region to another.[1] West Africa is a region marked by particularly high mortality rates. During the 1990s, this situation was characterized by stagnation or even regression in health progress for various reasons. In addition to the HIV/AIDS epidemic and the resurgence of malaria, the region faced the persistence or emergence of conflicts, political unrest, and the deterioration of healthcare services and socio-economic conditions. All the data used for this project was meticulously collected from a site called Global Health Data Exchange (http://ghdx.healthdata.org). This site provided an invaluable source for acquiring the necessary information for our study. The collected data was then subjected to a thorough analysis using RStudio, a statistical and graphical software widely recognized for its power and versatility. The data analysis revealed significant trends and correlations, which form the basis of our conclusions. Once these results were obtained, they were prepared to be mapped using QGIS software. QGIS is an open-source geographic information system (GIS) that offers advanced features for creating, editing, and analyzing geographic maps. The mapping of the results will allow for a clear and concise visualization of the information derived from our analysis. This visual representation will facilitate understanding and interpretation of the data by stakeholders, providing a geographical perspective on the results. In summary, the combined use of RStudio and QGIS has enabled us to transform raw data into actionable and visually compelling information, significantly contributing to the success of this project.

To address the concerning mortality rates in West Africa, this project explores the Theme of mortality caused by diseases in the region. The Subject focuses on analyzing specific diseases contributing to the increase in mortality rates and identifying strategies to mitigate their impact. The central Problem Statement asks: What is the effectiveness of healthcare systems in West Africa in preventing and treating fatal diseases?

The General Objective of this study is to evaluate the main causes of mortality in West Africa related to diseases, with the aim of proposing effective and sustainable solutions to reduce mortality and improve public health in the region. To achieve this, the study pursues three Specific Objectives:

• Evaluate the risk factors and more vulnerable populations.

• Assess the effectiveness of existing healthcare systems in terms of prevention.

• Propose awareness and education strategies to promote preventive health behaviors within communities. Additionally, this research builds on the following Hypotheses:

• Targeted prevention interventions, such as the distribution of insecticide-treated mosquito nets, vaccination campaigns, and awareness programs, have reduced mortality from malaria, meningitis, and HIV/AIDS, but their effectiveness is limited by gaps in coverage and community adherence.

• The lack of healthcare infrastructure in rural areas is the main challenge to improving access to healthcare and preventive services in West Africa.

• Integrating preventive care into primary healthcare services would enhance the efficiency of health systems in managing diseases such as typhoid and tuberculosis.

• Establishing sustainable financing mechanisms (universal health insurance, international subsidies) would strengthen the capacities of health systems in West Africa.

• Governments that invest in awareness and local research develop solutions better tailored to the realities of their populations.

CHAPTER I : LITERATURE REVIEW IDENTIFICATION OF THE MAIN DISEASES RESPONSIBLE FOR MORTALITY

The primary diseases contributing to mortality in West Africa include:

• Malaria: A leading cause of death, particularly among children under the age of five. • Tuberculosis: A significant concern with the emergence of multidrug-resistant cases and tuberculosis/HIV co-infection.

• HIV/AIDS: A major cause of death, especially among people living with HIV.

• Diabetes: The number of individuals living with diabetes is on the rise.

• Injuries: Physical trauma caused by shocks, blows, weapons, or hard objects affecting various body parts.

• Diarrhea Diseases: Among the leading causes of mortality and morbidity, particularly in children under five years of age.

• Meningitis: An infection of the spinal cord and the membranes surrounding the brain (meninges), caused by various viruses, bacteria, and fungi.

• Hepatitis B: Transmitted from mother to child during birth and delivery, causing approximately 1.1 million deaths.

• COVID-19: Caused by the SARS-CoV-2 coronavirus, primarily transmitted through close contact.

• Typhoid: A potentially fatal disease caused by the Salmonella bacteria. ANALYSIS OF RISK FACTORS (SOCIOECONOMIC, ENVIRONMENTAL, AND BEHAVIORAL)

II.1. SOCIOECONOMIC RISK FACTORS

Socioeconomic changes in West Africa encompass elements such as poverty, urbanization, and migration, which significantly affect living conditions and public health. For instance :

• Structural poverty: Limits access to adequate housing, healthcare, and education.

• Rapid urbanization: Creates challenges related to overcrowded living conditions, inadequate infrastructure, and poor sanitation, all of which exacerbate health vulnerabilities.

II.2. ENVIRONMENTAL RISK FACTORS

Environmental factors include climate change, deforestation, and pollution, which have profound impacts on health and ecosystems:

• Climate change: Leads to extreme weather events like droughts and floods, affecting water availability, food security, and living conditions.

• Deforestation and pollution: Contribute to changes in disease transmission patterns, particularly vector-borne diseases like malaria, while worsening respiratory and other health conditions.

II.3. BEHAVIORAL RISK FACTORS

Behavioral factors such as dietary habits, physical activity, and substance use play a significant role in individual health outcomes

. • Cultural practices: The use of traditional medicine in West Africa is common and can influence health and wellness positively or negatively, depending on the context.

• Fear-related behaviors (FRBs): These are individual or collective actions triggered by fear responses to perceived threats or exposure to potentially traumatic events. For instance, during the COVID-19 pandemic, fear-related behaviors significantly influenced health-seeking patterns and societal interactions. Addressing these behavioral dynamics is crucial for designing effective public health interventions and fostering resilience among affected populations.

III. Evaluation of the Effectiveness of Existing Healthcare Systems

III.1. ANALYSIS OF HEALTHCARE STRUCTURES (HOSPITALS, HEALTH CENTERS, MEDICAL PERSONNEL)

Hospitals and Health Centers The analysis of healthcare infrastructure in West Africa highlights several challenges and opportunities:

• Insufficient and outdated infrastructure: Many hospitals and health centers lack essential resources, such as modern equipment and adequate facilities.

• Access to healthcare: This remains a significant issue, particularly in rural areas where populations face difficulties reaching health facilities.

• Underfunding: Limited funding for health infrastructure restricts the capacity of facilities to deliver quality care.

Medical Personnel

• Shortage of qualified professionals: There is a significant deficit in trained doctors, nurses, and other healthcare professionals in the region.

• Retention challenges: Many healthcare workers leave the region in search of better opportunities elsewhere, leading to brain drain.

• Overburdened staff: Medical personnel often face heavy workloads and limited resources, which can negatively impact the quality of care.

III.2. EXAMINATION OF PREVENTION PROGRAMS AND PUBLIC HEALTH POLICIES Prevention Programs

• Disease control: Programs focus on reducing the prevalence of both communicable and non-communicable diseases, such as malaria, tuberculosis, HIV/AIDS, hypertension, diabetes, and neglected tropical diseases.

• Epidemics and health emergencies: The region frequently deals with outbreaks of diseases such as meningitis and fevers.

• Health promotion: Initiatives include education, basic hygiene, nutrition, and behavior change communication

. • Access to medication and vaccines: Efforts to improve access to essential medicines and vaccines aim to curb disease spread.

  1. PROPOSALS FOR STRENGTHENING PREVENTION AND AWARENESS

    To enhance prevention and awareness in West Africa, the following measures are critical: Improve Education and Community Awareness Develop and disseminate educational programs in schools, communities, and via media platforms to inform about diseases and prevention methods. Increase Access to Information Launch mass communication campaigns to educate the population on disease symptoms and encourage early medical intervention. Strengthen Healthcare Capacities Provide continuous training for healthcare professionals on new prevention and treatment techniques. Foster International Collaboration

    • Partner with international organizations, NGOs, and governments to mobilize resources and share best practices.

    • Promote and fund research projects on endemic diseases and effective prevention strategies.

    Develop Policies and Appropriate Legislation Implement robust public health policies to support disease prevention and management initiatives. Promote Hygiene and Sanitation • Provide subsidies and incentives for preventive practices, such as the use of insecticide-treated mosquito nets and vaccinations. • Invest in sanitation infrastructure and ensure access to clean water.

These measures, if effectively implemented, can significantly reduce disease burden and improve health outcomes across West Africa.

V.RESEARCH QUESTIONS

These are the key questions that guide this study: • What are the risk factors and socio-economic determinants associated with the prevalence of these diseases in West Africa?

• How have disease-specific prevention and control interventions impacted mortality in West Africa?

• What are the challenges and opportunities to improve access to healthcare and preventive services for these diseases in West Africa?

• How can healthcare systems be strengthened to better manage cases of malaria, diarrhea, meningitis, COVID-19, HIV/AIDS, tuberculosis, diabetes, hepatitis B, and typhoid in West Africa?

• What are the roles and responsibilities of governments, NGOs, and local communities in the fight against these diseases in West Africa?

• What lessons learned and best practices from other regions of the world can be applied to reduce mortality related to these diseases in West Africa?

• How can research and development initiatives contribute to identifying new solutions to reduce mortality related to these diseases in West Africa?

• What are the impacts of health education and awareness programs on the prevention and control of these diseases in West Africa?

CHAPTER II : MATERIALS AD METHODS

II.1. MATERIALS

II.1.1. POPULATION-SAMPLE

The study population comprised of twelve (12) countries in west Africa, namely: Benin, Côte d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Senegal, Sierra Leone, and Togo. (see figure 1).

Figure 1:The study area
Figure 1:The study area

II.1.2. DATA SOURCE Data

were obtained from the Globale Health Data Exchange (http://ghdx.healthdata.org) which is the world’s most comprehensive catalog of surveys, censuses, vital statistics, and other health-related data.

II..1.3. PRESENTATION OF DATA

This study focuses on data from 2021, sourced from GHDE. The data set comprises 10 variables, representing illnesses that are major causes of mortality in West Africa. These illnesses are categorized into two groups:
• Infectious Diseases (Effected Sickness)
• Non-Communicable Diseases (Non-Effected Sickness)

The table below provides an overview of the study data.

Table 1: Study data
Table 1: Study data

Remark: We did not encounter any missing data, and all the data were accurate and well-structured. With the assistance of GHDE, we encountered no issues with data cleaning, and all the information in the dataset was reliable and properly formatted.

II.1.4. TOOLS USED

II.1.4.1. RSTUDIO

For data analysis in this study, RStudio was used as the primary software tool. RStudio is an integrated development environment (IDE) for R, a programming language

widely used for statistical computing and graphics. The tool provides a user-friendly interface for data manipulation, statistical analysis, and visualization. Several key functions and packages within RStudio were employed to perform the analysis, specifically for Principal Component Analysis (PCA), data visualization, and overall data management.

II.1.4.2. EXCEL

Excel was used as an initial tool for organizing and structuring the dataset. It allowed for easy data entry, preliminary cleaning, and basic manipulation. After organizing the data, it was saved in CSV format to ensure compatibility with RStudio for more advanced statistical analysis and visualization. Excel provided a straightforward environment for handling data before transitioning to the more specialized tools in RStudio.

II.2. METHODOLOGY

II.2.1.RESEARCH APPROACH

This study utilized a quantitative research approach aimed at analyzing the relationships between mortality rates caused by infectious and non-infectious diseases in West African countries. Several statistical analyses were performed to gain insights into the factors influencing mortality variations. The choice of each analysis was driven by the specific research objectives and the nature of the data.

II.2.2. CHOICE OF METHODS

The research approach for this study was quantitative, utilizing Principal Component Analysis (PCA) to explore patterns and relationships within the data. PCA was chosen because it is an effective technique for reducing the dimensionality of large datasets while retaining as much variance as possible. This allows for a simplified representation of complex data, making it easier to identify underlying structures and key factors.

II.2.3. TYPES OF ANALYSIS CONDUCTED

Several types of analysis were conducted using RStudio, each chosen to complement the PCA and provide deeper insights into the mortality data:

II.2.3.1. CORRECTION ANALYSIS

Correlation analysis was conducted to examine the linear relationships between pairs of variables, such as mortality rates for different diseases. This was important to identify how strongly diseases are related to each other in terms of their impact on mortality. Correlation analysis complements PCA by providing specific insights into the strength and direction of relationships between variables, helping to validate or refine the patterns identified by PCA. For example, high correlations between malaria and other infectious diseases could suggest shared risk factors or transmission dynamics, which could then be explored further.

II.2.3.2. REGRESSION ANALYSIS

Regression analysis was used to model the relationship between the principal components derived from PCA and mortality rates for specific diseases. This allowed us to quantify the impact of each principal component on the mortality rates of individual diseases. Regression analysis complements PCA by providing a direct way to examine how the latent factors (identified in PCA) influence mortality rates, offering a more detailed understanding of the key variables driving mortality across the studied countries.

II.2.3.3. DATA VISUALIZATION

Data visualization was employed to provide graphical representations of the data and PCA results for easier interpretation. This included plots such as scatterplots, scree plots, and biplots, which help in understanding the relationships between variables and the principal components. Additionally, the results of variable contributions and dimensions were mapped using QGIS for spatial visualization. This helped to visually represent the geographical distribution of mortality patterns, further enhancing the interpretability of the statistical results.

CHAPTER III: RESULTS AND DISCUSSION

III.1. RESULTS

In this section, we present a comprehensive overview of the results obtained from our studies, emphasizing the key findings and their relevance to the research objectives.

III.1.1. PRINCIPAL COMPONENT ANALYSIS (PCA)

III.1.1.1. VARIANCE DISTRIBUTION

Table 2: Eigen values
Table 2: Eigen values
Figure 2 :Screen plot of the dimensions
Figure 2 :Screen plot of the dimensions

The figure 2 and table 2 above, shows the first four principal components (Dim.1 to Dim.4) account for 83.16% of the total variance, which is considered sufficient to effectively represent the underlying structure of the data. Key Contribution: The first two dimensions, Dim.1 and Dim.2, explain a combined total of 61.52% of the variance. These dimensions provide an adequate basis for a two-dimensional visualization, capturing the most significant patterns in the data

. III.1.1.2. DESCRIPTION DU PLAN 1-2

Figure 3:PCA variables graphe
Figure 3:PCA variables graphe

Figure 4:PCA graph of individual

The analysis highlights critical disparities and patterns in health-related mortality across West African countries. Figure 3 reveals distinct outliers, such as Niger and Guinea-Bissau, which may face unique health challenges requiring targeted interventions. In contrast, countries like Senegal, Mali, and Côte d’Ivoire form clusters, indicating shared mortality profiles likely influenced by similar socio-economic or healthcare conditions. Figure 4 identifies key drivers of mortality. Typhoid and Hepatitis B strongly influence Dim.1, suggesting their significant impact on the observed differences among countries. Meanwhile, Diabetes and HIV/AIDS dominate Dim.2, pointing to the growing burden of non-communicable and chronic diseases in the region. Additionally, strong correlations between some variables (e.g., HIV/AIDS and Tuberculosis) highlight possible co-morbidity patterns.

III.1.1.3. CLASSIFICATION

Figure 5: Classification Diagram
Figure 5: Classification Diagram

This analysis presents a hierarchical classification of West African countries based on mortality-related indicators, while also considering their geographical, cultural, economic, or historical similarities. Three main clusters emerge, providing valuable insights for targeted public health interventions: Cluster 1 (Green): Guinea-Bissau, Gambia, Togo These countries, characterized by strong homogeneity, stand out due to their geographical proximity and shared traits. This cluster can be referred to as “Small Coastal or Neighboring States.” Cluster 2 (Red): Guinea, Mali, Ghana, Senegal These nations form a transition zone between coastal and Sahelian regions, sharing significant economic and cultural characteristics. This group is named “Transition Countries between Coastal and Sahelian Regions.” Cluster 3 (Black): Niger, Liberia, Côte d’Ivoire, Sierra Leone, Benin This cluster includes diverse countries connected by similarities in their political or economic structures. It is identified as “Diverse or Landlocked States.” The reduction of inertia, highlighted in the dendrogram, reinforces the coherence of these groupings and can serve as a basis for designing strategies tailored to the specific needs of each cluster.

III.1.2. CORRELATION ANALYSIS

Figure 6: Correction graph
Figure 6: Correction graph

This correlation matrix highlights the relationships between various diseases based on mortality rates, with values ranging from -1 (strong negative correlation) to 1 (strong positive correlation). Significant findings include strong positive correlations, such as between meningitis and tuberculosis (0.56) and typhoid and malaria (0.58), indicating potential co-occurrence or shared risk factors. Conversely, strong negative correlations are observed between malaria and tuberculosis (-0.61) and malaria and injuries (-0.59), suggesting distinct patterns of prevalence or health priorities. Additionally, diabetes shows a notable positive correlation with tuberculosis (0.56), potentially reflecting shared comorbidities. These insights can guide targeted health interventions by highlighting disease interrelationships.

Figure 7: Correlation Diagram
Figure 7: Correlation Diagram

This pair plot visually displays the relationships between various diseases or health indicators, with each scatterplot comparing two variables (Pl, C, Tu, Bl, Di, Sida, Ty and Ha). The diagonal axis shows the variable labels. The scatterplots reveal patterns such as linear relationships, clustering, or a lack of correlation, providing valuable insights into the associations between variables. For instance, tightly clustered points indicate strong correlations, while scattered points suggest weaker relationships. This visualization complements the correlation matrix, offering a graphical validation of the relationships, highlighting trends and potential outliers, which can guide further analysis.

III.1.3. LINEAR REGRESSION ANALYSIS

Figure 8: Linear regression between malaria and COVID-19
Figure 8: Linear regression between malaria and COVID-19

Figure 8 illustrates the relationship between mortality rates from malaria (paludisme) and COVID-19 through a scatter plot with a fitted linear regression line. The data points represent individual observations, with the x-axis showing mortality rates for COVID-19 and the y-axis representing those for malaria. The regression line exhibits a negative slope, suggesting an inverse relationship between the two variables: regions with higher COVID-19 mortality rates tend to report lower malaria mortality rates. This trend could be attributed to factors such as reallocation of healthcare resources, variations in disease reporting, or differing regional health priorities.

III.1.4. DATA VISUALIZATION AND SPATIAL ANALYSIS

III.1.4.1. IDENTIFICATION OF MORTALITY RATE CLUSTERS

Figure 9: Prevalence graph of COVID-19
Figure 9: Prevalence graph of COVID-19
Figure 10: Prevalence graph of malaria
Figure 10: Prevalence graph of malaria

The bar charts compare the prevalence of COVID-19 and malaria across various countries, highlighting contrasting patterns. In the figure 9, Mali, Guinea, and Senegal show the highest COVID-19 prevalence, while Liberia, Sierra Leone, and Niger report lower rates. Conversely, figure 10 reveals that Sierra Leone, Niger, and Liberia have the highest malaria prevalence, with countries like Gambia and Togo exhibiting much lower rates. This contrast indicates that countries with high COVID-19 prevalence, such as Mali and Guinea, often have lower malaria prevalence, and vice versa. These differences may reflect variations in disease burden, healthcare priorities, resource allocation, and environmental factors, emphasizing the need for country-specific health interventions.

III.1.5. SPATIAL DISTRIBUTION OF MORTALITY DATA

The following map are presented to further investigate the spatial distribution of mortality data. The Dimension Map emphasizes key factors influencing mortality, while the Contribution Map illustrates how each factor contributes to the overall patterns. The Square of Cosine Map uncovers correlations between different variables, providing deeper insight into the complex relationships within the data. Together, these maps offer a comprehensive understanding of mortality trends across the region.

Figure 11: Dimensions map
Figure 11: Dimensions map
Figure 12: Contributions map
Figure 12: Contributions map
Figure 13: Map of cosine square
Figure 13: Map of cosine square

III.2. DISSCUSION

This study provides a comprehensive analysis of mortality pattern across West African countries, focusing on the interrelationships between infectious and non-communicable diseases. The results, derived from Principal Component Analysis (PCA), hierarchical clustering, and correlation analysis, reveal significant patterns that align with existing research while offering new insights into the evolving health landscape of the region.

III.2.1. PRINCIPAL COMPONENT ANALYSIS AND FACTORIAL MAPS

The PCA results indicate that the first four principal components account for 83.16% of the total variance, with Dim.1 and Dim.2 explaining over 60% of the variance. These dimensions highlight the key role of diseases like Typhoid, Hepatitis B, and HIV/AIDS in shaping mortality patterns in West Africa[2]. Previous studies, such as those by Joffe et al. (2020), have identified infectious diseases as primary drivers of mortality in sub-Saharan Africa, particularly in countries with limited healthcare access. Our results support these findings, especially in the case of Typhoid, which is still prevalent in many countries with poor sanitation systems.[3] On the other hand, the growing influence of non-communicable diseases like Diabetes and HIV/AIDS aligns with a global trend observed by WHO (2021), where chronic diseases are becoming more prominent even in lower-income settings, exacerbating the health burden.[3] The factorial maps visually illustrate the clustering of countries based on their mortality profiles. Countries like Senegal, Mali, and Côte d’Ivoire are clustered together, suggesting shared mortality patterns, which could be attributed to socio-economic factors such as urbanization, health system structures, and common health interventions.[4] Similar conclusions have been drawn in other regional studies, such as those by Sachs et al. (2019), which examined how socio-economic factors influence disease outcomes in West Africa.

III.2.2. HIERARCHICAL CLUSTERING AND MORTALITY CLUSTERS

The hierarchical clustering analysis identifies three distinct clusters of countries based on mortality-related indicators. The first cluster, including Guinea-Bissau, Gambia, and Togo, shows a high degree of homogeneity in mortality rates, indicating that these countries may face similar challenges in health care access or disease prevention.[5] Bennett et al. (2018) conducted a similar analysis of African countries, finding that smaller nations with limited healthcare infrastructure often show comparable disease patterns. This suggests that targeted health interventions, such as vaccination programs or improved healthcare infrastructure, could be beneficial in these countries. The second cluster, comprising Guinea, Mali, Ghana, and Senegal, reflects countries with relatively higher socio-economic development and healthcare access. However, they still share common health challenges, likely due to regional diseases and socio-economic conditions[4]. This finding is consistent with Okusanya et al. (2021), who noted that despite improvements in health systems, chronic diseases continue to rise in West Africa due to lifestyle changes, increasing urbanization, and the dual burden of infectious and non-communicable diseases. The third cluster, including Niger, Liberia, Côte d’Ivoire, Sierra Leone, and Benin, shows greater internal diversity in mortality rates, which may be due to varying healthcare policies, political stability, and other contextual factors. [6]The differences observed within this cluster echo the findings of Akanbi et al. (2020), who highlighted the importance of regional factors in shaping health outcomes, including political factors and public health strategies.

III.2.3. CORRELATION ANALYSIS AND DISEASE INTERRELATIONSHIPS

The correlation matrix revealed significant relationships between various diseases, with strong positive correlations between Meningitis and Tuberculosis (0.56) and Typhoid and Malaria (0.58).[7] These findings are consistent with studies such as Manneh et al. (2019), which reported a high co-morbidity rate between infectious diseases like Tuberculosis and Meningitis in West Africa, attributed to similar transmission routes and environmental factors. Additionally, the negative correlation between Malaria and Tuberculosis (-0.61) is noteworthy. This may reflect regional differences, as Malaria tends to be more concentrated in rural areas with lower healthcare infrastructure, while Tuberculosis is often found in urban centers.[8] Studies by Foley et al. (2018) have demonstrated similar patterns of disease distribution in sub-Saharan Africa, where rural-urban divides contribute to the contrasting epidemiological profiles of infectious diseases. The positive correlation between Diabetes and Tuberculosis (0.56) is also significant, highlighting the growing burden of both communicable and non-communicable diseases[9]. This finding is aligned with research by Akinmoladun et al. (2020), which showed an increasing prevalence of Diabetes alongside infectious diseases like Tuberculosis in the region. The coexistence of these diseases may be exacerbated by limited healthcare resources and the rising rates of urbanization and lifestyle changes.

III.2.4. IMPLICATIONS FOR PUBLIC HEALTH

The findings of this study underscore the need for integrated public health strategies that address both infectious and non-communicable diseases in West Africa. This dual burden of disease requires a holistic approach to healthcare, one that goes beyond the traditional focus on infectious diseases to also include chronic conditions like Diabetes and HIV/AIDS.[3] In line with recommendations from the World Health Organization (2021), countries in the region should invest in strengthening healthcare systems, improving disease surveillance, and implementing preventive measures for both types of diseases.[10] The identification of disease clusters and correlations also provides a valuable basis for targeted interventions. For instance, the strong correlation between Typhoid and Malaria suggests that joint interventions aimed at improving sanitation and controlling mosquito populations could help mitigate the impact of both diseases. Moreover, the clustering of countries with similar mortality profiles offers an opportunity for regional cooperation in addressing common health challenges.[11] Regional health strategies, as suggested by Sachs et al. (2019), could help to pool resources and expertise, leading to more effective disease management and prevention.

III.2.5. LIMITATIONS AND FUTURE RESEARCH

While this study provides important insights into mortality patterns in West Africa, it is important to consider its limitations. First, the analysis relied on available data, which may not account for all relevant factors influencing mortality, such as environmental changes or political instability. Additionally, the study focused on a limited number of diseases, and future research should incorporate a broader range of health indicators to provide a more comprehensive understanding of the health challenges facing the region. Future studies could also explore the role of environmental factors such as climate change, which may have an increasing influence on disease patterns in the coming decades.

CONCLUSION

This study has allowed us to shed light on the key factors contributing to mortality in West Africa and the complex relationships between communicable and non-communicable diseases. The results demonstrate the importance of honest strategies to improve public health in the region. We can therefore deduce that in order to reduce the mortality rate, it is important to make an increased investment in health infrastructure and the training of medical personnel, but also to carry out a collaborative approach including government and local communities, and finally to pay particular attention to country specifics to adapt public health interventions.

RECOMMENDATIONS

Based on the analyses carried out and the results obtained, the following recommendations are formulated to improve public health and reduce mortality in West Africa:

• Strengthening health infrastructure: Build and equip modern hospitals and health centers, particularly in rural areas and provide equitable access to quality care, particularly for vulnerable populations;

• Improving the capacity of medical personnel: Train and recruit more health professionals to overcome the current shortage and set up incentives to retain qualified workers in the countries of the region;

• Integrating preventive care into primary health services: Develop educational programs to raise awareness among communities about preventive practices and expand vaccination campaigns and distributions of impregnated mosquito nets to combat malaria;

• Promoting hygiene and sanitation: Invest in sanitation infrastructure and ensure access to drinking water and launch initiatives to raise awareness of the importance of personal and community hygiene;

• Strengthening research and international cooperation: Encourage local research on endemic diseases and their prevention and collaborate with NGOs and international partners to share resources and good practices;

• Increasing human resources for health: Launching accelerated training programs to fill the gap in qualified personnel, targeting critical specialties such as pediatrics and intensive care, providing financial incentives and career opportunities to retain health professionals in the region;

• Optimizing prevention campaigns: Strengthening vaccination programs to cover diseases such as meningitis, tuberculosis and COVID-19 and using large-scale awareness campaigns to promote hygiene, nutrition and the importance of antenatal care;

• Promoting equity in access to care: Expanding universal health insurance coverage to reduce financial barriers to accessing care and collaborating with international partners to mobilize funds to subsidize treatments for chronic diseases;

• Stimulate local research and regional collaboration: Support research initiatives on neglected tropical diseases and interactions between communicable and non-communicable diseases and strengthen cooperation between countries to pool resources and share best practices in public health;

ANNEXE

INTERVIEW GUIDE

 

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[8] « biomedicines-10-00577 ».

[9] R. Ratovoson, R. Laurent, et F. Meslé, « P15.1.6 Mortalité et santé en Afrique subsaharienne ».

[10] « Snapshot ». Consulté le: 7 décembre 2024. [En ligne]. Disponible sur: https://journals.lww.com/jhqonline/citation/1985/07000/THE_VISUAL_DISPLAY_OF_QUANTITATIVE_INFORMATION.12.aspx

[11] « H133678 ».