ARTICLE INFO

Keywords:

Malaria, West Africa, Economic Impact, Health Challenges, Public Health

ABSTRACT

This research and data analysis project evaluates the impact of malaria on West African countries in 2021, focusing on both health and economic challenges. Drawing on data from Our World in Data and other institutional sources (WHO, World Bank, UNICEF), the study adopts a quantitative approach that combines multidimensional statistical techniques including Principal Component Analysis (PCA), multiple linear regression, and hierarchical clustering with spatial mapping using QGIS. The objective is to identify the main determinants of malaria incidence, particularly in terms of health infrastructure, urban/rural distribution, poverty levels, and health investments. The results reveal significant disparities between countries, with lower incidences and mortality observed in states such as Senegal, and a heavy burden in others like Burkina Faso and Niger. Two principal dimensions, which explain nearly 72% of the data variance, underscore the close relationship between socioeconomic development, health management of malaria, and the impact of demographic and geographic dynamics.

Introduction

Malaria represents a major public health challenge in the West Africa region, with profound repercussions on the economy and development of the countries in West Africa. In 2021, this disease affected several million people across West Africa, leading to high morbidity and significant mortality, particularly among children under five and pregnant women. Despite efforts to combat it and advances in prevention strategies, malaria remains endemic, exerting substantial pressure on health systems and hindering the socioeconomic development of affected countries. The high incidence of malaria in West Africa is largely influenced by environmental, economic, and infrastructural factors. Inadequate sanitation systems, limited access to potable water, and poor health infrastructure promote the proliferation of vector mosquitoes and compromise the effectiveness of control interventions (UNICEF, 2021). These health inequalities exacerbate social disparities and undermine the achievement of the Sustainable Development Goals (SDGs), particularly those related to health and sanitation. This study aims to assess the impact of malaria in West African countries through a quantitative approach that examines its health and economic effects. More specifically, it seeks to answer the following questions: How does malaria affect the economic development of West African countries? What are the spatial and social disparities in malaria prevalence across West Africa? What are the main economic, environmental, and demographic factors influencing the spread of the disease? How do public health policies and investments influence the evolution of malaria? To carry out this analysis, we will utilize advanced statistical tools such as Principal Component Analysis (PCA) and geographic information systems (GIS) techniques in order to model the links between malaria and the relevant socioeconomic variables. This study thus aims to provide policymakers, economic stakeholders, and international organizations with concrete recommendations to strengthen the fight against malaria and mitigate its long-term impacts.

Literature Review

Malaria remains a major public health problem in Africa, affecting both health systems and economic development. In the West Africa region, characterized by limited infrastructure, heavy reliance on agriculture, and significant socioeconomic disparities, the burden of the disease results in considerable human and financial costs(1).Moreover, since the early 2000s, the deployment of targeted control strategies and interventions has helped reduce morbidity, although inequalities persist(2)&(3).

Socioeconomic Impact in the West Africa region
Direct and Indirect Costs

Malaria generates direct costs (medical treatments and preventive measures) as well as indirect costs (loss of productivity, school and work absenteeism)(4). These expenditures limit the investment capacity of households and hinder local development, particularly in rural areas where the burden of the disease perpetuates a vicious cycle of poverty(4)&(5).

Hindrance to Development and Regional Inequalities

Beyond the financial costs, the endemicity of malaria delays investment in essential infrastructures (health, education, housing)(5). Studies show that improving socioeconomic conditions could reduce exposure to vectors and facilitate access to care, but the inequalities between urban and rural areas remain a significant obstacle(5).

Epidemiological Dynamics and Environmental Factors
Transmission Conditions and Mapping

The distribution of malaria is closely linked to climatic and environmental characteristics. Global mapping studies have established the geographical distribution of endemicity(6).Moreover, the presence of asymptomatic infections contributes to the disease’s transmissibility, as evidenced by a review on the topic(7).

Climate Influence and Interannual Variability

Slight changes in temperature or precipitation can modify the mosquito breeding cycles, thus increasing the risk of outbreaks(8). In the already fragile West African contexts, this climatic variability further accentuates the transmission dynamics and poses additional challenges(8)&(9).

Challenges Related to Resistance and Preventive Measures

The emergence of resistance whether to drugs or insecticides coupled with logistical difficulties in distribution (insecticide-treated nets, indoor residual spraying), represents a major barrier to controlling the disease(9)&(10).

Malaria Control Initiatives and Therapeutic Innovations
Preventive Interventions

Preventive campaigns, such as indoor residual spraying and the distribution of insecticide-treated nets, have demonstrated their effectiveness in reducing malaria transmission(10)&(11).

Community-Based Approaches

Direct involvement of communities in the fight against malaria is essential. For example, studies conducted in West Africa have evaluated community interventions and shown significant improvements in adherence to preventive and therapeutic measures(12).

Therapeutic and Vaccinal Innovations

The development of the RTS,S vaccine represents a major breakthrough in the fight against malaria. Clinical trials have shown moderate efficacy in young children, thereby paving the way for its integration into malaria control programs(13).

Synergies Partnerships and Synergies

Multisectoral collaboration between public and private stakeholders mobilizes significant resources to intensify interventions. Structured partnerships facilitate access to prevention tools and reinforce the implementation of long-term strategies(14).

Perspectives and Recommendations for Future Research
Strengthening Surveillance Systems

The development of early warning surveillance tools based on regional indicators that integrate climatic and epidemiological data is essential for anticipating outbreaks and rapidly adapting interventions(15).

Cartographic and Spatial Approaches

Geospatial analysis of malaria distribution offers a better visualization of infection hotspots, allowing for the efficient targeting of vulnerable areas and equitable allocation of local resources(16).

Technological Innovations and Treatment Strategies

In the face of emerging resistance, research must continue to develop new therapeutic approaches. Innovation in antimalarial treatments is critical to sustainably reducing the incidence of the disease(10)&(17).

Economic Approach and Sustainability

Evaluating the direct and indirect costs of malaria is crucial for guiding public policies. Economic studies quantify the impact of the disease on households and health systems, thereby informing future investments(17)&(18).

Eradication Strategies and Modeling

Finally, the use of mathematical models to simulate the impact of interventions offers promising perspectives for exploring various eradication scenarios and optimizing resource allocation(19).

The analysis of the available data underlines that malaria in the West Africa region represents a multidimensional challenge, intertwining health, economic, and environmental issues. Although preventive and therapeutic interventions have reduced the incidence of the disease, regional disparities and emerging resistance necessitate continuous revision of strategies. Strengthening partnerships, integrating innovative surveillance tools, and employing predictive models are promising avenues to enhance the sustainable management of malaria and, ultimately, work toward its eradication.

Methodology

This study evaluates the impact of malaria on West African countries through a quantitative approach that combines multidimensional statistical analysis techniques with spatial mapping. The methodology is structured in three main stages: data collection and preparation, statistical and spatial analysis, and interpretation of the results leading to operational recommendations.

Data Collection and Preparation
Data Sources:

All the data used in this study originates from the international platform Our World in Data, which is renowned for its scientific rigor, methodological transparency, and comprehensive global coverage. These data are obtained through collaborations with institutions such as the World Health Organization (WHO), the World Bank, and UNICEF.

Variables Analyzed

The variables selected for this study encompass essential dimensions that are socioeconomic, health-related, and environmental, all of which are pertinent to understanding the malaria burden in West African countries. The target variable, malaria incidence (per 1,000 people at risk), measures the frequency of new cases within the exposed population, thereby providing a direct indicator of the disease’s prevalence. Among the explanatory variables, demographic factors such as the total population provide context for the health outcomes across different countries. Access to sanitation reflects hygiene conditions, which can significantly influence malaria transmission. The number of malaria-related deaths quantifies the disease’s severity, while the infant mortality rate serves as a broader indicator of the overall quality of the health system. Socioeconomic indicators include the percentage of the population living in extreme poverty, which highlights the vulnerability due to limited access to healthcare and preventive measures, as well as health expenditures as a percentage of GDP, which indicates national investment in health infrastructures. Lastly, environmental and infrastructure variables such as access to basic drinking water, urban population, and rural population help assess living conditions and disparities in healthcare access that influence the transmission dynamics of malaria. Although we already have access to data from Our World in Data, a hypothetical collection could have been undertaken by combining both a quantitative survey and a qualitative inquiry. The quantitative survey would have enabled the collection of data on key indicators such as population, sanitation, malaria-related mortality, extreme poverty, and access to water. It would have targeted households, affected individuals, rural and urban communities, as well as workers and students, with data being collected via KoboToolbox and KoboCollect for geolocated field data input. In parallel, a qualitative inquiry would have allowed for a deeper exploration of malaria-related issues by gathering testimonies from adults, healthcare professionals, community leaders, and policymakers. This approach would have provided a better understanding of treatment efficacy, preventive practices, and the challenges encountered. By combining these approaches, we would have achieved a comprehensive and multidimensional perspective on the impact of malaria in the West African region. To provide a clear idea of what the questionnaire and interview guide would have looked like, we have included sample versions in the appendix of the document.

The countries studied in this analysis are those within the West Africa region. Specifically, our research focuses on data from: Benin Burkina Faso Cape Verde Côte d’Ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo

Data Preparation :

Before commencing the analysis, data preparation is essential to ensure quality and uniformity. Quantitative variables were normalized to allow for consistent comparisons among them by applying the following normalization formula: Z=(X-μ)/σ Where: Z is the standardized value, X is the raw value, μ is the mean, and σ is the standard deviation. This step ensures that all variables are on the same scale, thereby facilitating their comparison and analysis.

Statistical Analysis Methods
Principal Component Analysis (PCA)

PCA was employed to reduce the dimensionality of the data while retaining the essential information. This method helped identify the main relationships between variables. The principal steps included:   • Extracting eigenvalues (λ > 1) to determine the significant principal components.   • Visualizing the relationships between variables using a correlation circle.   • Interpreting the factorial axes to identify the key factors influencing malaria incidence, infant mortality, access to water, etc. Multiple Linear Regression A multiple linear regression model was constructed to predict malaria incidence (the dependent variable) based on explanatory variables such as access to water, sanitation, infant mortality, and the percentage of GDP allocated to health. The model is expressed as:

Y= β_0+β_1 X_1+β_2 X_2+⋯+β_n X_n+ϵ Where: Y represents the malaria incidence, Xi are the explanatory variables, and ϵ denotes the residual error. The evaluation criteria include R², p-values, and residual plots to assess the quality of the model.

Hierarchical Clustering Analysis (HCA)

HCA was utilized to group West African countries based on their energy and socioeconomic characteristics. Ward’s method was employed to minimize intra-cluster variance and identify homogeneous groups. A dendrogram was generated to visualize the cluster hierarchy, allowing for a better understanding of the relationships between countries based on their health and economic characteristics. Geographic Analysis Using QGIS QGIS was used to visualize the geographical disparities in the data. The main steps involved:   • Importing geographic data in the form of shapefiles for West African countries.   • Linking the tabular data with the geographic entities via a common identifier.   • Creating choropleth maps to illustrate indicators such as access to electricity, urban/rural distribution, and other public health metrics (e.g., infant mortality and malaria incidence).   • Overlaying thematic layers representing economic and energy factors to better understand regional disparities. Validation Criteria for Results

To ensure the robustness and validity of the obtained results, several validation criteria were applied:

  • Normality of residuals and homoscedasticity of errors were verified to ensure that the assumptions of the multiple linear regression model were met.   • The clusters derived from the HCA were validated by comparing them with the results from the PCA to verify their coherence with the key extracted variables. Tools Used: • RStudio: For all statistical analyses (PCA, regression, HCA, K-means).   • QGIS: For geographic visualization of regional disparities.   • R Libraries: FactoMineR, factoextra, ggplot2, psych, Factoshiny, shiny, FactoInvestigate, corrplot, and DataExplorer. This combined methodology allows for a comprehensive exploration of the factors influencing the impact of malaria in the West African region by integrating both quantitative analyses and geographic visualizations. These steps contribute to a better understanding of the socioeconomic and health determinants of malaria and facilitate the formulation of targeted policy recommendations.

Results and Discussion

Results

Descriptive Analysis
Description of the Variables

Data Loading and Configuration

We begin by loading the necessary libraries and importing our dataset.

# Configuration des options et chargement des bibliothèques nécessaires
library(FactoMineR)
library(factoextra)
library(ggplot2)
library(psych)
library(Factoshiny)
library(shiny)
library(FactoInvestigate)
library(DataExplorer)
library(corrplot)
library(pander)
library(DT)

# Chargement des données
setwd("C:/Users/Fadil/Desktop/Projet RTI Groupe 13 S7 GEAAH")
donnees = donnees <- read.csv2(
  file         = "Variables RTI Groupe 13.csv",
  header       = TRUE,
  dec          = ".",
  row.names    = 1,
  fileEncoding = "latin1"
)

# Analyse de corrélation
numeric_data <- donnees[, sapply(donnees, is.numeric)]
matrice.cor <- cor(numeric_data, use = "pairwise.complete.obs")
datatable(round(matrice.cor, 3), options = list(pageLength = 20))
corrplot(matrice.cor, 
         method = "color", 
         type = "upper", 
         tl.col = "black", 
         tl.srt = 75,
         addCoef.col = "black")

The chart presents a correlation matrix of various socio-economic and health variables derived from our dataset. The color palette illustrates the strength and direction of these correlations, ranging from -1 (strong negative correlation, depicted in red) to 1 (strong positive correlation, depicted in blue), while the diagonal shows perfect self-correlation (1.00).

Malaria Deaths and Malaria Incidence

A strong positive correlation is observed between malaria-related deaths and malaria incidence, suggesting that countries experiencing high malaria incidence also witness a significant number of deaths due to the disease.

Infant Mortality and Malaria Deaths

There is a positive correlation between infant mortality and malaria-related deaths, highlighting the heightened vulnerability of children in regions with a high prevalence of malaria.

Extreme Poverty and Mortality Indicators

A positive link exists between extreme poverty and various mortality indicators (both overall and infant mortality), implying that poor living conditions and limited access to healthcare greatly contribute to a heavier health burden.

Sanitation and Malaria Incidence

The chart reveals a negative correlation between access to quality sanitation infrastructure and malaria incidence. This suggests that enhanced sanitation measures can play a crucial role in reducing the spread of the disease.

Health Expenditure (% of GDP) and Malaria Deaths

Countries that allocate a larger share of their GDP to health care tend to record fewer malaria-related deaths, reflecting a stronger capacity for health interventions.

Population

The variable representing population should be considered a control factor. Its influence on the other indicators underscores that the scale of a country’s population may amplify or modulate these correlations.

  1. Urban vs. Rural Disparities Urban Area vs. Rural Area The analysis highlights a marked difference between urban and rural regions. In countries where the rural population predominates, malaria incidence tends to be higher, probably due to limited access to healthcare, underdeveloped infrastructure, and environmental conditions conducive to the proliferation of disease vectors such as mosquitoes. Conversely, countries with a higher concentration of urban areas generally benefit from better access to health services, improved hygiene conditions, and a higher density of health infrastructure, which collectively contribute to a lower incidence of malaria.

    Results of the Principal Component Analysis (PCA)

# Principal Component Analysis (PCA)


resultat.acp = PCA(donnees, scale.unit = TRUE, ncp = 5, graph = TRUE)
## Warning in PCA(donnees, scale.unit = TRUE, ncp = 5, graph = TRUE): Missing
## values are imputed by the mean of the variable: you should use the imputePCA
## function of the missMDA package

# Visualisation de la biplot
fviz_pca_biplot(resultat.acp, repel = TRUE)

# Extraction et affichage des valeurs propres
val.propre = get_eigenvalue(resultat.acp)
pander(val.propre)
  eigenvalue variance.percent cumulative.variance.percent
Dim.1 4.674 46.74 46.74
Dim.2 2.457 24.57 71.31
Dim.3 1.285 12.85 84.15
Dim.4 0.7835 7.835 91.99
Dim.5 0.3652 3.652 95.64
Dim.6 0.2654 2.654 98.3
Dim.7 0.0984 0.984 99.28
Dim.8 0.06949 0.6949 99.97
Dim.9 0.002521 0.02521 100
Dim.10 1.268e-14 1.268e-13 100 
fviz_eig(resultat.acp, addlabels = TRUE, ylim = c(0, 50))

# Contribution des variables aux composantes principales
resultat.var = get_pca_var(resultat.acp)
pander(resultat.var$coord)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Population 0.1083 0.9683 -0.1366 0.1348 -0.01774
Sanitation -0.8112 0.1124 -0.2929 0.2513 0.3868
Morts.du.paludisme 0.1609 0.9576 -0.1349 0.1601 0.04315
Mortalite.infantile 0.8702 0.3496 0.0159 -0.1515 0.1815
Exteme.pauvrete 0.8085 0.1715 0.1136 0.4124 -0.2865
Part.du.PIB.Sante 0.1679 -0.1721 0.8345 0.4404 0.2112
Ressource.en.eau.de.base -0.8685 0.2101 0.03801 0.03965 -0.0782
Incidence.of.malaria 0.7093 0.2856 0.3404 -0.483 0.1646
zone.urbaine -0.8006 0.3759 0.4094 -0.1655 -0.1014
zone.rurale 0.8006 -0.3759 -0.4094 0.1655 0.1014 
pander(resultat.var$cor)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Population 0.1083 0.9683 -0.1366 0.1348 -0.01774
Sanitation -0.8112 0.1124 -0.2929 0.2513 0.3868
Morts.du.paludisme 0.1609 0.9576 -0.1349 0.1601 0.04315
Mortalite.infantile 0.8702 0.3496 0.0159 -0.1515 0.1815
Exteme.pauvrete 0.8085 0.1715 0.1136 0.4124 -0.2865
Part.du.PIB.Sante 0.1679 -0.1721 0.8345 0.4404 0.2112
Ressource.en.eau.de.base -0.8685 0.2101 0.03801 0.03965 -0.0782
Incidence.of.malaria 0.7093 0.2856 0.3404 -0.483 0.1646
zone.urbaine -0.8006 0.3759 0.4094 -0.1655 -0.1014
zone.rurale 0.8006 -0.3759 -0.4094 0.1655 0.1014 
pander(resultat.var$cos2)
Table continues below
  Dim.1 Dim.2 Dim.3 Dim.4
Population 0.01172 0.9376 0.01866 0.01817
Sanitation 0.658 0.01264 0.0858 0.06314
Morts.du.paludisme 0.02588 0.917 0.01819 0.02562
Mortalite.infantile 0.7572 0.1222 0.0002528 0.02296
Exteme.pauvrete 0.6536 0.02941 0.01291 0.1701
Part.du.PIB.Sante 0.02819 0.02963 0.6964 0.194
Ressource.en.eau.de.base 0.7543 0.04412 0.001445 0.001572
Incidence.of.malaria 0.5031 0.0816 0.1159 0.2332
zone.urbaine 0.6409 0.1413 0.1676 0.02738
zone.rurale 0.6409 0.1413 0.1676 0.02738
  Dim.5
Population 0.0003146
Sanitation 0.1496
Morts.du.paludisme 0.001862
Mortalite.infantile 0.03296
Exteme.pauvrete 0.08211
Part.du.PIB.Sante 0.04462
Ressource.en.eau.de.base 0.006115
Incidence.of.malaria 0.0271
zone.urbaine 0.01027
zone.rurale 0.01027 
pander(resultat.var$contrib)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Population 0.2508 38.17 1.452 2.319 0.08612
Sanitation 14.08 0.5145 6.679 8.058 40.96
Morts.du.paludisme 0.5536 37.32 1.416 3.27 0.5098
Mortalite.infantile 16.2 4.975 0.01967 2.93 9.024
Exteme.pauvrete 13.99 1.197 1.005 21.71 22.48
Part.du.PIB.Sante 0.6032 1.206 54.21 24.76 12.22
Ressource.en.eau.de.base 16.14 1.796 0.1124 0.2006 1.674
Incidence.of.malaria 10.76 3.321 9.022 29.77 7.419
zone.urbaine 13.71 5.75 13.04 3.495 2.813
zone.rurale 13.71 5.75 13.04 3.495 2.813 
# Visualisation des contributions des variables
fviz_pca_var(resultat.acp, col.var = "contrib", gradient.cols = c("blue", "orange", "red"), repel = TRUE, title = "Contribution des variables aux composantes principales")

fviz_contrib(resultat.acp, choice = "var", axes = 1, top = 10)

fviz_contrib(resultat.acp, choice = "var", axes = 2, top = 10)

fviz_contrib(resultat.acp, choice = "var", axes = 1:2, top = 10)

# Contribution des individus aux composantes principales
resultat.ind = get_pca_ind(resultat.acp)
pander(resultat.ind$coord)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Benin 0.604 -0.01024 -0.01219 -2.014 -0.1755
Burkina Faso 2.692 -0.7036 -0.2446 -0.2392 0.5258
Cap-Vert -5.086 -0.2826 0.4482 0.7568 0.5586
Cote d’Ivoire -1.093 0.4592 0.1049 -1.419 -0.07784
Gambie -1.251 -1.071 -1.485 0.6215 -0.2905
Ghana -1.57 0.3735 0.441 -0.09237 -1.68
Guinée 0.8978 -0.2461 -0.461 -1.229 0.5313
Guinee Bissau 1.214 -1.438 -0.2175 1.183 -0.06422
Libéria 0.5009 -0.2775 3.737 0.7754 0.3832
Mali 0.3264 0.3354 -0.3924 -0.5271 0.853
Mauritanie -2.11 -1.007 -1.045 0.1876 0.3168
Niger 4.963 -0.7371 -1.066 1.204 -0.2958
Nigéria 0.492 5.932 -0.4928 0.7386 0.09782
Senegal -2.455 -0.5935 -0.9455 0.5346 0.1917
Sierra Leone 1.65 -0.3296 1.029 -0.1978 0.2335
Togo 0.2252 -0.4046 0.6025 -0.2832 -1.109
7.131e-16 -2.119e-16 1.732e-16 -2.495e-16 1.93e-16 
pander(resultat.ind$cos2)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Benin 0.08051 2.316e-05 3.282e-05 0.8953 0.006795
Burkina Faso 0.7639 0.0522 0.006308 0.006032 0.02915
Cap-Vert 0.9342 0.002885 0.007255 0.02069 0.01127
Cote d’Ivoire 0.3202 0.05649 0.002947 0.5395 0.001623
Gambie 0.2609 0.1912 0.3681 0.06443 0.01407
Ghana 0.4221 0.02389 0.0333 0.001461 0.483
Guinée 0.2611 0.01962 0.06883 0.489 0.09143
Guinee Bissau 0.2669 0.3741 0.00856 0.2535 0.0007466
Libéria 0.01645 0.00505 0.9159 0.03942 0.009631
Mali 0.03207 0.03387 0.04637 0.08365 0.2191
Mauritanie 0.6259 0.1426 0.1536 0.004949 0.01411
Niger 0.8733 0.01926 0.04029 0.05139 0.003103
Nigéria 0.006665 0.9691 0.006686 0.01502 0.0002635
Senegal 0.7848 0.04588 0.1164 0.03723 0.004788
Sierra Leone 0.6372 0.02543 0.2479 0.009159 0.01276
Togo 0.02577 0.08321 0.1845 0.04077 0.6246
0.08083 0.007138 0.004768 0.009898 0.005921 
pander(resultat.ind$contrib)
  Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
Benin 0.4591 0.0002513 0.0006808 30.46 0.4959
Burkina Faso 9.118 1.185 0.2739 0.4295 4.453
Cap-Vert 32.55 0.1913 0.9197 4.3 5.026
Cote d’Ivoire 1.505 0.505 0.05038 15.12 0.09759
Gambie 1.968 2.745 10.1 2.9 1.359
Ghana 3.103 0.3341 0.8903 0.06406 45.43
Guinée 1.014 0.145 0.973 11.33 4.546
Guinee Bissau 1.856 4.948 0.2165 10.51 0.06643
Libéria 0.3157 0.1844 63.95 4.513 2.365
Mali 0.1341 0.2694 0.7052 2.086 11.72
Mauritanie 5.604 2.43 5.004 0.2643 1.617
Niger 30.99 1.301 5.202 10.88 1.409
Nigéria 0.3046 84.27 1.112 4.095 0.1541
Senegal 7.584 0.8434 4.093 2.146 0.592
Sierra Leone 3.426 0.2602 4.849 0.2938 0.8779
Togo 0.0638 0.392 1.662 0.6021 19.79
6.4e-31 1.075e-31 1.373e-31 4.675e-31 5.999e-31 
# Visualisation des contributions des individus
fviz_pca_ind(resultat.acp, col.ind = "cos2", gradient.cols = c("blue", "orange", "red"), repel = TRUE)

fviz_contrib(resultat.acp, choice = "ind", axes = 1, top = 8)

fviz_contrib(resultat.acp, choice = "ind", axes = 2, top = 8)

fviz_contrib(resultat.acp, choice = "ind", axes = 1:2, top = 8)

PCA was employed to reduce the complexity of our dataset and to identify the key dimensions that explain disparities in the impact of malaria across West Africa for 2021. The analysis summarizes the data into two principal components that capture most of the variance among socio-economic, health, and demographic variables.

• First Principal Component: Socio-Health Impact and Development Overview: This axis contrasts countries benefiting from robust healthcare infrastructures and favorable socio-economic conditions with those struggling with significant health and developmental challenges. On one end, nations with strong sanitation measures, high health expenditures (as a percentage of GDP), and reliable access to essential water resources tend to exhibit lower malaria incidence and mortality rates. On the other end, countries facing high levels of extreme poverty and poor sanitation typically report the opposite.

The correlation circle clearly shows that variables such as sanitation, health expenditure, and access to water have high loadings on this first component. Their strong contributions indicate that socio-economic development and health infrastructure are critical determinants of the malaria burden in the region. Implications for West Africa: Extending the analysis beyond just the UEMOA, these findings underscore that improved public health measures and socio-economic upliftment are essential strategies to mitigate malaria incidence throughout West Africa.

• Second Principal Component: Demographic Dynamics and Geographic Structure Overview: This component captures the contrasting demographic and geographic profiles among West African countries. It separates nations with rapidly growing, predominantly rural populations from those that are more urbanized. Countries with a larger rural population often face challenges like limited access to healthcare and basic infrastructure, correlating with higher malaria incidences. In contrast, urbanized countries benefit from concentrated health resources and better infrastructure, leading to a reduced disease burden.

Variables related to demographic structure—such as the proportion of rural versus urban population—load significantly on this second component. This emphasizes the importance of population distribution in influencing malaria outcomes.

The biplot visually represents the relative positions of West African countries based on their socio-economic and demographic characteristics. Clusters emerge that distinguish countries with robust health systems and urbanized demographics from those with pronounced rural characteristics and higher malaria impacts. Implications for West Africa: The biplot highlights that the burden of malaria is not uniform across the region. Tailored intervention strategies are necessary—ranging from infrastructural improvements in rural areas to continuing support for urban health systems—to address the region’s diverse needs.

Country Classification

# Classification hiérarchique sur les composantes principales
resultat.cah = HCPC(resultat.acp, nb.clust = -1, consol = FALSE, graph = FALSE)

# Visualisation de la classification hiérarchique
plot.HCPC(resultat.cah, choice = 'map', draw.tree = FALSE, title = 'Carte factorielle')

plot.HCPC(resultat.cah, choice = 'tree', ind.names = TRUE, centers.plot = FALSE, title = 'Dendrogramme hiérarchique 2D')

  1. Clustering Results The hierarchical clustering analysis—illustrated by Figure 5: Dendrogramme hiérarchique 2D groups 16 West African countries into three distinct clusters based on their socio-economic and health profiles related to malaria. The dendrogram displays the hierarchical relationships among these countries. The branch lengths indicate the similarity (or dissimilarity) between them, and the accompanying inertia gain chart shows the variance explained at each merging step. Based on this analysis, the clusters are interpreted as follows:

Cluster 1 (Black): This cluster includes countries such as Cape Verde, Senegal, Mauritania, and Gambia. Their grouping suggests that these nations share relatively favorable indicators regarding malaria outcomes. Their effective public health investments, robust health infrastructures, and well-implemented malaria control measures likely contribute to a lower malaria burden. Cluster 2 (Red):Nigeria forms its own distinct cluster. Its isolation in the dendrogram reflects its unique socio-economic and health characteristics factors that may be linked to its large population size, pronounced urban–rural disparities, and specific challenges in malaria management. Cluster 3 (Green): This cluster comprises the remaining countries (e.g., Sierra Leone, Guinea-Bissau, Liberia, Guinea, Burkina Faso, Niger, Benin, Mali, Togo, Ghana, and Côte d’Ivoire). The countries in Cluster 3 tend to exhibit a higher malaria burden. They share vulnerabilities such as underdeveloped health systems and a predominantly rural demographic structure that hinder effective prevention and control of malaria.

  1. Factorial Map Analysis (Biplot) The factorial map shown in Figure 6 projects these countries onto two principal dimensions that together explain a significant part of the total variance in the dataset: Dimension 1 (46,74% of total variance): This axis primarily reflects variables associated with malaria management and the quality of health infrastructure. Countries with higher scores on Dimension 1 typically those in Cluster 1 tend to have better-established healthcare systems and effective disease control measures, resulting in lower malaria incidence. Conversely, countries scoring lower on Dimension 1 (most of those in Cluster 3) face greater challenges in managing malaria. Dimension 2 (24,57% of total variance): This dimension captures socio-economic organization and demographic factors, specifically the urban–rural distribution of the population. Higher values on Dimension 2 are generally associated with nations that are more urbanized and economically developed, leading to improved access to healthcare. Lower scores indicate a predominance of rural characteristics, which is linked to limited healthcare accessibility and, consequently, a higher malaria burden.

The spatial distribution of countries in the biplot reinforces the clustering outcomes from the dendrogram. For example, countries in Cluster 1 consistently occupy more favorable positions along Dimension 1, indicating strong health infrastructures, while those in Cluster 3 are positioned lower on both dimensions highlighting their shared challenges in health service provision and socio-economic development. This corrected analysis, now based on 16 West African countries, underscores how differences in health infrastructure, public health investments, and demographic factors contribute to the diverse malaria outcomes across the region.

Discussion

Summary of Main Findings

The study revealed significant disparities in the impact of malaria across the West African countries, disparities that are mainly explained by socio-economic, health, and demographic factors. Countries with better healthcare infrastructure and higher health investments—such as Senegal—show significantly lower malaria incidence and mortality rates. Conversely, low-income countries like Burkina Faso and Niger bear a heavy burden of the disease. Statistical analyses, including PCA, highlighted two key dimensions explaining approximately 72% of the total data variance: on one hand, factors related to socio-health development (Dim1), and on the other, demographic and geographic dynamics (Dim2). Moreover, hierarchical clustering analysis grouped countries into four distinct clusters, revealing regional similarities and contrasts. A multiple regression model confirmed the significant impact of health investments, sanitation levels, and the proportion of the rural population on malaria. Interpretation i. Impact of Socio-Health and Economic Factors The results clearly indicate that the level of socio-health development measured notably by sanitation quality, access to safe water, and the percentage of GDP devoted to health is a major determinant of malaria’s impact. These findings are consistent with previous studies showing that countries capable of funding effective healthcare infrastructures significantly reduce malaria incidence and mortality. ii. Importance of Human Development The study reveals a strong correlation between human development indicators (education, health) and malaria control. Indeed, countries with better access to healthcare and higher education levels generally have more efficient prevention and treatment systems, which helps reduce the disease’s impact. iii. Disparities Between Rural and Urban Areas The analysis shows that malaria incidence rates are significantly higher in rural areas due to limited access to healthcare infrastructure and services. In contrast, greater urbanization, along with the concentration of health services, leads to better prevention and more effective treatment, thereby reducing the malaria burden. iv. Preventive Approaches and Their Limitations Although prevention measures such as the distribution of insecticide-treated nets, insecticide spraying, and community awareness have been implemented, their integration remains insufficient in public health plans. Centralized approaches often fail to effectively reach isolated populations, highlighting the need for decentralized solutions tailored to local contexts. v. Limitations of Traditional Approaches Centralized intervention models, which rely on national health structures, often prove inadequate for remote areas, thereby exacerbating inequalities in healthcare access. Decentralized, community-based strategies that promote proximity and adaptation to local realities could serve as more effective long-term alternatives.

Practical Implications
  1. Targeted Investments The findings suggest that countries must increase investment in health infrastructure, particularly in rural areas. Financial incentives and support programs could strengthen malaria prevention and treatment by improving access to safe drinking water and adequate sanitation systems.
  2. Decentralized Solutions To bridge the urban–rural gap, the development of decentralized solutions—such as community health centers, training of local health workers, and the use of mobile technologies for case tracking—is essential. These approaches would allow for more effective responses to the specific needs of the most vulnerable populations.
  3. Regional Cooperation Pooling resources and knowledge through regional initiatives is essential in the fight against malaria. The establishment of cross-border projects and collaboration between countries could strengthen prevention strategies and improve malaria management, particularly in regions where the disease poses a common challenge.
  4. Integration of Innovative Technologies The adoption of modern technologies—such as remote monitoring systems, geographic data tools like QGIS, and real-time analysis of health indicators—could contribute to better malaria management. These tools would help optimize resource allocation and better target high-risk areas. Study Limitations • Lack of Certain Variables: Factors such as governance, corruption, or foreign investment, which could also influence malaria management, could not be included due to data unavailability. • Linear Models: The use of linear models simplifies some complex relationships between variables, which may limit the full understanding of interrelations among the different determinants. This work, combining quantitative analyses (PCA, multiple regression, hierarchical clustering) and geographic visualizations, allows for an in-depth identification of the key determinants of malaria impact in the WAEMU region. The results provide a solid foundation for guiding targeted policy recommendations aimed at strengthening healthcare systems, improving access to care, and reducing inequalities between urban and rural areas.

Conclusion and Recommendations

Conclusion

This study highlights marked disparities in the impact of malaria within West African countries, confirming that socio-economic, health, and demographic factors play a crucial role in the disease’s prevalence and mortality. Statistical analyses, particularly PCA and hierarchical clustering, helped structure these disparities around two major dimensions: socio-health development and demographic dynamics. The results indicate that countries with better infrastructure and robust health policies are better able to limit the impact of malaria, while low-income countries with large rural populations remain severely affected. It thus appears that the fight against malaria cannot be addressed solely through a biomedical lens but must incorporate socio-economic and structural interventions. Public health policies must be tailored to local contexts and take into account disparities between urban and rural areas to ensure effective malaria transmission reduction.

Recommendations

Strengthening Health Infrastructure: Increase investments in access to drinking water and sanitation, particularly in rural areas, to improve sanitary conditions and limit malaria transmission vectors.
Decentralized and Community-Based Approaches: Develop local health networks, train community health workers, and promote local initiatives to ensure effective and context-specific healthcare for the most vulnerable populations.
Regional Cooperation and Resource Sharing: Establish cross-border partnerships to optimize malaria management, share best practices, and harmonize malaria control policies.
Integration of Technology and Geographic Analysis: Use tools like QGIS and health monitoring systems to improve case tracking, identify high-risk zones, and better allocate resources.
Increased Education and Awareness: Strengthen prevention campaigns and educational initiatives to encourage protective behaviors, especially in rural areas with high transmission.
Adapted Health Financing Policies: Increase national health budgets and establish sustainable financing mechanisms to ensure the continuity of prevention and treatment actions.

These recommendations aim to improve the effectiveness of malaria control strategies by integrating a multidimensional approach that combines health investment, decentralized care, and technological innovation. Coordinated and targeted action could significantly reduce healthcare access inequalities and alleviate the malaria burden in the region.

Appendix

Questionnaire

Guide d’entretien