—” title: “Analysis of Suicide Rates in West Africa author:”BA Bocar Alassane : 20230350 , IDANI Mechack Triomphant : 20210293 , Ki Elvire Ravelle : 20240923 , NIKIEMA Rakiswendé Faridatou 20210169 ,”
date: “2025-04-08” output: html_document —
#the remarks during the presentation
The remarks during the presentation focused on the need to name the groups in the dendrogram classification, improve the quality of the maps, develop a questionnaire, and complete the bibliography. We have made the necessary corrections
General Research Objective:
This study explores the causes of suicide in West Africa through a statistical analysis of socio-economic, health, and psychological data from 16 countries. The objective is to better understand the influencing factors using Principal Component Analysis (PCA), linear regression, and hierarchical clustering.
Introduction
Suicide is a complex, timeless phenomenon that affects all societies. It is defined as an intentional act of deliberately ending one’s own life. This phenomenon has been growing in recent years, representing a major public health challenge that also affects West Africa. Although often minimized due to cultural taboos in this region, the factors leading to it are multi-dimensional, including psychological, social, economic, biological, and environmental aspects.
In this region, socio-economic difficulties are numerous. Unemployment, poverty, social inequalities, and conflicts lead to distress and despair for many individuals. In addition, psychological disorders, stigma, and the lack of mental health care services further complicate the situation. Understanding these determinants is essential to implementing suicide prevention strategies and contributing to a more peaceful living environment. It is with this in mind that an analysis of the causes of suicide can be carried out on the West African population.
Literature Review
Several people die by suicide each year. The cause of this phenomenon is a set of several interconnected factors that can be distal or immediate. Faced with this reality, several studies on the causes of suicide have been conducted; however, those related exclusively to West Africa remain very limited. Among these factors, those linked to mental and psychological disorders are broad. For example, Zhuoyang Li et al. show that there is “an increased risk of suicide associated with mental disorders, in particular […] mood disorders, schizophrenia, anxiety disorders, and personality disorders” (1). Psychiatric illnesses increase this risk.
The majority of West African societies have inherited a social organization based on tradition and religion. This corresponds to a form of social satisfaction; social exclusion, stigma, and expectations of adhering to social and religious rules can expose certain individuals to great mental vulnerability, which leads to major depression that requires better access to health services. Indeed, according to Wanyoike and Becky Wanjiku, depression affects a person’s mental, emotional, and physical capacities (2), which can lead them to commit irreversible acts. Social pressure and social integration therefore influence suicidal behaviors, add Johnson and Barclay D (3), notably stigma which exposes individuals to a higher risk of suicide (4). The social impact is also very significant, hence “Manning argues that suicide stems from increased inequality and diminished intimacy” (5). The authors then discuss theoretical models explaining the importance of social support in maintaining mental health; the close links between mental health and social support have been identified in both population-based and clinical surveys.
Economic recessions affect the health of populations. Poor mental health has always been linked to the expression of financial hardship and poverty. Francis Tchégnonsi Tognon et al., in a descriptive study in Cobly (North Benin) on Suicide in Sub-Saharan Africa, noted that poverty ranked first with a rate of 32.7% out of a total of 52 suicide cases in that area (6). Low income, poverty, and unemployment affect living conditions and lifestyles, which become very unfavorable, especially in developing countries with low incomes (7). BAHI Jean Joel adds that being denied a position or being fired from one’s job can cause a man permanent anxiety, which could be the origin of a psychological and mental disorder, a warning sign of a suicidal act (8). Studies by Mahudjro AHOYO on the socio-economic determinants of suicide among individuals highlight that some people even resort to suicide due to lack of means to repay their credit, following the granting of numerous loans that plunge them into misery (9). Social expenses, which are obvious priorities, become burdensome for those responsible and thus a factor of stress and anxiety. According to Adam Skinner, “transitions from employment to unemployment tend to produce a significant increase in psychological distress” (10). The rise in unemployment is accompanied by an increase in mental disorders, and thus a probability of suicidal behavior. Jahoda believes that “distress among the unemployed is the consequence of the absence of five latent functions of employment” (11). The observed increase in suicide can therefore result from pronounced economic disruption. The lack of vision, added to the poverty of States, could also expose populations to suicidal behaviors. Dr. Yahaya Abou, Coordinator of the National Mental Health Program in Niger, has highlighted the impact of inadequate mental health policies in Niger and suicide, focusing on the issue of access to quality and financially affordable mental health care. In terms of mental health, Niger, like most West African countries, is considerably behind WHO standards (2015 data) (12). N’Guessan Elkhanan N’GORAN and Yann ZOLDAN (12) studied the cultural representation of depression and suicide among West African Francophones in Quebec and Côte d’Ivoire, emphasizing that the lack of resources, shortage of qualified caregivers, diagnostic errors, social stigma, and taboos related to mental disorders are all obstacles to access and delivery of effective care. These subjective considerations could be indicators of individual behavior, notably their decision to seek or not to seek help from specialists or even to commit acts such as suicide (13).
The use of substances may provide a state of well-being and temporary relief from moral suffering; however, it has very serious repercussions on health and social integration. Moreover, the consumption of tobacco and drugs causes behavioral disorders, according to Farber, Nuri B (14), which do not facilitate social relationships. Not to mention that quitting these substances has even more consequences, such as major depression or psychiatric disorders (15), in the sense that behavioral disorders and dependence on alcohol and drugs can lead to suicide. Drug use creates either physical or psychological dependence—or both. It is harmful to the individual and to society, as it creates addiction, meaning the subject is at the mercy of an intense craving. This refers to the “concept of disease applied to drug use,” Fernandez L, Sztulman H. (16). Depression affects not only the individual, but also those around them, and also represents a heavy social and economic burden according to WHO (13).
Presentation of the Study Area
West Africa, located in the western
region of the African continent, is an area of great importance due to
its geographical, cultural, economic, and socio-political diversity.
This region, composed of 16 member countries of the Economic Community
of West African States (ECOWAS), presents particular characteristics
that make it an essential field of observation for the study of various
issues, notably in terms of poverty, mental health, and suicide. Our
study focuses on the 16 West African countries, namely: Benin, Burkina
Faso, Cape Verde, Côte d’Ivoire, The Gambia, Ghana, Guinea,
Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal,
Sierra Leone, and Togo.
This study explores the causes of suicide in West Africa through a statistical analysis of socio-economic, health, and psychological data from 16 countries. The objective is to better understand the influencing factors using Principal Component Analysis (PCA), linear regression, and hierarchical clustering.
Loading Required Libraries
library(FactoMineR)## Warning: le package 'FactoMineR' a été compilé avec la version R 4.4.3library(factoextra)## Warning: le package 'factoextra' 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## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggplot2)
library(corrplot)## Warning: le package 'corrplot' a été compilé avec la version R 4.4.3## corrplot 0.95 loadedlibrary(car)## Warning: le package 'car' a été compilé avec la version R 4.4.3## Le chargement a nécessité le package : carData## Warning: le package 'carData' a été compilé avec la version R 4.4.3library(carData)
library(Hmisc)## Warning: le package 'Hmisc' a été compilé avec la version R 4.4.3##
## Attachement du package : 'Hmisc'## Les objets suivants sont masqués depuis 'package:base':
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## format.pval, unitslibrary(psych)## Warning: le package 'psych' a été compilé avec la version R 4.4.3##
## Attachement du package : 'psych'## L'objet suivant est masqué depuis 'package:Hmisc':
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## describe## L'objet suivant est masqué depuis 'package:car':
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## logit## Les objets suivants sont masqués depuis 'package:ggplot2':
##
## %+%, alphalibrary(clusterSim)## Warning: le package 'clusterSim' a été compilé avec la version R 4.4.3## Le chargement a nécessité le package : cluster## Le chargement a nécessité le package : MASSlibrary(DataExplorer)## Warning: le package 'DataExplorer' a été compilé avec la version R 4.4.3library(FactoInvestigate)## Warning: le package 'FactoInvestigate' a été compilé avec la version R 4.4.3library(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 4.4.3Data Import
data <- read.csv("data_2.csv", header = TRUE, sep = ";", dec = ",", row.names = 1)
head(data)## Suicide.rate mental.disorders poverty unemployement
## Benin 11.8 0.10 0.51 0.02
## Burkina Faso 15.4 0.10 0.31 0.05
## Cote d_Ivoire 12.6 0.10 0.51 0.10
## Gambie 8.3 0.12 0.25 0.06
## Ghana 8.1 0.11 0.14 0.03
## Guinee 8.2 0.11 0.26 0.06
## Gini.coefficient Working.poverty HDI Drug
## Benin 0.53 16.43 0.50 0.06
## Burkina Faso 0.56 28.97 0.45 0.06
## Cote d_Ivoire 0.63 12.08 0.53 0.07
## Gambie 0.57 15.57 0.49 0.06
## Ghana 0.60 18.80 0.60 0.07
## Guinee 0.47 16.52 0.47 0.063. Data Exploration and Correlation Matrix
mat_cor <- cor(data)
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(mat_cor, method="color", col=col(200), type="upper", order="hclust",
addCoef.col = "black", tl.col="black", tl.srt=90, sig.level = 0.1, insig = "blank", diag=FALSE)
rcorr(as.matrix(data))## Suicide.rate mental.disorders poverty unemployement
## Suicide.rate 1.00 0.09 0.36 -0.28
## mental.disorders 0.09 1.00 -0.40 0.06
## poverty 0.36 -0.40 1.00 -0.09
## unemployement -0.28 0.06 -0.09 1.00
## Gini.coefficient 0.38 0.11 0.19 0.07
## Working.poverty 0.12 -0.30 0.45 -0.49
## HDI -0.01 0.33 -0.32 0.33
## Drug 0.13 0.48 -0.07 0.40
## Gini.coefficient Working.poverty HDI Drug
## Suicide.rate 0.38 0.12 -0.01 0.13
## mental.disorders 0.11 -0.30 0.33 0.48
## poverty 0.19 0.45 -0.32 -0.07
## unemployement 0.07 -0.49 0.33 0.40
## Gini.coefficient 1.00 -0.06 0.35 0.47
## Working.poverty -0.06 1.00 -0.52 -0.23
## HDI 0.35 -0.52 1.00 0.69
## Drug 0.47 -0.23 0.69 1.00
##
## n= 15
##
##
## P
## Suicide.rate mental.disorders poverty unemployement
## Suicide.rate 0.7478 0.1849 0.3085
## mental.disorders 0.7478 0.1356 0.8317
## poverty 0.1849 0.1356 0.7508
## unemployement 0.3085 0.8317 0.7508
## Gini.coefficient 0.1593 0.6867 0.4937 0.7988
## Working.poverty 0.6609 0.2695 0.0909 0.0607
## HDI 0.9853 0.2312 0.2472 0.2243
## Drug 0.6499 0.0730 0.8048 0.1407
## Gini.coefficient Working.poverty HDI Drug
## Suicide.rate 0.1593 0.6609 0.9853 0.6499
## mental.disorders 0.6867 0.2695 0.2312 0.0730
## poverty 0.4937 0.0909 0.2472 0.8048
## unemployement 0.7988 0.0607 0.2243 0.1407
## Gini.coefficient 0.8260 0.1948 0.0737
## Working.poverty 0.8260 0.0454 0.4127
## HDI 0.1948 0.0454 0.0041
## Drug 0.0737 0.4127 0.0041##The analysis of the correlation matrix reveals key relationships that shed light on potential factors influencing suicide rates in West Africa:
Suicide Rate:
Positively correlated with poverty and working poverty → suggests that economic hardship increases suicide risk.
Negatively correlated with unemployment and HDI → possibly due to social safety nets or underreporting; high HDI reflects better health, education, and well-being access.
Mental Disorders:
Positively correlated with HDI and drug issues → may reflect better health system performance and diagnostic capacities.
Negatively correlated with poverty → likely due to underreporting in low-income areas.
Poverty & Working Poverty:
Strongly positively correlated with each other.
Negatively correlated with HDI and drug → indicating limited development and health service access in impoverished contexts.
Unemployment:
Negatively correlated with suicide rate and poverty → may reflect specific socio-economic contexts. Positively correlated with HDI and drug → possibly due to better infrastructure and health services. Gini Coefficient:
Moderately positively correlated with suicide rate and drug issues, suggesting that income inequality may increase social and psychological stress. HDI & Drug:
Very strongly correlated → reflecting the impact of development on awareness and reporting of health issues.
Principal Component Analysis (PCA)
pca_1 <- PCA(data, scale.unit = TRUE, ncp = 11, graph = FALSE)
fviz_eig(pca_1, addlabels = TRUE, ylim = c(0, 65))
fviz_pca_var(pca_1, col.var = "cos2", gradient.col = c("blue", "green", "red"), repel = TRUE)
fviz_pca_ind(pca_1, col.ind = "contrib", gradient.cols = c("blue", "green", "red"), repel = TRUE)
fviz_pca_biplot(pca_1, repel = TRUE, col.var = "blue", col.ind = "red")
Interpretation of the correlation circle
Suicide rate, poverty, and working poverty point in similar directions, indicating a positive correlation: as poverty and working poverty increase, the suicide rate also tends to rise.
Drug use, the Gini coefficient, and HDI also point in similar directions, showing that they are correlated with each other.
Mental disorders and unemployment point in nearly opposite directions to working poverty and poverty, suggesting a negative correlation: for example, in areas where poverty is high, mental disorders or unemployment may be less frequently reported (and vice versa).
The longer the arrow, the better the variable is represented in the plane formed by the first two components. For example, poverty, working poverty, drug use, and HDI are well represented, whereas mental disorders are somewhat less so.
variables
where the arrows represent the variables, and the cos2 value shows the quality of representation. The direction of the arrow indicates the direction of the relationship, and the length indicates the strength of the variable’s contribution to the dimensions. A longer arrow means the variable is significantly correlated with the considered dimension and well-represented. A shorter arrow means the variable is weakly correlated and poorly represented. The angle formed by two variables indicates the strength and direction of the correlation between them. Long arrows close to each other indicate a strong positive correlation, while long arrows far apart or opposite indicate strong negative correlations. For clarity, we associated colors with the variables based on length: green for well-correlated and well-represented variables, blue for moderately correlated and represented variables, and red for weakly correlated and poorly represented variables.
individus
PCA with two main dimensions, where the countries represent the studied individuals, and the cos2 value shows the quality of their representation on the factorial plane. We associated colors to facilitate understanding of representation: orange for well-represented individuals and red for poorly represented ones. Thus, countries close to each other on the graph share similar characteristics and could have similar expectations or performances in the considered dimensions.
Hierarchical Clustering on Principal Components (HCPC)
res.PCA <- PCA(data, graph=FALSE)
res.HCPC <- HCPC(res.PCA, nb.clust=3, consol=FALSE, graph=FALSE)
plot.HCPC(res.HCPC, choice='tree')
plot.HCPC(res.HCPC, choice='map', draw.tree=FALSE)
plot.HCPC(res.HCPC, choice='3D.map', ind.names=FALSE, centers.plot=FALSE)
#Mapping results 
dendogramme
First Group: Ghana, Mauritania, and Côte d’Ivoire These countries share common characteristics marked by a relatively higher level of human development, as well as greater exposure to mental disorders and modern social pressures. This could explain suicide rates more influenced by complex psychological and social factors, typical of societies transitioning toward greater urbanization and modernity. Second Group: Niger, Nigeria, Mali This group seems to occupy an intermediate zone. These countries share unstable socio-economic conditions where poverty is very present, but modern psychological factors (such as mental disorders or drugs) remain less visible or documented. These are countries that could quickly shift into one group or another, depending on their social or economic evolution. Third Group: Senegal, Sierra Leone, The Gambia, Burkina Faso, Liberia, Guinea-Bissau, Guinea, Benin, and Togo This group consists of countries with similar profiles, marked by persistent economic difficulties, poor access to mental health care, and precarious living conditions. Suicide here would be more influenced by structural factors like extreme poverty, unequal access to education, and limited future prospects. These countries represent the most socially vulnerable zone, where suicide may be linked to profound socio-economic despair.
#The names of the groups
Group 1: Transitional Refers to societies in transition, facing psychological challenges linked to modernity and urbanization.
Group 2: Unstable Highlights the socio-economic uncertainty in these countries, which are caught between poverty and emerging modern pressures.
Group 3: Vulnerable Emphasizes deep-rooted hardship, lack of opportunity, and structural factors that contribute to suicide risk.
Multiple Linear Regression
model1 <- lm(Suicide.rate ~ mental.disorders + poverty + unemployement + Gini.coefficient + Working.poverty + HDI + Drug, data = data)
summary(model1)##
## Call:
## lm(formula = Suicide.rate ~ mental.disorders + poverty + unemployement +
## Gini.coefficient + Working.poverty + HDI + Drug, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9706 -1.2879 -0.5536 0.6004 5.3674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.33740 17.21700 -0.368 0.724
## mental.disorders 48.42089 127.64153 0.379 0.716
## poverty 8.90902 8.31831 1.071 0.320
## unemployement -39.53680 39.96403 -0.989 0.355
## Gini.coefficient 19.30495 25.70199 0.751 0.477
## Working.poverty -0.05631 0.12083 -0.466 0.655
## HDI -4.37163 27.36070 -0.160 0.878
## Drug 58.84370 280.78634 0.210 0.840
##
## Residual standard error: 3.182 on 7 degrees of freedom
## Multiple R-squared: 0.3712, Adjusted R-squared: -0.2575
## F-statistic: 0.5905 on 7 and 7 DF, p-value: 0.7482Prediction Plot
data_vis <- data.frame(valeurs_reelles = data$Suicide.rate, predictions = predict(model1))
ggplot(data_vis, aes(x = valeurs_reelles, y = predictions)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "blue") +
labs(x = "Observed Values", y = "Predicted Values", title = "Linear Regression") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
regression
This graph illustrates a simple linear regression, highlighting the relationship between actual values (independent variable) and predictions (dependent variables). In this specific case, we used: The blue line represents the regression model used to predict values based on actual values. It is determined by the linear regression equation. The black dots represent individual observations used to build the regression model. Each point indicates how an actual value compares to the prediction generated by the model. Points below and above the line represent deviations between actual values and predictions. The in-depth analysis of the data reveals that suicide in this region is the result of a complex interaction between various socio-economic and psychosocial factors. On one hand, economic indicators such as poverty and working precariousness (Working Poverty), as well as income inequalities measured by the Gini coefficient, create an environment conducive to increased psychological distress. On the other hand, variables related to mental health, particularly mental disorders, and drug use, reflect a psychosocial vulnerability that amplifies suicide risk.
Discussion
At the conclusion of the Principal Component Analysis (PCA), two main dimensions emerge significantly in explaining the disparities related to suicide in West Africa: The first dimension, which we have named Economic Growth and Capacity to Meet Basic Needs, brings together strongly correlated variables such as the Human Development Index (HDI), substance use disorders (Drug), unemployment, and in-work poverty. The second dimension, which we have named Social Vulnerability and Psychological Fragility, is mainly influenced by the suicide rate, the Gini coefficient, and the level of poverty. The results highlight that mental disorders, addictions (alcohol/drugs), poverty, unemployment, and social inequalities are structural factors deeply linked to suicide rates in the region. For example, a high contribution of mental disorders (12.53%) to the first dimension suggests a direct link between mental health and economic conditions. This reality is amplified by a significant correlation with the HDI (24.76%), indicating that countries with lower human development are at greater risk. The analysis of the Gini coefficient, which contributes nearly 29% to the second dimension, shows that economic inequality, measured by wealth distribution, is a deep driver of social distress, potentially leading to extreme cases of despair such as suicide. The suicide rate, which loads heavily on the second dimension (32.99%), is particularly high in countries that combine low economic development, extreme poverty, significant inequalities, and unaddressed psychological fragility. These findings are consistent with many studies linking precariousness, lack of psychological support, loneliness, and marginalization to increased suicidal behavior. In West Africa, the lack of resources, specialized mental health centers, and public policies targeting these issues further weakens individuals at risk.
#Proposed Solutions
To reduce suicide risks and improve mental and social well-being, we recommend: Strengthening public mental health systems, including training professionals and raising awareness. Improving economic governance to reduce inequalities (Gini), notably through inclusive fiscal and social policies. Supporting decent employment, particularly for youth and working poor. Implementing addiction prevention programs, especially for alcohol and drug use. Investing in education and human development as levers for social resilience.
#CONCLUSION
Our objective was to identify the causes of suicide in West Africa using Principal Component Analysis (PCA). The analysis shows that suicide rate, poverty, and working poverty are positively correlated — as poverty increases, so does the suicide rate. Drug use, the Gini coefficient, and HDI also follow a similar direction, suggesting they are interrelated. In contrast, mental disorders and unemployment appear negatively correlated with poverty, indicating that in areas with higher poverty, these issues might be underreported. The length of each arrow reflects how well the variable is represented in the PCA plot; for instance, poverty, working poverty, drug use, and HDI are well represented, while mental disorders are slightly less so.
###Mapping results
#questionnaires
We conducted surveys on suicide in West Africa using the KoboToolbox platform. This tool allows me to design and distribute questionnaires tailored to the local context. The data collected help to better understand the factors associated with suicide in the region
#bibliographie
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