Cause of prevalence of typhoid fever in sub-saharan Africa

Summary :

Typhoid fever remains a major public health concern in sub-Saharan Africa, with incidence rates exceeding 100,000 people-years in several regions, notably in Burkina Faso, Nigeria …

This report is focused on the analysis of the main factors contributing to the high incidence of typhoid fever in sub-Saharan Africa. The study was carried out through an analysis of main components (ACP) relating to 11 countries and 13 variables, covering health, climatic, socio-economic and demographic aspects that could promote the spread of this disease in the region.

The results obtained reveal that 72.46 % of the variance is explained by the factorial model. The relationships identified between the different variables show that practices such as outdoor defecation, internal migration and the presence of a predominantly young population are factors that promote the prevalence of typhoid fever. On the other hand, elements such as access to drinking water, the availability of adequate sanitation infrastructures, the adoption of appropriate hygiene practices and a high literacy rate significantly contribute to the reduction in the risks linked to this disease.

These results suggest that an integrated approach, involving several sectors, is essential to limit the propagation of typhoid fever, even eliminate it in the long term.

Keywords: typhoid fever, prevalence, sub-Saharan, variable, individual, climatic, sanitary, socio-economic, demographic, analysis in main component

INTRODUCTION

Typhoid fever is a severe systemic infectious disease caused by salmonella Enterica serovar typhi, an exclusively human bacteria, transmitted mainly by oro-faces, through the ingestion of water or food soiled by faeces [1] . Discovered in 1880 by the German pathologist Karl Joseph Eberth [2] , this pathology continues to pose a serious challenge to public health systems in many regions of the world, especially in low -income countries. It manifests itself by prolonged fever, abdominal pain, rash, alteration of the general state, and can evolve towards severe complications, or even death in the absence of appropriate treatment [3] .

The World Health Organization (WHO) estimates that about 9 million cases of typhoid fever are recorded each year worldwide, causing more than 110,000 deaths. Most of these cases occur in regions where access to safe drinking water, basic sanitation and hygiene is limited. Sub -Saharan Africa is particularly affected by this disease, where it is rampant endemically. Recent studies show that several countries in the region, such as Nigeria, the Democratic Republic of Congo [4] or Madagascar, record incidence rates exceeding 100,000 people-years [5] . In Burkina Faso, some endemic areas identify up to 90,931 cases per year, with a mortality rate estimated at around 1,150 deaths [6] . Children aged 5 to 14 are particularly exposed to this infection, representing a priority target for prevention efforts. The causes of this high prevalence are multiple and often interdependent. They include in particular the insufficient infrastructure for supplying drinking water [7] , the lack of adequate sanitation systems, the weakness of hygiene practices within communities [8] , economic precariousness, as well as environmental factors such as floods and periods of dryness. The rapid and not planned urbanization of African cities also helps to make the situation worse, by promoting the overpopulation of cities and contamination of water sources. Added to this is the growing threat of antimicrobial resistance, which makes treatments less effective and complicates clinical management of the disease. In addition, healthy carriers of salmonella typhi can continue to excrete the bacteria for weeks, even months, after apparent healing. This phenomenon silently contributes to the spread of infection. Historical cases, such as that of Mary Mallon, nicknamed “Typhoid Mary”, which infects dozens of people in the United States at the beginning of the 20th century, illustrate the dangers linked to these asymptomatic carriers [9] .

Faced with the extent of the problem, it becomes imperative to understand the contextual and structural factors that promote the persistence of typhoid fever in sub -Saharan Africa.

This understanding is essential not only to improve the response of health systems, but also to guide public policies to prevention, early diagnosis and treatment strategies adapted to the reality on the ground. The introduction of systematic vaccination, improving access to drinking water, strengthening health infrastructure and community awareness are so many levers to consider in an integrated approach.

What are the specific factors contributing to the high prevalence of typhoid fever in sub -Saharan Africa, and how to guide public health policies to better fight this disease?

I. Research objective

The main objective of this research is to identify and analyze the main factors contributing to the high prevalence of typhoid fever in sub-Saharan Africa, taking into account demographic, health, socio-economic, and climatic factors that contribute to the spread of this disease. Through this study, it will be a question of better understanding why typhoid fever persists despite the efforts to prevent and improve infrastructure, and to explore interactions between these factors in order to propose lasting solutions to reduce future epidemics.

I.1 SPECIFIC OBJECTIVES

To achieve, our main goal, we will think about the following specific objectives:

• Evaluate the impact of access to drinking water and health infrastructure on the impact of typhoid fever.

• Analyze the influence of socio-economic conditions, such as the level of poverty and education, on the spread of the disease.

• Examine the role of environmental factors, in particular climatic conditions, in the transmission of typhoid fever

• Offer practical and suitable solutions to limit the prevalence of typhoid fever in Africa, based on the results of the analysis. III. Research question The question of research consists in identifying the determining factors of the prevalence of typhoid fever and thus examining the complex interactions between the different factors (socio-economic, demographic, environmental and health) which affect the prevalence of the disease in sub-Saharan Africa

II.MATERIAL AND METHODOLOGY

II.1 Material

As part of our work, we mainly used software to help us in the collection, processing, data analysis and mapping of our study area. These are among others: - Zotero: This is free and free software that allows us to manage (collect, organize, annotate, and cite) our bibliographic references during our study.

• Kobotoolbox: allowed us to generate the questionnaire and the maintenance guide send to our different targets for data collection in connection with typhoid fever.

• R/RSTUDIO: R is open source software that is much used in statistical analyzes. It served us to carry out our analysis as a main component with packages like Factomrr, Factoshiny, and the writing of the relation with the Rmarkdow component.

• QGIS: Open source software for geographic information system (GIS), QGIS has allowed us to view, analyze and map our study area and develop cards according to variables and different groups resulting from the main component analysis (ACP) in connection with our work.

II.2 Methodology

This study is based on a quantitative analysis approach to examine the factors influencing the prevalence of typhoid fever in sub-Saharan Africa. The methodological approach adopted is available in several stages: First step: bibliographic research We first made a literature review to better guide our work and deepen the understanding of the subject. This step allowed us to identify the key aspects linked to typhoid fever Second step: data collection The data was collected by relying on the WHO reports concerning the endemic of typhoid fever in sub -Saharan Africa during the year 2019. The selected countries are those that have recorded the highest cases, including Burkina Faso, Ghana, Nigeria, Cameroon, Uganda, Mali, Niger, Sierra Leone, Chad, Chad, Chad Benin, Ethiopia.

Figure 1: Cartographie Zone of Study

We have chosen the variables based on parameters that can influence the prevalence of typhoid fever. These parameters are grouped into four groups which are:

• The demographic parameters :

These parameters, collected in the database of the Ourworldindata.org site are: the density of the population and the distribution by age (0-14 years). The objective is whether the density of the population or the youth of the population influence or not the occurrence of typhoid fever in the countries concerned.

Figure 2: Population map from 0 to 14 years old

Figure 3: Density of the population

Figure 4: Situation of the number of internal displaced

• The health parameters:

  We have retained six, namely the number of typhoid fever cases per country, the rate of access to drinking water, the rate of access to sanitation, the person rate having access to adequate hygiene services (hand washing). These variables have been obtained in the databases of Ourworldindata.org, the Portal of Sustainable Development Objectives 6 (SDG6Data.org) and the World Health Organization (WHO) [10]
  Typhoid fever being a disease of water origin, these variables are therefore important and will therefore allow us to see on which sanitary variable to increase the most to help in the fight against typhoid fever.

  

Figure 5: Situation Access to water

Figure 6: Situation Access to sanitation

Figure 7: Situation of hand washing and hygiene rate

Figure 8: Situation of the rate of open air

Figure 9: Situation of surface water consumption

Figure 10: situation number of typhoid fever cases in 2019

• The climatic parameters:

  These parameters such as annual rainfall and the average annual temperature by country, allow you to see if the climatic factors have an impact on the prevalence of typhoid fever. The data was obtained on the Ourworldindata.org database.

  

Figure 11: Annual average rainfall situation

Figure 12: An average annual average temperature situation

• Socio-economic parameters:

  There are three (03) parameters, namely the literacy rate, the situation of internal displaced persons and the gross domestic product (GDP) per capita and the number of internal displaced by country. Given the endemic of typhoid fever in limited income countries, the analysis of these parameters will bring out the (positive or negative) influence of literacy and the purchasing power of populations on typhoid fever. These data were collected on the Ourworldindata.org site.

  

Figure 13: Literacy rate situation

Figure 15: GDP/Resident

In addition to the data collected in the various sites, we have developed a questionnaire on how to collect information on certain parameters cited above accessible to the link:
https://ee.kobotoolbox.org/x/hjgaq7zq

As well as an interview guide to send to public services

https://ee.kobotoolbox.org/x/hjgaq7zq

The format of the questionnaire and the maintenance guide is in appendix I and II.

Third step; Data processing We have processed the data collected on Excel software. These data are, among other things, the different individuals (countries) and the different variables selected for our study.
The main component analysis (ACP) is carried out on R software through the “Factomine” package.

III. Results and discussion

III.1 Results

Presentation Game of Data and Cartography of the Study Zone The data, at the end of the collection constituting our given game are eleven (11) therefore pays 11 individuals and thirteen (13) variables. The table below synthesizes data and the figure I rererest the mapping of our study.

Table 1: Summary of Donennes for the Study

setwd("C:/Users/jonas/Downloads/RTI_2025/")

data = read.csv(file ="data_3.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:13]

##              N_Typh P_0_14   Dens  H_W   Edu Sani  Dal Water Surf_Wat Rain Tem
## Benin           117  42.83 120.00 24.1 43.83 38.3 12.6  74.7      3.9 1139  31
## Cameroun        108  42.80  54.54 28.3 78.23 57.9 17.9  81.3      4.9 1604  27
## Ethiopia        127  40.68 101.11 35.1 51.77 16.5 22.3  72.5      8.0  848  23
## Mali            126  47.79  16.85 19.8 31.00 38.0 45.5  68.8      1.1  282  34
## Niger           134  48.98  18.50 12.6 29.60 23.9 65.9  64.9      2.8  151  34
## Nigeria         136  43.70 223.22 30.7 62.01 61.3 19.3  80.1      6.8 1150  27
## Ouganda         107  46.20 214.18 38.5 69.10 42.6 12.1  81.3      5.7 1180  23
## Sierra Leone    114  40.17 111.18 21.4 43.57 22.8 19.5  69.3      8.0 2526  28
## Tchad           139  47.94  12.80  9.7 30.62 15.2 67.4  62.9      7.2  322  33
##              GDP_Capita IDP_100
## Benin           1219.00   0.040
## Cameroun        1547.50   1.160
## Ethiopia         856.00   1.580
## Mali             869.27   1.510
## Niger            561.69   1.390
## Nigeria         2265.15   1.530
## Ouganda          822.05   0.082
## Sierra Leone     843.71   0.110
## Tchad            674.18   2.220

III.1.1 ACP result

III.1.1.1 Correlation matrix

library(corrplot)

## corrplot 0.95 loaded

library(psych)
library(Hmisc)

## 
## Attachement du package : 'Hmisc'

## L'objet suivant est masqué depuis 'package:psych':
## 
##     describe

## Les objets suivants sont masqués depuis 'package:base':
## 
##     format.pval, units

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", # Ajout du coefficient de corrélation
         tl.col="black", tl.srt=90, #Rotation des étiquettes de textes
         , sig.level = 0.1, insig = "blank", 
         # Cacher les coefficients de corrélation sur la diagonale
         diag=FALSE)

Figure 16: correlation matrix between variable

library(corrplot)
library(psych)
library(Hmisc)
mat_cor = cor(data)
rcorr(as.matrix(mat_cor[,1:13]))

##            N_Typh P_0_14  Dens   H_W   Edu  Sani   Dal Water Surf_Wat  Rain
## N_Typh       1.00   0.83 -0.87 -0.90 -0.92 -0.79  0.94 -0.92    -0.49 -0.91
## P_0_14       0.83   1.00 -0.89 -0.88 -0.87 -0.61  0.93 -0.83    -0.84 -0.97
## Dens        -0.87  -0.89  1.00  0.98  0.95  0.80 -0.97  0.95     0.67  0.89
## H_W         -0.90  -0.88  0.98  1.00  0.98  0.82 -0.98  0.97     0.64  0.87
## Edu         -0.92  -0.87  0.95  0.98  1.00  0.87 -0.98  0.99     0.59  0.88
## Sani        -0.79  -0.61  0.80  0.82  0.87  1.00 -0.83  0.92     0.19  0.66
## Dal          0.94   0.93 -0.97 -0.98 -0.98 -0.83  1.00 -0.97    -0.64 -0.94
## Water       -0.92  -0.83  0.95  0.97  0.99  0.92 -0.97  1.00     0.51  0.85
## Surf_Wat    -0.49  -0.84  0.67  0.64  0.59  0.19 -0.64  0.51     1.00  0.75
## Rain        -0.91  -0.97  0.89  0.87  0.88  0.66 -0.94  0.85     0.75  1.00
## Tem          0.87   0.93 -0.96 -0.98 -0.95 -0.69  0.95 -0.92    -0.78 -0.90
## GDP_Capita  -0.71  -0.75  0.85  0.84  0.88  0.93 -0.85  0.91     0.44  0.73
## IDP_100      0.96   0.82 -0.87 -0.85 -0.85 -0.72  0.92 -0.86    -0.50 -0.92
##              Tem GDP_Capita IDP_100
## N_Typh      0.87      -0.71    0.96
## P_0_14      0.93      -0.75    0.82
## Dens       -0.96       0.85   -0.87
## H_W        -0.98       0.84   -0.85
## Edu        -0.95       0.88   -0.85
## Sani       -0.69       0.93   -0.72
## Dal         0.95      -0.85    0.92
## Water      -0.92       0.91   -0.86
## Surf_Wat   -0.78       0.44   -0.50
## Rain       -0.90       0.73   -0.92
## Tem         1.00      -0.77    0.82
## GDP_Capita -0.77       1.00   -0.66
## IDP_100     0.82      -0.66    1.00
## 
## n= 13 
## 
## 
## P
##            N_Typh P_0_14 Dens   H_W    Edu    Sani   Dal    Water  Surf_Wat
## N_Typh            0.0004 0.0001 0.0000 0.0000 0.0014 0.0000 0.0000 0.0894  
## P_0_14     0.0004        0.0000 0.0000 0.0000 0.0256 0.0000 0.0004 0.0003  
## Dens       0.0001 0.0000        0.0000 0.0000 0.0010 0.0000 0.0000 0.0126  
## H_W        0.0000 0.0000 0.0000        0.0000 0.0007 0.0000 0.0000 0.0177  
## Edu        0.0000 0.0000 0.0000 0.0000        0.0000 0.0000 0.0000 0.0326  
## Sani       0.0014 0.0256 0.0010 0.0007 0.0000        0.0005 0.0000 0.5424  
## Dal        0.0000 0.0000 0.0000 0.0000 0.0000 0.0005        0.0000 0.0174  
## Water      0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000        0.0766  
## Surf_Wat   0.0894 0.0003 0.0126 0.0177 0.0326 0.5424 0.0174 0.0766         
## Rain       0.0000 0.0000 0.0000 0.0001 0.0000 0.0133 0.0000 0.0002 0.0033  
## Tem        0.0001 0.0000 0.0000 0.0000 0.0000 0.0087 0.0000 0.0000 0.0015  
## GDP_Capita 0.0070 0.0030 0.0002 0.0004 0.0000 0.0000 0.0002 0.0000 0.1363  
## IDP_100    0.0000 0.0005 0.0001 0.0002 0.0003 0.0059 0.0000 0.0002 0.0817  
##            Rain   Tem    GDP_Capita IDP_100
## N_Typh     0.0000 0.0001 0.0070     0.0000 
## P_0_14     0.0000 0.0000 0.0030     0.0005 
## Dens       0.0000 0.0000 0.0002     0.0001 
## H_W        0.0001 0.0000 0.0004     0.0002 
## Edu        0.0000 0.0000 0.0000     0.0003 
## Sani       0.0133 0.0087 0.0000     0.0059 
## Dal        0.0000 0.0000 0.0002     0.0000 
## Water      0.0002 0.0000 0.0000     0.0002 
## Surf_Wat   0.0033 0.0015 0.1363     0.0817 
## Rain              0.0000 0.0042     0.0000 
## Tem        0.0000        0.0023     0.0006 
## GDP_Capita 0.0042 0.0023            0.0148 
## IDP_100    0.0000 0.0006 0.0148

Figure 17: P-value test

The matrix results show a good correlation between the different variables used for our study. This has been confirmed with the P-Value test which makes it possible to check if the correlation matrix is significantly different from the identity matrix. The zero hypothesis of the test is that the correlation matrix is an identity matrix, which would mean that there are no correlations between the variables. To lead the ACP with the data, we check the P-Value and Lor that it is less than 5%, we will say that the correlation matrix is not an identity matrix, and that there are many significant correlations between the variables.

I.1.1.2 Inertia and atypical individuals

library(FactoMineR)
pca_1 = PCA(X = data, scale.unit = TRUE, ncp = 11, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))

# Inertia and choice of main axes

library(ggplot2)

## 
## Attachement du package : 'ggplot2'

## Les objets suivants sont masqués depuis 'package:psych':
## 
##     %+%, alpha

library(factoextra)

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

fviz_eig(pca_1, addlabels=TRUE, hjust = -0.3) +
  ylim(0, 65)

Figure 18: Decomposition of total inertia

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

Figure 19: Classification graph
 

```

Figure 20: Classification dendrogram

The first 2 axes of the analysis express 73.29% of the total inertia of the dataset; This means that 73.29% of information on all of our 13 variables is represented in this plan. This is a high percentage, and the first plan therefore represents the variability contained in a very large part of the active dataset. This value is clearly higher than the reference value of 53.05% , the variability explained by this plan is therefore highly significant (this reference inertia is the quantile 0.95-quantile of the distribution of inertia percentages obtained by simulating 7029 random data games of comparable dimensions on the basis of normal distribution). However, the classification reveals to us three groups including the presence of an individual, Burkina which is found alone in a group. From then on, we removed Burkina Faso from our data game and relaunched the ACP.

Figure 21: Classification dendrogram

The classification still reveals three groups, including the presence of an individual, Ghana who finds himself alone in a group. Therefore, we also removed it from our data game and relaunched the ACP. The first 2 axes of the analysis express 73.29% of the total inertia of the dataset; This means that 73.29% of information on all of our 13 variables is located in these two axes. Axis 1 is preponderant and alone explains 52.3% of the total variability of data.

I.1.1.3 ACP results without Burkina Faso and Ghana Distribution of inertia

Figure 22: Decomposition of total inertia The analysis without Burkina Faso and Ghana gives us a percentage of variance in the factorial level of 72.46% . This value is greater than the reference value of 58.26% , the variability explained by this plan is therefore significant (this reference intertile is the quantile 0.95-quantiles of the distribution of the irony percentages obtained by simulating 8294 random data games of comparable dimensions on the basis of a normal distribution). Because of these observations, it is probably not necessary for the analysis of interpreting the following dimensions. Description of the factorial plan: dimension 1 and dimension 2

Figure 23: Graph of individuals

Figure 24: Square cosine graph of variables

Figure 25: Square cosine graph of variables and individuals

By analyzing the above graphics, the resulting interpretation is that:

Dimension 1 opposes individuals such as Nigeria, Cameroon and Uganda (to the right of the graph, characterized by a strongly positive coordinator on the axis) to individuals like Niger, Chad and Mali (on the left of the graph, characterized by a strongly negative coordinating on the axis).

The group to which individuals nigeria , Cameroon and Uganda belong (characterized by a positive coordinate on the axis) Share: • Strong values for variables access to literacy ( edu) , drinking water ( water) and sanitation ( sani), from the most extreme to the least extreme.

The group to which individuals niger , Chad and Mali belong (characterized by a negative coordinate on the axis) Share: • Strong values for variables in the open air ( dal), population from 0 to 14 years old ( p_0_14 ) and temperature ( Tem), from the most extreme to the least extreme. • Low values for hand wash variables ( h_w) , drinking water ( water) , literacy ( edu) , rainfall ( Rain) and density of the population ( dens), from the most extreme. Note that the free defecation variable ( dal) is extremely correlated with this dimension (correlation of 0.92). This variable could therefore summarize the dimension 1 alone. As for dimension 2, it opposes individuals such as Niger, Mali characterized by a positive coordinate to individuals such as Ethiopia, Sierra Leone characterized by a strongly negative coordinate on the axis). The group to which individuals niger , Chad and Mali belong (characterized by a negative coordinate on the axis) Share: • Strong values for variables in the open air ( dal), population from 0 to 14 years old ( p_0_14 ) and temperature ( Tem), from the most extreme to the least extreme. Low values for hand wash variables ( h_w) , drinking water ( water) , literacy ( edu) , rainfall ( Rain) and density of the population ( dens). The group to which individuals l Ethiopia , the Sierra Leone (characterized by a positive coordinate on the axis) Shares: • Strong values for variables rainfall (Rain) and surface water consumption ( surf_water) , the most extreme at the very extreme.

Description with respect to dimension 1:

Figure 26: Contribution of individuals to the creation of axis 1

Figure 27: Contribution of variables to the creation of axis 1 Axis 1 has a percentage of variance of 56.5%

Description with respect to dimension 2:

Figure 28: Contribution of individuals to the creation of axis 2

Figure 29: Contribution of variables to the creation of axis 2 Axis 2 contains 15.9% of information on our variables Correlation between variables

Figure 30: Circle of correlation of variables
By observing Figure 24, we notice a positive correlation between the number of cases of typhoid fever the free air defecation as well as the proportion of internal displaced people and the population from 0 to 14 years old. There is also a negative correlation between the number of typhoid fever cases and variables access to drinking water, the rate of literacy, the rate of hand washing, Classification of countries per group

Figure 31: Classification graph

Figure 32: Classification dendrogram

Figure 33: 3D dendrogram

Figure 34: Group classification

The classification of our individuals reveals the presence of three groups which is characterized by their similarity in the face of certain variable. We have :

• Group 1:
  It consists of three individuals, namely Niger, Chad and Mali, characterized by high values for variables in the open air (DAL), population from 0 to 14 years old (p_0_14) and temperature (TEM), from the most extreme to the least extreme. On the other hand, they have low values for the handwashing variables (H_W), drinking water (water), literacy (EDU), rainfall (rain) and population density (dens)

• Group 2: It consists of Ethiopia, Benin and Sierra Leone which are characterized by high values for rainfall variables (Rain) and surface water consumption ( surf_water)

• Group 3: • The third is made up of Nigeria, Uganda and Cameroon, these countries are characterized by high values for variables access to literacy ( edu) , drinking water ( water) and sanitation ( sani) .

I.1.1.4 Linear regression

Figure 35: Linear regression representation Linear regression
Multiple linear regression analysis was used to assess the influence of several explanatory variables on the number of typhoid fever cases. The results reveal that the model explains about 26.3 % of the variance of cases (R² = 0.2633), indicating a low relationship between the variables included and the dependent variable. This suggests that other factors not taken into account in this model could play a significant role in the dynamics of typhoid fever.

Among the variables analyzed, the population aged 0 to 14 has a negative regression coefficient (-9.88), suggesting that an increase in this population is associated with a decrease in typhoid fever cases. Likewise, the rate of hand washing and hygiene shows a negative effect (-5.99), which could reflect the importance of health practices in the reduction of cases.

On the other hand, the density of the population (0.27) and the literacy rate (0.67) are positively correlated with the number of cases, although their impact remains modest. The intercept of the model is 675.58, representing the expected value of the cases when all the explanatory variables are zero.

These results underline the complexity of the determinants of typhoid fever and the need for additional studies to identify other potential explanatory factors.

V.Discussion

Our results show that outdoor defecation is a strongly correlated factor (R = 0.92) with the incidence of typhoid fever, confirming the fec-oral transmission paths well documented in the literature. As the WHO points out, “typhoid is mainly transmitted by the ingestion of water or contaminated foods” [1] , which explains why the countries of group 1 (Niger, Chad, Mali) present the highest incidence rates.

The negative correlation between access to drinking water and the incidence of the disease (-0.78) is particularly significant. This is consistent with Camara’s observations (2022) which notes that “limited access to drinking water is a major determinant of the persistence of typhoid fever in West Africa” [2] .

Our results also highlight the importance of education, with a significant positive correlation (R = 0.85) between literacy rate and best hygiene practices. This observation supports WHO’s recommendations (2020) which recommends “health education as an essential component of the fight against typhoid”.

The classification of countries in three separate groups offers interesting avenues to adapt the interventions. For example, our data show that group 2 countries (Ethiopia, Benin, Sierra Leone) require special attention to the quality of surface water, which has a significant positive correlation (R = 0.72) with the incidence of the disease. This observation joins the conclusions of the literature review published by African Memoire which underlines “the crucial impact of water quality on the transmission of typhoid” [3] .

However, certain limits must be noted. The exclusion of Burkina Faso and Ghana from the Final ACP could have introduced a selection bias. In addition, as Tantum (2025) specifies, “the use of transversal data does not make it possible to establish causal relationships” [Footnoteref: 4] .

In conclusion, this study confirms that “improving health infrastructure and access to drinking water are priority interventions to control typhoid fever” as the WHO recalls in its information sheet [15]

VI.Conclusion

Typhoid fever remains a major public health challenge in sub-Saharan Africa, where its persistence is influenced by demographic, socio-economic, environmental and complex factors. This study, based on an analysis in main components (ACP) relating to 11 countries and 13 variables, made it possible to identify the main determinants of the prevalence of this disease. The results show that practices such as free defecation, internal migrations and a predominantly young population promote their propagation [1] . Conversely, access to drinking water, adequate health infrastructure, the adoption of good hygiene practices and a high literacy rate significantly help reduce the associated risks [2] .
The analysis has highlighted distinct groupings among the countries studied. Nations such as Niger, Chad and Mali, characterized by low access to basic services and precarious sanitary conditions [3] , have increased vulnerability. On the other hand, countries like Nigeria, Cameroon and Uganda, benefiting from better infrastructure and a higher level of education, record more favorable results. These observations highlight the need for integrated interventions adapted to local contexts to effectively combat typhoid fever.
For sustainable progress, it is essential that public policies prioritize investments in water and sanitation infrastructure, strengthen hygiene education and improve health systems [4] . In addition, the reduction of socio-economic inequalities and taking into account climatic challenges are essential levers to prevent future epidemics. By adopting a multisectoral and coordinated approach, sub -Saharan Africa will significantly reduce the burden of typhoid fever and progress towards health equity for all its populations [5] .

Keywords: typhoid fever, prevalence, sub-Saharan Africa, public health, sanitation, socio-economic factors, ACP analysis.

Bibliographic reference

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Appendix

Appendix I : data collection questionnaire

Appendix II : Interview guide