Impact of Socio-Economic and Health Conditions on Child Health in Developing Countries

Summary

In the global context of public health, developing countries face complex challenges related to socio-economic and health conditions. These nations, often marked by economies based on agriculture, livestock and the exploitation of natural resources, have significant cultural and structural diversity. However, inequalities in access to health care, low levels of education and the prevalence of infectious diseases remain major obstacles to improving child health. This study explores the interactions between socio-economic and health conditions and their impact on child health in developing countries. Through an analysis of key indicators such as infant mortality, access to healthcare, nutrition, maternal education and disease prevalence, it highlights significant disparities between countries. The use of quantitative methods, including correlation matrices and comparative analyses, reveals that children from disadvantaged backgrounds are the most exposed to health risks, in particular because of the inadequacy of medical and educational infrastructure. The results show that countries with inclusive health and education policies in place see significant improvements in child health indicators. Conversely, those where investment in these areas remains insufficient continue to suffer from high rates of infant mortality and preventable diseases. These observations underscore the importance of an integrated approach combining strengthening primary health care, raising awareness of good prevention practices, and improving education systems to ensure healthy child development.

Introduction

Child health in developing countries is a major issue influenced by many socio-economic and health factors. Child mortality remains high in several regions of the world, particularly in Africa, due to precarious living conditions, limited access to health care and a lack of adequate health infrastructure. Differences in mortality exist from the earliest ages of life. Some were born from the chance of conception: sex, age of the mother, birth order, season of birth, etc. They are usually classified as biological determinants of infant mortality. Others are rooted in the social environment of origin, identified by the socio-professional group, nationality, level of education or mode of cohabitation of the parents, for example1. Preventable diseases, such as diarrhoea, malaria and pneumonia, continue to cause many deaths among children under five. Socio-economic conditions play a key role in child survival. Poverty limits access to medical care and vaccinations, while low parental literacy rates reduce awareness of good health and hygiene practices. In addition, the lack of infrastructure, such as the lack of safe drinking water and effective sanitation, increases children’s vulnerability to disease. In response to these challenges, many international and local initiatives have been put in place to improve child health. However, disparities persist between countries and populations, which requires an in-depth analysis of the factors impacting children’s health in order to propose appropriate and effective solutions.

Problematic

Child health in developing countries is strongly influenced by socio-economic and health factors. Despite efforts to improve access to health care and basic infrastructure, infant mortality rates remain a concern in many regions. Major challenges include poverty, poor access to health services, lack of sanitation and high prevalence of infectious diseases. The education of parents, especially mothers, also plays a key role in the prevention of childhood diseases and the adoption of health-promoting behaviours. Thus, the central question of this study is: To what extent do socio-economic and health conditions influence child health in developing countries, and what strategies can be put in place to reduce child mortality and improve child well-being? This issue makes it possible to explore the links between these different factors and to identify priority actions to improve the health of children in the most vulnerable regions

General Objective

The main objective of this study is to analyse the impact of socio-economic and health conditions on child health in developing countries. This analysis will provide a better understanding of the challenges related to child mortality and identify levers for action to improve child care and strengthen public health policies.

Specific Objectives

• To assess access to health care and its impact on child survival in several developing countries.
• To analyse the influence of the level of education of parents, especially mothers, on the prevention of childhood diseases and access to health services.
• Examine the role of sanitation infrastructure, such as access to clean water and hygiene facilities, in reducing childhood diseases.
• To study the impact of immunization programmes and public health policies on reducing child mortality.
• Propose strategies and recommendations based on available data to improve child health and reduce inequalities between countries. These objectives will help identify the main determinants of child health and propose solutions adapted to the contexts of developing countries.

Methodology and Tools Used

Study Area

The region stretches from the arid Sahel in the north to tropical coastal areas in the south, resulting in a wide variety of climates and ecosystems. It is experiencing rapid population growth, especially among young people despite this. A significant portion of the population lives below the poverty line, which limits access to food, clean water and health care leading to chronic malnutrition in children under five years of age and weakens their immune systems and increases the risk of disease. These representative villages were chosen because they reflect the highest infant mortality rates, characterized by water scarcity, poverty, as well as lack of sanitation infrastructure.

Data Collection

The analysis is based on data from Our World in Data. A selection of variables was made to measure the impact of socio-economic and health conditions on child health. Statistical (regressions, classifications) and cartographic analyses will be used to interpret the results.

Variables Studied

1. Child health indicators These indicators directly measure the health status of children in the countries studied:

-Infant mortality rate
A high infant mortality rate indicates difficult living conditions. The conundrum of the high maternal and infant mortality rate in Africa, particularly that of children under five, remains a major concern, even though every effort is being made to reverse the trend2.

-Malaria incidence Number of malaria cases per 100,000 population. The disease is a major cause of child mortality in sub-Saharan Africa. Malaria is an extremely serious human rights issue. Six of the eight Millennium Development Goals (MDGs) cannot be achieved without addressing this disease, which is both a cause and a consequence of poverty3.

-Diarrhoea-related deaths The consequences of the lack of drinking water and poor hygiene conditions are at the root of this mortality. Children are still dying from respiratory or diarrhoeal infections, which no longer pose a threat in industrialised countries, or from childhood diseases, such as measles, which vaccination could prevent4.

• Number of children who died of malaria: They show the direct impact of the spread of malaria in certain regions. In 2021, malaria remained the most prevalent parasitic endemic disease in the world, affecting 241 million people with 627,000 deaths. In Burkina Faso, it affected about 11.5 million people with 3983 deaths of children aged 0 to 5 years during the same period and thus constituting the first reason for consultation, hospitalization and death5.

-GDP per capita Low GDP can limit resources allocated to health and education. Countries that have made significant progress in terms of living standards and poverty reduction have not only focused on the quantitative aspect of growth but also on its qualitative aspect; they have improved the health, education and employment of their populations6.

-Literacy rate
Educational attainment of the adult population. A high rate is often associated with increased awareness of good health practices and better care for children. Individual factors such as education and maternal age have a positive effect on the variability of infant and under-five mortality7.

• Population in extreme poverty (%): Percentage of the population living on less than USD 1.90 per day. High poverty is correlated with limited access to medical care and insufficient nutrition. Under-one mortality rates have fallen sharply in Algeria over the past 50 years, from 150‰ in the late 1960s to almost 20‰ in 2015. This decline is mainly due to the fight against infectious diseases (exogenous mortality) and socio-economic and health progress8.

-Vaccination coverage Good immunization coverage is essential to prevent preventable childhood diseases. Immunization is one of the most cost-effective and life-saving public health interventions, including 2.5 million children each year. The average return on investment for each € invested is €34 compared to €14 on average in public health. Vaccination significantly improves the health and well-being of the population9.

-Access to handwashing Access to handwashing is an essential part of preventing the spread of infectious diseases. Here is an overview of the current situation regarding access to handwashing facilities around the world. Hand hygiene is a central component of safe and effective health care. It is a cost-effective public health measure that is essential for controlling diseases such as pneumonia and diarrhoea10. To enable people to wash their hands properly with soap at critical times (see box below), it is essential that they have access to the facilities they need to do so. Handwashing facilities should be widely available, accessible to all, and designed to encourage people to use them11. Hand washing is the best way to prevent the spread of germs12.

• Access to drinking water: Access to safe drinking water in Africa remains a major challenge, with significant disparities between regions and countries. Urban water services, like other essential services, face three main challenges. The first is that of numbers: even if population growth is declining today, it remains rapid (on average 4% per year), involves massive numbers and is accompanied by spatial sprawl in vast low-density peripheries where residential land tenure statuses are, if not always illegal, at least often precarious13. Drinking water is an essential resource for food security and human development15.

7.Target population The target populations identified are children under 5 years of age, mothers, poor rural and urban communities, as well as children in vulnerable situations (displaced or living in conflict zones), who are particularly exposed to risks related to malnutrition, preventable diseases and limited access to health care. These groups suffer from precarious socio-economic conditions that exacerbate health inequalities, justifying the need for specific and tailored interventions to improve child health and ensure equitable access to care and nutrition.

8. Data collection tools We used the KoboToolbox platform to create and manage survey forms to collect our data. Accompanied by the KoboCollect mobile app that can be installed on low-cost and easy-to-use devices, the data is easy to collect from the target population both online and offline.

• Data collected

9. Data processing and analysis tools R and Excel software were used for data processing and analysis. The results of the survey were compiled in Excel (see Supplementary Material: Table S1 for the data collected) and exported to R, a software program specializing in statistical processing and data visualization. Prior to any analysis, the data has undergone a rigorous cleaning process to identify and correct missing values and potential errors. First, a principal component analysis (PCA) was performed to reduce the dimensionality of the data while retaining essential information. This analysis identified the most influential variables on child health and visualized the relationships between them. The correlation matrix of the variables was calculated to assess the relationships between the different dimensions studied. Then, a hierarchical classification on principal components (HCPC) was carried out to group the countries according to their socio-economic and health similarities. This method has led to a better understanding of the disparities between groups of countries and to the identification of distinct profiles in terms of living conditions and child health. In addition, a linear regression was conducted to analyze the impact of socio-economic conditions (e.g. GDP per capita, access to health services) on the infant mortality rate. This approach quantified the relationships between the different variables and identified the factors that have the greatest influence on children’s health in developing countries. The analyses were visualized using a variety of graphs, including scatter plots, individual and variable contribution graphs, and dendrograms for classification. All of these methods made it possible to extract meaningful information and to support the conclusions on a solid statistical basis. To map the analyses carried out from R and Excel, we used the QGIS software. This allowed us to spatially represent the variables studied to better compare the impact of variables between localities. Thanks to this approach combining ACP, classification and regression, it was possible to draw clear lessons on the impact of socio-economic and health conditions on child health, thus making it possible to formulate recommendations adapted to the contexts of the countries studied.

10. Expected results

The objective of this part is to present the results obtained from the principal component analysis.

1. Analysis with Nigeria The analysis of our graph allowed us to detect an atypical individual (Nigeria).

   Reading axes and variance

• Dim 1 (47.35%) and Dim 2 (30.51%) together explain 77.86% of the total variance.

• The Dim 1 axis mainly separates Nigeria from other countries.

• The Dim 2 axis distinguishes Cape Verde, which is isolated at the top.

 Groups of countries observe

• Nigeria: it is very remote from others, indicating that it has very different socio-economic and health characteristics.

• Cape Verde: it also stands out, but in a different direction, probably because of its insularity and its economic particularities.

• A central core of countries (Senegal, Côte d’Ivoire, Togo, Gambia, Benin, etc.) share similar characteristics.

• Burkina Faso, Mali and Niger are close to each other, suggesting strong similarities.

 Interpretation

• Nigeria has a stronger economy and a larger population, which could explain its remoteness in the ACP. Indeed, Nigeria does not hide its aims of geopolitical leadership in Africa and “spokesman” of the continent on the world stage. The country considers that this role is a natural one, due to its population and its economic weight16.

• Cape Verde is an island country with a higher GDP per capita than most other West African countries, which could influence its unique position in the factor space. Cape Verde, a Sahelian island country located 500 km off the coast of Senegal, suffers from chronic droughts. The small size of the national territory (less than 4,000 km2 for the 9 inhabited islands), the absence of natural resources, and the Portuguese colonization for five centuries until independence in 1975 help to explain the migratory tradition, since the nineteenth century, of this Creole people17.

• Countries in the Sahel (Burkina Faso, Mali, Niger) face common challenges such as food insecurity and limited access to health care, which may bring them together in this analysis (FAO, 2021).

2. Analysis without Nigeria Analysis of the main correlations

 Strong positive correlation (Dark red, value close to +1)

• There is a strong positive correlation between TM (Mortality Rate) and SEI (Unsafe Water Source) This shows that economic and health conditions directly influence the mortality rate. The issue of social inequalities in health is a major public health issue. Taking it into account is essential for an effective health policy at different levels (national, regional and local), as well as for the implementation of health education programmes or actions for the population18.

• There is a strong positive correlation between EMP (child death from malaria) and PI (Malaria Index) which implies that the higher the rate of child deaths from malaria, the higher the malaria index.

• There is a strong positive correlation between PEP (Population in Extreme Poverty) and SEI which shows extreme poverty is strongly linked to unsanitary conditions. This shows that economic and health conditions have a direct influence on the mortality rate. The issue of social inequalities in health is a major public health issue. Taking it into account is essential for an effective health policy at different levels (national, regional and local), as well as for the implementation of health education programmes or actions for the population18.

 Strong negative correlation (Dark blue, value close to -1)

• PIB_H (GDP per capita) and MT (Mortality rate) High GDP is associated with a low infant mortality rate. A country’s income level is negatively correlated with burn mortality; the lower the income level, the moreMortality rateby burns is high. The equitable or inequitable distribution of income within a country is also correlated with burn mortality19.

• TA (Literacy Rate) and PEP (Population in Extreme Poverty) A higher literacy rate reduces the rate of extreme poverty. Low literacy has been associated with low productivity, high unemployment, low incomes and high rates of dependence on social assistance. Poverty has been linked to low literacy levels through suggestions, innuendo and statistics20.

 Interpretation

This graph highlights the links between socio-economic and health conditions and infant mortality. Targeted actions on education and economic growth are essential to improve the health of children in developing countries.

Correlation Circle

Correlation circle

 Dimension analysis

This graph illustrates the contribution of variables in Principal Component Analysis (PCA). The Dim1 axis (58.3%) captures the majority of the variance, followed by Dim2 (19.2%).

Variables correlated to Dim1 (Socio-economic factors)

• SEI, NALM, EMP, EMD: Linked to human development (education, employment, life expectancy). Dim2 Variables (Environmental Factors)

• VCH, TA, PIB_H: Impact of climate and the economy on health (e.g. malaria). Variables negatively correlated to Dim1

• TM, PI: Higher in countries with low human development.

 Interpretation:

Education and employment reduce infant mortality and poverty. An integrated policy is essential. Another area where change has been most significant is that of schools; Parity is almost assured, although girls tend to leave school earlier than boys. Reproductive health has also made great progress, with a significant decline in maternal and infant mortality21.

Graph of individuals

 Individuals Chart Analysis (ACP)

This graph represents the projection of the countries in the Dim1 (58.28%) - Dim2 (19.18%) factor plan, resulting from the Principal Component Analysis (PCA). Countries with a high contribution

• Niger, Cape Verde, Burkina Faso (red color): Strong influence on the construction of the axes.

• Niger stands out for its extreme contribution on Dim1, indicating marked socio-economic particularities (e.g. poverty, high infant mortality)

• Cape Verde and Burkina Faso mainly influence Dim2, illustrating distinct development dynamics.

 Low-contribution countries

• Togo, Côte d’Ivoire, Sierra Leone (in blue): Lower contribution, indicating low infant mortality and a developing economy

• They are grouped in the negative quadrant, indicating similar socioeconomic characteristics.

Explanatory Variances Chart
![Explanatory Variances Chart](images/figure7.png)

 Analysis of the Explanatory Variances Graph

This graph illustrates the proportion of variance explained by each principal component in the Principal Component Analysis (PCA).

Key results:

• Dim1 explains 58.3% of the total variance, which means that it contains the majority of the information in the data.

• Dim2 represents 19.2% of the variance, bringing the sum of the first two dimensions to 77.5%.

• Dim3 and Dim4 explain 9.5% and 5% respectively, with a marginal contribution from the other dimensions.

 Interpretation

• The sharp decay after Dim1 and Dim2 indicates a shoulder effect, suggesting that only the first two components are needed for a good representation of the data.

• Dimensions beyond the fourth one provide almost no information and can be ignored in the analysis. Conclusion PCA can be effectively reduced to the first two dimensions for optimal visualization and interpretation of results, without significant loss of information.

Hierarchical tree

 Analysis of the Hierarchical Classification Dendrogram (HAC)

This dendrogram represents the structure of similarity between countries according to the variables studied. It makes it possible to identify homogeneous groups. We observe: Three main groups, delimited by different colors.

 Interpretation of the Groups

The inertia graph in the top right shows that most of the variance is explained by the first three groupings. This justifies the choice of a classification into three main clusters.

Group 1 (Green): Countries with more difficult conditions: Niger, Mali, Burkina Faso (Sahel countries) These countries are often associated with low human development indicators, a higher prevalence of diseases such as malaria and a high dependence on subsistence agriculture. The situation of recurrent food and nutrition insecurity suffered by about 155 million children in the world, including 59 million in Africa, particularly those in the Sahel and the Horn of Africa, is a major problem of public health, development and global collective consciousness in this new context of globalization of prosperity, rights to survival, education and protection of children22.

Group 2 (Red): Medium-profile countries: Côte d’Ivoire, Ghana, Sierra Leone, Benin, Guinea These countries seem to share intermediate socio-economic conditions, with a mix of moderate incomes and varied health indicators. The results show that trade flows within ECOWAS member countries were highly asymmetric23.

Group 3 (Black): Countries with better relative conditions: Senegal, Togo, Liberia, Cape Verde, Gambia, Guinea Bissau This group includes countries with slightly better economic and health conditions, including Cape Verde, which has a relatively more stable and developed economy.

 Conclusion This dendrogram highlights significant socio-economic and health disparities between the countries studied, which corroborates previous research on the link between living conditions and child health.

Contribution of individuals

These graphs illustrate the contribution of countries to the different dimensions of Principal Component Analysis (PCA).

 General interpretation

• The bars show the percentage contribution of countries to the main axes of the ACP.

• The red line indicates the average contribution threshold: the countries above them have a significant influence on the structuring of the dimensions.

 Chart Analysis

First and Third Graph: Contribution to Sun-1 and Sun-1-2

• Niger dominates with a contribution of more than 30%, indicating that it strongly influences the structuring of the first two dimensions.

• Cape Verde and Burkina Faso follow with more than 20% and minus 10% respectively.

• Other countries such as Ghana, Mali and Guinea have a low contribution below the red line.

• Senegal and Liberia have a weaker influence.

 Interpretation:

• Niger and Cape Verde have socio-economic and health conditions, explaining their strong contribution.

• The other countries have a weak influence on the axis but remain important.

 Second Graph: Contribution to Sun-2

• Guinea has the highest contribution to this dimension at more than 20%, followed by Ghana and Niger.

• Cape Verde, Liberia and Benin are also well represented.

• Mali and Burkina Faso have a lower contribution on this axis.

 Interpretation:

• This second dimension could be linked to specific factors such as health infrastructure or access to education.

• Guinea and Ghana could represent contrasting profiles, for example in terms of economic or health stability.

Conclusion

Niger, Cape Verde and Burkina Faso are the countries most influential in ACP analysis. Guinea and Ghana also play a key role in the second dimension, linked to health conditions. Countries with a low contribution have little influence on the structuring of the axes, but their position must be analysed in the scatter plots. This analysis allows us to better understand the disparities between countries and their impacts on child health.

Variable contribution graphs

Linear regression  Analysis and Interpretation of Linear Regression: PEP ~ TM

Reading the Chart

• Blue dots represent individual observations.

• The red line is the adjusted linear regression.

• The shaded area represents the confidence interval around the regression.

 Interpretation of Results

Positive relationship between TM and PEP

• The slope of the regression line is positive, indicating that as PEP increases, so does TM.

• This could suggest that more precarious socio-economic and health conditions lead to an increase in MT. Scattering of points

• Although the trend is growing, the dots are quite scattered around the line.

• This means that the relationship is not perfectly linear, and other factors likely influence TM. Wide confidence interval

• The grey shadow indicates relatively high uncertainty, especially at the extremities.

Residues

 Analysis of Residual vs Fitted • This graph checks scedasticity, it describes how the residuals (prediction errors) vary according to the adjusted values. • Here, we observe a certain curved trend, suggesting that the variance of the errors is not constant. • Some points such as Niger and Côte d’Ivoire seem atypical.

 Q-Q Residual Analysis • This graph checks whether the residuals follow a normal distribution. • Here, the residues from Niger, Guinea and Côte d’Ivoire deviate significantly, suggesting that the residues are not strictly normal.  Scale-Location Analysis • This graph shows the variance of the residuals as a function of the adjusted values. • A horizontal line indicates a constant variance (homoscedasticity). • Here, the trend is slightly increasing, suggesting an increase in the variance of the residuals as the adjusted values increase, which reinforces the idea of heteroscedasticity.  Residuals vs Leverage This graph helps to identify influential points that could distort the model. Niger and Ghana have a strong influence (close to the Cooks thresholds distance).  Analysis The image shows a matrix of scatter plots, used to examine relationships between multiple variables. Each box represents a scatter plot between two variables, allowing correlations and trends to be visually identified. 11. ACP Results Model in GIS  Data Collection Questionnaire 12. Recommendations In the light of the results obtained through the ACP, the classification of countries according to their level of vulnerability and multiple regressions, several actions can be proposed to improve the health situation and reduce infant mortality in the most vulnerable countries:  Strengthening health infrastructure • Increase investments in access to safe drinking water and handwashing facilities in rural and disadvantaged areas. • Set up local health centres with basic services (consultation, vaccination, awareness-raising).

 Accelerating immunization coverage • Launch free and compulsory national vaccination campaigns, especially for children under 5 years of age. • Strengthen cold chain logistics and train more community health workers.

 Poverty alleviation • Develop conditional cash transfer programs targeting families in extreme poverty to promote schooling and child health. • Encourage income-generating activities and local entrepreneurship to improve living conditions.

 Improving education • Promote women’s literacy and girls’ education, which are key factors in reducing child mortality. • Conduct awareness-raising campaigns on hygiene, nutrition and child care, adapted to the local cultural context.

 Regional cooperation and sharing of experiences • Encourage countries with positive indicators to share good practices with more vulnerable countries. • Promote a coordinated regional approach, especially for communicable diseases such as malaria or diarrhoea.

13. Conclusion The study, conducted through in-depth statistical analyses, including Principal Component Analysis (NPA) and the classification of countries according to their level of vulnerability, highlighted the close links between socio-economic conditions, health conditions and child mortality in several African countries. The results show that poverty, poor access to safe drinking water, low vaccination coverage and inadequate health infrastructure are the main factors contributing to the vulnerability of populations, especially children. Countries in the most vulnerable groups have marked deficits in these areas. These findings underscore the need for targeted and coordinated interventions. The recommendations made emphasize the strengthening of health infrastructure, the promotion of vaccination, the fight against poverty and the development of education, especially for women. In short, sustainable vulnerability reduction requires ambitious, multisectoral policies that are based on objective data and adapted to local realities. The joint efforts of governments, international partners and communities are essential to building a healthier and more equitable future.

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SCRIPT R. STUDIO ACP

-Set the working directory (Change according to your machine)

setwd(“C:/Users/DELL E7470/Desktop/PROJET_RTI/Rstudio/ANALYSIS 2/without nigeria”)

-Loading the necessary packages

library(FactoMineR) library(factoextra) library(ggplot2) library(corrplot) library(psych)

-Loading data

data <- read.csv(“DONN.csv”, header = TRUE, sep = “;”, quote = “"”, dec=“.”, row.names=1, encoding=“UTF-8”)

-Quick data verification

dim(data) # Number of rows and columns head(data) # Preview of the first rows summary(data) # Descriptive statistics str(data) # Data structure sum(is.na(data)) # Checking for missing values

-Generate a basic dispersion matrix

pairs(data)

-Improved dispersion matrix with GGally

ggpairs(data)

-Correlation matrix of the first variables

mat_cor <- cor(data[, 1:10], use = “pairwise.complete.obs”) KMO(mat_cor) # Factor Analysis Suitability Check

-Visualization of the correlation matrix

corrplot(mat_cor, method = “circle”, type = “upper”, tl.cex = 0.7, col = colorRampPalette(c(“blue”, “white”, “red”))(200))

======== PRINCIPAL COMPONENT ANALYSIS (PCA) ========

Running the PCA with Centering and Reducing

resultats_pca <- PCA(X = data, scale.unit = TRUE, ncp = 5, graph = TRUE)

-Display of eigenvalues (variance explained)

valeurs_propres <- get_eigenvalue(resultats_pca) print(valeurs_propres)

-Eigenvalue graph (Scree plot)

fviz_eig(resultats_pca, addlabels = TRUE, ylim = c(0, 50))

============== CONTRIBUTION OF INDIVIDUALS================

-Visualization of individuals according to their contribution

fviz_pca_ind(resultats_pca, geom = “point”, col.ind = “contrib”, # Contribution coloring gradient.cols = c(“#00AFBB”, “#E7B800”, “#FC4E07”), repel = TRUE)