Determinants of access to basic sanitation service in Africa

GOUNA Hamidou: 2023028

KABORE SS Damaris: 2020066

TOE Hamadou:20200635

KABORE Yasmina Elsa:20210332

Summary : The study on the determinants of access to basic sanitation service in Africa highlights links between different socio-economic, demographic and sanitation indicators. The results show that factors such as rural population, education level and economic aspects are correlated with two axes of the principal component analysis (PCA). These variables are closely associated and can play an important role in understanding access to basic sanitation service in Africa. In summary, the study highlights the importance of a comprehensive and multidimensional approach to understanding the link between socio-economic, educational and economic factors in Africa. These findings underscore the need for effective policies and interventions aimed at promoting social well-being.

Introduction

In September 2015, world leaders gathered in New York to adopt the 2030 Agenda for Sustainable Development. This ambitious plan, dubbed a “plan for people, planet and prosperity,” aims to transform the world through 17 goals that span the social, economic, and environmental dimensions of sustainable development (Adil, Nadeem, & Malik, 2021) . Among these goals, SDG 6 is specifically dedicated to water and sanitation, with the aim of ensuring universal access and sustainable management of these resources. This goal highlights the critical importance of access to safe drinking water, safe sanitation, and sound management of water resources for human well-being, the environment, and development, while paying particular attention to sanitation services.

According to WHO and UNICEF, a sanitation service includes infrastructure and practices that maintain hygiene, protect public health and preserve the environment through the safe management of human waste and wastewater. This includes toilets, wastewater treatment systems, faecal sludge collection and solid waste management. The Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) classifies sanitation services into safely managed services, basic services (improved facilities that are not shared), limited services, unimproved facilities and no service (open defecation). These classifications help monitor and compare progress towards SDG 6.

In 2020, according to the JMP, only 40% of the population in Africa had access to basic sanitation, leaving more than half of the population without adequate access. The situation is even more critical in West and Central Africa, where these rates reach 36% and 23% respectively, with significant disparities. For example, Chad had an access rate of only 13% in 2020, compared to 59% for Senegal, despite different economic and demographic contexts. These inequalities raise the question of the factors influencing access to basic sanitation services in Africa.


The literature shows that various factors influence this access. Akpabio & Brown (2012) demonstrated that household income and education level play a key role in the choice of sanitation facilities in Nigeria. Shukla & Nayak (2017) confirmed that in Uttar Pradesh, India, literacy and education determine access to water and sanitation. Furthermore, social norms and collective behaviors also influence sanitation, as highlighted by McGranahan (2015) .

Other studies, such as Adams, Boateng, & Amoyaw (2016) in Ghana and Shukla & Nayak (2017) in India, have shown that household size can influence sanitation behaviors and infrastructure. In China, Luo et al. (2018) found that socioeconomic variables such as GDP per capita and literacy are determinant factors, while De (2018) found that non-economic factors, such as region and household size, play a major role in latrine use.

Finally, studies conducted in Ethiopia HAILU, MULU, & ABERA (2020) and Pakistan Zahid, (2018) ; Daud et al. (2017) have highlighted the impact of knowledge, practices, and resource availability on public health. These studies highlight that limited access to safe drinking water and sanitation facilities contribute significantly to waterborne diseases and mortality, especially among children.

Thus, understanding the factors that influence access to basic sanitation services is essential to reduce inequalities and improve health conditions in Africa and elsewhere.

Material and method

To ensure rigorous data collection, management and analysis, several digital tools were used. These software programs made it possible to optimize the various stages of the project, from field collection to analysis and presentation of the results. The main software programs used are as follows:

This software is a complete platform dedicated to field data collection. It was used to design collection forms, deploy surveys on mobile devices (smartphones or tablets) and centralize data collected in real time. Thanks to its user-friendly interface and offline compatibility, KoboToolbox facilitated the collection of information from households in areas sometimes without internet connection.

R is a powerful programming tool specifically designed for statistical analysis and data processing. The language was used to perform descriptive and inferential analyses, visualize results graphically, and manipulate large volumes of data efficiently. With its wide range of specialized libraries, R offers exceptional flexibility to meet the specific needs of this project.

This Geographic Information System (GIS) software played a key role in mapping and spatial analysis of the data. QGIS allows working with different geospatial data formats, including shapefiles and other vector files. In this project, it was used to visualize the locations of the countries studied, to overlay geographic data on maps, and to produce thematic maps useful for interpreting the results.

For bibliographic management, ZOTERO was used as the main tool. This free software facilitates the collection, organization and citation of bibliographic references needed to write documents. In addition to ensuring consistency in the formatting of citations and bibliographies, it allows the grouping and annotating of PDF files associated with the references, thus ensuring rigorous and centralized documentation throughout the project.

By combining these tools, this project benefited from a synergy between robust statistical analyses, detailed geographic visualizations and rigorous bibliographic documentation, which helped ensure the quality and credibility of the results.

The variables used in our study on twelve (12) countries are:

Percentage of population living in urban areas ( Washdata)

Percentage of population living in rural areas ( Washdata)

Open defecation rate ( Washdata)

Basic sanitation ( Washdata)

Population growth rate ( https://population.un.org/wpp/Download/)

Number of people with a certain level of education ( Wittgenstein Centre - processed by our data in world)

Population growth rate ( https://population.un.org/wpp/Download/)

Gross Domestic Product per capita ( https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023  )

·       Methods

Quiz

Survey Identification

            •Date :

            •Name of the locality:

            •Name of investigators:

            •           Team ID:

            •Household number:

Section 1: Information about the respondent

            1) Do we have informed consent from the respondent?

o   Yes

o   No

(If not, end the survey)

            2) Name and first name of the respondent:

            3) Gender of respondent:

o   Male

o   Female

            4) Level of education of the respondent:

o   None

o   Primary

o   Secondary

o   Superior

o   Other (specify):

            5) Respondent’s profession:

Section 2: Household Composition

            1) How many people live in the household?

            2) How many children under 5 years old are there in the household?

            3) Are there any elderly people (65 years and older) in the household?

o   Yes

o   No

            4) Are there any people with disabilities in the household?

o   Yes

o   No

Section 3: Access to latrines

1)    Does the household have a latrine?

o   Yes

o   No

(If not, skip to section 4)

2)    If yes, what type of latrine do you use?

o   Unimproved traditional latrines

o   Improved latrines (with slab)

o   Flush toilets

o   Community latrines shared with other households

o   Other (specify):

3)    Where are the latrines located?

o   Inside the courtyard

o   Outside the courtyard

o   Other (specify):

4)    Who uses the latrine in the household?

o   All household members

o   Adults only

o   Children only

o   Others (specify):

5)    How often are the latrines cleaned?

o   Daily

o   Every week

o   Every two weeks

o   Less often

o   Never

6)    What are the main challenges in using latrines?

o   Unpleasant odors

o   Lack of privacy

o   Too far from home

o   Risk of collapse

o   Lack of protection against rain

o   Other (specify):

7)    Is there a hand washing facility near the latrines?

o   Yes

o   No

8)    Does the household have regular access to water to maintain the latrines?

o   Yes

o   No

Section 4: Alternatives in the absence of latrines

1)    Where do household members relieve themselves in the absence of latrines?

o   Open defecation

o   Use of plastic bags

o   Other (specify):

2)    Where do children under 5 go to the bathroom?

o   In a plastic pot

o   Open defecation

o   Plastic bag

o   Other (specify):

3)    What do you do with the excrement of children under 5 years old?

o   Thrown into a latrine

o   Thrown outside or elsewhere

o   Buried

o   Other (specify):

Section 5: Economic and social factors

1)    What is the main source of household income?

o   Agriculture

o   Trade

o   Paid employment

o   Craftsmanship

o   Other (specify):

2)    What is the approximate monthly household income?

o   Less than 50,000 FCFA

o   Between 50,000 and 100,000 FCFA

o   More than 100,000 FCFA

3)    Did the household benefit from a sanitation assistance program?

o   Yes

o   No

(If yes, specify the type of assistance received):

4)    What are the main obstacles to building or using latrines?

o   High construction cost

o   Lack of space in the yard

o   Lack of information or awareness

o   Lack of institutional support

o   Cultural norms or beliefs

o   Other (specify):

Section 6: Perception and Awareness

1)    Do you think improved latrines are necessary for health?

o   Yes

o   No

o   Don’t know

2)    Have you been made aware of the benefits of improved latrines?

o   Yes, by a local organization

o   Yes, by public authorities

o   Yes, by other community members

o   No

3)    What do you consider to be the main benefits of improved latrines?

o   Disease Reduction

o   More comfort and privacy

o   Environmental protection

o   Other (specify):

·       Methodological approach

For this study, we adopted a qualitative approach combined with quantitative techniques, in order to better understand the determinants of access to improved sanitation. This method is particularly suited to populations with varying levels of literacy, while ensuring rigorous and comprehensive data collection.

ü  Sampling method

We opted for a reasoned and representative sampling strategy, based on the following criteria. First, the geographical stratification, in fact the localities were selected according to their geographical location (urban, semi-urban and rural areas) to understand the variations linked to the context. Then the size of the households. The sample includes households of different sizes in order to analyze the impact of family composition on access to sanitation. As other criteria we have socio-economic diversity in order to include households of different income levels to explore disparities linked to financial means. Finally we have access to services. Indeed, particular attention was paid to households with or without access to improved latrines.

ü  Survey method

We used structured interviews as the main data collection tool. These interviews were conducted through standardized questionnaires, administered face-to-face during door-to-door visits. This method overcomes potential literacy barriers and creates an interactive setting to collect reliable data.

ü  Structure of the questionnaire

The questionnaire, designed on the KoboToolbox platform, is structured in several sections to cover a full range of relevant data:

Section 1: General and demographic information

Ø  Personal data of the respondent

Ø  Household composition and characteristics

Section 2: Access to health infrastructure

Ø  Presence and type of latrines

Ø  Use and maintenance of facilities

Section 3: Economic factors

Ø  Income and cost of installing latrines

Ø  Aid programs or subsidies received

Section 4: Perceptions and awareness

Ø  Cultural beliefs and norms related to sanitation

Ø  Level of awareness of improved latrines

Section 5: Barriers and recommendations

Ø  Obstacles encountered in installing or using latrines

Ø  Household suggestions for improving access to sanitation

·       Data analysis

Data collected via KoboToolbox is exported for analysis using software such as R for statistical analysis and QGIS for mapping the results. This integrated approach ensures that the information collected is used to its full potential, allowing trends to be identified and recommendations to be made.

·       Organization of field teams

Team composition

The field teams are made up mainly of students, recruited for their ability to conduct surveys rigorously and use digital tools such as KoboToolbox. These students play the role of collection agents and are trained in advance on the objectives of the study, the use of digital tools and survey techniques.

The teams work under the supervision of local authorities, who play a facilitating role by allowing easier access to the target populations. This partnership with local leaders strengthens trust between the investigators and the communities, thus promoting smooth and efficient data collection.

Frequency and organization of surveys

Each group of interviewers is responsible for covering an average of 6 to 7 households per day, depending on population density and logistical constraints. Surveys


are conducted over a defined period, usually 3 to 4 weeks, to ensure comprehensive coverage of all planned locations. Strict planning is in place to avoid duplication and ensure that all targeted households are surveyed.


Data analysis and interpretation

Analysis with all individuals

setwd('C:/Users/OUEDRAOGO AWA/Documents/data')

library(FactoMineR)
library(ggplot2)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(corrplot)
## corrplot 0.94 loaded
dataF=read.csv('RTI_res.csv',sep = ';', dec = ',', row.names = 1)
Results=PCA(dataF, scale.unit = T, graph = F, ncp = 3)
#fviz_pca_biplot(Results, repel = T)
fviz_contrib(Results, choice = 'var', top = 11, axes = c(1,2))

fviz_contrib(Results, choice = 'ind', top = 11, axes = c(1,2))

The analysis of Figures 11 and 12 reveals two main findings: on the one hand, the variables with a contribution above the average on the factorial level are those relating to population and education; on the other hand, the individuals whose contribution largely exceeds the average of individual contributions are Chad and Nigeria. These two countries stand out clearly in our dataset, as illustrated in Figure 13, where each of them forms a distinct group, and Figure 14, which shows their isolation from the others.

Nigeria, in particular, stands out for its massive population, high urbanization, and its role among Africa’s leading economic powers. It is the most populous country on the continent and alone concentrates a significant share of Africa’s urban population. In contrast, Chad presents a very different profile: its population, predominantly rural, has limited access to education and health infrastructure, which makes it atypical in the context studied.  

Analysis without Chad and Nigeria

Correlation matrix and correlation circle

library(ggcorrplot)
## Warning: le package 'ggcorrplot' a été compilé avec la version R 4.4.2
dataF=read.csv('RTI.csv',sep = ';', dec = ',', row.names = 1)
cor_matrix=cor(dataF)
fviz_pca_var(Results, 
             col.var = "contrib",  # Coloration par contribution
             gradient.cols = c("blue", "yellow", "red"),  # Dégradé de couleurs
             repel = TRUE,  # Étiquettes non chevauchantes
             title = "Cercle des corrélations (Contributions)")

ggcorrplot(cor_matrix, lab = TRUE, lab_size = 2, 
           colors = c("red", "white", "blue"), 
           title = "Matrice de Corrélation")

From Figures 14 and 15 we observe the following points:

Correlation between national income per capita and education :
A positive correlation exists between national income per capita and educational variables (primary, middle school, secondary school and university). This indicates that an increase in national income per capita allows populations to access higher levels of education.

Correlation between GDP and national income per capita :
Gross Domestic Product (GDP) and national income per capita have a very strong positive correlation (1), showing that these two variables evolve jointly. Since national income per capita is correlated with educational variables, GDP is also associated with these variables, leading to similar effects.

Relationship between rural and urban population:
A strong negative correlation is observed between rural population and urban population. This suggests that as rural population decreases, urban population increases, reflecting a demographic shift towards urban areas.

Correlation between basic sanitation and lack of service :
There is a negative correlation between access to basic sanitation facilities and lack of service. This means that improving access to sanitation facilities reduces the rate of open defecation.

Inertia graph

fviz_screeplot(Results, 
               addlabels = TRUE,     # Ajouter les pourcentages sur le graphe
               barfill = "steelblue", # Couleur des barres
               barcolor = "black",   # Bordure des barres
               title = "Graphe d'inertie")

Figure 16 illustrates the variability of information contained in the different dimensions. We observe that the percentage of information progressively decreases as the number of dimensions increases. From two dimensions, we retain 70.5% of the total information present in our dataset.

Variable contribution graph

fviz_contrib(Results, choice = 'var', top = 11, axes = 1)

fviz_contrib(Results, choice = 'var', top = 11, axes = 2)

Figures 17 and 18 reveal that education-related variables (college, secondary and post-secondary) as well as economic variables contribute mainly to the construction of axis 1. On the other hand, sanitation-related variables (basic service and absence of service) and demographic variables (rural population, urban population and growth rate) play a predominant role in the formation of axis 2.

Individual contribution graph

fviz_contrib(Results, choice = 'ind', top = 11, axes = 1)

fviz_contrib(Results, choice = 'ind', top = 11, axes = 2)

We observe that individuals such as Ghana, Cameroon and Burkina Faso (Figure 19 and 20) contribute more to the construction of axis 1, while Gambia, Ghana, Senegal and Burkina Faso play a more important role in the formation of axis 2.

Factorial plan and quality of representation

fviz_pca_var(Results, 
             col.var = "cos2",  # Coloration par contribution
             gradient.cols = c("blue", "yellow", "red"),  # Dégradé de couleurs
             repel = TRUE,  # Étiquettes non chevauchantes
             title = "Cercle des corrélations (Cos2)")

plot.PCA(Results,title="Graphe des individus de l'ACP")

The graphs (figure 21 and 22) explain 71.5% of the variance, and the following observations can be made (figure):

For axis 1 , Ghana, Ivory Coast, Cameroon and Senegal make a positive contribution, while the other countries have a negative contribution.

For axis 2 , Guinea and Ivory Coast have a quasi-zero contribution (their ordinates are close to zero), Cameroon, Senegal and Gambia have negative ordinates, are close to zero), Cameroon, Senegal and Gambia have negative ordinates, while Togo, Burkina Faso and Ghana have a positive contribution.

PCA-biplot

fviz_pca_biplot(Results, repel = T)

The PCA-biplot graph (Figure 23) shows that countries such as Côte d’Ivoire, Cameroon, Ghana and Senegal have high values for variables related to education, economy, urban population and sanitation services, while countries such as Burkina Faso, Togo, Benin and Sierra Leone have significantly higher values for variables such as lack of services and rural population. In addition, Gambia stands out for the highest growth rate.

Classification graph

res.PCA<-PCA(dataF,ncp=2,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='tree',title='Arbre hiérarchique')

plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

plot.HCPC(res.HCPC,choice='3D.map',ind.names=FALSE,centers.plot=FALSE,angle=60,title='Arbre hiérarchique sur le plan factoriel')

The first group includes Guinea, Benin, Sierra Leone and Burkina Faso. These countries are characterized by a predominantly rural population and a low rate of access to basic sanitation services. The second group includes countries such as Gambia and Senegal, which have high values for the rate of access to basic sanitation services. Finally, the third group includes the most developed countries compared to the others, with a notable increase in the rate of access to sanitation.

Interpretation

After analyzing the different results obtained, we can name the different axes in order to facilitate interpretation.

We have axis 1 which will characterize countries like Cameroon, Ivory Coast and Ghana which are economically strong and have a relatively low rate of open defecation. And axis 2 grouping countries like Gambia where we have a high growth rate compared to other countries and a low rate of open defecation.

These results also reveal that low-income households cannot afford to access basic sanitation infrastructure. The cost of building sanitation facilities is a significant barrier for these populations.

In addition, rapid demographic growth often observed in high-density urban areas puts considerable pressure on existing sanitation infrastructure.

Finally, the geographical distribution of the population is also an obstacle to access to basic sanitation services. Urban populations have much easier access to basic sanitation facilities than rural populations.

Conclusion

Lack of sanitation is a major problem that spares no country in Africa today. The factors of access to basic sanitation service are several and multivariate. The rural population and economic aspects are two important elements to consider. Most of the countries that were the subject of this study are underdeveloped (limited in financial resources). It will be up to them to clearly define the targets that are the population in rural areas in order to improve the health situation in Africa.

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