HEALTH CARE AND WASH VERSUS COVID-19 OUTBREAK IN AFRICA

 

 

 

 

 

 

 

 

 

 

INTRODUCTION

As the COVID-19 crisis spreads around the world, it is essential to build on the experiences and lessons learned from China and Europe in the fight against this pandemic at a very high cost in human lives. One of the main lessons learned has been that those with the least access to essential services such as water will feel the most dramatic effects. An act as simple in our developed countries as frequent hand washing for at least 20 seconds could prevent the dramatic spread of the pandemic among the population (WHO/UNICEF, 2020). The lack of access to handwashing facilities is a risk factor in the case of lower respiratory infections (Wolf et al., 2014; Troeger et al., 2016; Prüss-Ustün et al. 2014; Prüss-Ustün et al. 2019; Stanaway et al., 2018), which are a leading cause of morbidity and mortality around the world (Troeger et al., 2016).

From this particular crisis, we have come to understand that public health depends on the security of water resources for all (Sustainable Development Goal 6 - SDG 6). Preventive measures are aimed to slow down the spread of COVID-19 virus, thus reducing the number of critically ill patients and providing precious time to increase hospital capacity. Such a strategy presupposes that at least three conditions are possible: 1) social distancing; 2) access to clean water and soap; and, 3) that the health care sectors are able to increase their capacity in a short period of time. These three assumptions are very complicated even for the richest African countries.

It is well known that 3 billion people, or 40% of the world's population, do not have access to basic handwashing facilities at home. In this context, expanding access to water becomes essential, but there is also an urgent need to create more resilient communities by addressing the fundamental problems of water insecurity. Without these basic measures, the growing pandemic could be especially difficult to control in developing countries, with the high risk of becoming a global problem again.

A fundamental question to be answered is how the transmission of COVID-19 could develop in an African context given its high levels of poverty, weak health systems and overpopulated urban areas. The virus could be particularly devastating, even though Africans have a great deal of experience in fighting infectious diseases such as the Ebola virus.

In this short note, we aim to identify African country profiles regarding the implementation of WASH services and other socio-economic variables and the correlation with endemic diseases. Our final idea is to show the potential vulnerability of African countries to COVID-19 and its potential expansion due to weak health care and WASH systems.

DATA SET, METHODS AND RESULTS

For this study, the annual data of 16 variables for the period 2000-2016 at the national level were retrieved from different sources (Table 1 in Annex). Incomplete data regarding handwashing facilities were estimated using basic sanitation and drinking water services as explanatory variables in a linear regression model.

The Principal Component Analysis (PCA) is an exploratory statistical tool that allow to find the most significant variables in a dataset. Our goal is to analyse the correlations between the different variables and to find out if the changes in variables related to mortality (total and lower respiratory infection deaths) during the selected period are linked to the development of Water Access, Sanitation and Hygiene (WASH) services, social changes (Population density, Urban population) and economic conditions (GDP, Migrant remittance, ODA). After identifying the variables that explain a large amount of the variance (the three first components explain up to 78.86%, see Table 2 in Annex), we use the K-Means and ACH (Agglomerative Hierarchical Clustering) algorithms to determine homogeneous groups of countries with similar profiles over the 17 years of data by linking the development of WASH services with (formal and informal) investments and respiratory diseases.

In 2016, three groups of countries¹ have been identified: Class A-2016 countries show the highest life expectancy, strongest healthcare system and WASH services, lowest mortality rates and highest rates of urbanization and migration remittance inflow; while Class C-2016 countries, show the lowest life expectancy, weakest healthcare system and WASH services, highest mortality rates and lowest rates of urbanization and migration remittance inflow. Therefore, it could be hypothesised that Class A-2016 countries are in a better position to tackle sanitarian emergencies, while Class C-2016 countries could be the most vulnerable ones.

The rest of countries are in Class B-2016, which shows intermediate values of these variables.

- Group A-2016 (better position to tackle sanitarian emergencies): Algeria; Mauritius; Seychelles; Egypt; Libya; Morocco; Tunisia; Cabo Verde.

- Group B-2016 (intermediate countries): Botswana; Sao Tome and Principe; South Africa; Gabon; Rwanda; Comoros; Namibia; Senegal; Djibouti; Ghana; Sudan; Gambia; Kenya; Equatorial Guinea; Madagascar; Malawi.

- Group C-2016 (the most vulnerable countries): Central African Republic; Chad; Sierra Leona; Somalia; Niger; Lesotho; Mali; Burkina Faso; Democratic Republic of the Congo; Guinea; Côte d'Ivoire; Cameroon; Mozambique; Nigeria; Eritrea; Benin; Guinea-Bissau; Togo; Burundi; Angola; Eswatini; Congo; Zimbabwe; Uganda; Tanzania; Zambia; Liberia; Ethiopia; Mauritania.

DISCUSSION

Our analyses show that any reduction in mortality in a country, in general, and in respiratory diseases in particular, is associated with increased investments in basic sanitation (population with basic handwashing facilities, including soap and water, basic drinking water and basic sanitation services). Our analyses also show that the poorest countries with increased population density have the highest mortality rates. On the other hand, based on the analyses, it could also be hypothesised that the Official Development Assistance (ODA) does not have a significant impact on improving sanitation services and thus on reducing respiratory diseases and increasing life expectancy in developing countries. In this case, the flow of ODA to the sanitation sector may not be sufficient to improve the overall national sanitation structure, but rather may have an impact at the individual - micro local level not visible at the country scale. On the contrary, the migration remittance inflows seem to have an impact on improving sanitation in households and local communities even if its structure is not efficient enough.

It is necessary to send short-term development assistance to improve diagnosis and protect the local health population and, secondly, to strengthen the healthcare structure not only in the specific case of the COVID-19 pandemic but also to fight other infectious diseases that are ravaging African countries every year.

In Sub-Saharan Africa, several high-mortality pathologies (apart from COVID-19) are prevalent: malaria, bacterial infections, tuberculosis, AIDS... These diseases are treatable and partly preventable, but their incidence could rise if most resources are directed to the COVID-19 outbreak. The solution could be to take advantage of economies of scale in current investments in health, to also combat other African endemic diseases such as diarrhea, gastrointestinal diseases and tuberculosis. The number of deaths in Africa due to these endemic diseases represents around 27% of the total (WHO, 2018). In 2016, the endemic diseases were equal to 2,383,263 distributed as follow: Lower Respiratory Infections (916,851 deaths – 10.4%), Diarrheal Diseases (652,791 deaths – 7.4%), Malaria (408,125 – 4.6%) and Tuberculosis (405.496 – 4.6%).

Figure 1 represents the potential risk (1 – Highest Risk; 0 – Lowest Risk) of the African countries to face Lower Respiratory Infection deaths considering the Healthcare System and WASH services in 2000, 2008 and 2016. The color of the circles represents the class (A, B, C) and its dimension is related to the total number of deaths per year.

The analysis of the 2000 - 2016 time series (Figure 2 - the potential risk in 3 different years: 2000, 2008, 2016) shows that even if the potential risks for African countries have decreased from 2000 to 2016 (see also Figure 1), they are far from being able to manage these endemic diseases and specifically low respiratory infections. Central and West African countries are particularly at risk together with Mozambique, Somalia, Lesotho and Eswatini. In this case, Central Africa Republic and Chad could be the most vulnerable countries with regard to the COVID-19 outbreak considering their health system and WASH services.

Figure 2 represents the potential risk (1 – Highest Risk; 0 – Lowest Risk) of African countries to deaths from Lower Respiratory Infections considering the quality of the Health System and WASH services in 2000, 2008 and 2016.

 

 

ANNEX

Table 1. Selected variables and sources

Variable	Source
Population	UN (2019)
Population density (people per km2)	World Bank (2020)
Urban population (% total population)	World Bank (2020)
Population living in big cities (> 1 million of inhabitants)	World Bank (2020)
Life expectancy at birth (years)	World Bank (2020)
Health expenditure (% GDP)	World Bank (2020)
Healthcare Access and Quality (HAQ) Index (0-100)	IHME, Fullman et al. (2018)
Total deaths	WHO (2018)
Lower respiratory infection deaths	WHO (2018)
Lower respiratory infection deaths in children (< 5 years)	WHO (2018)
Population using at least basic drinking-water services (%)	WHO/UNICEF-JMP (2017)
Population using at least basic sanitation services (%)	WHO/UNICEF-JMP (2017)
Population with basic handwashing facilities including soap and water (% )	WHO/UNICEF-JMP (2017)
GDP per capita (current US$)	World Bank (2020)
Migrant remittance inflows (current US$)	World Bank (2020)
Net official development assistance and official aid received (current US$)	World Bank (2020)

 

Table 2. Principal Component Analysis (PCA). Eigenvalues.

Table 3. Contribution of the Variables to the PCA1 component. The higher the variable value (max= 1), the higher the contribution to the PCA. The negative value represents the sign of the correlation. In this case, the total number of deaths is highly correlated with the other variables in the PCA, but with a negative ratio, i.e. the higher the basic sanitation, the lower the number of deaths in the country.

 

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