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Mortality rate and life Mortality rateand life
expectancy in Africa: expectancy in
Africa
the role of flood occurrence
Bismark Osei
Department of Economics, University of Ghana, Accra, Ghana
Mark Edem Kunawotor Received 30 July 2022Revised 20 September 2022
Department of Banking and Finance, University of Professional Studies, 25 November 2022
Accepted 29 December 2022
Accra, Ghana, and
Paul Appiah-Konadu
Department of Law, University of Brescia, Brescia, Italy
Abstract
Purpose – The purpose of this paper is to investigate the effect of flood occurrence on mortality rate and life
expectancy amongst 53 African countries.
Design/methodology/approach – The study utilizes panel data from the period 2000–2018 on 53 African
countries and system generalized method of moments (system GMM) for the analysis.
Findings –The result indicates that flood occurrence causes the destruction of health facilities and the spread
of diseases which reduces life expectancy. In addition, flood occurrence increases mortality rate amongst 53
African countries.
Research limitations/implications –
Practical implications – The study recommends that governments amongst African countries should
implement strategies being enshrined in Conference of Parties (COP, 2021) on climate change. This will help to
reduce the level of climate change and flood occurrence.
Originality/value – Previous studies focussed on the adverse effect of flood occurrence without considering
the issue of life expectancy amongst African countries. This study contributes to existing empirical studies by
examining the effect of flood occurrence on mortality rate and life expectancy amongst African countries.
Peer review – The peer review history for this article is available at: https://publons.com/publon/10.1108/
IJSE-07-2022-0508.
Keywords Life expectancy, Mortality rate, Flood occurrence, Health facility, Income inequality, Gross
domestic product (GDP)
Paper type Research paper
Introduction
Climate change has caused a lot of extreme weather events with respect to drought, heat
waves, extreme precipitation, flooding, hurricanes, landslides and bush fires (Gordon and
Wholeheartedly, the authors thank God for the immense knowledge and understanding He granted us
which has helped us to finish this paper. In addition, they thank Professor Edward Nketiah Amponsah
for his guidance for this paper.
Funding: This research received no specific grant from any funding agency in the public,
commercial, or not-for-profit sectors.
Disclosure statement: The authors report there are no competing interests to declare.
Data availability statement: The data that support the findings of this study are openly available in
[figshare] at [https://datatopics.worldbank.org/world-developmentindicators/] [https://databank.
worldbank.org/source/worldwide-governance-indicators/] [https://www.wider.unu.edu/project/wiid-%
E2%80%93-world-income-inequality-database/]
International Journal of Social
In the interest of transparency, data sharing and reproducibility, the author(s) of this article have Economics
made the data underlying their research openly available. It can be accessed by following the link here: © Emerald Publishing Limited
0306-8293
“DOI: 10.6084/m9.figshare.19603900.” DOI 10.1108/IJSE-07-2022-0508
IJSE Kate, 2016; Haitham and Jayant, 2014; Hilary et al., 2019). The excessive release of carbon
dioxide emission leads to higher atmospheric temperatures. This increases water bodies’
evaporation and cloud formation, this falls back to us in large quantities as rains and can lead
to flooding. UnitedNations Environment Program (UNEP) estimates indicate that, the level of
flooding has increased more than tenfold in Africa (UNEP, 2018).
According to Global Climate Risk Index (GCRI) report in 2019, amongst the top ten countries
heavily affected by climate change are countries from Africa: Mozambique, Zimbabwe, Niger,
South Sudan andMalawi were heavily affected by climate change (GCRI, 2019). This has led to
frequent flooding in countries like Mozambique, Niger, Sudan, Zimbabwe and others. Flood
occurrence deteriorates health facilities of affected countries. Aside the destruction of health
facilities, it leads to the spread of both water borne diseases (example typhoid fever, cholera and
others) and vector borne diseases for example malaria, dengue and others. In addition,
geographical areas that experience perennial levels of flooding discourage health personnel from
working in these affected regions (Hilary et al., 2019; Katie et al., 2018; Jouni, 2017). All these
undesirable conditions adversely affect the health of people.
A research carried out by Health Consumer Powerhouse Institute (HCPI, a think tank
based in Sweden) in 2018, indicated that in most part of Africa where there is persistent
flooding health personnel normally refuse employment posting to these areas due to the
health risk imposed by these flood occurrences (HCPI, 2018). This implies that countries in
Africa that experience persistent flooding will face the problem of unequitable distribution of
health personnel. This problem of unequitable distribution of health personnel results in the
rendering of poor health services. This leads to health related problems like morbidity rate,
mortality rate, reduced life expectancy, stunting and others (Johanna et al., 2019; Hilary et al.,
2019; Gordon and Kate, 2016; Katie et al., 2018).
Empirical studies carried out by Abbas and Routray (2014) and Collins et al. (2013) for
countries in LatinAmerica indicated that persistent flooding increasesmortality rate by 8.5%
and reduces life expectancy by 6.7%. With respect to African countries to what extent does
flooding affects mortality rate and life expectancy has not yet been explored with a recent
data set. There is the need to know the extent of the effect to help governments, foreign
stakeholders and other policy makers put up the needed strategies in order to help tackle the
adverse effect of climate change and flood occurrence in Africa. Hence, the study contributes
to existing empirical studies by examining the effect of flood occurrence onmortality rate and
life expectancy amongst African countries. Focussing onmortality rate and life expectancy is
appropriate. This is because statistics from the World Health Organization (WHO) indicates
that mortality rate amongst these countries has increased by 28% and life expectancy
reduced by 24% over the past five years (WHO, 2019).
Hence, there is the need to find out the causes of the situation and the appropriate
strategies to deal with that.
Literature review
This section focusses on both theoretical and empirical reviews to help enhance our
understanding on the subject matter.
Theoretical review
The study reviews a theory on behavioural change to help explain key issues on the subject
matter. Behavioural change theories explain how the behaviour of individuals changes over
the period of time. Specifically, it states how the environmental and social factors and other
character traits influence the behaviour of people. Amongst the numerous theories on
behavioural change, the study focusses on the theory of reasoned action.
The theory of reasoned action was developed by Icek Ajzen in 1985. The theory assumes Mortality rate
that individuals are rational in their endeavours in the sense that they consider the and life
consequences of their actions before they act. According to the theory, individuals’ intentions expectancy in
determine their behavioural change. The theory is relevant for the study because in most
African countries, the issue of flooding is sometimes caused by people’s irrational actions. Africa
Example throwing of plastic waste into gutters choke them leading to flooding. The
construction of buildings in waterways is also a typical example. Hence, there is the need for
behavioural change emanating from reasonable actions.
Empirical review
Empirical studies have been carried out on the subject matter; this section focusses on
reviewing these studies.
Damasceno-Junior et al. (2004) examined the mortality rate at Rio Paraguai (Brazil) after
extreme flooding. This region in Brazil experiences perennial flooding; hence, the study was
interested to find out its effect on mortality rate. The study gathered data from the residents
of Rio Paraguai based on severe flooding that occurred in 1995, for the analysis. Result of the
study indicated that mortality rate has increased by 9.1% over the past five years, due to
excessive flooding.
Collins et al. (2013) contribute to the study carried out by Damasceno-Junior et al. (2004) by
examining the people of Sudan vulnerability to flood occurrence and its associated health
risk. The study collected data from 589 households from communities in Aroma and
Tendellei for the analysis. The result indicates that 41% of rural households and 25% semi-
urban houses are highly vulnerable to flooding and its associated health risks like mental
illness, hepatitis A and E, dengue, malaria and others.
Murillo andTan (2017) fill the gap in existing studies carried out by Collins et al. (2013) and
Damasceno-Junior et al. (2004), by examining the effect of flooding on life expectancy when
you compare affected males and females in Southeast Asia. The study employed a regional
data set from the period 1995 to 2011 for the analysis. The result indicated that these
countries are highly vulnerable to the adverse effect of climate change. This reduces
socioeconomic conditions of males higher than females, thereby affecting their life
expectancy.
Vinet et al. (2019) contribute to existing empirical studies of Collins et al. (2013),
Damasceno-Junior et al. (2004) and Murillo and Tan (2017). The study examined mortality
rate caused by persistent flooding in the Mediterranean basin. The study employed 2011
pooling regional and national database on flood mortality from countries in the
Mediterranean basin for the analysis. The result indicated that flood-related deaths for
the south gradient of the Mediterranean basin are higher than that of the west–east
gradient.
Ferdous et al. (2020) contribute to existing empirical studies by focussing on structural
flood protection, population density and flood mortality in Jamuna River (Bangladesh). The
study employed primary and secondary data from Jamuna River. The study found that there
have been increasing levels of population density, flood-induced mortality and destruction of
valuable assets due to perennial flooding in Jamuna River.
Liu et al. (2022) contribute to these empirical studies reviewed by examining the patterns
of global flood occurrence effect on flood-induced mortality rate. The study employed data
covering the period between 1985 and 2019 for countries in Africa, Europe, Asia, North
America and South America for the analysis. The study found that flood-induced mortality
tends to be high, with Asian countries highly affected.
Borujeni et al. (2022) fill the gap in existing empirical studies (Collins et al., 2013;
Damasceno-Junior et al., 2004; Vinet et al., 2019; Murillo and Tan, 2017; Ferdous et al.,
IJSE 2020; Liu et al., 2022). The study examined social consequences of flooding on the quality
of life and life expectancy of flood victims of the Khuzestan province (Thailand). The
study employed data from 600 victims from the Khuzestan province for the analysis. The
study found that flood occurrence has adversely affected physical health, mental health,
social relations and life expectancy of these victims.
These studies and others (Gorman et al., 2003; Gelormino et al., 2015; Geary et al., 2021;
Diderichsen et al., 2019; Alvarenga et al., 2019;Walsh et al., 2020; Symonds et al., 2019; Pujolar
et al., 2016; Pierce, 2012; Nutbeam, 2004; Neagu et al., 2017) have not focussed on examining
the effect of flood occurrence on mortality rate and life expectancy amongst countries in
Africa with a recent data set. This study fills the yawning gap in existing studies by
employing a data set from 53 African countries to examine the subject matter. It is relevant to
undertake this study to help guide governments and other policy makers to enhance their
strategies towards the fight against climate change in order to help deal with the perennial
problem of flood occurrence.
Methodology
This section focusses on model specification, scope of the study, variables’measurement and
expected outcome.
Model specification
There is a higher tendency that a country experiencing higher levels of mortality rate and
reduced life expectancy in their previous year will have an effect on their current year’s
mortality rate and life expectancy. For example, assuming a country experiences a higher
mortality rate in 2014. This will adversely affect labour productivity, gross domestic product
(GDP) and tax revenue of the government, assuming majority of them are in the labour force.
This implies that government’s ability to raise enough revenue to improve the health system
will adversely be affected, leading to a higher mortality rate in 2015. Reduced life expectancy
will have the same effect.
Hence, the study employs a dynamic panel model for the analysis. A panel unit root test
was taken to check for the existence of a unit root in order to decidewhether to use generalized
method of moments (GMM) or systemGMM for the analysis. The result indicates existence of
a unit root (as shown in Table 1). Hence, system GMM is used to correct for the unit root for
robust results.
The general model for system GMM as specified by Hansen (1982) can be written as
Yit ¼ β0 þ β1Yit 1 þ β− 2Xit þ μi þ μt þ εit (1)
Yit 5 a dependent variable, Yit 1 ¼ a lag of a dependent variable and Xit 5 independent−
variables
Panel unit root test The panel unit root The Sargan test for validity
Test statistics statistic test p value of instruments
Inverse chi squared (p-value) 155.54 0.32
Table 1. Inverse normal (Z-value) 1.87 0.892
The panel unit root test Prob. > χ 0.27
and Sargan test Source(s): Authors’ creation
Ui;Ut and εit represent country fixed effect, time fixed effect and idiosyncratic error term, Mortality rate
respectively. and life
Based on (1), two equations are specified for the analysis; expectancy in
Mortalityit ¼ βo þ β1Mortalityi;t 1 þ β2Flooditþβ0X− it þ μi þ μt þ εit (2) Africa
In this equation, the study is interested to find out how flood occurrence affects mortality rate.
Flood occurrence leads to drowning and electrocuting people, wash away vehicles and others.
This is expected to increasemortality rate amongst these African countries.Xit represents the
vector of control variables that affect mortality rate. These include availability of health staff
(Healstaf), health facility (Heafac), income inequality (Incomeinequal), government health
expenditure (Healthexp), GDP, nutritional level (Nutri) and conflict occurrence (Conflict).
Expectancyit ¼ δo þ δ1Expectancyi;t 1 þ δ2Floodit þ δ3Floodit *Heafacþ δ3Flood− it
* Spdiseaseþ δ0Xit þ μi þ þ
(3)
μt εit
In this equation, the study is interested to find out how flood occurrence affects life
expectancy through the destruction of health facilities and the spread of diseases. Flood
occurrence is interacted with health facility and the spread of diseases. Flood occurrence by
destroying health facilities will adversely affect health services rendered. Without proper
measures put in place to fix these facilities, it is expected to reduce the life expectancy of the
citizenry. Again, as already reiterated flood occurrence leads to the spread of diseases,
without proper preventive measures, it is expected to reduce life expectancy. Xit represents
the vector of control variables: health staff (Healstaf), health facility (Heafac), income
inequality (Incomeinequal), government health expenditure (Healthexp), GDP, nutritional
levels (Nutri), conflicts occurrence (Conflict) and spread of diseases (Diseases).
Variables’ measurement, expected sign and source of data
This section presents variables for the study, their proxies and expected outcome. All the
variables are collected fromWorld Bank (World Development Indicators) with the exception
of income inequality which is collected from StandardizedWorld Income Inequality Database
(SWIID) from UNU-WIDER.
Dependent variable
Life expectancy measures the average number of years a person is expected to live. The
variable is measured in years. Mortality rate measures the number of deaths as per 100,000
populations which is attributed to household and ambient air pollution.
Independent variables
Flood occurrence is proxied by the level of water stress. It measures water level going
beyond excessive level after a place has experienced flooding. Flood occurrence causes the
destruction of health facilities and spread of diseases; this adversely affects the health of
people, as stated in the study of Abbas and Routray (2014). Hence, it is expected to reduce
life expectancy and increase mortality rate. The study wanted to include other variables:
droughts, heat waves, cold waves and landslides. But data were not available for majority
of these countries.
Control variables
Health facility is proxied by hospital beds. Hospital beds measure inpatient beds which are
available in public, private, general and specialized hospitals per 1,000 people. It is measured
IJSE in per 1,000 people. Governments’ ability to provide the needed health facilities contributes
towards reducing mortality rate and improving life expectancy as stated in the study of
Pujolar et al. (2016).
Availability of health staff is proxied by community health workers. Community health
workers are defined as the number of community health workers per 1,000 people. It is
measured in per 1,000 people. Making health staff available to render the needed health
services will lead to reducing mortality rate and improving life expectancy, as stated in the
study of Gorman et al. (2003).
Government health expenditure measures public expenditure incurred on health as a
share of GDP and government ability to spend on the health sector, when it comes to
provision of hospitals, training of health personnel and others.
These help to improve health services’ delivery to reduce mortality rate and improve life
expectancy as stated in the study of Neagu et al. (2017). The variable is measured in
percentage.
GDP per capita increasing overtime means that the standard of living of the citizenry
has improved. Thus the citizens are able to afford better health services and well-balanced
diet to reduce mortality rate and improve life expectancy. The variable is measured in
dollars.
Nutritional level is proxied by consumption of iodized salt as used in the study by
Johanna et al. (2019). Consumption of iodized salt measures the percentage of households
that use iodized salt for cooking. Improvement in the nutritional level of the citizenry helps
to build a good health system to withstand against diseases. Hence, it is expected to reduce
mortality rate and improve life expectancy. The variable is measured in the percentage.
Income inequality measures the extent of distribution of income within a country. It
ranges from 0 to 100, where 0 represents perfect equality and 100 represents perfect
inequality. Government’s inability to ensure equitable distribution of income will increase
mortality rate and reduce life expectancy. The reason is because individuals who receive less
income cannot afford better health services; this will adversely affect their health, as stated in
the study by Geary et al. (2021).
Conflict occurrence is proxied by the number of people displaced by conflict and violence,
as used in the study by Clare et al. (2009). This variable measures the number of people forced
to flee their homes due to armed conflict or violence. Conflict occurrence destroys health
facilities and produces harmful chemicals that pollute the environment. This leads to an
increasing mortality rate and reducing life expectancy. The variable is measured in the
number of people affected by conflict.
Spread of diseases is proxied by incidence of malaria cases. Incidence of malaria cases is
the number of new cases of malaria in a year per 1,000 people. Stagnant flooded water breeds
mosquitoes which lead to the prevalence rate of malaria. This increases mortality rate and
reduces life expectancy. The variable is measured in per 1,000 population. The study wanted
to include these diseases (typhoid fever, cholera and dengue) and the coverage rate of
immunization in the analysis. But the data that were available were for under five years;
hence they were not included.
Scope of the study
The study employs panel data covering the period 20002018 amongst 53 African countries
for the analysis. These 53African countries have been listed inTable 2. This period is selected
because during these periods, these countries have experienced perennial levels of flooding as
stated by UNEP (2018). Hence, the study is interested to find out the effect of flooding on
mortality rate and life expectancy amongst these countries and prescribes the best policy
direction.
Mortality rate
Algeria Cote divoire Kenya Nigeria Togo
and life
Angola Central African Rep Lesotho Niger Tanzania expectancy in
Benin Djibouti Liberia Rwanda Uganda
Burkina Faso Egypt Libya Sao Tomeo Zambia Africa
Botswana Equatorial Guinea Madagascar Senegal Zimbabwe
Burundi Eritrea Malawi Seychelles
Cameroun Ethiopia Mali Sierra Leone
Cape Verde Gabon Mauritania Somalia
Chad The Gambia Mauritius South Africa
Comoros Ghana Morocco Sudan
Congo, Dem. Guinea Mozambique South Sudan Table 2.
Congo, Rep. Guinea Bissau Namibia Tunisia List of countries used
Source(s): Authors’ creation for the study
Empirical results and discussion
This section discusses results from descriptive statistics, a panel unit root, the Sagan test and
system GMM.
Estimates from descriptive statistics, the panel unit root and the Sagan test
Estimates from flood occurrence show a mean value of 30.27, minimum value of 25.99 and
maximum value of 34.26 as shown in Table 3. The result does not show a wide difference in
flood occurrence amongst these countries. Life expectancy results indicate a mean value of
59.03, minimum value of 39.44 and maximum value of 76.69. The result does not show a wide
difference in the life expectancy amongst these countries.
Estimates from mortality rate indicate a mean value of 65.03, minimum value of 56.55 and
maximum value of 159.86. This indicates a wide difference in the mortality rate amongst these
countries. The decision to use either GMM or system GMM depends on the panel unit root test
as shown inTable 1. Estimate from the Fisher-type unit root test indicates p value of 0.32 which
means that there exists a unit root in the data; hence the use of systemGMM.The Sagan test for
validity of instruments as shown in Table 1 indicates that the instruments are valid.
Results from system generalized method of moments (system GMM)
This section that presents results from the system GMM analyses the effect of flood
occurrence on mortality rate and life expectancy.
Variable Observation Mean Standard deviation Minimum Maximum
Gross Domestic Product 956 2,197.27 3,179.11 111.93 5,942.61
Health facility 1,007 1.61 0.36 0.58 2.45
Health Staff 1,007 0.37 0.19 0.05 0.75
Spread of diseases 872 229.25 166.25 0.005 589.33
Gov’t health expenditure 954 1.73 1.11 0.062 6.049
Life expectancy 1,007 59.028 7.92 39.44 76.69
Nutritional level 1,007 71.22 7.88 54.48 84.8
Income inequality 967 54.29 7.71 31.88 74.23
Conflict occurrence 1,007 666,313.9 119,883.4 396,988.9 865,425
Mortality rate 1,007 65.03 22.49 56.55 159.86
Flood occurrence 1,007 30.27 2.66 25.99 34.26 Table 3.
Source(s): Authors’ creation Descriptive statistics
IJSE Effect of flood occurrence on mortality rate
Estimate from Table 4 indicates that the previous mortality rate (the lag mortality rate) has
negative effect on the current mortality rate with a value of 0.16. Put differently, the result
implies that the previous mortality rate reduces the current mortality rate. The result is
consistent with our expectation.
The study identified that these governments based on the information on their previous
mortality rate take proper measures to reduce their mortality rate. This accounted for the
results obtained.
Flood occurrence result indicates positive effect on mortality rate with a value of 0.021
when you control for health staff, GDP, health facility, government health expenditure,
nutritional levels, income inequality and conflict occurrence. Put differently, the result implies
that flood occurrence increases mortality rate amongst these countries. The result is
consistent with our expectation and results of these empirical studies (Babul et al., 2020; Maji
et al., 2021). Flood occurrence causes drowning and electrocuting people, submerging of cars
and other undesirable situations; these increasemortality rate amongst these countries. Flood
occurrence amongst these countries can be attributed to building on waterways, poor
drainage systems and choking gutters with plastic wastes and others.
Estimates from health facility and health staff indicate positive effect on mortality rate
with values of 0.39 and 0.417, respectively. Put differently, the results imply that services
rendered by health staff with these health facilities contribute towards increasing mortality
rate. The results are not consistent with our expectation and results from these empirical
Mortality/ (System GMM) (GMM) (System GMM) (GMM)
Life expectancy Mortality rate Mortality rate Life expectancy Life expectancy
Lag of Mortality 0.160*** 0.222***
(0.020) (0.027)
Flood occurrence 0.021*** 0.022*** 0.167*** 0.133**
(0.003) (0.004) (0.035) (0.048)
Health facility 0.390*** 0.349*** 0.109*** 0.020
(0.013) (0.016) (0.016) (0.022)
Health staff 0.417*** 0.454*** 0.577*** 0.582***
(0.032) (0.040) (0.018) (0.025)
Gov’t health expenditure 0.026 0.004 0.014 0.092***
(0.024) (0.023) (0.010) (0.011)
Income inequality 0.078*** 0.001 0.018*** 0.010**
(0.002) (0.007) (0.001) (0.003)
GDP 0.005 0.014 0.008 0.006
(0.011) (0.011) (0.007) (0.008)
Nutrition levels 0.013*** 0.017*** 0.002 0.002***
(0.001) (0.008) (0.004) (0.005)
Conflict occurrence 0.089*** 0.069*** 0.125*** 0.046**
(0.011) (0.014) (0.011) (0.015)
Lag of life expectancy 1.003*** 0.960***
(0.001) (0.002)
Flood*health facility 0.442*** 0.406***
(0.089) (0.119)
Flood*spread of disease 0.001*** 0.009***
(0.009) (0.001)
Table 4.
Effect of flood Spread of disease 0.003
*** 0.004***
occurrence on (0.003) (0.004)
mortality rate and life N 816 767 694 650
expectancy Source(s): Authors’ creation; standard errors are in parentheses: *p < 0.05, **p < 0.01 and ***p < 0.001
studies (Alvarenga et al., 2019; Diderichsen et al., 2019). Health services rendered by health Mortality rate
personnel have been identified as substandard due to poor maintenance and management of and life
health facilities; this accounted for the result obtained. A research was conducted by Health expectancy in
Consumer Powerhouse Institute (HCPI, a think tank based in Sweden) in 2018. The study
rated services rendered by health personnel and maintenance of health facilities in Africa as Africa
34.5 and 19.8%, respectively (HCPI, 2018). The study urges governments in Africa to work
towards improving these undesirable conditions.
Nutritional level estimate indicates negative effect on mortality rate with a value
of 0.013. Put differently, the result implies that improvement in the nutrition of citizens
helps to reduce mortality rate. The result is consistent with our expectation and results of
these empirical studies (Juan and Silvia, 2019; Cutler et al., 2006; Marielle et al., 2011; Collins
et al., 2013) ensuring proper nutritional practices, cutting down on excessive sugar intake,
frequent consumption of vegetables, fruits and herbs, consumption of smoked meats and
fishes, iodized salts’ usage and others. These protect the citizenry from high blood pressure,
high cholesterol, heart diseases, stroke, diabetes and others to reduce mortality rate.
Income inequality estimate indicates positive effect onmortality rate with a value of 0.078.
Put differently, these governments’ inability to ensure equitable distribution of income leads
to the increase in mortality rate.
The result is consistent with our expectation and results of these empirical studies (Abbas
and Routray, 2014; Johanna et al., 2019). Income inequality in Africa adversely affects lower
income earners, their ability to afford better health services and a well-balanced diet; this
increases mortality rate. Africa Development Bank (AfDB) indicated in 2018 that income
inequality is widening at an increasing rate. Hence, governments especially in West Africa,
Southern Africa and Central Africa should work towards reducing it.
Conflict occurrence estimate indicates positive effect on mortality rate with a value of
0.089. Put differently, the result indicates that occurrence of conflicts leads to an increasing
mortality rate amongst these countries. The result is consistent with our expectation and
results of these empirical studies (Symonds et al., 2019; Gurmesa et al., 2013; Katie et al., 2018).
Destructive activities that occur during conflicts contaminating water bodies with chemicals,
release of harmful chemicals into the atmosphere, shooting of people, food shortages and
others, increase mortality rate.
Effect of flood occurrence on life expectancy
From Table 4, previous year’s life expectancy estimate indicates positive effect on current
year’s life expectancy with a value of 1.003. This means that previous year’s life expectancy
contributes positively to improving life expectancy in the current year. Governments based
on the information on their previous year’s life expectancy take the necessary health
measures to improve their life expectancy; this accounted for the result.
Flood occurrence has negative effect on life expectancy with a value of 0.167. This
implies that flood occurrence directly reduces life expectancy. This is consistent with our
expectation and results of these empirical studies (Gurmesa et al., 2013; Haitham and Jayant,
2014; Maji et al., 2021). Flood occurrence directly destroys economic activities of affected
individuals. This adversely affects their standard of living which reduces their life
expectancy. The result for flood occurrence interaction with health facility indicates negative
effect on life expectancy with a value of 0.442. The result implies that flood occurrence
causes the destruction of health facilities to reduce life expectancy. The result is consistent
with our expectation and results of these empirical studies (Symonds et al., 2019; Reanos,
2021; Babul et al., 2020).
Flood occurrence leads to the destruction of properties like health facilities. This implies
that health personnel cannot render quality health service; this reduces life expectancy.
IJSE Statistics from Africa Union (AU) indicates that since 2000 flood occurrence has accounted
for 66% of disasters recorded in Africa (AU, 2018). Especially in countries like South Sudan,
Sudan, Somalia, Ethiopia and Kenya. This has destroyed a lot of properties including health
facilities which has adversely affected health services delivery and life expectancy.
The spread of diseases indicates negative effect on life expectancy with a value of0.003.
This implies that the spread of diseases directly reduces life expectancy. The result is
consistent with our expectation and the study result of Pujolar et al. (2016). The spread of
diseases, if not prevented, deteriorates people’s health system which reduces their life
expectancy. The result for flood occurrence interaction with the spread of diseases indicates
negative effect on life expectancy with a value of 0.001. Put differently, the result implies
that flood occurrence leads to the spread of diseases which reduces life expectancy of the
citizenry. This is consistent with our expectation. Stagnant flooded waters and the washing
away of dirty materials into water bodies lead to the spread of diseases like malaria, typhoid
fever, cholera, dengue and others. This reduces life expectancy if not prevented.
Estimates from health facility and health staff indicate negative effect on life expectancy
with values of 0.109 and 0.577. This implies that services rendered by health staff with
these health facilities reduce life expectancy. The result is not consistent with our expectation
and results of these empirical studies (Dialechti et al., 2011; Diderichsen et al., 2019). Health
staff inability to render quality services due to poor maintenance and management of health
facilities adversely affects the health of the citizenry. This reduces their life expectancy.
Income inequality estimate indicates negative effect on life expectancy with a value
of 0.018. Put differently, the result implies that the inability of governments to ensure
equitable distribution of income reduces life expectancy of the citizenry. The result is
consistent with our expectation and results of these empirical studies (Gelormino et al., 2015;
Nutbeam, 2004; Reanos, 2021). Income inequality adversely affects the standard of living of
low income earners. Leading to their inability to afford better health services and good
balance diet reduces life expectancy.
Conflict occurrence indicates negative effect on life expectancy with a value of 0.125.
This means that conflict occurrence reduces life expectancy of the citizenry. The result is
consistent with our expectation and the results of these empirical studies (Dialechti et al.,
2011; Adedeji et al., 2012).
Destructive activities of conflict that border on polluting water and air with chemicals
fromguns, shortage of food, slowing down economic activities and others adversely affect the
health of the citizenry which reduces their life expectancy.
Conclusion, policy implication and limitation
The issue of climate change has become a perennial problem in the world, of which Africa is
no exception. Excessive release of greenhouse gas emissions like carbon dioxide emission,
methane emission and others cause extreme weather events like flooding. Flood occurrence
and its destruction has been witnessed overall the world. In Africa, statistics from AU
indicates that since 2000s, flood occurrence has accounted for 66% of disasters in the
continent.
Hence, this study contributes to existing empirical studies by examining the effect of flood
occurrence on mortality rate and life expectancy amongst 53 African countries. These
countries desire to improve their mortality rate and life expectancy requires that issues of
flood occurrence andmeasures to prevent it should be taken seriously. Hence, this makes this
study relevant for policy makers. The result indicates that flood occurrence causes the
destruction of health facilities and the spread of diseases to reduce life expectancy. Result
from mortality rate indicates that flood occurrence through drowning and electrocuting
people, submerging of cars and other undesirable situations increase mortality rate.
In order to deal with climate change and its adverse effect of flood occurrence the study Mortality rate
recommends that governments amongst African countries should implement strategies and life
being enshrined in Conference of Parties (COP, 2021) on climate change. Countries should ban expectancy in
the use of plastics for packaging and adopt the usage of soil-decomposable packaging
material. Proportion of annual budget should be allocated towards the construction of Africa
drainage systems countrywide. Town and country planners should employ the use of
trackers, drones to monitor and prevent the construction of buildings on waterways. Future
empirical studies should focus on examining proper measures that needs to be
operationalized in order to develop sustainable and smart cities in Africa to deal with
climate change and flood occurrence.
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Corresponding author
Bismark Osei can be contacted at: bismarkosei84@yahoo.com
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