eCommons@AKU
./#-& **./&/0/" ./#-&
+1")"-
An integrated risk and vulnerability assessment
framework for climate change and malaria
transmission in East Africa
Esther Achieng Onyango
Griffith University
Oz Sahin
Griffith University
Alex Awiti
Aga Khan University("32&/&'0"!0
Cordia Chu
Griffith University
Brendan Mackey
Griffith University
+((+2/%&.*!!!&/&+*(2+-'./
%5," +))+*.'0"!0"./#-& "&
" +))"*!"!&//&+*
*4*$+%&*2&/&%0 '"4*&*/"$-/"!-&.'*!10(*"-&(&/4.."..)"*/#-)"2+-'#+-
(&)/" %*$"*!)(-&/-*.)&..&+*&*./#-& Malaria Journal, 15
Available at:
%5," +))+*.'0"!0"./#-& "&
Onyango
et al. Malar J (2016) 15:551
DOI 10.1186/s12936-016-1600-3
RESEARCH
An integrated risk andvulnerability
assessment framework forclimate change
andmalaria transmission inEast Africa
Esther Achieng Onyango
1*
, Oz Sahin
2,4
, Alex Awiti
3
, Cordia Chu
1
and Brendan Mackey
4
Abstract
Background: Malaria is one of the key research concerns in climate change-health relationships. Numerous risk
assessments and modelling studies provide evidence that the transmission range of malaria will expand with ris-
ing temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist
multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient under-
standing of the complex and interdependent factors that determine the risk and vulnerability of human populations
at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable
communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to
present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerabil-
ity to malaria due to climate change.
Results: Drawing upon published literature on existing frameworks, a systems approach was applied to characterize
the factors influencing the interactions between climate change and malaria transmission. This involved structural
analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal
loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework
that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.
Conclusions: A major conclusion was that this integrated assessment framework can be implemented using
Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with
stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to
malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that
incorporate both scientific and community perspectives.
Keywords: Integrated risk and vulnerability assessment, Climate change impact on malaria transmission, Systems
approach, Climate change and malaria risk, East Africa
© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(
http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/
publicdomain/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
Background
It is estimated that at least 3.3 billion people globally are
at risk of malaria infection. e disease is responsible for
over half a million deaths each year, mostly (90%) in sub-
Saharan Africa. Current climate change projections esti
-
mate an increase in the population at risk of malaria by
1.6 million by 2030 and 1.8 million by 2050 [
1, 2]. is
risk is significant in East Africa whereby rising temper
-
atures and changes in other climate conditions are pro-
jected to expand the transmission range of malaria into
geographic areas where communities were previously
unexposed to the disease [
3]. Understanding the extent to
which local communities are vulnerable to this risk and
how well they cope, is necessary to inform policies and
interventions for risk management.
Vulnerability is determined in part by changes in land
use and associated socio-economic and cultural factors
at the community level, which exacerbate climate change
Open Access
Malaria Journal
*Correspondence: esther.onyango@griffithuni.edu.au
1
Centre for Environment and Population Health, Griffith University,
School of Environment, 170 Kessels Road, Nathan 4111, Australia
Full list of author information is available at the end of the article
Page 2 of 12
Onyango
et al. Malar J (2016) 15:551
impacts on malaria transmission. Previous vulnerabil-
ity assessments have largely overlooked the influence
of these socio-economic and cultural factors, instead
emphasizing the biophysical influences on malaria trans
-
mission. While the evidence is abundant on increased
risk of malaria as a result of changing climate, more
robust understanding is needed of environmental, cul
-
tural and socioeconomic factors that influence malaria
transmission at the community and household levels.
is requires an integrated approach, which considers
climate along with the contribution of socio-economic
and cultural factors in order to explore current and future
risks and vulnerabilities to malaria transmission.
While there are general guidelines on conducting inte
-
grated risk and vulnerability assessments, there is not
one accepted method or approach in use that reflects
specific contexts and the availability of data. is paper
will provide a review of literature in climate change and
malaria transmission in East Africa, and use this previous
research to identify key variables in malaria transmission
in order to construct a systems conceptual model and an
integrated risk and vulnerability assessment framework.
The threat ofmalaria ina warmer world: climate change
andmalaria research inEast Africa
Warming over the African continent is faster than the
global average [
4]. Projections for the next century show
that most areas of the continent will exceed the 2 °C
threshold by the last two decades of this century under
medium scenarios and that under high scenarios this will
happen by mid-century and reach between 3 and 6 °C
by the end of the century [
3]. e malaria mosquito and
parasite are both sensitive to changes in climate and cli
-
mate variability and the projected rising temperatures
and changes in rainfall patterns will create favourable
conditions for mosquito breeding in many areas [
3, 4].
In East Africa, climate scenarios suggest longer malaria
transmission seasons and geographic expansion of the
disease into highland areas [
5–8]. According to pub
-
lished literature, the earliest malaria-climate connection
in the East African highlands was identified in the 1980s
when there was a series of malaria epidemics connected
to increases and anomalies in mean monthly maximum
temperatures and increase in rainfall in the highlands
[
9–12]. Since then, the frequency and size of epidem
-
ics increased with serious outbreaks in 1995, 1998 and
2002, corresponding to climate variations such as a sig
-
nificant increase (≥3°C) in mean temperatures [
9], high
rainfall [
10], drought and El Nino events [9, 13–17]. Con
-
currently, increasing human population and intensified
agricultural activities in the highlands has led to land use
changes that in turn have enhanced vector production.
At local scales, these changes in land cover, along with
differences in topography, result in micro-climatic vari
-
ability, raise surface temperatures by up to 2°C, may have
more of an impact on malaria transmission than climate
change alone [
18–22] and therefore should be included in
vulnerability and risk assessments.
Vulnerability assessments inclimate change andmalaria
research inEast Africa
Vulnerability studies, which have long been affiliated with
the disaster risk reduction and climate change adapta
-
tion communities [
23, 24] are now increasingly used to
map and interpret current and future risks related to
climate change. Vulnerability is determined in part by
human activities or interventions at the local level, which
may, if successful, counteract the negative impacts of cli
-
mate change. Furthermore, studies focused on projected
increases in malaria transmission as a result of changes in
climate should take into account the global decline of the
disease by 60% from 2000 to 2015 mainly as a result of
aggressive human interventions and treatment [
25–27].
erefore, a robust vulnerability assessment should not
only take into account the impact of the climate-induced
hazard to the population, but also the heterogeneity of
the population and for malaria transmission, the differ
-
ences in topography and hydrological characteristics of
the landscape and other biological and socio-economic
influences of transmission [
16, 28–36] in a holistic and
integrated manner [
4, 6, 7]. Such an approach can incor
-
porate an understanding of how changes in climate will
impact the current burden of the disease (biophysical
vulnerability). Moreover, this approach is also critical in
identifying vulnerable populations and their capacity to
respond (social vulnerability), taking into account other
factors that affect the current burden of malaria and the
effectiveness of current policies and programmes to man
-
age the disease [
37, 38].
Very few vulnerability assessments on climate change
and malaria in East Africa are in published literature. A
conceptual and methodological framework for model
-
ling of social vulnerability for the East African region
in a spatially explicit manner and independent of cur
-
rent disease prevalence, in order to provide options for
targeted interventions was presented by Kienberger and
Hagenlocher [
39]. Risk and vulnerability was framed
within the recent Inter Governmental Panel on Climate
Change (IPCC) definition [
40] in a dynamic and holis
-
tic manner and a number of related factors influencing
disease risk were considered. Analysis and results estab
-
lished links to risk governance, climate change adap-
tation and relevant intervention strategies to several
water-related vector borne diseases, including malaria.
In a related study, Bizimana et al. [
41] applied a com
-
posite indicator approach to assess social vulnerability
Page 3 of 12
Onyango
et al. Malar J (2016) 15:551
to malaria transmission in Rwanda at a district level. An
adapted vulnerability assessment framework [
39] was
used to identify indicators of different components of
vulnerability in terms of generic susceptibility (i.e., lack
-
ing capacity to anticipate) and biological susceptibility
(i.e., lacking capacity to cope or recover). Both studies
mapped the main indicators of social vulnerability to
malaria at district [
41] and East African regional [39] lev
-
els. While both of these approaches provide useful tools
for decision-making, only social vulnerability to malaria
was considered. Also, there is an assumption of homo
-
geneity of the population and landscape, which suggests
uniformity of indicators while in reality there are differ
-
ences in population and factors such as topography that
will have an impact on the weight of indicators. Both
papers acknowledge these limitations by suggesting that
interventions should take into account the relevance of
specific indicators of malaria vulnerability for different
regions [
39] and that future research should focus on an
integrated vulnerability assessment that combines both
environmental and social drivers [
41].
Further research by Hagenlocher and Castro [
42]
addressed some of these limitations by modelling multi-
dimensional vulnerability in Tanzania in a holistic and
spatially explicit manner, using estimates of entomologi
-
cal inoculation rate (EIR) i.e. risk of infective bite as a
proxy for malaria hazard. Causes of malaria risk and vul
-
nerability were demonstrated to vary considerably across
the country and risk, hazard and vulnerability maps that
allow prioritisation of areas for malaria control were
produced. By integrating malaria risk, vulnerability, and
contributing factors in a holistic framework, evidence
of issues that needed to be addressed locally to reduce
malaria risk while accounting for variability within dis
-
tricts was provided. A useful output was an easily adapt-
able modelling framework however; limitations included
incompatibility of the model with data that were not
available in a spatially disaggregated format. is means
that key vulnerability indicators such as acquired immu
-
nity to malaria, availability of malaria drugs, migra-
tion patterns, quality of the healthcare system, personal
beliefs, behaviours and social networks were not included
in the final model [
42].
More recently, Bizimana et al. [
43] integrated a set of
weighted vulnerability indicators to define homogenous
regions of social vulnerability to malaria in Rwanda.
Although a useful approach in determining targeted
interventions to specific high-risk areas and focus on fac
-
tors that influence vulnerability, the model limitations did
not allow for inclusion of key vulnerability indicators that
did not have quantitative measurements such as social
networks, migration and behavioural change. While these
studies provide suitable frameworks for assessment of
social vulnerability to malaria, none of them considered
climate change/variability and the associated biophysical
and social vulnerability at the same time. Biophysical and
socio-economic factors are interdependent and must be
considered simultaneously within an integrated systems
framework, which assesses risk and vulnerability of com
-
munities to malaria. erefore, using a systems approach,
this paper builds on previous research to develop a
framework for conducting an integrated risk and vul
-
nerability assessment of the interplay among biophysical
(especially climate change) and socio-economic and cul
-
tural factors on malaria transmission in East Africa.
Methods
Building the systems model was an iterative process that
involved problem definition and development of a con
-
ceptual model of the system under study. e model is
then used to suggest the development and calibration of a
Bayesian Belief Network (BBN) model. While there is not
a standardized procedure for system’s modelling, there are
some common steps as described by Sterman and Voinov
[
44, 45], which were adapted for our modelling process
(Fig.
1). is approach captures contextual and expert
knowledge of the system and then subjects the informa
-
tion to a structural analysis, which is used to formulate a
conceptual model in the form of an influence diagram. is
is the first step in developing a BBN model. BBN models
are a useful method for undertaking scenario simulations
because they can assimilate different kinds of data and
information including qualitative social survey results,
quantitative biophysical response functions, spatial envi
-
ronmental data, expert opinion and even missing data [
43].
Problem denition
is step involved an extensive literature review and
expert consultations on the key variables and relation
-
ships involved in the climate change and malaria trans-
mission cycle. ree key academics well versed in climate
change, malaria transmission and climate change-malaria
research in East Africa were contacted and consulted.
e experts were provided with contextual information
regarding the research and were interviewed on their
knowledge of the connections between climate change
and malaria transmission. Comprehensive reviews of cli
-
mate change and malaria transmission have been covered
in other papers [
21, 46–48]. Some of these studies have
developed suitable environmental, socio-demographic
and behavioural indicators of malaria risk at regional,
community and household levels [
15, 21, 33, 48–50]. is
previous knowledge and expert consultations were used
to capture relevant knowledge about the system and to
identify the relationships between key variables influenc
-
ing risk of malaria infection in East Africa.
Page 4 of 12
Onyango
et al. Malar J (2016) 15:551
Structural analysis
e malaria transmission cycle is a complex system with
multiple non-linear and often interacting variables of
climate change, environmental, biological and socio-
economic influences thus, conceptualizing such a system
is challenging. e cross-impact multiplication method
(CIMM) [
51] was used to undertake a structural analysis.
e structural analysis method revealed key system com
-
ponents and interactions from a candidate set identified
from the literature review and expert consultations and
followed a four-step iterative process:
a. Compilation of a candidate set of key variables from
the literature review and expert consultations;
b. Description of the relationships between variables
based on contextual knowledge and expert opinion.
e degree of influence between variables was rated
0 if there was no evidence of direct influence between
two variables. Otherwise, the strength of the rela
-
tionship was rated 1(low), 2 (medium), 3 (high) or 4
(potential);
c. Identification of key variables using the CIMM
approach which calculates the intensity of influence
and dependency between variables; and
d. e CIMM approach was also used to identify the rela
-
tionships between the identified variables of the system
through an analysis of the impact matrix by generat
-
ing a map of direct influence, which separates the vari-
ables into four types according to degree of influence:
(i) influential variables, which influence the system, but
are not dependent on other variables; (ii) relay variables,
which influence the system and are dependent on influ
-
ential variables; (iii) dependent variables, which repre-
sent the system’s output variables and; (iv) autonomous
variables, which are neither influential nor depend
-
ent and may or may not significantly affect the system
depending on the strength of their relationships.
Visualization ofthe systems conceptual model
After the structural analysis phase, an influence diagram
(also known as a causal loop diagram or CLD) was con
-
structed to visualize the key variables and interactions
of the system). In an influence diagram, variables repre
-
sent a stock of something or a quality of some kind that
can increase or decrease. e variables are connected or
linked by arrows that indicate a causal relationship; typi
-
cally, a flow of information, energy or materials that cause
a shift in the stock or quality of the affected variable. e
Fig. 1 A flow chart showing the adapted systems modelling process