Supplementary Information
Supplementary Information for “Climate change and the resurgence of malaria in
the East African highlands”
This material provides additional information on (1) the availability of the climate data
used in the analyses, (2) highland malaria and its importance in the global climate
change and health context, (3) recent published statements of the Intergovernmental
Panel on Climate Change (IPCC) and the United States National Academy of Sciences
on climate change and health and (4) a more comprehensive reproduction of Fig. 2.
1. Data Sources and Links
The global terrestrial surface climatology are distributed for a modest handling charge
by the Climatic Research Unit (CRU) at the University of East Anglia, Norwich (URL:
http://www.cru.uea.ac.uk/link). The monthly data are at 0.5 x 0.5 degree spatial
resolution (which equates to approximately 55 x 55 km at the equator) and available
from January 1901 to December 1995 for mean temperature (oC x 10), diurnal
temperature range (oC x 10), precipitation (mm), vapour pressure (hPa x 10), wet day
frequency (days) and cloud cover (oktas x 10).
2. Highland malaria and climate change
From the very beginnings of malaria research it was recognised that mosquito
bionomics and malaria transmission are extremely sensitive to climate1. More recently,
this has led to conjecture on the possible future impacts on disease transmission
resulting from anticipated climate changes2,3. A logical, if methodologically
contentious, extension has been attempts to quantify these impacts geographically based
on the outputs of climate change scenarios4-6. In this discussion, areas of high altitude
(usually but not exclusively considered to be above 1600m) have been viewed as
particularly at risk7, since the extrinsic incubation period of P. falciparum in anophelene
vectors is limited by low temperature8,9. It has been argued therefore, that temperature
increases will facilitate transmission amongst previously unexposed and hence
immunologically naïve populations and so precipitate a public health crisis7. For these
reasons areas prone to highland malaria were the focus of these analyses.
3. Policy Statements of International Scientific Originations
The following statements are quoted verbatim. From the Intergovernmental Panel on
Climate Change (IPCC) 10.
“Many vector-, food-, and water-borne infectious diseases are known to be sensitive to
changes in climatic conditions. From the results of most predictive model studies, there
is medium to high confidencea that, under climate change scenarios, there would be a net
increase in the geographic range of potential transmission of malaria and dengue-two
vector-borne infections each of which currently impinge on 40-505 of the world
populationb. Within their present ranges, these and many other infectious diseases
would tend to increase in incidence and seasonality-although regional decreases would
occur in some infectious diseases. In all cases, however, actual disease occurrence is
strongly influenced by local environmental conditions, socioeconomic circumstances,
and public health infrastructure.”
a
High is a 67-95% chance and medium a 33-67% chance.
b
Eight studies have modelled the effects of climate change on these disease, five on malaria and three on
dengue. Seven use a biological, or processed-based approach, and one uses a statistical approach.
From the National Research Council (NRC) of the United States11.
“Observation and modelling studies must be interpreted cautiously. There have
been numerous studies showing an association between climatic variations and disease
incidence, but such studies are not fully able to account for the complex web of
causation that underlies disease dynamics and thus may not be reliable indicators of
future changes. Likewise, a variety of models have been developed to simulate the
effects of climatic change on the incidence of diseases such as malaria, dengue, and
cholera. These models are useful heuristic tools for testing hypotheses and carrying out
sensitivity analyses, but they are not necessarily intended to serve as predictive tools,
and do not include processes such as physical/biological feedbacks and human
adaptation. Caution must be exercised in using these models to create scenarios of
future disease incidence, and to provide a basis for early warnings and policy
decisions.”
3. Comprehensive version of Figure 2
Given the importance of seeing the original meteorological time series for all highland
resurgence sites and the limited space available in the main text, Fig.2 is reproduced to
include the Kabale, Gikonko and Muhanga sites.
a
b
30
400
25
300
20
200
15
100
10
MAY 1995
SEP 1992
JAN 1994
JAN 1990
MAY 1991
MAY 1987
SEP 1988
SEP 1984
JAN 1986
JAN 1982
MAY 1983
JAN 1978
MAY 1979
SEP 1980
SEP 1976
JAN 1974
MAY 1975
JAN 1970
MAY 1971
SEP 1972
JAN 1994
MAY 1995
SEP 1992
JAN 1990
MAY 1991
SEP 1988
MAY 1987
SEP 1984
JAN 1986
JAN 1982
MAY 1983
MAY 1979
SEP 1980
0
SEP 1976
JAN 1978
JAN 1974
MAY 1975
SEP 1972
JAN 1970
MAY 1971
5
JAN 1970
point moving average (thick line).
g
30
5
0
JAN 1990
MAY 1991
SEP 1992
JAN 1994
MAY 1995
JAN 1990
MAY 1991
SEP 1992
JAN 1994
MAY 1995
MAY 1987
SEP 1988
10
MAY 1987
SEP 1988
100
SEP 1984
JAN 1986
15
SEP 1984
JAN 1986
200
JAN 1982
MAY 1983
20
JAN 1982
MAY 1983
25
JAN 1978
MAY 1979
SEP 1980
h
SEP 1976
300
JAN 1978
MAY 1979
SEP 1980
10
SEP 1976
e
JAN 1974
MAY 1975
30
MAY 1971
SEP 1972
JAN 1970
MAY 1995
JAN 1994
SEP 1992
JAN 1990
MAY 1991
SEP 1988
MAY 1987
SEP 1984
JAN 1986
MAY 1983
JAN 1982
MAY 1979
SEP 1980
SEP 1976
JAN 1978
JAN 1974
MAY 1975
SEP 1972
JAN 1970
MAY 1995
SEP 1992
JAN 1994
MAY 1991
JAN 1990
MAY 1987
SEP 1988
SEP 1984
JAN 1986
JAN 1982
MAY 1983
JAN 1978
MAY 1979
SEP 1980
SEP 1976
JAN 1974
MAY 1975
MAY 1971
SEP 1972
JAN 1970
MAY 1995
JAN 1994
SEP 1992
JAN 1990
MAY 1991
SEP 1988
MAY 1987
SEP 1984
JAN 1986
MAY 1983
JAN 1982
MAY 1979
SEP 1980
SEP 1976
JAN 1978
JAN 1974
MAY 1975
SEP 1972
MAY 1971
5
JAN 1974
MAY 1975
JAN 1970
MAY 1971
25
MAY 1971
SEP 1972
JAN 1970
MAY 1995
JAN 1994
SEP 1992
JAN 1990
MAY 1991
SEP 1988
MAY 1987
SEP 1984
JAN 1986
MAY 1983
JAN 1982
MAY 1979
SEP 1980
SEP 1976
JAN 1978
JAN 1974
MAY 1975
SEP 1972
MAY 1971
c
300
d
20
200
15
10
100
0
300
f
25
200
20
15
100
0
Figure 2 Meteorological time-series presented by row for Kericho, Kabale, Gikonko and
Muhanga. a, c, e and g show minimum (bottom), mean (middle) and maximum (top) monthly
temperatures (oC), plotted with a 13 point moving average (thick line) to reveal the long-term
movement in these data. b, d, f and h show total monthly rainfall (mm), again plotted with a 13
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