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School on Modelling Tools and Capacity Building in Climate and Public Health
GITHEKO Andrew
15 - 26 April 2013
Climate and Human Health Research Unit Centre for Global Health Research
Kenya Medical Research Institute Busia Road, P.O. Box 1578-40100, Kisuma
KENYA
Evidence based climate risk management in health: the case of epidemic malaria
DR ANDREW GITHEKO CLIMATE AND HUMAN HEALTH RESEARCH UNIT KEMRI KISUMU DECEMBER 2012
EVIDENCE BASED CLIMATE RISK MANAGEMENT IN HEALTH: THE CASE OF EPIDEMIC MALARIA
Sensitivity of VBD to Climate Change
HIGHLY SENSITIVE MALARIA DENGUE CHIKUNGUNYA RIFT VALLEY FEVER
LOW SENSITIVITY SCHSTOSOMIASIS FILARIASIS LEISHMANIA TRYMANOSOMIASIS YELLOW FEVER
NTD SENSITIVITY TO CLIMATE CHANGE
Major NTDS in Kenya are schstosomiasis, soil helminths, filariasis, leishnaniasis hydatid disaese The above pathogens are compleex organisms that have low sensitivity to changes in temperature. They exisst in endemic forms Dengue and other viral pathogens are highly sensitive to temperature variations and exist in endemic and epidemic forms.
Effects of climate change and variability on VBD
Climate change increases the suitability of new areas to disease transmission Climate variability drives epidemics
Mean annual temperature trends Kisumu
y = 0.012x + 29.32
y = 0.0217x + 16.485
15.5
16.0
16.5
17.0
17.5
18.0
18.5
28.0
28.5
29.0
29.5
30.0
30.5
31.0
Tem
pera
ture
Cel
cius
Tem
pera
ture
Cel
cius
Mean Max
Mean Min
Linear (Mean Max)
Linear (Mean Min)
Rainfall trends March April May in Kisumu
y = -1.3871x + 579.44
0
100
200
300
400
500
600
700
800
900
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
Mea
n m
onth
ly r
ainf
all m
m
Time (Years
MAMMAMLinear (MAM)
Malaria case anomalies in Nandi district western Kenya associated with El Nino weather
Malaria case anomalies
-200 0
200 400 600 800
1000 Ja
n-80
Jan-
82
Jan-
84
Jan-
86
Jan-
88
Jan-
90
Jan-
92
Jan-
94
Jan-
96
Jan-
98
Time in years
Cas
e an
omal
y
Malaria spreads to Central Kenya highlands because of climate change
16.5
17
17.5
18
18.5
19
1977 1980 1983 1986 1989 1992 1995 1998 2001
Year
Tem
pera
ture
Cel
sius
Temperature threshold for malaria transmission
Temperature trend Threshold temperature exceeded in 1993
Many episodes of malaria per year in “U” shaped valley
Many episodes of malaria per year in “U” shaped valley
Few episodes of malaria in “V” shaped valley ecosystem
0
5
10
15
20
25
30
0 5 10 15 20 25
Par
asit
e pr
eval
ence
(m
icro
scop
y)
Antibody prevalence
0
5
10
15
20
25
30
IGUHU EMUTETE SHIKONDI FORT TERNAN
MARANI
Mea
n pr
eval
ence
Rat
es (%
)
SITES
ANTIBODIES MICROSCOPY
Low malaria prevalence low immunity: high epidemic risk
V-shaped & plateau
U-shaped
Evolution of malaria epidemics
Heats permanent breeding habitats
Anomalous temperatures
Accelerated mosquito development
Rainfall expands breeding habitats
Accelerated parasite development In mosquitoes
Mosquito population amplification
Transmission amplification
Months
1 2 3 4
100xER mm2ii
RTRT
1002 xER mmii
xRTxRT
10018 xER mmii
xRTxRT
Additive model
Multiplicative model
Models using climate data
18+C model
Evolution of an epidemic in Multiplicative model (Kericho)
40C 391 mm 580% 657% 795.%
Sep-97 OCT NOV DEC JAN_98 FEB MAR APR
Vector distribution and control
In the highlands 98% of the malaria mosquitoes are found in houses less that 500 meters from breeding habitats at the valley bottom Targeting these houses for indoor resting spry is highly effective and efficient strategy for malaria epidemic control
Targeted IRS Iguhu Kakamega
Impacts of house modification
House modification can reduce the the numbers of indoor mosquitoes by 88% The modification stabilizes the indoor temperatures, cooling the houses during the day and keeping them warm at night. The ceiling improves the house esthetics This is local technology that is affordable and acceptable by the local populations
Malaria epidemics Climate variability can increase the number of malaria cases by 100-700% and mortality by 500% Areas are most affected by the epidemics are the highlands of Kenya Uganda, Tanzania Ethiopia, Rwanda and Burundi and Madagascar An early epidemic warning system was developed for early prediction and prevention of the epidemic
Adaptation to climate change Malaria
Develop climate based early epidemic prediction models Identify new areas affected by malaria transmission Identify epidemic hotspots Use universal application of insecticide treated bed nets Apply targeted Indoor residual Spraying (IRS)
ADAPT
THANK YOU
Research funded by
IDRC and DFID