2453-25

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