Presented by Madeleine Thomson, International Research Institute for Climate and Society and The Earth Institute at Columbia University, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.

1. :
Use of climate information in the
assessment of impact of malaria
interventions.
Dr Madeleine Thomson
Co-authors: Frank Zadravecz, Derek Willis, Tufa
Dinku, Bradfield Lyon, Remi Cousin, Gilma Mantilla
and Pietro Ceccato,.
Acknowledgements:
Ethiopian National Meteorological Agency, Tanzanian
Meteorological Agency
PMI-CDC USAID
Sixth MIM Pan-African Malaria Conference
October 6-11, 2013, Durban, South Africa
Symposium 38: October 9th, 2013
Analytic challenges in measuring impact of malaria control
programs: Methodological approaches, confounders and lessons
learned from the multi-agency malaria control impact evaluations
PAHO/WHO Collaborating Centre on early warning
systems for malaria and other climate sensitive diseases

2. Climate as a confounder for the measurement of
the impact of malaria interventions
 The RBM / Monitoring and Evaluation Reference Group
recommendation ….follow trends in the coverage of
malaria control interventions, all factors influencing
childhood mortality, malaria-associated morbidity.
 A minimal list of potential confounders, determined a
priori, should be available for all countries.
 The methodology indicates the need to explain
contextual (confounding) factors that affect the
epidemiology of malaria, like climate.

3. Why is climate unique?
 its climatology
 seasonality
 climate is routinely
measured
 diurnal rhythm and
 potential predictability at
multiple time scales
 weather
 seasonal
 decadal (not available)
 climate change
 and modeled
 by others – all over the
world

4. Rainfall in Africa
…why East Africa is unique
Rainfall amount
Rainfall invariability
Rourke (2011)

5. Predicted and actual trends in East African
rainfall
Left: Projected climate change 2080-2099) IPCC 4th
Assessment, Right: Observed decline in MAM rainfall 19792009) Funk (2011)

6. Recent and abrupt decline in the East African
long rains (Mar-Apr-May) – linked to decadal
sea- surface temperatures
Lyon, B., DeWitt, D., (2012) Geophysical Research
Letters

7. In the absence of control climate drives malaria
transmission across the continent
Gething, P.W., Patil, A.P.*, Smith, D.L., Gu
erra, C.A., Elyazar, I.R.F., Johnston
, G.L., Tatem, A.J. and Hay, S.I. (2011). A
new world malaria map: Plasmodium
falciparum endemicity in 2010. Malaria
Journal, 10: 378. *indicates equal
authorship.

8. Climate impacts on malaria at multiple
scales …..from national….
Anomalies in malaria incidence in Botswana is strongly
related to rainfall variability during the peak rainfall
season December – February. (1982-2003) using CMAP

9. ..to the local level (kericho tea estate)

10. But problems exist for measuring and
disseminating rainfall and temperature
information across scales
 So users:
 Do local analysis only
 Use satellite - derived estimates of
insufficient accuracy
 Use gridded data of poor resolution
 Use proxies for climate such as NDVI
 Ignore climate all together

11. Malaria decline in Eritrea relative to 1999
baseline
NDVI Vegetation Index (a proxy
for rainfall) also decreased
0
120
80
60
Mean
0
NDVI
Malaria incidence
Mean malaria incidence
100
0
40
0
ADDS NDVI (Dek mean)
20
ADDS NDVI (MVC)
0
0
CF NDVI (MVC)
1996
1996
1997
1998
1999
2000
Y EAR
2001
2002
2003
1998
1997
2000
1999
2002
2001
2003
Y EAR
Graves, P.M. et al., (2008) Effectiveness of malaria control in Eritrea, 1998 to 2003. Tropical
Medicine and International Health 13, (2) 218-228

12. Possible outcome if climate is not taken
into account………
Changes in observed malaria following
intervention (relative to baseline)
Changes in climate suitability
for malaria transmission
following intervention
(relative to baseline)
increase
decrease
no change
or increase
may underestimate
impact of
intervention
may obscure impact
of intervention
no change (average)
decrease
no effect
may overestimate
may obscure extent
impact of
of failure of
intervention
intervention

13. Ethiopia Confirmed Malaria Incidence / 1000 / year
in relation to climate
Combined sources from Ethiopia FMOH; Climate data from IRI
SST anomaly
LST anomaly
Incidence
2.5
10
2
8
1.5
6
1
4
0.5
2
0
0
SST or LST anomaly
Malaria conf cases per 1000 persons
12
-0.5
SST v Malaria Incidence y = 0.0268x - 0.157
R² = 0.18107
Graves et al., 2012

14. Enhanced National
Climate Services
(ENACTS) Ethiopia
New ENACTS products
combine locally
calibrated satellite
rainfall and temperature
(min and max) estimates
and all available quality
controlled ground-based
meteorological station
gauge data (>300 for
temperature and >600
for rainfall) available in
Ethiopia for the period
(1983-2010).

15. Climate suitability for malaria
transmission tool
Grover-Kopec, E., et al., An online operational rainfall-monitoring resource for epidemic malaria
early warning systems in Africa. Malaria Journal, 2005. 4(6).

16. Climate suitability for malaria
transmission tool

17. Climate Analysis Tool
Belg (Feb-May)
Kiremt (Jun-Sep)
Rainfall
Maximum
Temperature
Minimum
Temperature

18. WASP (Weighted Average
Standardised Precipitation ) TOOL

19. Rainfall analysis at the
Zone level –East Shoa

20. Malaria control and
elimination and economic
development in conflict in
East Shoa
Woreda and sub-Woreda
analysis

21. Building capacity to use the information
in Ethiopia

22. Enhanced National Climate Services
(ENACTS) Tanzania
Weighted Average Standardised Precipitation at National
level for Tanzania using a 1995-1999 baseline

23. Ethiopia
Changes in observed malaria following
intervention (relative to baseline)
Changes in climate suitability
for malaria transmission
following intervention
(relative to baseline)
increase
decrease
no change
or increase
may underestimate
impact of
intervention
may obscure impact
of intervention
no change (average)
decrease
no effect
may overestimate
may obscure extent
impact of
of failure of
intervention
intervention

24. Tanzania
Changes in observed malaria following
intervention (relative to baseline)
Changes in climate suitability
for malaria transmission
following intervention
(relative to baseline)
increase
decrease
no change
or increase
may underestimate
impact of
intervention
may obscure impact
of intervention
no change (average)
decrease
no effect
may overestimate
may obscure extent
impact of
of failure of
intervention
intervention

25. Conclusions
 The new ENACTS product for Ethiopia and Tanzania are suitable for
incorporation into national malaria impact assessments
 There is significant warming (approx. 0.2-3oC per decade) in many (but not
all) regions of Ethiopia and Tanzania over the last three decades.
 Ethiopian intervention period (2006-2010) was warmer and wetter than
baseline (2000-2005)
 Tanzanian intervention period (2000-2010) was warmer and substantially
drier than baseline period (1995-1999)
 Careful choice of baseline year(s) is key to reduce the impact of climate as a
confounder on malaria assessments
 Incorporation of climate data into statistical and mathematical models of
malaria at multiple scales is now feasible – but malaria data remains weak

26. :
Thank you
mthomson@iri.columbia.edu
PAHO/WHO Collaborating Centre on early warning
systems for malaria and other climate sensitive diseases