-
.
Amazon Malaria Initiative / Amazon Network for the Surveillance
of Anti-malarial Drug Resistance
Bogota, Colombia, March 1719, 2009
Climate Change and Malaria or Climate Risk Management in
Health
Stephen Connor, International Research Institute for Climate
& Society (IRI), The Earth Institute at Columbia University,
New York
PAHO/WHO Collaborating Centre on early warning systems for
malaria and other climate sensitive diseases
-
how can we get the knowledge benefit from recent advances in
climate science and observation
Into climate sensitive development sectors
to more effectively manage the associated risks affecting
vulnerable populations?sooner rather than later (e.g. MDG timeframe
2015)
-
Climate Change ^T 0.74C circa 100 years
-
And rainfall circa 100 years?Example: observed rainfall
variability in the Sahel 1900-2006.
long-term variability (linear trend),
decadal variability (after removing the linear trend)
inter-annual variability (after removing the linear and decadal
trends)
-
Malaria has also changed greatly in the past 100 years.
Americas: USA malaria declined as a result of changes in land
use and eradication which was declared in 1949 occasional import
malariaGuyana 1940s: 40,000 cases/1965: 22 cases/1994 84,017 cases
down again today
Europe: decline as a result of land use change/eradication some
resurgence >WWII. Eradication declared during 1950s and 1960s
occasional import malaria
Asia: India 1940s:circa 70 million cases/late 1950s circa
100,000/1970s >20 millionSri Lanka 1940s: circa 2 million
case/1963 17 cases/1967-68 massive resurgence but down again
today
Africa: Not included in the Global Eradication Campaign though
notable examplese.g. Swaziland 0 cases in 1972 - resurgence 1978 on
but down again today
-
Clearly its more complexmulti facetedMalariavspoverty
-
So does climate have a role to play ?Climate may impact on
health through a number of mechanisms
directly through cold or heat stress aggravating conditions such
as heart disease and respiratory conditions,
- and indirectly, for example through:
a) food security - nutritional status and immuno-suppression, b)
water source quality and water-borne diseasec) infectious diseases
malaria being a good example
-
Where (CS) disease is not adequately controlled . Then climate
information is relevant to informing on:
Seasonality in endemic disease
Shifts in the spatial distribution of endemic and epidemic
disease
Changes in risk of epidemic disease > Epidemic Early Warning
Systems
-
Climate and infectious disease Using Climate to Predict
Infectious Disease Epidemics. WHO 2005.. bacterial, viral and
protozoan ....other candidates, e.g some respiratory diseases not
included here. must remember socio economic factors very
important
Diseases include:Inter-annual variability:Sensitivity to
climate#:Climate variables:Influenza* * * * ** *T,RLeishmaniasis*
** * *>T,>RR.V. Fever* * ** * *>R,TMalaria* * * * ** * * *
*>R,T,HDengue* * * ** * *>R,T,H
-
Demand for integrated early warning systems Monitoring
and SurveillanceIntegrated MEWS gathering cumulative evidence
for early and focused epidemic preparedness and response (WHO
2004)
-
Demands for evidence-based health policyBefore using climate
information in routine decision making health policy advisors
need:
Evidence of the impact of climate variability on their specific
outcome of interest, and
Evidence that the information can be practically useful within
their decision frameworks, and
Evidence that using climate information is a cost-effective
means to improving health outcomes.. A case study
>>>>
-
An example: Malaria and MEWS in BotswanaBotswana straddles the
southern margins of malaria transmission in sub-Saharan Africa.The
incidence of malaria varies considerably from district to district
across the country showing a general north-south decreasing pattern
from more stable to less stable malaria.In Botswana the incidence
of malaria also varies considerably from year to year and as such
malaria is considered to be unstable and prone to periodic
epidemics (MoH 1999)
Chart1
0.0833586678
0.1531543615
0.2952740466
0.5620914097
1.2476005178
0.2745415745
7.3626056746
4.2772731631
1.4726555685
1.3438388418
0.305471802
10.5062815118
3.7455174182
1.5571186814
17.1698587823
12.9558331428
3.7107548535
7.9553579977
4.9074960255
2.805701595
0.7454551555
1.0384202284
1.9135764247
0.9406459483
Year
Incidence (per 1000)
Malaria incidence
Q1-Q5
CasesIncidenceLog incidence
YearConfirmedUnconfirmedPopulationConfirmedUnconfirmedEpidemicsRatioConfirmedUnconfirmed
19828533210196900.0830.32600.256-1.079-0.487
1983161116710512270.1531.11000.138-0.8150.045
1984320183110837390.2951.69000.175-0.5300.228
1985628186711172560.5621.67100.336-0.2500.223
19861437299411518111.2482.59900.4800.0960.415
1987326122811874340.2751.03400.265-0.5610.015
198890132158712241597.36317.63410.4180.8671.246
198953981484212620194.27711.76100.3640.6311.070
19901916845713010511.4736.50000.2270.1680.813
199117831201213267961.3449.05300.1480.1280.957
199241542931358554.201560490.3053.16000.097-0.5150.500
199314615407221391072.5677328410.50629.27410.3591.0211.466
19945335242511424369.293824373.74617.02600.2200.5741.231
19952271164511458463.010665431.55711.28000.1380.1921.052
199625641801011493372.7950341117.17053.63810.3201.2351.729
1997198111005791529118.1803304612.95665.77610.1971.1121.818
19985810596231565719.167506143.71138.08000.0970.5691.581
199912754728031603196.236255817.95545.41110.1750.9011.657
20008056715551641570.356476284.90743.58910.1130.6911.639
200147164828116808632.80628.72400.0980.4481.458
20021283289071721096.152609420.74516.79600.044-0.1281.225
20031830236571762292.326338891.03813.42400.0770.0161.128
20043453224041804474.5720711.91412.41600.1540.2821.094
20051738140191847666.49243450.9417.58700.124-0.0270.880
skewness1.773meanpre-19970.2627skewness-0.263
post-19970.1200
t test0.0004
Q1-Q5
Year
Incidence (per 1000)
Malaria incidence
Q6-Q12
TrendIncidence
YearMalariaRainRain^2InterventionVulnerabilityPost-policyNon-climateClimateAnomalyHighLow
1982-1.0791.823.31019820-3.712.63-0.6501
1983-0.8151.813.28019830-3.632.82-0.4601
1984-0.5301.953.80019840-3.563.03-0.2500
1985-0.2502.476.10019850-3.483.23-0.0400
19860.0962.385.66019860-3.413.510.2300
1987-0.5611.893.57019870-3.342.77-0.5001
19880.8673.6413.25019880-3.264.130.8510
19890.6313.9315.44019890-3.193.820.5410
19900.1682.425.86019900-3.113.280.0100
19910.1282.988.88019910-3.043.17-0.1100
1992-0.5151.712.92019920-2.962.45-0.8201
19931.0212.486.15019930-2.893.910.6410
19940.5743.3311.09019940-2.813.390.1100
19950.1921.893.57019950-2.742.93-0.3401
19961.2353.8014.44019960-2.673.900.6310
19971.1123.5512.60119971997-2.513.620.3410
19980.5692.184.75119981998-2.593.16-0.1100
19990.9012.657.02119991999-2.683.580.3110
20000.6914.8423.43120002000-2.773.460.1900
20010.4482.315.34120012001-2.863.310.0300
2002-0.1281.753.06120022002-2.952.82-0.4501
20030.0162.224.93120032003-3.043.05-0.2200
20040.2822.466.05120042004-3.133.410.1300
2005-0.0272.385.66120052005-3.213.19-0.0900
Regression results
Post-policyVulnerabilityInterventionRain^2RainInterceptRain^2RainInterceptMaximum
incidence
Coefficients-0.16290.0743325.3899-0.27042.0255-150.9926-0.27042.0255-3.27353.75
Standard
errors0.03660.015473.14610.06900.429130.43710.06230.38370.5482
R2, std
error0.88570.23880.00000.00000.00000.00000.75440.22110.0000
F,
DOF27.901818.00000.00000.00000.00000.000032.245121.00000.0000
Ssreg,
SSresid7.95771.02670.00000.00000.00000.00003.15311.02670.0000
Standard
errors4.45094.82904.44853.91894.72014.96084.34055.27855.9717
Q6-Q12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
DJF rainfall (mm/day)
Log incidence
Q13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Year
Detrended log incidence anomaly
Detrended malaria incidence
Q14
HighLow
YearHighLowRain indexIncidenceRain indexYearIncidenceRain
indexYear
1982012.790.853.791988-0.822.671992
1983012.780.643.361993-0.652.791982
1984002.920.633.791996-0.502.861987
1985003.350.543.781989-0.462.781983
1986003.290.343.781997-0.452.722002
1987012.860.313.471999-0.342.861995
1988103.790.233.291986-0.252.921984
1989103.780.193.472000-0.223.162003
1990003.320.133.352004-0.113.131998
1991003.630.113.751994-0.113.631991
1992012.670.033.242001-0.093.292005
1993103.360.013.321990-0.043.351985
1994003.75-0.043.3519850.013.321990
1995012.86-0.093.2920050.033.242001
1996103.79-0.113.6319910.113.751994
1997103.78-0.113.1319980.133.352004
1998003.13-0.223.1620030.193.472000
1999103.47-0.252.9219840.233.291986
2000003.47-0.342.8619950.313.471999
2001003.24-0.452.7220020.343.781997
2002012.72-0.462.7819830.543.781989
2003003.16-0.502.8619870.633.791996
2004003.35-0.652.7919820.643.361993
2005003.29-0.822.6719920.853.791988
meanst-test
High3.660.0006
Others2.87
Low2.780.0000
Others3.12
YearForecastLog incidenceIncidenceCasesEpidemicLow-risk
20064.620.2841.92355300
20071.93-0.4890.3259901
-
Vulnerability monitoringRoutine assessment of drug efficacy in
sentinel sites, susceptibility of the vector to insecticides,
coverage of IRS achieved each season
Regular assessment of drought-food security status from SADC
Drought Monitoring Centre - disseminates the information to the
epidemic prone DHTs
Recognises need for extra vigilance among its most vulnerable
groups, including those co-infected with HIV, TB, etc. Example in
practice: Botswana
-
Seasonal Climate ForecastingExample in Botswana .. SCF offers
good opportunities for planning and preparedness. NMCP strengthens
vector control measures and prepares emergency containers with
mobile treatment centresEvidence of impact of climate variability
on specific outcome of interest (Thomson, et al. Nature.
2006)Lead-time 5 months
-
Environmental monitoringExample in Botswana ENV monitoring
enables opportunities to focus and mobilise more localised
response, i.e. vector control and location of emergency treatment
centres.Adjusted malaria anomaliesEvidence of impact of climate
variability on specific outcome of interest (Thomson, et al. AJTMH.
2005)Lead-time 1 to 2 months
-
Case surveillanceExample in Botswana .. Of a number of
indicators (WHO 2004) the NMCP uses case thresholds defined for
three levels of alert
OKAVANGO SUB-DISTRICT
ACTION 1: When district notification reaches/exceeds 600
unconfirmed cases/week
DEPLOY EXTRA MANPOWER AS PER NATIONAL PLAN
Request 4 nurses from ULGS by telephone/fax
Collect the 4 nurses from districts directed by ULGS
Erect tents where needed
Catchment areas to deploy volunteers in hard-to-reach areas
Print bi-weekly newsletter to inform community about
epidemic
ACTION 2:When district notification reaches/exceeds 800
unconfirmed cases/week
DEPLOY MOBILE TEAMS PER DISTRICT PLAN
a) Each team to be up of a Nurse or FEW, a vehicle and a
driver
b) Deploy teams as follows:
TEAM AND DEPLOYMENT AREA
VEHICLE Reg No
Team A: Qangwa area
Council
Team B: Habu/ Tubu / Nxaunxau area
Council
Team C: Chukumuchu / Tsodilo / Nxaunxau area
Council
Team D: Shakawe clinic (vehicle and driver only)
DHT vehicle
Team E: Gani / Xaudum area
Gani HP vehicle
Team F: Mogotho / Tobera / Kaputura / Ngarange area
Mogotho HP vehicle
Team G: Seronga to Gudigwa area
Gudigwa HP vehicle
Team H: Seronga to Jao Flats
Boat
c) Deploy MO at Shakawe and 2 more nurses as per National
Manpower contingency plan
ACTION 3: When district notification reaches/exceeds 3000
unconfirmed cases /week
DECLARE DISTRICT DISASTER
a) Call for more outside help (manpower, vehicles, tents,
etc)
b) Convent some mobile stops to static treatment centres
c) Station nurses at the static treatment centres
d) Station GDA to assist nurse eg cooking for patients on
observation
e) Erect tents with beds and mattresses (6 10 beds/tents) at
selected centres
f) Station vehicles at selected centres
g) Deploy MO or FNP at Seronga
h) Station officer from MOH to co-ordinate epidemic control with
DHSCC
-
RBM: Southern African Regional MEWS activitiesEvidence for
practical application within a decision making framework (DaSilva,
et al. MJ 2004).Evidence for using environmental monitoring
(Thomson, et al. AJTMH 2005)Evidence for using seasonal forecasting
(Thomson, et al. Nature 2006).Evidence of timing/effectiveness
(Worrall, et al. TMIH 2007; Worrall, et al. 2008)
-
A test case for MEWS in the Southern Africa region
A wet year following three drought years (like 96/97) when major
regional epidemics had occurred
Classic post-drought epidemics have occurred periodically in
Southern Africas historyThe 2005/06 season in Southern Africa..
Chart2
-47.26232
-26.06732
-33.47732
168.3477
Sheet1
Dec 1979 - Feb 198042.48268
Dec 1980 - Feb 198176.96768
Dec 1981 - Feb 1982-83.39732
Dec 1982 - Feb 1983-84.13232
Dec 1983 - Feb 1984-71.66732
Dec 1984 - Feb 1985-24.73232
Dec 1985 - Feb 1986-32.68232
Dec 1986 - Feb 1987-77.09732
Dec 1987 - Feb 198879.99768
Dec 1988 - Feb 1989106.0677
Dec 1989 - Feb 1990-29.24732
Dec 1990 - Feb 199120.71768
Dec 1991 - Feb 1992-93.70232
Dec 1992 - Feb 1993-24.32732
Dec 1993 - Feb 199452.80268
Dec 1994 - Feb 1995-76.81232
Dec 1995 - Feb 199694.89268
Dec 1996 - Feb 199772.66268
Dec 1997 - Feb 1998-50.92232
Dec 1998 - Feb 1999-8.54732
Dec 1999 - Feb 2000188.1777
Dec 2000 - Feb 2001-39.71732
Dec 2001 - Feb 2002-89.78732
Dec 2002 - Feb 2003-47.26232
Dec 2003 - Feb 2004-26.06732
Dec 2004 - Feb 2005-33.47732
Dec 2005 - Feb 2006168.3477
Sheet1
Sheet2
Sheet3
-
Demonstrated progress..
-
And for application of the approach elsewhere ?in Colombia
(malaria & dengue)in East Africa (malaria, meningitis cholera
& RVF)in West Africa (malaria and meningitis)in South East Asia
(malaria, dengue and respiratory).. growing interest/demand from
other countries/regions:
-
Climate Risk Management for Health
Clearly we must take steps to mitigate Climate Change.
Howeverlearning to manage climate risk on a year to year basis is
undoubtedly our best method of adapting to climate changeA society
that manages current climate risks is less vulnerable - more
resilient giving it greater adaptive capacity to face the many
risks associated with climate change.
-
Need to:
Improve understanding of
climate-environment-disease-interaction. to build knowledge base
for risk management
Invest in effective control now & face the future with lower
disease burden
Develop more broadly informed surveillance systems to sustain
advances in control and ultimately elimination/eradication
-
Thank you for your attention
[email protected]
-
e.g. Seasonal climate and endemic malaria .Due to poor
epidemiological data in sub-Saharan Africa - climate data often
used to help model and map the distribution of disease.
Climate suitability for endemic malaria = 18-32C + 80mm +
RH>60%
-
e.g. the impact of climate trends.Very important consideration
when establishing baselines
-
e.g. Climate anomalies and epidemic malaria .Desert fringe
malaria e.g. Botswana
-
But - what is an epidemic? More cases than expected at a
particular place and time ? Where R0 temporarily goes above 1 ?
True epidemics infrequent (possibly cyclical) events in areas
where the disease does not normally occur e.g. warm arid/semi arid
zones and beyond the highland-fringe.
Unusually high peak in seasonal transmission
Neglect/breakdown of control resurgent outbreaks with subsequent
increase in endemicity level
Epidemics in complex emergencies transmission exacerbated by
population movement and political instability may include the above
and may be triggered by a climate anomaly
introduction of exotic vector (? rare ?)
The apparently simple question what is an epidemic? Comes up
time and time again. The simple definition of more cases than
expected at a particular place and time or when r0 rises above 1 is
not considered sufficient to help understand the variety of
situations which control services have to deal with.
In view of this WHOs Technical Support Network on Epidemic
Prevention and Control have recently suggested the a range of
definitions to characterize particular circumstances:
~ True epidemics infrequent, but possibly cyclical, events in
areas where the disease does not normally occur e.g. the warm arid
and semi arid zones~ Unusual seasonal transmission where the peak
in seasonal transmission is higher than expected~ Resurgent
outbreaks where previously controlled malaria emerges as a result
of control breakdown or neglect
A further definition is required for epidemics in complex
emergencies where transmission is unusually high as a result of
populations movements and political instability may include
components of the above and may be triggered by a climate
anomaly
