amazon malaria initiative / amazon network for the surveillance of anti-malarial drug resistance
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Amazon Malaria Initiative / Amazon Network for the Surveillance of Anti-malarial Drug Resistance Bogota, Colombia, March 17–19, 2009 “Climate Change and Malaria” or Climate Risk Management in Health Stephen Connor, International Research Institute for Climate & Society (IRI), - PowerPoint PPT PresentationTRANSCRIPT
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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
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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)
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Climate Change ^T 0.74C circa 100 years
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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)
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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
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Clearly its more complexmulti facetedMalariavspoverty
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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
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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
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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
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Demand for integrated early warning systems Monitoring
and SurveillanceIntegrated MEWS gathering cumulative evidence for early and focused epidemic preparedness and response (WHO 2004)
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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 >>>>
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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
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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
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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
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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
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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
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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)
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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
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Demonstrated progress..
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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:
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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.
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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
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Thank you for your attention
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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%
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e.g. the impact of climate trends.Very important consideration when establishing baselines
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e.g. Climate anomalies and epidemic malaria .Desert fringe malaria e.g. Botswana
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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