. Amazon Malaria Initiative / Amazon Network for the Surveillance of Anti-malarial Drug Resistance Bogota, Colombia, March 17–19, 2009 “Climate Change.

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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), 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 0.74ºC circa 100 years

And rainfall circa 100 years?

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Long term trend: 18% Variance

Inter-decadal variability: 27% Variance

Inter-annual variability: 55% Variance

Example: observed rainfall variability in the Sahel 1900-2006.

(a) long-term variability (linear trend),

(b) decadal variability (after removing the linear trend)

(c) inter-annual variability (after removing the linear and decadal trends)

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 million

Sri 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 examples

e.g. Swaziland 0 cases in 1972 - resurgence 1978 on – but down again today

Malar

ia

T i m e

Malaria has also changed greatly in the past 100 years….

Clearly its more complex…multi faceted…

Malariavs

poverty

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 disease

c) 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

Diseases include:

Inter-annual variability:

Sensitivity to climate#:

Climate variables:

Influenza * * * * * * * <T

Meningitis * * * * * * * >T,<H,>R

Leishmaniasis * * * * * >T,>R

R.V. Fever * * * * * * >R,<T

Cholera * * * * * * * * * * >T

Malaria * * * * * * * * * * >R,T,H

Dengue * * * * * * * >R,T,H

.. bacterial, viral and protozoan ..

..other candidates, e.g some respiratory diseases not included here….

… must remember socio economic factors very important…

Monitoring

and Surveillance

Demand for integrated early warning systems …Integrated MEWS gathering cumulative evidence for early and focused epidemic preparedness and response (WHO 2004)

Climate

Env-Info

Demands for evidence-based health policy

Before 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 Botswana

Malaria incidence

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ce (

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Botswana 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)

Malaria incidence

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Policy Changes

Vulnerability monitoring

Routine 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 Forecasting

5115N =

Adjusted malaria anomalies

highmediumlow

Forec

asted

rainf

all -

DEME

TER

NDJ (

mm/da

y)

4.0

3.5

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Example in Botswana ….. SCF offers good opportunities for planning and preparedness. NMCP strengthens vector control measures and prepares emergency containers with mobile treatment centres

Evidence of impact of climate variability on specific outcome of interest (Thomson, et al. Nature. 2006)

Lead-time 5 months

Environmental monitoring

Example in Botswana …ENV monitoring enables opportunities to focus and mobilise more localised response, i.e. vector control and location of emergency treatment centres….

5125N =

Standardised malaria incidence anomaly quartiles

>75%<25%

CMAP

DJF

quad

ratic

mode

l

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1993

Adjusted malaria anomalies

Evidence of impact of climate variability on specific outcome of interest (Thomson, et al. AJTMH. 2005)

Lead

-tim

e 1

to 2

mon

ths

Case surveillance

Example 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

Threshold 1- 600 unconfirmed cases/week >>> Action Plan 1.

Threshold 2- 1000 unconfirmed cases/week >>> Action Plan 2.

Threshold 3- 3000 unconfirmed cases/week >>> Action Plan 3.

RBM: Southern African Regional MEWS activities

Evidence 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)

2005-06

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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 Africa’s history

The 2005/06 season in Southern Africa…..

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. However……

learning to manage climate risk on a year to year basis is undoubtedly our best method of adapting to climate change

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OBS

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A 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]

PAHO/WHO Collaborating Centre on early warning systems for malaria and climate sensitive diseases

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

= 18-32ºC + 80mm + RH>60%

Temporal information useful for developing seasonal disease calendars for control planning purposes

e.g. the impact of climate trends….

Changes in malaria <endemicity (Faye et al 1995)

Changes in meningitis>southward extension of ‘Meningitis Belt’ >epidemic frequency (Molesworth et al 2003)

Very important consideration when establishing baselines

30 year drought >?

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 ?)