Transcript Mexico meeting 2004 - Amazon Malaria Initiative

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
200
And rainfall circa 100 years?
150
Long term trend: 18% Variance
100
50
Example: observed rainfall variability
in the Sahel 1900-2006.
0
-50
-100
-150
-200
1900 1910 1920
1930 1940 1950 1960 1970 1980
1990 2000 2010
200
(a) long-term variability (linear trend),
150
Inter-decadal variability: 27% Variance
100
50
0
(b) decadal variability (after removing
the linear trend)
-50
-100
-150
-200
1900 1910
200
1920 1930 1940 1950
1960 1970 1980 1990
2000 2010
Inter-annual variability: 55% Variance
150
(c) inter-annual variability (after
removing the linear and decadal
trends)
100
50
0
-50
-100
-150
-200
1900 1910
1920 1930 1940 1950
1960 1970 1980 1990
2000 2010
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’ malaria
Guyana 1940s: 40,000 cases/1965: 22 cases/1994 84,017 cases – down again today
M
a
Europe:
decline as a result of land use change/eradication – some resurgence >WWII.
l
Eradication declared during 1950s and 1960s – occasional ‘import’ malaria
a
r
Asia:
i India 1940s:circa 70 million cases/late 1950s circa 100,000/1970s >20 million
a Lanka – 1940s: circa 2 million case/1963 17 cases/1967-68 massive resurgence –
Sri
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
Time
Clearly its more complex…multi faceted…
Malaria
vs
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 Sensitivity
variability:
to climate#:
Climate
variables:
Influenza
*****
**
<T
Meningitis
****
***
>T,<H,>R
**
***
>T,>R
***
***
>R,<T
Cholera
*****
*****
>T
Malaria
*****
*****
>R,T,H
Dengue
****
***
>R,T,H
Leishmaniasis
R.V. Fever
.. bacterial, viral and
protozoan ..
..other candidates, e.g
some respiratory
diseases not included
here….
… must remember
socio economic factors
very important…
Demand for integrated early warning systems …
Integrated MEWS gathering cumulative evidence for early and
focused epidemic preparedness and response (WHO 2004)
Monitoring
Climate
Env-Info
and Surveillance
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
Botswana straddles
the southern margins of
Policy Changes
malaria transmission in sub-Saharan Africa.
20.000
18.000
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.
14.000
12.000
Malaria incidence
10.000
20.000
8.000
18.000
10.000
8.000
6.000
4.000
Year
4
20
04
20
0
0
20
02
20
02
20
0
6
19
98
20
00
19
9
19
94
2
19
98
19
9
19
90
4
Year
8
0.000
19
96
19
8
2.000
19
86
19
90
19
88
19
86
19
84
19
82
also varies
0.000 considerably from year to
year – and as such malaria is
considered to be ‘unstable’ and prone
to periodic epidemics (MoH 1999)
12.000
19
94
19
8
2.000
In Botswana
the incidence of malaria
14.000
19
82
4.000
Incidence (per 1000)
16.000
6.000
19
92
Incidence (per 1000)
16.000
Vulnerability monitoring
Example in practice: Botswana …
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.
Seasonal Climate Forecasting
Example in Botswana ….. SCF offers
good opportunities for planning and
preparedness. NMCP strengthens vector
control measures and prepares
emergency containers with mobile
treatment centres
4.0
3.5
3.0
2.5
2.0
1.5
1.0
.5
N=
5
11
5
low
medium
high
Adjusted malaria anomalies
Evidence of impact of climate variability on specific
outcome of interest (Thomson, et al. Nature. 2006)
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….
2.0
1.5
1.0
.5
1993
0.0
-.5
-1.0
-1.5
-2.0
N=
5
<25%
12
5
>75%
Adjusted
malaria
Standardised malaria
incidenceanomalies
anomaly quartiles
Evidence of impact of climate variability on specific outcome
of interest (Thomson, et al. AJTMH. 2005)
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
Threshold 1- 600 unconfirmed
cases/week >>> Action Plan 1.
ACTION 2: When district notification reaches/exceeds 800 unconfirmed cases/week
Threshold 2- 1000 unconfirmed
cases/week >>> Action Plan 2.
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
Team A: Qangwa area
Team B: Habu/ Tubu / Nxaunxau area
Team C: Chukumuchu / Tsodilo / Nxaunxau area
Team D: Shakawe clinic (vehicle and driver only)
Team E: Gani / Xaudum area
Team F: Mogotho / Tobera / Kaputura / Ngarange area
Team G: Seronga to Gudigwa area
Team H: Seronga to Jao Flats
Council
Council
Council
DHT vehicle
Gani HP vehicle
Mogotho HP vehicle
Gudigwa HP vehicle
Boat
Reg No
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)
b)
c)
d)
e)
f)
g)
h)
Call for more outside help (manpower, vehicles, tents, etc)
Convent some mobile stops to static treatment centres
Station nurses at the static treatment centres
Station GDA to assist nurse eg cooking for patients on observation
Erect tents with beds and mattresses (6 – 10 beds/tents) at selected centres
Station vehicles at selected centres
Deploy MO or FNP at Seronga
Station officer from MOH to co-ordinate epidemic control with DHSCC
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)
The 2005/06 season in
Southern Africa…..
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
2005-06
200
150
100
50
0
-50
-100
Dec 2002 - Feb Dec 2003 - Feb Dec 2004 - Feb Dec 2005 - Feb
2003
2004
2005
2006
Demonstrated progress…..
And for application of the approach elsewhere ?
.. growing interest/demand from other countries/regions:
in Colombia
(malaria &
dengue)
in West Africa
in South East Asia
(malaria and
(malaria, dengue
meningitis)
and respiratory)
in East Africa
(malaria, meningitis
cholera & RVF)
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
700
650
600
550
500
450
400
350
OBS
Linear
Decadal
300
250
200
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
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….
30 year drought >?
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
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 ?)