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Climate change and human health
in search of magic numbers…
NCAR Summer colloquium
28 July 2004
R Sari Kovats
Centre on Global Change and Health
Dept of Public Health and Policy
London School of Hygiene and Tropical Medicine
STRATOSPHERIC OZONE DEPLETION
-Global problem
-Health and environmental impacts
-Skin cancer
-Cataracts
Information from epidemiological
studies
Modelling impacts of climate change
Greenhouse gas
emissions scenarios
2050
2100
Defined by IPCC
Time
2020s
2050s
Global climate scenarios:
Generates series of maps of
predicted future distribution of
climate variables
30 year averages
2080s
Impact models
Estimates of populations at risk or
attributable burden of disease
2020s
2050s
2080s
World
Deaths, 2000
F
M
Africa
Both
High child,
high adult
High child,
very high
adult
M
M
F
F
(000)
(000)
(000)
(000)
(000)
(000)
(000)
Tobacco
3 893
1 014
4 907
43
7
84
26
Alcohol
1 638
166
1 804
53
15
125
30
163
41
204
5
1
1
0
Unsafe water, sanitation hygiene
895
835
1 730
129
103
207
169
Urban air pollution
411
388
799
11
11
5
5
Indoor smoke from solid fuels
658
961
1 619
93
80
118
101
Lead exposure
155
79
234
5
4
4
3
Climate change
76
78
154
9
9
18
18
Risk factors for injury
291
19
310
14
1
18
1
Carcinogens
118
28
146
1
0
1
1
Airborne particulates
217
26
243
3
0
3
0
Ergonomic stressors
0
0
0
0
0
0
0
Noise
0
0
0
0
0
0
0
Addictive substances
Illicit drugs
Environmental risks
Occupational risks
Estimated death and DALYs attributable to climate change.
Selected conditions in developing countries
Floods
Malaria
Diarrhoea
Malnutrition
120 100
80
60
40
20
0
2
Deaths (thousands)
4
6
8
10
DALYs (millions)
2000
2020
Health-impact models
• Process-based/Biological models
– Malaria/vectorial capacity [MIASMA]
– Heat budget models
• Empirical statistical
–
–
–
–
Temp-mortality (Kalkstein, Moser, etc.)
Temp –Diarrhoeal disease
Rainfall -flood-death
Temp/rainfall- Dengue, Malaria [spatial correlations]
Incubation period
Survival probability
Biting frequency
1
0.35
50
0.3
30
20
10
0.8
(per day)
(per day)
(days)
40
0.25
0.2
0.15
0.2
0
15
20
25
30
35
40
0
10
15
Temp (°C)
20
25
30
35
40
10
1
0.8
0.6
0.4
0.2
0
14
17
20
23
26
29
32
15
20
25
30
Temp (°C)
Temp (°C)
TRANSMISSION POTENTIAL
Martens et al. 1999,
van Lieshout et al. 2004
0.4
0.1
0.05
0
0.6
35
Temperature (°C)
38
41
35
40
Can global models reveal regional
vulnerability?
• Increase: East Africa, central Asia, Russian Federation
• Decrease: central America, Amazon
[within current vector limits]
Change of consecutive months
A1
> +2
A2
+2
-2
< -2
B1
B2
Mid-range
scenario
(SRES B2 greenhouse gas
emission scenario, best
guess climate sensitivity)
Present
2050
2100
Present
2050
2100
High-range
scenario
(SRES A2 greenhouse gas
emission scenario, high
climate sensitivity)
Potential distribution of Aedes aegypti in the North Island based on 10°C midwinter
isotherm limit for a mid- and high-range climate change scenario.
Source: Hotspots dengue fever risk model developed by the International Global Change Institute, University of
Waikato, with the assistance of funding from the Health Research Council
Empirical-stats models
• EXTRAPOLATION
– Can you extrapolate the exposure-response relationship
beyond the bounds of the observed temperature range?
• VARIATION
– Can you extrapolate the exposure-response relationship
derived from a different population.
• ADAPTATION
– Responses to climate change - acclimatization
• MODIFICATION
– What is likely?–
– changes to exposure response relationship
Predicted distribution of the malaria vector (mosquito
Anopheles atroparvus)
in present day Europe, and in the 2080s with SRES A2 climate scenario.
[Kuhn, LSHTM, 2002]
Current climate
2080s
Temperature-salmonellosis [fully adjusted models]
lcl
ucl
rr
lcl
ucl
1500
rr
300
England & Wales
Switzerland
1000
200
500
100
0
0
0
5
10
Average 2 month temperature
lcl
ucl
15
20
0
rr
10
Average 2 month temperature
lcl
ucl
20
rr
80
150
Netherlands
Scotland
60
100
40
50
0
20
0
5
10
Average 2 month temperature
15
20
0
5
10
Average 2 month temperature
15
Netherlands: time series
250
Total weekly cases
200
150
100
50
0
1984 1986
1988 1990
1992 1994
1996 1998 2000 2002
Climate change and air pollution,
UK Health Assessment 2002
Pollutant
2020s
2050s
2080s
Particles
Large decrease
Large decrease
Large decrease
Ozone (assuming no
threshold)
Large increase (by
about 10%)
Large increase
(by about
20%)
Large increase (by
about 40%)
Ozone (assuming a
threshold)
Small increase
Small increase
Small increase
Nitrogen dioxide
Small decrease
Small decrease
Small decrease
Sulphur dioxide
Large decrease
Large decrease
Large decrease
Outcomes...
• Shift in “climate envelope”
• Additional population at risk
– Definitions of risk
• Relative risk
• Absolute risk
– additional/excess cases/deaths
– Disability-adjusted life-year [DALY]
COSTS
Simplified causal web linking
exposures and outcomes
WHO model
Distal SocioEconomic Causes
Proximal Causes
Physiological and
Pathophysiological
Causes
Outcomes
D1
P1
Pa 1
O1
D2
P2
Pa 2
O2
D3
P3
Pa 3
Attributable fractions vs attributable
deaths/cases
• Population change
– Growth
– Ageing
– Countries have national projections
• Which baseline disease incidence used to estimate
attributable cases.
– Current or future?
Scenarios
• Climate
– Averages, extremes
• Population
– Population growth ✔✔
– Population ageing ✔
– Urbanisation, coastal migration
• “socio-economic”
Non climate scenarios
• Vector presence/abundance
• Baseline disease prevalence
•
•
•
•
– Cardiovascular disease
– HIV/AIDS
Millennium Development Goals
Population
Income/GDP per capita/PPP per capita
Technology
– Malaria vaccine
• Qualitative “Knowledge is King, Big is Beautiful”
Relevance of attributable vs avoidable burden
• Avoidable burden more policy-relevant
• Why calculate attributable burden?
WHO Definitions…
• A health impact assessment is a combination of
procedures or methods by which a proposed policy,
programme or project may be judged as to the
effects it may have on the health of a population.
• The basic principles underlying such an assessment
are democracy, equity, sustainable development and
evidence-based advice.
Uncertainty
• Climate scenario
–
–
–
–
>1 climate model
>4 emissions scenarios
Regional model
Downscaling
• Exposure response relationship
– Key uncertainties/assumptions in the models
– Confidence intervals
– Monte Carlo simulation/Bayes
Qualitative
Level of agreement, consensus
High
Low
Low
Established but
incomplete
Well-established
Speculative
Competing
explanations
Amount of evidence
High
three research tasks
Empirical studies
[epidemiology]
learn
?analogues
mechanisms
detection
attribution
2004
Past
[climate/weather-health
relationships]
Present
[highland malaria]
predictive
modelling
2010
2080
Future
[map malaria]
Country
Reference
Antigua and Barbuda
O'Marde and Michael, 2000 – UNEP Country Study
Australia
McMichael et al, 2002
Cameroon
UNEP/ Ministry of Environment and Forestry, Cameroon, 1998
Canada
Duncan et al., 1997
Fiji
de Wet and Hales, 2000
Japan
Ando et al, 1998
Kiribati
Taeuea, de Wet and Hales, 2000
New Zealand
Woodward et al. 2001
Panama
Sempris E and Lopez R, eds. 2001 - ANAM/UNDP
Portugal
Casimiro and Calheiros, 2002
South Africa
UNEP Country study 2000
Sri Lanka
Ratnasari 1998
St Lucia
St Lucia National Communication, chapter 4.
United Kingdom
Dept of Health, 2002
United States
Patz et al., 2000 + various documents
Zambia
Phiri amd Msiska, 1998