Transcript Estimating the Climate-Attributable Burden of Disease

HOW HOT IS HOT?
Paul Wilkinson
Public & Environmental Health Research Unit
London School of Hygiene & Tropical Medicine
Keppel Street
London WC1E 7HT (UK)
CLIMATE OR WEATHER?
1 HEAT WAVES
2 TEMPERATURE-RELATED IMPACTS
3 ECOLOGICAL IMPACTS
HEAT WAVES & TEMPERATURE
•
Episode analysis
- transparent
- risk defined by comparison to local baseline
•
Regression analysis
- uses all data
- requires fuller data and analysis of confounders
- can be combined with episode analysis
No. of deaths/day
PRINCIPLES OF EPISODE ANALYSIS
Smooth function of date
Triangle: attributable
deaths
Smooth function of date
with control for influenza
Period of heat
Influenza ‘epidemic’
Date
60
40
20
0
0
100
200
Mean daily temperature (degrees Celsius)
80
300
DEATHS, LONDON, 2003
01jan2003
01apr2003
01jul2003
Date
01oct2003
01jan2004
01jan2003
0
0
200
01apr2003
20
40
60
300
01jul2003
Date
80
16 August
1 August
Mean daily temperature (degrees Celsius)
100
DEATHS, LONDON, 2003
01oct2003
01jan2004
01jan2003
0
0
200
01apr2003
20
40
60
300
01jul2003
Date
80
16 August
1 August
Mean daily temperature (degrees Celsius)
100
DEATHS, LONDON, 2003
01oct2003
01jan2004
01jan2003
0
0
200
01apr2003
20
40
60
300
01jul2003
Date
80
16 August
1 August
Mean daily temperature (degrees Celsius)
100
DEATHS, LONDON, 2003
01oct2003
01jan2004
16jul2003
60
40
20
0
0
100
200
Mean daily temperature (degrees Celsius)
80
300
DEATHS, LONDON, 2003
30jul2003
13aug2003
Date
27aug2003
MORTALITY IN PARIS, 1999-2002 v 2003
peak: 13 Aug
INTERPRETATION
•
•
Common sense, transparent
Relevant to PH warning systems
But
• How to define episode?
- relative or absolute threshold
- duration
- composite variables
• Uses only selected part of data
• Most sophisticated analysis requires same methods as
time-series regression
60
40
20
0
0
100
200
Mean daily temperature (degrees Celsius)
80
300
DEATHS, LONDON, 2001-2003
01jan2001
01jan2002
01jan2003
Date
01jan2004
100
150
200
250
300
DEATHS, LONDON, 2001-2003
0
10
20
Mean temperature / Celsius
30
150
125
100
75
50
25
0
0
100
200
300
400
500
Frequency / Predicted excess deaths a day
600
TEMPERATURE-RELATED DEATHS, LONDON, 2001-2003
0
5
10
15
20
Mean temperature / Celsius
25
30
TIME-SERIES REGRESSION
•
Short-term temporal associations
•
Usually based on daily data (for heat) over several
years
•
Similar to any regression analysis but with specific
features
•
Methodologically sound as same population
compared with itself day by day
STATISTICAL ISSUES 1
•
Time-varying confounders
influenza
day of the week, public holidays
pollution
•
Secular trend
•
Season
STATISTICAL ISSUES 1I
•
Shape of exposure-response function
smooth functions
linear splines
•
Lags
simple lags
distributed lags
•
Temporal auto-correlation
Source: Anderson HR, et al. Air pollution and daily mortality in London: 1987-92. Br
Med J 1996; 312:665-9
THE MODEL…
(log) rate = ß0 +
ß1(high temp.)
ß2(low temp.)
ß3(pollution)
+
+
ß2=cold slope
+
ß4(influenza)
+
ß5(day, PH)
+
ß6(season)
+
ß7(trend)
ß1=heat slope
measured
confounders
unmeasured
confounders
LAGS
•
Heat impacts short: 0-2 days
Cold impacts long: 0-21 days
•
Vary by cause-of-death
- CVD: prompt
- respiratory: slow
•
Should include terms for all relevant lags
LONDON, 1986-96: LAGS FOR COLD-RELATED MORTALITY
% INCREASE IN MORTALITY
/ ºC FALL IN TEMPERATURE
ALL CAUSE
CARDIOVASCULAR
1.85
1.9
1.8
1.85
1.75
1.8
1.7
1.75
1.65
1.7
0
5
10
15
0
RESPIRATORY
5
10
15
NON-CARDIORESPIRATORY
1
4.2
4.1
.9
4
.8
3.9
3.8
.7
0
5
10
15
DAYS OF LAG
0
5
10
15
SANTIAGO: COLD-RELATED MORTALITY
CARDIO-VASCULAR DISEASE
R1.05 1.0
*
*
1.0
*
*
*
*
*
*
*
*
*
*
*
*
* *
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
* *
*
*
0
5
1 0 1 5 2 0
L a g
SANTIAGO: COLD-RELATED MORTALITY
RESPIRATORY DISEASE
R
0.95 1.0 1.05 1.0 1.05
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
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*
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*
*
*
*
*
*
* *
*
*
*
*
*
*
0
5
1 0 1 5 2 0
L a g
THRESHOLDS, SLOPES & LAGS
LAG: 0-1 DAYS
HEAT
LAG: 0-13 DAYS
COLD
SOFIA
BUCHAREST
140
140
120
120
100
100
80
80
-10
20 30
3040
40
-10 0 10 20
DELHI
SOFIA
140
140
120
120
100
100
80
80
-100 0101020203030
-10
40 40
CHIANGMAI
MEXICO
Threshold for
BANGKOK
CHIANGMAI
Threshold for
cold effect
heat effect
140
140
Variation in ‘heat slope’ & attributable deaths with threshold
40
SOFIA, 0-1
DAY LAG
30
20
10
0
Pop attrib frac
-10
% change
-15
-10
Cutpoint
-5
0
5
10
Threshold
15
20
25
30
CONTROLLNG FOR SEASON
TEMPERATURE
MORTALITY
SEASON
X
?
Infectious disease
Diet
Human behaviours
UNRECORDED FACTORS
METHODS OF SEASONAL CONTROL
• Moving averages
• Fourier series (trigonometric terms)
• Smoothing splines
• Stratification by date
• Other…
SUMMARY:TIME-SERIES STUDIES
•
Provide evidence on short-term associations of
weather and health
•
‘Robust’ design
•
Repeated finding of direct h + c effects
•
Some uncertainties over PH significance
•
Uncertainties in extrapolation to future
(No historical analogue of climate change)
HOW HOT IS HOT?
Depends on…
•
Climate!
(Threshold tends to be higher in warmer climates >
acclimatization or adaptation)
•
Characteristics of heat (esp. duration)
•
Characteristics of the population
But
•
Heat effect identified in (almost) all populations studied
to date
•
Some evidence for steep increases in risk at extreme
high temperatures
ASSESSMENT OF FUTURE HEALTH IMPACTS
GHG emissions
scenarios
Defined by IPCC
GCM model:
Generates series of
maps of predicted
future distribution of
climate variables
Health impact model
Generates comparative
estimates of the regional
impact of each climate
scenario on specific health
outcomes
Conversion to GBD
‘currency’ to allow
summation of the effects
of different health impacts
Level
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
Age group (years)
0-4
5-14
1.0
1.0
1.2
1.2
1.7
1.7
1.0
1.0
1.2
1.2
1.7
1.7
1.0
1.0
1.2
1.2
1.7
1.7
1.0
1.0
1.2
1.2
1.7
1.7
1.0
1.0
1.2
1.2
1.7
1.7
15-29
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
30-44
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
45-59
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
60-69
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
70+
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
1.0
1.2
1.7
Relative mortality (% of daily average)
Heat-related mortality, Delhi
Temperature
distribution
140
120
100
80
0
10
20
30
Daily mean temperature /degrees Celsius
40
BUT FIVE REASONS TO HESITATE…
• EXTRAPOLATION
(going beyond the data)
• VARIATION
(..in weather-health relationship -- largely unquantified)
• ADAPTATION
(we learn to live with a warmer world)
• MODIFICATION
(more things will change than just the climate)
• ANNUALIZATION
(is the climate impact the sum of weather impacts)
VECTOR-BORNE
DISEASE
100
0
50
Deaths per 100,000
150
Malaria mortality rates by region
Sub-Saharan
Africa
Source: WHO
North & West
Africa
Asia/
Pacific
Latin
America
Developed
countries
Mosquito
Biting frequency
Incubation period
Survival probability
0.2
1
50
0.8
40
(days)
(per day)
0.3
(per day)
Parasite
0.6
0.4
30
20
0.1
0
10
15
20
25
30
35
40
0.2
10
0
0
10
Temp (°C)
15
20
25
30
35
40
Temp (°C)
TRANSMISSION POTENTIAL
1
0.8
0.6
0.4
0.2
0
14 17 20 23 26 29 32 35 38 41
Temperature (°C)
15
20
25
30
Temp (°C)
35
40
SO,TEMPERATURE IMPORTANT BUT…
• NON-CLIMATE INFLUENCES
• OTHER CLIMATIC FACTORS
• TREATMENTS / ERADICATION PROGRAMMES
CONTACT DETAILS
Sari Kovats
Paul Wilkinson
Public & Environmental Health Research Unit
London School of Hygiene & Tropical Medicine
Keppel Street
London
WC1E 7HT
(UK)
www.lshtm.ac.uk
Tel: +44 (0)20 7972 2415
Fax: +44 (0)20 7580 4524
[email protected]
[email protected]