Transcript Estimating the Climate-Attributable Burden of Disease

TIME SERIES ANALYSIS FOR
STUDIES OF WEATHER AND HEALTH
Paul Wilkinson
Public & Environmental Health Research Unit
London School of Hygiene & Tropical Medicine
Keppel Street
London WC1E 7HT (UK)
www.lshtm.ac.uk [email protected]
150
100
75
50
25
0
Cardiovascular deaths/day
125
LONDON, 1990 - 1994
01jan1990
01jan1991
CVD deaths
01jan1992
01jan1993
Mean temperature
01jan1994
CLIMATE OR WEATHER?
TWO APPROACHES
•
Episode analysis
- transparent
- risk defined by comparison to local baseline
•
Regression analysis of all days of year
- uses full data set
- requires fuller data and analysis of confounders
- can be combined with episode analysis
EPISODES
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
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 for
regression of all days of year
REGRESSION OF ALL DAYS
TIME-SERIES
•
Short-term temporal associations
•
Usually day to day fluctuations over several years
•
Similar to any regression analysis but with specific
features
•
Methodologically sound
(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
-12
0
10
20
30
(2 day
mean)
TEMPERATURETemperature
DEPENDENCE OF DAILY
MORTALITY,
LONDON
140
100
85
-12
0
10
20
30
Temperature (2 week mean)
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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*
0
5
1 0 1 5 2 0
L a g
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
CONTROLLNG FOR SEASON
TEMPERATURE
MORTALITY
SEASON
X
X
Infectious disease
Diet
Human behaviours
UNRECORDED FACTORS
METHODS OF SEASONAL CONTROL
• Moving averages
• Fourier series (trigonometric terms)
• Smoothing splines
• Stratification by date
• Other…
EFFECT OF INCREASING SEASONAL CONTROL
Gradient of cold-related mortality, London
All cause
3.5
3
% change
2.5
2
1.5
1
0.5
0
4df
7df
10df
decade 1
12df
4df
7df
10df
decade 2
12df
4df
7df
10df
decade 3
12df
4df
7df
10df
decade 4
12df
SEASONAL MORTALITY, GB
Unadjust ed
Adjust ed f or low t emperat ure
150
Relative mort alit y
Relative mort alit y
150
125
100
75
50
125
100
75
50
1
2
3
4
5
6
7
month
8
9
10
11
12
1
Adjust ed f or influenza count
3
4
5
6
7
month
8
9
10 11
12
Adjust ed f or month low t emperat ure and influenza
150
Relative mort alit y
150
Relative mort alit y
2
125
100
75
50
125
100
75
50
1
2
3
4
5
6
7
month
8
9
10
11
12
1
2
3
4
5
6
7
month
8
9
10 11
12
SEASONAL FLUCTUATION IN MORTALITY, GB
Month-to-month variation in mortality (adjusted for region
and time-trend) accounted for 17% of annual all-cause
mortality but only:
- 7.8% after adjustment for temperature
- 12.6% after adjustment for influenza A counts
- 5.2% after adjustment for both
FUTURE IMPACTS
Seasonal mortality pattern, Delhi
Daily deaths
60
40
20
0
01jan1991
01jan1993
31dec1994
Delhi, India:
Average annual pattern of temperature, rainfall and
daily mortality (data for all 1991-94 years, averaged, by day of year)
150
40
40
150
Daily temperature
30
30
100
100
Daily deaths
Deaths
20
Temperature
10
20
10
50
50
0
0
Monthly rainfall
0
-10
0
1st Jan
Jan 1
-10
1st July
July 1
Dec 31
McMichael et al, in press
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
RISK ASSESSMENT FOR CLIMATE CHANGE
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
BUT FOUR 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)
HOSPITALIZATIONS FOR DIARRHOEA, LIMA PERU
Shaded region corresponds to 1997-98 El Niño event
Daily hospitalizations for diarrhoea
Daily temperature
1993
Source: Checkley et al, Lancet 2000
1997
CHANGING VULNERABILITY
• Changes in population
- Demographic structure (age)
- Prevalence of weather-sensitive disease
• Environmental modifiers
• Adaptive responses
- Physiological habituation (acclimatization)
- Behavioural change
- Structural adaptation
- PH interventions
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)
INTERMISSION…
TIME SERIES ANALYSIS FOR
STUDIES OF WEATHER AND HEALTH
Part 2
HARVESTING
FRAILTY MODEL
It
General
population
Frail
population,
Nt
Dt
Nt = Nt-1 + It - Dt-1
Death
IDEALIZED SCHEMA
MORTALITY
A
B
HEAT
Period of
averaging
strong correlation
weaker
absent
ALL-CAUSE MORTALITY
P
e
r
c
n
t
a
g
o
f
m
e
n
u
b
r
o
f
d
e
a
t
h
s
95 10 105 10 15 120
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5
1 0 1 5 2 0 2 5
Me a n
te m
1
*
0.5
*
0
*
-0.5
PERCENTAGE INCREASE IN MORTALITY
PER ºC BELOW COLD THRESHOLD
1.5
ALL CAUSE MORTALITY: HEAT DEATHS
*
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* * * * * * *
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0
5
10
Lag
15
20
CUMULATIVE EXCESS RISK OF HEAT DEATH AS A
FUNCTION OF London
INCREASING LAG: LONDON
Excess risk (95% CI)
6
4
2
0
-2
---------------
---------------------
-------------
-4
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
lag
CUMULATIVE EXCESS RISK OF HEAT DEATH AS A
FUNCTION OF Delhi
INCREASING LAG: DELHI
Excess risk (95% CI)
6
4
2
0
----------------
-2
-4
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
lag
CUMULATIVE EXCESS RISK OF HEAT DEATH AS A
FUNCTION OF INCREASING LAG: SAO PAULO
Sao Paolo
Excess risk (95% CI)
6
4
-
2
0
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-2
-4
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30
lag
UNCERTAINTIES IN FUTURE
HEALTH IMPACTS
(1) EXTRAPOLATION
80
80
HEAT DEATHS
-10
Monterrey, Mexico
0 10 20 30 40
-1
?
M
MORTALITY
(% of annual average)
MONTERREY
140
120
100
80
140
120
100
80
-10 0 10 20 30 40
SALVADOR
MEAN DAILY TEMPERATURE
/ degrees Celsius
-10
S
(2) VARIATION
Mortality (% of annual average)
Daily mortality in relation to mean temperature
during preceding two days
LJUBLJANA
140
120
100
80
BUCHAREST
140
120
100
80
-10 0 10 20 30 40
MONTERREY
140
120
100
80
140
120
100
80
-10 0 10 20 30 40
MEXICO
CHIANGMAI
SALVADOR
140
120
100
80
140
120
100
80
-10 0 10 20 30 40
-10 0 10 20 30 40
BANGKOK
140
120
100
80
-10 0 10 20 30 40
SANTIAGO
140
120
100
80
-10 0 10 20 30 40
-10 0 10 20 30 40
-10 0 10 20 30 40
SAO PAULO
140
120
100
80
DELHI
140
120
100
80
-10 0 10 20 30 40
140
120
100
80
-10 0 10 20 30 40
SOFIA
CAPE TOWN
140
120
100
80
-10 0 10 20 30 40
Mean daily temperature in degrees Celsius
-10 0 10 20 30 40
Daily mortality in relation to mean temperature
during preceding two weeks
LJUBLJANA
140
120
100
80
BUCHAREST
140
120
100
80
-10 0 10 20 30 40
140
120
100
80
-10 0 10 20 30 40
MONTERREY
140
120
100
80
-10 0 10 20 30 40
140
120
100
80
BANGKOK
140
120
100
80
-10 0 10 20 30 40
SANTIAGO
140
120
100
80
-10 0 10 20 30 40
-10 0 10 20 30 40
-10 0 10 20 30 40
SAO PAULO
140
120
100
80
-10 0 10 20 30 40
140
120
100
80
CHIANGMAI
-10 0 10 20 30 40
SALVADOR
DELHI
-10 0 10 20 30 40
MEXICO
140
120
100
80
140
120
100
80
SOFIA
CAPE TOWN
140
120
100
80
-10 0 10 20 30 40
-10 0 10 20 30 40
(3) ADAPTATION
Threshold for heat impacts
Thresholds for heat-related mortality: 12 lower- &
middle-income cities
30
20
10
Positive slope
suggests adaptation
0
-10
20
25
30
35
Maximum daily mean temperature
12.5° C
12
11
S P 01
10
9
8
7
7.0° C
Risk of death relative to annual minimum
10-week moving average
2
1.75
High standardized
heating costs
1.5
1.25
Low standardized
heating costs
1
.75
1Jan
1Apr
1Jul
Day of year
1Oct
31Dec
Seasonal variation in deaths from cardiovascular disease by cost of
home heating. England, 1986-1996.
CARDIOVASCULAR MORTALITY IN RELATION TO HOME HEATING:
ENGLAND, 1986-96
Standardized
indoor temp.
/deg Celsius
Mortality
(deaths/day)
Winter:non-winter ratio*
Winter
Nonwinter
Unadjusted
Adjusted for
deprivation
1
<14.8
0.8
(1080)
0.6
(1568)
1.39
(1.28,1.50)
1.38
(1.16,1.63)
2
14.8-
0.7
(973)
0.6
(1580)
1.24
(1.15,1.35)
1.24
(1.05,1.47)
3
16.6-
0.7
(869)
0.5
(1442)
1.21
(1.11,1.31)
1.21
(1.02,1.44)
4
18.4-
0.7
(957)
0.6
(1569)
1.22
(1.13,1.32)
1.23
(1.04,1.46)
5
19.4-27.0
0.8
(1055)
0.7
(1906)
1.11
(1.03,1.20)
1.13
(0.96,1.34)
* All ratios adjusted for region
(4) EFFECT MODIFICATION
Table. Change in population health and deaths attributable to cold over the 20th
century
Percentage of
deaths by age &
cause:
Period
1900-10
1927-37
1954-64
1986-96
0-14 years
15-64 years
65+ years
38.5%
32.0%
29.4%
13.3%
40.5%
46.1%
4.9%
31.4%
63.7%
1.5%
18.8%
79.7%
Cardiovascular
Respiratory
Other
12.1%
18.9%
69.0%
27.9%
20.0%
52.1%
33.3%
14.1%
52.6%
42.3%
14.0%
43.7%
12.5
(10.1, 14.9)
11.2
(8.40, 14.0)
8.74
(5.93, 11.5)
5.42
(4.13, 6.69)
Percent of deaths
attributable to cold
ALL CAUSE MORTALITY
0
50
0
50
100 150 200 250 300
1927 to 1937
100 150 200 250 300
1900 to 1910
1900
1902
1904
1906
1908
1910
1927
1929
1933
1935
1937
0
50
0
50
100 150 200 250 300
1986 to 1996
100 150 200 250 300
1954 to 1964
1931
1954
1956
1958
1960
1962
1964
1986
1988
1990
1992
1994
1996
All-cause
40
20
0
-20
-20
0
20
40
60
1927-1937
60
1900-1910
-10
0
10
20
30
-10
0
20
30
20
30
40
0
20
-20
-20
0
20
40
60
1986-1996
60
1954-1964
10
-10
0
10
20
30
-10
0
Temperature (degrees Celsius)
10
IMPLICATIONS FOR MONITORING
HEALTH IMPACTS OF CLIMATE CHANGE
METHODOLOGICAL ISSUES
•
•
•
•
Gradual change
Year to year fluctuation
Secular trends
Modifiers
- physiological acclimatization
- structural and behavioural adaptation
- specific protection measures
• Attribution
MONITORING
1. Measurement of trend
in disease rates
Confounded by secular
trends: un-interpretable
unless v. specific marker
2. Measurement of trend in
attributable disease:
direct method
Based on analysis of
(short-term) climatedisease relationships
3. Application of climatedisease relationships to
measured changes in
climate: indirect method
Depends on understanding
effect modification or
assumption of its absence
30
Deaths in
June & July
10000
20
5000
10
Days over
27 Celsius
0
1986
1988
1990
1992
Year
Deaths in June & July, London, 1986-1996
1994
1996
0
Days over 27ºC
Deaths in June & July
15000
30
Heat
deaths
.8
25
20
.6
15
.4
10
Days over
27 Celsius
.2
0
1986
1988
1990
1992
1994
year
Deaths attributable to heat, London, 1986-1996
1996
5
0
Days over 27ºC
Percent attributable to heat
1
Temperature /degrees Celsius
22
1996
1986
Central England mean June & July temperatures, 1900-1999
18
14
Regression slope,
1986-1996
10
6
1900
1920
1940
Year
1960
1980
2000
20
Average Global
Temperature
(OC)
19
This presents a rate-of-change
IPCC
(2001)
estimates
problem
for many
natural
a 1.4-5.8 oC increase
systems/processes
High
18
17
Central estimate = 2.5 oC
(+ increased variability)
16
Low
15
Band of historical
climatic variability
14
13
1860
1900
1950
Year
2000
2050
2100
NEEDED EVIDENCE
MITIGATION
Evidence for change that benefits health & lowers emissions
•
Reduction in greenhouse gas emissions/energy use
•
Social, economic and technological changes
ADAPTATION/PREPAREDNESS
Evidence that can influence health in short and longer term
•
Understanding of weather-health > climate-health relationships
•
Vulnerability in terms of impacts, geographical distribution and
population characteristics
•
Public protection through:
public health system (short-medium term)
infrastructure, adaptation
CONTACT DERTAILS
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]