HOW HOT IS HOT?

HOW HOT IS HOT?

Paul WilkinsonPublic & Environmental Health Research Unit

London School of Hygiene & Tropical MedicineKeppel 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

death

s/day

Date

Influenza ‘epidemic’

Period of heat

Smooth function of date with control for

influenza

Smooth function of date

Triangle: attributable deaths

PRINCIPLES OF EPISODE ANALYSIS

020

4060

80M

ean

daily

tem

per

atu

re (

degr

ees

Ce

lsiu

s)

010

020

030

0D

eat

hs

01jan2003 01apr2003 01jul2003 01oct2003 01jan2004Date

DEATHS, LONDON, 2003

1 A

ugu

st

16 A

ugus

t

020

4060

80M

ean

daily

tem

per

atu

re (

degr

ees

Ce

lsiu

s)

010

020

030

0D

eat

hs



1 A

ugu

st

16 A

ugus

t

020

4060

80M

ean

daily

tem

per

atu

re (

degr

ees

Ce

lsiu

s)

010

020

030

0D

eat

hs



1 A

ugu

st

16 A

ugus

t

020

4060

80M

ean

daily

tem

per

atu

re (

degr

ees

Ce

lsiu

s)

010

020

030

0D

eat

hs



020

4060

80M

ean

daily

tem

per

atu

re (

degr

ees

Ce

lsiu

s)

010

020

030

0D

eat

hs

16jul2003 30jul2003 13aug2003 27aug2003Date


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

020

4060

80M

ean

daily

tem

per

atu

re (

deg

ree

s C

elsi

us)

010

020

030

0D

eat

hs

01jan2001 01jan2002 01jan2003 01jan2004Date

DEATHS, LONDON, 2001-2003

100

150

200

250

300

De

aths

pe

r d

ay

0 10 20 30Mean temperature / Celsius

DEATHS, LONDON, 2001-2003

025

5075

100

125

150

Fre

quen

cy /

Pre

dict

ed

exc

ess

deat

hs a

da

y

010

020

030

040

050

060

0T

ota

l exc

ess

dea

ths

(ris

k x

freq

)

0 5 10 15 20 25 30Mean temperature / Celsius

TEMPERATURE-RELATED DEATHS, LONDON, 2001-2003

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

• Time-varying confoundersinfluenzaday of the week, public holidayspollution

• Secular trend

• Season

STATISTICAL ISSUES 1

STATISTICAL ISSUES 1I

• Shape of exposure-response functionsmooth functionslinear splines

• Lagssimple lagsdistributed 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.)

ß1=heat slopeß2=cold slope

+

ß3(pollution) +

ß4(influenza) +

ß5(day, PH)

measured confounders+

ß6(season) +

ß7(trend)unmeasured confounders

LAGS

• Heat impacts short: 0-2 daysCold 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

% I

NC

RE

AS

E I

N M

OR

TA

LIT

Y/

ºC F

ALL

IN

TE

MP

ER

AT

UR

E

DAYS OF LAG

ALL CAUSE

0 5 10 151.65

1.7

1.75

1.8

1.85

CARDIOVASCULAR

0 5 10 151.7

1.75

1.8

1.85

1.9

RESPIRATORY

0 5 10 153.8

3.9

4

4.1

4.2

NON-CARDIORESPIRATORY

0 5 10 15.7

.8

.9

1

Lag

RR

0 5 10 15 20

1.0

00

1.0

05

1.0

10

*

**

** * * *

**

**

**

** * *

* *

*

*

*

* * **

**

**

**

** * * * * *

*

*

SANTIAGO: COLD-RELATED MORTALITYCARDIO-VASCULAR DISEASE

Lag

RR

0 5 10 15 20

0.9

95

1.0

00

1.0

05

1.0

10

1.0

15

*

* **

* * * * * * * * * * **

* * *

*

*

*

*

* * **

* * * * * * * * ** * *

* *

*

SANTIAGO: COLD-RELATED MORTALITYRESPIRATORY DISEASE

THRESHOLDS, SLOPES & LAGS

LJUBLJANA

-10 0 10 20 30 40

80100120140

BUCHAREST

-10 0 10 20 30 40

80100120140

SOFIA

-10 0 10 20 30 40

80100120140

DELHI

-10 0 10 20 30 40

80100120140

MONTERREY

-10 0 10 20 30 40

80100120140

MEXICO

-10 0 10 20 30 40

80100120140

CHIANGMAI

-10 0 10 20 30 40

80100120140

BANGKOK

-10 0 10 20 30 40

80100120140

SALVADOR

-10 0 10 20 30 40

80100120140

SAO PAULO

-10 0 10 20 30 40

80100120140

SANTIAGO

-10 0 10 20 30 40

80100120140

CAPE TOWN

-10 0 10 20 30 40

80100120140

LAG: 0-1 DAYSHEAT

LJUBLJANA

-10 0 10 20 30 40

80100120140

BUCHAREST

-10 0 10 20 30 40

80100120140

SOFIA

-10 0 10 20 30 40

80100120140

DELHI

-10 0 10 20 30 40

80100120140

MONTERREY

-10 0 10 20 30 40

80100120140

MEXICO

-10 0 10 20 30 40

80100120140

CHIANGMAI

-10 0 10 20 30 40

80100120140

BANGKOK

-10 0 10 20 30 40

80100120140

SALVADOR

-10 0 10 20 30 40

80100120140

SAO PAULO

-10 0 10 20 30 40

80100120140

SANTIAGO

-10 0 10 20 30 40

80100120140

CAPE TOWN

-10 0 10 20 30 40

80100120140

LAG: 0-13 DAYSCOLD

Threshold for heat effect

Threshold for cold effect

Cutpoint

302520151050-5-10-15

40

30

20

10

0

-10

Pop attrib frac

% change

Threshold

Variation in ‘heat slope’ & attributable deaths with threshold

SOFIA, 0-1 DAY LAG

CONTROLLNG FOR SEASON

TEMPERATURE

MORTALITYSEASON

Infectious disease

Diet

UNRECORDED FACTORS

Human behaviours

X ?

• Moving averages

• Fourier series (trigonometric terms)

• Smoothing splines

• Stratification by date

• Other…

METHODS OF SEASONAL CONTROL

• 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)

SUMMARY: TIME-SERIES STUDIES

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

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

GHG emissions scenarios Defined by IPCC

GCM model: Generates series of maps of predicted future distribution of climate variables

Level Age group (years)0-4 5-14 15-29 30-44 45-59 60-69 70+

1 1.0 1.0 1.0 1.0 1.0 1.0 1.02 1.2 1.2 1.2 1.2 1.2 1.2 1.23 1.7 1.7 1.7 1.7 1.7 1.7 1.71 1.0 1.0 1.0 1.0 1.0 1.0 1.02 1.2 1.2 1.2 1.2 1.2 1.2 1.23 1.7 1.7 1.7 1.7 1.7 1.7 1.71 1.0 1.0 1.0 1.0 1.0 1.0 1.02 1.2 1.2 1.2 1.2 1.2 1.2 1.23 1.7 1.7 1.7 1.7 1.7 1.7 1.71 1.0 1.0 1.0 1.0 1.0 1.0 1.02 1.2 1.2 1.2 1.2 1.2 1.2 1.23 1.7 1.7 1.7 1.7 1.7 1.7 1.71 1.0 1.0 1.0 1.0 1.0 1.0 1.02 1.2 1.2 1.2 1.2 1.2 1.2 1.23 1.7 1.7 1.7 1.7 1.7 1.7 1.7

ASSESSMENT OF FUTURE HEALTH IMPACTS

0 10 20 30 40

80

100

120

140

Heat-related mortality, DelhiR

ela

tive m

ort

alit

y (

% o

f d

aily

avera

ge)

Daily mean temperature /degrees Celsius

Temperature distribution

• 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)

BUT FIVE REASONS TO HESITATE…

VECTOR-BORNE DISEASE

050

100

150

Dea

ths

per

100,

000

Sub-Saharan Africa

North & West Africa

Asia/Pacific

Latin America

Developed countries

Malaria mortality rates by region

050

100

150

Dea

ths

per

100,

000

Sub-Saharan Africa

North & West Africa

Asia/Pacific

Latin America

Developed countries

Malaria mortality rates by region

Source: WHO

TRANSMISSION POTENTIAL

0

0.2

0.4

0.6

0.8

1

14 17 20 23 26 29 32 35 38 41

Temperature (°C)

Incubation period

0

10

20

30

40

50

15 20 25 30 35 40

(day

s)

Temp (°C)

Survival probability

0

0.2

0.4

0.6

0.8

1

10 15 20 25 30 35 40

(per

day

)Temp (°C)

ParasiteBiting frequency

0

0.1

0.2

0.3

10 15 20 25 30 35 40

(per

day

)

Temp (°C)

Mosquito

• NON-CLIMATE INFLUENCES

• OTHER CLIMATIC FACTORS

• TREATMENTS / ERADICATION PROGRAMMES

SO, TEMPERATURE IMPORTANT BUT…

CONTACT DETAILS

Sari KovatsPaul Wilkinson

Public & Environmental Health Research UnitLondon School of Hygiene & Tropical MedicineKeppel StreetLondonWC1E 7HT(UK)

www.lshtm.ac.ukTel: +44 (0)20 7972 2415Fax: +44 (0)20 7580 4524

[email protected]@lshtm.ac.uk

http://www.lshtm.ac.uk/

Documents

HOW HOT IS HOT?