HOW HOT IS HOT? - PowerPoint PPT Presentation

1 / 40
About This Presentation
Title:

HOW HOT IS HOT?

Description:

HOW HOT IS HOT – PowerPoint PPT presentation

Number of Views:50
Avg rating:3.0/5.0
Slides: 41
Provided by: tonym162
Category:
Tags: hot | how

less

Transcript and Presenter's Notes

Title: HOW HOT IS HOT?


1
HOW HOT IS HOT?
Paul WilkinsonPublic Environmental Health
Research UnitLondon School of Hygiene Tropical
MedicineKeppel StreetLondon WC1E 7HT (UK)
2
CLIMATE OR WEATHER?
3
1 HEAT WAVES 2 TEMPERATURE-RELATED IMPACTS 3
ECOLOGICAL IMPACTS
4
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

5
PRINCIPLES OF EPISODE ANALYSIS
No. of deaths/day
Date
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
MORTALITY IN PARIS, 1999-2002 v 2003
peak 13 Aug
13
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

14
(No Transcript)
15
(No Transcript)
16
(No Transcript)
17
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

18
STATISTICAL ISSUES 1
  • Time-varying confounders influenza day of the
    week, public holidays pollution
  • Secular trend
  • Season

19
STATISTICAL ISSUES 1I
  • Shape of exposure-response function smooth
    functions linear splines
  • Lags simple lags distributed lags
  • Temporal auto-correlation

20
(No Transcript)
21
(No Transcript)
22
Source Anderson HR, et al. Air pollution and
daily mortality in London 1987-92. Br Med J
1996 312665-9
23
THE MODEL
(log) rate ß0 ß1(high temp.) ß2(low
temp.)
24
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

25
LONDON, 1986-96 LAGS FOR COLD-RELATED MORTALITY
26
SANTIAGO COLD-RELATED MORTALITYCARDIO-VASCULAR
DISEASE
27
SANTIAGO COLD-RELATED MORTALITYRESPIRATORY
DISEASE
28
SANTIAGO COLD-RELATED MORTALITYALL CAUSES
29
  • THRESHOLDS, SLOPES LAGS

30
LAG 0-1 DAYSHEAT
31
Variation in heat slope attributable deaths
with threshold
SOFIA, 0-1 DAY LAG
Threshold
32
CONTROLLNG FOR SEASON
TEMPERATURE
MORTALITY
SEASON
UNRECORDED FACTORS
Infectious disease
Diet
Human behaviours
33
METHODS OF SEASONAL CONTROL
  • Moving averages
  • Fourier series (trigonometric terms)
  • Smoothing splines
  • Stratification by date
  • Other

34
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)

35
HOW HOT IS HOT?
  • Depends on
  • Climate!(Threshold tends to be higher in warmer
    climates gt 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

36
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
37
Heat-related mortality, Delhi
Relative mortality ( of daily average)
Daily mean temperature /degrees Celsius
38
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)

39
VECTOR-BORNE DISEASE
Source WHO
40
Parasite
Mosquito
TRANSMISSION POTENTIAL
1
0.8
0.6
0.4
0.2
0
14
17
20
23
26
29
32
35
38
41
Temperature (C)
41
SO, TEMPERATURE IMPORTANT BUT
  • NON-CLIMATE INFLUENCES
  • OTHER CLIMATIC FACTORS
  • TREATMENTS / ERADICATION PROGRAMMES

42
CONTACT DETAILSSari KovatsPaul
WilkinsonPublic 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 4524sari.kovats_at_lshtm.ac.ukpaul.
wilkinson_at_lshtm.ac.uk
Write a Comment
User Comments (0)
About PowerShow.com