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HOW HOT IS HOT?

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.

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HOW HOT IS HOT?

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  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. Smooth function of date Triangle: attributable deaths Smooth function of date with control for influenza Period of heat Influenza ‘epidemic’ PRINCIPLES OF EPISODE ANALYSIS No. of deaths/day Date

  6. MORTALITY IN PARIS, 1999-2002 v 2003 peak: 13 Aug

  7. 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

  8. 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

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

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

  11. Source: Anderson HR, et al. Air pollution and daily mortality in London: 1987-92. Br Med J 1996; 312:665-9

  12. ß1=heat slope ß2=cold slope + ß3(pollution) + ß4(influenza) + ß5(day, PH) measured confounders + ß6(season) + ß7(trend) unmeasured confounders THE MODEL… (log) rate = ß0 + ß1(high temp.) + ß2(low temp.)

  13. 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

  14. 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 5 10 15 % INCREASE IN MORTALITY / ºC FALL IN TEMPERATURE RESPIRATORY NON-CARDIORESPIRATORY 1 4.2 4.1 .9 4 .8 3.9 3.8 .7 0 5 10 15 0 5 10 15 DAYS OF LAG LONDON, 1986-96: LAGS FOR COLD-RELATED MORTALITY

  15. SANTIAGO: COLD-RELATED MORTALITYCARDIO-VASCULAR DISEASE

  16. SANTIAGO: COLD-RELATED MORTALITYRESPIRATORY DISEASE

  17. THRESHOLDS, SLOPES & LAGS

  18. LAG: 0-13 DAYSCOLD Threshold for heat effect Threshold for cold effect LAG: 0-1 DAYSHEAT

  19. Variation in ‘heat slope’ & attributable deaths with threshold SOFIA, 0-1 DAY LAG Threshold

  20. X ? CONTROLLNG FOR SEASON TEMPERATURE MORTALITY SEASON UNRECORDED FACTORS Infectious disease Diet Human behaviours

  21. METHODS OF SEASONAL CONTROL • Moving averages • Fourier series (trigonometric terms) • Smoothing splines • Stratification by date • Other…

  22. 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)

  23. 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

  24. 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 1.0 2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3 1.7 1.7 1.7 1.7 1.7 1.7 1.7 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

  25. Temperature distribution Heat-related mortality, Delhi Relative mortality (% of daily average) Daily mean temperature /degrees Celsius

  26. 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)

  27. VECTOR-BORNE DISEASE Source: WHO

  28. Mosquito Parasite Survival probability Incubation period Biting frequency 1 50 0.3 0.8 40 0.6 30 0.2 (per day) (days) (per day) 0.4 20 0.1 0.2 10 0 0 0 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 Temp (°C) Temp (°C) 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)

  29. SO, TEMPERATURE IMPORTANT BUT… • NON-CLIMATE INFLUENCES • OTHER CLIMATIC FACTORS • TREATMENTS / ERADICATION PROGRAMMES

  30. 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@lshtm.ac.ukpaul.wilkinson@lshtm.ac.uk

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