1. HOW HOT IS HOT? Paul Wilkinson Public & Environmental Health Research Unit London School of Hygiene & Tropical Medicine Keppel Street London 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. No. of deaths/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

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

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

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

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

23. THE MODEL… (log) rate =ß 0 + ß 1 (high temp.)+ ß 2 (low temp.) ß 1 =heat slope ß 2 =cold slope + ß 3 (pollution)+ ß 4 (influenza)+ ß 5 (day, PH) measured confounders + ß 6 (season)+ ß 7 (trend) unmeasured confounders

24. 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

25. LONDON, 1986-96: LAGS FOR COLD-RELATED MORTALITY % INCREASE IN MORTALITY / ºC FALL IN TEMPERATURE DAYS OF LAG ALL CAUSE 051015 1.65 1.7 1.75 1.8 1.85 CARDIOVASCULAR 051015 1.7 1.75 1.8 1.85 1.9 RESPIRATORY 051015 3.8 3.9 4 4.1 4.2 NON-CARDIORESPIRATORY 051015.7.8.9 1

26. SANTIAGO: COLD-RELATED MORTALITY CARDIO-VASCULAR DISEASE

27. SANTIAGO: COLD-RELATED MORTALITY RESPIRATORY DISEASE

28. SANTIAGO: COLD-RELATED MORTALITY ALL CAUSES

29. THRESHOLDS, SLOPES & LAGS

30. LAG: 0-1 DAYS HEAT LAG: 0-13 DAYS COLD Threshold for heat effect Threshold for cold effect

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

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

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

34. 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

35. 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

36. 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 ASSESSMENT OF FUTURE HEALTH IMPACTS

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

38. 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…

39. VECTOR-BORNE DISEASE Source: WHO

40. TRANSMISSION POTENTIAL 0 0.2 0.4 0.6 0.8 1 14172023262932353841 Temperature (°C) Incubation period 0 10 20 30 40 50 152025303540 (days) Temp (°C) Survival probability 0 0.2 0.4 0.6 0.8 1 10152025303540 (per day) Temp (°C) Parasite Biting frequency 0 0.1 0.2 0.3 10152025303540 (per day) Temp (°C) Mosquito

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

42. 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 sari.kovats@lshtm.ac.uk paul.wilkinson@lshtm.ac.ukwww.lshtm.ac.uk