1. 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.ukwww.lshtm.ac.uk paul.wilkinson@lshtm.ac.uk

2. 0 25 50 75 100 125 150 Cardiovascular deaths/day 01jan199001jan199101jan199201jan199301jan1994 CVD deaths Mean temperature LONDON, 1990 - 1994

3. CLIMATE OR WEATHER?

4. 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

5. EPISODES

6. 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

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 for regression of all days of year

8. REGRESSION OF ALL DAYS

9. 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)

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

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

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

15. TEMPERATURE DEPENDENCE OF DAILY MORTALITY, LONDON

16. 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

17. 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

18. 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

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

20. SANTIAGO: COLD-RELATED MORTALITY RESPIRATORY DISEASE

21. SANTIAGO: COLD-RELATED MORTALITY ALL CAUSES

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

23. CONTROLLNG FOR SEASON TEMPERATURE MORTALITY SEASON Infectious disease Diet UNRECORDED FACTORS Human behaviours XX

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

25. EFFECT OF INCREASING SEASONAL CONTROL Gradient of cold-related mortality, London

26. SEASONAL MORTALITY, GB

27. 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 SEASONAL FLUCTUATION IN MORTALITY, GB

28. FUTURE IMPACTS

29. Seasonal mortality pattern, Delhi Daily deaths

30. Delhi, India: Average annual pattern of temperature, rainfall and daily mortality (data for all 1991-94 years, averaged, by day of year) 1st Jan1st July 0 50 100 150 -10 0 20 30 40 Jan 1 July 1 Dec 31 150 100 50 0 40 30 20 10 0 - Daily deaths Daily temperature Monthly rainfall Temperature Deaths McMichael et al, in press

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

32. 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 RISK ASSESSMENT FOR CLIMATE CHANGE

33. 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) BUT FOUR REASONS TO HESITATE…

34. Source: Checkley et al, Lancet 2000 Daily hospitalizations for diarrhoea Daily temperature 19971993 HOSPITALIZATIONS FOR DIARRHOEA, LIMA PERU Shaded region corresponds to 1997-98 El Niño event

35. SOI (Southern Oscillation Index) Hales and Woodward, 1999 El Nino years La Nina years

36. Changes in population - Demographic structure (age) - Prevalence of weather-sensitive disease Environmental modifiers Adaptive responses - Physiological habituation (acclimatization) - Behavioural change - Structural adaptation - PH interventions CHANGING VULNERABILITY

37. 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

38. INTERMISSION…

39. TIME SERIES ANALYSIS FOR STUDIES OF WEATHER AND HEALTH Part 2

40. HARVESTING

41. FRAILTY MODEL General population Frail population, N t Death DtDt ItIt N t = N t-1 + I t - D t-1

42. IDEALIZED SCHEMA MORTALITY HEAT A B weaker absent strong correlation Period of averaging

43. ALL-CAUSE MORTALITY

44. ALL CAUSE MORTALITY: HEAT DEATHS Lag PERCENTAGE INCREASE IN MORTALITY PER ºC BELOW COLD THRESHOLD 05101520 -0.5 0 0.5 1 1.5 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

45. CUMULATIVE EXCESS RISK OF HEAT DEATH AS A FUNCTION OF INCREASING LAG: LONDON

46. CUMULATIVE EXCESS RISK OF HEAT DEATH AS A FUNCTION OF INCREASING LAG: DELHI

47. CUMULATIVE EXCESS RISK OF HEAT DEATH AS A FUNCTION OF INCREASING LAG: SAO PAULO

48. CUMULATIVE EXCESS RISK OF HEAT DEATH: DELHI, SAO PAULO, LONDON

49. UNCERTAINTIES IN FUTURE HEALTH IMPACTS

50. (1) EXTRAPOLATION

51. MEAN DAILY TEMPERATURE / degrees Celsius MORTALITY (% of annual average) ? HEAT DEATHS Monterrey, Mexico

52. (2) VARIATION

53. ISOTHURM STUDY: annual pattern of temperature, rainfall and daily mortality (data for all years averaged, by day of the year) LJUBLJANABUCHARESTSOFIADELHI MONTEREYMEXICO CITYCHIANG MAIBANGKOK SALVADORSAO PAULOSANTIAGOCAPE TOWN

54. Mortality (% of annual average) Mean daily temperature in degrees Celsius Daily mortality in relation to mean temperature during preceding two days

55. Daily mortality in relation to mean temperature during preceding two weeks

56. (3) ADAPTATION

57. Threshold for heat impacts Maximum daily mean temperature 20253035 -10 0 10 20 30 Thresholds for heat-related mortality: 12 lower- & middle-income cities Positive slope suggests adaptation

59. Risk of death relative to annual minimum 10-week moving average Day of year 1Jan1Apr1Jul1Oct31Dec.75 1 1.25 1.5 1.75 2 High standardized heating costs Low standardized heating costs Seasonal variation in deaths from cardiovascular disease by cost of home heating. England, 1986-1996.

60. CARDIOVASCULAR MORTALITY IN RELATION TO HOME HEATING: ENGLAND, 1986-96 Standardized indoor temp. /deg Celsius Mortality (deaths/day) Winter:non-winter ratio* WinterNon- winter UnadjustedAdjusted 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 19.4-27.00.8 (1055) 0.7 (1906) 1.11 (1.03,1.20) 1.13 (0.96,1.34) * All ratios adjusted for region

61. Slope becomes shallower if home is warmer Mortality Outdoor temperature / degrees Celsius

62. (4) EFFECT MODIFICATION

63. Table. Change in population health and deaths attributable to cold over the 20 th century Percentage of deaths by age & cause: Period 1900-101927-371954-641986-96 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% Percent of deaths attributable to cold 12.5 (10.1, 14.9) 11.2 (8.40, 14.0) 8.74 (5.93, 11.5) 5.42 (4.13, 6.69)

65. Temperature (degrees Celsius)

66. -5 0 5 10 15 1900-101927-371956-66 1986-96 1900-10 1927-371956-661986-96 15-64 years65+ years Population attributable fraction (PAF) of deaths from cold PAF decade

67. IMPLICATIONS FOR MONITORING HEALTH IMPACTS OF CLIMATE CHANGE

68. METHODOLOGICAL ISSUES Gradual change Year to year fluctuation Secular trends Modifiers - physiological acclimatization - structural and behavioural adaptation - specific protection measures Attribution

69. 1. Measurement of trend in disease rates 2.Measurement of trend in attributable disease: direct method 3.Application of climate- disease relationships to measured changes in climate: indirect method Confounded by secular trends: un-interpretable unless v. specific marker Based on analysis of (short-term) climate- disease relationships Depends on understanding effect modification or assumption of its absence MONITORING

71. Deaths in June & July, London, 1986-1996 Deaths in June & July Year Days over 27ºC 198619881990199219941996 0 5000 10000 15000 0 10 20 30 Deaths in June & July Days over 27 Celsius

72. Deaths attributable to heat, London, 1986-1996 Percent attributable to heat year Days over 27ºC 198619881990199219941996 0.2.4.6.8 1 0 5 10 15 20 25 30 Heat deaths Days over 27 Celsius

75. Band of historical climatic variability 20 15 190021002000 14 16 17 18 13 19 Average Global Temperature ( O C) Year 205019501860 Low High Central estimate = 2.5 o C (+ increased variability) IPCC (2001) estimates a 1.4-5.8 o C increase This presents a rate-of-change problem for many natural systems/processes

76. 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

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