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TIME SERIES ANALYSIS FOR STUDIES OF WEATHER AND HEALTH

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.uk paul.wilkinson@lshtm.ac.uk. Mean temperature. LONDON, 1990 - 1994. 150. 125.

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TIME SERIES ANALYSIS FOR STUDIES OF WEATHER AND HEALTH

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  1. TIME SERIES ANALYSIS FOR STUDIES OF WEATHER AND HEALTH Paul WilkinsonPublic & Environmental Health Research UnitLondon School of Hygiene & Tropical MedicineKeppel StreetLondon WC1E 7HT (UK) www.lshtm.ac.ukpaul.wilkinson@lshtm.ac.uk

  2. Mean temperature LONDON, 1990 - 1994 150 125 100 75 Cardiovascular deaths/day 50 25 0 01jan1990 01jan1991 01jan1992 01jan1993 01jan1994 CVD deaths

  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. 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

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

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

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

  13. TEMPERATURE DEPENDENCE OF DAILY MORTALITY, LONDON

  14. 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.)

  15. 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

  16. 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

  17. SANTIAGO: COLD-RELATED MORTALITYCARDIO-VASCULAR DISEASE

  18. SANTIAGO: COLD-RELATED MORTALITYRESPIRATORY DISEASE

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

  20. X 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. EFFECT OF INCREASING SEASONAL CONTROLGradient of cold-related mortality, London

  23. SEASONAL MORTALITY, GB

  24. SEASONAL FLUCTUATION IN MORTALITY, GB 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

  25. FUTURE IMPACTS

  26. Seasonal mortality pattern, Delhi Daily deaths

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

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

  29. 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 RISK ASSESSMENT FOR CLIMATE CHANGE 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

  30. BUT FOUR 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)

  31. HOSPITALIZATIONS FOR DIARRHOEA, LIMA PERUShaded region corresponds to 1997-98 El Niño event Daily hospitalizations for diarrhoea Daily temperature 1993 1997 Source: Checkley et al, Lancet 2000

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

  33. 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)

  34. INTERMISSION…

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

  36. HARVESTING

  37. It Dt General population Frail population, Nt Death FRAILTY MODEL Nt = Nt-1 + It - Dt-1

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

  39. ALL-CAUSE MORTALITY

  40. ALL CAUSE MORTALITY: HEAT DEATHS * 1.5 1 * 0.5 * * * * * PERCENTAGE INCREASE IN MORTALITY PER ºC BELOW COLD THRESHOLD * * * * * * * * * * * * * 0 * * * * * * * * * * * * * * * * * * -0.5 * * * * 0 5 10 15 20 Lag

  41. CUMULATIVE EXCESS RISK OF HEAT DEATH AS AFUNCTION OF INCREASING LAG: LONDON

  42. CUMULATIVE EXCESS RISK OF HEAT DEATH AS AFUNCTION OF INCREASING LAG: DELHI

  43. CUMULATIVE EXCESS RISK OF HEAT DEATH AS AFUNCTION OF INCREASING LAG: SAO PAULO

  44. UNCERTAINTIES IN FUTURE HEALTH IMPACTS

  45. (1) EXTRAPOLATION

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

  47. (2) VARIATION

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

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