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Analyzing the Health Effects of Weather, Climate and Climate Change: Training Course Chapter 6

This training course chapter explores the analysis of health effects related to weather, climate, and climate change. It covers different types of analysis, including observational and model-based approaches, and discusses the principles and methods used in analyzing health impacts. The chapter also examines the inter-annual variation and seasonality of diseases and provides evidence on short-term associations between weather and health.

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Analyzing the Health Effects of Weather, Climate and Climate Change: Training Course Chapter 6

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  1. Protecting our Health from Climate Change: a Training Course for Public Health Professionals Chapter 6: Analyzing the Health Effects of Weather, Climate and Climate Change

  2. Some definitions Weather: the day-to-day atmospheric conditions in a specific place at a specific time Climate: the average state of the atmosphere and the underlying land or water in a specific region over a specific time scale Climate change: variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer)

  3. Types of analysis OBSERVATIONAL Episodes or event analysis: heat wave, flood, drought… Time-series analysis: mortality vs temperature, precipitation Seasonality: diarrhoea, aero-allergens Changes in geographical distribution: temperature/precipitation vs VBDs MODEL-BASED Health burdens: risk assessments Decisions analysis of health impact of policy options

  4. Decades Climate change Climate Seasonality Weather Years Months Days FUTUREIMPACTS(mid century onwards) HISTORICALEVIDENCE(recent past) Conventional epidemiology,observation Models, synthesis, ‘triangulation’

  5. Daily changes: two approaches Episode analysis - transparent - risk defined by comparison to local baseline Regression analysis of all days of year (time-series) - uses full data set - requires fuller data and analysis of confounders - can be combined with episode analysis

  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. Time-series regression Short-term temporal associations Daily/weekly Suitable for episodes or effects of local fluctuations in meteorological parameters U- or V-shape of temperature-response function Different lags

  9. Parameterization: hockey-stick models f(ti, ß) = ßctc,i + ßhth,i Relative risk tc,i = max[tc - ti,0] ‘cold’ th,i = max[th - ti,0] ‘heat’ Cold slope Heat slope Tc Th Temperature Minimum mortality range

  10. 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.)

  11. 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

  12. 2 1.5 2 1 0 5 10 15 1.5 1 0 5 10 15 Lags for cold-related mortality, London Cardiovascular death Respiratory death Increase in mortality/degrees Celsius below cold threshold Time lag (days)

  13. Threshold for heat effect Threshold for cold effect LAG: 0-1 DAYS(HEAT) LAG: 2-13 DAYS(COLD) Relative risk Relative risk Temperature Temperature

  14. strong correlation weaker absent Mortality displacement: schema MORTALITY A B HEAT Period of averaging

  15. Constrained distributed lag model: “harvesting” interpretation ‘Prompt’ adverse effects Deficit 3 weeks later - harvesting?

  16. X X Controlling for season TEMPERATURE MORTALITY SEASON UNRECORDED FACTORS Infectious disease Diet Human behaviours

  17. Methods of seasonal control Moving averages Fourier series (trigonometric terms): Fn(x) = a0 + (a1cos(x) + b1sin(x))+ … + (ancos(nx) + bnsin(nx)) where a0, b0, a1, b1,… are coefficients of Fn(x) Smoothing splines Stratification by date Other…

  18. Inter-annual variation: example of dengue epidemics in the South Pacific 1970-1998 10 2.0 9 1.5 8 La Nina years SOI (Southern Oscillation Index) 1.0 7 0.5 6 SOI 5 0.0 number of epidemics 4 -0.5 3 -1.0 2 El Nino years -1.5 1 0 -2.0 1980 1982 1984 1986 1988 1990 1992 1994 1998 1970 1972 1974 1976 1978 1996 Hales and Woodward, 1999

  19. Seasonality Cases of diarrhoeal disease Current distribution Distribution under global warming? Date of year

  20. Summary of time-series Provide evidence on short-term associations of weather and health Robust design Repeated finding of direct heat + cold effects Some uncertainties over PH significance Uncertainties in extrapolation to future(No historical analogue of climate change)

  21. Changes in geographical distribution of disease (1) BIOLOGICAL MODELS Use of (laboratory derived) biological evidence (2) STATISTICAL MODELS Analyses of disease prevalence or vector abundance in relation to geographical factors

  22. Bites per day Incubation period (days) P(S) per day

  23. Estimated population at risk of dengue fever:(A) 1990, (B) 2085 Source. Hales S et al. Lancet (online) 6 August 2002. http://image.thelancet.com/extras/01art11175web.pdf

  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 1.0 1 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 1.0 1 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 1.7 3 1.7 1.7 1.7 1.7 1.7 1.7 Future burdens: risk assessment 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) 140 Relative mortality (% of daily average) 120 100 80 0 10 20 30 40 Daily mean temperature /degrees Celsius

  26. Uncertainties • 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)

  27. 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

  28. Conclusions Most methods of ‘climate’ attribution based on analysis of weather-health associations: episode analysis, time-series, seasonality, inter-annual variations Relevance to climate change limited by uncertainties over multiple effect-modifiers – changes in vulnerability of population & health Modelling intrinsic to assessment of likely future burdens and the effect of adaptation options, but entails many uncertainties

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