1. Climate change and human health in search of magic numbers… NCAR Summer colloquium 28 July 2004 R Sari Kovats Centre on Global Change and Health Dept of Public Health and Policy London School of Hygiene and Tropical Medicine

2. STRATOSPHERIC OZONE DEPLETION -Global problem -Health and environmental impacts -Skin cancer -Cataracts Information from epidemiological studies

3. Impact models Estimates of populations at risk or attributable burden of disease Greenhouse gas emissions scenarios Defined by IPCC Global climate scenarios: Generates series of maps of predicted future distribution of climate variables 30 year averages Modelling impacts of climate change 2020s 2050s 2080s 2020s2050s2080s

4. High child, high adult High child, very high adult M F Both MFMF (000) Addictive substances Tobacco3 8931 0144 907 43 7 84 26 Alcohol1 638 1661 804 53 15 125 30 Illicit drugs 163 41 204 5 1 1 0 Environmental risks Unsafe water, sanitation hygiene 895 8351 730 129 103 207 169 Urban air pollution 411 388 799 11 5 5 Indoor smoke from solid fuels 658 9611 619 93 80 118 101 Lead exposure 155 79 234 5 4 4 3 Climate change 76 78 154 9 9 18 Occupational risks Risk factors for injury 291 19 310 14 1 18 1 Carcinogens 118 28 146 1 0 1 1 Airborne particulates 217 26 243 3 0 3 0 Ergonomic stressors 0 0 0 0 0 0 0 Noise 0 0 0 0 0 0 0 WorldAfrica Deaths, 2000

5. Deaths (thousands)DALYs (millions) 20002020 Estimated death and DALYs attributable to climate change. Selected conditions in developing countries Floods Malaria Diarrhoea Malnutrition 0204060801001200246810

6. Health-impact models Process-based/Biological models –Malaria/vectorial capacity [MIASMA] –Heat budget models Empirical statistical –Temp-mortality (Kalkstein, Moser, etc.) –Temp –Diarrhoeal disease –Rainfall -flood-death –Temp/rainfall- Dengue, Malaria [spatial correlations]

7. 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) Biting frequency 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 10152025303540 Temp (°C) (per day) Survival probability 0 0.2 0.4 0.6 0.8 1 10152025303540 (per day) Temp (°C) Martens et al. 1999, van Lieshout et al. 2004

8. Can global models reveal regional vulnerability? Increase: East Africa, central Asia, Russian Federation Decrease: central America, Amazon [within current vector limits] A1 B2 A2 B1

9. Potential distribution of Aedes aegypti in the North Island based on 10°C midwinter isotherm limit for a mid- and high-range climate change scenario. Source: Hotspots dengue fever risk model developed by the International Global Change Institute, University of Waikato, with the assistance of funding from the Health Research Council Present2050 2100 Present2050 2100 Mid-range scenario (SRES B2 greenhouse gas emission scenario, best guess climate sensitivity) High-range scenario (SRES A2 greenhouse gas emission scenario, high climate sensitivity)

10. Empirical-stats models EXTRAPOLATION –Can you extrapolate the exposure-response relationship beyond the bounds of the observed temperature range? VARIATION –Can you extrapolate the exposure-response relationship derived from a different population. ADAPTATION –Responses to climate change - acclimatization MODIFICATION –What is likely?– –changes to exposure response relationship

11. Predicted distribution of the malaria vector (mosquito Anopheles atroparvus) in present day Europe, and in the 2080s with SRES A2 climate scenario. [Kuhn, LSHTM, 2002] Current climate2080s

12. Temperature-salmonellosis [fully adjusted models] England & Wales Scotland Switzerland Netherlands

13. Netherlands: time series 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 250 200 150 100 50 0 Total weekly cases

14. Climate change and air pollution, UK Health Assessment 2002 Pollutant2020s2050s2080s ParticlesLarge decrease Ozone (assuming no threshold) Large increase (by about 10%) Large increase (by about 20%) Large increase (by about 40%) Ozone (assuming a threshold) Small increase Nitrogen dioxideSmall decrease Sulphur dioxideLarge decrease

15. Outcomes... Shift in “climate envelope” Additional population at risk –Definitions of risk Relative risk Absolute risk –additional/excess cases/deaths –Disability-adjusted life-year [DALY]  COSTS

16. Simplified causal web linking exposures and outcomes WHO model

17. Attributable fractions vs attributable deaths/cases Population change –Growth –Ageing –Countries have national projections Which baseline disease incidence used to estimate attributable cases. –Current or future?

18. Scenarios Climate –Averages, extremes Population –Population growth ✔✔ –Population ageing ✔ –Urbanisation, coastal migration “socio-economic”

19. Non climate scenarios Vector presence/abundance Baseline disease prevalence –Cardiovascular disease –HIV/AIDS Millennium Development Goals Population Income/GDP per capita/PPP per capita Technology –Malaria vaccine Qualitative “Knowledge is King, Big is Beautiful”

20. Relevance of attributable vs avoidable burden Avoidable burden more policy-relevant Why calculate attributable burden?

21. WHO Definitions… A health impact assessment is a combination of procedures or methods by which a proposed policy, programme or project may be judged as to the effects it may have on the health of a population. The basic principles underlying such an assessment are democracy, equity, sustainable development and evidence-based advice.

22. Uncertainty Climate scenario –>1 climate model –>4 emissions scenarios –Regional model –Downscaling Exposure response relationship –Key uncertainties/assumptions in the models –Confidence intervals –Monte Carlo simulation/Bayes

23. Qualitative Low High Established but incomplete Speculative Competing explanations Well-established Amount of evidence Level of agreement, consensus

24. Past[climate/weather-healthrelationships]Future [map malaria] Present [highland malaria] learn?analoguesmechanismsdetectionattribution predictive modelling three research tasks Empirical studies [epidemiology] 200420102080

25. CountryReference Antigua and BarbudaO'Marde and Michael, 2000 – UNEP Country Study AustraliaMcMichael et al, 2002 CameroonUNEP/ Ministry of Environment and Forestry, Cameroon, 1998 CanadaDuncan et al., 1997 Fijide Wet and Hales, 2000 JapanAndo et al, 1998 KiribatiTaeuea, de Wet and Hales, 2000 New ZealandWoodward et al. 2001 PanamaSempris E and Lopez R, eds. 2001 - ANAM/UNDP PortugalCasimiro and Calheiros, 2002 South AfricaUNEP Country study 2000 Sri LankaRatnasari 1998 St LuciaSt Lucia National Communication, chapter 4. United KingdomDept of Health, 2002 United StatesPatz et al., 2000 + various documents ZambiaPhiri amd Msiska, 1998