Climate change and human health

in search of magic numbers…NCAR Summer colloquium

28 July 2004

R Sari KovatsCentre on Global Change and Health

Dept of Public Health and PolicyLondon School of Hygiene and Tropical Medicine

STRATOSPHERIC OZONE DEPLETION

-Global problem-Health and environmental impacts

-Skin cancer-Cataracts

Information from epidemiologicalstudies

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 variables30 year averages

Modelling impacts of climate change

2020s

2050s

2080s

Time

2050 2100

2020s 2050s 2080s

  

High child,high adult

High child,very high

adult

  M F Both M F M F

(000) (000) (000) (000) (000) (000) (000)

Addictive substances

Tobacco 3 893 1 014 4 907 43 7 84 26

Alcohol 1 638 166 1 804 53 15 125 30

Illicit drugs 163 41 204 5 1 1 0

Environmental risks

Unsafe water, sanitation hygiene 895 835 1 730 129 103 207 169

Urban air pollution 411 388 799 11 11 5 5

Indoor smoke from solid fuels 658 961 1 619 93 80 118 101

Lead exposure 155 79 234 5 4 4 3

Climate change 76 78 154 9 9 18 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

WorldWorld AfricaAfricaDeaths, 2000Deaths, 2000

Deaths (thousands) DALYs (millions)

2000 2020

Estimated death and DALYs attributable to climate change.Selected conditions in developing countries

Floods

Malaria

Diarrhoea

Malnutrition

020406080100120 0 2 4 6 8 10

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]

TRANSMISSION POTENTIAL

0

0.2

0.4

0.6

0.8

1

14 17 20 23 26 29 32 35 38 41

Temperature (°C)

Incubation period

0

10

20

30

40

50

15 20 25 30 35 40

(day

s)Biting frequency

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

10 15 20 25 30 35 40

Temp (°C)

(per

day

)

Survival probability

0

0.2

0.4

0.6

0.8

1

10 15 20 25 30 35 40

(per

day

)

Temp (°C) Temp (°C)

Martens et al. 1999, van Lieshout et al. 2004

Can global models reveal regional vulnerability?

• Increase: East Africa, central Asia, Russian Federation• Decrease: central America, Amazon[within current vector limits]

C hange o f consecutive m onths

> +2

+2

-2

< -2

A1

B2

A2

B1

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

Present 2050 2100

Present 2050 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)

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

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 climate 2080s

Average 2 month temperature

lcl rr ucl

0 5 10 15 20

0

500

1000

1500

Temperature-salmonellosis [fully adjusted models]

England & Wales

Average 2 month temperature

lcl rr ucl

0 5 10 15 20

0

50

100

150

Average 2 month temperature

lcl rr ucl

0 5 10 15

20

40

60

80

Scotland

Average 2 month temperature

lcl rr ucl

0 10 20

0

100

200

300

Switzerland

Netherlands

sa

lm

yearwk1984w1 1986w1 1988w1 1990w1 1992w1 1994w1 1996w1 1998w1 2000w12002w1

0

50

100

150

200

250

Netherlands: time series

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

250

200

150

100

50

0

Tot

al w

eekl

y ca

ses

Climate change and air pollution,

UK Health Assessment 2002Pollutant 2020s 2050s 2080s

Particles Large decrease Large decrease Large 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 Small increase Small increase

Nitrogen dioxide Small decrease Small decrease Small decrease

Sulphur dioxide Large decrease Large decrease Large decrease

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

P Pa O

Distal Socio-Economic Causes

Proximal CausesPhysiological and

PathophysiologicalCauses

Outcomes

P Pa O

P Pa

1

2

3

1

2

3

1

2

D

D

D

1

2

3

Simplified causal web linking exposures and outcomes

WHO model

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?

Scenarios

• Climate– Averages, extremes

• Population– Population growth ✔✔– Population ageing ✔– Urbanisation, coastal migration

• “socio-economic”

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”

Relevance of attributable vs avoidable burden

•Avoidable burden more policy-relevant

•Why calculate attributable burden?

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.

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

Qualitative

Low

Low

High

High

Established butincomplete

Speculative Competing explanations

Well-established

Amount of evidence

Lev

el o

f ag

reem

ent,

cons

ensu

s

PastPast[climate/weather-health[climate/weather-health

relationships]relationships]

FutureFuture[map malaria][map malaria]

PresentPresent[highland malaria][highland malaria]

learnlearn?analogues?analoguesmechanismsmechanisms

detectiondetectionattributionattribution

predictive predictive modellingmodelling

three research tasksthree research tasks

Empirical studiesEmpirical studies[epidemiology][epidemiology]

2004 2010 2080

Country Reference

Antigua and Barbuda O'Marde and Michael, 2000 – UNEP Country Study

Australia McMichael et al, 2002

Cameroon UNEP/ Ministry of Environment and Forestry, Cameroon, 1998

Canada Duncan et al., 1997

Fiji de Wet and Hales, 2000

Japan Ando et al, 1998

Kiribati Taeuea, de Wet and Hales, 2000

New Zealand Woodward et al. 2001

Panama Sempris E and Lopez R, eds. 2001 - ANAM/UNDP

Portugal Casimiro and Calheiros, 2002

South Africa UNEP Country study 2000

Sri Lanka Ratnasari 1998

St Lucia St Lucia National Communication, chapter 4.

United Kingdom Dept of Health, 2002

United States Patz et al., 2000 + various documents

Zambia Phiri amd Msiska, 1998