Weather, climate and health

Simon LloydLondon School of Hygiene

and Tropical Medicine

Health outcomes influenced by climate: likely impacts of climate change

Figure 8.3: Ch 8 Human Health, IPCC 4AR

Health outcomes influenced by climate: likely impacts of climate change

Figure 8.3: Ch 8 Human Health, IPPC 4AR

• Impact on existing burden of disease

• Various exposures of interest

• At times, complex links

Health outcomes influenced by climate: likely impacts of climate change

Figure 8.3: Ch 8 Human Health, IPPC 4AR

• Impact on existing burden of disease

• Various exposures of interest

• At times, complex links

Long term changes in means

Daily or weekly changes

Interannual variability

Extreme weather events

Health outcomes influenced by climate: likely impacts of climate change

Figure 8.3: Ch 8 Human Health, IPPC 4AR

• Impact on existing burden of disease

• Various exposures of interest

• At times, complex links

Exposure – outcome relationships

Exposure

Epidemiology at individual level

Outcome

Smoking Cardiovascular disease

Exposure – outcome relationships

Exposure

Epidemiology at individual level

Outcome

Smoking Cardiovascular disease

Weather/climate and health:

• Exposure at population level

• Pathway (often) indirect and complex

• Impact moderated by vulnerability

Climate change and health: Risks and Responses, WHO 2003.

Climate change and health: Risks and Responses, WHO 2003.

Climate change and health: Risks and Responses, WHO 2003.

Climate change and health: Risks and Responses, WHO 2003.

Types of evidence for health effects

Spatial studies• Climate as an explanatory variable in the distribution of

the disease or the disease vector

Temporal studies• short term (daily, weekly) changes • inter-annual climate variability • longer term (decadal) changes in the context of

detecting early effects of climate change.

Health impacts of individual extreme events• heat waves, floods, storms, droughts

Diarrhoea and average weather

Diarrhoea and average weather

Diarrhoea rates

Diarrhoea and average weather

Temperature

Diarrhoea rates

Mechanism: pathogen survival

Diarrhoea and average weather

Temperature

Diarrhoea ratesHigh rainfall

Mechanism: water supply contamination (water quality)

Diarrhoea and average weather

Temperature

Diarrhoea ratesHigh rainfall

Low rainfall

Mechanism: use of unprotected water sources; reduced hygiene behaviour (water quantity)

Diarrhoea and average weather

Temperature

Diarrhoea ratesHigh rainfall

Low rainfall

Water andsanitation

Malnutrition

etc.

Climate type

Socioeconomic conditions

Data

Outcome

Exposure

Co-variates

Data

Outcome

Exposure

Co-variates

• Diarrhoea morbidity

• Children under 5 years

• Low and middle income countries

• 36 study sites

Data

Outcome

Exposure

Co-variates

• Av. temperature over study period

• Av. rainfall over study period

CRU TS 2.1 dataset

• Underlying climate

Köppen climate classification

Data

Outcome

Exposure

Co-variates

• Socioeconomic conditions

• Water and sanitation

• Setting: urban, rural, slum

Study sites

Lloyd, Kovats & Armstrong. Clim Res 2007; 34:119-27

Diarrhoea cases vs average weather

Guatemala

Costa Rica

Indonesia

Guatemala

India

India

India Guatemala

Indonesia

Kenya

Brazil

Brazil

Egypt

Ghana

Bangladesh

Peru

Costa Rica

Nigeria

India

Brazil

Argentina

Peru

Zimbabwe

Thailand

Papua New Guinea

China

India

Egypt

-10

12

3Lo

g ep

iso

des/

child

-ye

ar

0 100 200 300 400Average precipitation (mm/month)

r=-0.36

Rainfall and children 6-11 months old

-10

12

3Lo

g ep

iso

des/

child

-ye

ar5 10 15 20 25 30

Average mean temperature (degrees C)

Tropical Arid

Temperate Boreal forest

r=-0.03

Temperature and children aged one year

Average monthly rainfall and log diarrhoea incidence in children aged 6-11 months, showing country

Average temperature and log diarrhoea incidence in children aged 1 year, showing climate classification.

Lloyd, Kovats & Armstrong. Clim Res 2007; 34:119-27

Regression results

Variable Model 1 Model 2 Model 3 Model 4

GDP 1.00 (1.00-1.00) 1.00 (1.00-1.00) 1.00 (1.00-1.00) 1.00 (1.00-1.00)

Precipitation 0.97 (0.94-1.00) 0.97 (0.94-1.00) 0.96 (0.93-0.99)

Temperature 1.02 (0.97-1.07) 1.03 (0.97-1.09)

Setting: rural 1

urban 0.73 (0.42-1.29)

slum 1.01 (0.45-2.28)

mixed 1.63 (0.38-6.93)

All models are adjusted for age group. Coefficients indicate the change in diarrhoea rate (episodes per child-year) for each: 10USD (in 2000 USDs) increase in GDP/capita; 10mm/month increase in average monthly rainfall; and, 1C increase in average temperature

Lloyd, Kovats & Armstrong. Clim Res 2007; 34:119-27

Regression results

Variable Model 1 Model 2 Model 3 Model 4

GDP 1.00 (1.00-1.00) 1.00 (1.00-1.00) 1.00 (1.00-1.00) 1.00 (1.00-1.00)

Precipitation 0.97 (0.94-1.00) 0.97 (0.94-1.00) 0.96 (0.93-0.99)

Temperature 1.02 (0.97-1.07) 1.03 (0.97-1.09)

Setting: rural 1

urban 0.73 (0.42-1.29)

slum 1.01 (0.45-2.28)

mixed 1.63 (0.38-6.93)

All models are adjusted for age group. Coefficients indicate the change in diarrhoea rate (episodes per child-year) for each: 10USD (in 2000 USDs) increase in GDP/capita; 10mm/month increase in average monthly rainfall; and, 1C increase in average temperature

Diarrhoea ↓ 4% (1 – 7%) for

for each 10mm/month rainfall ↑

Lloyd, Kovats & Armstrong. Clim Res 2007; 34:119-27

Extreme events: coastal flooding

• Spatial scale– WHO GBD regions

Extreme events: coastal flooding

• Spatial scale– WHO GBD regions

• Event and outcome data– International Emergency Disaster Database,

University of Louvain, Belgium.

EM-DAT

Region Events Killed Killed/event Affected Affected/event

AP_HI 83 2715 33 2 180 872 26 276

As_C 2 0 0 2800 1400

As_E 161 9736 60 221 000 000 1 372 671

As_S 105 185 095 1763 105 000 000 1 000 000

As_SE 247 33 461 135 109 000 000 441 296

Au 28 63 2 18 925 676

Ca 185 4478 24 15 300 000 82 703

Eu_C 6 13 2 8000 1333

Eu_E 21 71 3 329 326 15 682

Eu_W 36 236 7 3 475 200 96 533

LA_A 1 518 518 580 000 580 000

LA_C 109 23 799 218 11 000 000 100 917

LA_S - - - - -

LA_T 1 4 4 150 000 150 000

NA_HI 59 2447 41 10 400 000 176 271

NA_ME 6 127 21 180 050 30 008

Oc 94 688 7 1 623 948 17 276

SSA_C 1 17 17 0 0

SSA_E 62 2472 40 8 404 123 135 500

SSA_S 4 125 31 1 132 000 283 000

SSA_W 4 33 8 9322 2331

Initial estimates of impacts of extratropical & tropical cyclones, 1980 - 2007 [EMDAT, 2008]

Region Events Killed Killed/event Affected Affected/event

AP_HI 83 2715 33 2 180 872 26 276

As_C 2 0 0 2800 1400

As_E 161 9736 60 221 000 000 1 372 671

As_S 105 185 095 1763 105 000 000 1 000 000

As_SE 247 33 461 135 109 000 000 441 296

Au 28 63 2 18 925 676

Ca 185 4478 24 15 300 000 82 703

Eu_C 6 13 2 8000 1333

Eu_E 21 71 3 329 326 15 682

Eu_W 36 236 7 3 475 200 96 533

LA_A 1 518 518 580 000 580 000

LA_C 109 23 799 218 11 000 000 100 917

LA_S - - - - -

LA_T 1 4 4 150 000 150 000

NA_HI 59 2447 41 10 400 000 176 271

NA_ME 6 127 21 180 050 30 008

Oc 94 688 7 1 623 948 17 276

SSA_C 1 17 17 0 0

SSA_E 62 2472 40 8 404 123 135 500

SSA_S 4 125 31 1 132 000 283 000

SSA_W 4 33 8 9322 2331

Initial estimates of impacts of extratropical & tropical cyclones, 1980 - 2007 [EMDAT, 2008]

Region Events Killed Killed/event Affected Affected/event

AP_HI 83 2715 33 2 180 872 26 276

As_C 2 0 0 2800 1400

As_E 161 9736 60 221 000 000 1 372 671

As_S 105 185 095 1763 105 000 000 1 000 000

As_SE 247 33 461 135 109 000 000 441 296

Au 28 63 2 18 925 676

Ca 185 4478 24 15 300 000 82 703

Eu_C 6 13 2 8000 1333

Eu_E 21 71 3 329 326 15 682

Eu_W 36 236 7 3 475 200 96 533

LA_A 1 518 518 580 000 580 000

LA_C 109 23 799 218 11 000 000 100 917

LA_S - - - - -

LA_T 1 4 4 150 000 150 000

NA_HI 59 2447 41 10 400 000 176 271

NA_ME 6 127 21 180 050 30 008

Oc 94 688 7 1 623 948 17 276

SSA_C 1 17 17 0 0

SSA_E 62 2472 40 8 404 123 135 500

SSA_S 4 125 31 1 132 000 283 000

SSA_W 4 33 8 9322 2331

Initial estimates of impacts of extratropical & tropical cyclones, 1980 - 2007 [EMDAT]

140 000 in a single event in 1991

Interannual variability: ENSO

• Human impact of natural disasters increases during El Niño

• ENSO associated with infectious diseases in some areas, esp cholera risk and malaria epidemics

• ENSO and seasonal climate forecasts may have public health use

Interannual variability: ENSO

• Human impact of natural disasters increases during El Niño

• ENSO associated with infectious diseases in some areas, esp cholera risk and malaria epidemics

• ENSO and seasonal climate forecasts may have public health use

Global disaster burden associated with El Niño over a 30 year period (1964-1993)

• Exposure: consensus El Niño years

• Outcome: affected by a natural disaster

0

10

20

30

40

50

60

70

8019

64

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

Inci

denc

e pe

r 10

00

El Niño years

World population affected by natural disasters

Possible El Niño years Non-El Niño years

Rate per 1000 people, 1964-93

Bouma, Kovats et al. Lancet 1997; 350:1435-8

0

10

20

30

40

50

60

70

80

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

Inci

denc

e pe

r 10

00World population affected by natural disasters

Rate per 1000 people, 1964-93

Bouma, Kovats et al. Lancet 1997; 350:1435-8

Strongest association between El Niño and drought (including food shortage and famine):

• sub-Saharan Africa, South America, and South and West Asia

Extreme rainfall and tropical cyclones: mixed effects by region

-20

-15

-10

-5

0

5

10

15

20

25

30

N-2 N-1 Nino year N+1 N+2

Inci

denc

e /1

000

(dev

.tren

d)El Niño: a natural disaster cycle?

Bouma, Kovats et al. Lancet 1997; 350:1435-8

-20

-15

-10

-5

0

5

10

15

20

25

30

N-2 N-1 Nino year N+1 N+2

Inci

denc

e /1

000

(dev

.tren

d)

150

million

people

El Niño: a natural disaster cycle?

Bouma, Kovats et al. Lancet 1997; 350:1435-8

Climate and respiratory health in children

Climate and respiratory health in children

Previous studies

↓ temperature range

↓ relative humidity range

Asthma ↑ with:

↑ temperature in coldest month

↑ mean annual temperature

Data needs and queries

• How well does data represent climate at particular study sites?

• Are there areas that are not well represented?

• Can data be used to quantify elements of events?• E.g. rainfall in floods; extent, intensity & duration of drought.

• How well are ENSO events and their associated impacts on climate events represented?