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David N. Fisman 1 Infectious Disease Transmission and Climate Change David N. Fisman, MD MPH FRCPC Institute of Medicine Workshop on Climate Change, the Indoor Environment, and Public Health Washington, D.C. June 19, 2009 Projections on Climate Change in North America Intergovernmental Panel on Climate Change (IPCC) 4 th Assessment, 2007 Increased temperatures Increased rainfall Increased drought & wildfire Increased frequency of “extreme” weather events

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Page 1: Infectious Disease Transmission and Climate Change/media/Files/Activity Files...Infectious Disease Transmission and Climate Change ... • Accelerating pace of global warming (IPCC,

David N. Fisman

1

Infectious Disease Transmission and Climate Change

David N. Fisman, MD MPH FRCPC

Institute of Medicine Workshop on Climate Change, the Indoor Environment, and Public Health

Washington, D.C.

June 19, 2009

Projections on Climate Change in North America

Intergovernmental Panel on Climate Change (IPCC) 4th Assessment, 2007

◦ Increased temperatures

◦ Increased rainfall

◦ Increased drought & wildfire

◦ Increased frequency of “extreme” weather events

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David N. Fisman

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• Accelerating pace of global warming (IPCC, 4th Assessment Report, 2007)

NOAA Report (June 12, 2009)

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David N. Fisman

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Health Effects of Climate Change

• Direct consequences

– Heat-related mortality.

– Injuries (e.g., due to hurricanes, tornadoes and fires).

– Displacement of populations (coastal flooding, desertification).

• Indirect consequences

– Changes in the incidence and distribution of infectious diseases.

– More complex causal pathways: enhanced infectious disease transmission due to displacement of populations.

Impact on Invasive Bacterial Disease

• Vector-borne bacterial disease: changing ecosystems, ranges of amplifying hosts and insect vectors.

• Communicable diseases (esp. respiratory pathogens): perturbations of seasonal patterns of transmission; mass movement and crowding of populations via social disruption.

• Bacterial diseases with environmental reservoirs: Effects on food, water sources; “innoculation” via extreme weather events.

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The Physical Environment and Disease Transmission

Ambient Environment and Influenza A Transmission

• Guinea pig model of influenza A “rediscovered” by Palese and colleagues.

» Lowen A.C. et al., PNAS 2006.

• Notably “seasonal” pathogen: model facilitates evaluation of role of ambient environment in transmission.– Transmission enhanced by low relative humidity, low

temperature.» Lowen A.C. et al., PLoS Pathogens 2007.

– Differential impact of temperature on aerosol transmission and transmission via contact.

» Lowen A.C. et al., J. Virology 2008.

• Seasonality of community influenza epidemics vs. LTC outbreaks?

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Transmission, Temperature and Humidity (Influenza A Virus)

Source: Lowen AC et al., PLoS Pathogens, 2007

Increasing Relative Humidity Increasing Temperature

Inferring Environmental Impact

on Disease Transmission:

Insights from Seasonality

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David N. Fisman

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Houston, Texas

Is it really the season?

• Establishing causal links between environmental factors and disease occurrence may be difficult when the disease is seasonal.

• Relationships may be confounded with underlying factors– e.g. increased incidence during certain types of weather might just reflect

population risk behaviour

– Strong correlation is necessary but NOT sufficient

• Aggregation of exposures may lead to “ecological fallacy”.

R² = 0.9389

0

10

0 2 4 6 8 10 12

Cases per week

[Slide courtesy of Laura Kinlin and Alexander White]

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David N. Fisman

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Seasonally Oscillating

Environmental Exposures,

Philadelphia0

10

20

30

40

01/1994 01/1996 01/1998 01/2000 01/2002 01/2004 01/2006 01/2008

TMAX (C) MAXCIE/10

Delaware River dissolved O2 (*2)

Date

Approaches to Managing Seasonally Oscillating Confounders

• Count data: regression models with oscillatory smoothers (e.g., Fast Fourier Transform).

• Uncommon events: case crossover design (CXD).– Useful for evaluating impact of brief, transient,

repeated exposure.

– Traditional CXD case serves as own control.• In environmental CXD, evaluate exposures upstream

from “case day” (hazard period) vs. exposures during “control period.”

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Residual (Excess) Deaths, Relative to Model

Supplementary Figure: Schematic diagram of control selection strategy for case-

crossover study. Each row represents a 3-week time block. Hazard and control

periods (matched by day-of-week) are selected from the 3-week time block, resulting

in random directionality of control selection.

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Pneumococcus—Philadelphia

0

2

4

6

8

10

Jan-02 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06

Date

Ca

se

s

0

5

10

15

20

25

An

nu

alize

d In

cid

en

ce

pe

r 1

00

,00

0

[Source White ANJ et al., BMC Infectious Diseases, 2009]

Pneumococcus—Philadelphia

Univariable Models

Meteorological Element IRR (95% CI) P

Cooling-degree Days (oC) 0.92 (0.90 – 0.94) <0.001

Mean Temperature (oC) 0.96 (0.95 – 0.97) <0.001

Relative Humidity (%) 0.98 (0.97 – 0.99) 0.002

UV Index 0.89 (0.87 – 0.92) <0.001

Sulphur Oxides (ppm x 100) 1.73 (1.27 – 2.37) 0.002

Average Wind Speed (km/h) 1.01 (1.006 – 1.015) <0.001

Multivariable Modelsa

IRR (95% CI) P

0.97 (0.94 – 1) 0.06

... ... ...

... .. ...

0.74 (0.59 – 0.83) 0.01

... ... ...

... ... ...

[Source White ANJ et al., BMC Infectious Diseases, 2009]

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From: Sultan B, Labadi K, Guégan JF, Janicot S (2005) Climate Drives the Meningitis Epidemics Onset in West Africa. PLoS Med 2(1): e6

African Meningitis Belt (2)

Seasonality of Case Occurrence

• The seasonal distribution of IMD cases was similar in all cities (χ2=11.03;P=0.27),

with peak incidence in the winter and spring

• Poisson regression models confirmed the oscillatory nature of meningococcal

infections (P for seasonality <0.001 for London, Philadelphia and Sydney;P=0.10

for Toronto).

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12

Pe

rcen

tage

of

case

s

Month

Philadelphia

Sydney (shifted by 6 months)

London

[Laura Kinlin, IMED Vienna 2009]

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Seasonality of Case Occurrence

• The seasonal distribution of IMD cases was similar in all cities (χ2=11.03;P=0.27),

with peak incidence in the winter and spring

• Poisson regression models confirmed the oscillatory nature of meningococcal

infections (P for seasonality <0.001 for London, Philadelphia and Sydney;P=0.10

for Toronto).

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12

Per

cen

tage

of

case

s

Month

Philadelphia

Sydney (shifted by 6 months)

Toronto

London

[Laura Kinlin, IMED Vienna 2009]

UVB Radiation and Invasive Meningococcal Disease

INCUBATION

Kinlin L.M. et al., Am. J. Epidemiol. 2009

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Odds Ratio per Unit Increase in UV Index

.5 1 1.2

Combined

Sydney

Philadelphia

Toronto

London

Summary of Findings

Philadelphia Toronto London Sydney

Poisson analysis(i.e. long-term effects)

Humidity (+) - Carbon monoxide (+)Sulphur dioxide (+)

Humidity (-)Wind speed (+)

Carbon monoxide (+)Sulphur dioxide (+)

Case-crossover analysis(i.e., acute effects)

UV index (-) - - -

[Laura Kinlin, IMED Vienna 2009]

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What Can we Conclude?

• Despite apparently synchronous seasonal patterns of case occurrence,

environmental predictors of invasive meningococcal disease occurrence are

not consistent across regions

• Seasonal fluctuations in infection may be caused by small exogenous changes

interacting with population dynamics

• Effects of climate change may be region-specific and, consequently, difficult to

predict

[Laura Kinlin, IMED Vienna 2009]

Parental Smoking and IMD RiskRisk Factors for IMD in Aukland Children (Baker M et al., Ped Infect Dis J,

2000)

-1

0

1

2

3

Crowding

(number per

room)

Recent

analgesic

use (marker

of illness)

Recent social

gatherings

Number of

household

smokers

Sharing

(food, drink,

pacifier)

Respiratory

symptoms

(cough,

coryza, etc.)

in household

member

Od

ds

Rat

io (

Ln S

cale

)

0.37

1.0

2.7

20.1

7.4 OR 1.4 (95% CI 1 to 1.8)

OR 1.5 (95% CI 1 to 2.5)

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Influenza Season Severity and Environmental Conditions

Influenza Epidemic Severity and Cold

Cause New York metropolitan area Illinois and Indiana

No. of deaths

95% confidence interval

No. of deaths 95% confidence interval

H3N2 1,492 361 to 2,624 2,126 1,004 to 3,249

H1N1 –88 –560 to 384 127 –338 to 592

B 774 –21 to 1,571 549 –173 to 1,271

Cold 1,640 –1,815 to 5,097 1,646 –2,504 to 5,796

Total 3,819 66 to 7,572 4,447 62 to 8,832

TABLE 3. Annual pneumonia and influenza deaths, with 95% confidence intervals, categorized by causes appearing anywhere in the death record, attributed by the regional surveillance regression model for the New York metropolitan area and the states of Illinois and Indiana, 1979–2001 (threshold = 10°C).

Dushoff J. et al., Am J Epidemiol 2006

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El Niño and Diminished Influenza Mortality and “Space-Time Correlation” of P&I Death

Source: Viboud C et al., Euro. J. Epidemiol. 2004;Choi KM et al., Public Health 2005.

France California

Pathogen-Pathogen Interactions

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Respiratory Virus Activity and IMD: Central Ontario

[Tuite A.R. et al., ESPID, Nice, France, May 4-8, 2010]

OR per 100 case increase in influenza A: 2.46 (1.34 to 4.48)

OR per 100 case increase in RSV: 4.31 (1.14 to 16.32)

Effects on Multi-Week Time Scales (Poisson Regression)

Coefficient IRR 95% CI P-value

Influenza A (1 week lag)* 1.098 1.058-1.139 <0.001

Influenza A (3 week lag)* 0.929 0.890-0.970 <0.001

UV Index (weekly average) 0.969 0.943-0.997 0.027

UV Index (weekly average, 1 week lag) 0.949 0.923-0.976 <0.001

Sine(week) 1.29 1.218-1.365 <0.001

Cosine(week) 1.045 0.913-1.195 0.522

Year 1.009 0.995-1.022 0.205

*Per 100 case increase in FluWatch reports from Ontario.

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Vector Autoregressive Model 1: Predict IPD

Independent Variable and Lag Coefficient 95% CI P-value P (Granger)

Pneumococcus

1 week 0.141 0.046 to 0.236 0.004

2 week 0.038 -0.058 to 0.134 0.436

3 week 0.037 -0.055 to 0.128 0.432

Influenza A

1 week 0.0148 0.0071 to 0.0226 <0.001 <0.001

2 week -0.0088 -0.0216 to 0.0040 0.176

3 week -0.0025 -0.0104 to 0.0054 0.538

[Kuster S., in preparation]

Vector Autoregressive Model 2: Predict Influenza A

Independent Variable and Lag Coefficient 95% CI P-value P (Granger)

Pneumococcus

1 week 0.203 -0.944 to 1.35 0.729 0.998

2 week -0.103 -1.264 to 1.058 0.862

3 week -0.185 -1.286 to 0.916 0.742

Influenza A

1 week 1.3035 1.2095 to 1.3975 <0.001

2 week -0.3448 -0.4993 to -0.1903 <0.001

3 week -0.1365 -0.2325 to -0.0405 0.005

[Kuster S., in preparation]

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Conclusions

• Global climate change: major implications for human health.

• Impact of climate change on seasonally oscillating bacterial respiratory pathogens less clear.– “Not just weather”.

• Complex interplay between meteorology, pollutants, and pathogens.

– Influenza: may see mitigation (or de-seasonality) via warming, changing humidity.

– Environmental drivers of seasonality may be geographically specific.

Acknowledgements

• Team: Laura M. Kinlin, Alexander N.J. White, Marija Vasilevska, Caitlin McCabe, Ashleigh R. Tuite, Christina Chan, Tanya Hauck.

• Collaborators: Dr. Caroline Johnson (PDPH), Dr. Allison McGeer (Mt. Sinai), Dr. Jeff Kwong (ICES), Dr. Fran Jamieson (OAHPP), Dr. Stefan Kuster (Mt. Sinai), Dr. Amy L. Greer (PHAC), Dr. Victoria Ng (University of Guelph).

• Funders:– Ontario Early Researcher Award Program

– U.S. National Institute of Allergy and Infectious Diseases (R21-AI065826)

– SickKids Foundation

– Ontario Agency for Health Protection and Promotion