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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) 4th Assessment, 2007
◦ Increased temperatures
◦ Increased rainfall
◦ Increased drought & wildfire
◦ Increased frequency of “extreme” weather events
David N. Fisman
2
• Accelerating pace of global warming (IPCC, 4th Assessment Report, 2007)
NOAA Report (June 12, 2009)
David N. Fisman
3
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.
David N. Fisman
4
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?
David N. Fisman
5
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
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]
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.”
David N. Fisman
8
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.
David N. Fisman
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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]
David N. Fisman
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Pneumococcus—Philadelphia
.2.4
.6.8
11
.2
Age Group
Inc
ide
nc
e R
ate
Ra
tio
[Source White ANJ et al., BMC Infectious Diseases, 2009]
Global Distribution of IMDStephens 2007
David N. Fisman
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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]
David N. Fisman
12
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
David N. Fisman
13
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]
David N. Fisman
14
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)
David N. Fisman
15
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
David N. Fisman
16
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
David N. Fisman
17
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.
David N. Fisman
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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]
David N. Fisman
19
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