Transcript Health applications for climate data Pollen Grains Shubhayu Saha Climate and Health Program Centers for Disease Control and Prevention CDC, National Center for Environmental Health.

Health applications for climate data
Pollen Grains
Shubhayu Saha
Climate and Health Program
Centers for Disease Control and Prevention
CDC, National Center for Environmental Health
Presenter Disclosures
Shubhayu Saha
"The findings and conclusions in this
report are those of the author(s) and do
not necessarily represent the official
view of Centers for Disease Control
CDC, National Center for Environmental Health
Outline
Climate-sensitive health outcomes
CDC’s role in translation and capacity building
Example of establishing health-weather
associations
Projecting future health burden
CDC, National Center for Environmental Health
National Climate Assessment –
Health implications
Temperature extremes
Aeroallergens
Vectorborne disease
Injuries from extreme weather events
Wildfire
CDC, National Center for Environmental Health
Mortality risk from heat waves
Andersen and Bell, 2011, Environmental Health Perspectives
CDC, National Center for Environmental Health
Temperature increase and change in length of Ragweed season
Ziska et al., 2011 PNAS
CDC, National Center for Environmental Health
Vectorborne diseases
17029
cases
Changes in georgaphical distribution
Longer transmission season
Higher tick densities
24364
cases
Weather-related motor vehicle fatalities (Marmor et al, JAPH 2006)
CDC, National Center for Environmental Health
CDC, National Center for Environmental Health
1. Forecasting
Climate Impacts
and Assessing
Vulnerabilities
5. Evaluating
Impact and
Improving Quality
of Activities
Building
Resilience
Against
Climate
Effects
4. Developing and
Implementing a
Climate and Health
Adaptation Plan
2. Projecting the
Disease Burden
3. Assessing
Public Health
Interventions
Climate and Health Program, National Center for Environmental Health
Generating County-level Measures
Step 1:
Creating population
weighted county centroid
Geometric centroid
of census blocks
Population weighted
County centroid
County boundary
Step 2:
Selecting the grid cell that
contains the population
weighted county centroid
Step 3:
County-level values
obtained by averaging
values of all the 9 grid cells
CDC, National Center for Environmental Health
NLDAS grid
Grid cell containing the
population weighted
centroid
Adjacent grid cells
NLDAS-based maximum temperature (F)
Daily Comparison: Scatter plot by Climate Region
r = 0.91
t = 0.76
r = 0.88
t = 0.69
r = 0.92
t = 0.76
r = 0.87
t = 0.70
r = 0.87
t = 0.70
r = 0.90
t = 0.72
r = 0.90
t = 0.75
r = 0.82
t = 0.64
r = 0.89
t = 0.71
Station-based maximum temperature (F)
Comparison for May – September 2006
The National Environmental Public Health Tracking Network
The network provides data on:



Extreme heat days and events
Heat vulnerability
Health effects associated with extreme heat
http://ephtracking.cdc.gov/showHome.action
CDC, National Center for Environmental Health
The National Environmental Public Health Tracking Network
http://ephtracking.cdc.gov/showHome.action
CDC, National Center for Environmental Health
 What is the temporal association of
Hyperthermia-related ED visit with different
measures of ambient heat?
 How does this association vary by place?
CDC, National Center for Environmental Health
Data elements
For 141 Metropolitan Statistical Areas in continental US:
 National Climatic Data Center:
Daily temperature, humidity
30 year daily normal for maximum temperature
 Spatial Synoptic classification
 MarketScan health data:
ED visit of Hyperthermia by date, county of healthcare, age, gender
 Air pollution data:
Daily monitor-level PM2.5 and Ozone data
CDC, National Center for Environmental Health
Analytical strategy
Case crossover design – same patient treated as Case and Control
 Half-month time-stratified control selection
Calendar month
1
8
15
22
Patient 1
Patient 2
Patient 3
Case day
CDC, National Center for Environmental Health
Control day
31
Is the temperature different leading to an ED visit?
CDC, National Center for Environmental Health
Conditional logistic regression
A
B
C
D
Cases
11270
11270
3828
3828
Control days
14070
14070
4738
4738
Maximum temperature oF
1.15 (1.14-1.15)
1.15 (1.14-1.15)
1.15 (1.14-1.17)
1.16 (1.15-1.17)
1.12 (0.96-1.30)
1.12 (0.83-1.52)
1.13 (0.84-1.54)
Heat wave indicator1
PM 2.5 concentration
(mg/m3)
1.02 (1.01-1.03)
Ozone concentration (ppm)
1.00 (1.00-1.00)
Holiday indicator2
1.24 (0.82-1.88)
Model AIC
15163
15163
CDC, National Center for Environmental Health
5071
5083
86.00
0.40
84.00
0.20
82.00
0.00
Latitude category oN
CDC, National Center for Environmental Health
Max Temp
Z score
Z-score of Max Temp on day of ED visit
0.60
>= 42
88.00
<=40 & <42
0.80
<=38 & <40
90.00
<=36 & <38
1.00
<=34 & <36
92.00
<=32 & <34
1.20
<=30 & <32
94.00
<30
Maximum temperature on day of ED visit
Temperature profile on ED visit days change by place
Odds ratio of ED visit associated with extreme heat by Latitude
5
11
23
13
8
7
15
12
1.18
1.16
1.14
1.12
1.10
1.08
Latitude category oN
CDC, National Center for Environmental Health
>= 42
<=40 & <42
<=38 & <40
<=36 & <38
<=34 & <36
<=32 & <34
<=30 & <32
1.06
<30
Odds Ratio
1.20
Random Effects meta-analysis of Odds Ratios of ED visit
Northwest
West North Central
1.16
1.16
(1.07, 1.26)
(1.07, 1.26)
East North Central
1.18
(1.14, 1.21)
2
11
8
Northeast
1
West
1.15
(1.13, 1.17)
Central
Southwest
1.12
(1.09, 1.14)
13
1.17
(1.16, 1.19)
1.05
(1.02, 1.10)
South
1.12
(1.10, 1.13)
8
4
22
Southeast
1.14
(1.12, 1.16)
25
CDC, National Center for Environmental Health
Benefit Mapping and Analysis tool EPA (Neal Fann, ISEE 2009)
CDC, National Center for Environmental Health
Study
Location
Temperature
exposure
Climate model
Downscaling
Jackson et al. 2010
WA state
Humidex
HadCM (A1B), PCM1 (B1) x
Hayhoe et al., 2010
Chicago
Spatial Synoptic
Classification
GFDLCM2.1, HadCM3,
PCM
Statistical
Knowlton et al., 2007 NYC
Mean Temperature GISS-MM5
Dynamic
Hayhoe et al., 2004
Maximum Apparent
PCM, HadCM3
Temperature
Statistical
LA
Kalkstein et al., 1997 44 US cities
Spatial Synoptic
Classification
GFDL, UKMO, Max Planck
x
model
(Environmental Health Perspective, 2011)
CDC, National Center for Environmental Health
Li, Horton, Kinney (Nature, 2013)
CDC, National Center for Environmental Health
Conclusion
• Extreme weather events and their health impacts
• Short vs long term decision-making horizon in public
health
• Small spatial scale, as many health vulnerabilities are
highly localized
• Need for translating climate projections to an interested
but uninformed health community
• Building regional collaborations
CDC, National Center for Environmental Health