CDC, National Center for Environmental Health

CDC, National Center for Environmental Health

Health applications for climate data

Shubhayu Saha

Climate and Health ProgramCenters for Disease Control and Prevention

Pollen Grains


Presenter Disclosures

"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

Shubhayu Saha


Outline

Climate-sensitive health outcomes

CDC’s role in translation and capacity building

Example of establishing health-weather associations

Projecting future health burden


National Climate Assessment –

Health implications

Temperature extremesAeroallergensVectorborne diseaseInjuries from extreme weather eventsWildfire


Andersen and Bell, 2011, Environmental Health Perspectives

Mortality risk from heat waves


Ziska et al., 2011 PNAS

Temperature increase and change in length of Ragweed season

Vectorborne diseases

Changes in georgaphical distributionLonger transmission seasonHigher tick densities

17029 cases

24364 cases


Weather-related motor vehicle fatalities (Marmor et al, JAPH 2006)


1. Forecasting Climate Impacts and Assessing Vulnerabilities

2. Projecting the Disease Burden

3. Assessing Public Health Interventions

4. Developing and Implementing a

Climate and Health Adaptation

Plan

5. Evaluating Impact and

Improving Quality of Activities

BuildingResilience

AgainstClimateEffects

Climate and Health Program, National Center for Environmental Health


Generating County-level Measures

Population weightedCounty centroidCounty boundary

Geometric centroid of census blocks

Step 1: Creating population

weighted county centroid

Grid cell containing the population weighted centroid

NLDAS grid

Step 2: Selecting the grid cell

that contains the

populationweighted county

centroidStep 3: County-level values obtained by averaging values of all the 9 grid

cells

Adjacent grid cells

Daily Comparison: Scatter plot by Climate Region

Comparison for May – September 2006

NLD

AS-b

ased

max

imum

tem

pera

ture

(F)

Station-based maximum temperature (F)

r = 0.91 t = 0.76

r = 0.88 t = 0.69

r = 0.92 t = 0.76

r = 0.87t = 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


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


The National Environmental Public Health Tracking Network

http://ephtracking.cdc.gov/showHome.action


What is the temporal association of Hyperthermia-related ED visit with different measures of ambient heat?

How does this association vary by place?


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


Analytical strategy

1 8 15 22 31

Calendar month

Patient 1

Patient 2

Patient 3

Case crossover design – same patient treated as Case and Control

Half-month time-stratified control selection

Case day Control day


Is the temperature different leading to an ED visit?


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)

Heat wave indicator1 1.12 (0.96-1.30) 1.12 (0.83-1.52) 1.13 (0.84-1.54)

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 5071 5083

Conditional logistic regression


<30

<=30

& <

32

<=32

& <

34

<=34

& <

36

<=36

& <

38

<=38

& <

40

<=40

& <

42

>= 4

2

82.00

84.00

86.00

88.00

90.00

92.00

94.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Max TempZ scoreLatitude category oN

Max

imum

tem

pera

ture

on

day

of E

D vi

sit

Z-sc

ore

of M

ax T

emp

on d

ay o

f ED

visi

t

Temperature profile on ED visit days change by place


Odds ratio of ED visit associated with extreme heat by Latitude

<30

<=30

& <

32

<=32

& <

34

<=34

& <

36

<=36

& <

38

<=38

& <

40

<=40

& <

42

>= 4

21.06

1.08

1.10

1.12

1.14

1.16

1.18

1.20

Latitude category oN

Odd

s Rati

o

5 11 23 13 8 7 15 12


Random Effects meta-analysis of Odds Ratios of ED visit

Central

1.17 (1.16, 1.19)

West

1.12 (1.09, 1.14)

South

1.12 (1.10, 1.13)

West North Central

1.16 (1.07, 1.26)

East North Central

1.18 (1.14, 1.21)

Northwest

1.16 (1.07, 1.26)

Southwest

1.05 (1.02, 1.10)

Southeast

1.14 (1.12, 1.16)

Northeast

1.15 (1.13, 1.17)

2

8

1

4

25

8

22

13

11


Benefit Mapping and Analysis tool EPA (Neal Fann, ISEE 2009)


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 LA Maximum Apparent Temperature PCM, HadCM3 Statistical

Kalkstein et al., 1997 44 US cities Spatial Synoptic Classification

GFDL, UKMO, Max Planck model x

(Environmental Health Perspective, 2011)


Li, Horton, Kinney (Nature, 2013)


• 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

Conclusion

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