Transcript Community Level Indicators of Heat Related Morbidity in

Maggie M. Kovach, Christopher M. Fuhrmann, Charles E. Konrad II
Southeast Regional Climate Center
University of North Carolina at Chapel Hill
Conor Harrison
Department of Geography
University of North Carolina at Chapel Hill
Previous Literature
•
What geographic locations are at greater risk
for heat-related illness?
– Urban areas are higher risk for heat illness
due to higher temperatures (CDC, 2004),
(Jones et al. 1982), (Harlan et al. 2006) (Reid
et. al 2009)
dailykos.com
•
What specific populations are at risk?
– Young adults and working population
experience higher rates of heat related illness
in NC (Lippmann in review)
– Poor, minorities, socially isolated, elderly
(CDC, 2004)
USA Today
Previous Literature
•
Are agricultural workers at greater risk for HRI?
– In the US, North Carolina accounts for 57% of all heat related deaths
among crop workers from 1992 to 2006 (Luginbuhl et al. 2008)
– African Americans, Latino workers (Richardson and Gregory 1997,
Richardson and Mirabelli 2002).
EPA
Agricultural Worker Health Project
: David Bacon
ers.usda.gov
Data Sources
Census 2000 Data
Potential Relationship to HRI
Race: (Hispanic, Black, White)
Populations most vulnerable to heat
Citizenship: (Naturalized, Non-Citizen, Spanish
speakers)
Agricultural workers/social isolation
Income: (food stamps, below $20,000, median
household income)
Wealth or poverty
Housing Type: (Mobile home, multihouse, rental
occupancy)
Wealth or poverty/Social isolation
Electricity source:(LPG, natural gas, electricity,
heating oil)
Rural or Urban/Poverty
National Land Cover Database (2008)
Potential Relationship to HRI
Developed Land: High intensity, medium
intensity, Low intensity
Rural or Urban/Geographic Locations
Cultivated Crops: 30 total crops (e.g. tobacco,
corn, apples, oats, peanuts)
Agriculture workers/Microclimate of
fields
Forest: Evergreen, Mixed forest, deciduous
forest, woodland
Cooling potential from vegetation
North Carolina Disease Event Tracking and
Epidemiologic Tool (NC DETECT)
Dates Available: 01/01/2007 – 12/31/2008
ICD 9: 992
Methodology
1.) Transform data to a similar spatial scale.
2.) Evaluate relationship between heat-related hospital admissions and land cover &
socioeconomic variables through Pearson correlations.
3.) Perform regression analysis
of risk factors associated
with heat-related illness.
Geographically Weighted Regression is a spatial regression technique that
models spatially varying relationships. It generates a separate regression
equation for each census tract based on the values of neighboring census
tracts.
ED heat admissions for North Carolina
ED HRI admission
per 100,000 people
N = 2590 ED Visits (Entire State)
N = 2248 ED Visits (Piedmont and
Coastal Plain)
Rural
Urban
Where is HRI geographically located?
Variables
R
Developed Land
Population Density
Natural Gas (Urban)
Median Year Built
Multi-house
Renters
Evergreen Land
LPG (Rural)
Woodland
-0.34
-0.31
-0.27
-0.26
-0.25
-0.24
0.32
0.29
0.22
*p-values < 0.05
Developed Land
Evergreen Land Cover
Rural populations of North Carolina are at increased risk for
heat related illness compared to urban populations.
Is poverty associated with increased HRI?
Variables
R
Mobile Homes 0.37
Mobile Homes
With the exception of mobile homes, correlations are weak for
HRI and other measures of poverty (i.e. food stamps, median
income, home values below $10,000, incomes below $20,000).
Are specific populations at greater risk HRI?
Variables
R
Citizens
Caucasian
Non Citizens
Nationalized
Spanish speaking
Hispanic
0.14
0.07
-0.12
-0.11
-0.11
-0.08
Non-Citizens
*p-values < 0.05
Caucasian Population
Correlations are weak for HRI and different minority populations.
Are specific farm
laborers at higher
risk for HRI?
Variables
R
All Crops
Corn
Soybean
Fruits &Vegetables
Wheat Crops
Tobacco & Cotton
0.20
0.17
0.15
0.13
0.12
0.10
*p-values < 0.05
Fruits and Vegetables
Wheat Crops
Of the 30 crops examined
only a few were
correlated with HRI.
All Crops
Geographically Weighted Regression Analysis
Variables: Home values below $10,000, Rental Occupancy,
Mobile Homes, Cropland (all crops)
Local R2 values:
Local R2 values: these values range between 0.0 and 1.0 and indicate how well the
local regression model fits observed HRI admissions. In this model, the R2 predicts
up to 0.700 in particular areas .
Geographically Weighted
Regression Analysis
Tobacco Crops
Cropland
Coefficient
The positive relationship between crops
and HRI is located in the Northern
Piedmont and Northern Coastal Plain,
where soybean, tobacco and cotton
agriculture is located.
Corn Crops
Soybean Crops
Cotton Crops
Geographically Weighted
Regression Analysis
Home Values below
10,000 Coefficient
Rental Occupancy
Coefficient
These maps display
the relationship
between the
coefficients and HRI.
Reds are positive
and blues are
negative.
GWR Coefficients
-200 - -100
-99 - 0
Mobile Homes
Coefficient
1 - 100
101 - 330
331 - 595
Summary
• In North Carolina, heat related illness (HRI) is found predominately in rural
areas with no development, low population density, and locations with more
“green space.”
•
Mobile homes, a proxy for rural poverty, increase a community’s risk for
heat-related illness. Other indicators for poverty such as food stamps, income
below $20,000 or home value below $10,000 have less influence on HRI.
• No correlations were observed for minority populations and HRI. However,
previous heat mortality research found that minority populations are less likely
to seek care (Richardson and Mirabelli 2002).
• Agriculture is positively correlated with HRI in the Northern Piedmont and
Northern Coastal Plain of North Carolina, where the tobacco, cotton and
soybeans are the predominate cash crops.
• In the Sandhills and Southern Coastal Plain of North Carolina, socioeconomic
factors such as income and mobile homes increase the likelihood of HRI.
Current Work
• Incorporate NC-DETECT data for
2009, 2010
• Examine heat wave, non-heat wave
heat related ED heat admissions,
ages of HRI ED patients.
• Incorporate climate information
with individual and neighborhood
risk factors to model heat risk.
Agricultural Worker Health Project : David Bacon
Acknowledgements:
NC Division of Public Health
NC-DETECT
Southeast Regional Climate Center
The NC DETECT Data Oversight Committee does not take responsibility for the scientific
validity or accuracy of methodology, results, statistical analyses or conclusions presented.
Contact: [email protected]