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Community Level Indicators of Heat Related Morbidity in North Carolina. 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 - PowerPoint PPT Presentation
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Community Level Indicators of Heat Related Morbidity in North
Carolina
Maggie M. Kovach, Christopher M. Fuhrmann, Charles E. Konrad IISoutheast Regional Climate Center
University of North Carolina at Chapel Hill
Conor HarrisonDepartment 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)
• 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)
dailykos.com
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
Census 2000 Data Potential Relationship to HRIRace: (Hispanic, Black, White) Populations most vulnerable to heatCitizenship: (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 HRIDeveloped 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
Data Sources
North Carolina Disease Event Tracking and Epidemiologic Tool (NC DETECT)
Dates Available: 01/01/2007 – 12/31/2008ICD 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 HRI admission per 100,000 people
N = 2590 ED Visits (Entire State)N = 2248 ED Visits (Piedmont and Coastal Plain)
ED heat admissions for North Carolina
Where is HRI geographically located?Variables R
Developed Land -0.34Population Density -0.31
Natural Gas (Urban) -0.27Median Year Built -0.26
Multi-house -0.25Renters -0.24
Evergreen Land 0.32LPG (Rural) 0.29Woodland 0.22
Developed Land
Evergreen Land Cover*p-values < 0.05
Rural populations of North Carolina are at increased risk for heat related illness compared to urban populations.
Urb
an
Rur
al
Variables RMobile Homes 0.37
Mobile Homes
Is poverty associated with increased HRI?
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).
Non-Citizens
Caucasian Population
Variables RCitizens 0.14
Caucasian 0.07Non Citizens -0.12Nationalized -0.11
Spanish speaking -0.11Hispanic -0.08
*p-values < 0.05
Are specific populations at greater risk HRI?
Correlations are weak for HRI and different minority populations.
Fruits and Vegetables
Wheat Crops
All Crops
Variables RAll Crops 0.20
Corn 0.17Soybean 0.15
Fruits &Vegetables 0.13Wheat Crops 0.12
Tobacco & Cotton 0.10*p-values < 0.05
Are specific farm laborers at higher
risk for HRI?
Of the 30 crops examined only a few were correlated with HRI.
Variables: Home values below $10,000, Rental Occupancy, Mobile Homes, Cropland (all crops)
Geographically Weighted Regression Analysis
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 .
Corn Crops
Cotton Crops
Soybean Crops
CroplandCoefficient
Tobacco Crops
Geographically Weighted Regression Analysis
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.
Geographically Weighted Regression Analysis
Home Values below 10,000 Coefficient
Rental Occupancy Coefficient
Mobile HomesCoefficient
GWR Coefficients-200 - -100
-99 - 0
1 - 100
101 - 330
331 - 595
These maps display the relationship between the coefficients and HRI.
Reds are positive and blues are negative.
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-DETECTSoutheast 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]