Online Program

Assessment of cancer risk disparity indicators using air toxics data in South Carolina

Wednesday, November 6, 2013

Sacoby Wilson, MS, PhD, Maryland Institute for Applied Environmental Health, University of Maryland, College Park, MD
Hongmei Zhang, PhD, Arnold School of Public Health, Epidemiology and Biostatistics, University of South Carolina, Columbia, SC
Kristen Burwell, MPH, Maryland Institute for Applied Environmental Health (MIAEH), University of Maryland - College Park, College Park, MD
Dayna Campbell, MS, PhD(c), Dept of Health Services Policy and Management, University of South Carolina, Arnold School of Public Health, Columbia, SC
LaShanta Rice, MPH, PhD(c), Health Promotion, Education and Behavior, University of South Carolina, Columbia, SC
Ashok Samantapudi, BS, University of South Carolina-Arnold School of Public Health, University of South Carolina, Columbia, SC
Edith M. Williams, PhD, MS, Institute for Partnerships to Eliminate Health Disparities, University of South Carolina, Columbia, SC
Background: The purpose of this study was to assess cancer risk disparities in SC using the USEPA's National Air Toxics Assessment (NATA) data and additional indicators such as sociodemographic characteristics from the 2000 US Census Bureau and Segregation (including Dissimilarity and Isolation Indices), Townsend, and Diversity Index.

Methods: NATA risk data for varying risk categories were linked with 2000 census data and analyzed using R. Simple linear regression between indices or sociodemographic variables and cancer risk were used to quantify relationships while controlling for urban-rural effects. Percent high cancer risk tracts (cancer risk > 90th percentile of all tracts) were calculated in each quartile for every index and sociodemographic variable. Relative risk and 95% confidence intervals (CI) were estimated by comparing the first quartile with the latter three quartiles. The level of significance for differences in the percent high cancer risk between the first and latter quartiles was calculated for major, area, on-road, and non-road source cancer risk.

Results: The cancer risk for 95% of tracts was 42/million which is lower than the national average (50/million). Cancer risk from on-road sources greatly contributed to all source risk when excluding background sources. There were no significant differences in percent high major source cancer risk within different quartiles of all indices; however, area cancer risk was higher in areas with a higher Isolation Index. When considering the urban-rural effect impacts, all of the coefficients were significant which means that the influence of both indices and sociodemographic status on cancer risk was different in urban and rural areas. The adjusted R-square (0.47) of the Isolation Index and percent urban area was the highest among all indices and sociodemographic variables.

Conclusion: On-road sources and Isolation Index may be the best indicators of cancer risk and should be considered when addressing cancer disparities in SC.

Learning Areas:

Environmental health sciences

Learning Objectives:
Demonstrate how exposures to air toxics may impact estimated cancer risk in rural versus urban environments. Assess the impact of sociodemographic factors and segregation indices on estimated cancer risk associated with air toxics in South Carolina. Identify solutions to mitigate health disparities related to exposure to toxic air pollution from various sources.

Keyword(s): Cancer, Environmental Exposures

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am an Assistant Professor at Maryland Institute for Applied Environmental Health and director of The Community Engagement, Environmental Justice and Health (CEEJH) lab. I have expertise in exposure science and applied environmental health including community-based exposure assessment, environmental justice science, social epidemiology, environmental health disparities, built environment, air pollution monitoring, and community-based participatory research. I am trained in secondary data analysis, advanced geographic information systems and spatial methods, and other quantitative and qualitative approaches.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.