1. The Role of Poverty in Prostate Cancer in African-Americans
M. Norman Oliver, M.D., M.A.,
Assistant Professor, University of Virginia Departments of Family Medicine, Health Evaluation Sciences, and Anthropology;
Eric Smith, M.S.,
University of Virginia Department of Family Medicine;
Mir Siadaty, M.S., M.D.,
University of Virginia Department of Health Evaluation Sciences

2. Acknowledgements
Research supported by grants from the National Cancer Institute (1 K07 CA ) and the University of Virginia Paul Mellon Prostate Cancer Research Institute.

3. Prostate cancer scourge
Second leading cause of cancer death among men
Unequal burden: African-Americans have both prostate cancer incidence and mortality rates twice that of whites
Reasons for disparities unclear. Diet? Environmental exposures? Access to health care? Genetic susceptibility?
Dietary fat intake, particularly from red meat, seems associated with increased incidence of prostate cancer. Perhaps differences in dietary practices contribute to racial and ethnic disparities in prostate cancer. Rates for both African-Americans and whites are higher in agricultural areas. Perhaps an association with exposure to pesticides and fertilizers?

4. Data description VCR: Incident prostate cancer cases 1990-99
Geocoded 78% (nearly 31,000 cases)
Cases aggregated to census block group level for comparison to sociodemographic variables in the 1990 U.S. Census
Census variables: low education (<12yrs), high education (≥16yrs), income, % rural – all stratified by racial category
Geocoding = lat/long; block group ~ 1,200, tract ~ 4,000
Data entered into a geographic information system. We calculated average annual incidence rates for prostate cancer for African-Americans and whites at the block group level, and then we mapped these incidence rates.

5.
Patterns? Lots of variance, so hard to tell. We therefore smoothed the maps to more easily discern patterns. (Smoothing -> oak tree in pines) Tobler’s first law of geography: everything is related; however, things nearer to each other are more related than they are to things further apart.

6. LOW
HIGH

8. LOW
HIGH

9. LOW
HIGH

10. 0-6
7-17
18-26
27-47
48-100

11. LOW
HIGH

12. Poverty increases going from the northeast of the state toward the southwest; however, prostate cancer incidence rates increase going from the southwest to the northeast. We therefore see an apparent inverse relationship between these two variables.
So, we grossly see apparent patterns in our data. Are these patterns statistically significant, or just random noise? We performed a statistical test (Tango’s Maximum Excess Events Test) to test for any significant clustering of prostate cancer incidence in the state. The Tango test indicated there was statistically significant clustering. Where? We used a local spatial scan statistic (SaTScan) to calculate where local clustering is occurring.

13. Results of SaTScan test
Results of SaTScan test. How do these clusters compare to patterning of poverty in the state?

14. LOW
Levels of poverty are not the only sociodemographic variables of interest in our project. So, we ran a rank correlation test, comparing the incidence rate to pct below poverty, pct low ed, pct blk, medhsinc, pcthighed, pct rural.
HIGH

15. Correlation graph

16. Limitations of study
VCR a poor-quality cancer registry; problems with data collection
Only used 78% of VCR data, with large fall out of rural data
U.S. Census suppresses some data at block group level
Incidence rates calculated using average 1990 and 2000 populations
Spatial and temporal lag: a general challenge for GIScience in public health

17. Next steps
GIS in cancer control research: 1) statistical modeling of incidence and mortality to identify predictors for intervention 2) surveillance – identify clusters for further investigation
Develop statistical models of VCR data
Diet surveys/ethnographic studies in areas of low and high incidence
Repeat these steps on data from southeastern U.S.

18. The Role of Poverty in Prostate Cancer in African-Americans
M. Norman Oliver, M.D., M.A.,
Assistant Professor, University of Virginia Departments of Family Medicine, Health Evaluation Sciences, and Anthropology;
Eric Smith, M.S.,
University of Virginia Department of Family Medicine;
Mir Siadaty, M.S., M.D.,
University of Virginia Department of Health Evaluation Sciences