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Mapping and Cancer Disparities

Mapping and Cancer Disparities. Robin Taylor Wilson, PhD Assistant Professor Epidemiology Division Department of Public Health Sciences Penn State College of Medicine. Overview. Colon Cancer in Pennsylvania Exploratory Spatial Data Analysis (ESDA) Software Development.

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Mapping and Cancer Disparities

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  1. Mapping and Cancer Disparities Robin Taylor Wilson, PhD Assistant Professor Epidemiology Division Department of Public Health Sciences Penn State College of Medicine

  2. Overview • Colon Cancer in Pennsylvania • Exploratory Spatial Data Analysis (ESDA) Software Development

  3. Why use spatial data analysis? • Help prioritize cancer control and identify areas of success • e.g. left- and right-sided colon cancer screening • Help to understand access to screening and treatment in relation to stage at diagnosis • Classify environmental exposures not readily assessed by questionnaire or biomarkers • e.g. organophosphate pesticides • e.g. UVB index • Provide etiologic clues to disease • e.g. neighborhood context and physical activity • May prevent a major cancer cluster investigation

  4. Spatial Analysis of Left- and Right-Sided Colon Cancer in Pennsylvania • Rationale: • The incidence of colon cancer in Pennsylvania is higher than the US all-races rate • Understanding left-sided spatial clustering may help focus cancer screening efforts in the state • Purpose: • Determine whether geographic clustering of colon cancer incidence occurs and whether cluster patterns differ by stage and site (left- versus right-sided) • Identify county-level characteristics associated with incidence

  5. Methods: Colon Cancer in PA • Cases diagnosed between 1994-2002 were identified through the Pennsylvania Cancer Registry • Principal components analysis was used to create a county-level SES index based on 7 variables from the 2000 Census • County-level colon cancer screening and obesity prevalence were derived from the Behavioral Risk Factor Surveillance System • SaTScan was used to determine geographic clustering by county, adjusted for confounding variables • Poisson regression estimated incidence rate ratios (IRR) and 95% confidence intervals (CI) according to the county-distribution of age, sex, race, SES index, obesity, screening, and rural residence as defined by the Beale rural-urban continuum

  6. Colon Cancer: Case Characteristics

  7. Components of SES Index: Principal Component 1: 72% of variance

  8. Colon Cancer Incidence: Gender, Race and SES Results

  9. Right-sided Left-sided Gender Race Age Gender Race Age SES Gender Race Age Screening Gender Race Age Obesity SatScan: Spatial Cluster Results

  10. Gender, Race, Age, SES Gender, Race, Age, Screening

  11. Summary: Colon Cancer in PA • Lower SES was associated with higher left-sided colon cancer incidence (IRR=1.11, 95% CI: 1.03-1.19, p-trend=0.003), adjusting for age, sex, race, screening and obesity. • Clustering of late stage cancer persisted in two metro regions, including a three-county area in the north east (Beale=2) and two southeastern counties (Beale=1). • Adjustment for SES revealed a cluster of late stage cancer incidence in rural non-metro counties in the western portion of the state.

  12. Conclusion: Colon Cancer in PA • Geographic clustering of left-sided and late stage colon cancer occurs. • The association between left-sided colon cancer and low SES requires replication.

  13. Exploratory Spatial Data Analysis • Incidence Mapping • SatScan • O/E • Local Moran’s I • Star Plot overlay • Parallel Coordinate Plots

  14. GeoViz Toolkit

  15. ESDA Example: Prostate Cancer Health Disparities • Multiple State Partners • New York • New Jersey • Iowa • South Carolina • Kentucky • Georgia • Pennsylvania • County-level and Census-Tract analyses

  16. Prostate Cancer Incidence, White Population, 2000

  17. Acknowledgements • Penn State Department of Public Health Sciences • Eugene Lengerich • Erik Lehman • Yihai Liu • Penn State Department of Geography • Frank Hardisty • Mark Gahegan • Dan Addyson • State Partners • Gene Weinberg, Pennsylvania Department of Health • Tim Aldrich and Shannon Shropshire, East Tennessee State University • Jay Christian, University of Kentucky • Michele West and Gerard Rushton, University of Iowa • Frank Boscoe, School of Public Health, SUNY • Colleen McLaughlin, NY State Department of Health • Stanley Weiss, University of Medicine and Dentistry of New Jersey

  18. Thank you

  19. Figure: Map of counties in Pennsylvania.

  20. Panel 1 2 3 4 Adjustedfor Gender Race Age Gender Race Age SES Gender Race Age Screening Gender Race Age Obesity SatScan: High and Low Clustering Right Sided Left Sided

  21. Correlation between Model Covariates: SES Index, Screening prevalence and Obesity

  22. Right Colon Left Colon Anatomy of colon and rectum

  23. SatScan: Observed to Expected Results

  24. Obesity Gender, Race, Age

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