I. GIS database development

Geographic-based Research and Applications at the National CLinda Williams Pickle1, Mary H. Ward1, Dan J. Grauman1, Daniel B. Carr1, John R. Nuckols2, Michael J. Barrett3, James E. Cucinelli3, S

Brunton4, Deborah M. Winn1 (1National Cancer Institute, Bethesda, MD; 2Department of Environmental Health, Colorado State University, Ft. Collins, CO; 3IMS, Inc., Silver Spring, MD, 4Ti

A. Environmental exposure assessmentB. Statistical modeling

C. Outlier detection for cancer sD. Cluster identification

II. Spatial data analysis

A. Environmental exposure assessment• GIS can provide information about potential 

environmental exposures that cannot be obtained through traditional epidemiologic methods• Study in south central Nebraska demonstrated use of satellite imagery to reconstruct historical crop patterns

Ward et al. Env Health Perspectives,2000

B. Statistical modeling• Cancer incidence prediction project goal: model data from NCI 

cancer registries (470 counties), predict # cases in all states• Use hierarchical Poisson regression models to characterize associations between cancer incidence and mortality, sociodemographic, lifestyle factors by county• These factors explain spatial variation so well that no spatialcorrelation is needed in the model• Extensions of original models:

– Spatio-temporal prediction of cancer rates by state– Predicted incidence is used to predict prevalence– Predicted incidence is used to calculate % completeness of case ascertainment for each cancer registry

MaCoSoySor

Output: Predicted inc

Female breast can

AK HI

SEER

(1999+)

Registries

SEER

NCI cancer registries

Landsat imagery

(color infrared display

of bands 4,2,1)

wheat

Farm Service Agency 

historical aerial photos

with crops noted

+

Classified land co

A. Enterprise GIS

• Goal: Provide comprehensive, consistent, easily maintained geospatial data for NCI division staff• Content: Cancer rates and trends; sociodemographic, medical resource, behavioral risk factor data• System architecture: 

- Visual Basic Form called from a VB .NET dll from within ArcMap- This program makes an internet call to a PHP program which retrieves data from Sybase database- Resulting text file is written to user’s hard drive, joined to shapefile

Selection: Output: map or text file

Nassau and Suffolk counties - detailed dataBuffer counties within 50 km - less detailExtended area within 100 miles - limited data

Geographic Extent of LI GIS

B. Geographic Information System for Breast Cancer Studies on Long IslandA Resource for researchers and the public interested in breast cancer patterns on Long Island

The LI GIS is a 

geographic 

information system 

(GIS) comprising 

data with statistical 

and spatial 

extensions. The LI 

GIS is designed to 

study the potential 

relationships 

between 

environmental 

exposures and 

breast cancer on 

Long Island. It also 

can be used to study 

other diseases.

The LI GIS is one of a series of major 

studies and initiatives within the Long 

Island Breast Cancer Study Project 

(LIBCSP), congressionally mandated 

activities to understand breast cancer 

incidence rates on Long Island. 

Researchers can apply to use the entire 

LI GIS and/or the LI GIS statistical 

software and spatial extensions. Apply 

online at www.healthgis-li.com

The LI GIS warehouse contains over 80 datasets covering:

- Topographic; - Demographic; - Health outcome, including relative breast cancer incidence; and- Environmental data for Nassau and Suffolk counties, 

and to a lesser extent on surrounding counties. 

Arial interpolator – interpolating zip codePopulation from census tract population

Disease rate calculator – calculating directlyAdjusted rate for selected census tracts Researcher’s toolbox: 

A full suite of GIS software and extensions related to study of breast cancer

- ESRI ArcGIS software suite

- ArcView & ArcInfo- Spatial Analysis & 3D Analyst

Extensions for epidemiological studies

- Case File Formatter- Disease Rate Calculator- Areal Interpolator- Cluster Analysis Tool (using 

SaTScan)

- Empiricial Bayes Tool- EpiAnalyst- S-Plus Spatial Stats- Geographic masking- SAS

- Oracle 9i

- Online User’s Guide- Additional ArcView extensions and software

Empirical Bayes Tool – stabilizes data.Deals with small numbers issues for local data

Cluster analysis – checking for clusters ofSample cases in Suffolk County (SaTScan)

C. Related collaborative research by Surveillance, Epidemiology & End Results (SEER) cancer registries (Rapid Response Surveillance Studies)

- Development of high resolution population distribution data for cancer control- Assessment of the accuracy of geocoding by geographic scale, impact of errors on cancer rates

C. Outlier detection for cancer surveillance• Can we detect significant outliers (unusual occurrences) 

of the # of new cancer cases?• Applied an empirical Bayes data mining algorithm to test data (DuMouchel & Pregibon, Proc KDD, 2001; Lincoln Technologies, Inc)

• Method assumes Poisson distribution of # cases, estimates Relative Risk = observed/expected• Lung cancer mortality, white males, 1950-69

• Smoothed map provided expected # cases per county• Algorithm compared actual # cases to this expectation• Found known “hot spot” in MT, site of copper smelter

Lung cancer mortality rates among white males, 1950

Observed rates Smoothed 

(expected p

Covariate data available for all counties:• cancer mortality rates• sociodemographic factors 

(income, schooling, etc.)

• medical facilities• cancer screening utilization• smoking, obesity, no insurance

Smoothed by co

D. Cluster identification• Are apparent map clusters real or random noise?• SaTScan software identifies most likely significant cluster over

space, time or both• Algorithm: spatial scan statistic for Poisson or Bernoulli event data, adjusts for population heterogeneity & covariates• Originally identified circular clusters, new version scans for elliptical clusters, various shapes & angles• Software: www.satscan.org

• Recently extended to clusters of survival ratesDeveloped by Martin Kulldorff: Stat in Med, 1995, 1996; Communications in Statistics, 1997; Am J Epidemiology, 1997; Am J Public Health, 1998

Mos

Pickle et al., Atlas of United States Mortality, NCHS, 1996 

Breast cancer mortality rates

Pickle, Feuer, maps by count5435, 2003 (av