Spatial Statistics for Cancer Surveillance Martin Kulldorff Harvard Medical School and Harvard Pilgrim Health Care

Two Applications of Spatial Data and GIS in Cancer Research Studies of Specific Hypotheses: Evaluate the relationship between cancer and geographical variables of interest such as radon, pesticide use or income levels, adjusting for geographical variation. Surveillance: Evaluate the geographical variation of cancer, adjusting for known or suspected variables such as age, gender or income.

Reasons for Geographical Cancer Surveillance Disease Etiology Known Etiology but Unknown Presence Health Services Public Education Outbreak Detection New Diseases

Cancer Prevention and Control Are people in some geographical area at higher risk of brain cancer? This could be due to environmental, socio-economical, behavioral or genetic risk factors.

Cancer Prevention and Control Are there geographical differences in the access to and/or use of early detection programs, such as mammography screening?

Cancer Prevention and Control Are there geographical differences in the access to and/or use of state-of-the-art breast cancer treatment?

Different Types of Cancer Data Count Data: Incidence, Mortality, Prevalence Categorical Data: Stage, Histology, Treatment Continuous Data: Survival

For Incidence and Mortality Poisson Data Numerator: Number of Cases Denominator: Person-years at risk

For Prevalence Bernoulli Data (0/1 Data) Numerator: People with Thyroid Cancer Denominator: Those without Thyroid Cancer Note: When prevalence is low, a Poisson model is a very good approximation for Bernoulli data.

For Stage, Histology and Treatment Bernoulli Data (0/1 Data) Numerator: Cases of a specific type, e.g. late stage. Denominator: All cases. Ordinal Data For example: Stage 1, 2, 3, 4

For Survival Survival Data Length of Survival (Censored Data is Common)

Data Aggregation (spatial resolution) Exact Location Census Block Group Zip Code Census Tract County State

Data Aggregation Same level of aggregation usually needed due to data availability. Less aggregation is typically better as more information is retained. Many statistical methods can be used irrespectively of aggregation level.

Geographical Cancer Surveillance 1. Mapping Rates and Proportions 2. Smoothed Maps 3. Tests for Spatial Randomness 4. Spatial Scan Statistic 5. Global Clustering Tests 6. Brain Cancer Mortality 7. Survival Data Course Outline

Space-Time Cancer Surveillance 8. Space-Time Scan Statistic for the Early Detection of Disease Outbreaks Statistical Software 9. SaTScan Demonstration Course Outline

WELCOME AT ANY TIME Comments and Questions Software and Slide Presentation AVAILABLE FROM THE WEB