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 CasesDenominator: Person-years at risk

For Prevalence

Bernoulli Data (0/1 Data)

Numerator: People with Thyroid CancerDenominator: 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 Surveillance1. Mapping Rates and Proportions2. Smoothed Maps3. Tests for Spatial Randomness4. Spatial Scan Statistic5. Global Clustering Tests6. Brain Cancer Mortality7. Survival Data

Course Outline

Space-Time Cancer Surveillance8. Space-Time Scan Statistic for the Early

Detection of Disease Outbreaks

Statistical Software9. SaTScan Demonstration

Course Outline

WELCOME AT ANY TIME

Comments and Questions

Software and Slide Presentation

AVAILABLE FROM THE WEB