Using ArcGIS/SaTScan to detect higher than expected breast cancer incidence

Jim Files, BSAppathurai Balamurugan, MD, MPH

Overview

• Breast Cancer incidence

• Study objectives

• Methods

• Results

• Conclusions and recommendations

Study Objectives

• To identify geographic areas in AR with higher proportion of excess cases of breast cancer.

• To plan treatment and rehabilitative services for women with breast cancer in these areas.

Methods

• Using SaTScan/ArcGIS to identify geographic areas with higher proportion of excess cases.

• Models used:

- Poisson Model

- Space-Time Permutation

SaTScan

• SaTScan Software is available for free from NCI

• SaTScan uses the Spatial Scan Statistic developed by Martin Kulldorff for the National Cancer Institute

• Gives health agencies ability to quickly assess potential cancer clusters.

Spatial scan statistic

• Circles of different sizes (from zero up to 50 % of the population size)

• For each circle a likelihood ratio statistic is computed based on the number of observed and expected cases within and outside the circle and compared with the likelihood L0 under the null hypothesis.

SaTScan

• To evaluate reported spatial or space-time disease clusters, to see if they are statistically significant.

• To test whether a disease is randomly distributed over space, over time or over space and time.

• To perform geographical surveillance of disease, to detect areas of significantly high or low rates.

• To perform repeated time-periodic disease surveillance for the early detection of disease outbreaks.

Poisson Model

• With the Poisson model, the number of cases in each location is Poisson-distributed.

• Under the null hypothesis, and when there are no covariates, the expected number of cases in each area is proportional to its population size, or to the person-years in that area.

• Purely spatial analysis was conducted using poisson model

Space-Time Permutation Model

• For the Space-Time Permutation model, the number of observed cases in a cluster is compared to what would have been expected if the spatial and temporal locations of all cases were independent of each other so that there is no space-time interaction.

Data Analysis

• Incidence cases from ACCR

• 2000 Census block groups

• ArcGIS for data geocoding, preparation and display.

Data Prep Model

Data Prep Model

Results from Poisson Model

• Locations with most likely clusters identified and displayed using ArcGIS

• Expected cases – 2,395

• Observed cases – 3,016

• Observed / expected – 1.259

• Test Statistic – 96.531

• P-Value - 0.001

Results from Poisson Model

Inference from Poisson Model

• Most likely areas with higher than expected cases of breast cancer are centered around

- Hot Spring, Pulaski, and Dallas Counties in the Central region.

- Greene, Craighead, and Mississippi Counties in the northeast region

Inference from Poisson Model

• Pros

- Expected number of cases proportional to population size

- Diseases of long latency

• Cons

- Purely spatial (Less time specific)

- Less sensitive for a dynamic population

Results from STP Model

• Locations with most likely clusters identified and displayed using ArcGIS

• Time frame: 2003/8/1 - 2004/1/31• Expected cases – 30• Observed cases – 63• Observed / expected – 2.120• Test Statistic – 14.101• P-value - <0.05

Results from STP Model

Results from STP Model

Inference from STP Model

• Most likely areas with higher than expected cases of breast cancer are centered around

-Cleburne, Van Buren, and White Counties in the north central part of the state

Inference from STP Model

• Pros

- Information on cases alone sufficient - Accounts for time changes

• Cons- Population shift bias: Ignores

population dynamics over time

- Longer study period

Overlay of the Two Models

Inference

Poisson Model is preferred to calculate higher than expected cases in our scenario due to following reasons:

• Since breast cancer is a disease of long latency

• Arkansas has a relatively stable population

Recommendations

• Future methods should focus on accounting for time and space in calculating higher than expected cases for diseases of long latency.

• Also, adjusting for covariates like age, race, SES, and urban/rural would be critical.

Any Questions?Jim Files

GIS CoordinatorArkansas Central Cancer Registry

E-mail: james.files@arkansas.gov      Web:www.healthyarkansas.com/arkcancer/arkcancer.html

Tel: 501.661.2959