using arcgis/satscan to detect higher than expected breast cancer incidence jim files, bs appathurai...
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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: [email protected] Web:www.healthyarkansas.com/arkcancer/arkcancer.html
Tel: 501.661.2959