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

Using ArcGIS/SaTScan to detect higher than expected breast cancer incidence. Jim Files, BS Appathurai Balamurugan, MD, MPH. Overview. Breast Cancer incidence Study objectives Methods Results Conclusions and recommendations. Study Objectives.

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

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  1. Using ArcGIS/SaTScan to detect higher than expected breast cancer incidence Jim Files, BS Appathurai Balamurugan, MD, MPH

  2. Overview • Breast Cancer incidence • Study objectives • Methods • Results • Conclusions and recommendations

  3. 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.

  4. Methods • Using SaTScan/ArcGIS to identify geographic areas with higher proportion of excess cases. • Models used: - Poisson Model - Space-Time Permutation

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. Data Analysis • Incidence cases from ACCR • 2000 Census block groups • ArcGIS for data geocoding, preparation and display.

  11. Data Prep Model

  12. Data Prep Model

  13. 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

  14. Results from Poisson Model

  15. 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

  16. 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

  17. 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

  18. Results from STP Model

  19. Results from STP Model

  20. 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

  21. 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

  22. Overlay of the Two Models

  23. 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

  24. 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.

  25. Any Questions? Jim Files GIS Coordinator Arkansas Central Cancer Registry E-mail: james.files@arkansas.gov      Web:www.healthyarkansas.com/arkcancer/arkcancer.html Tel: 501.661.2959

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