Preventing Chronic Disease | Applying Spatial Analysis Tools in Public Health: An Example Using SaTScan to Detect Geographic Targets for Colorectal Cancer Screening Interventions - CDC
Applying Spatial Analysis Tools in Public Health: An Example Using SaTScan to Detect Geographic Targets for Colorectal Cancer Screening Interventions
Recinda L. Sherman, MPH; Kevin A. Henry, PhD; Stacey L. Tannenbaum, PhD; Daniel J. Feaster, PhD; Erin Kobetz, PhD; David J. Lee, PhD
Suggested citation for this article:
Sherman RL, Henry KA, Tannenbaum SL, Feaster DJ, Kobetz E, Lee DJ. Applying Spatial Analysis Tools in Public Health: An Example Using SaTScan to Detect Geographic Targets for Colorectal Cancer Screening Interventions. Prev Chronic Dis 2014;11:130264. DOI: http://dx.doi.org/10.5888/pcd11.130264
Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan’s default settings does not always produce pertinent results.
Corresponding Author: Recinda L. Sherman, North American Association of Central Cancer Registries. Central Cancer Registries, Inc, 2121 West White Oaks Dr, Suite B, Springfield, IL 62704-7412. Telephone: 217-698-0800, Ext 6. E-mail: firstname.lastname@example.org
Author Affiliations: Kevin A. Henry, Rutgers University, School of Public Health, Cancer Institute of New Jersey; Stacey L. Tannenbaum, University of Miami Miller School of Medicine and University of Miami Sylvester Comprehensive Cancer Center; Daniel J. Feaster, Erin Kobetz, David J. Lee, University of Miami Miller School of Medicine.
- Melnick AL. Introduction to geographic information systems in public health. Gaithersburg (MD): Aspen Publishers; 2002.
- Winn DM, Blot WJ, Shy CM, Pickle LW, Toledo A, Fraumeni JF Jr. Snuff dipping and oral cancer among women in the southern United States. N Engl J Med 1981;304(13):745–9. CrossRef PubMed
- Tagnon I, Blot WJ, Stroube RB, Day NE, Morris LE, Peace BB, et al. Mesothelioma associated with the shipbuilding industry in coastal Virginia. Cancer Res 1980;40(11):3875–9. PubMed
- Horner MJ, Altekruse SF, Zou A, Wideroff L, Katki HA, Stinchcomb DG. US geographic distribution of prevaccine era cervical cancer screening, incidence, stage, and mortality. Cancer Epidemiol Biomarkers Prev 2011;20(4):591–9. CrossRef PubMed
- Abe T, Martin IB, Roche LM. Clusters of census tracts with high proportions of men with distant-stage prostate cancer incidence in New Jersey, 1995 to 1999. Am J Prev Med 2006;30(2 Suppl):S60–6. CrossRef PubMed
- Gregorio DI, Kulldorff M, Barry L, Samociuk H. Geographic differences in invasive and in situ breast cancer incidence according to precise geographic coordinates, Connecticut, 1991–95. Int J Cancer 2002;100(2):194–8. CrossRef PubMed
- Henry KA, Sherman R, Roche LM. Colorectal cancer stage at diagnosis and area socioeconomic characteristics in New Jersey. Health Place 2009;15(2):505–13. CrossRef PubMed
- Klassen AC, Kulldorff M, Curriero F. Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors. Int J Health Geogr 2005;4(1):1. CrossRef PubMed
- Meliker JR, Jacquez GM, Goovaerts P, Copeland G, Yassine M. Spatial cluster analysis of early stage breast cancer: a method for public health practice using cancer registry data. Cancer Causes Control 2009;20(7):1061–9. CrossRef PubMed
- Henry KA, Niu X, Boscoe FP. Geographic disparities in colorectal cancer survival. Int J Health Geogr 2009;8:48. CrossRef PubMed
- Johnson GD. Small area mapping of prostate cancer incidence in New York State (USA) using fully Bayesian hierarchical modelling. Int J Health Geogr 2004;3(1):29. CrossRef PubMed
- Lian M, Schootman M, Doubeni CA, Park Y, Major JM, Stone RA, et al. Geographic variation in colorectal cancer survival and the role of small-area socioeconomic deprivation: a multilevel survival analysis of the NIH–AARP Diet and Health Study Cohort. Am J Epidemiol 2011;174(7):828–38.CrossRef PubMed
- Roche LM, Skinner R, Weinstein RB. Use of a geographic information system to identify and characterize areas with high proportions of distant stage breast cancer. J Public Health Manag Pract 2002;8(2):26–32. CrossRef PubMed
- Rushton G, Armstrong MP, Gittler J, Breene BR, Pavlik CE, West MM, et al. , editors. Geocoding health data: the use of geographic codes in cancer prevention and control, research and practice. Boca Raton (FL): CRC Press; 2007.
- Rushton G, Peleq I, Banerjee A, Smith G, West M. Analyzing geographic patterns of disease incidence: rates of late-stage colorectal cancer in Iowa. J Med Syst 2004;28(3):223–36. CrossRef PubMed
- Schootman M, Jeffe DB, Lian M, Gillanders WE, Aft R. The role of poverty rate and racial distribution in the geographic clustering of breast cancer survival among older women: a geographic and multilevel analysis. Am J Epidemiol 2009;169(5):554–61. CrossRef PubMed
- Vieira V, Webster T, Weinberg J, Aschengrau A. Spatial analysis of bladder, kidney, and pancreatic cancer on upper Cape Cod: an application of generalized additive models to case-control data. Environ Health 2009;8:3. PubMed
- Vieira VM, Webster T, Weinberg J, Aschengrau A. Spatial-temporal analysis of breast cancer in upper Cape Cod, Massachusetts. Int J Health Geogr 2008;7:46. CrossRef PubMed
- Boscoe FP, McLaughlin C, Schymura MJ, Kielb CL. Visualization of the spatial statistic using nested circles. Health Place 2003;9(3):273–7. CrossRefPubMed
- Talbot TO, LaSelva GD. Geographic aggregation tool, version 1.2. Troy (NY): New York State Health Department, 2010.
- Wang F, Guo D, McLafferty S. Constructing geographic areas for cancer data analysis: a case study on late-stage breast cancer risk in Illinois. Appl Geogr 2012;35(1-2):1–11. CrossRef PubMed
- Waller LA, Gotway CA. Applied spatial statistics for public health data. Hoboken (NJ): Wiley–Interscience; 2004.
- Talbot TO, Kulldorff M, Forand SP, Haley VB. Evaluation of spatial filters to create smoothed maps of health data. Stat Med 2000;19(17-18):2399–408. CrossRef PubMed
- Mather FJ, Chen VW, Morgan LH, Correa CN, Shaffer JG, Srivastav SK, et al. Hierarchical modeling and other spatial analyses in prostate cancer incidence data. Am J Prev Med 2006;30(2 Suppl):S88–100. CrossRef PubMed
- Huang L, Pickle LW, Das B. Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases. Stat Med 2008;27(25):5111–42. CrossRef PubMed
- Jackson MC, Huang L, Luo J, Hachey M, Feuer E. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers. Int J Health Geogr 2009;8:55 CrossRef PubMed
- Chen J, Roth RE, Naito AT, Lengerich EJ, MacEachren AM. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of US cervical cancer mortality. Int J Health Geogr 2008;7:57. CrossRef PubMed
- Behavioral Risk Factor Surveillance System. Atlanta (GA): Centers for Disease Control and Prevention, Division of Behavioral Surveillance, Public Health Surveillance and Informatics Program Office; 2010. http://apps.nccd.cdc.gov/brfss/. Accessed July 16, 2013.
- Sherman R, Henry K, Lee D. Addressing colorectal cancer disparities: the identification of geographic targets for screening interventions in Miami-Dade County, Florida. Conference proceeding from the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, HEALTHGIS. 2012 Nov 6-9; Redondo Beach, CA. http://sysrun.haifa.il.ibm.com/hrl/healthgis2012/papers/healthgis-03.pdf. Accessed July 15, 2013.
- PDQ® genetics of colorectal cancer. Bethesda (MD): National Cancer Institute; 2013. http://cancer.gov/cancertopics/pdq/genetics/colorectal/HealthProfessional. Accessed May 15, 2013.
- Howlader N, Noone AM, Krapcho M, Garshell J, Neyman N, Altekruse SF, et al. , editors. SEER cancer statistics review, 1975–2010. Bethesda (MD): National Cancer Institute; 2013. http://seer.cancer.gov/csr/1975_2010/. Accessed April 20, 2013.
- Kulldorff M, Huang L, Konty K. A scan statistic for continuous data based on the normal probability model. Int J Health Geogr 2009;8:58. CrossRefPubMed
- Kulldorff M. SaTScan user guide for version 9.0. 2010. http://www.satscan.org/. Accessed April 21, 2013.
- Kulldorff M, Huang L, Pickle L, Duczmal L. An elliptic spatial scan statistic. Stat Med 2006;25(22):3929–43. CrossRef PubMed
- Florida cancer data system. Miami (FL): Florida Department of Health, Bureau of Epidemiology; 2011. https://fcds.med.miami.edu/scripts/fcdspubrates/production/main.html. Accessed May 5, 2013.
- Abrams AM, Kleinman K, Kulldorff M. Gumbel based p-value approximations for spatial scan statistics. Int J Health Geogr 2010;9:61. CrossRefPubMed
- Pinheiro PS, Sherman R, Fleming LE, Gomez-Marin O, Huang Y, Lee DJ, et al. Validation of ethnicity in cancer data: which Hispanics are we misclassifying? J Registry Manag 2009;36(2):42–6. PubMed
- Han J, Feuer R, Stinchcomb D, Tatalovich Z, Lewis D, Zhu L. Optimizing maximum window size for scan statistics (oral presentation). Louisville (KY): Annual Meeting of North American Association of Central Cancer Registries; 2011. http://www.naaccr.org/LinkClick.aspx?fileticket=hR6UMTigRM4%3D&tabid=257&mid=732. Accessed July 16, 2013.
No hay comentarios:
Publicar un comentario