Evaluation of the performance of tests for spatial randomness on prostate cancer data

• Published in: International journal of health geographics 2009-07, Vol.8 (1), p.41-41
• Main Authors: Hinrichsen, Virginia L, Klassen, Ann C, Song, Changhong, Kulldorff, Martin
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Cancer; Care and treatment; Cluster Analysis; Data Interpretation, Statistical; Demography; Diagnosis; Distribution; Humans; Male; Maryland - epidemiology; Middle Aged; Predictive Value of Tests; Prostate cancer; Prostatic Neoplasms - epidemiology; Registries - statistics & numerical data; Risk Factors; Spatial analysis (Statistics); Young Adult
• ISSN: 1476-072X; 1476-072X
• DOI: 10.1186/1476-072X-8-41

Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real unadjusted data and data adjusted for individual- and area-level covariates has not been reported previously.We evaluated data on prostate cancer histologic tumor grade and stage of disease at diagnosis for incident cases of prostate cancer reported to the Maryland Cancer Registry during 1992-1997. We analyzed unadjusted data as well as expected counts from models that were adjusted for individual-level covariates (race, age and year of diagnosis) and area-level covariates (census block group median household income and a county-level socioeconomic index). We chose 3 spatial clustering tests that are commonly used to evaluate the geographic distribution of disease: Cuzick-Edwards' k-NN (k-Nearest Neighbors) test, Moran's I and Tango's MEET (Maximized Excess Events Test). For both grade and stage at diagnosis, we found that Cuzick-Edwards' k-NN and Moran's I were very sensitive to the percent of population parameter selected. For stage at diagnosis, all three tests showed that the models with individual- and area-level adjustments reduced clustering the most, but did not reduce it entirely. Based on this specific example, results suggest that these tests provide useful tools for evaluating spatial clustering of disease characteristics, both before and after consideration of covariates.