Area-level factors associated with spatial variation of prostate cancer incidence for black men

Getachew Dagne, Folakemi Odedina, Nickyjeanna Aime, Mary Ellen Young


Purpose: Black men are disproportionately affected by prostate cancer (CaP) compared to any other racial/ethnic groups within the United States. Identifying CaP hotspots along with associated local area-level risk factors is crucial to tackling the significant burden of CaP and the disparity seen in Black men. The objective of this study was to determine the scope of geographical variation in CaP incidences and to assess the degree to which this variation is associated with county-level risk and protective factors.

Methods: The study population was Black men diagnosed with prostate cancer between 2006-2010 in Florida. County-level CaP incidence rates were computed as the ratios of the numbers of new CaP cases diagnosed between 2006 and 2010 to the corresponding 2000 US census population of Black men 20 and over years old data (US Census 2000). Other county-level environmental and health care factors were also obtained. A random effects Poisson model and Geographical Information System (GIS) were used to map and assess the spatial patterns of CaP incidences in 67 Florida counties. These statistical techniques involved a Bayesian approach for estimating the underlying county-specific CaP risk since the data are very sparse.

Results: The findings showed that an increasing CaP incidence of Black Men in Florida  was significantly associated with an increasing unemployment rate ( 2     with 95% CI: (.0025, .2703), does not include zero suggesting significance) and with increasing number of physicians per capita after controlling for other county characteristics. There was a negative association between poverty and CaP incidence. Regarding spatial distribution of CaP incidence, we observed that there are clustering and hotspots of high CaP incidence rates in Palm Beach county in South Florida, and Alachua and Marion counties in north Florida.

Conclusion: Our findings showed that indicators of socioeconomic status and accessibility of health care services such as poverty, unemployment and health care providers are important variables that explain spatial variation of prostate cancer incidence rates of Black Men. Better understanding of such risk factors and identifying specific counties with a disproportionate burden of CaP disease may help formulate targeted interventions and resource allocation by state and local public officials


Bayesian inference, Health disparity, Prostate cancer, Poisson model

Full Text:



American Cancer Society (ACS). Cancer facts & figures 2016. American Cancer Society, Atlanta; 2016.

Powell IJ. Prostate cancer and African-American men. Oncol. 1977;11:599-618.

Powell IJ. Prostate cancer in the African-American: is this a different disease? Semin Urol. 1988;16:221-6.

Merrill RM, Brawley OW. Prostate cancer incidence and mortality rates among white and black men. Epidemiology. 1997;8:126-31.

Brawley OW, Knopf K, Thompson I. The epidemiology of prostate cancer Part II: the risk factors. Semin Urol Oncol. 1988;16:193-201.

Montie JE, Pienta KJ. A unifying model to explain the increased incidence and higher mortality of prostate cancer in black men. Urology. 1999;53:1073-6.

Du XL, Fang S, Coker AL et al. Racial disparity and socioeconomic status in association with survival in older men with local/regional stage prostate carcinoma: findings from a large community-based cohort. Cancer. 2006;106:1276-85.

Goovaerts P, Xiao H, Gwede CK et al. Impact of age, race and socio-economic status on temporal trends in late-stage prostate cancer diagnosis in Florida. Spat Stat. 2015;14:321-37.

Toledano M, Jarup L, Best N, et al. Spatial and temporal trends of testicular cancer in great Britain. Br J Cancer. 2001;84:1482-7.

Jarup L, Toledano MB, Best N, et al. Geographical epidemiology of prostate cancer in great Britain. Int J Cancer. 2002;97:695-9.

Boffetta P, Nyberg F. Contribution of environmental factors to cancer risk. Br Med Bull. 2003;68:71-94.

Waller LA , Gotway CA. Applied spatial statistics for public health data. New York: John Wiley, 2004.

Goovaerts P, Xiao H. Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: a multiscale joinpoint regression analysis. Int J Health Geogr. 2011;10:63.

Xiao H, Tan F, Goovaerts P. Racial and geographic disparities in late-stage prostate cancer diagnosis in Florida. J Health Care Poor Underserved. 2011;22(4):187-99.

Signorello LB, Adami H. Prostate Cancer. In Textbook of Cancer Epidemiology Edited by: Adami H, Hunter H, Trichopoulos D. Oxford, Oxford University Press; 400-428. 2002.

Hernandez MN, Fleming LE, MacKinnon JA, et al. Cancer in Florida Persons of African Descent 1988-2007. Miami: Florida Cancer Data System; 2010.

Lawson A, Bohning D, Biggeri A. Disease Mapping and Risk Assessment for Public Health. 2010.

Lunn DJ, Thomas A, Best N et al. WinBUGS -a Bayesian modelling framework: concepts, structure, an extensibility. Stat and Computing. 2000;10:325-7.

Boscoe F P, Johnson C J, Sherman RL et al. The Relationship between area poverty rate and site-specific cancer incidence in the United States. Cancer. 2014;120(14):2191-8.

Liu L, Cozen W, Bernstein L, et al. Changing relationship between socioeconomic status and prostate cancer incidence. J Natl Cancer Inst. 2001;93(9):705-9.

Rundle A, Neckerman KM, Sheehan D et al. A prospective study of socioeconomic status, prostate cancer screening and incidence among men at high risk for prostate cancer. Cancer Causes Control. 2013; 24(2):297-303.

Freedman DA. Ecological inference and the ecological fallacy. International Encyclopedia for the Social and Behavioral Sciences. 2001;6:4027-30.

Greenland S, Morgenstern H. Ecological bias, confounding and effect modification. Int J Epidemiology. 1989; 18:269-74.

Prentice R, Sheppard L. Aggregate data studies of disease risk factors. Biometrika. 1995; 82:113-25.

Lawson A. Statistical methods in spatial epidemiology. 2001.

Best N, Richardson S, Thomson A. A comparison of Bayesian spatial models for disease mapping. Stat Methods Med Res. 2005;14:35-59.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.


International Journal of Cancer Therapy and Oncology (ISSN 2330-4049)

© International Journal of Cancer Therapy and Oncology (IJCTO)

To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.


Number of visits since October, 2013