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Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis

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Abstract

Background

Racial and socio-economic status (SES) disparities exist in prostate cancer (PrCA) incidence and mortality. Less is known regarding how geographical factors, including neighborhood social vulnerability and distance traveled to receive care, affect PrCA risk. The purpose of this research was to use the Veterans Administration Medical System, which provides a unique means for studying PrCA epidemiology among diverse individuals with ostensibly equal access to healthcare, to determine whether area-level characteristics influence PrCA incidence while accounting for individual-level risk factors.

Methods

From the US Veteran’s Health Administration (VHA) electronic medical records (EMR) database from January 1999 to December 2015, we identified 3,736 PrCA patients and 104,017 cancer-free controls from South Carolina (SC). The VHA EMRs were linked to the US census which provided area-level factors. US census data were used to construct the Social Vulnerability Index which is a continuous composite measure of area-level vulnerability and was divided into tertiles for modeling purposes. Data were analyzed using a Bayesian multivariate conditional autoregressive model (CAR) which accounted for individual-level factors, area-level factors, spatial random effects, and autocorrelation, which were used to identify areas of higher- or lower-than-expected PrCA incidence after controlling for risk factors.

Results

As expected, after accounting for age (sixfold and 13-fold increases in men 40–50 years and > 50 years, respectively), race was an important risk factor, with threefold higher odds among Blacks in the fully adjusted model [ORadj 2.98 (2.77, 3.20)]. After accounting for all other factors, residing in a ZIP code tabulated areas (ZCTA) with the greatest level social vulnerability versus the lowest, least vulnerable ZCTA’s, increased PrCA risk by 39% [ORadj 1.39 (1.11, 1.75)].

Conclusions

While accounting for known risk factors for PrCA, including age, race, and marital status, we found geographic areas in SC characterized by higher than average social vulnerability with higher rates of incident PrCA among veterans. Outreach for screening, education, and care coordination may be needed for veterans in these areas.

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References

  1. Siegel RL, Miller KD (2018) Jemal A (2018) Cancer statistics. CA Cancer J Clin 68(1):7–30. https://doi.org/10.3322/caac.21442

    Article  PubMed  Google Scholar 

  2. Adami H-O, Hunter DJ, Trichopoulos D (2008) Textbook of cancer epidemiology. Monographs in epidemiology and biostatistics, 2nd ed. vol 36. Oxford University Press, Oxford, New York

    Book  Google Scholar 

  3. Clegg LX, Reichman ME, Miller BA, Hankey BF, Singh GK, Lin YD, Goodman MT, Lynch CF, Schwartz SM, Chen VW, Bernstein L, Gomez SL, Graff JJ, Lin CC, Johnson NJ, Edwards BK (2009) Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes Control 20(4):417–435. https://doi.org/10.1007/s10552-008-9256-0

    Article  PubMed  Google Scholar 

  4. Hebert JR, Hurley TG, Olendzki BC, Teas J, Ma Y, Hampl JS (1998) Nutritional and socioeconomic factors in relation to prostate cancer mortality: a cross-national study. J Natl Cancer Inst 90(21):1637–1647. https://doi.org/10.1093/jnci/90.21.1637

    Article  CAS  PubMed  Google Scholar 

  5. McMahon DM, Burch JB, Hebert JR, Hardin JW, Zhang J, Wirth MD, Youngstedt SD, Shivappa N, Jacobsen SJ, Caan B, Van Den Eeden SK (2019) Diet-related inflammation and risk of prostate cancer in the California Men's Health Study. Ann Epidemiol 29:30–38. https://doi.org/10.1016/j.annepidem.2018.10.008

    Article  PubMed  Google Scholar 

  6. Mohseni R, Abbasi S, Mohseni F, Rahimi F, Alizadeh S (2019) Association between dietary inflammatory index and the risk of prostate cancer: a meta-analysis. Nutr Cancer 71(3):359–366. https://doi.org/10.1080/01635581.2018.1516787

    Article  PubMed  Google Scholar 

  7. Prostate Cancer in the United States in (2015) by State. Interactive Cancer Atlas, CDC

    Google Scholar 

  8. Klassen AC, Curriero FC, Hong JH, Williams C, Kulldorff M, Meissner HI, Alberg A, Ensminger M (2004) The role of area-level influences on prostate cancer grade and stage at diagnosis. Prev Med 39(3):441–448. https://doi.org/10.1016/j.ypmed.2004.04.031

    Article  PubMed  Google Scholar 

  9. Oliver MN, Smith E, Siadaty M, Hauck FR, Pickle LW (2006) Spatial analysis of prostate cancer incidence and race in Virginia, 1990–1999. Am J Prev Med 30(2 Suppl):S67–S76. https://doi.org/10.1016/j.amepre.2005.09.008

    Article  PubMed  Google Scholar 

  10. Xiao H, Gwede CK, Kiros G, Milla K (2007) Analysis of prostate cancer incidence using geographic information system and multilevel modeling. J Natl Med Assoc 99(3):218–225

    PubMed  PubMed Central  Google Scholar 

  11. National Center for Health Statistics (U.S.) (2012) Healthy people 2010: final review. DHHS publication, vol no (PHS) 2012–1038. U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville

  12. DeChello LM, Gregorio DI, Samociuk H (2006) Race-specific geography of prostate cancer incidence. Int J Health Geogr 5:59. https://doi.org/10.1186/1476-072X-5-59

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hebert JR, Daguise VG, Hurley DM, Wilkerson RC, Mosley CM, Adams SA, Puett R, Burch JB, Steck SE, Bolick-Aldrich SW (2009) Mapping cancer mortality-to-incidence ratios to illustrate racial and sex disparities in a high-risk population. Cancer 115(11):2539–2552. https://doi.org/10.1002/cncr.24270

    Article  PubMed  Google Scholar 

  14. Hsu CE, Mas FS, Miller JA, Nkhoma ET (2007) A spatial-temporal approach to surveillance of prostate cancer disparities in population subgroups. J Natl Med Assoc 99(1):72–80

    PubMed  PubMed Central  Google Scholar 

  15. Wagner SE, Bauer SE, Bayakly AR, Vena JE (2013) Prostate cancer incidence and tumor severity in Georgia: descriptive epidemiology, racial disparity, and geographic trends. Cancer Causes Control 24(1):153–166. https://doi.org/10.1007/s10552-012-0101-0

    Article  PubMed  Google Scholar 

  16. Wagner SE, Hurley DM, Hebert JR, McNamara C, Bayakly AR, Vena JE (2012) Cancer mortality-to-incidence ratios in Georgia: describing racial cancer disparities and potential geographic determinants. Cancer 118(16):4032–4045. https://doi.org/10.1002/cncr.26728

    Article  PubMed  Google Scholar 

  17. Goovaerts P, Xiao H (2011) Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: a multiscale joinpoint regression analysis. Int J Health Geogr 10:63. https://doi.org/10.1186/1476-072X-10-63

    Article  PubMed  PubMed Central  Google Scholar 

  18. Goovaerts P, Xiao H (2012) The impact of place and time on the proportion of late-stage diagnosis: the case of prostate cancer in Florida, 1981–2007. Spat Spatio-temporal Epidemiol 3(3):243–253. https://doi.org/10.1016/j.sste.2012.03.001

    Article  Google Scholar 

  19. Zhou H, Lawson AB, Hebert JR, Slate EH, Hill EG (2008) A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer. Stat Med 27(9):1468–1489. https://doi.org/10.1002/sim.3024

    Article  PubMed  Google Scholar 

  20. Zhou H, Lawson AB, Hebert JR, Slate EH, Hill EG (2008) Joint spatial survival modeling for the age at diagnosis and the vital outcome of prostate cancer. Stat Med 27(18):3612–3628. https://doi.org/10.1002/sim.3232

    Article  PubMed  PubMed Central  Google Scholar 

  21. Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley series in probability and statistics, Wiley, Hoboken

    Book  Google Scholar 

  22. Prostate Cancer in the United States in 2011–2015 by State. Interactive Cancer Atlas. CDC

  23. ZIP Code™ Tabulation Areas (ZCTAs™). US Department of Commerce. https://www.census.gov/geo/reference/zctas.html

  24. Department of Veterans Affairs. VA Medical SAS® Files (121VA10P2). VA Corporate Data Center Operations, Austin Information Technology Center, Austin

  25. Department of Veterans Affairs. VA Vital Status File (121VA10P2). VA Corporate Data Center Operations, Austin Information Technology Center, Austin

  26. United States Census Bureau. 2010 Census.U.S. Census Bureau. 2010. https://www2.census.gov/census_2010/. Accessed Aug 2014

  27. United States Census Bureau. 2011 Five Year American Community Survey.U.S. Census Bureau. 2011. https://www2.census.gov/acs2011_5yr/. Accessed Aug 2014

  28. United States Census Bureau. TIGER/Line® Shapefiles. https://www.census.gov/cgi-bin/geo/shapefiles/index.php. Accessed Aug 2014

  29. Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q. https://doi.org/10.1111/1540-6237.8402002

    Article  Google Scholar 

  30. Schmidtlein MC, Deutsch RC, Piegorsch WW, Cutter SL (2008) A sensitivity analysis of the social vulnerability index. Risk Anal 28(4):1099–1114. https://doi.org/10.1111/j.1539-6924.2008.01072.x

    Article  PubMed  Google Scholar 

  31. SAS 9.4. SAS Institute Inc., Cary

  32. Kleinbaum DG, Kupper LL, Nizam A, Rosenberg ES (2013) Applied regression analysis and other multivariable methods, 5th edn. Cengage Learning, Boston

    Google Scholar 

  33. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, Rastam L, Larsen K (2006) A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 60(4):290–297. https://doi.org/10.1136/jech.2004.029454

    Article  PubMed  PubMed Central  Google Scholar 

  34. Geweke J (1992) Evaluating the accuracy of sampling‐based approaches to the calculation of posterior moments. In Paper presented at the Bayesian Statistics, vol 4

  35. Lunn D (2013) The BUGS book: a practical introduction to Bayesian analysis. texts in statistical science. CRC Press/Taylor & Francis Group, Boca Raton

  36. Development Core Team R (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  37. Penchansky R, Thomas JW (1981) The concept of access: definition and relationship to consumer satisfaction. Med Care 19(2):127–140

    Article  CAS  PubMed  Google Scholar 

  38. Delamater PL, Messina JP, Shortridge AM, Grady SC (2012) Measuring geographic access to health care: raster and network-based methods. Int J Health Geogr 11(1):15. https://doi.org/10.1186/1476-072X-11-15

    Article  PubMed  PubMed Central  Google Scholar 

  39. Obertova Z, Brown C, Holmes M, Lawrenson R (2012) Prostate cancer incidence and mortality in rural men—a systematic review of the literature. Rural Remote Health 12(2):2039

    CAS  PubMed  Google Scholar 

  40. WWAMI Rural Health Research Center. https://depts.washington.edu/uwruca/

  41. Cheng I, Witte JS, McClure LA, Shema SJ, Cockburn MG, John EM, Clarke CA (2009) Socioeconomic status and prostate cancer incidence and mortality rates among the diverse population of California. Cancer Causes Control 20(8):1431–1440. https://doi.org/10.1007/s10552-009-9369-0

    Article  PubMed  PubMed Central  Google Scholar 

  42. Gilligan T (2005) Social disparities and prostate cancer: mapping the gaps in our knowledge. Cancer Causes Control 16(1):45–53. https://doi.org/10.1007/s10552-004-1291-x

    Article  PubMed  Google Scholar 

  43. Coker AL, Sanderson M, Ellison GL, Fadden MK (2006) Stress, coping, social support, and prostate cancer risk among older African American and Caucasian men. Ethn Dis 16(4):978–987

    PubMed  Google Scholar 

  44. Newell GR, Pollack ES, Spitz MR, Sider JG, Fueger JJ (1987) Incidence of prostate cancer and marital status. J Natl Cancer Inst 79(2):259–262

    CAS  PubMed  Google Scholar 

  45. Tyson MD, Andrews PE, Etzioni DA, Ferrigni RG, Humphreys MR, Swanson SK, Castle EK (2013) Marital status and prostate cancer outcomes. Can J Urol 20(2):6702–6706

    PubMed  Google Scholar 

  46. Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, Lehmann HP, Hripcsak G, Hartzog TH, Cimino JJ, Saltz JH (2013) Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care 51(8 Suppl 3):S30–S37. https://doi.org/10.1097/MLR.0b013e31829b1dbd

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rao GA, Mann JR, Bottai M, Uemura H, Burch JB, Bennett CL, Haddock KS, Hebert JR (2013) Angiotensin receptor blockers and risk of prostate cancer among United States veterans. J Clin Pharmacol 53(7):773–778. https://doi.org/10.1002/jcph.98

    Article  PubMed  PubMed Central  Google Scholar 

  48. Rao GA, Mann JR, Shoaibi A, Bennett CL, Nahhas G, Sutton SS, Jacob S, Strayer SM (2014) Azithromycin and levofloxacin use and increased risk of cardiac arrhythmia and death. Ann Fam Med 12(2):121–127. https://doi.org/10.1370/afm.1601

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rao GA, Mann JR, Shoaibi A, Pai SG, Bottai M, Sutton SS, Haddock KS, Bennett CL, Hebert JR (2013) Angiotensin receptor blockers: are they related to lung cancer? J Hypertens 31(8):1669–1675. https://doi.org/10.1097/HJH.0b013e3283621ea3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Shoaibi A, Rao GA, Cai B, Rawl J, Haddock KS, Hebert JR (2017) Prostate specific antigen-growth curve model to predict high-risk prostate cancer. Prostate 77(2):173–184. https://doi.org/10.1002/pros.23258

    Article  CAS  PubMed  Google Scholar 

  51. Grubesic TH, Matisziw TC (2006) On the use of ZIP codes and ZIP code tabulation areas (ZCTAs) for the spatial analysis of epidemiological data. Int J Health Geogr 5:58. https://doi.org/10.1186/1476-072X-5-58

    Article  PubMed  PubMed Central  Google Scholar 

  52. United States. Congress. Senate. Committee on Labor and Human Resources. (1998) Health Insurance Portability and Accountability Act of 1996, first-year implementation concerns: hearing before the Committee on Labor and Human Resources, United States Senate, One Hundred Fifth Congress, second session … 19 March 1998. S hrg, vol 105–490. U.S. G.P.O.: For sale by the U.S. G.P.O., Supt. of Docs., Congressional Sales Office, Washington

  53. United States. Congress (104th 2nd session: 1996) (1996) Health Insurance Portability and Accountability Act of 1996: conference report (to accompany H.R. 3103). Report/104th Congress, 2nd session, House of Representatives 104-736. U.S. G.P.O., Washington

  54. Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci USA 105(7):2301–2306. https://doi.org/10.1073/pnas.0710375105

    Article  PubMed  PubMed Central  Google Scholar 

  55. Fryar CD, Herrick K, Afful J, Ogden CL (2016) Cardiovascular disease risk factors among male veterans, U.S., 2009–2012. Am J Prev Med 50 (1):101–105. https://doi.org/10.1016/j.amepre.2015.06.011

    Article  Google Scholar 

  56. Liu Y, Sayam S, Shao X, Wang K, Zheng S, Li Y, Wang L (2017) Prevalence of and trends in diabetes among veterans, United States, 2005–2014. Prev Chronic Dis 14:E135. https://doi.org/10.5888/pcd14.170230

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA

    Peter Georgantopoulos, Jan M. Eberth & James R. Hébert

  2. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

    Peter Georgantopoulos, Jan M. Eberth, Bo Cai, Gowtham Rao, Charles L. Bennett & James R. Hébert

  3. Southern Network On Adverse Reactions (SONAR), South Carolina Center of Economic Excellence for Medication Safety, College of Pharmacy, University of South Carolina, Columbia, SC, USA

    Peter Georgantopoulos & Charles L. Bennett

  4. Columbia VA Health Care System, Columbia, SC, USA

    Peter Georgantopoulos, Charles L. Bennett & Kathlyn S. Haddock

  5. College of Health and Public Affairs, University of Central Florida, Orlando, USA

    Christopher Emrich

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  1. Peter Georgantopoulos
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Contributions

PG developed the study idea under the mentorship of JME, BO, GR, CLB, and JRH. PG obtained, managed, cleaned, and analyzed the data. PG drafted the manuscript and was responsible for incorporating and resolving all manuscript edits and comments. JRH and JME guided PG through all epidemiological aspects of the study. BO guided PG through all biostatistical aspects of the study. GR guided PG in acquiring and cleaning the data. CLB guided PG through all clinical considerations of the study. CE guided PG in developing the Social Vulnerability Index for ZIP code tabulated areas. KSH guided PG through all required Department of Veteran’s Affairs requirements necessary to conduct a study using Veteran Health Administration data. JRH provided PG guidance in overseeing and managing all aspects of this study. JME, BO, GR, CLB, CE, KSH, and JRH reviewed, edited, and commented on all drafts of this manuscript.

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Correspondence to Peter Georgantopoulos.

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Georgantopoulos, P., Eberth, J.M., Cai, B. et al. Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis. Cancer Causes Control 31, 209–220 (2020). https://doi.org/10.1007/s10552-019-01263-2

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