Mindy C DeRouen1,2,3,4, Clayton W Schupp5, Juan Yang5,6,7, Jocelyn Koo5, Andrew Hertz5, Salma Shariff-Marco5,6,7,8, Myles Cockburn9, David O Nelson5, Sue A Ingles9, Iona Cheng5,6,7,8, Esther M John5,10,11, Scarlett L Gomez5,6,7,8. 1. Cancer Prevention Institute of California, Fremont, CA, USA. MDeRouen@psg.ucsf.edu. 2. Department of Epidemiology and Biostatistics, University of California, San Francisco, Mission Hall, 550 16th Street, 2nd Floor, UCSF Box 0560, San Francisco, CA, 94143, USA. MDeRouen@psg.ucsf.edu. 3. Greater Bay Area Cancer Registry, Fremont, CA, USA. MDeRouen@psg.ucsf.edu. 4. UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA. MDeRouen@psg.ucsf.edu. 5. Cancer Prevention Institute of California, Fremont, CA, USA. 6. Department of Epidemiology and Biostatistics, University of California, San Francisco, Mission Hall, 550 16th Street, 2nd Floor, UCSF Box 0560, San Francisco, CA, 94143, USA. 7. Greater Bay Area Cancer Registry, Fremont, CA, USA. 8. UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA. 9. Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA. 10. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. 11. Department of Health Research Policy (Epidemiology), Stanford University School of Medicine, Stanford, CA, USA.
Abstract
PURPOSE: The reasons behind socio-economic disparities in prostate cancer incidence remain unclear. We tested the hypothesis that individual-level factors act jointly with neighborhood-level social and built environment factors to influence prostate cancer risk and that specific social and built environment factors contribute to socio-econmic differences in risk. METHODS: We used multi-level data, combining individual-level data (including education and known prostate cancer risk factors) for prostate cancer cases (n = 775) and controls (n = 542) from the San Francisco Bay Area Prostate Cancer Study, a population-based case-control study, with contextual-level data on neighborhood socio-economic status (nSES) and specific social and built environment factors from the California Neighborhoods Data System. Multivariable logistic regression models were used to compute adjusted odds ratios separately for localized and advanced stage prostate cancer while controlling for neighborhood clustering. RESULTS: We found a more than twofold increased risk of both localized and advanced prostate cancer with increasing levels of nSES, and decreased risk of advanced prostate cancer with increasing levels of education. For localized disease, the nSES association was largely explained by known prostate cancer risk factors and specific neighborhood environment factors; population density, crowding, and residential mobility. For advanced disease, associations with education and nSES were not fully explained by any available individual- or neighborhood-level factors. CONCLUSIONS: These results demonstrate the importance of specific neighborhood social and built environment factors in understanding risk of localized prostate cancer. Further research is needed to understand the factors underpinning the associations between individual- and neighborhood-level SES and risk of advanced prostate cancer.
PURPOSE: The reasons behind socio-economic disparities in prostate cancer incidence remain unclear. We tested the hypothesis that individual-level factors act jointly with neighborhood-level social and built environment factors to influence prostate cancer risk and that specific social and built environment factors contribute to socio-econmic differences in risk. METHODS: We used multi-level data, combining individual-level data (including education and known prostate cancer risk factors) for prostate cancer cases (n = 775) and controls (n = 542) from the San Francisco Bay Area Prostate Cancer Study, a population-based case-control study, with contextual-level data on neighborhood socio-economic status (nSES) and specific social and built environment factors from the California Neighborhoods Data System. Multivariable logistic regression models were used to compute adjusted odds ratios separately for localized and advanced stage prostate cancer while controlling for neighborhood clustering. RESULTS: We found a more than twofold increased risk of both localized and advanced prostate cancer with increasing levels of nSES, and decreased risk of advanced prostate cancer with increasing levels of education. For localized disease, the nSES association was largely explained by known prostate cancer risk factors and specific neighborhood environment factors; population density, crowding, and residential mobility. For advanced disease, associations with education and nSES were not fully explained by any available individual- or neighborhood-level factors. CONCLUSIONS: These results demonstrate the importance of specific neighborhood social and built environment factors in understanding risk of localized prostate cancer. Further research is needed to understand the factors underpinning the associations between individual- and neighborhood-level SES and risk of advanced prostate cancer.
Entities:
Keywords:
Built environment; Disparities; Education; Neighborhood socioeconomic status; Prostate cancer risk; Race/ethnicity
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