Aimee Palumbo1, Yvonne Michael2, Terry Hyslop3,4. 1. Department of Epidemiology and Biostatistics, Drexel University, Nesbitt Hall, 3215 Market St, 5th Floor, Philadelphia, PA, 19104, USA. aimee.palumbo@glink.drexel.edu. 2. Department of Epidemiology and Biostatistics, Drexel University, Nesbitt Hall, 3215 Market St, 5th Floor, Philadelphia, PA, 19104, USA. 3. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. 4. Duke Cancer Institute, Durham, NC, USA.
Abstract
PURPOSE: Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. METHODS: Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. RESULTS: Complete data was available for 1,664 breast cancer patients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. CONCLUSIONS: Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts.
PURPOSE: Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. METHODS:Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. RESULTS: Complete data was available for 1,664 breast cancerpatients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. CONCLUSIONS: Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts.
Entities:
Keywords:
Breast cancer; Health disparities; Methodology, modeling, and biostatistics; Neighborhoods
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