OBJECTIVE: To test whether reported associations between race/ethnicity and breast cancer estrogen receptor (ER) status are inflated due to missing ER data, lack of socioeconomic data, and use of the odds ratio (OR) rather than the prevalence ratio (PR). METHODS: We geocoded and added census tract socioeconomic data to all cases of primary invasive breast cancer (n = 42,420) among women diagnosed between 1998 and 2002 in two California cancer registries (San Francisco Bay Area; Los Angeles County) and analyzed the data using log binomial regression. RESULTS: Adjusting for socioeconomic position and tumor characteristics, in models using the imputed data, reduced the PR for the black versus white excess risk of being ER--from 1.76 (95% CI: 1.66, 1.86; adjusted for age and catchment area) to 1.47 (95% CI: 1.38, 1.56). The latter parameter estimate was 16% greater (i.e., 1.56) in models excluding women with missing ER data, and was 43% greater when estimated using the OR (i.e., 1.82). CONCLUSION(S): Studies on race/ethnicity and ER status that fail to account for missing data and socioeconomic data and report the OR are likely to yield inflated estimates of racial/ethnic disparities in ER status.
OBJECTIVE: To test whether reported associations between race/ethnicity and breast cancerestrogen receptor (ER) status are inflated due to missing ER data, lack of socioeconomic data, and use of the odds ratio (OR) rather than the prevalence ratio (PR). METHODS: We geocoded and added census tract socioeconomic data to all cases of primary invasive breast cancer (n = 42,420) among women diagnosed between 1998 and 2002 in two California cancer registries (San Francisco Bay Area; Los Angeles County) and analyzed the data using log binomial regression. RESULTS: Adjusting for socioeconomic position and tumor characteristics, in models using the imputed data, reduced the PR for the black versus white excess risk of being ER--from 1.76 (95% CI: 1.66, 1.86; adjusted for age and catchment area) to 1.47 (95% CI: 1.38, 1.56). The latter parameter estimate was 16% greater (i.e., 1.56) in models excluding women with missing ER data, and was 43% greater when estimated using the OR (i.e., 1.82). CONCLUSION(S): Studies on race/ethnicity and ER status that fail to account for missing data and socioeconomic data and report the OR are likely to yield inflated estimates of racial/ethnic disparities in ER status.
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