Breast cancer trends among black and white women in the United States
ABSTRACT Overall US breast cancer mortality rates are higher among black women than white women, and the disparity is widening. To investigate this disparity, we examined incidence data and changes in mortality trends according to age, year of death (calendar period), and date of birth (birth cohort). Calendar period mortality trends reflect the effects of new medical interventions, whereas birth cohort mortality trends reflect alterations in risk factors.
Incidence data were obtained from the Connecticut and National Cancer Institute Surveillance, Epidemiology, and End Results registries and mortality data were obtained from the National Center for Health Statistics. Changes in age, period, and cohort mortality trends were analyzed with Poisson regression.
For both races, breast cancer incidence rates for localized and regional disease diverged in the late 1970s. Almost concurrently, overall mortality rates diverged among blacks and whites. For both races, mortality increases with age, but blacks have higher mortality at age younger than 57. The calendar period curves revealed declining mortality for whites over the entire study period. For blacks, calendar period mortality declined until the late 1970s, and then sharply increased. After 1994, calendar period mortality declined for both. For women born between 1872 and 1950, trends in mortality were similar for blacks and whites. For women born after 1950, mortality decreased more rapidly for blacks.
The widening racial disparity in breast cancer mortality seems attributable to calendar period rather than birth cohort effects. Thus, differences in response or access to newer medical interventions may largely account for these trends.
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ABSTRACT: The effect of race on breast cancer outcome is confounded by tumor and treatment heterogeneity. We examined a cohort of women with stage II-III breast cancer treated uniformly with neoadjuvant chemotherapy to identify factors associated with racial differences in chemotherapeutic response and long-term survival. Using a prospective database, we identified women with stage II-III breast cancer treated with neoadjuvant chemotherapy from 1998 to 2011. Race was categorized as African-American (AA) or non-AA. Preplanned subtype analyses were stratified by hormone receptor (HR) and HER2. Pathologic response to chemotherapy (pCR), time to recurrence (TTR), and overall survival (OS) were assessed using logistic regression, Kaplan-Meier method, and Cox proportional hazards regression analyses. Of 349 women identified, 102 (29 %) were AA, who were younger (p = 0.03), more obese (p < 0.001), and less likely to have HR+/HER2- tumors (p = 0.01). No significant differences in pCR rate by race were found. At median follow-up of 6.5 years, AA had worse TTR (hazard ratio 1.51, 95 % CI 1.02-2.24), which was attenuated in multivariable modeling, and there was no significant difference in OS. When stratified by HR, worse outcomes were limited to HR+AA (TTR hazard ratio 1.85, 95 % CI 1.09-3.14; OS hazard ratio 2.42 95 % CI 1.37-4.28), which remained significant in multivariable analysis including pCR rate and BMI. With long-term follow-up, racial disparity in outcome was limited to HR+ breast cancer, with no apparent contribution of chemotherapy sensitivity. This suggests that disparity root causes may be driven by HR+ factors such as unmeasured molecular differences, endocrine therapy sensitivity, or adherence.Breast Cancer Research and Treatment 03/2015; 71(24 Supplement). DOI:10.1007/s10549-015-3350-2 · 4.20 Impact Factor
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ABSTRACT: Abstract: Analysis, precise interpretation and forecasting of incidence and mortality rates are very important aspects in epidemiology and demography. The purpose of this study is to apply two different methodologies, namely the FTS (functional time series) models and APC (age-period-cohort) models on a single data set. We intend to compare the results obtained and to see the performances of these two approaches. We used FTS models on age-specific incidence rates taking age as a continuous function that is varying by time. Here we examined the age variations by using FPC (functional principal component) analysis. We also obtained the forecast of the entire incidence curve. Then we applied the APC models on the same data where we explored the age, period and cohort effects separately. We illustrated these approaches by using lung cancer incidence rates for males in Denmark, obtained from R-package “Epi” available on CRAN (Comprehensive R Archive Network). It was found that there was a rapid increase in lung cancer incidence rates in Denmark since 1960, and the highest rates were seen in the year 1985. After that, the rates started to level off. A continuous increment was also found in incidence rates since 1958 birth cohorts. These rates stabilized in 1905 and started decreasing since 1925. The first four basis functions of the FTS model explained about 98.5%, 1.2%, 0.2%, and 0.1% of the total variation, respectively. We also obtained 20-year predictions and suggested that future trends for the male lung cancer incidence rates in Denmark will decrease in all ages.