BACKGROUND: Triple-negative breast cancer (TNBC) has been associated with a more aggressive histology, poorer prognosis, and nonresponsiveness to hormone therapy. It is imperative that cancer research identify factors that drive disparities and focus on prevention. METHODS: Using the United States Cancer Statistics database, the authors examined differences between TNBCs compared with all other breast cancers with regard to age, race/ethnicity, and stage at diagnosis. RESULTS: A total of 1,151,724 cases of breast cancer were identified from 2010 through 2014, with the triple-negative phenotype accounting for approximately 8.4% of all cases. In unadjusted analyses, non-Hispanic black women (odds ratio [OR], 2.27; 95% CI, 2.23-2.31) and Hispanic women (OR, 1.22; 95% CI, 1.19-1.25) had higher odds of diagnosis when compared with non-Hispanic white women. Women aged <40 years had the highest odds of diagnosis compared with women aged 50 to 64 years (OR, 1.95; 95% CI, 1.90-2.01). Diagnosis at American Joint Committee on Cancer stage III and beyond conferred higher odds of the diagnosis of TNBC (OR for stage III, 1.69 [95% CI, 1.68-1.72]; and OR for stage IV, 1.47 [95% CI, 1.43-1.51]). Results varied slightly in adjusted analyses. CONCLUSIONS: The results of the current study demonstrated that there is a significant burden of disease in TNBC diagnosed among women of color, specifically non-Hispanic black women, and younger women. Additional studies are needed to determine drivers of disparities between race, age, and stage of disease at diagnosis. Cancer 2019;125:3412-3417.

Background We determined whether the current SEER registries are representative of the nation’s cancer cases or the socio-demographic characteristics. Methods We used breast cancer (BC) and colorectal cancer (CRC) cases diagnosed 2004–2009 from the US Cancer Statistics (USCS) database. Cases were classified into groups residing in SEER coverage areas and the other areas. We compared difference between SEER and non-SEER areas in: age–race-specific proportions of late-stage BC or CRC, area demographics and socioeconomic factors, and data quality. Results For late-stage CRC diagnosis, SEER areas contained lower proportions of people with other race and higher proportions of Asian and Hispanic females aged <40, than non-SEER areas. For late-stage BC diagnosis, SEER and non-SEER estimates were comparable. SEER areas had lower percentages of whites and higher percentages of young people, were more urban, and had higher percentage of poor, lower educational attainment, and higher unemployment. SEER areas also tended to have a higher percentage of case completeness than non-SEER areas. Conclusion Overall, SEER registries were not significantly different from non-SEER areas in terms of average age– race-specific proportions of late-stage BC or CRC, except for estimates of late-stage CRC for other race and young Asian and Hispanic women. Although case completeness was better in SEER areas than non-SEER areas, SEER areas had greater economic disadvantage and greater minority diversity among the population. This study demonstrated a need for caution in using SEER data and discussed advantages of using the more complete USCS database.

In the United States, renal cell carcinoma (RCC) has rapidly increased in incidence for over two decades. The most common histologic subtypes of RCC, clear cell, papillary, and chromophobe have distinct genetic and clinical characteristics; however, epidemiologic features of these subtypes have not been well characterized, particularly regarding any associations between race, disease subtypes, and recent incidence trends. Using data from the Surveillance, Epidemiology, and End Results (SEER) Program, we examined differences in the age-adjusted incidence rates and trends of RCC subtypes, including analysis focusing on racial differences. Incidence rates increased over time (2001–2009) for all three subtypes. However, the proportion of white cases with clear cell histology was higher than among blacks (50% vs. 31%, respectively), whereas black cases were more likely than white cases to have papillary RCC (23% vs. 9%, respectively). Moreover, papillary RCC incidence increased more rapidly for blacks than whites (P < 0.01) over this period. We also observed that increased incidence of papillary histology among blacks is not limited to the smallest size strata. We observed racial differences in proportionate incidence of RCC subtypes, which appear to be increasing over time; this novel finding motivates further etiologic, clinical, molecular, and genetic studies.Using national data, we observed a higher proportion of black renal cell carcinoma (RCC) cases with papillary histology compared to Caucasian cases. We also observed time trends in black-white incidence differences in histologic RCC subtypes, with rapid increases in the disproportionate share of black cases with papillary histology.

No abstract

Background: Mammography is essential for early detection of breast cancer and both reduced morbidity and increased survival among breast cancer victims. Utilization is lower than national guidelines, and evidence of a recent decline in mammography use has sparked concern. We demonstrate that regression models estimated over pooled samples of heterogeneous states may provide misleading information regarding predictors of health care utilization and that comprehensive cancer control efforts should focus on understanding these differences and underlying causal factors. Our study population includes all women over age 64 with breast cancer in the Surveillance Epidemiology and End Results (SEER) cancer registries, linked to a nationally representative 5% reference sample of Medicare-eligible women located in 11 states that span all census regions and are heterogeneous in racial and ethnic mix. Combining women with and without cancer in the sample allows assessment of previous cancer diagnosis on propensity to use mammography. Our conceptual model recognizes the interplay between individual, social, cultural, and physical environments along the pathways to health care utilization, while delineating local and more distant levels of influence among contextual variables. In regression modeling, we assess individual-level effects, direct effects of contextual factors, and interaction effects between individual and contextual factors.