After applying stringent exclusion criteria, we identified a sample of 1,474 newly-diagnosed early-onset breast cancer cases (). Six percent of patients were black, 8% Hispanic, 4% Asian, 2% Jewish, 55% non-Jewish white, and 26% “other or unknown” race/ethnicity (). Seventeen percent had family incomes under $50,000 per year. Only 30% (n=446) of our study sample was tested for BRCA1/2 mutations. There was substantial variation in testing rates by patient characteristics absent adjustment for confounders or differential durations of observation. Fourteen percent of those tested underwent testing prior to their treatment. The median time from diagnosis to testing was 4 months and 91% were tested within one year of diagnosis ().
Application of Exclusion Criteria to Identify New Cancer Patients
Cumulative distribution of time from diagnosis to genetic testing for patients receiving testing
Our primary findings relate to the multivariate model with unrestricted follow-up (). Our model truncated at 1 year follow-up gave results that were nearly indistinguishable from the unrestricted model, so we only report the latter. We found a number of important predictors of BRCA1/2 testing among this cohort of newly diagnosed women. As one would expect, women of Jewish ethnicity, a key indicator of high risk for BRCA1/2 mutations, were nearly three times as likely to receive testing compared to non-Jewish white women (HR 2.83, 95% CI 1.52–5.28). Though a contemporaneous diagnosis of ovarian cancer would also warrant BRCA1/2 testing, there were very few such diagnoses in the study sample and we did not find a significant association between ovarian cancer diagnosis and BRCA1/2 testing.
Proportional hazard models of the probability of BRCA1/2 testing a,b,c
Controlling for other risk factors and all other covariates, black and Hispanic women were significantly less likely to receive BRCA1/2 testing compared to non-Jewish white women (HR 0.34, 95% CI 0.18–0.64; HR 0.52, 95% CI 0.33–0.81, respectively). There was a monotonically increasing likelihood of testing as a function of family income, though these associations were not statistically significant. However, when income was coded as an ordinal variable, a statistically significant association emerged, where women with family incomes >$150,000/year were 1.99 times as likely to be tested as women with incomes <$30,000/year (95% CI 1.10–28.51; results not shown).
Several other factors were also significantly associated with the probability of testing. Patients who received chemotherapy, radiation therapy, or hormone therapy were more likely to receive testing than those not receiving those therapies (HR 1.72, 95% CI 1.36–2.18; HR 1.24, 95% CI 1.01–1.52; and HR 1.29, 95% CI 1.06–1.58 respectively). Women covered by an HMO insurance product were less likely to be tested than those with a POS product (HR 0.73, 95% CI 0.54–0.99). Those living in the South were more likely to be tested than patients in the Northeast (HR 1.46, 95% CI 1.07–2.00). After adjusting for covariates, the likelihood of testing increased consistently and substantially over the study period, with women diagnosed in 2007 3.79 times as likely to be tested as those diagnosed in 2004 (95% CI 2.59–5.55).
We conducted a sensitivity analysis regarding the potential for bias in measuring differences between blacks or Hispanics and non-Jewish whites due to the misclassification of Jewish women as non-Jewish white women. If Jewish women more likely to be tested were misclassified as non-Jewish white women, that would increase the difference between non-Jewish whites and groups less likely to be tested. We intentionally reclassified the 32 women in the study identified as Jewish as non-Jewish white and recalculated our hazard ratios. The new hazard ratios (not reported) were nearly identical to those reported in . This suggests there would have to be an implausibly high degree of misclassification to drive the differences we calculate between non-Jewish white women and black and Hispanic women.