Ovarian cancer: predictors of early-stage diagnosis

Cancer Causes Control. 2010 Aug;21(8):1203-11. doi: 10.1007/s10552-010-9547-0. Epub 2010 Apr 3.

Abstract

Objectives: Despite the lack of effective screening, almost 20% of women with ovarian cancer are diagnosed at an early stage of disease, when the prognosis is favorable. This study sought to elucidate tumor-related, census-based socioeconomic indicators, and demographic characteristics associated with early diagnosis of epithelial ovarian cancer (EOC).

Methods: The study population included 16,228 women diagnosed with epithelial ovarian cancer from 1996 through 2006 and reported to the California Cancer Registry. Women diagnosed with stage I tumors were compared to those diagnosed with stage III or IV disease with respect to several demographic and tumor-related characteristics. Logistic regression was used to estimate adjusted odds ratios (OR) and associated 95% confidence intervals.

Results: Age at diagnosis, tumor histology, tumor size, laterality, and grade were all strongly associated with EOC early stage at diagnosis. However, after adjusting for all relevant factors in this study, other disparities were detected. Compared with white women, the likelihood of being diagnosed with early-stage disease was significantly lower among African Americans (OR = 0.78, 95% CI = 0.55-0.92), and significantly higher among women with private insurance compared to those either uninsured or covered by Medicaid (OR = 1.6, 95% CI = 1.18-2.05).

Conclusion: These findings suggest that, in addition to tumor biology, disparities in access to care may have a significant effect on the timely diagnosis of epithelial ovarian cancer.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Early Detection of Cancer
  • Early Diagnosis
  • Female
  • Humans
  • Ovarian Neoplasms / diagnosis*
  • Ovarian Neoplasms / economics
  • Ovarian Neoplasms / pathology
  • Predictive Value of Tests
  • Socioeconomic Factors