Charlotte C. Sun

Use Patterns of Levonorgestrel-Releasing Intrauterine System among American Women

Abstract Levonorgestrel-releasing intrauterine system (LNG-IUS) use is approved by the FDA for contraception and heavy menorrhagia. More importantly, it effectively treats endometrial hyperplasia, a precursor to endometrial cancer. Therefore, LNG-IUS use is associated with potential endometrial cancer risk reduction, but current use patterns in the United States are unknown. We analyzed LNG-IUS use prevalence among women ages 18 to 50 years using a weighted statistical analysis of the 2017 to 2019 National Survey of Family Growth. Summary statistics were stratified by race and ethnic group and known endometrial cancer sociodemographic and health risk factors and assessed statistically with bivariate Rao–Scott χ2 tests. A multivariable logistic regression model was developed to explore LNG-IUS use predictors. Current LNG-IUS use in the United States was 6.9% [95% confidence interval (CI), 5.9%–8.1%]. LNG-IUS use was lower in Hispanic women compared with White women [adjusted OR (AOR), 0.7; 95% CI, 0.5–1.0]. Compared with women with ≤high school education, LNG-IUS use was higher for women with ≥college degree (AOR, 2.0; 95% CI, 1.3–3.1). Parous (AOR, 2.6; 95% CI, 1.7–3.9) and insured (AOR, 1.7; 95% CI, 1.0–3.1) women had higher odds of LNG-IUS use, whereas women with diabetes (AOR, 0.3; 95% CI, 0.1–0.7) had lower odds of LNG-IUS use. No differences in LNG-IUS use were observed by endometrial cancer risk factors of women’s body mass index, age of menarche, hypertension, and personal history of cancer. More research is needed to establish the potential benefits of LNG-IUS use on endometrial cancer, which will further highlight potential opportunities for population-level primary prevention to address the growing incidence of endometrial cancer. Prevention Relevance: This study describes the characteristics of American women using the LNG-IUS. Reproductive-age women (especially Hispanic, with lower education, nulliparous, uninsured, and with diabetes) have lower LNG-IUS use odds. These groups may benefit from LNG-IUS use for endometrial cancer primary prevention, conditioned that LNG-IUS use is proven effective in reducing endometrial cancer incidence.

Algorithm to Identify Incident Epithelial Ovarian Cancer Cases Using Claims Data

PURPOSE To create an algorithm to identify incident epithelial ovarian cancer cases in claims-based data sets and evaluate performance of the algorithm using SEER-Medicare claims data. METHODS We created a five-step algorithm on the basis of clinical expertise to identify incident epithelial ovarian cancer cases using claims data for (1) ovarian cancer diagnosis, (2) receipt of platinum-based chemotherapy, (3) no claim for platinum-based chemotherapy but claim for tumor debulking surgery, (4) removed cases with nonplatinum chemotherapy, and (5) removed patients with prior claims with personal history of ovarian cancer code to exclude prevalent cases. We evaluated algorithm performance using SEER-Medicare claims data by creating four cohorts: incident epithelial ovarian cancer, a 5% random sample of cancer-free Medicare beneficiaries, a 5% random sample of incident nonovarian cancer, and prevalent ovarian cancer cases. RESULTS Using SEER tumor registry data as the gold standard, our algorithm correctly classified 89.9% of incident epithelial ovarian cancer cases (cohort n = 572) and almost 100% of cancer-free controls (n = 97,127), nonovarian cancer (n = 714), and prevalent ovarian cancer cases (n = 3,712). The overall algorithm sensitivity was 89.9%, the positive predictive value was 93.8%, and the specificity and negative predictive value were > 99.9%. Patients were more likely to be correctly classified as incident ovarian cancer if they had stage III or IV disease compared with early stage I or II disease (93.5% v 83.7%, P < .01), and grade 1-4 compared with unknown grade tumors (93.8% v 81.4%, P < .01). CONCLUSION Our algorithm correctly identified most incident epithelial ovarian cancer cases, especially those with advanced disease. This algorithm will facilitate research in other claims-based data sets where cancer registry data are unavailable.