Polycystic Ovary Syndrome Underdiagnosis Patterns by Individual-level and Spatial Social Vulnerability Measures

OBJECTIVE: To use electronic health records (EHR) data at Boston Medical Center (BMC) to identify individual-level and spatial predictors of missed diagnosis, among those who meet diagnostic criteria for polycystic ovary syndrome (PCOS). METHODS: The BMC Clinical Data Warehouse was used to source patients who presented between October 1, 2003, and September 30, 2015, for any of the following: androgen blood tests, hirsutism, evaluation of menstrual regularity, pelvic ultrasound for any reason, or PCOS. Algorithm PCOS cases were identified as those with International Classification of Diseases (ICD) codes for irregular menstruation and either an ICD code for hirsutism, elevated testosterone lab, or polycystic ovarian morphology as identified using natural language processing on pelvic ultrasounds. Logistic regression models were used to estimate odds ratios (ORs) of missed PCOS diagnosis by age, race/ethnicity, education, primary language, body mass index, insurance type, and social vulnerability index (SVI) score. RESULTS: In the 2003-2015 BMC-EHR PCOS at-risk cohort (n = 23 786), there were 1199 physician-diagnosed PCOS cases and 730 algorithm PCOS cases. In logistic regression models controlling for age, year, education, and SVI scores, Black/African American patients were more likely to have missed a PCOS diagnosis (OR = 1.69 95% CI, 1.28, 2.24) compared to non-Hispanic White patients, and relying on Medicaid or charity for insurance was associated with an increased odds of missed diagnosis when compared to private insurance (OR = 1.90 95% CI, 1.47, 2.46, OR = 1.90 95% CI, 1.41, 2.56, respectively). Higher SVI scores were associated with increased odds of missed diagnosis in univariate models. CONCLUSION: We observed individual-level and spatial disparities within the PCOS diagnosis. Further research should explore drivers of disparities for earlier intervention.