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. 2025 May 19;110(6):1657-1666.
doi: 10.1210/clinem/dgae705.

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

Emily L Silva  1 , Kevin J Lane  2 , Jay Jojo Cheng  3 , Zachary Popp  2 , Breanna D van Loenen  2 , Brent Coull  1   4 , Jaime E Hart  1   5 , Tamarra James-Todd  1   6 , Shruthi Mahalingaiah  1   7
Affiliations

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

Emily L Silva et al. J Clin Endocrinol Metab. .

Abstract

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.

Keywords: PCOS; clinical diagnosis; disparities; polycystic ovary syndrome.

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Figures

Figure 1.
Figure 1.
Proposed conceptual diagram for disparities in PCOS, PCOS comorbidities, and PCOS diagnoses. Abbreviations: PCOS, polycystic ovary syndrome.
Figure 2.
Figure 2.
Time trends for number of International Classification of Diseases, ninth revision, PCOS and algorithm PCOS cases per year at the Boston Medical Center between 2003 and 2025 (n = 1929). Abbreviations: PCOS, polycystic ovary syndrome.
Figure 3.
Figure 3.
Odds ratios for the probability of missed diagnoses by age, race/ethnicity, education, and insurance type among those who meet the criteria for polycystic ovary syndrome diagnosis (n = 1929) for women aged 18 to 45 attending Boston Medical Center between 2003 and 2015.
Figure 4.
Figure 4.
Odds ratios for the probability of missed diagnoses by Social Vulnerability Index overall and factor scores among those who meet the criteria for polycystic ovary syndrome diagnosis (n = 1929) for women aged 18 to 45 attending Boston Medical Center between 2003 and 2015.
See this image and copyright information in PMC

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