Associations between social determinants of health and clinical diagnosis time of uterine fibroids and endometriosis: a cross-sectional electronic health record-based study

Background This study characterizes the association between self-reported race/ethnicity, insurance type, and preferred language with clinical diagnosis time for uterine fibroids (UF) and endometriosis patients, defined as time from first symptom consultation to diagnosis date.

We extracted electronic health record data for patients of NYU Langone Health diagnosed with UF or endometriosis between 2017–2023. We utilized negative binomial regressions and a quantitative intersectionality methodology for the analysis.

For UF patients, preferring Spanish (adjusted rate ratio [aRR] = 1.09, 95% confidence interval = [0.94, 1.26]) or a non-Spanish, non-English language (aRR = 1.16 [0.87, 1.55]) was associated with longer diagnosis time compared with English, albeit confidence intervals were wide. For endometriosis, non-Hispanic Black (aRR = 1.35 [0.99, 1.85]) and Hispanic/Latina patients (aRR = 1.40 [1.10, 1.78]) had longer diagnosis times compared with non-Hispanic White patients, and patients who had public insurance compared with private insurance (aRR = 1.20 [0.96, 1.50]). There was no evidence of interactions between these variables, but there was large heterogeneity in diagnosis time for NH Asian UF patients and Hispanic/Latina endometriosis patients.

We found evidence that these social determinants of health are associated with diagnosis time for UF and endometriosis patients, an integral step towards delivering equitable care for UF and endometriosis patients. Similar content being viewed by others Abbreviations - UF: - Uterine fibroids - aRR: - Adjusted rate ratio - CI: - Confidence interval - SDOH: - Social determinants of health - NYULH: - NYU Langone Health - EHR: - Electronic health records - NH: - Non-Hispanic - FHC: - Federal Health Center - OP: - Outpatient - MAIHDA: - Multilevel analysis of individual heterogeneity and discriminatory accuracy - VPC: - Variance partition coefficient - PCV: - Proportional changes in variance explained

I would like to acknowledge all the NYULH clinical experts who volunteered their expertise on using the electronic health records. I would like to acknowledge Eunsil Seok for computational and statistical support in the completion of this analysis. Finally, I would like to acknowledge the advice and guidance of Onchee Yu. Funding MC acknowledges funding from the National Research Foundation Graduate Research Fellowship Program (20-A0-00–1005789). The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR001445, via the Clinical Translational Science Institute at New York University Grossman School of Medicine. Author information Authors and Affiliations Corresponding author Ethics declarations Ethics approval and consent to participate This study was reviewed by NYU Langone Institutional Review Board in accordance with the ethical principals of the Common Rule and was deemed exempt (s22-00924) under 45 CFR 46.104(d). As the study was deemed exempt and compliant with confidentiality and anonymization national regulations, informed consent to participate was deemed unnecessary for this study. NYU Langone Health IRBs operate in accordance with Good Clinical Practices (GCP) and applicable laws and regulations. Consent for publications Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. About this article Cite this article Charifson, M., Hong, C., Fair, A. et al. Associations between social determinants of health and clinical diagnosis time of uterine fibroids and endometriosis: a cross-sectional electronic health record-based study. BMC Women's Health (2026). https://doi.org/10.1186/s12905-026-04893-2 Received: Accepted: Published: DOI: https://doi.org/10.1186/s12905-026-04893-2