Identifying Geographic Cold Spots of PCOS Diagnosis in Texas: A Spatial Analysis of Underdiagnosis and Rural Disparities.

Context: Polycystic ovary syndrome (PCOS) is a common yet underdiagnosed endocrine disorder with substantial reproductive and metabolic consequences. Although disparities in PCOS care have been documented, few studies have employed spatial methods to identify areas of potential underdiagnosis.

Objective: This study uses geospatial analysis to detect cold spots of PCOS clinical encounters across Texas and investigates neighborhood characteristics associated with these areas.

Methods: We analyzed inpatient and outpatient encounter data from the Texas Public Use Data File (PUDF) between 2018 and 2024 to identify PCOS-related visits (International Classification of Diseases, revision 10: E28.2). ZIP code tabulation area (ZCTA)-level PCOS encounter prevalence was calculated per 1000 females and stabilized using empirical Bayes smoothing to account for rate instability. The Anselin local Moran's I statistic was used to detect spatial clusters. ZCTAs with statistically significant low-prevalence clusters (cold spots) were identified. Logistic regression assessed associations between cold spot status and neighborhood-level variables, including rural-urban commuting area codes, socioeconomic indicators, and health-related factors.

Results: Cold spots were concentrated in rural and periurban areas, suggesting potential underdiagnosis in communities with limited health-care access. This highlights the need for targeted public health interventions, including expanded provider training and diagnostic outreach in rural settings.

Conclusion: Significant spatial disparities in PCOS diagnosis suggest differential health-care access, diagnostic practices, or population health behaviors across the state. Targeted health interventions in rural communities may improve PCOS recognition and care. Further research is needed to explore the role of infrastructure and provider practices in causing these geographic disparities.