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Background <p>Adequate geographic access to eating disorder treatment is essential for timely care. Yet the distribution of in-person and telehealth programs, and their accessibility across urban and rural settings and across communities with different socioeconomic conditions, remains unclear.</p> Methods <p>We combined an official registry of eating disorder programs from the National Alliance for Eating Disorders with an AI-augmented web corpus built from U.S. domains using ISTARI.AI and large language models. After cleaning and linkage, we analyzed 328 registry centers for in-person care and an augmented set of 2,045 physical sites for proximity checks; telehealth programs were mainly limited to intensive outpatient and partial hospitalization. In-person accessibility was estimated at the census tract level with the two-step floating catchment area method (2SFCA) using program-based capacity. Telehealth accessibility used an unbounded two-step virtual catchment area (u2SVCA) that discounts demand by internet subscription. We also derived tract-level proximity indicators within 30 miles, namely nearest distance and the best available facility size. Population and covariates came from the American Community Survey and the 2020 Census. Multivariable ordinary least squares models related accessibility to median household income, rurality, education, insurance, and race or ethnicity. Correlation-based sensitivity analyses compared metrics across data sources and service regimes that either permit cross-state care or restrict care to within-state.</p> Results <p>Large language models classified employee size from web summaries with an accuracy of 0.641 and Cohen’s kappa of 0.176. In-person access concentrates in metropolitan corridors, with longer distances and lower accessibility in rural tracts; allowing cross-state care improves proximity near many borders, while within-state constraints reduce reachable capacity across interior states. Telehealth per capita availability varies by state, and effective telehealth access declines after discounting by subscription, with lower values across parts of the South and interior West and the longest mean distances in Alaska. Regression models show strong rural and income gradients. Higher income and urban residence are associated with shorter distance and higher accessibility, while rurality is associated with poorer access for both in-person and telehealth measures. Conditional associations for Black and Latine population shares are small once socioeconomic factors are included. Proximity metrics from the AI-augmented set are moderately correlated with registry-based 2SFCA scores, and telehealth and in-person accessibility show lower but nonzero correlation, suggesting that telehealth and in-person measures capture partially distinct dimensions of potential access, while cross-source differences may also reflect coverage and measurement differences.</p> Conclusions <p>Eating disorder treatment access remains uneven across the United States. In-person capacity is sparse outside metropolitan regions, and telehealth expands potential reach but remains constrained by broadband subscription and state-based administrative boundaries. Combining registry data with AI-augmented provider information and paired in-person and telehealth accessibility models can help identify communities with limited access. These findings support policy efforts to expand cross-state practice pathways, improve broadband affordability and availability, and increase transparency in program capacity reporting.</p>

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Spatial and telehealth accessibility to eating disorder treatment in the United States: evidence from registry and LLM-augmented data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月31日

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  • Lingbo Liu,
  • Chuying Huo,
  • Ariel L. Beccia,
  • Tracy K. Richmond,
  • S. Bryn Austin

摘要

Background

Adequate geographic access to eating disorder treatment is essential for timely care. Yet the distribution of in-person and telehealth programs, and their accessibility across urban and rural settings and across communities with different socioeconomic conditions, remains unclear.

Methods

We combined an official registry of eating disorder programs from the National Alliance for Eating Disorders with an AI-augmented web corpus built from U.S. domains using ISTARI.AI and large language models. After cleaning and linkage, we analyzed 328 registry centers for in-person care and an augmented set of 2,045 physical sites for proximity checks; telehealth programs were mainly limited to intensive outpatient and partial hospitalization. In-person accessibility was estimated at the census tract level with the two-step floating catchment area method (2SFCA) using program-based capacity. Telehealth accessibility used an unbounded two-step virtual catchment area (u2SVCA) that discounts demand by internet subscription. We also derived tract-level proximity indicators within 30 miles, namely nearest distance and the best available facility size. Population and covariates came from the American Community Survey and the 2020 Census. Multivariable ordinary least squares models related accessibility to median household income, rurality, education, insurance, and race or ethnicity. Correlation-based sensitivity analyses compared metrics across data sources and service regimes that either permit cross-state care or restrict care to within-state.

Results

Large language models classified employee size from web summaries with an accuracy of 0.641 and Cohen’s kappa of 0.176. In-person access concentrates in metropolitan corridors, with longer distances and lower accessibility in rural tracts; allowing cross-state care improves proximity near many borders, while within-state constraints reduce reachable capacity across interior states. Telehealth per capita availability varies by state, and effective telehealth access declines after discounting by subscription, with lower values across parts of the South and interior West and the longest mean distances in Alaska. Regression models show strong rural and income gradients. Higher income and urban residence are associated with shorter distance and higher accessibility, while rurality is associated with poorer access for both in-person and telehealth measures. Conditional associations for Black and Latine population shares are small once socioeconomic factors are included. Proximity metrics from the AI-augmented set are moderately correlated with registry-based 2SFCA scores, and telehealth and in-person accessibility show lower but nonzero correlation, suggesting that telehealth and in-person measures capture partially distinct dimensions of potential access, while cross-source differences may also reflect coverage and measurement differences.

Conclusions

Eating disorder treatment access remains uneven across the United States. In-person capacity is sparse outside metropolitan regions, and telehealth expands potential reach but remains constrained by broadband subscription and state-based administrative boundaries. Combining registry data with AI-augmented provider information and paired in-person and telehealth accessibility models can help identify communities with limited access. These findings support policy efforts to expand cross-state practice pathways, improve broadband affordability and availability, and increase transparency in program capacity reporting.