Currently submitted to: JMIR Public Health and Surveillance
Date Submitted: Nov 2, 2025
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Exploring the Association Between Social Determinants of Health and Telehealth Utilization for ADHD Among Adults Using Machine Learning: A Cross-Sectional Study
ABSTRACT
Background:
Attention-Deficit/Hyperactivity Disorder (ADHD) affects an estimated 6% of adults in the United States and contributes to a significant economic burden. Telehealth has emerged as a vital tool in the management of ADHD, offering improved access to care, especially for individuals in underserved communities. Despite its growing role, there remain critical gaps in understanding how social determinants of health (SDOH) influence disparities in tele- health utilization for ADHD treatment.
Objective:
This study analyzed data from the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS) Round 2: ADHD (October–November 2023). Adults were classified into three groups: never diagnosed, previously diagnosed, and currently diagnosed with ADHD. The study aimed to: (1) compare the distribu- tion of SDOH across ADHD status groups and the general adult population to identify factors associated with ADHD diagnosis; (2) assess the homogeneity of SDOH distributions across ADHD groups; (3) evaluate telehealth utiliza- tion among adults currently diagnosed with ADHD; and (4) examine the relationship between SDOH and telehealth use for ADHD treatment.
Methods:
Multivariable logistic regression (MVLR) served as a benchmark model, while machine learning (ML) models—including regularized linear regression, support vector machine (SVM), random forest (RF), LightGBM, multilayer perceptron (MLP), and Few-Shot Learning (FSL)—were trained to identify key predictors.
Results:
A total of 7,009 survey responses were analyzed: 124 had a past diagnosis, and 444 were currently diagnosed, the remaining do not have an ADHD diagnosis, resulting in an ADHD prevalence of 6.3%. Adults with current ADHD were more likely to be male, single, younger, white, non-homeowners, and frequent users of online health resources. They also reported lower education, income, and financial security. About 70% used telehealth for counseling and prescriptions, but only one-quarter received insurance reimbursement. Nineteen SDOH elements across four domains were identified as predictors. ML models outperformed MVLR, with RF achieving the highest recall (0.73) and strong F1 (0.62). Affordability, coverage, and household resources were key predictors.
Conclusions:
Despite widespread internet access, disparities in telehealth use for ADHD persist. ML models captured complex patterns and outperformed traditional approaches. Future research should incorporate inclusive data collec- tion and stratified modeling to better represent disadvantaged populations and inform equitable access strategies.
Citation
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