Disparities Between Treatment Need and Utilization for Substance Use Disorders in the United States A Population-Level Decomposition Analysis
DOI:
https://doi.org/10.71465/fbg725Keywords:
Substance use disorders, treatment utilization, unmet treatment need, health disparities, population health, decomposition analysisAbstract
Substance use disorders (SUDs) represent a persistent public health challenge in the United States, yet gaps remain between population-level treatment need and actual service utilization. This study examined temporal trends and structural disparities in SUD treatment need and receipt from 2012 to 2023 using nationally representative survey data from 412,836 adults. Treatment need was defined based on DSM-aligned diagnostic criteria, while treatment receipt was identified through self-reported engagement with specialty or non-specialty SUD services within the past year. Weighted prevalence estimates and age-standardized rates were calculated annually. A multivariable decomposition approach was applied to quantify the relative contributions of demographic, socioeconomic, insurance, and geographic factors to observed treatment gaps. Over the study period, the estimated prevalence of treatment need increased from 8.1% to 10.6%, while treatment receipt rose modestly from 12.4% to 15.8% among those in need. Decomposition analysis indicated that insurance coverage expansion accounted for 29.3% of the increase in treatment utilization, whereas income instability and rural residence contributed to 34.7% of the persistent unmet need. Younger adults aged 18–25 exhibited the largest absolute treatment gap, with only 18.2% receiving care despite meeting diagnostic criteria. These findings highlight that recent improvements in treatment access have been uneven and insufficient to offset rising need, underscoring the importance of structural and policy-level interventions to reduce persistent disparities in SUD care delivery.
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Copyright (c) 2026 Daniel R. Hughes, Charlotte M. Walker, Emily J. Thompson (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.