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Deaf and hard of hearing (DHH) American Sign Language users experience significant mental health-related disparities compared with non-DHH English speakers. Yet there is little empirical evidence documenting this priority population's communication access in mental health and substance use treatment facilities. This study measured mental health and substance use treatment facilities' noncompliance to Section 1557 of the Affordable Care Act (ACA), which requires health care facilities receiving government funds to provide effective communication access, such as a sign language interpreter, to DHH patients. Using nationally representative data from the Substance Abuse and Mental Health Services Administration, we found that 41 percent of mental health facilities and 59 percent of substance use treatment facilities receiving public funds reported not providing services in sign language in 2019 and were thus noncompliant with the ACA's mandate to provide accessible communication to DHH patients. We mapped these data to display state-level noncompliance, and we make detailed recommendations at the policy, facility, and provider levels. These include monitoring noncompliance among government-funded facilities, expanding state-by-state mental health licensure reciprocity and telehealth policies to improve access to American Sign Language-fluent mental health professionals and addiction counselors, establishing systematic processes to collect information on disability-related accommodation needs, and increasing the workforce of DHH American Sign Language-Ouent providers. [ABSTRACT FROM AUTHOR]

Background: College students are a priority population for health insurance literacy interventions. Yet, there are few psychometric studies on measuring health insurance knowledge – a core construct of health insurance literacy. Methods: We administered a health insurance survey to 2,250 college students. We applied Classical Test Theory and Item Response Theory methods to estimate psychometric properties of the Kaiser Family Foundation's 10-item health insurance knowledge quiz. Results: The scale is unidimensional, and a two-parameter logistic model best fit the data. IRT estimates indicated varying item discriminations (a range: 0.717–2.578) and difficulties (b range: −0.913–1.790). Precision of measurement was maximized for students half a standard deviation below the mean (θ = −0.686) health insurance knowledge ability. Conclusions: This scale can be used to identify gaps in health insurance knowledge among college students and be applied in clinical and community health education practice. [ABSTRACT FROM AUTHOR]