Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted intersectional MAIHDA.

This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,722), we conducted design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated age-standardized prevalence and excess/reduced prevalence attributable to two-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9-19.7% (past-year) and 4.5-36.5% (lifetime). Approximately 12.7% (past-year) and 12.5% (lifetime) of total individual variance were attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past-year: 10.1%; lifetime: 16.5%) indicated a further source of heterogeneity around averages with groups experiencing excess/reduced prevalence compared to main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods.

Sampling weighting strategies in causal mediation analysis.

BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis. METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation "cross-world" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH). RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions. DISCUSSION: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

Identifying Optimal Methods for Addressing Confounding Bias When Estimating the Effects of State-level Policies.

BACKGROUND: Policy evaluation studies that assess how state-level policies affect health-related outcomes are foundational to health and social policy research. The relative ability of newer analytic methods to address confounding, a key source of bias in observational studies, has not been closely examined. METHODS: We conducted a simulation study to examine how differing magnitudes of confounding affected the performance of 4 methods used for policy evaluations: (1) the two-way fixed effects difference-in-differences model; (2) a 1-period lagged autoregressive model; (3) augmented synthetic control method; and (4) the doubly robust difference-in-differences approach with multiple time periods from Callaway-Sant'Anna. We simulated our data to have staggered policy adoption and multiple confounding scenarios (i.e., varying the magnitude and nature of confounding relationships). RESULTS: Bias increased for each method: (1) as confounding magnitude increases; (2) when confounding is generated with respect to prior outcome trends (rather than levels), and (3) when confounding associations are nonlinear (rather than linear). The autoregressive model and augmented synthetic control method had notably lower root mean squared error than the two-way fixed effects and Callaway-Sant'Anna approaches for all scenarios; the exception is nonlinear confounding by prior trends, where Callaway-Sant'Anna excels. Coverage rates were unreasonably high for the augmented synthetic control method (e.g., 100%), reflecting large model-based standard errors and wide confidence intervals in practice. CONCLUSIONS: In our simulation study, no single method consistently outperformed the others, but a researcher's toolkit should include all methodologic options. Our simulations and associated R package can help researchers choose the most appropriate approach for their data.

A Tutorial for Propensity Score Weighting for Moderation Analysis With Categorical Variables: An Application Examining Smoking Disparities Among Sexual Minority Adults.

OBJECTIVE: To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects with categorical variables. RESEARCH DESIGN: Tutorial illustrating the key steps for estimating and testing moderation using observational data. Steps include: (1) examining covariate overlap across treatment groups within levels of the moderator; (2) estimating the PS weights; (3) evaluating whether PS weights improved covariate balance; (4) estimating moderated treatment effects; and (5) assessing the sensitivity of findings to unobserved confounding. Our illustrative case study uses data from 41,832 adults from the 2019 National Survey on Drug Use and Health to examine if gender moderates the association between sexual minority status (eg, lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence. RESULTS: For our case study, there were no noted concerns about covariate overlap, and we were able to successfully estimate the PS weights within each level of the moderator. Moreover, balance criteria indicated that PS weights successfully achieved covariate balance for both moderator groups. PS-weighted results indicated there was significant evidence of moderation for the case study, and sensitivity analyses demonstrated that results were highly robust for one level of the moderator but not the other. CONCLUSIONS: When conducting moderation analyses, covariate imbalances across levels of the moderator can cause biased estimates. As demonstrated in this tutorial, PS weighting within each level of the moderator can improve the estimated moderation effects by minimizing bias from imbalance within the moderator subgroups.

The Role of Pre-Treatment Traumatic Stress Symptoms in Adolescent Substance Use Treatment Outcomes.

Background: Prominent theories suggest that individuals with co-occurring traumatic stress symptoms (TSS) and substance use (SU) may be less responsive to SU treatment compared to those with SU only. However, empirical findings in adult samples are mixed, and there has been limited work among adolescents. This study assesses the association between TSS and SU treatment outcomes among trauma-exposed adolescents, using statistical methods to reduce potential confounding from important factors such as baseline SU severity. Method: 2,963 adolescents with lifetime history of victimization received evidence-based SU treatment in outpatient community settings. At baseline, 3- and 6-months, youth were assessed using the Global Appraisal of Individual Needs Traumatic Stress Scale and the Substance Frequency Scale. Propensity score weighting was used to mitigate potential confounding due to baseline differences in sociodemographic characteristics and SU across youth with varying levels of TSS. Results: Propensity score weighting successfully balanced baseline differences in sociodemographic factors and baseline SU across youth. Among all youth, mean SU was lower at both 3- and 6- month follow-up relative to baseline, indicating declining use. After adjusting for potential confounders, we observed no statistically significant relationship between TSS and SU at either 3- or 6-month follow-up. Conclusions: Based on this investigation, conducted among a large sample of trauma-exposed youth receiving evidence-based outpatient SU treatment, baseline TSS do not appear to be negatively associated with SU treatment outcomes. However, future research should examine whether youth with TSS achieve better outcomes through integrative treatment for both SU and TSS.

The results of this study provide keenly needed evidence that, among youth with prior victimization, presence and level of traumatic stress symptoms at substance use treatment initiation does not lead to significantly worse treatment outcomes for youth in outpatient treatment. This suggests that evidence-based outpatient substance use treatment modalities may be effective at improving substance use outcomes even when co-existing traumatic stress symptoms are present.

National Trends in Buprenorphine Treatment for Opioid Use Disorder From 2007 to 2018.

BACKGROUND: Buprenorphine is a key medication to treat opioid use disorder (OUD). Since its approval in 2002, buprenorphine access has grown markedly, spurred by major federal and state policy changes. This study characterizes buprenorphine treatment episodes during 2007 to 2018 with respect to payer, provider specialty, and patient demographics. METHODS: In this observational cohort study, IQVIA Real World pharmacy claims data were used to characterize trends in buprenorphine treatment episodes across four time periods: 2007-2009, 2010-2012, 2013-2015, and 2016-2018. RESULTS: In total, we identified more than 4.1 million buprenorphine treatment episodes among 2 540 710 unique individuals. The number of episodes doubled from 652 994 in 2007-2009 to 1 331 980 in 2016-2018. Our findings indicate that the payer landscape changed dramatically, with the most pronounced growth observed for Medicaid (increased from 17% of episodes in 2007-2009 to 37% of episodes in 2016-2018), accompanied by relative declines for both commercial insurance (declined from 35 to 21%) and self-pay (declined from 27 to 11%). Adult primary care providers (PCPs) were the dominant prescribers throughout the study period. The number of episodes among adults older than 55 increased more than 3-fold from 2007-2009 to 2016-2018. In contrast, youth under age 18 experienced an absolute decline in buprenorphine treatment episodes. Buprenorphine episodes increased in length from 2007-2018, particularly among adults over age 45. CONCLUSIONS: Our findings demonstrate that the U.S. experienced clear growth in buprenorphine treatment-particularly for older adults and Medicaid beneficiaries-reflecting some key health policy and implementation success stories. Yet, since the prevalence of OUD and fatal overdose rate have also approximately doubled during this period, the observed growth in buprenorphine treatment did not demonstrably impact the pronounced treatment gap. To date, only a minority of individuals with OUD currently receive treatment, indicating continued need for systemic efforts to equitably improve treatment uptake.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Disparities in Use/Misuse of Specific Illicit and Prescription Drugs among Sexual Minority Adults in a National Sample.

Background: Compared to heterosexual adults, lesbian, gay, and bisexual (LGB) adults have higher rates of any illicit drug use and any prescription drug misuse, yet disparities regarding specific drugs remain poorly characterized. Methods: We examined disparities by sexual identity and sex for 8 illicit and prescription drugs using 2015-2019 National Survey on Drug Use and Health data. Outcomes included past-year use/misuse of cocaine/crack, hallucinogens, inhalants, methamphetamine, heroin, prescription opioids, prescription stimulants, prescription tranquilizers/sedatives, and level of polydrug use/misuse (2 substances; 3+ substances). For each outcome, odds ratios relative to heterosexual adults of same sex were estimated using logistic regression controlling for demographics; significant estimates were interpreted as a disparity. Results: Among gay men, significant disparities were present for all drugs except prescription stimulants and heroin; inhalant use was particularly elevated. Bisexual women exhibited significant disparities for every drug examined, as did bisexual men (except heroin). Among lesbian/gay women, disparities were only present for prescription opioids and stimulants. Relative to heterosexual peers, use of 3+ substances was 3 times higher among gay men and bisexual women and 2 times higher among bisexual men. Conclusions: Consistent with minority stress theory, prevalences of illicit and prescription drug use/misuse were 2-3 times higher among LGB adults than heterosexual adults. Illicit drug use should not be perceived as only impacting gay/bisexual men - bisexual women had similar - or higher - prevalences of hallucinogen, cocaine, methamphetamine, and heroin use. Yet, in contrast to bisexual women, lesbian/gay women did not exhibit disparities for any illicit drugs.

Moving beyond the classic difference-in-differences model: a simulation study comparing statistical methods for estimating effectiveness of state-level policies.

BACKGROUND: Reliable evaluations of state-level policies are essential for identifying effective policies and informing policymakers' decisions. State-level policy evaluations commonly use a difference-in-differences (DID) study design; yet within this framework, statistical model specification varies notably across studies. More guidance is needed about which set of statistical models perform best when estimating how state-level policies affect outcomes. METHODS: Motivated by applied state-level opioid policy evaluations, we implemented an extensive simulation study to compare the statistical performance of multiple variations of the two-way fixed effect models traditionally used for DID under a range of simulation conditions. We also explored the performance of autoregressive (AR) and GEE models. We simulated policy effects on annual state-level opioid mortality rates and assessed statistical performance using various metrics, including directional bias, magnitude bias, and root mean squared error. We also reported Type I error rates and the rate of correctly rejecting the null hypothesis (e.g., power), given the prevalence of frequentist null hypothesis significance testing in the applied literature. RESULTS: Most linear models resulted in minimal bias. However, non-linear models and population-weighted versions of classic linear two-way fixed effect and linear GEE models yielded considerable bias (60 to 160%). Further, root mean square error was minimized by linear AR models when we examined crude mortality rates and by negative binomial models when we examined raw death counts. In the context of frequentist hypothesis testing, many models yielded high Type I error rates and very low rates of correctly rejecting the null hypothesis (< 10%), raising concerns of spurious conclusions about policy effectiveness in the opioid literature. When considering performance across models, the linear AR models were optimal in terms of directional bias, root mean squared error, Type I error, and correct rejection rates. CONCLUSIONS: The findings highlight notable limitations of commonly used statistical models for DID designs, which are widely used in opioid policy studies and in state policy evaluations more broadly. In contrast, the optimal model we identified--the AR model--is rarely used in state policy evaluation. We urge applied researchers to move beyond the classic DID paradigm and adopt use of AR models.

Association between disease progression and depression onset in persons with radiographic knee osteoarthritis.

OBJECTIVES: Osteoarthritis (OA) disease progression may lead to deteriorating psychosocial function, but it is unclear what aspects of disease severity are related to the onset of depression. This study assessed which components of OA disease progression cumulatively contribute to depression onset in persons with radiographic knee OA. METHODS: Osteoarthritis Initiative participants (n = 1651) with radiographic disease (Kellgren-Lawrence grade ≥2) in one or both knees and below the screening threshold for probable depression [Center for Epidemiological Studies Depression (CES-D) scale <16] at baseline were included. Disease severity was measured from baseline to the third annual follow-up visit using joint space width, 20-meter gait speed, and the Western Ontario and McMaster Universities Osteoarthritis Index pain subscale, each categorized into quintiles. Depression onset (CES-D ≥ 16) was assessed annually at four follow-up visits. Marginal structural models that account for time-dependent confounding and attrition evaluated the association between each time-varying disease severity measure and depression onset. RESULTS: Each disease severity measure exhibited a non-linear relationship concerning the probability of depression onset, with the higher quintiles generally being associated with a larger risk. The highest quintile (relative to the lowest) of joint space width and gait speed were both significantly associated with depression onset. By contrast, none of the higher pain quintiles compared with the lowest were significantly associated with the onset of depression. CONCLUSION: Faster disease progression as measured by either worsening structural severity or decreasing physical performance corresponds to an increased risk of depression among individuals with radiographic knee OA.

Heterogeneity in Prescription Opioid Pain Reliever Misuse Across Age Groups: 2015-2017 National Survey on Drug Use and Health.

BACKGROUND: Prescription opioid misuse among older adults has received little attention to date. Potential age variation in characteristics of and motivations for prescription opioid misuse has not been fully characterized yet has important implications for preventing diversion and misuse. OBJECTIVE: To examine (1) age-specific patterns of source of misused prescription opioid pain relievers and motives for misuse and (2) age-specific and source-specific associations with opioid use disorder (OUD), heroin use, benzodiazepine misuse, and OUD treatment utilization. DESIGN: Cross-sectional study using 3 waves (2015-2017) of the National Survey on Drug Use and Health (68% average response rate) PARTICIPANTS: Respondents aged 12 and older with past-year prescription opioid pain reliever misuse (n = 8228) MAIN MEASURES: Source for the most-recently misused prescription pain reliever (categorized as medical, social, or illicit/other), motive for last episode of misuse, OUD, heroin use, benzodiazepine misuse, and OUD treatment. KEY RESULTS: Adults 50 and older comprised approximately 25% of all individuals reporting past-year prescription opioid misuse. A social source was most common for individuals under age 50 while a medical source was most common for individuals 50 and older. The most commonly reported motive for misuse was to "relieve physical pain"; the frequency of this response increased across age groups (47% aged 12-17 to 87% aged 65+). Among adults age 50 and older with prescription opioid misuse, 17% met criteria for OUD, 15% reported past-year benzodiazepine misuse, and 3% reported past-year heroin use. CONCLUSIONS: Physicians continue to be a direct source of prescription opioids for misuse, particularly for older adults. Ongoing clinical initiatives regarding optimal opioid prescribing practices are needed in addition to effective non-opioid strategies for pain management. Clinical initiatives should also include screening adult and adolescent patients for non-medical use of prescription opioids as well as improving access to OUD treatment for individuals of all ages.

Human Papillomavirus Vaccine Effectiveness Against Incident Genital Warts Among Female Health-Plan Enrollees, United States.

We examined the effectiveness of human papillomavirus vaccination by dose number and spacing against incident genital warts in a cohort of 64,517 female health-plan enrollees in the United States during 2006-2012. Eligible recipients were classified into groups by regimen: 0, 1, 2 (<6 months apart), 2 (≥6 months apart), or 3 doses. They were followed until a genital wart diagnosis, loss to follow-up, or the end of study. Propensity score weights were used to balance baseline differences across groups. To account for latent genital warts before vaccination, we applied 6- and 12-month buffer periods from last and first vaccine dose, respectively. Incidence rates and hazard ratios were calculated using Poisson regression and Cox models. The propensity score-weighted incidence rate per 100,000 person-years was 762 among unvaccinated participants. Using 6- and 12-month buffer periods, respectively, incidence rates were 641 and 257 for 1 dose, 760 and 577 for the 2-dose (<6-month interval) regimen, 313 and 194 for the 2-dose (≥6-month interval) regimen, and 199 and 162 among 3-dose vaccinees; vaccine effectiveness was 68% and 76% for the 2-dose (≥6-month interval) regimen and 77% and 80% in 3-dose vaccinees compared with unvaccinated participants. Vaccine effectiveness was not significant among vaccinees receiving 1-dose and 2-dose (<6-month interval) regimens compared with unvaccinated participants. Our findings contribute to a better understanding of the real-world effectiveness of HPV vaccination.

Substance use behaviors and the timing of family formation during young adulthood.

The impact of substance use on the life course of young adults can be substantial, yet few studies have examined to what extent early adult substance use behaviors are related to the timing of family formation, independent of confounding factors from adolescence. Using panel data from the Monitoring the Future study (N~20,000), the current study examined the associations between three substance use behaviors (i.e., cigarette use, binge drinking, and marijuana use) and the timing of family formation events in young adulthood. Survival analysis and propensity score weighting addressed pre-existing differences between substance users and non-users in the estimation of the timing of union formation (i.e., marriage, cohabitation) and parenthood. Results for young adult substance users showed general patterns of reduced rates of marriage and parenthood, and increased cohabitation during young adulthood. Variations were evident by substance and sex.

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood estimation (TMLE) is a well-established alternative method with desirable statistical properties. TMLE is a doubly robust maximum-likelihood-based approach that includes a secondary "targeting" step that optimizes the bias-variance tradeoff for the target parameter. Under standard causal assumptions, estimates can be interpreted as causal effects. Because TMLE has not been as widely implemented in epidemiologic research, we aim to provide an accessible presentation of TMLE for applied researchers. We give step-by-step instructions for using TMLE to estimate the average treatment effect in the context of an observational study. We discuss conceptual similarities and differences between TMLE and 2 common estimation approaches (G-computation and inverse probability weighting) and present findings on their relative performance using simulated data. Our simulation study compares methods under parametric regression misspecification; our results highlight TMLE's property of double robustness. Additionally, we discuss best practices for TMLE implementation, particularly the use of ensembled machine learning algorithms. Our simulation study demonstrates all methods using super learning, highlighting that incorporation of machine learning may outperform parametric regression in observational data settings.

Quasi-Experimental Study of Functional Family Therapy Effectiveness for Juvenile Justice Aftercare in a Racially and Ethnically Diverse Community Sample.

Functional Family Therapy (FFT) is an intensive community-based treatment program designed to reduce youth behavior problems such as violence, drug use, and other delinquency. Although there is evidence of FFT efficacy and effectiveness with predominantly White samples, there is very little evidence with racial/ethnic minority samples. In light of the over-representation of African American and Latino youth in the juvenile justice system, this study examined the effectiveness of FFT and an adaptation of FFT to probation supervision, called Functional Family Probation (FFP), among a predominantly Latino and African American sample of youth returning home from court-ordered out-of-home placements (OHP). Propensity score weighting was used to compare the likelihood of subsequent OHPs among youth receiving standard probation (Comparison group), and youth receiving FFT (with standard probation), youth receiving FFP (instead of standard probation), and youth receiving FFT in combination with FFP. Results indicated that youth receiving FFT (both with standard probation and FFP), relative to Comparison youth receiving standard probation only, had significantly lower likelihood of OHP during the first two months following release, but this advantage disappeared in later months. Youth receiving only FFP also had lower likelihood of OHP than Comparison youth in the first two months, though not significantly. These findings provide encouraging evidence of positive effects of FFT, in combination with FFP or standard probation, among a diverse sample of juvenile justice system-involved youth.

Growing importance of high-volume buprenorphine prescribers in OUD treatment: 2009-2018.

BACKGROUND: We examined the number and characteristics of high-volume buprenorphine prescribers and the nature of their buprenorphine prescribing from 2009 to 2018. METHODS: In this observational cohort study, IQVIA Real World retail pharmacy claims data were used to characterize trends in high-volume buprenorphine prescribers (clinicians with a mean of 30 or more active patients in every month that they were an active prescriber) during 2009-2018. Very high-volume prescribing (mean of 100+ patients per month) was also examined. RESULTS: Overall, 94,491 clinicians prescribed buprenorphine dispensed during 2009-2018. The proportion of active prescribers meeting high-volume criteria increased from 7.4 % in 2009 to 16.7 % in 2018. High-volume prescribers accounted for 80 % of dispensed buprenorphine prescriptions during 2009-2018; very high-volume prescribers accounted for 26 %. Adult primary care physicians consistently comprised the majority of high-volume prescribers. Addiction specialists were much more likely to be high-volume prescribers compared to other specialties, including psychiatrists and pain specialists. By 2018, the proportion of prescriptions from high-volume prescribers paid by Medicaid had doubled to 40 %, accompanied by a decline in both self-pay and commercial insurance. High-volume prescribers were overwhelmingly concentrated in urban counties with the highest fatal overdose rates. In 2018, the highest density of high-volume prescribers was in New England and the mid-Atlantic region. CONCLUSIONS: Growth in high-volume prescribers outpaced the overall growth in buprenorphine prescribers across 2009-2018. High-volume prescribers play an increasingly central role in providing medication for OUD in the U.S., yet results indicate key regional variation in the availability of high-volume buprenorphine prescribers.

Availability of Mental Telehealth Services in the US.

Importance: Telehealth utilization for mental health care remains much higher than it was before the COVID-19 pandemic; however, availability may vary across facilities, geographic areas, and by patients' demographic characteristics and mental health conditions. Objective: To quantify availability, wait times, and service features of telehealth for major depressive disorder, general anxiety disorder, and schizophrenia throughout the US, as well as facility-, client-, and county-level characteristics associated with telehealth availability. Design, Settings, and Participants: Cross-sectional analysis of a secret shopper survey of mental health treatment facilities (MHTFs) throughout all US states except Hawaii from December 2022 and March 2023. A nationally representative sample of 1938 facilities were contacted; 1404 (72%) responded and were included. Data analysis was performed from March to July 2023. Exposure: Health facility, client, and county characteristics. Main Outcome and Measures: Clinic-reported availability of telehealth services, availability of telehealth services (behavioral treatment, medication management, and diagnostic services), and number of days until first telehealth appointment. Multivariable logistic and linear regression analyses were conducted to assess whether facility-, client-, and county-level characteristics were associated with each outcome. Results: Of the 1221 facilities (87%) accepting new patients, 980 (80%) reported offering telehealth. Of these, 97% (937 facilities) reported availability of counseling services; 77% (726 facilities), medication management; and 69% (626 facilities) diagnostic services. Telehealth availability did not differ by clinical condition. Private for-profit (adjusted odds ratio [aOR], 1.75; 95% CI, 1.05-2.92) and private not-for-profit (aOR, 2.20; 95% CI, 1.42-3.39) facilities were more likely to offer telehealth than public facilities. Facilities located in metropolitan counties (compared with nonmetropolitan counties) were more likely to offer medication management services (aOR, 1.83; 95% CI, 1.11-3.00) but were less likely to offer diagnostic services (aOR, 0.67; 95% CI, 0.47-0.95). Median (range) wait time for first telehealth appointment was 14 (4-75) days. No differences were observed in availability of an appointment based on the perceived race, ethnicity, or sex of the prospective patient. Conclusions and Relevance: The findings of this cross-sectional study indicate that there were no differences in the availability of mental telehealth services based on the prospective patient's clinical condition, perceived race or ethnicity, or sex; however, differences were found at the facility-, county-, and state-level. These findings suggest widespread disparities in who has access to which telehealth services throughout the US.