Therapeutic Potential of Psychedelic Drugs: Navigating High Hopes, Strong Claims, Weak Evidence, and Big Money.

Therapeutic claims about many psychedelic drugs have not been evaluated in any studies of even modest rigor. The science of psychedelic drugs is strengthening however, making it easier to differentiate some promising findings amid the hype that suffuses this research area. Ketamine has risks of adverse side effects (e.g., addiction and cystitis), but multiple studies suggest it can benefit individuals with treatment-resistant depression. Other therapeutic signals from psychedelic drug research that merit rigorous replication studies include 3,4-Methylenedioxymethamphetamine (MDMA) for post-traumatic stress disorder (PTSD) and psilocybin for depression, end of life dysphoria, and alcohol use disorder. The precise mechanisms through which psychedelic drugs can produce benefit and harm are not fully understood. Rigorous research is the best path forward for evaluating the therapeutic potential and mechanisms of psychedelic drugs. Policies governing the clinical use of these drugs should be informed by evidence and prioritize the protection of public health over the profit motive.

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives.

Postsurgical falls have significant patient and societal implications but remain challenging to identify and track. Detecting postsurgical falls is crucial to improve patient care for older adults and reduce healthcare costs. Large language models (LLMs) offer a promising solution for reliable and automated fall detection using unstructured data in clinical notes. We tested several LLM prompting approaches to postsurgical fall detection in two different healthcare systems with three open-source LLMs. The Mixtral-8×7B zero-shot had the best performance at Stanford Health Care (PPV = 0.81, recall = 0.67) and the Veterans Health Administration (PPV = 0.93, recall = 0.94). These results demonstrate that LLMs can detect falls with little to no guidance and lay groundwork for applications of LLMs in fall prediction and prevention across many different settings.

Studying the association Between longitudinal non-dense breast tissue and breast cancer risk: a joint modelling approach.

Conflicting results have appeared in the literature on whether the amount of non-dense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer, and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and non-dense tissue trajectories and the risk of breast cancer in a large, Swedish, screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies, e.g. ignoring available longitudinal data. Overall, we did not find strong evidence supporting an association between non-dense tissue and the risk of incident breast cancer. We hypothesize that (1) previous studies have not been able to isolate the effect of non-dense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of non-dense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of non-dense tissue on breast cancer risk is likely to be small/not clinically relevant.

Which Social Media Platforms Provide the Most Informative Data for Monitoring the Opioid Crisis?

Background and Aims: Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have leveraged data from platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened their usability in opioid overdose surveillance systems. Here, we evaluate the potential of a broad set of platforms to detect emerging trends in the opioid crisis. Design: We identified 72 online platforms with a substantial global user base or prior citations in opioid-related research. We evaluated each platform's fit with our definition of social media, size of North American user base, and volume of opioid-related discourse. We created a shortlist of 11 platforms that met our criteria. We documented basic characteristics, volume and nature of opioid discussion, official policies regulating drug-related discussion, and data accessibility of shortlisted platforms. Setting: USA and Canada. Measurements: We quantified the volume of opioid discussion by number of platform-specific Google search hits for opioid terms. We captured informal language by including slang generated using a large language model. We report the number of opioid-related hits and proportion of opioid-related hits to hits for common nouns. Findings: We found that TikTok, YouTube, and Facebook have the most potential for use in opioid-related surveillance. TikTok and Facebook have the highest relative amount of drug-related discussions. Language on TikTok was predominantly informal. Many platforms offer data access tools for research, but changing company policies and user norms create instability. The demographics of users varies substantially across platforms. Conclusions: Social media data sources hold promise for detecting trends in opioid use, but researchers must consider the utility, accessibility, and stability of data on each platform. A strategy mixing several platforms may be required to cover all demographics suffering in the epidemic.

Crafting effective regulatory policies for psychedelics: What can be learned from the case of cannabis?

The turn of the century brought a resurgence of interest in psychedelics as a treatment for addiction and other psychiatric conditions, accompanied by extensive positive media attention and private equity investment. Government regulatory bodies in Australia, Israel, Canada and the United States now permit use of psychedelics for medical purposes. In the United States, citizen action and corporate financing have led to petitions and ballot initiatives to legalize psilocybin and other psychedelics for medical and recreational use. Given this momentum, policymakers must grapple with important questions that define whether and how psychedelics are made available to the public, as well as how companies produce and promote them. The current push to broaden the production, sale, and use of psychedelics bears many parallels to the movement to legalize cannabis in the United States and other nations-most notably, the use of poorly-evidenced therapeutic claims to create a de facto recreational market via the health care system. Experience with cannabis highlights the value of debating the question of legalization for nonmedical use as such rather than misrepresenting it as a medical issue. The lessons of cannabis policy also suggest a need to challenge hyping of psychedelic research findings; to promote rigorous clinical research on dosing and potency; to minimize the influence of for-profit industry in shaping policies to their economic advantage; and to coordinate federal, state, and local governments to regulate the manufacture, sale and distribution of psychedelic drugs (regardless of whether they are legalized for medical and/or recreational use).

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

A Randomized Controlled Trial of a Pay-for-Performance Initiative to Reduce Costs of Care for High-Need Psychiatric Patients.

OBJECTIVE: Pay-for-performance (P4P) initiatives hold promise for improving health care delivery but are rarely applied to behavioral health or tested in randomized controlled trials (RCTs). This RCT examined the effectiveness of a P4P initiative to reduce total cost of 24-hour care among patients with high needs for psychiatric care in a large county in California. METHODS: From August 2016 to March 2022, a total of 652 adult residents of Santa Clara County, California, were enrolled in a P4P initiative (mean±SD age=46.7±13.3 years, 61% male, 51% White, and 60% diagnosed as having a bipolar or psychotic disorder). Participants were randomly assigned to usual full-service partnerships from the county (N=327) or a comparable level of care from a contractor who agreed to a schedule of financial penalties and rewards based on whether enrollees (N=325) used more or less care than a historical cohort of similar county patients. The primary outcome was total cost of 24-hour psychiatric services. Secondary outcomes were costs of each of the 24-hour care services. RESULTS: The proportion of the total sample that used 24-hour psychiatric services decreased over the 36-month study period. Intent-to-treat analyses revealed no differences between the two study conditions in total care costs during the follow-up period. No significant care utilization differences were observed between the two conditions in most of the individual 24-hour services. CONCLUSIONS: A P4P initiative for high-need patients was no more effective than usual care for reducing costs of 24-hour psychiatric care.

Estimated effectiveness and cost-effectiveness of opioid use disorder treatment under proposed U.S. regulatory relaxations: A model-based analysis.

AIM: To assess the effectiveness and cost-effectiveness of buprenorphine and methadone treatment in the U.S. if exemptions expanding coverage for substance use disorder services via telehealth and allowing opioid treatment programs to supply a greater number of take-home doses of medications for opioid use disorder (OUD) continue (Notice of Proposed Rule Making, NPRM). DESIGN SETTING AND PARTICIPANTS: Model-based analysis of buprenorphine and methadone treatment for a cohort of 100,000 individuals with OUD, varying treatment retention and overdose risk among individuals receiving and not receiving methadone treatment compared to the status quo (no NPRM). INTERVENTION: Buprenorphine and methadone treatment under NPRM. MEASUREMENTS: Fatal and nonfatal overdoses and deaths over five years, discounted lifetime per person QALYs and costs. FINDINGS: For buprenorphine treatment under the status quo, 1.21 QALYs are gained at a cost of $19,200/QALY gained compared to no treatment; with 20% higher treatment retention, 1.28 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment, and the strategy dominates the status quo. For methadone treatment under the status quo, 1.11 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment. In all scenarios, methadone provision cost less than $20,000/QALY gained compared to no treatment, and less than $50,000/QALY gained compared to status quo methadone treatment. CONCLUSIONS: Buprenorphine and methadone OUD treatment under NPRM are likely to be effective and cost-effective. Increases in overdose risk with take-home methadone would reduce health benefits. Clinical and technological strategies could mitigate this risk.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

BACKGROUND: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

A pilot, randomized clinical trial: Left dorsolateral prefrontal cortex intermittent theta burst stimulation improves treatment outcomes in veterans with alcohol use disorder.

BACKGROUND: Transcranial magnetic stimulation (TMS) offers a promising treatment avenue to modulate brain function in alcohol use disorder (AUD). To the best of our knowledge, this pilot study is the first randomized, double-blind, sham-controlled trial to deliver intermittent theta burst stimulation to the left dorsolateral prefrontal cortex (DLPFC) among US veterans with AUD. We hypothesized that 20 sessions of real TMS are tolerable and feasible. As a secondary line of inquiry, we hypothesized that, relative to sham TMS, individuals receiving real TMS would experience greater reductions in 6-month relapse rates, anhedonia, and alcohol cue-reactivity. METHODS: Veterans (n = 17, one woman) were enrolled in a double-blind, sham-controlled trial (2-3 sessions/day; 7-10 days; 600 pulses/session; 20 sessions). Pre- and posttreatment assessments included responses to self-report questionnaires and functional magnetic resonance imaging measures of alcohol cue-reactivity. Alcohol consumption was assessed for 6 months. Linear mixed-effects models were constructed to predict posttreatment craving, mood, and cue-reactivity. RESULTS: Individuals who received active iTBS (n = 8) were less likely to relapse within 3 months after treatment than the sham-treated group (n = 9) (OR = 12.0). Greater reductions in anhedonia were observed following active iTBS (Cohen's d = -0.59), relative to sham (d = -0.25). Alcohol cue-reactivity was reduced following active iTBS and increased following sham within the left insula (d = -0.19 vs. 0.51), left thalamus (d = -0.28 vs. 0.77), right insula (d = 0.18 vs. 0.52), and right thalamus (d = -0.06 vs. 0.62). CONCLUSIONS: Relative to sham, we demonstrate that 20 sessions of real left DLPFC iTBS reduced the likelihood of relapse for at least 3 months. The potential utility of this approach is underscored by observed decreases in anhedonia and alcohol cue-reactivity-strong predictors of relapse among veterans. These initial data offer a valuable set of effect sizes to inform future clinical trials in this patient population.

Familial risk of postpartum psychosis.

Objective: Postpartum psychosis, a mood disorder triggered by childbirth, is one of the most severe psychiatric conditions, with high risks of suicide and infanticide if untreated. While it is evident that genetic factors play a crucial role in disorder risk, the exact extent of their importance is yet to be determined. Methods: This cohort study consisted of 1,648,759 women from the Swedish nationwide registers, of whom 2,514 (0.15%) experienced postpartum psychosis within three months of their first-ever childbirth. We estimated the relative recurrence risk of postpartum psychosis for female full siblings and cousins as a measure of familial, genetic, and environmental risk. Results: Relative recurrence risk of postpartum psychosis in full siblings was 10.69 (95% CI=6.60-16.26) when adjusted for year of and age at childbirth. Although cousins showed an elevated relative recurrence risk, these results did not reach statistical significance (1.78, 95% CI=0.70-3.62). Despite the higher familial risk of postpartum psychosis among full siblings, the absolute risk for women with an affected sibling is relatively low, estimated at 1.55% within the entire population. Conclusions: The observed increased risk of postpartum psychosis in full siblings suggests both genetic and shared environmental influences. However, the lack of significant results in cousins hampers a definitive distinction between these factors. Furthermore, despite increased relative recurrence risk in siblings, their overall likelihood of developing postpartum psychosis remains low. Our study underscores the need for further research to better understand the intricate interplay of genetics and environment in the development of postpartum psychosis.

Breast Cancer Incidence After a False-Positive Mammography Result.

Importance: False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear. Objectives: To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result. Design, Setting, and Participants: This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023. Exposure: A false-positive mammography result. Main Outcomes and Measures: Breast cancer incidence and mortality. Results: The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15). Conclusions and Relevance: This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.

Does the advent of depot therapy represent a step change in our understanding of opioid use disorder and its treatment?

After years of minimal innovation in pharmacotherapeutics, impressive outcomes in the treatment of opioid use disorder are being obtained from a new way of delivering an old medication; long-acting injectable formulations of buprenorphine appear to produce compelling reductions in relapse to illicit opioid use not only during use but also following depot discontinuation. This commentary discusses potential mechanisms behind this observation, asks if the removal of the need for daily oral opioid agonist dosing furthers our understanding of addiction treatment and whether we should therefore consider expanding access to depot formulations.

Leadership Styles Experienced During Military Service Predict Later Anhedonic Depressive Symptoms and Self-Efficacy in Veterans With Alcohol Use Disorder.

INTRODUCTION: Lifetime and past-year alcohol use disorder (AUD) prevalence is significantly higher in US Armed Services Veterans than in non-veterans across adulthood. This study examined the associations of perceived transformational leadership styles (TLS) experienced during military service and anhedonic depression and self-efficacy related to confidence to abstain or reduce alcohol consumption in Veterans seeking treatment for AUD. The ramifications of perceived leadership styles on multiple aspects of follower psychiatric functioning, including depressive and PTSD symptomatology, during and after military service, may be substantial and enduring. Higher anhedonic depression and lower abstinence self-efficacy are related to increased risk of relapse after treatment. We predicted Veterans, in treatment for AUD, who reported higher perceived levels of transformational leadership during military service, demonstrate lower anhedonic depressive symptoms and higher alcohol abstinence self-efficacy. MATERIALS AND METHODS: Veterans with AUD (n = 60; 50 ± 14 years of age) were recruited from residential treatment at the VA Palo Alto Health Care System. All procedures were approved by the VA Palo Alto Health Care System and Stanford University institutional review boards. A series of mediation analyses were completed with The Multifactor Leadership Questionnaire measures of TLS (average across leadership measures [transformational leadership average; TLS average]) as predictor and the Alcohol Abstinence Self-Efficacy Scale, Mood and Anxiety Symptom Questionnaire, anhedonic depression subscale, as dependent measures. PTSD Checklist for DSM-5 score was tested as a mediator variable. RESULTS: Higher reported perceived TLS average during military service was significantly related to lower anhedonic depressive symptoms. Higher TLS average was related to higher self-efficacy to resist alcohol use in contexts involving experience of physical issues and withdrawal/cravings and urges. These relationships were not mediated by PTSD symptomatology or duration of military service, age, education, time since military service, military branch, combat exposure, or current psychiatric diagnosis. CONCLUSIONS: The significant associations of perceived TLS during military service with anhedonic depression and alcohol use self-efficacy are clinically relevant because these measures are associated with relapse risk after AUD treatment. Further study of the implications of perceived TLS during military service for AUD and other substance use disorder treatment outcome is warranted in Veterans.

Drug-related deaths among housed and homeless individuals in the UK and the USA: comparative retrospective cohort study.

BACKGROUND: The UK and USA currently report their highest number of drug-related deaths since records began, with higher rates among individuals experiencing homelessness. AIMS: Given that overdose prevention in homeless populations may require unique strategies, we evaluated whether substances implicated in death differed between (a) housed decedents and those experiencing homelessness and (b) between US and UK homeless populations. METHOD: We conducted an internationally comparative retrospective cohort study utilising multilevel multinomial regression modelling of coronial/medical examiner-verified drug-related deaths from 1 January 2012 to 31 December 2021. UK data were available for England, Wales and Northern Ireland; US data were collated from eight county jurisdictions. Data were available on decedent age, sex, ethnicity, housing status and substances implicated in death. RESULTS: Homeless individuals accounted for 16.3% of US decedents versus 3.4% in the UK. Opioids were implicated in 66.3 and 50.4% of all studied drug-related deaths in the UK and the USA respectively. UK homeless decedents had a significantly increased risk of having only opioids implicated in death compared with only non-opioids implicated (relative risk ratio RRR = 1.87, 95% CI 1.76-1.98, P < 0.001); conversely, US homeless decedents had a significantly decreased risk (RRR = 0.37, 95% CI 0.29-0.48, P < 0.001). Methamphetamine was implicated in two-thirds (66.7%) of deaths among US homeless decedents compared with 0.4% in the UK. CONCLUSIONS: Both the rate and type of drug-related deaths differ significantly between homeless and housed populations in the UK and USA. The two countries also differ in drugs implicated in death. Targeted programmes for country-specific implicated drug profiles appear warranted.

Understanding predictors of mental health and substance use treatment utilization among US adults: A repeated cross-sectional study.

Background: Understanding discrepancies in mental health and substance use treatment utilization can help identify inequities in access to health services. We investigate mental health and substance use treatment utilization as function of demographic and social determinants, as well as pre-existing mental health and substance use disorders. Methods: In this repeated cross-sectional study, we used the 2017-2019 National Survey on Drug Use and Health data on US adults above age 18. Two logistic regression models were conducted, using predictors of age, gender, race/Hispanicity, sexual identity, education, insurance, family income, and past year mental health and substance use disorders, with outcomes of mental health or substance use treatment utilization. Weighted estimates of substance use disorders and insurance types and Pearson's correlation tests of vulnerability among age, gender, and treatment type were reported. Findings: Racial minorities, uninsured populations, sexual minorities, and females had lower odds of receiving mental health treatment, while older populations, lower income groups, and dual eligible enrollees had higher odds. Individuals with substance use disorders but no mental illness had higher odds of receiving mental health treatment. Those utilizing mental health treatment were mostly of high income, privately insured, and using cannabis, cocaine, and opioids. Older populations, men, and Medicaid only enrollees had higher odds of receiving substance use disorder treatment, whereas racial minorities had lower odds. Distribution of income, insurance type, and substance use were more widespread than mental health treatment. Interpretation: Mental health treatment can be used as an avenue for substance use treatment, particularly opioid use disorders. It is important to target vulnerable populations, like racial minorities and uninsured populations to improve access to mental health and substance use treatment.