Latent trajectories of anxiety and depressive symptoms among adults in early treatment for nonmedical opioid use

Increases in primary opioid use disorder diagnoses co-occurring with anxiety or depressive disorder diagnoses in mental health treatment in the United States, 2015-2019

BACKGROUND

Opioid use disorders (OUDs) often co-occur with anxiety and depressive disorders. While the proportion of mental health (MH) treatment facilities providing substance use treatment has increased, the proportion of these facilities able to simultaneously treat MH and substance use decreased. This warrants investigation into the integrated treatment needs of persons with a primary OUD diagnosis treated in MH treatment facilities.

METHODS

Using the Mental Health Client Level Data, we examined a sample of N = 83,975 adults with OUD as their primary diagnosis who received treatment from a MH treatment facility in the United States from 2015 to 2019. Joinpoint regression was used to examine annual trends of the number of individuals with co-occurring anxiety or depression diagnoses.

RESULTS

Most of the sample were men (53.7%) and received treatment in a community-based program (93.3%). Approximately 17% of the sample had either an anxiety or depressive disorder diagnosis. Approximately 9% of our sample had an anxiety disorder diagnosis, and 10% had a depressive disorder diagnosis. An increase in the number of individuals with a co-occurring anxiety disorder diagnosis from 2015 to 2019 was identified (annual percent change (APC) = 61.4; 95% confidence interval (CI) = [10.0, 136.9]; p =.029). An increase in the number of individuals with a co-occurring depressive disorder diagnosis from 2015 to 2019 was identified (APC = 39.0; 95% CI = [7.4; 79.9]; p =.027).

CONCLUSIONS

This study highlights increases in adults receiving MH treatment for OUD having co-occurring anxiety or depression diagnoses, furthering the importance of integrated dual disorder treatment.

Patterns of demoralization and anhedonia during early substance use disorder treatment and associations with treatment attrition

BACKGROUND

Although depressive symptoms represent a promising therapeutic target to promote recovery from substance use disorders (SUD), heterogeneity in their diagnostic presentation often hinders the ability to effectively tailor treatment. We sought to identify subgroups of individuals varying in depressive symptom phenotypes (i.e., demoralization, anhedonia), and examined whether these subgroups were associated with patient demographics, psychosocial health, and treatment attrition.

METHODS

Patients (N = 10,103, 69.2 % male) were drawn from a dataset of individuals who presented for admission to SUD treatment in the US. Participants reported on their demoralization and anhedonia approximately weekly for the first month of treatment, and on their demographics, psychosocial health, and primary substance at intake. Longitudinal latent profile analysis examined patterns of demoralization and anhedonia with treatment attrition as a distal outcome.

RESULTS

Four subgroups of individuals emerged: (1) High demoralization and anhedonia, (2) Remitting demoralization and anhedonia, (3) High demoralization, low anhedonia, and (4) Low demoralization and anhedonia. Relative to the Low demoralization and anhedonia subgroup, all the other profiles were more likely to discontinue treatment. Numerous between-profile differences were observed with regard to demographics, psychosocial health, and primary substance.

LIMITATIONS

The racial and ethnic background of the sample was skewed towards White individuals; future research is needed to determine the generalizability of our findings to minoritized racial and ethnic groups.

CONCLUSIONS

We identified four clinical profiles that varied in the joint course of demoralization and anhedonia. Findings suggest specific subgroups might benefit from additional interventions and treatments that address their unique mental health needs during SUD recovery.

Predictors of Suicidal Ideation During Residential Substance Use Treatment

Background: Individuals with substance use disorders (SUDs) and co-occurring chronic health and/or psychiatric conditions face unique challenges in treatment and may be at a greater risk for suicidal ideation relative to persons with SUD alone.

Methods: In a sample of individuals entering residential SUD treatment in 2019 and 2020 (N = 10,242), we tested adjusted and unadjusted associations between suicidal ideation and (1) psychiatric symptoms and (2) chronic health conditions at treatment intake and during treatment using logistic and generalized logistic models.

Results: Over a third of the sample endorsed suicidal ideation at intake, though the prevalence of suicidal ideation decreased during treatment. In both adjusted and unadjusted models, individuals who reported past-month self-harm, those who reported a lifetime suicide attempt, and individuals who screened positive for co-occurring anxiety, depression, and/or posttraumatic stress disorder were at elevated risk of endorsing suicidal ideation at intake and during treatment (P values < .001). In unadjusted models, chronic pain (odds ratio [OR] = 1.51, P < .001) and hepatitis C virus (OR = 1.65, P < .001) were associated with an elevated risk for suicidal ideation at intake, and chronic pain was associated with elevated risk for suicidal ideation during treatment (OR = 1.59, P < .001).

Conclusions: Increasing accessibility to integrated treatments (ie, those that address psychiatric and chronic health conditions) for patients experiencing suicidal ideation may be beneficial in residential SUD treatment settings. Developing predictive models to identify those most at risk of suicidal ideation in real time remains a relevant direction for future work.

Trajectories of depression among patients in treatment for opioid use disorder: A growth mixture model secondary analysis of the XBOT trial

BACKGROUND AND OBJECTIVES

To inform clinical practice, we identified subgroups of adults based on levels of depression symptomatology over time during opioid use disorder (OUD) treatment.

METHODS

Participants were 474 adults in a 24-week treatment trial for OUD. Depression symptoms were measured using the 17-item Hamilton Depression Rating Scale (HAM-D) at nine-time points. This was a secondary analysis of the Clinical Trials Network Extended-Release Naltrexone versus Buprenorphine for Opioid Treatment (XBOT) trial using a growth mixture model.

RESULTS

Three distinct depression trajectories were identified: Class 1 High Recurring-10% with high HAM-D with initial partial reductions (of HAM-D across time), Class 2 Persistently High-5% with persistently high HAM-D, and Class 3 Low Declining-85% of the participants, with low HAM-D with early sustained reductions. The majority (low declining) had levels of depression that improved in the first 4 weeks and then stabilized across the treatment period. In contrast, 15% (high recurring and persistently high) had high initial levels that were more variable across time. The persistently high class had higher rates of opioid relapse.

DISCUSSION AND CONCLUSIONS

In this OUD sample, most depressive symptomatology was mild and improved after medication treatment for opioid use disorder (MOUD). Smaller subgroups had higher depressive symptoms that persisted or recurred after the initiation of MOUD. Depressive symptoms should be followed in patients initiating treatment for OUD, and when persistent, should prompt further evaluation and consideration of antidepressant treatment.

SCIENTIFIC SIGNIFICANCE

This study is the first to identify three distinct depression trajectories among a large clinical sample of individuals in MOUD treatment.

Psychosocial Correlates of Opioid Use Profiles among Young Adults in a Longitudinal Study across 6 US Metropolitan Areas

Background: Examining opioid use profiles over time and related factors among young adults is crucial to informing prevention efforts. Objectives: This study analyzed baseline data (Fall 2018) and one-year follow-up data from a cohort of 2,975 US young adults (Mage=24.55, 42.1% male; 71.7% White; 11.4% Hispanic). Multinomial logistic regression was used to examine: 1) psychosocial correlates (i.e. adverse childhood experiences [ACEs], depressive symptoms, parental substance use) of lifetime opioid use (i.e. prescription use vs. nonuse, nonmedical prescription [NMPO] use, and heroin use, respectively); and 2) psychosocial correlates and baseline lifetime use in relation to past 6-month use at one-year follow-up (i.e. prescription use vs. nonuse and NMPO/heroin use, respectively). Results: At baseline, lifetime use prevalence was: 30.2% prescription, 9.7% NMPO, and 3.1% heroin; past 6-month use prevalence was: 7.6% prescription, 2.5% NMPO, and 0.9% heroin. Compared to prescription users, nonusers reported fewer ACEs and having parents more likely to use tobacco, but less likely alcohol; NMPO users did not differ; and heroin users reported more ACEs and having parents more likely to use cannabis but less likely alcohol. At one-year follow-up, past 6-month use prevalence was: 4.3% prescription, 1.3% NMPO, and 1.4% heroin; relative to prescription users, nonusers were less likely to report baseline lifetime opioid use and reported fewer ACEs, and NMPO/heroin users were less likely to report baseline prescription opioid use but more likely heroin use. Conclusions: Psychosocial factors differentially correlate with young adult opioid use profiles, and thus may inform targeted interventions addressing different use patterns and psychosocial risk factors.

Patterns of polysubstance use and clinical comorbidity among persons seeking substance use treatment: An observational study

INTRODUCTION

Polysubstance use is common among individuals seeking treatment for substance use disorders (SUD). However, we know less about patterns and correlates of polysubstance use among treatment-seeking populations. The current study aimed to identify latent patterns of polysubstance use and associated risk factors in persons entering SUD treatment.

METHODS

Patients (N = 28,526) being admitted for substance use treatment reported on their use of thirteen substances (e.g., alcohol, cannabis, cocaine, amphetamines, methamphetamines, other stimulants, heroin, other opioids, benzodiazepines, inhalants, synthetics, hallucinogens, and club drugs) in the month before treatment and prior to the month before treatment. Latent class analysis (LCA) determined the relationship between class membership and gender, age, employment status, unstable housing, self-harm, overdose, past treatment, depression, generalized anxiety disorder, and/or post-traumatic stress disorder (PTSD).

RESULTS

Identified classes included: 1) Alcohol primary, 2) Moderate probability of past-month alcohol, cannabis, and/or opioid use; 3) Alcohol primary, Lifetime cannabis and cocaine use; 4) Opioid primary, Lifetime use of alcohol, cannabis, hallucinogens, club drugs, amphetamines, and cocaine; 5) Moderate probability of past-month alcohol, cannabis, and/or opioid use, Lifetime use of various substances; 6) Alcohol and cannabis primary, Lifetime use of various substances; and 7) High past-month polysubstance use. Individuals who engaged in past-month polysubstance use attended to face elevated risk of screening positive for recent unstable housing, unemployment, depression, anxiety, PTSD, self-harm, and overdose.

CONCLUSIONS

Current polysubstance use is associated with significant clinical complexity. Tailored treatments that reduce harms resulting from polysubstance use and related psychiatric comorbidity may improve treatment outcomes in this population.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Intra-individual variability and stability of affect and craving among individuals receiving medication treatment for opioid use disorder

Affect and craving are dynamic processes that are clinically relevant in opioid use disorder (OUD) treatment, and can be quantified in terms of intra-individual variability and stability. The purpose of the present analysis was to explore associations between opioid use and variability and stability of affect and craving among individuals receiving medication treatment for OUD (MOUD). Adults (N = 224) with OUD in outpatient methadone or buprenorphine treatment completed ecological momentary assessment (EMA) prompts assessing positive affect, negative affect, opioid craving, and opioid use. Dynamic structural equation modeling (DSEM) was used to quantify person-level indices of magnitude and stability of change. Beta regression was used to examine associations between intra-individual variability and stability and proportion of opioid-use days, when controlling for overall intensity of affect and craving. Results suggested that greater magnitude of craving variability was associated with opioid use on a greater proportion of days, particularly among individuals with lower average craving. Low average positive affect was also associated with higher proportion of days of use. Individuals who experience substantial craving variability in the context of lower average craving may be particularly vulnerable to opioid use during treatment. Ongoing assessment of craving may be useful in identifying treatment needs. Examining correlates of intra-individual variability and stability in MOUD treatment remains a relevant direction for future work.