Digital mental health interventions as stand-alone vs. augmented treatment as usual.

BACKGROUND: Smartphone-based digital mental health interventions (DMHI) have been described as a purported solution to meet growing healthcare demands and lack of providers, but studies often don't account for whether patients are concurrently in another treatment modality. METHODS: This preregistered quasi-experimental intent-to-treat study with 354 patients enrolled in a therapist-supported DMHI examined the treatment effectiveness of the Meru Health Program (MHP) as a stand-alone treatment as compared to the MHP in combination with any other form of treatment, including (1) in-person therapy, (2) psychotropic medication use, and (3) in-person therapy and psychotropic medication use. RESULTS: Patients with higher baseline depressive and anxiety symptoms were more likely to self-select into multiple forms of treatment, an effect driven by patients in the MHP as adjunctive treatment to in-person therapy and psychotropic medication. Patients in combined treatments had significantly higher depressive and anxiety symptoms across treatment, but all treatment groups had similar decreasing depressive and anxiety symptom trajectories. Exploratory analyses revealed differential treatment outcomes across treatment combinations. Patients in the MHP in combination with another treatment had higher rates of major depressive episodes, psychiatric hospitalization, and attempted death by suicide at baseline. CONCLUSIONS: Patients with higher depressive and anxiety symptoms tend to self-select into using DMHI in addition to more traditional types of treatment, rather than as a stand-alone intervention, and have more severe clinical characteristics. The use the MHP alone was associated with improvement at a similar rate to those with higher baseline symptoms who are in traditional treatments and use MHP adjunctively.

Therapist-supported digital mental health intervention for depressive symptoms: A randomized clinical trial.

Depression is a chronic and debilitating mental disorder. Despite the existence of several evidence-based treatments, many individuals suffering from depression face myriad structural barriers to accessing timely care which may be alleviated by digital mental health interventions (DMHI). Accordingly, this randomized clinical trial (ClinicalTrials.gov: NCT04738084) investigated the efficacy of a newer version of the therapist-supported and guided DMHI, the Meru Health Program (MHP), which was recently enhanced with heart rate variability biofeedback and lengthened from 8- to 12-weeks duration, among people with elevated depression symptoms (N = 100, mean age 37). Recruited participants were randomized to the MHP (n = 54) or a waitlist control (n = 46) condition for 12 weeks. The MHP group had greater decreases in depression symptoms compared to the waitlist control (d = -0.8). A larger proportion of participants in the MHP group reported a minimal clinically important difference (MCID) in depression symptoms than participants in the waitlist control group (39.1 % vs. 9.8 %, χ2(1) = 9.90, p = .002). Similar effects were demonstrated for anxiety symptoms, quality of life, insomnia, and resilience. The results confirm the utility of the enhanced MHP in reducing depression symptoms and associated health burdens.

Improvements in Work Productivity and Activity Impairment Among Adults With Anxiety or Depressive Symptoms Participating in a Relational Agent-Delivered Digital Mental Health Intervention.

OBJECTIVES: The study aimed to assess changes between baseline and end of treatment in work-related absenteeism, presenteeism, productivity, and nonwork-related activity impairment and estimate cost savings associated with observed improvements. METHODS: Data from 91 employed adult participants who enrolled in a single-arm, exploratory study of a relational agent-delivered digital mental health intervention and completed Work Productivity and Activity Impairment assessments were analyzed; overall work productivity improvement was multiplied by the overall and education-adjusted US median annual salary to arrive at potential cost savings estimates. RESULTS: Adjusted models indicated more than 20% improvements in presenteeism, work productivity impairment, and activity impairment, yielding cost-savings estimates between $14,000 and more than $18,000 annually. CONCLUSIONS: Relational agent-delivered digital mental health interventions may be associated with improvements in work productivity and activity impairment, which could result in a sizable cost savings.

Longitudinal outcomes of a therapist-supported digital mental health intervention for depression and anxiety symptoms: A retrospective cohort study.

PURPOSE: This study examined treatment outcomes (depression and anxiety symptoms) up to 24 months after completion of a therapist-supported digital mental health intervention (DMHI). METHODS: The sample consisted of 380 participants who participated in an eight-week DMHI from February 6, 2017 to May 20, 2019. Participants reported depression and anxiety symptoms at eight timepoints from baseline to 24 months. Mixed-effects modelling was used to investigate symptom changes over time. The proportion of participants meeting criteria for treatment response, clinically significant change, and remission of depression and anxiety symptoms were calculated, including proportions demonstrating each outcome sustained up to each timepoint. RESULTS: Multivariate analyses yielded statistically significant reductions in depression (ß = -5.40) and anxiety (ß = -3.31) symptoms from baseline to end of treatment (8 weeks). Symptom levels remained significantly reduced from baseline through 24 months. The proportion of participants meeting criteria for clinical treatment outcomes remained constant over 24 months, although there were linear decreases in the proportions experiencing sustained clinical outcomes. CONCLUSIONS: Treatment gains were made for depression and anxiety symptoms at the end of treatment and up to 24 months. Future studies should determine the feasibility of integrating post-treatment programmes into DMHIs to address symptom deterioration.

Preliminary Effectiveness of a Therapist-Supported Digital Mental Health Intervention in Reducing Suicidal Ideation.

Suicidal ideation (SI) is a significant public health concern with increasing prevalence. Therapist-supported digital mental health interventions (DMHI) are an emergent modality to address common mental health problems like depression and anxiety, although less is known about SI. This study examined SI trajectories among 778 patients who participated in a therapist-supported DMHI using multilevel models during and up to 6-months post-treatment. Estimates of associated suicide attempts and deaths by suicide were calculated using published data linking PHQ-9-assessed SI to records of suicide attempts and deaths by suicide. The proportion of participants reporting no SI significantly increased between baseline and end-of-treatment (78.02% to 91.00%). Effect sizes of SI changes between baseline and end-of-treatment, 3-month, and 6-month follow-ups were 0.33 (95%CI = 0.27-0.38), 0.32 (95%CI = 0.27-0.38), and 0.32 (95%CI = 0.27-0.38), respectively. Results also indicated an estimated 30.49% reduction (95%CI = 25.15%-35.13%) in suicide attempts and death by suicide across treatment. This study provides preliminary evidence of the effectiveness of a therapist-supported DMHI in reducing SI.

Engagement, Satisfaction, and Mental Health Outcomes Across Different Residential Subgroup Users of a Digital Mental Health Relational Agent: Exploratory Single-Arm Study.

BACKGROUND: Mental illness is a pervasive worldwide public health issue. Residentially vulnerable populations, such as those living in rural medically underserved areas (MUAs) or mental health provider shortage areas (MHPSAs), face unique access barriers to mental health care. Despite the growth of digital mental health interventions using relational agent technology, little is known about their use patterns, efficacy, and favorability among residentially vulnerable populations. OBJECTIVE: This study aimed to explore differences in app use, therapeutic alliance, mental health outcomes, and satisfaction across residential subgroups (metropolitan, nonmetropolitan, or rural), MUAs (yes or no), and MHPSAs (yes or no) among users of a smartphone-based, digital mental health intervention, Woebot LIFE (WB-LIFE). WB-LIFE was designed to help users better understand and manage their moods and features a relational agent, Woebot, that converses through text-based messages. METHODS: We used an exploratory study that examined data from 255 adults enrolled in an 8-week, single-arm trial of WB-LIFE. Analyses compared levels of app use and therapeutic alliance total scores as well as subscales (goal, task, and bond), mental health outcomes (depressive and anxiety symptoms, stress, resilience, and burnout), and program satisfaction across residential subgroups. RESULTS: Few study participants resided in nonmetropolitan (25/255, 10%) or rural (3/255, 1%) areas, precluding estimates across this variable. Despite a largely metropolitan sample, nearly 39% (99/255) resided in an MUA and 55% (141/255) in an MHPSA. There were no significant differences in app use or satisfaction by MUA or MHPSA status. There also were no differences in depressive symptoms, anxiety, stress, resilience, or burnout, with the exception of MUA participants having higher baseline depressive symptoms among those starting in the moderate range or higher (Patient Health Questionnaire-8 item scale≥10) than non-MUA participants (mean 16.50 vs 14.41, respectively; P=.01). Although working alliance scores did not differ by MHPSA status, those who resided in an MUA had higher goal (2-tailed t203.47=2.21; P=.03), and bond (t203.47=1.94; P=.05) scores at day 3 (t192.98=2.15; P=.03), and higher goal scores at week 8 (t186.19=2.28; P=.02) as compared with those not living in an MUA. CONCLUSIONS: Despite the study not recruiting many participants from rural or nonmetropolitan populations, sizable proportions resided in an MUA or an MHPSA. Analyses revealed few differences in app use, therapeutic alliance, mental health outcomes (including baseline levels), or satisfaction across MUA or MHPSA status over the 8-week study. Findings suggest that vulnerable residential populations may benefit from using digital agent-guided cognitive behavioral therapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05672745; https://clinicaltrials.gov/study/NCT05672745.

Changes in stress, burnout, and resilience associated with an 8-week intervention with relational agent "Woebot".

Background: Research investigating the potential for digital mental health interventions with integrated relational agents to improve mental health outcomes is in its infancy. By delivering evidence-based mental health interventions through tailored, empathic conversations, relational agents have the potential to help individuals manage their stress and mood, and increase positive mental health. Aims: The aims of this study were twofold: 1) to assess whether a smartphone app delivering mental health support through a relational agent, Woebot, is associated with changes in stress, burnout, and resilience over 8 weeks, and 2) to identify demographic and clinical factors associated with changes in these outcomes. Method: This exploratory, non-randomized, single-armed, open-labeled trial was conducted from May to July 2022. A total of 256 adults (mean age 39 ± 13.35; 72 % females) recruited through social media advertising enrolled in the study. Participants completed an 8-week intervention period during which they were invited to use a smartphone app called Woebot-LIFE that delivers cognitive behavioral therapy through a relational agent called "Woebot". Participant-reported measures of stress, burnout, and resilience were collected at Baseline, and Week 8. Changes in these outcomes during the study period were assessed. Bivariate and stepwise multiple regression modeling was used to identify sociodemographic and clinical factors associated with observed changes over the 8-week study period. Results: Exposure to Woebot-LIFE was associated with significant reductions in perceived stress and burnout and significantly increased resilience over the 8-week study period. A greater reduction in stress was observed among those with clinically elevated mood symptoms (i.e., Patient Health Questionnaire-8 or Generalized Anxiety Disorder 7-item scores ≥10) at baseline compared to those without; however, the differences in the improvements in resilience scores and burnout between the two groups were not statistically significant. Although a difference in the magnitude of change in stress was observed for participants with and without clinically elevated mood symptoms at baseline, significant improvements in stress, burnout, and resilience over the 8-week study period were observed for both groups. Bivariate analyses showed that race, insurance type, and baseline level of resilience were associated with changes in each of the outcomes, though baseline resilience was the only factor that remained significantly associated with changes in the outcomes in the stepwise multiple regression analyses. Conclusion: Results of this single-arm, exploratory study suggest that conversational agent-guided mental health interventions such as Woebot-LIFE may be associated with reduced stress and burnout and increased resilience in both clinical and non-clinical populations.

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention.

OBJECTIVE: Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes. METHODS: We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics. RESULTS: Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals. CONCLUSIONS: Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Trajectories of depression symptoms in a therapist-supported digital mental health intervention: a repeated measures latent profile analysis.

PURPOSE: Major depression affects 10% of the US adult population annually, contributing to significant burden and impairment. Research indicates treatment response is a non-linear process characterized by combinations of gradual changes and abrupt shifts in depression symptoms, although less is known about differential trajectories of depression symptoms in therapist-supported digital mental health interventions (DMHI). METHODS: Repeated measures latent profile analysis was used to empirically identify differential trajectories based upon biweekly depression scores on the Patient Health Questionnaire-9 (PHQ-9) among patients engaging in a therapist-supported DMHI from January 2020 to July 2021. Multivariate associations between symptom trajectories with sociodemographics and clinical characteristics were examined with multinomial logistic regression. Minimal clinically important differences (MCID) were defined as a five-point change on the PHQ-9 from baseline to week 12. RESULTS: The final sample included 2192 patients aged 18 to 82 (mean = 39.1). Four distinct trajectories emerged that differed by symptom severity and trajectory of depression symptoms over 12 weeks. All trajectories demonstrated reductions in symptoms. Despite meeting MCID criteria, evidence of treatment resistance was found among the trajectory with the highest symptom severity. Chronicity of major depressive episodes and lifetime trauma exposures were ubiquitous across the trajectories in a multinomial logistic regression model. CONCLUSIONS: These data indicate that changes in depression symptoms during DMHI are heterogenous and non-linear, suggesting a need for precision care strategies to address treatment resistance and increase engagement. Future efforts should examine the effectiveness of trauma-informed treatment modules for DMHIs as well as protocols for continuation treatment and relapse prevention.

Examining the temporal dynamics of anxiety and depressive symptoms during a therapist-supported, smartphone-based intervention for depression: Longitudinal observational study.

OBJECTIVE: This study examined the temporal dynamics of anxiety and depressive symptoms during a 12-week therapist-supported, smartphone-delivered digital health intervention for symptoms of depression and anxiety. METHODS: A total of 290 participants were included in the present analyses (age Mean = 39.64, SD = 10.25 years; 79% female; 54% self-reported psychotropic medication use). Linear mixed models were used to examine the concurrent anxiety-depression association and (2) the lead-lag anxiety-depression relationship, with greater anxiety predicted to precede an increase in depression. RESULTS: In support of Hypothesis 1, greater anxiety during the current biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment. In support of Hypothesis 2, greater anxiety during the prior biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment but not vice-versa. CONCLUSION: These findings demonstrate that anxiety and depressive symptoms may overlap and fluctuate in concert, with anxiety symptoms predicting subsequent depressive symptoms but not vice-versa. With sensitivity to study limitations, implications for future intervention designs are discussed.

Changes in Quality of Life and Loneliness Among Middle-Aged and Older Adults Participating in Therapist-Guided Digital Mental Health Intervention.

Background: This study aimed to examine the effects of a 12-week multicomponent mobile app-delivered intervention, the Meru Health Program (MHP), on mental health quality of life (QoL) and loneliness among the middle-aged and older adults with depression symptoms. Methods: The eligible participants (M age = 57.06, SD = 11.26 years) were enrolled in the MHP, a therapist-supported mobile intervention. Using a non-randomized pre-post design, change in mental health QoL [WHO QoL Brief (WHOQOL-BREF) psychological health] and loneliness (UCLA Loneliness Scale) from baseline to post-treatment were examined. Time of enrollment [pre- vs. post-coronavirus disease 2019 (COVID-19)] was included as a between-subjects factor in the repeated measures analyses. Results: Forty-two participants enrolled prior to the COVID-19 pandemic; eight enrolled after the pandemic began. Among the pre-COVID-19 enrollees, increase in mental health QoL, F(1, 38) = 12.61, p = 0.001, η2 = 0.25 and decreases in loneliness emerged, F(1, 38) = 5.42, p = 0.025, η2 = 0.13. The changes in mental health QoL, but not loneliness, held for the combined sample, such as post-COVID-19 enrollees, F(1, 44) = 6.02, p = 0.018, η2 = 0.12. The regression analyses showed that increases in mindfulness were associated with the increased mental health QoL and decreased loneliness. Conclusion: Therapist-supported digital mental health interventions, such as the MHP, have the potential to improve mental health QoL and decrease loneliness among the middle-aged and older adults. The findings for loneliness may not hold during the periods of mandated isolation. Instead, therapists supporting digital interventions may need to tailor their approach to target loneliness.

Significant reduction in depressive symptoms among patients with moderately-severe to severe depressive symptoms after participation in a therapist-supported, evidence-based mobile health program delivered via a smartphone app.

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of depression. As such, in an intent-to-treat study we examined whether 218 patients with at least moderately severe depressive symptoms (PHQ-9 ≥ 15) experienced significant reductions in depressive symptoms after participation in a therapist-supported, evidence-based mobile health (mHealth) program, Meru Health Program (MHP). Patients with moderately severe and severe depressive symptoms at pre-program assessment experienced significant decreases in depressive symptoms at end-of treatment (mean [standard deviation] PHQ-9 reduction = 8.30 [5.03], Hedges' g = 1.64, 95% CI [1.44, 1.85]). Also, 34% of patients with at least moderately severe depressive symptoms at baseline and 29.9% of patients with severe depressive symptoms (PHQ-9 ≥ 20) at baseline responded to the intervention at end-of-treatment, defined as experiencing ≥50% reduction in PHQ-9 score and a post-program PHQ-9 score lower than 10. Limitations include use lack of a control group and no clinical diagnostic information. Future randomized trials are warranted to test the MHP as a scalable solution for patients with more severe depressive symptoms.

Feasibility and Efficacy of the Addition of Heart Rate Variability Biofeedback to a Remote Digital Health Intervention for Depression.

A rise in the prevalence of depression underscores the need for accessible and effective interventions. The objectives of this study were to determine if the addition of a treatment component showing promise in treating depression, heart rate variability-biofeedback (HRV-B), to our original smartphone-based, 8-week digital intervention was feasible and whether patients in the HRV-B ("enhanced") intervention were more likely to experience clinically significant improvements in depressive symptoms than patients in our original ("standard") intervention. We used a quasi-experimental, non-equivalent (matched) groups design to compare changes in symptoms of depression in the enhanced group (n = 48) to historical outcome data from the standard group (n = 48). Patients in the enhanced group completed a total average of 3.86 h of HRV-B practice across 25.8 sessions, and were more likely to report a clinically significant improvement in depressive symptom score post-intervention than participants in the standard group, even after adjusting for differences in demographics and engagement between groups (adjusted OR 3.44, 95% CI [1.28-9.26], P = .015). Our findings suggest that adding HRV-B to an app-based, smartphone-delivered, remote intervention for depression is feasible and may enhance treatment outcomes.

Residential Transience Among Adults: Prevalence, Characteristics, and Association with Mental Illness and Mental Health Service Use.

This study examined the association between frequent residential mobility (i.e., residential transience) and mental illness, mental health service use, and unmet need for services. Data are from the 2010 to 2014 National Surveys on Drug Use and Health (n = ~ 229,600). Logistic regression models examined the relationship between proximal (past year) and distal (past 2-5 years) residential transience and past year any mental illness (AMI), serious mental illness (SMI), mental health service use among adults with mental illness, and unmet need for services. Adults with transience had greater odds of AMI and SMI than those without transience. Proximal and distal transience were unrelated to past year mental health service use among adults with mental illness, but the odds of unmet need for services were greater among adults with transience compared with those without, suggesting a level of unmet service need among those with transience.

Trends in mental health service use by age among adults with serious mental illness.

PURPOSE: This study examines trends in mental health service use among 18- to 64-year-old adults with serious mental illness (SMI). METHODS: Data are from approximately 22,200 adults with SMI who participated in the National Survey on Drug Use and Health, an annual nationally representative survey of the U.S. civilian, noninstitutionalized population. A regression restricted spline modeled the trend in mental health service use by age among adults with SMI. RESULTS: Approximately 20 to 50% of adults with SMI did not receive past-year mental health services. The odds of past-year service use increased by 3% per year until age 52 years. CONCLUSIONS: From age 18 to 52 years, age incrementally increases the likelihood that an adult with SMI makes treatment contact.

Primary Care Interventions to Prevent Child Maltreatment: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.

Importance: Child maltreatment, also referred to as child abuse and neglect, can result in lifelong negative consequences. Objective: To update the evidence on interventions provided in or referable from primary care to prevent child maltreatment for the US Preventive Services Task Force. Data Sources: PubMed, Cochrane Library, EMBASE, and trial registries through December 18, 2017; references; experts; literature surveillance through July 17, 2018. Study Selection: English-language fair- and good-quality randomized clinical trials that (1) included children with no known exposure to maltreatment and no signs or symptoms of current or past maltreatment, (2) evaluated interventions feasible in a primary care setting or that could result from a referral from primary care, and (3) reported abuse or neglect outcomes or proxies for abuse or neglect (eg, injury with a specificity for abuse, visits to the emergency department, hospitalization). Data Extraction and Synthesis: Two reviewers independently assessed titles/abstracts, full-text articles, and study quality; a third resolved conflicts when needed. When at least 3 similar trials were available, random-effects meta-analyses were conducted. Main Outcomes and Measures: Direct measures (including reports to child protective services and removal of the child from the home) or proxy measures of abuse or neglect; behavioral, emotional, mental, or physical well-being; and harms. Results: Twenty-two trials (33 publications) were included (N = 11 132). No significant association was found between interventions and reports to child protective services within 1 year of intervention completion (10.6% vs 11.9%; pooled odds ratio [OR], 0.94 [95% CI, 0.72-1.23]; 10 trials [n = 2444]) or removal of the child from the home within 1 to 3 years of follow-up (3.5% vs 3.7%; pooled OR, 1.09 [95% CI, 0.16-7.28]; 4 trials [n = 609]). No statistically significant associations were observed between interventions and outcomes for emergency department visits in the short term (<2 years), hospitalizations, child development, school performance, and prevention of death. Nonsignificant results from single trials led to a conclusion of insufficient evidence for injuries, failure to thrive, failure to immunize, school attendance, and other measures of abuse or neglect. Inconsistent results led to a conclusion of insufficient evidence for long-term (≥2 years) outcomes for reports to child protective services (ORs range from 0.48 to 1.13; 3 trials [n = 1690]), emergency department visits (1 of 2 trials reported significant differences) and internalizing and externalizing behavior symptoms (3 of 6 trials reported reductions in behavior difficulties). No eligible trials on harms of interventions were identified. Conclusions and Relevance: Interventions provided in or referable from primary care did not consistently prevent child maltreatment. No evidence on harms is available.

Screening for Depression in Pediatric Primary Care.

PURPOSE OF REVIEW: To review the clinical practice guideline landscape for depression screening in pediatric primary care and to identify current gaps in knowledge. RECENT FINDINGS: Various organizations have recommendations that support screening for depression in pediatric primary care, although some differ based on the age of the child. To date, guidelines have been made based on indirect evidence of efficacy. For example, indirect evidence shows that several screening tools exist for use in primary care, and various primary care-administered or referred treatments for childhood depression have some evidence of efficacy (particularly among adolescents). In addition to determining the applicability of this evidence to younger children, more research is needed on the direct net benefits of screening and to identify factors that facilitate its effective implementation. Indirect evidence supports the benefits of screening for depression in pediatric primary care; most organizations that publish screening guidelines recommend its use.

Which mental disorders are associated with the greatest impairment in functioning?

OBJECTIVE: The objective of this study is to estimate the comparative associations of mental disorders with three measures of functional impairment: the Global Assessment of Functioning (GAF); the number of days in the past 12 months of total inability to work or carry out normal activities because of emotions, nerves, or mental health (i.e., days out of role); and a modified version of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). METHODS: Secondary data analysis of the linked Mental Health Surveillance Study and the National Survey on Drug Use and Health (n = 5653), nationally representative population surveys conducted in the United States. Generalized linear models assessed the independent effects of mental disorders on each measure of functional impairment, controlling for mental disorder comorbidity, physical health disorders, and sociodemographic factors. RESULTS: The results varied across measures of functional impairment. However, mood disorders generally tended to be associated with the greatest functional impairment, anxiety disorders with intermediate impairment, and substance use disorders with the least impairment. All 15 disorders were significantly associated with the GAF score in multiple regression models, eight disorders were significantly associated with the WHODAS score, and three disorders were significantly associated with days out of role. CONCLUSIONS: Our results highlight the value of complementary measures of functional impairment.

Comorbid mental disorders among adults in the mental health surveillance survey.

PURPOSE: To examine the prevalence and correlates of mental disorder comorbidity in the adult U.S. household population. METHODS: Data are from a nationally representative sample of noninstitutionalized, civilian adults aged 18 years or older (n = 5653) who participated in the 2008-2012 Mental Health Surveillance Study. Mental disorders, including substance use disorders, were assessed by clinical interviewers using a semistructured diagnostic instrument. Analyses examined co-occurrence of mental disorders and associations with sociodemographic, functional impairment, and treatment correlates. RESULTS: Approximately one-third of adults (31.1%, or more than 15 million) with a past-year mental disorder had a co-occurring mental disorder. Correlates of comorbidity in adjusted models included being of young age, being of non-Hispanic white race/ethnicity, having low family income, and living in a large metropolitan area. Adults with comorbid mental disorders had lower mean levels of functioning and were more likely to report past-year treatment than adults with a single disorder; they also had higher estimates of past-year perceived unmet need for care (21.7% vs. 11.6%, P < .01). CONCLUSIONS: About one in three adults with a mental disorder have a co-occurring mental disorder. Elucidating factors associated with co-occurrence may lend clues to shared etiologies, help improve prevention efforts, facilitate early identification, and improve treatment regimens.