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ObjectiveIn the United States, adult cannabis use has increased over time, but less information is available on time trends in cannabis use disorder. The authors used Veterans Health Administration (VHA) data to examine change over time in cannabis use disorder diagnoses among veterans, an important population subgroup, and whether such trends differ by age group (

ObjectivesPeople with severe mental illness are 10 times more likely to have HIV/ AIDS than the general population, yet little is known about the characteristics and frequency of recognition of pre-existing HIV/AIDS diagnoses among inpatients with severe mental illness. This study examines documentation rates of pre-existing HIV/ AIDS among inpatients within psychiatric hospitals in New York State.DesignRetrospective cohort study to examine recognition of pre-existing HIV/AIDS among psychiatric inpatients.MethodsPatient-level Medicaid claims records were linked with hospital and regional data for people admitted to psychiatric inpatient units in New York State. Presence of HIV/AIDS diagnoses prior to psychiatric hospitalization was coded for each inpatient (n = 14 602). Adjusted odds ratios of undocumented HIV/AIDS diagnoses at the time of discharge were calculated using logistic regression analyses.ResultsAbout 5.1% (741/14 602) of unique psychiatric inpatients had pre-existing HIV/AIDS diagnoses. Of these inpatients, 58.3% (432/741) were not coded as having HIV/AIDS upon discharge. Higher rates of missed detection were associated with younger age, non-Hispanic white race/ethnicity, shorter length of stay, more distal coding of an HIV/AIDS diagnosis, and fewer HIV/AIDS-related Medicaid claims in the past year. Hospitals with higher readmission rates also had higher rates of undetected HIV/AIDS diagnoses.ConclusionOver half of inpatients previously diagnosed with HIV/AIDS did not have their HIV-positive status noted upon discharge from psychiatric hospitalization. This finding underscores how frequently clinically significant medical comorbidities fail to be incorporated into psychiatric treatment and treatment planning. Inpatient clinicians are missing important opportunities to optimize HIV/AIDS treatment and reduce morbidity and mortality.

BackgroundSubstance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality.MethodsVeterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR).ResultsAmong COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment.ConclusionsIn VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.

ObjectiveThis study examined the extent to which prehospital treatment engagement is related to posthospital follow-up treatment among psychiatric inpatients and whether the effects of inpatient discharge planning on posthospital follow-up treatment vary by level of pretreatment engagement in care.MethodsNew York State Medicaid and other administrative databases were used to examine service use by 18,793 adult patients discharged to the community after inpatient psychiatric care in 2012-2013. Outcomes included attending an outpatient mental health service within 7 days and within 30 days after discharge. The sample was stratified by whether patients had high, partial, low, or no engagement in outpatient psychiatric services in the 6 months before admission.ResultsScheduling an outpatient appointment as part of the patient's discharge plan was significantly associated with attending outpatient psychiatric appointments, regardless of the patient's level of engagement in care before admission. The differences were most pronounced for patients who had not received any outpatient care in the 6 months before admission. When an appointment was scheduled, these patients were three times more likely to follow up with care within 7 days and more than twice as likely to follow up within 30 days than were patients without a scheduled appointment.ConclusionsThe likelihood of psychiatric inpatients following up with outpatient psychiatric care was directly related to their level of outpatient care engagement before hospital admission. Even among those who had not been engaged in outpatient care, inpatient discharge planning was associated with a greater likelihood of receiving follow-up outpatient care.

Glutamate neurotransmission is a prioritized target for antipsychotic drug development. Two metabotropic glutamate receptor 2/3 (mGluR2/3) agonists (pomaglumetad [POMA] and TS-134) were assessed in two Phase Ib proof of mechanism studies of comparable designs and using identical clinical assessments and pharmacoBOLD methodology. POMA was examined in a randomized controlled trial under double-blind conditions for 10-days at doses of 80 or 320 mg/d POMA versus placebo (1:1:1 ratio). The TS-134 trial was a randomized, single-blind, 6-day study of 20 or 60 mg/d TS-134 versus placebo (5:5:2 ratio). Primary outcomes were ketamine-induced changes in pharmacoBOLD in the dorsal anterior cingulate cortex (dACC) and symptoms reflected on the Brief Psychiatric Rating Scale (BPRS). Both trials were conducted contemporaneously. 95 healthy volunteers were randomized to POMA and 63 to TS-134. High-dose POMA significantly reduced ketamine-induced BPRS total symptoms within and between-groups (p

ObjectiveShortened life expectancy of people with mental disorders has been attributed to medical comorbidities, yet these conditions remain under-recognized and under-treated. This study characterizes the medical demands placed on inpatient psychiatric units to help guide medical assessment and management practices in these settings.MethodsMedicaid claims records and clinician data were linked with hospital and regional data for individuals with a principal diagnosis of any mental disorder admitted to psychiatric inpatient units in New York State from 2012 to 2013. A modified Elixhauser Comorbidity Index (ECI) score was calculated for each unique individual (n = 14,458). Adjusted odds ratios (AORs) of having a medical comorbidity were calculated using logistic regression analyses.Results74.9% of psychiatric inpatients had at least one medical comorbidity, including 57.5% of people ages 18-24. Higher rates of medical comorbidity were associated with older age, female gender, non-schizophrenia diagnoses, and engagement in care prior to hospitalization. Patients with medical comorbidities had lower odds (AOR 0.54; 99% CI 0.35-0.83) of being treated in hospitals with 100 or more total beds compared to smaller hospitals.ConclusionsA high prevalence of common medical diagnoses among psychiatric inpatients underscores the importance of adequate detection and medical treatment of medical comorbidities in psychiatric inpatient settings.

Ketamine is an uncompetitive N-methyl-d-aspartate (NMDA) glutamate receptor antagonist. It induces effects in healthy individuals that mimic symptoms associated with schizophrenia. We sought to root these experiences in altered brain function, specifically aberrant resting state functional connectivity (rsfMRI). In the present study, we acquired rsfMRI data under ketamine and placebo in a between-subjects design and analyzed seed-based measures of rsfMRI using large-scale networks, dorsolateral prefrontal cortex (DLPFC) and sub-nuclei of the thalamus. We found ketamine-induced alterations in rsfMRI connectivity similar to those seen in patients with schizophrenia, some changes that may be more comparable to early stages of schizophrenia, and other connectivity signatures seen in patients that ketamine did not recreate. We do not find any circuits from our regions of interest that correlates with positive symptoms of schizophrenia in our sample, although we find that DLPFC connectivity with ACC does correlate with a mood measure. These results provide support for ketamine's use as a model of certain biomarkers of schizophrenia, particularly for early or at-risk patients.

BackgroundThe pathophysiology of late-life depression (LLD) is complex and heterogeneous, with age-related processes implicated in its pathogenesis. This study examined the cross-sectional and longitudinal association between depressive symptoms and a baseline multibiomarker algorithm of biological age (BA) that aggregates indicators of inflammatory, metabolic, cardiovascular, lung, liver, and kidney functioning.MethodData were analyzed from 2,776 men and women from the prospective observational Health Aging and Body Composition Study, who had both evaluable chronological age (CA) and BA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale.ResultsA covariate-adjusted regression model showed that BA (B = 0.03, p = .0471) but not CA (B = -0.01, p = .7185) is associated with baseline CES-D scores. The mean baseline BA for individuals with a CES-D ≥ 10 was 1.28 years greater than in those with a CES-D < 10. Comparatively, there is only a 0.05-year difference in mean CA between the two depression groups. A covariate-adjusted longitudinal model found that baseline BA predicts CES-D score at follow-up (B = 0.04, p = .0058), whereas CA does not (B = 0.03, p = .4125). Additionally, an older BA significantly predicted a CES-D ≥ 10 (B = 0.02, p = .032) over a 10-year period.ConclusionsA multibiomarker index of an older adult's BA outperformed their CA in predicting subsequent increased and clinically significant depressive symptoms. This result supports the evolving view of LLD as a brain disorder resulting from deleterious age-associated changes across numerous physiological systems.

ImportanceDespite strong theoretical rationale and preclinical evidence, several glutamate-targeted treatments for schizophrenia have failed in recent pivotal trials, prompting questions as to target validity, compound inadequacy, or lack of target engagement. A key limitation for glutamate-based treatment development is the lack of functional target-engagement biomarkers for translation between preclinical and early-stage clinical studies. We evaluated the utility of 3 potential biomarkers-ketamine-evoked changes in the functional magnetic imaging (fMRI) blood oxygen level-dependent response (pharmacoBOLD), glutamate proton magnetic resonance spectroscopy (1H MRS), and task-based fMRI-for detecting ketamine-related alterations in brain glutamate.ObjectiveTo identify measures with sufficient effect size and cross-site reliability to serve as glutamatergic target engagement biomarkers within early-phase clinical studies.Design, setting, and participantsThis randomized clinical trial was conducted at an academic research institution between May 2014 and October 2015 as part of the National Institute of Mental Health-funded Fast-Fail Trial for Psychotic Spectrum Disorders project. All raters were blinded to study group. Healthy volunteers aged 18 to 55 years of either sex and free of significant medical or psychiatric history were recruited from 3 sites. Data were analyzed between November 2015 and December 2016.InterventionsVolunteers received either sequential ketamine (0.23 mg/kg infusion over 1 minute followed by 0.58 mg/kg/h infusion over 30 minutes and then 0.29 mg/kg/h infusion over 29 minutes) or placebo infusions.Main outcomes and measuresKetamine-induced changes in pharmacoBOLD, 1H MRS, and task-based fMRI measures, along with symptom ratings. Measures were prespecified prior to data collection.ResultsOf the 65 volunteers, 41 (63%) were male, and the mean (SD) age was 31.1 (9.6) years; 59 (91%) had at least 1 valid scan. A total of 53 volunteers (82%) completed both ketamine infusions. In pharmacoBOLD, a highly robust increase (Cohen d = 5.4; P

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out. [ABSTRACT FROM AUTHOR]