Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Gabapentin Enacarbil Extended-Release Versus Placebo: A Likely Responder Reanalysis of a Randomized Clinical Trial.

BACKGROUND: We reanalyzed a multisite 26-week randomized double-blind placebo-controlled clinical trial of 600 mg twice-a-day Gabapentin Enacarbil Extended-Release (GE-XR), a gabapentin prodrug, designed to evaluate safety and efficacy for treating alcohol use disorder. In the original analysis (n = 338), published in 2019, GE-XR did not differ from placebo. Our aim is to advance precision medicine by identifying likely responders to GE-XR from the trial data and to determine for likely responders if GE-XR is causally superior to placebo. METHODS: The primary outcome measure in the reanalysis is the reduction from baseline of the number of heavy drinking days (ΔHDD). Baseline features including measures of alcohol use, anxiety, depression, mood states, sleep, and impulsivity were used in a random forest (RF) model to predict ΔHDD to treatment with GE-XR based on those assigned to GE-XR. The resulting RF model was used to obtain predicted outcomes for those randomized to GE-XR and counterfactually to those randomized to placebo. Likely responders to GE-XR were defined as those predicted to have a reduction of 14 days or more. Tests of causal superiority of GE-XR to placebo were obtained for likely responders and for the whole sample. RESULTS: For likely responders, GE-XR was causally superior to placebo (p < 0.0033), while for the whole sample, there was no difference. Likely responders exhibited improved outcomes for the related outcomes of percent HDD and drinks per week. Compared with unlikely responders, at baseline likely responders had higher HDDs; lower levels of anxiety, depression, and general mood disturbances; and higher levels of cognitive and motor impulsivity. CONCLUSIONS: There are substantial causal benefits of treatment with GE-XR for a subset of patients predicted to be likely responders. The likely responder statistical paradigm is a promising approach for analyzing randomized clinical trials to advance personalized treatment.

Speech-based markers for posttraumatic stress disorder in US veterans.

BACKGROUND: The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls. METHODS: Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm. RESULTS: The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders. CONCLUSIONS: This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.

Outcomes of The BODY Project: A Program to Halt Obesity and Its Medical Consequences in High School Students.

Adolescent obesity continues to be a major public health issue with a third of American adolescents being overweight or obese. Excess weight is associated with cardiovascular risk factors and pre-diabetes. High school students identified as carrying excess weight [body mass index (BMI) ≥25 kg/m(2), or BMI percentile ≥85 %] were invited to participate in The BODY Project, an intervention that included a medical evaluation and a personalized medical report of the results of that evaluation sent to the parent/guardian at home. The medical evaluation and report was repeated 12 months later. The reports also contained advice on how the individual student could modify their lifestyle to improve the specific medical parameters showing abnormalities. Outcomes were change in BMI, blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), fasting glucose, and fasting insulin. Students participating in The BODY Project intervention demonstrated modest, yet significant, reductions in BMI (p < 0.001) 1 year later, and also had significant improvements in systolic blood pressure (p < 0.001) and cholesterol profile (HDL p = 0.002; LDL p < 0.001) at follow-up. The BODY Project, by means of a minimal educational program anchored on the principle of teachable moments around the students' increased perception of their own risk for disease from the medical abnormalities uncovered, demonstrates evidence of potential effectiveness in addressing adolescent obesity.

A treeless absolutely random forest with closed-form estimators of expected proximities.

We introduce a simple variant of a Purely Random Forest, an Absolute Random Forest (ARF) for clustering. At every node splits of units are determined by a randomly chosen feature and a random threshold drawn from a uniform distribution whose support, the range of the selected feature in the root node, does not change. This enables closed-form estimators of parameters, such as pairwise proximities, to be obtained without having to grow a forest. The probabilistic structure corresponding to an ARF is called a Treeless Absolute Random Forest (TARF). With high probability, the algorithm will split units whose feature vectors are far apart and keep together units whose feature vectors are similar. Thus, the underlying structure of the data drives the growth of the tree. The expected value of pairwise proximities is obtained for three pathway functions. One, a completely common pathway function, is an indicator of whether a pair of units follow the same path from the root to the leaf node. The properties of TARF-based proximity estimators for clustering and classification are compared to other methods in eight real-world data sets and in simulations. Results show substantial performance and computing efficiencies of particular value for large data sets.

Quality Improvement Framework to Examine Health Care Disparities in Behavioral Emergency Management in the Inpatient Medical Setting: A Consultation-Liaison Psychiatry Health Equity Project.

BACKGROUND: De-escalation of behavioral emergencies in the inpatient medical setting may involve restrictive clinical interventions that directly challenge patient autonomy. OBJECTIVE: We describe a quality improvement framework used to examine associations between patient characteristics and behavioral emergency de-escalation strategies. This project may inform other Consultation-Liaison Psychiatry teams seeking to promote equity in care. METHODS: We examined behavioral emergency response team (BERT) management at an urban, tertiary-care medical center in the United States over a 3-year period. BERT data from an existing dataset were combined with demographic information from the hospital's electronic medical record. Race and ethnic identities were categorized as Black, Hispanic, Asian, White, and unknown. BERT events were coded based on the most restrictive intervention utilized per unique patient. Cross-tabulations and adjusted odds ratios from multivariate logistic regression were used to identify quality improvement targets in this exploratory project. RESULTS: The sample included N = 902 patients and 1532 BERT events. The most frequent intervention reached was verbal de-escalation (n = 419 patients, 46.45%) and the least frequent was 4-point restraints (n = 29 patients, 3.2%). Half of BERT activations for Asian and a third for Hispanic patients required interpreter services. Anxiety and cognitive disorders and 2 BERT interventions, verbal de-escalation, and intramuscular/intravenous/ medications, were significantly associated with race/ethnic category. The most restrictive intervention for BERTs involving Black and Asian patients were verbal de-escalation (60.1%) and intramuscular/intravenous(53.7%), respectively. These proportions were higher compared with other race/ethnic groups. There was a greater percentage of patients from the unknown (6.3%) and Black (5.9%) race/ethnic groups placed in 4-point restraints compared with other groups (3.2%) that did not reach statistical significance. A logistic regression model predicting 4-point restraints indicated that younger age, multiple BERTs, and violent behavior as a reason for BERT activation, but not race/ethnic group, resulted in significantly higher odds. CONCLUSIONS: This project illustrates that a quality improvement framework utilizing existing clinical data can be used to engage in organizational introspection and identify potential areas of bias in BERT management. Our findings suggest opportunities for further exploration, enhanced education, and programmatic improvements regarding BERT intervention; 4-point restraints; interpreter services; and the influence of race on perception of psychopathology.

Screening for PTSD and TBI in Veterans using Routine Clinical Laboratory Blood Tests.

Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

A General Iterative Clustering Algorithm.

The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. A RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a General Iterative Cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the Extremely Randomized Tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette Score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.

A likely responder approach for the analysis of randomized controlled trials.

OBJECTIVE: To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS: Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS: A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION: The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.

The Nathan Kline Institute cultural competency assessment scale: psychometrics and implications for disparity reduction.

The NKI Cultural Competency Assessment Scale measures organizational CC in mental health outpatient settings. We describe its development and results of tests of its psychometric properties. When tested in 27 public mental health settings, factor analysis discerned three factors explaining 65% of the variance; each factor related to a stage of implementation of CC. Construct validity and inter-rater reliability were satisfactory. In tests of predictive validity, higher scores on items related to linguistic and service accommodations predicted a reduction in service disparities for engagement and retention outcomes for Hispanics. Disparities for Blacks essentially persisted independent of CC scores.

CRF serum levels differentiate PTSD from healthy controls and TBI in military veterans.

BACKGROUND AND OBJECTIVE: Posttraumatic stress disorder (PTSD) is a serious and frequently debilitating psychiatric condition that can occur in people who have experienced traumatic stessors, such as war, violence, sexual assault and other life-threatening events. Treatment of PTSD and traumatic brain injury (TBI) in veterans is challenged by diagnostic complexity, partially due to PTSD and TBI symptom overlap and to the fact that subjective self-report assessments may be influenced by a patient's willingness to share their traumatic experiences and resulting symptoms. Corticotropin-releasing factor (CRF) is one of the main mediators of hypothalamic pituitary adrenal (HPA)-axis responses in stress and anxiety. METHODS AND RESULTS: We analyzed serum CRF levels in 230 participants including heathy controls (64), and individuals with PTSD (53), TBI (70) or PTSD+TBI (43) by enzyme immunoassay (EIA). Significantly lower CRF levels were found in both the PTSD and PTSD+TBI groups compared to healthy control (PTSD vs Controls: P=0.0014, PTSD + TBI vs Controls: P=0.0011) and chronic TBI participants (PTSD vs TBI: P<0.0001PTSD + TBI vs TBI: P<0.0001) , suggesting a PTSD-related mechanism independent from TBI and associated with CRF reduction. CRF levels negatively correlated with PTSD severity on the CAPS-5 scale in the whole study group. CONCLUSIONS: Hyperactivation of the HPA axis has been classically identified in acute stress. However, the recognized enhanced feedback inhibition of the HPA axis in chronic stress supports our findings of lower CRF in PTSD patients. This study suggests that reduced serum CRF in PTSD should be further investigated. Future validation studies will establish if CRF is a possible blood biomarker for PTSD and/or for differentiating PTSD and chronic TBI symptomatology.

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Estimating treated prevalence and service utilization rates: assessing disparities in mental health.

There is considerable public concern about health disparities among different cultural/racial/ethnic groups. Important process measures that might reflect inequities are treated prevalence and the service utilization rate in a defined period of time. We have previously described a method for estimating N, the distinct number who received service in a year, from a survey of service users at a single point in time. The estimator is based on the random variable 'time since last service', which enables the estimation of treated prevalence. We show that this same data can be used to estimate the service utilization rate, E(J), the mean number of services in the year. If the sample is typical with respect to the time since last visit, the MLE of E(J) is asymptotically unbiased. Confidence intervals and a global test of equality of treated prevalence and service utilization rates among several groups are given. A data set of outpatient mental health services from a county in New York State for which the true values of the parameters are known is analyzed as an illustration of the methods and an appraisal of their accuracy.

Predeployment neurocognitive functioning predicts postdeployment posttraumatic stress in Army personnel.

OBJECTIVE: The Fort Campbell Cohort study was designed to assess predeployment biological and behavioral markers and build predictive models to identify risk and resilience for posttraumatic stress disorder (PTSD) following deployment. This article addresses neurocognitive functioning variables as potential prospective predictors. METHOD: In a sample of 403 soldiers, we examined whether PTSD symptom severity (using the PTSD Checklist) as well as posttraumatic stress trajectories could be prospectively predicted by measures of executive functioning (using two web-based tasks from WebNeuro) assessed predeployment. RESULTS: Controlling for age, gender, education, prior number of deployments, childhood trauma exposure, and PTSD symptom severity at Phase 1, linear regression models revealed that predeployment sustained attention and inhibitory control performance were significantly associated with postdeployment PTSD symptom severity. We also identified two posttraumatic stress trajectories utilizing latent growth mixture models. The "resilient" group consisted of 90.9% of the soldiers who exhibited stable low levels of PTSD symptoms from pre- to postdeployment. The "increasing" group consisted of 9.1% of the soldiers, who exhibited an increase in PTSD symptoms following deployment, crossing a threshold for diagnosis based on PTSD Checklist scores. Logistic regression models predicting trajectory revealed a similar pattern of findings as the linear regression models, in which predeployment sustained attention (95% CI of odds ratio: 1.0109, 1.0558) and inhibitory control (95% CI: 1.0011, 1.0074) performance were significantly associated with postdeployment PTSD trajectory. CONCLUSIONS: These findings have clinical implications for understanding the pathogenesis of PTSD and building preventative programs for military personnel. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Trends in the inpatient mental health treatment of children and adolescents in US community hospitals between 1990 and 2000.

CONTEXT: Previous work has demonstrated marked changes in inpatient mental health service use by children and adolescents in the 1980s and early 1990s, but more recent, comprehensive, nationally representative data have not been reported. OBJECTIVE: To describe trends in inpatient treatment of children and adolescents with mental disorders between 1990 and 2000. DESIGN AND SETTING: Analysis of the Healthcare Cost and Utilization Project Nationwide Inpatient Sample, a nationally representative sample of discharges from US community hospitals sponsored by the Agency for Healthcare Research and Quality. PATIENTS: Patients aged 17 years and younger discharged from US community hospitals with a principal diagnosis of a mental disorder. MAIN OUTCOME MEASURES: Changes in the number and population-based rate of discharges, total inpatient days and average length of stay, charges, diagnoses, dispositions, and patient demographic and hospital characteristics. RESULTS: Although the total number of discharges, population-based discharge rate, and daily charges did not significantly change between 1990 and 2000, the total number of inpatient days and mean charges per visit each fell by approximately one half. Median length of stay declined 63% over the decade from 12.2 days to 4.5 days. Declines in median and mean lengths of stay were observed for most diagnostic categories and remained significant after controlling for changes in background patient and hospital characteristics. Discharge rates for psychotic and mood disorders as well as intentional self-injuries increased while rates for adjustment disorders fell. Discharges to short-term, nursing, and other inpatient facilities declined. CONCLUSIONS: The period between 1990 and 2000 was characterized by a transformation in the length of inpatient mental health treatment for young people. Community hospitals evaluated, treated, and discharged mentally ill children and adolescents far more quickly than 10 years earlier despite higher apparent rates of serious illness and self-harm and fewer transfers to intermediate and inpatient care.

The nonlinear relationship between cerebrospinal fluid Aß42 and tau in preclinical Alzheimer's disease.

Cerebrospinal fluid (CSF) studies consistently show that CSF levels of amyloid-beta 1-42 (Aß42) are reduced and tau levels increased prior to the onset of cognitive decline related to Alzheimer's disease (AD). However, the preclinical prediction accuracy for low CSF Aß42 levels, a surrogate for brain Aß42 deposits, is not high. Moreover, the pathology data suggests a course initiated by tauopathy contradicting the contemporary clinical view of an Aß initiated cascade. CSF Aß42 and tau data from 3 normal aging cohorts (45-90 years) were combined to test both cross-sectional (n = 766) and longitudinal (n = 651) hypotheses: 1) that the relationship between CSF levels of Aß42 and tau are not linear over the adult life-span; and 2) that non-linear models improve the prediction of cognitive decline. Supporting the hypotheses, the results showed that a u-shaped quadratic fit (Aß2) best describes the relationship for CSF Aß42 with CSF tau levels. Furthermore we found that the relationship between Aß42 and tau changes with age-between 45 and 70 years there is a positive linear association, whereas between 71 and 90 years there is a negative linear association between Aß42 and tau. The quadratic effect appears to be unique to Aß42, as Aß38 and Aß40 showed only positive linear relationships with age and CSF tau. Importantly, we observed the prediction of cognitive decline was improved by considering both high and low levels of Aß42. Overall, these data suggest an earlier preclinical stage than currently appreciated, marked by CSF elevations in tau and accompanied by either elevations or reductions in Aß42. Future studies are needed to examine potential mechanisms such as failing CSF clearance as a common factor elevating CSF Aßxx analyte levels prior to Aß42 deposition in brain.

Making the cut: Depression screening in urban general hospital clinics for culturally diverse Latino populations.

OBJECTIVES: We examined whether the cut-point 10 for the Patient Health Questionnaire-9 (PHQ9) depression screen used in primary care populations is equally valid for Mexicans (M), Ecuadorians (E), Puerto Ricans (PR) and non-Hispanic whites (W) from inner-city hospital-based primary care clinics; and whether stressful life events elevate scores and the probability of major depressive disorder (MDD). METHODS: Over 18-months, a sample of persons from hospital clinics with a positive initial PHQ2 and a subsequent PHQ9 were administered a stressful life event questionnaire and a Structured Clinical Interview to establish an MDD diagnosis, with oversampling of those between 8 and 12: (n=261: 75 E, 71 M, 51 PR, 64 W). For analysis, the sample was weighted using chart review (n=368) to represent a typical clinic population. Receiver Operating Characteristics analysis selected cut-points maximizing sensitivity (Sn) plus specificity (Sp). RESULTS: The optimal cut-point for all groups was 13 with the corresponding Sn and Sp estimates for E=(Sn 73%, Sp 71%), M=(76%, 81%), PR=(81%, 63%) and W=(80%, 74%). Stressful life events impacted screen scores and MDD diagnosis. CONCLUSIONS: Elevating the PHQ9 cut-point for inner-city Latinos as well as whites is suggested to avoid high false positive rates leading to improper treatment with clinical and economic consequences.

Tenant outcomes in supported housing and community residences in New York City.

OBJECTIVE: This study examined whether outcomes in housing, clinical status, and well-being of persons with severe mental illness and a history of homelessness differ between those in supported housing and those in community residences, two housing arrangements that substantially differ in the level of independence that is offered to its tenants. METHODS: A quasi-experimental 18-month follow-up study was conducted with 157 persons newly entering supported housing and community residences. The housing models accepted persons with similar illness characteristics and homelessness histories, so that the inability to randomly assign tenants to housing types could be compensated for by propensity scoring methods. Tenure in housing was examined by using survival models. Analyses of other outcomes used hierarchical linear and regression models in both intent-to-treat (N=139) and true-stayer (N=80) analyses. RESULTS: Tenure in housing did not differ by housing type. Substantial proportions of tenants in both models remained housed during the follow-up period. Tenants in supported housing reported greater housing satisfaction in terms of autonomy and economic viability. Over time some tenants in supported housing reported greater feelings of isolation. Independent of housing type, symptoms of depression or anxiety at housing entry increased the risk of poorer outcomes. CONCLUSIONS: The models of supported housing were viable portals of entry into community housing for homeless persons, even for consumers with characteristics indicating that they would have been more likely to be placed in community residences. The results suggest that greater clinical attention should be paid to persons who exhibit depression or anxiety when entering housing.