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.

Posttraumatic stress disorder symptom trajectories within the first year following emergency department admissions: pooled results from the International Consortium to predict PTSD.

BACKGROUND: Research exploring the longitudinal course of posttraumatic stress disorder (PTSD) symptoms has documented four modal trajectories (low, remitting, high, and delayed), with proportions varying across studies. Heterogeneity could be due to differences in trauma types and patient demographic characteristics. METHODS: This analysis pooled data from six longitudinal studies of adult survivors of civilian-related injuries admitted to general hospital emergency departments (EDs) in six countries (pooled N = 3083). Each study included at least three assessments of the clinician-administered PTSD scale in the first post-trauma year. Latent class growth analysis determined the proportion of participants exhibiting various PTSD symptom trajectories within and across the datasets. Multinomial logistic regression analyses examined demographic characteristics, type of event leading to the injury, and trauma history as predictors of trajectories differentiated by their initial severity and course. RESULTS: Five trajectories were found across the datasets: Low (64.5%), Remitting (16.9%), Moderate (6.7%), High (6.5%), and Delayed (5.5%). Female gender, non-white race, prior interpersonal trauma, and assaultive injuries were associated with increased risk for initial PTSD reactions. Female gender and assaultive injuries were associated with risk for membership in the Delayed (v. Low) trajectory, and lower education, prior interpersonal trauma, and assaultive injuries with risk for membership in the High (v. Remitting) trajectory. CONCLUSIONS: The results suggest that over 30% of civilian-related injury survivors admitted to EDs experience moderate-to-high levels of PTSD symptoms within the first post-trauma year, with those reporting assaultive violence at increased risk of both immediate and longer-term symptoms.

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.

Exact distribution of a maximally selected Wilcoxon and a new hybrid test of symmetry.

Recently, a maximally selected normalized Wilcoxon, whose asymptotic distribution is a Brownian Bridge, was proposed for testing symmetry of a distribution about zero. The test sequentially discards observations whose absolute value is below increasing thresholds. The Wilcoxon is obtained at each threshold, and the maximum is the test statistic. We develop a recursive function for the exact distribution of a modification of the Max Wilcoxon test (MW) and provide critical values and a program for computing the p-value for a sample. A new hybrid test that combines the sign and MW tests is introduced. The power of MW and the new hybrid test are compared with Modarres and Gastwirth's hybrid test (MGH) and the Max McNemar (MM), under the generalized lambda distributions (GLD) family and two normal mixture models. The MW and the new hybrid test outperform the MGH, which is superior to the MM test in the GLD family. In one mixture model, MM is the least powerful test and the remaining three are essentially equivalent. In the second mixture model, when the zero median assumption is nearly valid, the MW test does well; its performance degrades when this assumption is violated. In the latter case, the MM performs better than MW for the same degree of skewness because the MM simultaneously tests both symmetry and zero median. Data from a genetic study of monozygotic twins discordant for major depressive disorder is used to illustrate the new tests.

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 maximally selected test of symmetry about zero.

The problem of testing symmetry about zero has a long and rich history in the statistical literature. We introduce a new test that sequentially discards observations whose absolute value is below increasing thresholds defined by the data. McNemar's statistic is obtained at each threshold and the largest is used as the test statistic. We obtain the exact distribution of this maximally selected McNemar and provide tables of critical values and a program for computing p-values. Power is compared with the t-test, the Wilcoxon Signed Rank Test and the Sign Test. The new test, MM, is slightly less powerful than the t-test and Wilcoxon Signed Rank Test for symmetric normal distributions with nonzero medians and substantially more powerful than all three tests for asymmetric mixtures of normal random variables with or without zero medians. The motivation for this test derives from the need to appraise the safety profile of new medications. If pre and post safety measures are obtained, then under the null hypothesis, the variables are exchangeable and the distribution of their difference is symmetric about a zero median. Large pre-post differences are the major concern of a safety assessment. The discarded small observations are not particularly relevant to safety and can reduce power to detect important asymmetry. The new test was utilized on data from an on-road driving study performed to determine if a hypnotic, a drug used to promote sleep, has next day residual effects.

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.

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.

Percentage of Heavy Drinking Days Following Psilocybin-Assisted Psychotherapy vs Placebo in the Treatment of Adult Patients With Alcohol Use Disorder: A Randomized Clinical Trial.

Importance: Although classic psychedelic medications have shown promise in the treatment of alcohol use disorder (AUD), the efficacy of psilocybin remains unknown. Objective: To evaluate whether 2 administrations of high-dose psilocybin improve the percentage of heavy drinking days in patients with AUD undergoing psychotherapy relative to outcomes observed with active placebo medication and psychotherapy. Design, Setting, and Participants: In this double-blind randomized clinical trial, participants were offered 12 weeks of manualized psychotherapy and were randomly assigned to receive psilocybin vs diphenhydramine during 2 day-long medication sessions at weeks 4 and 8. Outcomes were assessed over the 32-week double-blind period following the first dose of study medication. The study was conducted at 2 academic centers in the US. Participants were recruited from the community between March 12, 2014, and March 19, 2020. Adults aged 25 to 65 years with a DSM-IV diagnosis of alcohol dependence and at least 4 heavy drinking days during the 30 days prior to screening were included. Exclusion criteria included major psychiatric and drug use disorders, hallucinogen use, medical conditions that contraindicated the study medications, use of exclusionary medications, and current treatment for AUD. Interventions: Study medications were psilocybin, 25 mg/70 kg, vs diphenhydramine, 50 mg (first session), and psilocybin, 25-40 mg/70 kg, vs diphenhydramine, 50-100 mg (second session). Psychotherapy included motivational enhancement therapy and cognitive behavioral therapy. Main Outcomes and Measures: The primary outcome was percentage of heavy drinking days, assessed using a timeline followback interview, contrasted between groups over the 32-week period following the first administration of study medication using multivariate repeated-measures analysis of variance. Results: A total of 95 participants (mean [SD] age, 46 [12] years; 42 [44.2%] female) were randomized (49 to psilocybin and 46 to diphenhydramine). One participant (1.1%) was American Indian/Alaska Native, 3 (3.2%) were Asian, 4 (4.2%) were Black, 14 (14.7%) were Hispanic, and 75 (78.9%) were non-Hispanic White. Of the 95 randomized participants, 93 received at least 1 dose of study medication and were included in the primary outcome analysis. Percentage of heavy drinking days during the 32-week double-blind period was 9.7% for the psilocybin group and 23.6% for the diphenhydramine group, a mean difference of 13.9%; (95% CI, 3.0-24.7; F1,86 = 6.43; P = .01). Mean daily alcohol consumption (number of standard drinks per day) was also lower in the psilocybin group. There were no serious adverse events among participants who received psilocybin. Conclusions and Relevance: Psilocybin administered in combination with psychotherapy produced robust decreases in percentage of heavy drinking days over and above those produced by active placebo and psychotherapy. These results provide support for further study of psilocybin-assisted treatment for AUD. Trial Registration: ClinicalTrials.gov Identifier: NCT02061293.

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).

Model-based multiplicity estimation of population size.

A survey is conducted at w of K selection units or lists, e.g. health care institutions or weeks in a year, to estimate N, the total number of individuals with particular characteristics. Our estimator utilizes two items determined for each survey participant: the number, u, among the w lists in S and the number, j, among all K lists on which each survey participant appears. In its traditional form, selection units are chosen using probability sampling and the statistical properties of the estimator derive from the sampling mechanism. Here, selection units are purposively chosen to maximize the chance that they are 'typical' and a model-based analysis is used for inference. If the sample is typical, the ML estimators of N and E(J) are unbiased. If a condition on the second moment of U/J is satisfied, the model-based variance of the estimator of N based on a purposively chosen typical sample is smaller than one based on a randomly chosen sample. Methods to test whether the typical assumption is valid using data from the survey are not yet available. The importance of proper selection of the sample to maximize the chance that it is typical and model breakdown does not occur must be emphasized.

Electronic self-administered screening for substance use in adult primary care patients: feasibility and acceptability of the tobacco, alcohol, prescription medication, and other substance use (myTAPS) screening tool.

BACKGROUND: The TAPS Tool is a substance use screening and brief assessment instrument that was developed for use in primary care medical settings. It is one of the first screening instruments to provide rapid assessment of all commonly used substance classes, including illicit and prescription opioids, and is one of the only available screeners designed and validated in an electronic self-administered format (myTAPS). This secondary analysis of data from the TAPS Tool validation study describes the feasibility and acceptability of the myTAPS among primary care patients. METHODS: Adult patients (N = 2000) from five primary care clinics completed the TAPS Tool on a tablet computer (myTAPS), and in an interviewer-administered format. Requests for assistance and time required were tracked, and participants completed a survey on ease of use, utilization of audio guidance, and format preference. Logistic regression was used to examine outcomes in defined subpopulations, including groups that may have greater difficulty completing an electronic screener, and those that may prefer an electronic self-administered approach. RESULTS: Almost all participants (98.3%) reported that the myTAPS was easy to use. The median time to complete myTAPS screening was 4.0 min (mean 4.48, standard deviation 2.57). More time was required by participants who were older, Hispanic, Black, or reported non-medical prescription drug use, while less time was required by women. Assistance was requested by 25% of participants, and was more frequently requested by those who with lower education (OR = 2.08, 95% CI 1.62-2.67) or age > 65 years (OR = 2.79, 95% CI 1.98-3.93). Audio guidance was utilized by 18.3%, and was more frequently utilized by participants with lower education (OR = 2.01, 95% CI 1.54-2.63), age > 65 years (OR = 1.79, 95% CI 1.22-2.61), or Black race (OR = 1.30, 95% 1.01-1.68). The myTAPS format was preferred by women (OR = 1.29, 95% CI 1.00-1.66) and individuals with drug use (OR = 1.43, 95% CI 1.09-1.88), while participants with lower education preferred the interviewer-administered format (OR = 2.75, 95% CI 2.00-3.78). CONCLUSIONS: Overall, myTAPS screening was feasible and well accepted by adult primary care patients. Clinics adopting electronic screening should be prepared to offer assistance to some patients, particularly those who are older or less educated, and should have the capacity to use an interviewer-administered approach when required.

Extended-release vs. oral naltrexone for alcohol dependence treatment in primary care (XON).

BACKGROUND: Extended-release naltrexone (XR-NTX, Vivitrol®) and daily oral naltrexone tablets (O-NTX) are FDA-approved mu opioid receptor antagonist medications for alcohol dependence treatment. Despite the efficacy of O-NTX, non-adherence and poor treatment retention have limited its adoption into primary care. XR-NTX is a once-a-month injectable formulation that offers a potentially more effective treatment option in reducing alcohol consumption and heavy drinking episodes among persons with alcohol use disorders. METHODS: This pragmatic, open-label, randomized controlled trial examines the effectiveness of XR-NTX vs. O-NTX in producing a Good Clinical Outcome, defined as abstinence or moderate drinking (<2 drinks/day, men; <1 drink/day, women; and < 2 heavy drinking occasions/month) during the final 20 of 24 weeks of primary care-based Medical Management treatment for alcohol dependence. Secondary aims will estimate the cost effectiveness of XR-NTX vs. O-NTX, in conjunction with primary-care based Medical Management for both groups, and patient-level characteristics associated with effectiveness in both arms. Alcohol dependent persons are recruited from the community into treatment in a New York City public hospital primary care setting (Bellevue Hospital Center) for 24 weeks of either XR-NTX (n = 117) or O-NTX (n = 120). RESULTS: We describe the rationale, specific aims, design, and recruitment results to date. Alternative design considerations and secondary aims and outcomes are reported. CONCLUSIONS: XR-NTX treatment in a primary care setting is potentially more efficacious, feasible, and cost-effective than oral naltrexone when treating community-dwelling persons with alcohol use disorders. This study will estimate XR-NTX's treatment and cost effectiveness relative to oral naltrexone.