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RAND researchers developed a model and tools to support a centralized, systematic, and ongoing process to aid the Department of Defense in making decisions around continued support and expansion of psychological health and brain injury programs.

The cowpea aphid, Aphis craccivora, is a major insect pest of cowpea in Africa. Volatile organic compounds (VOCs) mediate plant–arthropod interactions that could be used in the management of insect pests. In this study, we established the VOC profile involved in the interaction between A. craccivora and four cowpea cultivars, namely Ex‐Luanda, Katumani 80, Machakos 66 and Ken Kunde 1. Behavioural assays were conducted to study host preference and gas chromatography‐mass spectrometry (GC/MS) for chemical analysis of volatiles. In preference assays, alate A. craccivora had no significant preference for any of the four cowpea cultivars tested. However, in the olfactometer assays, the aphids showed a significant preference for odours from cultivar Ex‐Luanda compared to Katumani 80. Machakos 66 and Ken Kunde 1 elicited neutral responses. In pairwise comparisons, alate A. craccivora did not distinguish between odours of respective cowpea cultivars. GC/MS analysis identified 23 compounds in the volatiles of the four cowpea cultivars. Not all compounds were detected in all cowpea cultivars, and the detected compounds amounts varied in each cultivar. Of these, only four compounds (hexanal, (E)‐2‐hexenal, 1‐octen‐3‐ol and p‐xylene) were emitted in significantly different quantities in the four cultivars. A blend of hexanal and (E)‐2‐hexenal added to cowpea cultivar Ex‐Luanda decreased its attractiveness to A. craccivora compared to the control. Our findings showed differential attractiveness of VOCs of cowpea cultivars to A. craccivora, suggesting that VOCs could be used in the management of A. craccivora. ; Federal Ministry for Economic Cooperation and Development, Germany ; Swedish International Development Cooperation Agency ; Swiss Agency for Development and Cooperation ; Government of Kenya ; German Academic Exchange Service ; Peer Review

Supporting the mental health needs of veterans is a national priority. Over the past decade, there have been several studies describing the needs of the veteran population, particularly those who served in the post-9/11 era, calling for improved access to high-quality mental health services. In response, the federal government has expanded funding and services to meet increasing demand. At the same time, there has also been a proliferation of nongovernmental support to improve services for veterans in local communities. Often, in an attempt to deploy resources quickly, new programs and services are implemented without a full understanding of the specific needs of the population. This article discusses findings and recommendations from a study designed to gather information on the mental health–related needs facing veterans in the Detroit metropolitan area to identify gaps in the support landscape and inform future investments for community-level resources to fill the identified gaps.

Objective: The purpose of this study was to explore the interrelationship between driver distraction and characteristics of driver behavior associated with reduced highway traffic efficiency. Background: Research on the three-phase traffic theory and on behavioral driving suggests that a number of characteristics associated with efficient traffic flow may be affected by driver distraction. Previous studies have been limited, however, by the fact that researchers typically do not allow participants to change lanes, nor do they account for the impact of varying traffic states on driving performance. Methods: Participants drove in three simulated environments with differing traffic congestion while both using and not using a cell phone. Instructed only to obey the speed limit, participants were allowed to vary driving behaviors, such as those involving forward following distance, speed, and lane-changing frequency. Results: Both driver distraction and traffic congestion were found to significantly affect lane change frequency, mean speed, and the likelihood of remaining behind a slower-moving lead vehicle. Conclusions: This research suggests that the behavioral profile of "cell phone drivers," which is often described as compensatory, may have far-reaching and unexpected consequences for traffic efficiency. Application: By considering the dynamic interplay between characteristics of traffic flow and driver behavior, this research may inform both public policy regarding in-vehicle cell phone use and future investigations of driving behavior.

Introduction: Hepatitis C virus (HCV), the leading cause of advanced liver disease, has enormous economic burden. Identification of patients at risk of treatment failure could lead to interventions that improve cure rates. Objectives: Our goal was to develop and evaluate a prediction model for HCV treatment failure. Methods: We analyzed HCV patients initiating direct-acting antiviral therapy at four United States institutions. Treatment failure was determined by lack of sustained virologic response (SVR) 12 weeks after treatment completion. From 20 patient-level variables collected before treatment initiation, we identified a subset associated with treatment failure in bivariate analyses. In a derivation set, separate predictive models were developed from 100 bootstrap samples using logistic regression. From the 100 models, variables were ranked by frequency of selection as predictors to create four final candidate models, using cutoffs of ≥80%, ≥50%, ≥40%, and all variables. In a validation set, predictive performance was compared across models using area under the receiver operating characteristic curve. Results: In 1,253 HCV patients, overall SVR rate was 86.1% (95% CI = 84.1%, 88.0%). The AUCs of the four final candidate models were: ≥80% = 0.576; ≥50% = 0.605; ≥40% = 0.684; all = 0.681. The best performing model (≥40%) had significantly better predictive ability than the ≥50% (p = 0.03) and ≥80% models (p = 0.02). Strongest predictors of treatment failure were older age, history of hepatocellular carcinoma, and private (vs. government) insurance. Conclusion: This study highlighted baseline factors associated with HCV treatment failure. Treatment failure prediction may facilitate development of data-driven clinical tools to identify patients who would benefit from interventions to improve SVR rates.