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Abstract Background Adolescents with chronic medical conditions (CMC) use alcohol and marijuana at levels equal to or even greater than their peers without CMC and are more likely to initiate substance use at 14 years or younger. Approximately 33% of adolescents with CMC binge drink alcohol and 20% use marijuana. When using substances, adolescents with CMC are at elevated risk for problem use and adverse consequences given their medical conditions. Although there has recently been progress integrating substance use services into adult hospitals, there has been almost no implementation of standardized substance use services into pediatric hospitals for adolescents with CMC. Screening, Brief Intervention, and Referral to Treatment (SBIRT) for adolescents is an evidence-based, public health approach to promote the early detection and intervention of risky alcohol use in high-risk youth. This paper describes a study protocol combining two leading implementation science frameworks, the Consolidated Framework for Implementation Research (CFIR) and the Health Equity Implementation framework (HEIF), to engage pediatric hospital partners (hospital staff and clinicians, patients with CMC, and caregivers) to identify and specify contextual determinants of SBIRT implementation, which can be used to derive implementation strategies to optimize SBIRT adoption, reach, and fidelity. Method This study will use semi-structured interviews and focus groups with pediatric hospital partners (e.g., hospital staff and clinicians, adolescent patients, and caregivers) to identify SBIRT implementation determinants, using semi-structured interview and focus group guides that integrate CFIR and HEIF dimensions. Discussion Understanding implementation determinants is one of the first steps in the implementation science process. The use of two determinant frameworks highlighting a comprehensive set of determinants including health equity and justice will enable identification of barriers and facilitators that will then map on to strategies that address these factors. This study will serve as an essential precursor to further work evaluating the feasibility of and the degree of engagement with SBIRT among this vulnerable pediatric population.

Abstract Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems.

BackgroundAdolescence is a vulnerable developmental period, characterized by high rates of mental health concerns, yet few adolescents receive treatment. Public libraries support adolescents by providing them with access to teen programming, technological resources, and have recently been providing mental health services. Digital mental health (DMH) services may help libraries provide scalable mental health solutions for their adolescent patrons and could be well positioned to address the mental health needs of historically underrepresented racial and ethnic (HURE) adolescents; however, little research has been conducted on the compatibility of DMH services with adolescent patron mental health needs or resource needs of library workers supporting them.MethodsThe research team formed a partnership with a public library, which serves a large HURE adolescent population. We conducted needs assessment and implementation readiness interviews with 17 library workers, including leadership, librarians, and workers with specialized areas of practice. Interview questions focused on library infrastructure, as well as library needs and preferences around the design and implementation of DMH services for adolescents. We used the Consolidated Framework for Implementation Research as guiding implementation determinant framework to code and analyze the interview transcripts.ResultsOur findings revealed library workers play an important role in guiding patrons to desired resources and share a goal of implementing adolescent DMH resources into the library and elevating marginalized adolescents’ voices. Existing library resources, such as the library's role as a safe space for adolescents in the community, close relationships with external and community organizations, and availability of no-cost technological resources, could help facilitate the implementation of DMH services. Barriers related to community buy-in, mental health stigma, and library worker confidence in supporting adolescent mental health could affect service implementation.ConclusionsOur findings suggest public libraries are highly promising settings to deploy DMH services for adolescents. We identified important determinants that may impact the implementation of DMH services in public library settings. Special considerations are needed to design services to meet the mental health needs of HURE adolescent populations and those adolescents’ most experiencing health inequities.

BackgroundBlack boys and men from disinvested communities are disproportionately survivors and perpetrators of youth violence. Those presenting to emergency departments with firearm-related injuries also report recent substance use. However, young Black men face several critical individual and systemic barriers to accessing trauma-focused prevention programs. These barriers contribute to service avoidance, the exacerbation of violence recidivism, substance use relapse, and a revolving-door approach to prevention. In addition, young Black men are known to be digital natives. Therefore, technology-enhanced interventions offer a pragmatic and promising opportunity to mitigate these barriers, provide vital life skills for self-led behavior change, and boost service engagement with vital community resources. ObjectiveThe study aims to systematically adapt and pilot-test Boosting Violence-Related Outcomes Using Technology for Empowerment, Risk Reduction, and Life Skills Preparation in Youth Based on Acceptance and Commitment Therapy (BrotherlyACT), a culturally congruent, trauma-focused digital psychoeducational and service-engagement tool tailored to young Black men aged 15-24 years. BrotherlyACT will incorporate microlearning modules, interactive safety planning tools for risk assessment, goal-setting, mindfulness practice, and a service-engagement conversational agent or chatbot to connect young Black men to relevant services. MethodsThe development of BrotherlyACT will occur in 3 phases. In phase 1, we will qualitatively investigate barriers and facilitators influencing young Black men’s willingness to use violence and substance use prevention services with 15-30 young Black men (aged 15-24 years) who report perpetrating violence and substance use in the past year and 10 service providers (aged >18 years; any gender; including health care providers, street outreach workers, social workers, violence interrupters, community advocates, and school staff). Both groups will be recruited from community and pediatric emergency settings. In phase 2, a steering group of topic experts (n=3-5) and a youth and community advisory board comprising young Black men (n=8-12) and service providers (n=5-10) will be involved in participatory design, alpha testing, and beta testing sessions to develop, refine, and adapt BrotherlyACT based on an existing skills-based program (Achieving Change Through Values-Based Behavior). We will use user-centered design principles and the Assessment, Decision, Administration, Production, Topical, Experts, Integration, Training, and Testing framework to guide this adaptation process (phase 2). In phase 3, a total of 60 young Black men will pilot-test the adapted BrotherlyACT over 10 weeks in a single-group, pretest-posttest design to determine its feasibility and implementation outcomes. ResultsPhase 1 data collection began in September 2021. Phases 2 and 3 are scheduled to start in June 2023 and end in September 2024. ConclusionsThe development and testing of BrotherlyACT is a crucial first step in expanding an evidence-based psychoeducational and service-mediating intervention for young Black men involved in violence. This colocation of services shifts the current prevention strategy from telling them why to change to teaching them how. International Registered Report Identifier (IRRID)PRR1-10.2196/43842

BackgroundDespite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design. ObjectiveThe objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms. MethodsA total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data. ResultsAlmost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores. ConclusionsLocation and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions.

BackgroundSubstance use, particularly binge drinking of alcohol and noninjection substance use, is associated with increased risk for HIV infection among youth, but structured substance use screening and brief intervention are not often provided as part of HIV risk reduction. ObjectiveThe purpose of the study was to test the efficacy of a fully automated electronic screening and brief intervention, called Step Up, Test Up, to reduce alcohol misuse among adolescents and young adults presenting for HIV testing. Secondary objectives were reduction in sexual risk and uptake of pre-exposure prophylaxis (PrEP) for HIV prevention. MethodsYouth aged 16 years to 25 years who presented for HIV testing at community-based locations were recruited for study participation. Those who screened at moderate to high risk on the Alcohol Use Disorders Identification Test were randomized (1:1) to either an electronic brief intervention or a time-attention control. The primary outcome was change in alcohol use at 1, 3, 6, and 12-month follow-ups. Negative binomial and log binomial regression analyses with generalized estimating equations were conducted to evaluate the intervention efficacy. ResultsAmong a sample of 329 youth, there were no significant differences in alcohol use outcomes between conditions over time or at the 1, 3, 6, or 12-month time points. In terms of secondary outcomes, there was evidence of reduction in condomless insertive anal sex under the influence of alcohol and drugs at 12 months compared with 3 months in the intervention versus the attention control condition (incidence rate ratio=0.15, 95% CI 0.05-0.44); however, there were no other significant differences in sexual risk and no difference in PrEP engagement. ConclusionsWe found no effect of electronic brief intervention to reduce alcohol use and some effect on sexual risk among youth aged 16 years to 25 years who present for HIV testing. Trial RegistrationClinicalTrials.gov number NCT02703116; https://clinicaltrials.gov/ct2/show/NCT02703116 International Registered Report Identifier (IRRID)RR2-10.1186/s12889-020-8154-6

BackgroundThe COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. ObjectiveWe applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. MethodsThis retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. ResultsTopic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier’s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). ConclusionsAmong patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Jatropha is a non-edible, important bioenergy plant, which can grow in marginalized land. The seeds possess about 36% oil and this would be converted into biodiesel or biojet-fuel. Jatropha provides an option for sustainable feed and fuel production due to its inherent qualities including hardy nature, drought tolerance and surviving with limited amount of water, tolerance to unfavorable conditions and excessive moisture. However, heterozygosity, low productivity and poor understanding of its genome are the major impediments to elite line development. Further, classical breeding and advanced technological investments remain limited owing to long juvenile phase and breeding cycles. Scientific technologies that lead to identification of elite genotypes and development of high yielding elite Jatropha lines and effective methods of detoxification of seeds needs immediate priority. Efficient tissue culture system, doubled haploids (DH) and genomic tools are increasingly made available to improve the seed yield and its oil quantity through the development of geminivirus disease resistant lines. The application of advanced, sequencing technologies has presented a repertoire of genomic information for this important yet orphan crop. In the present investigation, we highlight the achievements made in Jatropha towards development of high yielding, virus resistant elite lines and hybrids with yield potential ranging from 3 to 5 tons per hectare in a year, which is a first ever report in the world. We also developed potential biotechnological tools such as genetic transformation, genome editing and next generation genomics tools including linkage maps and QTLs for accelerating breeding efforts through marker assisted selection. Because of our concerted and continuous efforts during the past 15 years, we have overcome all the obstacles and developed high yielding, disease resistant hybrids/lines, advanced cultivation technologies with thorough knowledge on Agronomy of the Jatropha crop. The Jatropha cultivation technology developed for the first time in the world, could open up new avenues for higher yield productivity of commercial viability.

Abstract Background Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. Methods An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. Results Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99–0.99) across the encounter and 0.98 (95% CI 0.98–0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77–0.84) and 0.72 (95% CI 0.68–0.75). For the first 24 h, they were 0.75 (95% CI 0.71–0.78) and 0.61 (95% CI 0.57–0.64). Conclusions Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

Abstract Background A lack of consensus on the optimal outcome measures to assess the efficacy and effectiveness of interventions for the treatment of opioid use disorder (OUD) has hampered the pooling of research data for evidence synthesis and clinical guidelines. A core outcome set (COS) is a minimum set of outcome measures that are recommended for all studies of a particular condition. The National Drug Abuse Treatment Clinical Trials Network (CTN) Core Outcome Set for OUD (COS-OUD) is a development study to identify core constructs, meaningful outcomes, and their optimal measurement for all efficacy and effectiveness studies of OUD treatment and service delivery. Methods/design Overseen by an expert workgroup, a modified, stepwise, e-Delphi methodology will be used to gain consensus among a panel of clinical practitioners and researchers involved in the treatment of OUD, who are members of the CTN. Sequential rounds of anonymous, online questionnaires will be used to identify, rate the importance of, and refine a core outcome set. A consensus threshold will be achieved if at least 70% of the panel rate the measure as critical for inclusion in the COS-OUD. Where consensus is not reached or there are suggestions for new measures, these will be brought forward to a further round of review prior to a consensus meeting. Products from this study will be communicated via peer-reviewed scientific journals and conferences. Discussion This initiative will develop a COS for OUD intervention trials, treatment studies, and service delivery and will support the pooling of research and clinical practice data and efforts to develop measurement-based care within the OUD treatment cascade. Trial registration http://www.comet-initiative.org/Studies/Details/1579

Abstract Background Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. Methods An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration. Results Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUI-based convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms ‘Heroin’ and ‘Victim of abuse’. Conclusions We demonstrate good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI. Herein we share a PHI-free, trained opioid misuse classifier for other researchers and health systems to use and benchmark to overcome privacy and security concerns.

Abstract Background Rates of homelessness have been increasing in recent years, thereby necessitating a more direct approach to treating this complex social problem. Homeless youth have disproportionately high rates of untreated mental health problems and are therefore particularly vulnerable to the effects of homelessness during the transition period from adolescence to adulthood. Methods The study team developed a shelter-based clinic and collected clinical measures on youth who attended this clinic from October 2016 through June 2018. Results Youth attended an average number of three sessions, but there was a significant drop in follow-up after the first (intake) appointment. Depression, anger, and adjustment disorder emerged as the most common presenting mental health concerns identified by clinicians in the intake appointment, and trauma was identified as a significant complaint for those youth who returned for a second session. Conclusion Mental health care is needed in this population, but future studies should explore alternative approaches to retaining homeless youth in treatment and in designing targeted trauma-informed interventions.

Abstract Background Sexually exploited youth are disconnected from societal tethers and need support systems, which makes them some of the most vulnerable of youth. This heightened level of vulnerability increases their risk for violence, abuse, ongoing sexual exploitation and all its sequelae. The purpose of this study was to examine an evidence-based intervention called STRIVE (support to reunite, involve and value each other) that has been a successful family re-engagement strategy with newly homeless youth. We sought to explore its contextual relevance for youth with risk factors for sexual exploitation and identify necessary adaptations to reduce risk factors for sexual exploitation. We deliberately took an intersectional approach in conducting this study. Methods Six community-based focus groups were conducted with youth at risk for sexual exploitation and their service providers. Each group was recorded, transcribed, coded, and thematically analyzed. Results Results from 29 youth and 11 providers indicate that there are unique considerations that must be taken into account while working with youth at risk for sexual exploitation to ensure effective service delivery and/or ethical research. Emergent themes included: setting the stage by building rapport and acknowledging experiences of structural violence, protect and hold which balances youth’s need for advocacy/support with their caregivers’ need for validation/understanding, and walking the safety tightrope by assessing risks and safety planning. Discussion Focus groups are an effective methodology when working with traditionally disempowered populations particularly in gaining a range of perspectives to meet unique needs/preferences. Youth at risk for commercial sexual exploitation needs require strengths-based, individualized, multi-systemic approaches.

The aim of this paper is to analyze the queuing model entitled to cost optimization in bulk arrival and a batch service retrial queuing system with threshold, server failure, non-disruptive service, and multiple vacations. When bulk arrival of customers find the server is busy, then all customers will join in the orbit. On the other hand, if the server is free, then batch service will be provided according to the general bulk service rule. Batch size varies from a minimum of one and a maximum of ‘b’ number of customers. Customers in the orbit seek service one by one through constant retrial policy whenever the server is in idle state. The server may encounter failure during service. If the server fails, then ‘renewal of service station’ will be considered with probability . If there is no server failure with probability in the service completion or after the renewal process and if the orbit is empty, the server then leaves for multiple vacations. The server stays on vacation until the orbit size reaches the value N. For this proposed queuing model, a probability generating function of the orbit size will be obtained by using the supplementary variable technique and various performance measures will be presented with suitable numerical illustrations. A real time application is also discussed for this system. Additionally, a cost effective model is developed for this queuing model.

Background: Basal cell carcinoma (BCC) is a common malignancy among light-skin races. Chronic solar exposure is the most important precipitating factor. In India, BCC is the second in order among the nonmelanoma skin cancers, the first being squamous cell carcinoma. Actual incidence and prevalence of this tumor in various geographical regions of India remain undefined. Aim: The aim is to study the demographic, clinical, and histopathological parameters of the patients suffering from BCC residing in a rural area of northern Karnataka. Materials and Methods: Retrospective data analysis of the patients suffering from BCC was performed from the records of dermatology outpatients' department of a tertiary healthcare center in north Karnataka. Results: A total of 41 cases of BCC were examined during 12 years. The duration of the lesions varied from 6 months to 20 years. Female patients outnumbered males in a ratio of 3.36:1. Majority of the patients were homemakers and agricultural workers. Nodular, noduloulcerative, and pigmented BCC were the most common clinical pattern and 9 (28.1%) cases of morpheaform BCC were recorded. Four patients had genodermatoses; three were xeroderma pigmentosum and one case of nevoid BCC syndrome. These patients had multiple lesions of BCC. Classical histopathological features were recorded in all cases. One specimen showed evidence of metatypical epithelioma. Conclusion: Most of the patients in this series belonged to the rural part of North Karnataka, predominantly homemakers and agricultural workers. Females were more commonly affected, attributable to the intermittent type of solar exposure. Morpheaform BCC accounted for less than a quarter of cases, and multiple BCCs associated with genodermatoses were seen in four cases.

Background: Intensive treatment programmes (ITPs) for posttraumatic stress disorder (PTSD) produce large symptom reductions and have generally higher completion rates compared to traditional weekly care. Although ITPs do not appear to increase substance use, it has yet to be determined whether their effectiveness differs for veterans with and without hazardous alcohol use (HAU). Objective: This study examined the effectiveness of a 3-week Cognitive Processing Therapy-based ITP for 538 veterans with PTSD (66.0% male; mean age = 41.22 years) and with (n = 193) or without HAU (n = 343) for reducing PTSD and depression symptoms. Method: Veterans’ PTSD (PCL-5) and depression (PHQ-9) symptoms were assessed at pre-treatment, during treatment, and at post-treatment. HAU (AUDIT-C total score ≥4 for males; ≥3 for females) was measured at intake. Results: Treatment completion rates were high for both individuals who endorsed HAU (92.68%) and those who did not (93.37%), likely due to veterans being housed near the treatment facility. Mixed effects regression models revealed a significant time by alcohol use interaction when predicting both PCL-5 (p

BackgroundAddiction medicine consultation services (ACS) may improve outcomes of hospitalized patients with substance use disorders (SUD). Our aim was to examine the difference in length of stay and the hazard ratio for a routine hospital discharge between SUD patients receiving and not receiving ACS.MethodsStructured EHR data from 2018 of 1,900 adult patients with a SUD-related diagnostic code at an urban academic health center were examined among 35,541 total encounters. Cox proportional hazards regression models were fit using a cause-specific approach to examine differences in hospital outcome (i.e., routine discharge, leaving against medical advice, in-hospital death, or transfer to another level of care). Models were adjusted for age, sex, race, ethnicity, insurance status, and comorbidities.ResultsLength of stay was shorter among encounters with a SUD that received a SUIT consultation versus those admissions that did not receive one (5.77 v. 6.54 days, pConclusionsThe SUIT consultation service was associated with a reduced length of stay and an increased hazard of a routine discharge. The SUIT model may serve as a benchmark and inform other health systems attempting to improve outcomes in SUD patient cohorts.

Abstract Background Intensive delivery of evidence-based treatment for posttraumatic stress disorder (PTSD) is becoming increasingly popular for overcoming barriers to treatment for veterans. Understanding how and for whom these intensive treatments work is critical for optimizing their dissemination. The goals of the current study were to evaluate patterns of PTSD and depression symptom change over the course of a 3-week cohort-based intensive outpatient program (IOP) for veterans with PTSD, examine changes in posttraumatic cognitions as a predictor of treatment response, and determine whether patterns of treatment outcome or predictors of treatment outcome differed by sex and cohort type (combat versus military sexual trauma [MST]). Method One-hundred ninety-one veterans (19 cohorts: 12 combat-PTSD cohorts, 7 MST-PTSD cohorts) completed a 3-week intensive outpatient program for PTSD comprised of daily group and individual Cognitive Processing Therapy (CPT), mindfulness, yoga, and psychoeducation. Measures of PTSD symptoms, depression symptoms, and posttraumatic cognitions were collected before the intervention, after the intervention, and approximately every other day during the intervention. Results Pre-post analyses for completers (N = 176; 92.1% of sample) revealed large reductions in PTSD (d = 1.12 for past month symptoms and d = 1.40 for past week symptoms) and depression symptoms (d = 1.04 for past 2 weeks). Combat cohorts saw a greater reduction in PTSD symptoms over time relative to MST cohorts. Reduction in posttraumatic cognitions over time significantly predicted decreases in PTSD and depression symptom scores, which remained robust to adjustment for autocorrelation. Conclusion Intensive treatment programs are a promising approach for delivering evidence-based interventions to produce rapid treatment response and high rates of retention. Reductions in posttraumatic cognitions appear to be an important predictor of response to intensive treatment. Further research is needed to explore differences in intensive treatment response for veterans with combat exposure versus MST.

BackgroundApproaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes.MethodsThis was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes.FindingsThe analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, pConclusionsA 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions.

Monodispersed CuO octahedral crystals were successfully synthesized using a low-temperature co-precipitation method. Zinc doping in CuO created surface defects that enhanced oxygen adsorption on the surface crucial for gas sensing applications. Pure and Zn-doped CuO sensor films were realized using the doctor blade method. The sensor films showed selective response towards a low concentration of NO2 at a lower operating temperature of 150 °C. Doping with Zn causes the resistance of the sensor film to decrease due to the enhancement of charge carriers with an analogous improvement in the sensor response. The observed decrease in sensor resistance agreed well with the findings of the work function studies. Zinc doping resulted in an increase in work function by 180 meV which, after NO2 exposure, was found to increase by a further 130 meV, attributed to the oxidizing behavior of the test gas.

Youth homelessness is a growing problem in the United States. The experience of homelessness appears to have numerous adverse consequences, including psychiatric and substance use disorders. This study compared the frequencies of psychiatric disorders, including substance use, between homeless youth (18–24 years-old) in San Francisco (N = 31) and Chicago (N = 56). Subjects were administered the Mini International Neuropsychiatric Interview (M.I.N.I.) to assess DSM-IV-TR diagnoses and substance use disorders. Eighty-seven percent of the San Francisco youth, and 81% of the Chicago youth met criteria for at least one M.I.N.I. psychiatric diagnosis. Nearly two-thirds of the youth in both samples met criteria for a mood disorder. Approximately one-third met criteria for an anxiety disorder. Thirty-two percent of the San Francisco sample and 18% of the Chicago met criteria for Antisocial Personality Disorder. Approximately 84% of the San Francisco youth and 48% of the Chicago youth met criteria for a substance-related disorder, and more substances were used by San Francisco youth. In conclusion, the high rate of psychiatric disorders in homeless youth provides clear evidence that the mental health needs of this population are significant. Implications are discussed.

BackgroundDespite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design.ObjectiveThe objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms.MethodsA total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data.ResultsAlmost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores.ConclusionsLocation and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions.