Evidence Based Mental Health最新文献

{"title":"Prevalences of comorbid anxiety disorder and daily smartphone-based self-reported anxiety in patients with newly diagnosed bipolar disorder.","authors":"Sharleny Stanislaus,&nbsp;Klara Coello,&nbsp;Hanne Lie Kjærstad,&nbsp;Kimie Stefanie Ormstrup Sletved,&nbsp;Ida Seeberg,&nbsp;Mads Frost,&nbsp;Jakob Eyvind Bardram,&nbsp;Rasmus Nejst Jensen,&nbsp;Maj Vinberg,&nbsp;Maria Faurholt-Jepsen,&nbsp;Lars Vedel Kessing","doi":"10.1136/ebmental-2021-300259","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300259","url":null,"abstract":"<p><strong>Background: </strong>Around 40% of patients with bipolar disorder (BD) additionally have anxiety disorder. The prevalence of anxiety in patients with newly diagnosed BD and their first-degree relatives (UR) has not been investigated.ObjectiveTo investigate (1) the prevalence of a comorbid anxiety diagnosis in patients with newly diagnosed BD and their UR, (2) sociodemographic and clinical differences between patients with and without a comorbid anxiety diagnosis and (3) the association between smartphone-based patient-reported anxiety and observer-based ratings of anxiety and functioning, respectively.</p><p><strong>Methods: </strong>We recruited 372 patients with BD and 116 of their UR. Daily smartphone-based data were provided from 125 patients. SCAN was used to assess comorbid anxiety diagnoses.</p><p><strong>Findings: </strong>In patients with BD, the prevalence of a comorbid anxiety disorder was 11.3% (N=42) and 10.3% and 5.9% in partial and full remission, respectively. In UR, the prevalence was 6.9%. Patients with a comorbid anxiety disorder had longer illness duration (p=0.016) and higher number of affective episodes (p=0.011). Smartphone-based patient-reported anxiety symptoms were associated with ratings of anxiety and impaired functioning (p<0.001).</p><p><strong>Limitations: </strong>The SCAN interviews to diagnose comorbid anxiety disorder were carried out regardless of the participants' mood state.Clinical implicationsThe lower prevalence of anxiety in newly diagnosed BD than in later stages of BD indicates that anxiety increases with progression of BD. Comorbid anxiety seems associated with poorer clinical outcomes and functioning and smartphones are clinically useful for monitoring anxiety symptoms.</p><p><strong>Trial registration number: </strong>ClinicalTrials.gov Registry (NCT02888262).</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"137-144"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231557/pdf/ebmental-2021-300259.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10214193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidelines to understand and compute the number needed to treat.","authors":"Valentin Vancak,&nbsp;Yair Goldberg,&nbsp;Stephen Z Levine","doi":"10.1136/ebmental-2020-300232","DOIUrl":"https://doi.org/10.1136/ebmental-2020-300232","url":null,"abstract":"<p><strong>Objective: </strong>We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.</p><p><strong>Methods: </strong>The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis <i>et al</i>'s NNT, Kraemer and Kupfer's NNT and the Furukawa and Leucht's NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.</p><p><strong>Results: </strong>The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.</p><p><strong>Conclusion: </strong>This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"131-136"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231569/pdf/ebmental-2020-300232.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10212217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of COVID-19 on telepsychiatry at the service and individual patient level across two UK NHS mental health Trusts.","authors":"James Sw Hong,&nbsp;Rebecca Sheriff,&nbsp;Katharine Smith,&nbsp;Anneka Tomlinson,&nbsp;Fathi Saad,&nbsp;Tanya Smith,&nbsp;Tomas Engelthaler,&nbsp;Peter Phiri,&nbsp;Catherine Henshall,&nbsp;Roger Ede,&nbsp;Mike Denis,&nbsp;Pamina Mitter,&nbsp;Armando D'Agostino,&nbsp;Giancarlo Cerveri,&nbsp;Simona Tomassi,&nbsp;Shanaya Rathod,&nbsp;Nick Broughton,&nbsp;Karl Marlowe,&nbsp;John Geddes,&nbsp;Andrea Cipriani","doi":"10.1136/ebmental-2021-300287","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300287","url":null,"abstract":"<p><strong>Background: </strong>The effects of COVID-19 on the shift to remote consultations remain to be properly investigated.</p><p><strong>Objective: </strong>To quantify the extent, nature and clinical impact of the use of telepsychiatry during the COVID-19 pandemic and compare it with the data in the same period of the 2 years before the outbreak.</p><p><strong>Methods: </strong>We used deidentified electronic health records routinely collected from two UK mental health Foundation Trusts (Oxford Health (OHFT) and Southern Health (SHFT)) between January and September in 2018, 2019 and 2020. We considered three outcomes: (1) service activity, (2) in-person versus remote modalities of consultation and (3) clinical outcomes using Health of the Nation Outcome Scales (HoNOS) data. HoNOS data were collected from two cohorts of patients (cohort 1: patients with ≥1 HoNOS assessment each year in 2018, 2019 and 2020; cohort 2: patients with ≥1 HoNOS assessment each year in 2019 and 2020), and analysed in clusters using superclasses (namely, psychotic, non-psychotic and organic), which are used to assess overall healthcare complexity in the National Health Service. All statistical analyses were done in Python.</p><p><strong>Findings: </strong>Mental health service activity in 2020 increased in all scheduled community appointments (by 15.4% and 5.6% in OHFT and SHFT, respectively). Remote consultations registered a 3.5-fold to 6-fold increase from February to June 2020 (from 4685 to a peak of 26 245 appointments in OHFT and from 7117 to 24 987 appointments in SHFT), with post-lockdown monthly averages of 23 030 and 22 977 remote appointments/month in OHFT and SHFT, respectively. Video consultations comprised up to one-third of total telepsychiatric services per month from April to September 2020. For patients with dementia, non-attendance rates at in-person appointments were higher than remote appointments (17.2% vs 3.9%). The overall HoNOS cluster value increased only in the organic superclass (clusters 18-21, n=174; p<0.001) from 2019 to 2020, suggesting a specific impact of the COVID-19 pandemic on this population of patients.</p><p><strong>Conclusions and clinical implications: </strong>The rapid shift to remote service delivery has not reached some groups of patients who may require more tailored management with telepsychiatry.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":" ","pages":"161-166"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39466124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder in children and adolescents: a systematic review and network meta-analysis.","authors":"Yajie Xiang, Andrea Cipriani, Teng Teng, Cinzia Del Giovane, Yuqing Zhang, John R Weisz, Xuemei Li, Pim Cuijpers, Xueer Liu, Jürgen Barth, Yuanliang Jiang, David Cohen, Li Fan, Donna Gillies, Kang Du, Arun V Ravindran, Xinyu Zhou, Peng Xie","doi":"10.1136/ebmental-2021-300346","DOIUrl":"10.1136/ebmental-2021-300346","url":null,"abstract":"<p><strong>Background: </strong>Available evidence on the comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder (PTSD) in children and adolescents remains uncertain.</p><p><strong>Objective: </strong>We aimed to compare and rank the different types and formats of psychotherapies for PTSD in children and adolescents.</p><p><strong>Methods: </strong>We searched eight databases and other international registers up to 31 December 2020. The pairwise meta-analyses and frequentist network meta-analyses estimated pooled standardised mean differences (SMDs) and ORs with random-effects model. Efficacy at post-treatment and follow-up, acceptability, depressive and anxiety symptoms were measured.</p><p><strong>Findings: </strong>We included 56 randomised controlled trials with 5327 patients comparing 14 different types of psychotherapies and 3 control conditions. For efficacy, cognitive processing therapy (CPT), behavioural therapy (BT), individual trauma-focused cognitive-behavioural therapy (TF-CBT), eye movement desensitisation and reprocessing, and group TF-CBT were significantly superior to all control conditions at post-treatment and follow-up (SMDs between -2.42 and -0.25). Moreover, CPT, BT and individual TF-CBT were more effective than supportive therapy (SMDs between -1.92 and -0.49). Results for depressive and anxiety symptoms were similar to the findings for the primary outcome. Most of the results were rated as 'moderate' to 'very low' in terms of confidence of evidence.</p><p><strong>Conclusions: </strong>CPT, BT and individual TF-CBT appear to be the best choices of psychotherapy for PTSD in young patients. Other types and different ways of delivering psychological treatment can be alternative options. Clinicians should consider the importance of each outcome and the patients' preferences in real clinical practice.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"153-160"},"PeriodicalIF":6.6,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0c/75/ebmental-2021-300346.PMC8543231.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10269967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correspondence on \"How can we estimate QALYs based on PHQ-9 scores? Equipercentile linking analysis of PHQ-9 and EQ-5D\" by Furukawa <i>et al</i>.","authors":"Matthew Franklin,&nbsp;Tracey Young","doi":"10.1136/ebmental-2021-300265","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300265","url":null,"abstract":"Furukawa et al posed the question: how can we estimate qualityadjusted life years (QALYs) based on Patient Health Questionnaire-9 (PHQ-9) scores? They recommend equipercentile linking analysis between the depression severity PHQ-9 and preferencebased EQ5D threelevel version (EQ5D3L; UK value set), the latter used to estimate utility data for QALYs. Furukawa et al refer to the process of ‘crosswalking’, whereby the practice of fitting a statistical model to health utility data has been referred to as ‘mapping’ and 'crosswalking’. Furukawa et al reference two mappingrelated papers (their references 7 and 9); however, their analysis seems to have missed rigorous mapping methodology and previous studies which have used these mapping processes, alongside other conceptual considerations when wanting to ‘crosswalk’/‘map’ from a nonpreferencebased (often conditionspecific) measure such as the PHQ-9 to the preferencebased EQ5D3L. Clear guidance for mapping has been set out by Wailoo et al. A case for equipercentile linking for mapping has been made based on suggested limitations of the more commonly used regression methods; the case for regression is described by Alava et al. A systematic review of mapping studies published in 2019 states: ‘There were 180 papers with 233 mapping functions in total [identified]...The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with [...] greater reporting of predictive ability of mapping functions’. From this review, the majority of mapping functions were generated to obtain EQ5D3L/EQ5D fivelevel version (EQ5D5L)/childfriendly EQ5D version (EQ5DY) scores (n=147) among other preferencebased measure scores; eg, ShortForm SixDimension (SF6D, n=45). Furukawa et al reference one study, which was also identified by Mukuria et al, which maps from the PHQ-9 to the SF6D (not EQ5D3L), which concluded that: ‘mapping from mental health conditionspecific measures, such as the widely used PHQ-9, GAD [(Generalized Anxiety Disorder)] and HADS [(Hospital Anxiety and Depression Scale)], may not be an appropriate approach to generating EQ5D and SF6D scores as these measures focus on specific symptoms and not on the wider impact of mental health conditions’ (their reference 7). Furukawa et al is mapping and therefore existing rigorous mapping methods should be used and compared with the suggested equipercentile linking analysis. We recommend not using the suggested conversion table by Furukawa et al until further conceptual and statistical analyses have been conducted, including reporting of performance statistics to allow method performance to be judged and compared against existing mapping studies in the empirical literature. We make this recommendation on the basis that Furukawa et al currently provides no reported performance statistics or comparisons to suggest the potential predictive ability of using the conversion table; therefore there is no way to judge t","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"e5"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1136/ebmental-2021-300265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10565607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of treatment dosage and duration from free-text prescriptions: an application to ADHD medications in the Swedish prescribed drug register.","authors":"Le Zhang,&nbsp;Tyra Lagerberg,&nbsp;Qi Chen,&nbsp;Laura Ghirardi,&nbsp;Brian M D'Onofrio,&nbsp;Henrik Larsson,&nbsp;Alexander Viktorin,&nbsp;Zheng Chang","doi":"10.1136/ebmental-2020-300231","DOIUrl":"https://doi.org/10.1136/ebmental-2020-300231","url":null,"abstract":"<p><strong>Background: </strong>Accurate estimation of daily dosage and duration of medication use is essential to pharmacoepidemiological studies using electronic healthcare databases. However, such information is not directly available in many prescription databases, including the Swedish Prescribed Drug Register.</p><p><strong>Objective: </strong>To develop and validate an algorithm for predicting prescribed daily dosage and treatment duration from free-text prescriptions, and apply the algorithm to ADHD medication prescriptions.</p><p><strong>Methods: </strong>We developed an algorithm to predict daily dosage from free-text prescriptions using 8000 ADHD medication prescriptions as the training sample, and estimated treatment periods while taking into account several features including titration, stockpiling and non-perfect adherence. The algorithm was implemented to all ADHD medication prescriptions from the Swedish Prescribed Drug Register in 2013. A validation sample of 1000 ADHD medication prescriptions, independent of the training sample, was used to assess the accuracy for predicted daily dosage.</p><p><strong>Findings: </strong>In the validation sample, the overall accuracy for predicting daily dosage was 96.8%. Specifically, the natural language processing model (NLP1 and NLP2) have an accuracy of 99.2% and 96.3%, respectively. In an application to ADHD medication prescriptions in 2013, young adult ADHD medication users had the highest probability of discontinuing treatments as compared with other age groups. The daily dose of methylphenidate use increased with age substantially.</p><p><strong>Conclusions: </strong>The algorithm provides a flexible approach to estimate prescribed daily dosage and treatment duration from free-text prescriptions using register data. The algorithm showed a good performance for predicting daily dosage in external validation.</p><p><strong>Clinical implications: </strong>The structured output of the algorithm could serve as basis for future pharmacoepidemiological studies evaluating utilization, effectiveness, and safety of medication use, which would facilitate evidence-based treatment decision-making.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":" ","pages":"146-152"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1136/ebmental-2020-300231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25539775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing the clinical interpretability of PHQ-9 through equipercentile linking with health utility values by EQ-5D-3L.","authors":"Toshi A Furukawa,&nbsp;Stephen Z Levine,&nbsp;Claudia Buntrock,&nbsp;Pim Cuijpers","doi":"10.1136/ebmental-2021-300299","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300299","url":null,"abstract":"In our recent paper, we presented the results of the equipercentile linking analysis between the Patient Health Questionnaire (PHQ-9) and the EuroQol Five Dimentions Three Levels (EQ5D3L) in order to increase the clinical interpretability of the PHQ-9 scores and their changes. Our paper was based on the clinical approach to linking that has been applied to various scales in psychiatry. 3 Drs Franklin and Young made some important comments on our approach and we will try our best to clarify the concerns they raise. Drs Franklin and Young cite the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Practices for Outcomes Research Task Force Report for studies mapping nonpreferencebased measures of health to generic preferencebased measures. This guideline was prepared mainly for mapping exercises ‘to inform a specific costeffectiveness analysis’ (p. 19). Thus, their items are often concerned with the matching between a dataset that allowed mapping and a dataset for economic analysis. However, the purpose of our study was not to perform any specific costeffectiveness analysis. The ISPOR report recommends regression methods, and many of their reporting items are about the details of the regression models. However, there are good arguments that equipercentile linking is superior to the regression methods for the purpose of scalealignment, mainly due to regression to the mean inherent in any regression models. We used the equipercentile linking, a nonparametric approach that makes no distinction between independent or dependent variables for our more general purpose to link PHQ-9 scores with health utility values. Our model therefore did not adjust for covariates. It is then important to describe the samples on which the linking was performed, as we did in our report: participants of internet cognitive behavioural therapy trials, mainly in their 30s through 50s and predominantly female, without specific physical comorbidities. Their baseline depression severity ranged equally through subthreshold, mild, moderate and severe depression. We agree with Drs Franklin and Young, and undoubtedly with many others, that depression is only one aspect of quality of life and that any mapping from only one domain to the whole construct can be misleading. It is appropriate to remember that the correlations between PHQ-9 and EQ5D3L were 0.5 at best in our sample and could have been lower if we included more variable samples. Any linking based on such data cannot be strong enough for individual prediction, but must be used judiciously for grouplevel evaluations. We discussed such limitations in our original publication. Whether regression models would allow more exact prediction remains an empirical question. By including strong covariates and by improving the conceptual overlap with a preferencebased instrument they may, and we agree with Drs Franklin and Young that we need to compare such models with the equipercentile approach, with due at","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":" ","pages":"e6"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1136/ebmental-2021-300299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39216507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Call for emergency action to limit global temperature increases, restore biodiversity and protect health.","authors":"Lukoye Atwoli,&nbsp;Abdullah H Baqui,&nbsp;Thomas Benfield,&nbsp;Raffaella Bosurgi,&nbsp;Fiona Godlee,&nbsp;Stephen Hancocks,&nbsp;Richard Horton,&nbsp;Laurie Laybourn-Langton,&nbsp;Carlos Augusto Monteiro,&nbsp;Ian Norman,&nbsp;Kirsten Patrick,&nbsp;Nigel Praities,&nbsp;Marcel Gm Olde Rikkert,&nbsp;Eric J Rubin,&nbsp;Peush Sahni,&nbsp;Richard Smith,&nbsp;Nicholas J Talley,&nbsp;Sue Turale,&nbsp;Damián Vázquez","doi":"10.1136/ebmental-2021-300328","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300328","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":" ","pages":"e4"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/7e/ebmental-2021-300328.PMC8543188.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39384586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brief Evidence-Based Interventions for Child and Adolescent Mental Health","authors":"P. Lusk, B. Melnyk","doi":"10.1891/9780826167279.0017","DOIUrl":"https://doi.org/10.1891/9780826167279.0017","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"50 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76022265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimbursement for Mental/Behavioral Health Services in Primary Care","authors":"N. Herendeen","doi":"10.1891/9780826167279.0016","DOIUrl":"https://doi.org/10.1891/9780826167279.0016","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"1 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72968785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}