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Objectives Our objective was to assess the effects of mental health interventions for children, adolescents, and adults not quarantined or undergoing treatment due to COVID-19 infection. Methods We searched 9 databases (2 Chinese-language) from December 31, 2019, to March 22, 2021. We included randomised controlled trials of interventions to address COVID-19 mental health challenges among people not hospitalised or quarantined due to COVID-19 infection. We synthesized results descriptively due to substantial heterogeneity of populations and interventions and risk of bias concerns. Results We identified 9 eligible trials, including 3 well-conducted, well-reported trials that tested interventions designed specifically for COVID-19 mental health challenges, plus 6 other trials with high risk of bias and reporting concerns, all of which tested standard interventions (e.g., individual or group therapy, expressive writing, mindfulness recordings) minimally adapted or not specifically adapted for COVID-19. Among the 3 well-conducted and reported trials, 1 ( N = 670) found that a self-guided, internet-based cognitive-behavioural intervention targeting dysfunctional COVID-19 worry significantly reduced COVID-19 anxiety (standardized mean difference [SMD] 0.74, 95% confidence interval [CI], 0.58 to 0.90) and depression symptoms (SMD 0.38, 95% CI, 0.22 to 0.55) in Swedish general population participants. A lay-delivered telephone intervention for homebound older adults in the United States ( N = 240) and a peer-moderated education and support intervention for people with a rare autoimmune condition from 12 countries ( N = 172) significantly improved anxiety (SMD 0.35, 95% CI, 0.09 to 0.60; SMD 0.31, 95% CI, 0.03 to 0.58) and depressive symptoms (SMD 0.31, 95% CI, 0.05 to 0.56; SMD 0.31, 95% CI, 0.07 to 0.55) 6-week post-intervention, but these were not significant immediately post-intervention. No trials in children or adolescents were identified. Conclusions Interventions that adapt evidence-based strategies for feasible delivery may be effective to address mental health in COVID-19. More well-conducted trials, including for children and adolescents, are needed.

ObjectiveTo synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic.DesignSystematic review.Data sourcesMedline, PsycINFO, CINAHL, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints.Eligibility criteria for selecting studiesStudies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies.ResultsAs of 11 April 2022, 94 411 unique titles and abstracts including 137 unique studies from 134 cohorts were reviewed. Most of the studies were from high income (n=105, 77%) or upper middle income (n=28, 20%) countries. Among general population studies, no changes were found for general mental health (standardised mean difference (SMD)change0.11, 95% confidence interval −0.00 to 0.22) or anxiety symptoms (0.05, −0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses.ConclusionsHigh risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health).Review registrationPROSPERO CRD42020179703.

ImportanceWomen and gender-diverse individuals have faced disproportionate socioeconomic burden during COVID-19. There have been reports that this has translated into greater negative changes in mental health, but this has been based on cross-sectional research that has not accounted for pre-COVID-19 differences.ObjectiveTo compare mental health symptom changes since pre-COVID-19 by sex or gender.Data SourcesMEDLINE, PsycINFO, CINAHL, EMBASE, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework (December 31, 2019 to August 30, 2021).Study SelectionEligible studies compared mental health symptom changes from pre-COVID-19 to COVID-19 by sex or gender.Data Extraction and SynthesisData was extracted by a single reviewer with validation by a second reviewer. Adequacy of study methods and reporting was assessed using an adapted version of the Joanna Briggs Institute Checklist for Prevalence Studies. A restricted maximum-likelihood random-effects meta-analyses was conducted.Main Outcomes and MeasuresAnxiety symptoms, depression symptoms, general mental health, and stress measured continuously or dichotomously.Results12 studies (10 unique cohorts) were included. All compared females or women to males or men; none included gender-diverse individuals. Continuous symptom change differences were not statistically significant for depression (standardized mean difference [SMD]= 0.12, 95% CI -0.09 to 0.33; 4 studies, 4,475 participants; I2=69.0%) and stress (SMD= - 0.10, 95% CI -0.21 to 0.01; 4 studies, 1,533 participants; I2=0.0%), but anxiety (SMD= 0.15, 95% CI 0.07 to 0.22; 4 studies, 4,344 participants; I2=3.0%) and general mental health (SMD= 0.15, 95% CI 0.12 to 0.18; 3 studies, 15,692 participants; I2=0.0%) worsened more among females or women than males or men during COVID-19. There were no significant differences in changes in proportion above a cut-off: anxiety (difference= -0.05, 95% CI -0.20 to 0.11; 1 study, 217 participants), depression (difference= 0.12, 95% CI -0.03 to 0.28; 1 study, 217 participants), general mental health (difference= -0.03, 95% CI -0.09 to 0.04; 3 studies, 18,985 participants; I2=94.0%), stress (difference= 0.04, 95% CI -0.10 to 0.17; 1 study, 217 participants).Conclusion and RelevanceMental health outcomes did not differ or were worse by amounts below thresholds for clinical significance for women compared to men.RegistrationPROSPERO (CRD42020179703).KEY MESSAGESQuestionDid mental health symptoms worsen more for females or women than males or men in COVID-19?FindingsWe reviewed almost 65,000 citations and identified 12 studies that provided data to directly compare mental health symptom changes from pre-COVID-19 to during COVID-19 for females or women versus males or men. Statistically significant, but small, sex- or gender-based differences were found in 2 of 8 mental health outcomes.MeaningMental health changes among females or women were not significantly different from males or men for most outcomes, and differences that were identified were small and less than minimally important difference thresholds.

Shortened versions of self-reported questionnaires may be used to reduce respondent burden. When shortened screening tools are used, it is desirable to maintain equivalent diagnostic accuracy to full-length forms. This manuscript presents a case study that illustrates how external data and individual participant data meta-analysis can be used to assess the equivalence in diagnostic accuracy between a shortened and full-length form. This case study compares the Patient Health Questionnaire-9 (PHQ-9) and a 4-item shortened version (PHQ-Dep-4) that was previously developed using optimal test assembly methods. Using a large database of 75 primary studies (34,698 participants, 3,392 major depression cases), we evaluated whether the PHQ-Dep-4 cutoff of ≥ 4 maintained equivalent diagnostic accuracy to a PHQ-9 cutoff of ≥ 10. Using this external validation dataset, a PHQ-Dep-4 cutoff of ≥ 4 maximized the sum of sensitivity and specificity, with a sensitivity of 0.88 (95% CI 0.81, 0.93), 0.68 (95% CI 0.56, 0.78), and 0.80 (95% CI 0.73, 0.85) for the semi-structured, fully structured, and MINI reference standard categories, respectively, and a specificity of 0.79 (95% CI 0.74, 0.83), 0.85 (95% CI 0.78, 0.90), and 0.83 (95% CI 0.80, 0.86) for the semi-structured, fully structured, and MINI reference standard categories, respectively. While equivalence with a PHQ-9 cutoff of ≥ 10 was not established, we found the sensitivity of the PHQ-Dep-4 to be non-inferior to that of the PHQ-9, and the specificity of the PHQ-Dep-4 to be marginally smaller than the PHQ-9.

ObjectivesThe rapid pace, high volume, and limited quality of mental health evidence that has been generated during COVID-19 poses a barrier to understanding mental health outcomes. We sought to summarize results from studies that compared mental health outcomes during COVID-19 to outcomes assessed prior to COVID-19 in the same cohort in the general population and in other groups for which data have been reported.DesignLiving systematic review.Data SourcesMEDLINE (Ovid), PsycINFO (Ovid), CINAHL (EBSCO), EMBASE (Ovid), Web of Science Core Collection: Citation Indexes, China National Knowledge Infrastructure, Wanfang, medRxiv (preprints), and Open Science Framework Preprints (preprint server aggregator).Eligibility criteria for selecting studiesFor this report, we included studies that compared general mental health, anxiety symptoms, or depression symptoms, assessed January 1, 2020 or later, to the same outcomes collected between January 1, 2018 and December 31, 2019. Any population was eligible. We required ≥ 90% of participants pre-COVID-19 and during COVID-19 to be the same or the use of statistical methods to address missing data. For population groups with continuous outcomes for at least two studies in an outcome domain, we conducted restricted maximum-likelihood random-effects meta-analyses. Worse COVID-19 mental health outcomes are reported as positive. Risk of bias of included studies was assessed using an adapted version of the Joanna Briggs Institute Checklist for Prevalence Studies.ResultsAs of April 11, 2022, we had reviewed 94,411 unique titles and abstracts and identified 137 unique eligible studies with data from 134 cohorts. Almost all studies were from high-income (105, 77%) or upper-middle income (28, 20%) countries. Among adult general population studies, we did not find changes in general mental health (standardized mean difference of change [SMDchange= 0.11, 95% CI -0.00 to 0.22) or anxiety symptoms (SMDchange= 0.05, 95% CI -0.04 to 0.13), but depression symptoms worsened minimally (SMDchange= 0.12, 95% CI 0.01 to 0.24). Among women or females, mental health symptoms worsened by minimal to small amounts in general mental health (SMDchange= 0.22, 95% CI 0.08 to 0.35), anxiety symptoms (SMDchange= 0.20, 95% CI 0.12 to 0.29), and depression symptoms (SMDchange= 0.22, 95% CI 0.05 to 0.40). Of 27 other analyses across outcome domains, among subgroups other than women or females, 5 analyses suggested minimal or small amounts of symptom worsening, and 2 suggested minimal or small symptom improvements. No other subgroup experienced statistically significant changes across outcome domains. In the 3 studies with data from March to April 2020 and later in 2020, symptoms either were unchanged from pre-COVID-19 at both time points or increased initially then returned to pre-COVID-19 levels. Heterogeneity measured by the I2statistic was high (e.g., > 80%) for most analyses, and there was concerning risk of bias in most studies.ConclusionsHigh risk of bias in many studies and substantial heterogeneity suggest that point estimates should be interpreted cautiously. Nonetheless, there was general consistency across analyses in that most symptom change estimates were close to zero and not statistically significant, and changes that were identified were of minimal to small magnitudes. There were, however, small negative changes for women or females in all domains. It is possible that gaps in data have not allowed identification of changes in some vulnerable groups. Continued updating is needed as evidence accrues.Funding:Canadian Institutes of Health Research (CMS-171703; MS1-173070; GA4-177758; WI2-179944); McGill Interdisciplinary Initiative in Infection and Immunity Emergency COVID-19 Research Fund (R2-42).Registration:PROSPERO (CRD42020179703); registered on April 17, 2020.

BackgroundScalable interventions to address COVID-19 mental health are needed. Our objective was to assess effects of mental health interventions for community-based children, adolescents, and adults.MethodsWe searched 9 databases (2 Chinese-language) from December 31, 2019 to March 22, 2021. We included randomised controlled trials with non-hospitalised, non-quarantined participants of interventions to address COVID-19 mental health challenges. We synthesized results descriptively but did not pool quantitatively due to substantial heterogeneity of populations and interventions and concerns about risk of bias.FindingsWe identified 9 eligible trials, including 3 well-conducted, well-reported trials that tested interventions designed specifically for COVID-19 mental health challenges, plus 6 trials of standard interventions (e.g., individual or group therapy, expressive writing, mindfulness recordings) minimally adapted for COVID-19, all with risk of bias concerns. Among the 3 COVID-19-specific intervention trials, one (N = 670) found that a self-guided, internet-based cognitive-behavioural intervention targeting dysfunctional COVID-19 worry significantly reduced COVID-19 anxiety (standardized mean difference [SMD] 0.74, 95% CI 0.58 to 0.90) and depression symptoms (SMD 0.38, 95% CI 0.22 to 0.55) in Swedish general population participants. A lay-delivered telephone intervention for homebound older adults in the United States (N = 240) and a peer-moderated education and support intervention for people with a rare autoimmune condition from 12 countries (N = 172) significantly improved anxiety (SMD 0.35, 95% CI 0.09 to 0.60; SMD 0.31, 95% CI 0.03 to 0.58) and depressive symptoms (SMD 0.31, 95% CI 0.05 to 0.56; SMD 0.31, 95% CI 0.07 to 0.55) 6-weeks post-intervention, but these were not significant immediately post-intervention. No trials in children or adolescents were identified.InterpretationInternet-based programs for the general population and lay-or peer-delivered interventions for vulnerable groups may be effective, scalable options for public mental health in COVID-19. More well-conducted trials, including for children and adolescents, are needed.FundingCanadian Institutes of Health Research (CMS-171703; MS1-173070); McGill Interdisciplinary Initiative in Infection and Immunity Emergency COVID-19 Research Fund (R2-42).RegistrationPROSPERO (CRD42020179703); registered on April 17, 2020.RESEARCH IN CONTEXTEvidence before this studyWe searched for systematic reviews of randomised controlled trials of interventions to address mental health challenges in COVID-19. We used searches from our living systematic review, which were not limited by study design and reviewed citations through April 29, 2021 from MEDLINE, PsycINFO, CINAHL, EMBASE, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv (preprints), and Open Science Framework Preprints (preprint server aggregator). We identified 4 systematic reviews of interventions for COVID-19 mental health with search dates between April and September 2020. None, however, included evidence from any completed randomised controlled trials on mental health interventions for community-based children, adolescents, or adults during COVID-19.Added value of this studyOur systematic review is the only living systematic review on COVID-19 community-based mental health interventions registered in PROSPERO and, to the best of our knowledge, the first systematic review to synthesize evidence on completed randomised controlled trials of COVID-19 mental health interventions. The sheer volume of evidence being published in COVID-19 poses a barrier to effective synthesis and policy response. We reviewed over 45,000 citations in any language and distilled this to 9 verified eligible community-based trials. Of these, there were 3 well-conducted trials of interventions designed specifically to be scalable to address challenges of public mental health in COVID-19. One trial showed that internet-based cognitive behavioural therapy in the Swedish general population (N = 670) reduced COVID-19 anxiety and symptoms of depression. Trials that tested a lay-delivered telephone support intervention for homebound older adults in the United States (N = 240) and a peer-moderated group intervention for people with a rare autoimmune condition from 12 countries (N = 172) also found that they improved mental health outcomes, although not all outcomes were statistically significant.Implications of all the available evidenceEffective, scalable, and feasibly delivered mental health interventions are needed for the general public and vulnerable groups as lockdown restrictions continue, even intermittently, and because COVID-19 mental health implications will likely persist beyond the pandemic. Although we identified only 3 high-quality trials, they demonstrated approaches that can be feasibly adopted to meet the needs of adults in the general public and vulnerable groups. The successful internet-based cognitive behavioural therapy intervention was made available to the Swedish general public free-of-charge following testing and suggests that online tools tailored for specific concerns in COVID-19 may represent an efficient way of addressing public mental health. Two lay-and peer-delivered interventions, consistent with pre-COVID-19 evidence, suggest that low-intensity, non-professionally delivered, support-oriented approaches can be leveraged among vulnerable groups. The absence of trials of interventions for children and adolescents underlines the need for evidence on scalable strategies for this population, including school-based approaches.

Objectives: Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. Methods: We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random‐effects meta‐analysis was used to compare prevalence with EPDS cutoffs versus the SCID. Results: Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (≥9 to ≥14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%–34.5%) for EPDS ≥ 9 to 9.0% (95% CI: 6.8%–11.9%) for EPDS ≥ 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%–12.3%). EPDS ≥14 provided pooled prevalence closest to SCID‐based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: 13.7%, 12.3%). Conclusion: EPDS ≥14 approximated SCID‐based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation., This study was funded by the Canadian Institutes of Health Research (CIHR, KRS‐140994). Ms. Lyubenova was supported by the Mitacs Globalink Research Internship Program. Ms. Neupane was supported by G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University. Drs. Levis and Wu were supported by Fonds de recherche du Québec‐Santé (FRQS) Postdoctoral Training Fellowships. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Ms. Azar was supported by a FRQS Masters Training Award. The primary study by Barnes et al. was supported by a grant from the Health Foundation (1665/608). The primary study by Beck et al. was supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the University of Connecticut Research Foundation. The primary study by Helle et al. was supported by the Werner Otto Foundation, the Kroschke Foundation, and the Feindt Foundation. Prof. Robertas Bunevicius, MD, PhD (1958‐2016) was Principal Investigator of the primary study by Bunevicius et al., but passed away and was unable to participate in this project. The primary study by Chaudron et al. was supported by a grant from the National Institute of Mental Health (grant K23 MH64476). The primary study by Tissot et al. was supported by the Swiss National Science Foundation (grant 32003B 125493). The primary study by Tendais et al. was supported under the project POCI/SAU‐ESP/56397/2004 by the Operational Program Science and Innovation 2010 (POCI 2010) of the Community Support Board III and by the European Community Fund FEDER. The primary study by Garcia‐Esteve et al. was supported by grant 7/98 from the Ministerio de Trabajo y Asuntos Sociales, Women's Institute, Spain. The primary study by Howard et al. was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Numbers RP‐PG‐1210‐12002 and RP‐DG‐1108‐10012) and by the South London Clinical Research Network. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The primary study by Phillips et al. was supported by a scholarship from the National Health and Medical and Research Council (NHMRC). The primary study by Nakić Radoš et al. was supported by the Croatian Ministry of Science, Education, and Sports (134‐0000000‐2421). The primary study by Navarro et al. was supported by grant 13/00 from the Ministry of Work and Social Affairs, Institute of Women, Spain. The primary study by Pawlby et al. was supported by a Medical Research Council UK Project Grant (number G89292999N). The primary study by Quispel et al. was supported by Stichting Achmea Gezondheid (grant number z‐282). Dr. Robertson‐Blackmore was supported by a Young Investigator Award from the Brain and Behavior Research Foundation and NIMH grant K23MH080290. The primary study by Rochat et al. was supported by grants from the University of Oxford (HQ5035), the Tuixen Foundation (9940), the Wellcome Trust (082384/Z/07/Z and 071571), and the American Psychological Association. Dr. Rochat receives salary support from a Wellcome Trust Intermediate Fellowship (211374/Z/18/Z). The primary study by Prenoveau et al. was supported by The Wellcome Trust (grant number 071571). The primary study by Stewart et al. was supported by Professor Francis Creed's Journal of Psychosomatic Research Editorship fund (BA00457) administered through University of Manchester. The primary study by Tandon et al. was funded by the Thomas Wilson Sanitarium. The primary study by Tran et al. was supported by the Myer Foundation who funded the study under its Beyond Australia scheme. Dr. Tran was supported by an early career fellowship from the Australian National Health and Medical Research Council. The primary study by Vega‐Dienstmaier et al. was supported by Tejada Family Foundation, Inc, and Peruvian‐American Endowment, Inc. Drs. Benedetti and Thombs were supported by FRQS researcher salary awards.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions.

The Maternal Mental Health in Canada, 2018/2019, survey reported that 18% of 7,085 mothers who recently gave birth reported "feelings consistent with postpartum depression" based on scores ≥7 on a 5-item version of the Edinburgh Postpartum Depression Scale (EPDS-5). The EPDS-5 was designed as a screening questionnaire, not to classify disorders or estimate prevalence; the extent to which EPDS-5 results reflect depression prevalence is unknown. We investigated EPDS-5 ≥7 performance relative to major depression prevalence based on a validated diagnostic interview, the Structured Clinical Interview for DSM (SCID)., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Canadian Institutes of Health Research (CIHR, KRS-140994). Drs. Thombs and Benedetti were supported by Fonds de recherche du Qu´ebec–Sant´e (FRQS) researcher salary awards. Dr. Levis was supported by a FRQS Postdoctoral Training Fellowship. Ms. Lyubenova was supported by theMitacs Globalink Research Internship Program. Ms. Neupane was supported by G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University. Dr. Wu was supported by a FRQS Postdoctoral Training Fellowship. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. Ms. Azar was supported by a FRQS Masters Training Award. The primary study by Barnes et al. was supported by a grant from the Health Foundation (1665/608). The primary study by Beck et al. was supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the University of Connecticut Research Foundation. The primary study by de Figueiredo et al. was supported by Fundacão de Amparo à Pesquisa do Estado de São Paulo. The primary study by Tendais et al. was supported under the project POCI/SAU-ESP/56397/2004 by the Operational Program Science and Innovation 2010 (POCI 2010) of the Community Support Board III and by the European Community Fund FEDER. The primary study by Helle et al. was supported by the Werner Otto Foundation, the Kroschke Foundation, and the Feindt Foundation. The primary study by Howard et al. was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Numbers RPPG-1210-12002 and RP-DG-1108-10012) and by the South London Clinical Research Network. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The primary study by Phillips et al. was supported by a scholarship from the National Health and Medical and Research Council (NHMRC). The primary study by Nakic´ Radoˇs et al. was supported by the Croatian Ministry of Science, Education, and Sports (134-0000000-2421). The primary study by Quispel et al. was supported by Stichting Achmea Gezondheid (grant number z-282). The primary study by Rochat et al. was supported by grants from the University of Oxford (HQ5035), the Tuixen Foundation (9940), the Wellcome Trust (082384/Z/07/Z and 071571), and the American Psychological Association. Dr. Rochat receives salary support from a Wellcome Trust Intermediate Fellowship (211374/Z/18/Z). The primary study by Prenoveau et al. was supported by The Wellcome Trust (grant number 071571). The primary study by Stewart et al. was supported by Professor Francis Creed’s Journal of Psychosomatic Research Editorship fund (BA00457) administered through University of Manchester. The primary study by Tandon et al. was funded by the Thomas Wilson Sanitarium. The primary study by Tran et al. was supported by the Myer Foundation who funded the study under its Beyond Australia scheme. Dr. Tran was supported by an early career fellowship from the Australian National Health and Medical Research Council. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The primary study by Vega-Dienstmaier et al. was supported by Tejada Family Foundation, Inc, and Peruvian-American Endowment, Inc. No other authors reported funding for primary studies or for their work on this study.

Objectives Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale – depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. Methods We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. Results 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was −21.1% to 19.5%. Conclusions HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview.

Importance The Patient Health Questionnaire depression module (PHQ-9) is a 9-item self-administered instrument used for detecting depression and assessing severity of depression. The Patient Health Questionnaire-2 (PHQ-2) consists of the first 2 items of the PHQ-9 (which assess the frequency of depressed mood and anhedonia) and can be used as a first step to identify patients for evaluation with the full PHQ-9. Objective To estimate PHQ-2 accuracy alone and combined with the PHQ-9 for detecting major depression. Data sources MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science (January 2000-May 2018). Study selection Eligible data sets compared PHQ-2 scores with major depression diagnoses from a validated diagnostic interview. Data extraction and synthesis Individual participant data were synthesized with bivariate random-effects meta-analysis to estimate pooled sensitivity and specificity of the PHQ-2 alone among studies using semistructured, fully structured, or Mini International Neuropsychiatric Interview (MINI) diagnostic interviews separately and in combination with the PHQ-9 vs the PHQ-9 alone for studies that used semistructured interviews. The PHQ-2 score ranges from 0 to 6, and the PHQ-9 score ranges from 0 to 27. Results Individual participant data were obtained from 100 of 136 eligible studies (44 318 participants; 4572 with major depression [10%]; mean [SD] age, 49 [17] years; 59% female). Among studies that used semistructured interviews, PHQ-2 sensitivity and specificity (95% CI) were 0.91 (0.88-0.94) and 0.67 (0.64-0.71) for cutoff scores of 2 or greater and 0.72 (0.67-0.77) and 0.85 (0.83-0.87) for cutoff scores of 3 or greater. Sensitivity was significantly greater for semistructured vs fully structured interviews. Specificity was not significantly different across the types of interviews. The area under the receiver operating characteristic curve was 0.88 (0.86-0.89) for semistructured interviews, 0.82 (0.81-0.84) for fully structured interviews, and 0.87 (0.85-0.88) for the MINI. There were no significant subgroup differences. For semistructured interviews, sensitivity for PHQ-2 scores of 2 or greater followed by PHQ-9 scores of 10 or greater (0.82 [0.76-0.86]) was not significantly different than PHQ-9 scores of 10 or greater alone (0.86 [0.80-0.90]); specificity for the combination was significantly but minimally higher (0.87 [0.84-0.89] vs 0.85 [0.82-0.87]). The area under the curve was 0.90 (0.89-0.91). The combination was estimated to reduce the number of participants needing to complete the full PHQ-9 by 57% (56%-58%). Conclusions and relevance In an individual participant data meta-analysis of studies that compared PHQ scores with major depression diagnoses, the combination of PHQ-2 (with cutoff ≥2) followed by PHQ-9 (with cutoff ≥10) had similar sensitivity but higher specificity compared with PHQ-9 cutoff scores of 10 or greater alone. Further research is needed to understand the clinical and research value of this combined approach to screening.

Objectives: Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores >= 10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 >= 10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. Study Design and Setting: Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. Results: A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 >= 10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 >= 10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 >= 14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 >= 14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). Conclusion: PHQ-9 >= 10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies. (C) 2020 Elsevier Inc. All rights reserved.

Objective Two previous individual participant data meta-analyses (IPDMAs) found that different diagnostic interviews classify different proportions of people as having major depression overall or by symptom levels. We compared the odds of major depression classification across diagnostic interviews among studies that administered the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). Methods Data accrued for an IPDMA on HADS-D diagnostic accuracy were analysed. We fit binomial generalized linear mixed models to compare odds of major depression classification for the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and Mini International Neuropsychiatric Interview (MINI), controlling for HADS-D scores and participant characteristics with and without an interaction term between interview and HADS-D scores. Results There were 15,856 participants (1942 [12%] with major depression) from 73 studies, including 15,335 (97%) non-psychiatric medical patients, 164 (1%) partners of medical patients, and 357 (2%) healthy adults. The MINI (27 studies, 7345 participants, 1066 major depression cases) classified participants as having major depression more often than the CIDI (10 studies, 3023 participants, 269 cases) (adjusted odds ratio [aOR] = 1.70 (0.84, 3.43)) and the semi-structured SCID (36 studies, 5488 participants, 607 cases) (aOR = 1.52 (1.01, 2.30)). The odds ratio for major depression classification with the CIDI was less likely to increase as HADS-D scores increased than for the SCID (interaction aOR = 0.92 (0.88, 0.96)). Conclusion Compared to the SCID, the MINI may diagnose more participants as having major depression, and the CIDI may be less responsive to symptom severity.

BackgroundDifferent diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.AimsTo evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.MethodData collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.ResultsA total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).ConclusionsThe MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.Declaration of interestDrs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

BackgroundItem 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.MethodsWe conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.Results16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).ConclusionsPHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22–0.24 lower compared to fully structured interviews and 0.06–0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82–0.92) and 0.86 (0.82–0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.

BACKGROUND: Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9. METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy. RESULTS: 16,742 participants (2,097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0·996 (95% confidence interval 0·996 to 0·996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0·02 (−0·06 to 0·00) and more specific by 0·01 (0·00 to 0·01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0·00 to 0·05 (0·03 at cutoff 10), and specificity was within 0·01 for all cutoffs (0·00 to 0·01). CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

We evaluated whether sample sizes in different arms of two-arm parallel group randomized controlled trials of nonregulated interventions were systematically closer in size than would plausibly occur by chance if simple randomization had been applied.We searched PubMed for trials of nonregulated health care interventions that did not report using restricted randomization from journals in behavioral sciences and psychology, nursing, nutrition and dietetics, rehabilitation, and surgery. We emailed trial authors to clarify randomization procedures.We identified 148 nonregulated intervention trials that indicated they used simple randomization. Difference in trial arm sizes was smaller than would be predicted by chance if simple randomization had occurred in all trials (P 0.001). Rather than approximately half of the trials being within a 50% prediction interval for the difference, 96% had differences within this interval. Results were similar and statistically significant (P 0.001) for trials that were published in journals with impact factors ≥ 4 and when stratified by type of nonregulated intervention.There is a need for education and better understanding of clinical trial methods to ensure that randomization procedures are implemented as intended and reported fully and accurately.

Objectives The objectives were to determine the proportion of eligible randomized controlled trials (RCTs) that contributed data to individual participant data meta-analyses (IPDMAs) and explore associated factors. Study Design and Setting IPDMAs with ≥10 eligible RCTs were identified by searching MEDLINE, EMBASE, CINAHL, and Cochrane May 1, 2015 to February 13, 2017. Mixed-effect logistic regression was used to identify factors associated with data contribution. Results Of 774 eligible RCTs from 35 included IPDMAs, 517 (67%, 95% confidence interval [CI]: 63%–70%) contributed data. Compared to RCTs from journals with low-impact factors (0–2.4), RCTs from journals with higher impact factors were more likely to contribute data: impact factor 5.0–9.9, odds ratio [OR] 2.6, 95% CI: 1.37–4.86; impact factor: 10.0–19.9, OR: 5.7, 95% CI: 3.0–10.8; impact factor >20.0, OR: 4.6, 95% CI: 1.9–11.4. RCTs from the United Kingdom were more likely to contribute data than those from the United States (reference; OR: 2.4, 95% CI, 1.3–4.6). There was an increase in OR per publication year (OR: 1.05, 95% CI: 1.02–1.09). Conclusion The country where RCTs are conducted, impact factor of the journal where RCTs are published, and RCT publication year were associated with data contribution in IPDMAs with ≥10 eligible RCTs.

Importance Many interventions that are important to the health care of patients are not subject to regulation by the US Food and Drug Administration (FDA) or comparable regulatory bodies in other nations. Objective To determine whether specialty journals that publish trials of primarily nonregulated health care interventions require prospective registration and whether the prospective registration policies are associated with the publication of prospectively registered trials, trials with adequately registered outcomes, and trials with primary outcomes consistent with the registered primary outcomes. Design and Methods PubMed was searched daily, from March 18, 2016, to September 17, 2016, for nonregulated intervention randomized clinical trials. The search included all journals in the Clarivate Analytics Science Citation Index Expanded categories of behavioral sciences, nursing, nutrition and dietetics, psychology, rehabilitation, and surgery. Trials of interventions not subject to FDA regulation were included. One investigator extracted journal registration policy and trial registration status. Two investigators independently extracted trial registration and publication characteristics. Main Outcomes and Measures For journals, the main outcome was the trial registration policy. For trials, the main outcomes were prospective registration, adequacy of outcome registration, and concordance of registered with published primary outcomes. Results In total, 953 nonregulated intervention trials published in 254 journals were identified. Prospective registration was required for publication by 29 (11.4%) of 254 journals, and an additional 12 journals (4.7%) had conditional date-based requirements. Only 189 (19.8%) of the 953 trials were registered prospectively, including 33 of 98 published in journals with prospective registration policies as compared with 156 of 855 in journals without policies (33.7% vs 18.2%; P = .004). Among the 17 journals that required prospective registration and had at least 2 included trials, none had a prospective registration of more than 50%. In journals with policies, only 3 of 98 trials included primary outcomes consistent with prospectively, adequately registered outcomes, as compared with 34 of 852 trials in journals without policies (3.1% vs 4.0%; P = .62). Conclusions and Relevance Few journals in behavioral sciences or psychology, nursing, nutrition and dietetics, rehabilitation, and surgery require prospective trial registration, and those with existing registration policies rarely enforce them; this finding suggests that strategies for encouraging prospective registration of clinical trials not subject to FDA regulation should be developed and tested.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Background: The objective of this study was to develop and validate a short form of the Patient Health Questionnaire-9 (PHQ-9), a self-report questionnaire for assessing depressive symptomatology, using objective criteria. Methods: Responses on the PHQ-9 were obtained from 7,850 English-speaking participants enrolled in 20 primary diagnostic test accuracy studies. PHQ unidimensionality was verified using confirmatory factor analysis, and an item response theory model was fit. Optimal test assembly (OTA) methods identified a maximally precise short form for each possible length between one and eight items, including and excluding the ninth item. The final short form was selected based on prespecified validity, reliability, and diagnostic accuracy criteria. Results: A four-item short form of the PHQ (PHQ-Dep-4) was selected. The PHQ-Dep-4 had a Cronbach's alpha of 0.805. Sensitivity and specificity of the PHQ-Dep-4 were 0.788 and 0.837, respectively, and were statistically equivalent to the PHQ-9 (sensitivity = 0.761, specificity = 0.866). The correlation of total scores with the full PHQ-9 was high (r = 0.919). Conclusion: The PHQ-Dep-4 is a valid short form with minimal loss of information of scores when compared to the full-length PHQ-9. Although OTA methods have been used to shorten patient-reported outcome measures based on objective, prespecified criteria, further studies are required to validate this general procedure for broader use in health research. Furthermore, due to unexamined heterogeneity, there is a need to replicate the results of this study in different patient populations.

Objectives: A previous individual participant data meta-analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire-9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum. Methods: Data accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics. Results: Among fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased. Conclusion: Different interviews may not classify major depression equivalently., This study was funded by the Canadian Institutes of Health Research (CIHR, KRS-140994). Ms. Levis was supported by a CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Awards. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Dr. Wu was supported by an Utting Postdoctoral Fellowship from the Jewish General Hospital, Montreal, Quebec, Canada. Ms. Azar was supported by a Fonds de recherche du Québec - Santé (FRQS) Masters Training Award. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. The primary study by Alvarado et al. was supported by the Ministry of Health of Chile. The primary study by Barnes et al. was supported by a grant from the Health Foundation (1665/608). The primary study by Beck et al. was supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the University of Connecticut Research Foundation. The primary study by Helle et al. was supported by the Werner Otto Foundation, the Kroschke Foundation, and the Feindt Foundation. Prof. Robertas Bunevicius, MD, PhD (1958–2016) was principal investigator of the primary study by Bunevicius et al. but passed away and was unable to participate in this project. The primary study by Couto et al. was supported by the National Counsel of Technological and Scientific Development (CNPq; Grant 444254/2014-5) and the Minas Gerais State Research Foundation (FAPEMIG; Grant APQ-01954-14). The primary study by Chaudron et al. was supported by a grant from the National Institute of Mental Health (Grant K23 MH64476). The primary study by Figueira et al. was supported by the Brazilian Ministry of Health and by the National Counsel of Technological and Scientific Development (CNPq; Grant 403433/2004-5). The primary study by de Figueiredo et al. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo. The primary study by Tissot et al. was supported by the Swiss National Science Foundation (Grant 32003B 125493). The primary study by Fernandes et al. was supported by grants from the Child: Care Health and Development Trust and the Department of Psychiatry, University of Oxford, Oxford, UK, and by the Ashok Ranganathan Bursary from Exeter College, University of Oxford. Dr. Fernandes was supported by a National Institute for Health Research (NIHR) academic clinical fellowship. The primary study by Tendais et al. was supported under the project POCI/SAU-ESP/56397/2004 by the Operational Program Science and Innovation 2010 (POCI 2010) of the Community Support Board III and by the European Community Fund FEDER. The primary study by Fisher et al. was supported by a grant under the Invest to Grow Scheme from the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs. The primary study by Garcia-Esteve et al. was supported by Grant 7/98 from the Ministerio de Trabajo y Asuntos Sociales, Women's Institute, Spain. The primary study by Howard et al. was supported by the NIHR under its Programme Grants for Applied Research Programme (Grants RP-PG-1210-12002 and RPDG-1108-10012) and by the South London Clinical Research Network. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The primary study by Phillips et al. was supported by a scholarship from the National Health and Medical and Research Council (NHMRC). The primary study by Roomruangwong et al. was supported by the Ratchadaphiseksomphot Endowment Fund 2013 of Chulalongkorn University (CU-56-457-HR). The primary study by Naki c Radoš et al. was supported by the Croatian Ministry of Science, Education, and Sports. The primary study by Navarro et al. was supported by Grant 13/00 from the Ministry of Work and Social Affairs, Institute of Women, Spain. The primary study by Usuda et al. was supported by Grants-in-Aid for Young Scientists (A) from the Japan Society for the Promotion of Science (primary investigator: Daisuke Nishi, MD, PhD) and by an Intramural Research Grant for Neurological and Psychiatric Disorders from the National Center of Neurology and Psychiatry, Japan. Dr. Robertson-Blackmore was supported by a Young Investigator Award from the Brain and Behavior Research Foundation and NIMH Grant K23MH080290. The primary study by Rochat et al. was supported by grants from University of Oxford (HQ5035), the Tuixen Foundation (9940), the Wellcome Trust (082384/Z/07/Z and 071571), and the American Psychological Association. Dr. Rochat receives salary support from a Wellcome Trust Intermediate Fellowship (211374/Z/18/Z). The primary study by Rowe et al. was supported by the diamond Consortium, beyondblue Victorian Centre of Excellence in Depression and Related Disorders. The primary study by Comasco et al. was supported by funds from the Swedish Research Council (VR: 521-2013-2339, VR: 523-2014-2342), the Swedish Council for Working Life and Social Research (FAS: 2011-0627), the Marta Lundqvist Foundation (2013, 2014), and the Swedish Society of Medicine (SLS-331991). The primary study by Prenoveau et al. was supported by the Wellcome Trust (Grant 071571). The primary study by Stewart et al. was supported by Professor Francis Creed's Journal of Psychosomatic Research Editorship fund (BA00457) administered through University of Manchester. The primary study by Su et al. was supported by grants from the Department of Health (DOH94F044 and DOH95F022) and the China Medical University and Hospital (CMU94-105, DMR-92-92, and DMR94-46). The primary study by Tandon et al. was supported by the Thomas Wilson Sanitarium. The primary study by Tran et al. was supported by the Myer Foundation who funded the study under its Beyond Australia scheme. Dr. Tran was supported by an early career fellowship from the Australian National Health and Medical Research Council. The primary study by Vega-Dienstmaier et al. was supported by Tejada Family Foundation, Inc. and Peruvian-American Endowment, Inc. The primary study by Yonkers et al. was supported by a National Institute of Child Health and Human Development grant (5 R01HD045735). Drs. Benedetti and Thombs were supported by FRQS researcher salary awards.

The extent that randomized controlled trials (RCTs) accurately reflect intervention effectiveness depends on the completeness and accuracy of published results. A previous study found that only 40% of 63 RCTs published in top behavioral health journals in 2008-2009 clearly declared primary and secondary outcomes and only 21% were registered. The objective of this study was to conduct a five-year follow-up to assess outcome reporting clarity, proportion of registered trials, and adequacy of outcome registration in RCTs in top behavioral health journals.Eligible studies were RCTs published in Annals of Behavioral Medicine, Health Psychology, Journal of Psychosomatic Research, and Psychosomatic Medicine from January 2013 to October 2014.Of 76 RCT publications reviewed, only 25 (32.9%) adequately declared primary or secondary outcomes, whereas 51 (67.1%) had multiple primary outcomes or did not define outcomes. Of the 76 trials, 40 (52.6%) had been registered. Only 3 studies registered a single primary outcome and time point of assessment prior to enrolling patients, and registered and published outcomes were discrepant in 1 of the 3 studies. No studies were adequately registered as per Standard Protocol Items: Recommendation for Interventional Trials guidelines. Compared to 5 years prior, the proportion of published trials with adequate outcome declaration decreased from 39.7% to 32.9% (p=0.514). The proportion of registered trials increased from 20.6% to 52.6% (p0.001).The quality of published outcome declarations and trial registrations remains largely inadequate. Greater attention to trial registration and outcome definition in published reports is needed.

Contains fulltext : 191821.pdf (Publisher’s version ) (Closed access) Objective: Exercise is associated with improved health in many medical conditions. Little is known about the exercise habits of people with systemic sclerosis (SSc, or scleroderma). This study assessed the proportion of individuals with SSc who exercise and associations of demographic and disease variables with exercise. Additionally, the weekly amount of time spent exercising and the types of exercise performed were assessed among patients exercising. Methods: The sample consisted of adult participants with SSc enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort who completed baseline questionnaires from March 2014 through August 2015. Baseline questionnaires included questions on exercise habits, physician-reported medical characteristics, self-report demographic characteristics, the Health Assessment Questionnaire-Disability Index, Patient Health Questionnaire-9, and Patient-Reported Outcomes Measurement Information System-29. Results: Of 752 patients, 389 (51.7%) reported presently engaging in exercise, and these patients exercised on average 4.7 h [standard deviation (SD) = 2.8] per week. Among patients who reported exercising, walking was most commonly reported (n = 295, 75.8%). In bivariate analyses, present exercise was associated with more education, lower body mass index, some (versus no) alcohol consumption, non-smoking, limited/sine disease subtype, absence of skin thickening, lower disability, higher physical function, lower symptoms of anxiety and depression, less fatigue, lower sleep disturbance, higher ability to participate in social roles and activities, and less pain. Conclusions: Approximately half of SSc patients reported that they are currently exercising with walking being the most common form of exercise. Understanding exercise patterns and factors associated with exercise will help better inform intervention programs to support exercise for patients with SSc. •Implications for rehabilitation. •Systemic sclerosis is a rare autoimmune rheumatic disease associated with great morbidity and highly diverse presentation. •Approximately half of people with both limited and diffuse systemic sclerosis report exercising. •Most exercisers walk, but patients engage in a wide variety of exercise-related activities. •Individually designed exercise programs are most likely to support and encourage exercise in patients with diverse disease manifestations. 7 p.

Introduction The Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) has been recommended for depression screening in medically ill patients. Many existing HADS-D studies have used exploratory methods to select optimal cut-offs. Often, these studies report results from a small range of cut-off thresholds; cut-offs with more favourable accuracy results are more likely to be reported than others with worse accuracy estimates. When published data are combined in meta-analyses, selective reporting may generate biased summary estimates. Individual patient data (IPD) meta-analyses can address this problem by estimating accuracy with data from all studies for all relevant cut-off scores. In addition, a predictive algorithm can be generated to estimate the probability that a patient has depression based on a HADS-D score and clinical characteristics rather than dichotomous screening classification alone. The primary objectives of our IPD meta-analyses are to determine the diagnostic accuracy of the HADS-D to detect major depression among adults across all potentially relevant cut-off scores and to generate a predictive algorithm for individual patients. We are already aware of over 100 eligible studies, and more may be identified with our comprehensive search. Methods and analysis Data sources will include MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Eligible studies will have datasets where patients are assessed for major depression based on a validated structured or semistructured clinical interview and complete the HADS-D within 2 weeks (before or after). Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values, and a predictive algorithm for individual patients will be generated. Ethics and dissemination The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.

Background Negative symptoms are known to be present in the prodromal stage of psychotic disorders, yet little is known about their prevalence. Studies examining the presence of negative symptoms in ultra-high risk (UHR) populations have shown some limitations, notably failing to control depression. The objective of this study was to examine the prevalence of negative symptoms in the presence of significant levels of depression and in the absence of such symptoms (primary negative symptoms) over 1 year and to examine differences in negative symptoms in psychosis converters and non-converters. Methods Participants were 123 individuals at UHR for the development of psychosis receiving follow-up for a period of 2 years. Negative symptoms and depression were measured using the Scale for the Assessment of Negative Symptoms and the Montgomery-Asberg Depression Scale at baseline, 6 and 12 months post-admission. Results At baseline, the prevalence of negative symptoms and primary negative symptoms was 76.4% and 32.7%, respectively. Whereas the prevalence of negative symptoms was significantly decreased at 6 months, the prevalence of primary negative symptoms was similar at all time points. Negative symptoms at baseline were not different between later converters and non-converters to psychosis. Conclusion Our findings confirm the presence of secondary and primary negative symptoms in individuals at UHR, but suggest a differential trajectory of both measures over time. Future studies should include larger UHR groups and focus on the investigation of intra-individual changes in primary negative symptoms over time and further explore their potential role for psychosis conversion.

Introduction Studies of the diagnostic accuracy of depression screening tools often used data-driven methods to select optimal cut-offs. Typically, these studies report results from a small range of cut-off points around whatever cut-off score is identified as most accurate. When published data are combined in meta-analyses, estimates of accuracy for different cut-off points may be based on data from different studies, rather than data from all studies for each cut-off point. Thus, traditional meta-analyses may exaggerate accuracy estimates. Individual patient data (IPD) meta-analyses synthesise data from all studies for each cut-off score to obtain accuracy estimates. The 10-item Edinburgh Postnatal Depression Scale (EPDS) is commonly recommended for depression screening in the perinatal period. The primary objective of this IPD meta-analysis is to determine the diagnostic accuracy of the EPDS to detect major depression among women during pregnancy and in the postpartum period across all potentially relevant cut-off scores, accounting for patient factors that may influence accuracy (age, pregnancy vs postpartum). Methods and analysis Data sources will include Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science. Studies that include a diagnosis of major depression based on a validated structured or semistructured clinical interview administered within 2 weeks of (before or after) the administration of the EPDS will be included. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values. Analyses will evaluate data from pregnancy and the postpartum period separately, as well as combining data from all women in a single model. Ethics and dissemination This study does not require ethics approval. Dissemination will include journal articles and presentations to policymakers, healthcare providers and researchers. Systematic review registration PROSPERO 2015:CRD42015024785.

EnglishTo the Editor In their meta-analysis,1 Dr Mata and colleagues combined point and period estimates from 54 studies and reported a 29% prevalence of “depression or depressive symptoms” among resident physicians. This is more than 4 times the 12-month general population period prevalence for major depressive disorder (MDD) in North America.2,3 EnglishMs Levis and colleagues comment on our finding of high levels of depression and depressive symptoms among residents—based primarily on data from self-report inventory studies—and question whether these levels are truly elevated compared with criterion-based MDD in the general population. We included multiple subanalyses to assess whether the depression found in residents was due to inaccurate self-report inventories rather than a true increase in pathology. In a subanalysis examining 7 prospective studies on more than 4200 residents using the same inventory to assess depressive symptoms before and during residency, we found a median absolute increase in symptoms of 15.8% (relative risk, 4.5) with the onset of training. This analysis, which controlled for inventory and cutoff score, showed that the prevalence of preresidency depressive symptoms was essentially the same as that of the general population and suggests that the depression levels identified in the primary studies were an effect of residency. If Levis and colleagues were correct that elevated resident depression was an artifact of self-report inventories, then spuriously elevated depression levels would likely have appeared in the preresidency assessments as well. However, this was not the case.