for the Depression Screening Data (DEPRESSD) PHQ Collaboration 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.

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Objective To update a previous individual participant data meta-analysis and determine the accuracy of the Patient Health Questionnaire-9 (PHQ-9), the most commonly used depression screening tool in general practice, for detecting major depression overall and by study or participant subgroups. Design Systematic review and individual participant data meta-analysis. Data sources Medline, Medline In-Process, and Other Non-Indexed Citations via Ovid, PsycINFO, Web of Science searched through 9 May 2018. Review methods Eligible studies administered the PHQ-9 and classified current major depression status using a validated semistructured diagnostic interview (designed for clinician administration), fully structured interview (designed for lay administration), or the Mini International Neuropsychiatric Interview (MINI; a brief interview designed for lay administration). A bivariate random effects meta-analytic model was used to obtain point and interval estimates of pooled PHQ-9 sensitivity and specificity at cut-off values 5-15, separately, among studies that used semistructured diagnostic interviews (eg, Structured Clinical Interview for Diagnostic and Statistical Manual), fully structured interviews (eg, Composite International Diagnostic Interview), and the MINI. Meta-regression was used to investigate whether PHQ-9 accuracy correlated with reference standard categories and participant characteristics. Results Data from 44 503 total participants (27 146 additional from the update) were obtained from 100 of 127 eligible studies (42 additional studies; 79% eligible studies; 86% eligible participants). Among studies with a semistructured interview reference standard, pooled PHQ-9 sensitivity and specificity (95% confidence interval) at the standard cut-off value of ≥10, which maximised combined sensitivity and specificity, were 0.85 (0.79 to 0.89) and 0.85 (0.82 to 0.87), respectively. Specificity was similar across reference standards, but sensitivity in studies with semistructured interviews was 7-24% (median 21%) higher than with fully structured reference standards and 2-14% (median 11%) higher than with the MINI across cut-off values. Across reference standards and cut-off values, specificity was 0-10% (median 3%) higher for men and 0-12 (median 5%) higher for people aged 60 or older. Conclusions Researchers and clinicians could use results to determine outcomes, such as total number of positive screens and false positive screens, at different PHQ-9 cut-off values for different clinical settings using the knowledge translation tool at www.depressionscreening100.com/phq . Study registration PROSPERO CRD42014010673.

Major depressive disorder (MDD) and bipolar disorder (BD) lack robust biomarkers useful for screening purposes in a clinical setting. A systematic review of the literature was conducted on metabolomic studies of patients with MDD or BD through the use of analytical platforms such as in vivo brain imaging, mass spectrometry, and nuclear magnetic resonance. Our search identified a total of 7,590 articles, of which 266 articles remained for full-text revision. Overall, 249 metabolites were found to be dysregulated with 122 of these metabolites being reported in two or more of the studies included. A list of biomarkers for MDD and BD established from metabolites found to be abnormal, along with the number of studies supporting each metabolite and a comparison of which biological fluids they were reported in, is provided. Metabolic pathways that may be important in the pathophysiology of MDD and BD were identified and predominantly center on glutamatergic metabolism, energy metabolism, and neurotransmission.Using online drug registries, we also illustrate how metabolomics can facilitate the discovery of novel candidate drug targets. K E Y W O R D Sbiochemical, biomarker panel, mood disorders, omics

Objective To evaluate the accuracy of the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) to screen for major depression among people with physical health problems. Design Systematic review and individual participant data meta-analysis. Data sources Medline, Medline In-Process and Other Non-Indexed Citations, PsycInfo, and Web of Science (from inception to 25 October 2018). Review methods Eligible datasets included HADS-D scores and major depression status based on a validated diagnostic interview. Primary study data and study level data extracted from primary reports were combined. For HADS-D cut-off thresholds of 5-15, a bivariate random effects meta-analysis was used to estimate pooled sensitivity and specificity, separately, in studies that used semi-structured diagnostic interviews (eg, Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders ), fully structured interviews (eg, Composite International Diagnostic Interview), and the Mini International Neuropsychiatric Interview. One stage meta-regression was used to examine whether accuracy was associated with reference standard categories and the characteristics of participants. Sensitivity analyses were done to assess whether including published results from studies that did not provide raw data influenced the results. Results Individual participant data were obtained from 101 of 168 eligible studies (60%; 25 574 participants (72% of eligible participants), 2549 with major depression). Combined sensitivity and specificity was maximised at a cut-off value of seven or higher for semi-structured interviews, fully structured interviews, and the Mini International Neuropsychiatric Interview. Among studies with a semi-structured interview (57 studies, 10 664 participants, 1048 with major depression), sensitivity and specificity were 0.82 (95% confidence interval 0.76 to 0.87) and 0.78 (0.74 to 0.81) for a cut-off value of seven or higher, 0.74 (0.68 to 0.79) and 0.84 (0.81 to 0.87) for a cut-off value of eight or higher, and 0.44 (0.38 to 0.51) and 0.95 (0.93 to 0.96) for a cut-off value of 11 or higher. Accuracy was similar across reference standards and subgroups and when published results from studies that did not contribute data were included. Conclusions When screening for major depression, a HADS-D cut-off value of seven or higher maximised combined sensitivity and specificity. A cut-off value of eight or higher generated similar combined sensitivity and specificity but was less sensitive and more specific. To identify medically ill patients with depression with the HADS-D, lower cut-off values could be used to avoid false negatives and higher cut-off values to reduce false positives and identify people with higher symptom levels. Trial registration PROSPERO CRD42015016761.

Objectives: The rapid pace, high volume, and limited quality of mental health evidence being generated during COVID-19 poses a barrier to effective decision-making. The objective of the present report is to compare mental health outcomes assessed during COVID-19 to outcomes prior to COVID-19 in the general population and other population groups. Design: Living systematic review. Data Sources: MEDLINE (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). The initial search was conducted on April 13, 2020 with ongoing weekly updates. Eligibility criteria for selecting studies: For 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. 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 three studies in an outcome domain, we conducted restricted maximum-likelihood random-effects meta-analyses. Results: As of March 22, 2021, we had identified 36 unique eligible studies with data from 33 cohorts. All reported COVID-19 outcomes between March and June 2020, and 3 studies also reported outcomes between September and November 2020. Estimates of changes in general mental health were close to zero in the general population (standardized mean difference [SMD] = 0.02, 95% CI -0.11 to 0.16, I2 = 94.6%; 4 studies, N = 19,707) and among older adults (SMD = 0.02, 95% CI -0.11 to 0.16, I2 = 90.4%; 4 studies, N = 5,520) and university students (SMD = -0.01, 95% CI -0.33 to 0.30, I2 = 92.0%; 3 studies, N = 3,372). Changes in anxiety symptoms were close to zero and not statistically significant in university students (SMD = 0.00, 95% CI -0.35 to 0.36, I2 = 95.4%; 5 studies, N = 1,537); women or females (SMD = 0.02, 95% CI -0.35 to 0.39, I2 = 92.3%; 3 studies, N = 2,778); and men or males (SMD = 0.07, 95% CI -0.01 to 0.15; I2 = 0.01%; 3 studies, N = 1,250); anxiety symptoms increased, however, among people with pre-existing medical conditions (SMD = 0.27, 95% CI 0.01 to 0.54, I2 = 91.0%; 3 studies, N = 2,053). Changes in depression symptoms were close to zero or small and not statistically significant among university students (SMD = 0.19, 95% CI -0.08 to 0.45, I2 = 91.8%; 5 studies, N = 1,537); people with pre-existing medical conditions (SMD = 0.01, 95% CI -0.15 to 0.17, I2 = 14.9%; 3 studies, N = 2,006); women or females (SMD = 0.21, 95% CI -0.14 to 0.55, I2 = 91.2%; 3 studies, N = 2,843); and men or males (SMD = 0.00, 95% CI -0.21 to 0.22; I2 = 92.3%; 4 studies, N = 3,661). In 3 studies with data from both March to June 2020 and September to November 2020, symptoms were unchanged from pre-COVID-19 at both time points or there were increases at the first assessment that had largely dissipated by the second assessment. Conclusions: Evidence does not suggest a widespread negative effect on mental health symptoms in COVID-19, although it is possible that gaps in data have not allowed identification of changes in some vulnerable groups. Continued updating is needed as evidence accrues.

<b><i>Background:</i></b> 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. <b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> Data were included for 54 of 72 identified eligible studies (<i>n</i> participants = 16,688, <i>n</i> 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). <b><i>Conclusions:</i></b> The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.