Michael von Glischinski1, Ruth von Brachel2, Gerrit Hirschfeld3. 1. Universität Witten Herdecke, Witten, Germany. 2. Mental Health Research & Treatment Center, Ruhr-Universität Bochum, Bochum, Germany. 3. Faculty of Business Management and Health, Bielefeld University of Applied Sciences, Bielefeld, Germany. gerrit.hirschfeld@fh-bielefeld.de.
Abstract
INTRODUCTION: The Beck Depression Inventory revised (BDI-II) is widely used tool to screen for depression. The aim of the present study was to systematically review and synthesize studies that determined optimal cut points for the BDI-II. METHOD: We identified 27 studies that tried to identify optimal cut points for the BDI-II. Study quality was assessed using QUADAS criteria. Cut points and their variability were analyzed descriptively, via simulation and synthesized with a diagnostic meta-analysis. Analysis was performed on all studies and subgroups based on the setting (psychiatric, somatic, healthy). RESULTS: Cut points identified as optimal ranged from 10 to 25 across all studies. Simulation-based estimations of the variability inherent in studies show that much of the between-study differences may be attributed to random fluctuations. Diagnostic meta-analysis across all studies revealed that a cut point of 14.5 (95% CI 12.75-16.44) is optimal, yielding a sensitivity of 0.86 and a specificity of 0.78. Analyses within the different settings suggest using sample-specific cut points, specifically 18.18 in psychiatric settings, and 12.9 in primary care settings and healthy populations. CONCLUSION: Most studies aimed at determining optimal cut points fail to acknowledge that reported results are only estimates and subject to random fluctuations resulting in conflicting recommendations for practitioners. Taking into account these fluctuations, we find that practitioners should use different cut points to screen for depression in primary care and healthy populations (a score of 13 and higher indicates depression) and psychiatric settings (a score of 19 and higher indicates depression). Methods to describe this variability and meta-analysis to synthesize findings across studies should be used more widely.
INTRODUCTION: The Beck Depression Inventory revised (BDI-II) is widely used tool to screen for depression. The aim of the present study was to systematically review and synthesize studies that determined optimal cut points for the BDI-II. METHOD: We identified 27 studies that tried to identify optimal cut points for the BDI-II. Study quality was assessed using QUADAS criteria. Cut points and their variability were analyzed descriptively, via simulation and synthesized with a diagnostic meta-analysis. Analysis was performed on all studies and subgroups based on the setting (psychiatric, somatic, healthy). RESULTS: Cut points identified as optimal ranged from 10 to 25 across all studies. Simulation-based estimations of the variability inherent in studies show that much of the between-study differences may be attributed to random fluctuations. Diagnostic meta-analysis across all studies revealed that a cut point of 14.5 (95% CI 12.75-16.44) is optimal, yielding a sensitivity of 0.86 and a specificity of 0.78. Analyses within the different settings suggest using sample-specific cut points, specifically 18.18 in psychiatric settings, and 12.9 in primary care settings and healthy populations. CONCLUSION: Most studies aimed at determining optimal cut points fail to acknowledge that reported results are only estimates and subject to random fluctuations resulting in conflicting recommendations for practitioners. Taking into account these fluctuations, we find that practitioners should use different cut points to screen for depression in primary care and healthy populations (a score of 13 and higher indicates depression) and psychiatric settings (a score of 19 and higher indicates depression). Methods to describe this variability and meta-analysis to synthesize findings across studies should be used more widely.
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