Background: Major depressive disorder may be present in 10–20% of patients in medical settings. Routine screening for depression has been recommended to improve depression management. However, studies that have examined the diagnostic accuracy of depression screening tools typically have used data-driven, exploratory methods to select optimal cutoffs. Typically, these studies report results from a small range of cutoff points around whatever cutoff score is most accurate in that given study. When data from these published studies are combined in meta-analyses, estimates of accuracy for different cutoff points are often based on data from different studies, rather than having data from all studies for each possible cutoff point. As a result, traditional meta-analyses can generate grossly exaggerated estimates of accuracy. Individual patient data (IPD) meta-analyses can be used to address this problem by synthesizing data from all studies for each cutoff score to obtain precise, unbiased diagnostic accuracy estimates.
Objectives: The DEPRESsion Screening Data (DEPRESSD) Registry was created as a data repository for IPD meta-analyses of depression screening accuracy. The Registry is accumulating datasets from original studies with diagnostic accuracy data for common depression screening tools, which will result in large enough samples to accurately estimate accuracy across all relevant cutoff scores. It will also allow analyses of moderating factors that may influence accuracy (e.g., age, gender, diagnosis).
Methods: Authors of eligible published studies are being invited to contribute original data to the Registry. Datasets will be eligible for this project if they include a DSM or ICD diagnosis ofMDD based on a validated structured or semi-structured diagnostic interview administered within two weeks of the administration of one or more depression screening tools included in the Registry.
Conclusions: This Registry will provide a mechanism to obtain realistic estimates of depression screening tool accuracy, which currently appears to be substantially exaggerated.