Detecting depression in persons living in long-term care: a systematic review and meta-analysis of diagnostic test accuracy studies.

Objective Depressive disorders are common in long-term care (LTC), however, there is no one process used to detect depressive disorders in this setting. Our goal was to describe the diagnostic accuracy of depression detection tools used in LTC settings. Methods We conducted a systematic review and meta-analysis of diagnostic accuracy measures. The databases PubMed, EMBASE, PsycINFO and CINAHL were searched from inception to 10 September 2021. Studies involving persons living in LTC, assisted living residences or facilities, comparing diagnostic accuracy of depression tools with a reference standard, were included. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess risk of bias. Results We identified 8,463 citations, of which 20 studies were included in qualitative synthesis and 19 in meta-analysis. We identified 23 depression detection tools (including different versions) that were validated against a reference standard. At a cut-off point of 6 on the Geriatric Depression Scale-15 (GDS-15), the pooled sensitivity was 73.6% (95% confidence interval (CI) 43.9%–76.5%), specificity was 76.5% (95% CI 62.9%–86.7%), and an area under the curve was 0.83. There was significant heterogeneity in these analyses. There was insufficient data to conduct meta-analysis of other screening tools. The Nursing Homes Short Depression Inventory (NH-SDI) had a sensitivity ranging from 40.0% to 98.0%. The 4-item Cornell Scale for Depression in Dementia (CSDD) had the highest sensitivity (67.0%–90.0%) for persons in LTC living with dementia. Conclusions There are 23 tools validated for detection of depressive disorders in LTC, with the GDS-15 being the most studied. Tools developed specifically for use in LTC settings include the NH-SDI and CSDD-4, which provide briefer options to screen for depression. However, more studies of both are needed to examine tool accuracy using meta-analyses. [ABSTRACT FROM AUTHOR]