Development of a Minimum Data Set-based depression rating scale for use innursing homes

Background: depression is common but under-diagnosed in nursing-home reside nts. There is a need for a standardized screening instrument which incorpor ates daily observations of nursing-home staff. Aim: to develop and validate a screening instrument for depression using it ems from the Minimum Data Set of the Resident Assessment Instrument. Methods: we conducted semi-structured interviews with 108 residents from tw o nursing homes to obtain depression ratings using the 17-item Hamilton Dep ression Rating Scale and the Cornell Scale for Depression in Dementia. Nurs ing staff completed Minimum Data Set assessments. In a randomly assigned de rivation sample (n = 81), we identified Minimum Data Set mood items that we re correlated (P < 0.05) with Hamilton and Cornell ratings. These items wer e factored using an oblique rotation to yield five conceptually distinct fa ctors. Using linear regression, each set of factored items was regressed ag ainst Hamilton and Cornell ratings to identify a core set of seven Minimum Data Set mood items which comprise the Minimum Data Set Depression Rating S cale. We then tested the performance of the Minimum Data Set Depression Rat ing Scale against accepted cut-offs and psychiatric diagnoses. Results: a cutpoint score of 3 on the Minimum Data Set Depression Rating Sc ale maximized sensitivity (94% for Hamilton, 78% for Cornell) with minimal loss of specificity (72% for Hamilton, 77% for Cornell) when tested against cut-offs for mild to moderate depression in the derivation sample. Results were similar in the validation sample. When tested against diagnoses of ma jor or non-major depression in a subset of 82 subjects, sensitivity was 91% and specificity was 69%, Performance compared favourably with the 15-item Geriatric Depression Scale. Conclusion: items from the Minimum Data Set can be organized to screen for depression in nursing-home residents. Further testing of the instrument is now needed.