Assessing depression in a geriatric cancer population

评估老年癌症患者的抑郁症

阅读:1

Abstract

OBJECTIVE: To examine the ability of three popular self-report measures of depression to assess depression in a geriatric cancer setting. METHOD: Cancer patients 70 years or older and on active treatment completed the Geriatric Depression Scale-Short Form, the Hospital Anxiety and Depression Scale, and the Center for Epidemiological Studies Depression Scale-Revised, and were interviewed using the depression module of the Structured Clinical Interview for DSM disorders (SCID) as the 'gold standard.' Analyses included calculating internal consistency, ROC curves, and the sensitivity and specificity to detect major depression (MDD) or minor depression (i.e. subthreshold depression). RESULTS: In a sample of 201 cancer patients (85% White; 64% completed college degree or higher), all three of the self-report measures produced adequate internal consistency and predicted depression greater than chance. However, the published cutoff scores for detecting MDD produced inadequate sensitivity, suggesting these scores will miss as many as 33%-83% of geriatric cancer patients who are depressed. Revised cutoff scores were lower than published cutoff scores. CONCLUSION: Although these measures produced good internal consistency and were better than chance at predicting depression in a geriatric cancer sample, the published cutoff scores for these measures did not perform well in predicting MDD nor minor depression. Of the three measures, the CES-D appeared to have the most utility. This data suggests that these popular screening measures may be inadequate for reliably identifying depression in a geriatric cancer population. Researchers and clinicians, therefore, should use caution when selecting depression measures for geriatric cancer patients and consider using the lower cut-off scores presented here.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。