Accuracy of Self-Reported Items for the Screening of Depression in the General Population

自我报告项目在普通人群抑郁症筛查中的准确性

阅读:2

Abstract

INTRODUCTION: Though self-reported items (SRD, self-reported depression) are commonly used in health surveys and cohort studies, their metric properties as a depression indicator remain unclear. The aims were to evaluate the measurement properties of SRD using the Patient Health Questionnaire-8 (PHQ-8) as reference and to identify factors related to the agreement between both indicators. METHODS: Data from the European Health Interview Survey in Spain in 2014/2015 (n = 22,065) were analyzed. Two indicators of depression were considered: SRD based on two items yes/no (positive: both yes), and the PHQ-8 (positive ≥ 10). Socioeconomic factors and use of health services were considered as independent variables. The prevalence of depression, sensitivity, specificity, global agreement, and positive and negative predictive values (PPV and NPV) of SRDs were evaluated using the PHQ-8 as a reference. Logistic regression models were fitted to determine factors associated with the agreement between indicators. RESULTS: The prevalence of depression was lower when assessed with PHQ-8 (5.9%) than with SRD (7.7%). SRD sensitivity and PPV were moderate-low (52.9% and 40.4%, respectively) whereas global agreement, specificity, and NPV were high (92.7%, 95.1%, and 97.0%, respectively). Positive agreement was associated with marital status, country of birth, employment status, and social class. Negative agreement was related to all independent variables except country of birth. CONCLUSIONS: SRD items tend to overestimate the current prevalence of depression. While its use in health surveys and cohorts may be appropriate as a quick assessment of possible depression, due to their low sensitivity, its use in clinical contexts is questionable.

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。