Literature DB >> 11809355

Assessing the performance of a new depression screener for primary care (PC-SAD).

William H Rogers1, Ira B Wilson, Kathleen M Bungay, Diane J Cynn, David A Adler.   

Abstract

As many as 50% of patients with major depression seen in primary care settings are not diagnosed. To facilitate efficient identification of primary care patients with depression, we developed a new patient-administered depression screening instrument (PC-SAD) that produces a DSM-IV diagnosis, and compared its performance to other screeners that yield DSM-IV diagnoses. To assess validity, the diagnostic accuracy of the PC-SAD was compared with the Inventory to Diagnose Depression (IDD) and the PRIME-MD-PHQ (PHQ) in a convenience sample (N = 312) of health plan members, primary care outpatients, and psychiatric patients (with diagnoses). The screeners were compared with each other and with psychiatric diagnoses to assess their relative performance. Disagreement among the three screeners was formally tested using a triangulation approach that incorporates a statistical likelihood model. Of patients diagnosed as depressed using the IDD, 84.2% were also depressed by the PC-SAD (sensitivity). Of patients not diagnosed as depressed by the IDD, 94.7% were not depressed by the PC-SAD (specificity). Using the triangulation method the sensitivities were 87.2% (PC-SAD), 88.4% (IDD), and 60.7% (PHQ). The specificities were 95.0% (PC-SAD), 92.7% (IDD), and 98.3% (PHQ). The performance of the PC-SAD and the IDD was comparable. The PHQ was less sensitive than either of those. The PC-SAD respondent burden strikes a balance between the very short PHQ, and the longer IDD, and has the lowest (easiest) Flesch-Kincaid reading level. Investigators, clinicians, and health plans that want a DSM-IV-based depression screener can choose from among these three instruments, with known tradeoffs in sensitivity, respondent burden, and readability.

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Mesh:

Year:  2002        PMID: 11809355     DOI: 10.1016/s0895-4356(01)00430-9

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  21 in total

1.  A randomized clinical trial of a telephone depression intervention to reduce employee presenteeism and absenteeism.

Authors:  Debra Lerner; David A Adler; William H Rogers; Hong Chang; Annabel Greenhill; Elina Cymerman; Francisca Azocar
Journal:  Psychiatr Serv       Date:  2015-03-01       Impact factor: 3.084

2.  Racial/Ethnic disparities in ART adherence in the United States: findings from the MACH14 study.

Authors:  Jane M Simoni; David Huh; Ira B Wilson; Jie Shen; Kathy Goggin; Nancy R Reynolds; Robert H Remien; Marc I Rosen; David R Bangsberg; Honghu Liu
Journal:  J Acquir Immune Defic Syndr       Date:  2012-08-15       Impact factor: 3.731

3.  Stochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression.

Authors:  Niels Smits; Matthew D Finkelman; Henk Kelderman
Journal:  Appl Psychol Meas       Date:  2015-06-29

4.  Improving work outcomes of dysthymia (persistent depressive disorder) in an employed population.

Authors:  David A Adler; Debra Lerner; Zachary L Visco; Annabel Greenhill; Hong Chang; Elina Cymerman; Francisca Azocar; William H Rogers
Journal:  Gen Hosp Psychiatry       Date:  2015-04-08       Impact factor: 3.238

5.  The Relationship Between Mentally Unhealthy Days and Depressive Symptoms Among Older Adults Over Time.

Authors:  Kimberly A Skarupski; Matthew M Zack; Julia L Bienias; Paul A Scherr; Denis A Evans
Journal:  J Appl Gerontol       Date:  2011-04

6.  Diagnosing depression: there is no blood test.

Authors:  Roanne Thomas-MacLean; Janet Stoppard; Baukje Bo Miedema; Sue Tatemichi
Journal:  Can Fam Physician       Date:  2005-08       Impact factor: 3.275

7.  Unemployment, job retention, and productivity loss among employees with depression.

Authors:  Debra Lerner; David A Adler; Hong Chang; Leueen Lapitsky; Maggie Y Hood; Carla Perissinotto; John Reed; Thomas J McLaughlin; Ernst R Berndt; William H Rogers
Journal:  Psychiatr Serv       Date:  2004-12       Impact factor: 3.084

8.  Stages of change for adherence to antiretroviral medications.

Authors:  Becky L Genberg; Yoojin Lee; William H Rogers; Cynthia Willey; Ira B Wilson
Journal:  AIDS Patient Care STDS       Date:  2013-10       Impact factor: 5.078

Review 9.  Four types of barriers to adherence of antiretroviral therapy are associated with decreased adherence over time.

Authors:  Becky L Genberg; Yoojin Lee; William H Rogers; Ira B Wilson
Journal:  AIDS Behav       Date:  2015-01

10.  Report of the Indo-US health care summit 2009 - Mental health section.

Authors:  Anand K Pandurangi; Nimesh G Desai
Journal:  Indian J Psychiatry       Date:  2009 Oct-Dec       Impact factor: 1.759

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