Are one or two simple questions sufficient to detect depression in cancer and palliative care? A Bayesian meta-analysis View Full Text


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Article Info

DATE

2008-05-27

AUTHORS

A J Mitchell

ABSTRACT

The purpose of this study is to examine the value of one or two simple verbal questions in the detection of depression in cancer settings. This study is a systematic literature search of abstract and full text databases to January 2008. Key authors were contacted for unpublished studies. Seventeen analyses were found. Of these, 13 were conducted in late stage palliative settings. (1) Single depression question: across nine studies, the prevalence of depression was 16%. A single 'depression' question enabled the detection of depression in 160 out of 223 true cases, a sensitivity of 72%, and correctly reassured 964 out of 1166 non-depressed cancer sufferers, a specificity of 83%. The positive predictive value (PPV) was 44% and the negative predictive value (NPV) 94%. (2) Single interest question: there were only three studies examining the 'loss-of-interest' question, with a combined prevalence of 14%. This question allowed the detection of 60 out of 72 cases (sensitivity 83%) and excluded 394 from 459 non-depressed cases (specificity of 86%). The PPV was 48% and the NPV 97%. (3) Two questions (low mood and low interest): five studies examined two questions with a combined prevalence of 17%. The two-question combination facilitated a diagnosis of depression in 138 of 151 true cases (sensitivity 91%) and gave correct reassurance to 645 of 749 non-cases (specificity 86%). The PPV was 57% and the NPV 98%. Simple verbal methods perform well at excluding depression in the non-depressed but perform poorly at confirming depression. The 'two question' method is significantly more accurate than either single question but clinicians should not rely on these simple questions alone and should be prepared to assess the patient more thoroughly. More... »

PAGES

1934-1943

References to SciGraph publications

  • 2004-05-25. High levels of untreated distress and fatigue in cancer patients in BRITISH JOURNAL OF CANCER
  • 2005-01. Relationship of depression to patient satisfaction: findings from the barriers to breast cancer study in BREAST CANCER RESEARCH AND TREATMENT
  • 2007-07-13. A longitudinal evaluation of the Center for Epidemiologic Studies-Depression scale (CES-D) in a Rheumatoid Arthritis Population using Rasch Analysis in HEALTH AND QUALITY OF LIFE OUTCOMES
  • 2001-05-22. Depression in palliative care: a pragmatic report from the Expert Working Group of the European Association for Palliative Care in SUPPORTIVE CARE IN CANCER
  • 2004-03. Nonadherence in Adolescent Oncology Patients: Preliminary Data on Psychological Risk Factors and Relationships to Outcome in JOURNAL OF CLINICAL PSYCHOLOGY IN MEDICAL SETTINGS
  • 2001-01-15. How successful are oncologists in identifying patient distress, perceived social support, and need for psychosocial counselling? in BRITISH JOURNAL OF CANCER
  • 2004-08-10. Rapid screening for depression – validation of the Brief Case-Find for Depression (BCD) in medical oncology and palliative care patients in BRITISH JOURNAL OF CANCER
  • 2004-10-09. Depressive symptom patterns and their consequences for diagnosis of affective disorders in cancer patients in SUPPORTIVE CARE IN CANCER
  • 2006-06-12. Evaluation of the Edinburgh Post Natal Depression Scale using Rasch analysis in BMC PSYCHIATRY
  • 1994-10. Can oncologists detect distress in their out-patients and how satisfied are they with their performance during bad news consultations? in BRITISH JOURNAL OF CANCER
  • 2001-04-15. Psychiatric morbidity and its recognition by doctors in patients with cancer in BRITISH JOURNAL OF CANCER
  • 2004-01-20. Major depression in outpatients attending a regional cancer centre: screening and unmet treatment needs in BRITISH JOURNAL OF CANCER
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    http://scigraph.springernature.com/pub.10.1038/sj.bjc.6604396

    DOI

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    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/18506146


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