Literature DB >> 24681108

How are patient populations characterized in studies investigating depression in advanced cancer? Results from a systematic literature review.

Elene Janberidze1, Marianne Jensen Hjermstad2, Dagny Faksvåg Haugen3, Katrin Ruth Sigurdardottir4, Erik Torbjørn Løhre5, Hanne Cathrine Lie6, Jon Håvard Loge7, Stein Kaasa5, Anne Kari Knudsen5.   

Abstract

CONTEXT: Prevalence rates of depression in patients with advanced cancer vary considerably. This may be because of heterogeneous samples and use of different assessment methods. Adequate sample descriptions and consistent use of measures are needed to be able to generalize research findings and apply them to clinical practice.
OBJECTIVES: Our objective was twofold: First, to investigate which clinically important variables were used to describe the samples in studies of depression in patients with advanced cancer; and second, to examine the methods used for assessing and classifying depression in these studies.
METHODS: PubMed, PsycINFO, Embase, and CINAHL were searched combining search term groups representing "depression," "palliative care," and "advanced cancer" covering 2007-2011. Titles and abstracts were screened, and relevant full-text articles were evaluated independently by two authors. Information on 32 predefined variables on cancer disease, treatment, sociodemographics, depression-related factors, and assessment methods was extracted from the articles.
RESULTS: After removing duplicates, 916 citations were screened of which 59 articles were retained. Age, gender, and stage of the cancer disease were the most frequently reported variables. Depression-related variables were rarely reported, for example, antidepressant use (17%) and previous depressive episodes (12%). Only 25% of the studies assessed and classified depression according to a validated diagnostic system.
CONCLUSION: Current practice for describing sample characteristics and assessing depression varies greatly between studies. A more standardized practice is recommended to enhance the generalizability and utility of findings. Stakeholders are encouraged to work toward a common standard for sample descriptions.
Copyright © 2014 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Advanced cancer; assessment; depression; generalizability; palliative care

Mesh:

Year:  2014        PMID: 24681108     DOI: 10.1016/j.jpainsymman.2013.11.013

Source DB:  PubMed          Journal:  J Pain Symptom Manage        ISSN: 0885-3924            Impact factor:   3.612


  4 in total

1.  Patients in palliative care-Development of a predictive model for anxiety using routine data.

Authors:  Sonja Hofmann; Stephanie Hess; Carsten Klein; Gabriele Lindena; Lukas Radbruch; Christoph Ostgathe
Journal:  PLoS One       Date:  2017-08-03       Impact factor: 3.240

2.  Mental health care in oncology. Contemporary perspective on the psychosocial burden of cancer and evidence-based interventions.

Authors:  R Caruso; W Breitbart
Journal:  Epidemiol Psychiatr Sci       Date:  2020-01-09       Impact factor: 6.892

3.  The Palliative Radiotherapy and Inflammation Study (PRAIS) - protocol for a longitudinal observational multicenter study on patients with cancer induced bone pain.

Authors:  Ragnhild Habberstad; Trude Camilla Salvesen Frøseth; Nina Aass; Tatiana Abramova; Theo Baas; Siri Tessem Mørkeset; Augusto Caraceni; Barry Laird; Jason W Boland; Romina Rossi; Elena Garcia-Alonso; Hanne Stensheim; Jon Håvard Loge; Marianne Jensen Hjermstad; Ellen Bjerkeset; Asta Bye; Jo-Åsmund Lund; Tora Skeidsvoll Solheim; Ola Magne Vagnildhaug; Cinzia Brunelli; Jan Kristian Damås; Tom Eirik Mollnes; Stein Kaasa; Pål Klepstad
Journal:  BMC Palliat Care       Date:  2018-09-28       Impact factor: 3.234

4.  Depressive and anxiety symptoms among Japanese cancer survivors: Japan cancer survivorship research project.

Authors:  Motoki Endo; Kentaro Matsui; Rie Akaho; Kiyomi Mitsui; Yan Yan; Yuya Imai; Yuito Ueda; Go Muto; Gautam A Deshpande; Yasuhisa Terao; Satoru Takeda; Mitsue Saito; Kazuhiko Hayashi; Katsuji Nishimura; Takeshi Tanigawa
Journal:  BMC Cancer       Date:  2022-02-02       Impact factor: 4.430

  4 in total

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