Evidence map›Paper›PMID 31106980›Full record

ArticleCancer medicine2019

Patterns and predictors of self-reported clinical diagnosis and treatment for depression in prostate cancer survivors.

Daniel O Erim, Jeannette T Bensen, James L Mohler, Elizabeth T H Fontham, Lixin Song, Laura Farnan, Scott E Delacroix, Edward S Peters, Theodora N Erim, Ronald C Chen and 1 more

Abstract read
In one paragraph

Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Quality of life in the year after new diagnosis with advanced prostate cancer for Black and White individuals living in the US.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Article
  4. A cross-sectional study on demoralization in prostate cancer patients: the role of masculine self-esteem, depression, and resilience.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022
    Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Daniel O ErimDepartment of Health Policy and Management, Gillings School of Global Public Health, the University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-6604-7640
Jeannette T BensenDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina.
James L MohlerDepartment of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, New York.
Elizabeth T H FonthamLouisiana State University Health Sciences Center, School of Public Health, New Orleans, Louisiana.
Lixin SongLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Laura FarnanLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Scott E DelacroixDepartment of Urology, Louisiana State University, New Orleans, Louisiana.
Edward S PetersLouisiana State University Health Sciences Center, School of Public Health, New Orleans, Louisiana.
Theodora N Erim
Ronald C ChenLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Bradley N GaynesDepartment of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAppropriate depression care is a cancer-care priority. However, many cancer survivors live with undiagnosed and untreated depression. Prostate cancer survivors may be particularly vulnerable, but little is known about their access to depression care. The goal of this study was to describe patterns and predictors of clinical diagnosis and treatment of depression in prostate cancer survivors.

methodsGeneralized estimating equations were used to evaluate indicators of self-reported clinical diagnosis and treatment depression as a function of individual-level characteristics within a longitudinal dataset. The data were from a population-based cohort of North Carolinian prostate cancer survivors who were enrolled from 2004 to 2007 on the North Carolina-Louisiana Prostate Cancer Project (N = 1,031), and prospectively followed annually from 2008 to 2011 on the Health Care Access and Prostate Cancer Treatment in North Carolina (N = 805).

resultsThe average rate of self-reported clinical diagnosis of depression was 44% (95% CI: 39%-49%), which declined from 60% to 40% between prostate cancer diagnosis and 5-7 years later. Factors associated with lower odds of self-reported clinical diagnosis of depression include African-American race, employment, age at enrollment, low education, infrequent primary care visits, and living with a prostate cancer diagnosis for more than 2 years. The average rate of self-reported depression treatment was 62% (95% CI: 55%-69%). Factors associated with lower odds of self-reported depression treatment included employment and living with a prostate cancer diagnosis for 2 or more years.

conclusionProstate cancer survivors experience barriers when in need of depression care.

Indexed as

Practice Patterns, Physicians'AdultAgedCancer SurvivorsDepressionDisease ManagementHumansMaleMiddle AgedPatient SatisfactionPrognosisProstatic NeoplasmsPublic Health SurveillanceSelf ReportSurveys and Questionnairesclinical diagnosisclinical recognitiondepression treatmentpredictorsprostate cancer

Identifiers

PMID31106980
PMCPMC6639178

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.