Evidence map›Paper›PMID 27082440›Full record

ArticlePloS one2016

Determinants of Quality of Life for Breast Cancer Patients in Shanghai, China.

Bei Yan, Li-Ming Yang, Li-Peng Hao, Chen Yang, Lei Quan, Li-Hong Wang, Zheng Wu, Xiao-Pan Li, Yu-Tang Gao, Qiao Sun and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 2 pooled it
6.0field-weighted citation impact, top 3% of its field
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

63 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.

  1. The effect of cognitive behavioral therapy on the quality of life of breast cancer patients: a systematic review and meta-analysis of randomized controlled trials.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021
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3 more citing papers are in PubMed but not listed here.

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 at 4 institutions in 2 countries.

Bei YanShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Li-Ming YangShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Li-Peng HaoShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Chen YangShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Lei QuanDivision of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America.
Li-Hong WangShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Zheng WuShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Xiao-Pan LiShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Yu-Tang GaoDepartment of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Qiao SunShanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Jian-Min YuanDivision of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America.
Shanghai Municipal Center For Disease Control Prevention · CNRenji Hospital · CNSouth China University of Technology · CNUniversity of Pittsburgh · US

Funding

Prospective studies of cancer etiology and prevention in Shanghai and SingaporeR01CA144034 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YUAN, JIAN-MIN · 2010 to 2014
$7.0M
Cancer epidemiology cohorts in Shanghai and Singapore UM1CA182876 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YUAN, JIAN-MIN · 2014 to 2018
$4.3M
NCI NIH HHS R01 CA144034NCI NIH HHS R01CA144034NCI NIH HHS UM1 CA182876NCI NIH HHS UM1CA182876
6 · The paper itself

Abstract

purposeTo evaluate the association of social support status, health insurance and clinical factors with the quality of life of Chinese women with breast cancer.

methodsInformation on demographics, clinical characteristics, and social support status was collected from 1,160 women with newly diagnosed breast cancer in Shanghai, China. The Perceived Social Support Scale was used to assess different sources of social support for breast cancer patients. The quality of life was evaluated using the Functional Assessment of Cancer Therapy-Breast Cancer that consisted of five domains: breast cancer-specific, emotional, functional, physical, and social & family well-being. Multivariate linear regression models were used to evaluate the associations of demographic variables, clinical characteristics, and social support status with the quality of life measures.

resultsAdequate social support from family members, friends and neighbors, and higher scores of Perceived Social Support Scale were associated with significantly improved quality of life of breast cancer patients. Higher household income, medical insurance plans with low copayment, and treatment with traditional Chinese medicine for breast cancer all were associated with higher (better) scores of quality of life measures whereas patients receiving chemotherapy had significantly lower scores of quality of life.

conclusionSocial support and financial aids may significantly improve the quality of life of breast cancer survivors.

Indexed as

Quality of LifeAdultAgedBreast NeoplasmsChinaFemaleFriendsHumansInsurance, HealthMiddle AgedMultivariate AnalysisSocial ClassSocial SupportSurveys and QuestionnairesSurvivors

Identifiers

PMID27082440
PMCPMC4833339
OpenAlexW2339624027

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.