Evidence map›Paper›PMID 40378349›Full record

ArticlePloS one2025

Multivariable prediction of early malignancy detection among first-degree relatives of patients with hereditary colorectal cancer based on the health belief model: A cross-sectional study from the largest hereditary colorectal cancer cohort in China.

Liyuan Yang, Xiaojun Chen, Mengjiao Zhong, Zhiyong Pan, Xiaodan Wu

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Liyuan YangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.
Xiaojun ChenSchool of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China.
Mengjiao ZhongSun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Zhiyong PanDepartment of Neurology, Yueyang People's Hospital, Hunan Normal University, Yueyang, China.
Xiaodan WuSun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID https://orcid.org/0000-0002-5622-376X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFirst-degree relatives (FDRs) of hereditary colorectal cancer are at an increased risk of cancer and receiving early detection and surveillance on malignancies is an efficacious strategy to reduce cancer-related morbidity and mortality. But there is rare information on screening in this cancer high-risk group in China.

objectiveThe aim of this study was to explore the detection and surveillance on malignancies and its predictors among FDRs of patients with hereditary CRC based on the largest hereditary colorectal cancer cohort from China.

methodsWe conducted an exploratory, descriptive, cross-sectional study. 530 FDRs were recruited from December 2021 to December 2022, evaluated using the questionnaire on knowledge, attitudes and behavior of malignancies early detection and the Champion's Health Belief Model Scale. The main outcome were identified using logistic regression analysis.

resultsAmong all the FDRs, only 122 (23.0%) underwent malignancies early detection. The predictors of malignancies early detection included knowledge score (OR = 1.117, P < 0.001), sex (OR = 0.244, P < 0.001), age (OR = 4.627, P < 0.001), marital status (OR = 3.815, P < 0.001), chronic disease history (OR = 2.945, P < 0.01), diagnosis of index patient (OR = 2.876, P < 0.001), attitude about "cancer is preventable" (OR = 3.405, P < 0.05) and "one needs malignancies early detection even if feel healthy" (OR = 16.477, P < 0.001), perceived susceptibility (OR = 1.106, P < 0.05) and self-efficacy (OR = 1.244, P < 0.001).

conclusionThe uptake of malignancy early detection among FDRs should be improved. Some demographic and health-related characteristics, knowledge score, perceived susceptibility and self-efficacy were the most important predictors of malignancies early detection. Enhancing the recognition of clinical features of hereditary CRC and offering personalized genetic counseling, along with tailored cancer risk assessments, could further optimize individual cancer surveillance and prevention strategies, helping to reduce the risk of malignancies in high-risk populations.

Indexed as

Colorectal NeoplasmsEarly Detection of CancerHealth Belief ModelAdultAgedChinaCross-Sectional StudiesFamilyFemaleGenetic Predisposition to DiseaseHealth Knowledge, Attitudes, PracticeHumansMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40378349
PMCPMC12084039

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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.