Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Perceived beliefs about breast cancer and breast cancer screening are important predictors for mammography utilization. This study adapted and validated the Champion's scale in Peru. This scale measures perceived susceptibility for breast cancer and perceived benefits and barriers for mammography.
Methods: A cross-sectional study was conducted among women ages 40 to 65 attending outpatient gynecology services in a public hospital in Peru. A group of experts developed and pre-tested a Spanish version of the Champion's scale to assess its comprehensibility (N=20). Factor analysis, internal consistency, and test-retest reliability analyses were performed (N=285). Concurrent validity compared scores from participants who had a mammogram and those who did not have it in the previous 15 months. T-test and multiple regression analysis adjusting for socio-demographic factors, mammography knowledge and other preventive behaviors were performed.
Results: The construct validity and reliability were optimal. Cronbach-Alpha coefficients were 0.75 (susceptibility), 0.72 (benefits) and 0.86 (barriers). Concurrent validity analysis showed an association between barriers and mammography screening use in bivariate (22.3±6.7 vs. 30.2±7.6; p<0.001) and multiple regression analysis (OR=0.28, 95% CI=0.18-0.43). Ages 50-60 years (OR=2.35, 95% CI=1.19-4.65), history of prior Papanicolaou test (OR=3.69, 95% CI=1.84-7.40), and knowledge about breast cancer and mammography (OR=3.69, 95% CI=1.84-7.40) were also independently associated with mammography screening use.
Conclusion: Concurrent validity analysis showed that the Champion's scale has important limitations for assessing perceived susceptibility for breast cancer and perceived benefits for mammography among Peruvian women. There is still a need for developing valid and reliable instruments for measuring perceived beliefs about breast cancer and mammography screening among Peruvian women.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3250928 | PMC |
| http://dx.doi.org/10.1186/1472-6874-11-54 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comprehensive Overview of Breast Cancer in India.
JCO Glob Oncol
May 2025
Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India.
Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.
Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.
An economic scenario analysis of implementing artificial intelligence in BreastScreen Norway-Impact on radiologist person-years, costs and effects.
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
View Article and Find Full Text PDFGermline rare variants in cancer susceptibility genes and subsequent neoplasm risk after childhood cancer.
J Natl Cancer Inst
September 2025
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States.
Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.
Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.
STmiR: A Novel XGBoost-based framework for spatially resolved miRNA activity prediction in cancer transcriptomics.
PLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFIntegrative network toxicology and molecular docking reveal 4-Nonylphenol's multifaceted mechanisms in breast cancer pathogenesis.
PLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.