METHODS: We conducted focus groups among healthy English-speaking Malay women in Singapore, aged 40 to 69 years, using a structured guide developed through literature review, expertise input and participant refinement. Thematic analysis was conducted to extract dominant themes representing key motivators and barriers to screening and genetic testing. We used grounded theory to interpret results and derive a framework of understanding, with implications for improving uptake of services.
RESULTS: Five focus groups (four to six participants per group) comprising 27 women were conducted to theme saturation. Major themes were (a) spiritual and religious beliefs act as barriers towards uptake of screening and genetic testing; (b) preference for traditional medicine competes with Western medicine recommendations; (c) family and community influence health-related decisions, complexed by differences in intergenerational beliefs creating contrasting attitudes towards screening and prevention.
CONCLUSIONS: Decisions to participate in breast cancer screening and genetic testing are influenced by cultural, traditional, spiritual/religious, and intergenerational beliefs. Strategies to increase uptake should include acknowledgement and integration of these beliefs into counseling and education and collaboration with key influential Malay stakeholders and leaders.
METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.
RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.
CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
METHODS: A case-control study comprising 134 breast cancer patients and 265 cancer-free controls were conducted. Dietary intakes were assessed using a validated food frequency questionnaire (FFQ), from which the HEI-2015 score was calculated. Logistic regression was used to derive the odds ratios (ORs) for measuring the association between HEI-2015 scores and breast cancer risk.
RESULTS: Subjects in the top quartile of HEI-2015 had a 46% lower chance of breast cancer compared with subjects in the bottom quartile (OR 0.54; 95% CI 0.30, 0.98). After adjustment for potential confounders such as age, age at menarche, oral contraceptive drug use, menopausal status, marital status, body mass index, smoking and education level, the association between HEI-2015 score and a lower risk of breast cancer was enhanced (OR 0.32; 95% CI 0.16, 0.65).
CONCLUSION: We successfully demonstrated that a higher HEI-2015 score was associated with a reduced breast cancer risk.
METHODS: The authors conducted a qualitative study using in-depth interviews and focus group discussions with 98 participants representing 23 LMICs in Eastern Europe, Central Asia, East and Southern Africa, and Latin America.
RESULTS: Despite geographic, cultural, and socioeconomic differences, the common themes that emerged from the data across the 3 regions are strikingly similar: trust, knowledge gaps, stigma, sharing experiences, and sustainability. The authors identified common facilitators (training/education, relationship building/networking, third-party facilitators, and communication) and barriers (mistrust, stigma, organizational fragility, difficulty translating HIC strategies) to establishing trust, collaboration, and advancing cancer advocacy efforts. To the authors' knowledge, the current study is the first to describe the role that coalitions and regional networks play in advancing breast cancer advocacy in LMICs across multiple regions.
CONCLUSIONS: The findings of the current study corroborate the importance of investing in 3-way partnerships between CSOs, political leaders, and health experts. When provided with information that is evidence-based and resource appropriate, as well as opportunities to network, advocates are better equipped to achieve their goals. The authors propose that support for CSOs focuses on building trust through increasing opportunities for engagement, disseminating best practices and evidence-based information, and fostering the creation of platforms for partnerships and networks.