Unveiling Sociocultural Barriers to Breast Cancer Awareness Among the South Asian Population: Case Study of Bangladesh and West Bengal, India
Bibliographic record
Abstract
BACKGROUND: Bangladesh and West Bengal, India, are 2 densely populated South Asian neighboring regions with many socioeconomic and cultural similarities. In dealing with breast cancer (BC)-related issues, statistics show that people from these regions are having similar problems and fates. According to the Global Cancer Statistics 2020 and 2012 reports, for BC (particularly female BC), the age-standardized incidence rate is approximately 22 to 25 per 100,000 people, and the age-standardized mortality rate is approximately 11 to 13 per 100,000 for these areas. In Bangladesh, approximately 90% of patients are at stages III or IV, compared with 60% in India. For the broader South Asian population, this figure is 16%, while it is 11% in the United States and the United Kingdom. These statistics highlight the need for an urgent investigation into the reasons behind these regions' late diagnoses and treatment. OBJECTIVE: Early detection is essential for managing BC and reducing its impact on individuals. However, raising awareness in diverse societies is challenging due to differing cultural norms and socioeconomic conditions. We aimed to interview residents to identify barriers to BC awareness in specific regions. METHODS: We conducted semistructured interviews with 17 participants from West Bengal and Bangladesh through Zoom (Zoom Video Communications). These were later transcribed and translated into English for qualitative data analysis. All our participants were older than 18 years, primarily identified as female, and most were married. RESULTS: We have identified 20 significant barriers to effective BC care across 5 levels-individual, family, local society, health care system, and country or region. Key obstacles include neglect of early symptoms, reluctance to communicate, societal stigma, financial fears, uncertainty about treatment costs, inadequate mental health support, and lack of comprehensive health insurance. To address these issues, we recommend context-specific solutions such as integrating BC education into middle and high-school curricula, providing updates through media channels like talk shows and podcasts, promoting family health budgeting, enhancing communication at cultural events and religious gatherings, offering installment payment plans from health care providers, encouraging regular self-examination, and organizing statewide awareness campaigns. In addition, social media can be a powerful tool for raising mass awareness while respecting cultural and socioeconomic norms. CONCLUSIONS: Fighting BC or any fatal disease is challenging and requires support from various dimensions. However, studies show that raising mass awareness is crucial for the early detection of BC. By adopting a sensitive and well-informed approach, we aim to improve the early detection of BC and help reduce its impact on South Asian communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".