Unveiling Sociocultural Barriers to Breast Cancer Awareness Among the South Asian Population: Case Study of Bangladesh and West Bengal, India
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».