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Breast Cancer Beliefs as Potential Targets for Breast Cancer Awareness Efforts to Decrease Late-Stage Presentation in Uganda

Domaine:

healthcare

Type de record:

paper
Créateur:
JohYamBenDon
Éditeur:
Ame
Hôte:
Purpose To assess breast cancer beliefs in Uganda and determine whether these beliefs are associated with factors potentially related to nonparticipation in early detection. Methods A survey with open- and close-ended items was conducted in a community sample of Ugandan women to assess their beliefs about breast cancer. Linear regression was used to ascertain associations between breast cancer beliefs and demographic factors potentially associated with early detection, including socioeconomic factors, health care access, prior breast cancer knowledge, and personal detection practices. Results Of the 401 Ugandan women surveyed, most had less than a primary school education and received medical care at community health centers. Most women either believed in or were unsure about cultural explanatory models for developing breast cancer (> 82%), and the majority listed these beliefs as the most important causes of breast cancer (69%). By comparison, ≤ 45% of women believed in scientific explanatory risks for developing breast cancer. Although most believed that regular screening and early detection would find breast cancer when it is easy to treat (88% and 80%, respectively), they simultaneously held fatalistic attitudes toward their own detection efforts, including belief or uncertainty that a cure is impossible once they could self-detect a lump (54%). Individual beliefs were largely independent of demographic factors. Conclusion Misconceptions about breast cancer risks and benefits of early detection are widespread in Uganda and must be addressed in future breast cancer awareness efforts. Until screening programs exist, most breast cancer will be self-detected. Unless addressed by future awareness efforts, the high frequency of fatalistic attitudes held by women toward their own detection efforts will continue to be deleterious to breast cancer early detection in sub-Saharan countries like Uganda.

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Poster presented at the Deep Learning Indaba 2023 by Bonaventure F. P. Dossou