Developing a community‑based breast cancer risk prediction tool for resource‑poor settings



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Journal of Education and Health Promotion


BACKGROUND: With an estimation of every two women newly diagnosed with breast cancer, one dies. It is accounted that 1 in 28 women is likely to develop breast cancer during her lifetime. Developing a risk prediction tool by assessing the prevalence of known risk factors in the community will help public health intervention. METHODOLOGY: A cross‑sectional study was conducted among 18–64‑year‑old women to gather the prevalence of known breast cancer risk factors, through a community survey (sample survey). In this multistage random number‑based cluster sampling study, the results were compiled, collated, and analyzed in rates and proportions. Statistical conclusions were made using spreadsheets (Microsoft) and the values were converted into ordinal values using modified Likert scale and median was used to estimate central values. The estimated prevalence of these known risk factors was re‑assorted for analysis and these re‑assorted data were categorized into range of values across the communities. The internal validity of the survey questionnaire was measured using Cronbach’s alpha (α). RESULTS: The analysis of 558 participants was performed for the known risk factors for breast cancer including participant’s age, age at menarche, marriage, first childbirth, menopause, family history of breast cancer and benign breast disease, history of abortion, and body mass index. Based on the estimated prevalence of these risk factors, a community‑based risk prediction tool was developed with Cronbach’s α score of medium internal validity. CONCLUSIONS: The risk assessment tool has collated most of the risk factors of breast cancer that are capable of being measured at community level. The survey findings concluded that the community under survey was bearing moderate risk for breast cancer for women


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BRCA1, Breast cancer, Cluster sampling, Gail Model, Risk assessment tool, Risk factors, Screening


Pillai D, Hossain SS, Chattu VK. Developing a community-based breast cancer risk prediction tool for resource-poor settings. J Edu Health Promot 2019;8:106.