BACKGROUND: Very little is known about breast carcinoma risk factors for American Indian/Alaska Native (AI/AN) women undergoing screening. The Gail model has been a useful tool for predicting the risk of breast carcinoma in several populations. It has not been applied systematically to AI/AN women. METHODS: The current study was a retrospective review of 1458 screening mammograms performed for AI/AN women. The authors applied the Gail model to estimate both absolute risk and relative risk for breast carcinoma for AI/AN women screened in South Dakota, Arizona, and Alaska. RESULTS: The mean age of the women was 52.4 years. The onset of menses was not significantly different than expected. The average age at first birth was 20 years, very few women were nulliparous, and few women were age > 30 years at first live birth. The proportion of women reporting a first- or second-degree relative with breast carcinoma was similar to the proportion in the general population. The results of the model indicated an overall average relative risk that ranged from 1.42 to 2.69 compared with white American women, depending on the model assumptions used. Using a modified Gail model and calculating an imputed absolute risk, the expected incidence of breast carcinoma in this population increased to rates of 170-180 per 100,000 in the next 10 years, a significant increase over the Surveillance, Epidemiology and End Results-derived incidence rates from 1988 to 1992 of 31.6 per 100,000 for AI women in New Mexico and 78.9 per 100,000 for AN women. CONCLUSIONS: The model indicated a likelihood of increasing rates of breast carcinoma in the study population. The data obtained were useful in generating preliminary estimates of breast carcinoma risk in the study population, for which no prospective population survey has been completed. The inherent weaknesses in the current retrospective study indicated the need for a large-scale prospective data collection to confirm these exploratory findings. Copyright 2004 American Cancer Society.
BACKGROUND: Very little is known about breast carcinoma risk factors for American Indian/Alaska Native (AI/AN) women undergoing screening. The Gail model has been a useful tool for predicting the risk of breast carcinoma in several populations. It has not been applied systematically to AI/AN women. METHODS: The current study was a retrospective review of 1458 screening mammograms performed for AI/AN women. The authors applied the Gail model to estimate both absolute risk and relative risk for breast carcinoma for AI/AN women screened in South Dakota, Arizona, and Alaska. RESULTS: The mean age of the women was 52.4 years. The onset of menses was not significantly different than expected. The average age at first birth was 20 years, very few women were nulliparous, and few women were age > 30 years at first live birth. The proportion of women reporting a first- or second-degree relative with breast carcinoma was similar to the proportion in the general population. The results of the model indicated an overall average relative risk that ranged from 1.42 to 2.69 compared with white American women, depending on the model assumptions used. Using a modified Gail model and calculating an imputed absolute risk, the expected incidence of breast carcinoma in this population increased to rates of 170-180 per 100,000 in the next 10 years, a significant increase over the Surveillance, Epidemiology and End Results-derived incidence rates from 1988 to 1992 of 31.6 per 100,000 for AI women in New Mexico and 78.9 per 100,000 for AN women. CONCLUSIONS: The model indicated a likelihood of increasing rates of breast carcinoma in the study population. The data obtained were useful in generating preliminary estimates of breast carcinoma risk in the study population, for which no prospective population survey has been completed. The inherent weaknesses in the current retrospective study indicated the need for a large-scale prospective data collection to confirm these exploratory findings. Copyright 2004 American Cancer Society.
Authors: Basem Saleh; Mohamed A Elhawary; Moataz E Mohamed; Islam N Ali; Menna S El Zayat; Hadeer Mohamed Journal: Breast Cancer Res Treat Date: 2021-04-14 Impact factor: 4.872
Authors: Judith S Kaur; Robert A Vierkant; Timothy Hobday; Daniel Visscher Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-03 Impact factor: 4.254
Authors: Fei Gao; David Machin; Khuan-Yew Chow; Yu-Fan Sim; Stephen W Duffy; David B Matchar; Chien-Hui Goh; Kee-Seng Chia Journal: BMC Cancer Date: 2012-11-19 Impact factor: 4.430