Family history of cancer and its association with breast cancer risk perception and repeat mammography

Abstract

Objectives: We examined the strength of association between family history of breast cancer and family history of other cancers with breast cancer risk perception and repeat mammography.

Methods: The sample included 6706 women, aged 46 to 74 years, with no breast cancer history. Multinomial logistic regression assessed the association between family history of cancer and breast cancer risk perception. Structural equation modeling estimated the relationship between family history of cancer and repeat mammography.

Results: Breast cancer risk perception was strongly associated with family history of breast cancer in the mother or mother and sister (odds ratio [OR] = 32.15; P < .001); family history of breast cancer in the sister, daughter, or male first-degree relative (OR = 6.6-8.4; P < .001); and maternal history of other cancers (OR = 1.38-2.73; P < .001). For repeat mammography, women with maternal history of breast cancer had a mean increase of 0.50 more mammograms in the past 6 years compared with women without maternal history of breast cancer (P < .001).

Conclusions: Breast cancer risk perception was associated with the type of cancer found in first-degree relatives and with the person's relationship to the family member with cancer. Family history of breast cancer affected repeat mammography behavior.

FIGURE 1—

Structural equation model: family history of cancer predicting risk perception and repeat mammography. Note. Emphasized lines indicate statistical significance found; dotted line indicates statistical significance found for selected family members. Paths a and b covariates not shown but included in the model were age, income, race/ethnicity, education, nativity, and geographic region. Paths c and d covariates not shown but included in the model were age, income, race/ethnicity, education, nativity, geographic region, insurance status, physician recommendation, no care because of cost, delayed care because of cost, and system barriers. ^aThe model is just identified and accounts for 31% of the variance in repeat mammography. ^bFor every 1 standard score that the latent variable of frequency of mammograms changes, the observed score of frequency of mammograms is predicted to change 0.83 standard units, given a measurement error of 0.3 units.