Do breast cancer risk factors differ among those who do and do not undertake mammography screening?

Beckmann, Kerri; Roder, David; Hiller, Janet E.; Farshid, Gelareh; Lynch, John

doi:10.1177/0969141313510293

Cited by 14 publications

(27 citation statements)

References 31 publications

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“…[3][4][5][6]12 Such variability is puzzling, and it might be due to the differences in assessment methods or to differences in study samples. 13 If screening allows diagnosis of the disease at an earlier stage, the most appropriate method to evaluate mortality gain would be to assess specific mortality rather than overall mortality. 6 In the present study we found a decrease in BC-specific mortality of 12.4% in screened patients compared with unscreened patients of all age groups, and a reduction of 10% in the unscreened group aged 50-69 years, figures that are similar to our previous work.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Screened and Nonscreened Breast Cancer Patients in Relation to Age: A 2-Institution Study

Barco

Chabrera

Font

et al. 2015

Clinical Breast Cancer

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Section: Discussionmentioning

confidence: 99%

Comparison of Screened and Nonscreened Breast Cancer Patients in Relation to Age: A 2-Institution Study

Barco

Chabrera

Font

et al. 2015

Clinical Breast Cancer

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“…Decreasing the age gradient resulted in slightly higher estimates (14.5% for all BC and 8.3% for IBC) while increasing the slope resulted in lower estimates of overdiagnosis (13.1% and 6.9%, respectively). In further sensitivity analysis in which we adjusted for higher breast cancer risk among screening participants compared with non‐participants based on risk differences noted in a recent survey conducted in SA, estimates of over‐diagnosis were considerably lower than in the original unadjusted analysis. For example, estimates of over‐diagnosis were 1% for IBC and 8% for IBC and DCIS combined after adjustment for 10% higher breast cancer risk among screening participants.…”

Section: Resultsmentioning

confidence: 94%

“…Although we adjusted our estimates for SEP (at the neighbourhood level) and area of residence in our logistic regression models, other potential confounders could not be included due to lack of data at the individual level. Recent surveys examining differences in breast cancer risk profiles among SA women indicated higher prevalence of several breast cancer risk factors among breast screen participants compared with non‐participants . BSSA participants were more likely to have had a family history of breast cancer, higher prevalence of past or current hormone replacement therapy (HRT) use and increased frequency of breast biopsy or breast surgery.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we tested the effect of altering the age specific incidence rates, by increasing and decreasing the slope for the age trend by 20%. Finally, to account for any unmeasured confounding due to screening participants potentially having a higher risk of breast cancer compared with non‐participants (based on recent SA survey findings), we recalculated our estimates of over‐diagnosis by decreasing the incidence rate for screening participants by 2 different orders of magnitude (5 and 10%) and applying these revised rates across each of the screening time windows.…”

Section: Methodsmentioning

confidence: 99%

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A novel case–control design to estimate the extent of over‐diagnosis of breast cancer due to organised population‐based mammography screening

Beckmann

Lynch

Hiller

et al. 2014

Intl Journal of Cancer

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Debate about the extent of breast cancer over-diagnosis due to mammography screening has continued for over a decade, without consensus. Estimates range from 0 to 54%, but many studies have been criticized for having flawed methodology. In this study we used a novel study design to estimate over-diagnosis due to organised mammography screening in South Australia (SA). To estimate breast cancer incidence at and following screening we used a population-based, age-matched case-control design involving 4,931 breast cancer cases and 22,914 controls to obtain OR for yearly time intervals since women's last screening mammogram. The level of over-diagnosis was estimated by comparing the cumulative breast cancer incidence with and without screening. The former was derived by applying ORs for each time window to incidence rates in the absence of screening, and the latter, by projecting pre-screening incidence rates. Sensitivity analyses were undertaken to assess potential biases. Over-diagnosis was estimated to be 8% (95%CI 2-14%) and 14% (95%CI 8-19%) among SA women aged 45 to 85 years from 2006-2010, for invasive breast cancer and all breast cancer respectively. These estimates were robust when applying various sensitivity analyses, except for adjustment for potential confounding assuming higher risk among screened than non-screened women, which reduced levels of over-diagnosis to 1% (95%CI 5-7%) and 8% (95%CI 2-14%) respectively when incidence rates for screening participants were adjusted by 10%. Our results indicate that the level of over-diagnosis due to mammography screening is modest and considerably lower than many previous estimates, including others for Australia.

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“…Having a family history of breast cancer is also known to increase breast density and has been suggested to increase screening participation . Screening participation is also higher in women with a high socio‐economic status, which appears to be associated with dense breasts as well .…”

Section: Discussionmentioning

confidence: 99%

Breast cancer screening effect across breast density strata: A case–control study

Waal¹,

Ripping²,

Verbeek³

et al. 2016

Intl Journal of Cancer

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Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening programme , which invites women (aged 50-74 years) biennially. Performance measures were determined. Furthermore, a case-control study was performed for women having dense and women having fatty breasts. Breast density was assessed visually with a dichotomized Wolfe scale. Breast density data were available for cases. The prevalence of dense breasts among controls was estimated with age-specific rates from the general population. Sensitivity analyses were performed on these estimates. Screening performance was better in the fatty than in the dense group (sensitivity 75.7% vs 57.8%). The mortality reduction appeared to be smaller for women with dense breasts, with an odds ratio (OR) of 0.87 (95% CI 0.52-1.45) in the dense and 0.59 (95% CI 0.44-0.79) in the fatty group. We can conclude that high density results in lower screening performance and appears to be associated with a smaller mortality reduction. Breast density is thus a likely candidate for risk-stratified screening. More research is needed on the association between density and screening harms.Breast cancer is the cancer that causes most death in women worldwide, even though mortality has been decreasing over time.

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Do breast cancer risk factors differ among those who do and do not undertake mammography screening?

Abstract: South Australian women who participated in the population-based mammography screening have a slightly higher prevalence of breast cancer risk factors. This also applies to those who undertook private screening.

Cited by 14 publications

References 31 publications

Comparison of Screened and Nonscreened Breast Cancer Patients in Relation to Age: A 2-Institution Study

Comparison of Screened and Nonscreened Breast Cancer Patients in Relation to Age: A 2-Institution Study

A novel case–control design to estimate the extent of over‐diagnosis of breast cancer due to organised population‐based mammography screening

Breast cancer screening effect across breast density strata: A case–control study

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