Modeling the dissemination of mammography in the United States.
ABSTRACT This paper presents a methodology for piecing together disparate data sources to obtain a comprehensive model for the use of mammography screening in the US population for the years 1975-2000.
Two aspects of mammography usage, the age that a woman receives her first mammography and the interval between subsequent mammograms, are modeled separately. The initial dissemination of mammography is based on cross-sectional self report data from national surveys and the interval length between screening exams is fit using longitudinal mammography registry data.
The two aspects of mammography usage are combined to simulate screening histories for individual women that are representative of the US population. Simulated mammography patterns for the years 1994-2000 were found to be similar to observed screening patterns from the state level mammography registry for Vermont.
The model presented gives insight into screening practices over time and provides an alternative public health measure for screening usage in the US population. The comprehensive description of mammography use from its introduction represents an important first step to understanding the impact of mammography on breast cancer incidence and mortality.
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ABSTRACT: Background Molecular characterization of breast cancer allows subtype-directed interventions. Estrogen receptor (ER) is the longest-established molecular marker. Methods We used six established population models with ER-specific input parameters on age-specific incidence, disease natural history, mammography characteristics, and treatment effects to quantify the impact of screening and adjuvant therapy on age-adjusted US breast cancer mortality by ER status from 1975 to 2000. Outcomes included stage-shifts and absolute and relative reductions in mortality; sensitivity analyses evaluated the impact of varying screening frequency or accuracy. Results In the year 2000, actual screening and adjuvant treatment reduced breast cancer mortality by a median of 17 per 100 000 women (model range = 13-21) and 5 per 100 000 women (model range = 3-6) for ER-positive and ER-negative cases, respectively, relative to no screening and no adjuvant treatment. For ER-positive cases, adjuvant treatment made a higher relative contribution to breast cancer mortality reduction than screening, whereas for ER-negative cases the relative contributions were similar for screening and adjuvant treatment. ER-negative cases were less likely to be screen-detected than ER-positive cases (35.1% vs 51.2%), but when screen-detected yielded a greater survival gain (five-year breast cancer survival = 35.6% vs 30.7%). Screening biennially would have captured a lower proportion of mortality reduction than annual screening for ER-negative vs ER-positive cases (model range = 80.2%-87.8% vs 85.7%-96.5%). Conclusion As advances in risk assessment facilitate identification of women with increased risk of ER-negative breast cancer, additional mortality reductions could be realized through more frequent targeted screening, provided these benefits are balanced against screening harms.JNCI Journal of the National Cancer Institute 11/2014; 106(11). DOI:10.1093/jnci/dju289 · 15.16 Impact Factor
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ABSTRACT: Black women in the District of Columbia (DC) have the highest breast cancer mortality in the US. Local cancer control planners are interested in how to most efficiently reduce this mortality. An established simulation model was adapted to reflect the experiences of Black women in DC and estimate the past and future impact of changes in use of screening and adjuvant treatment. The model estimates that the observed reduction in mortality that occurred from 1975 to 2007 attributable to screening, treatment, and both was 20.2%, 25.7%, and 41.0% respectively. The results suggest that, by 2020, breast cancer mortality among Black women in DC could be reduced by 6% more by initiating screening at age 40 vs. age 50. Screening annually may also reduce mortality to a greater extent than biennially, albeit with a marked increase in false positive screening rates. This study demonstrates how modeling can provide data to assist local planners as they consider different cancer control policies based on their individual populations.04/2012; 2012(2012). DOI:10.1155/2012/241340