Other Achievements: Highlights
- Modeling comorbidities to determine age of screening cessation
- Race-specific modeling
- Evaluating the impact of new screening modalities
- Can the probability of overdiagnosis in early detection programs be estimated?
Modeling comorbidities to determine age of screening cessation
Two of the Breast Working Group models (E and GE) participated in an analysis with other CISNET sites to estimate the harms and benefits of cancer screening by age and comorbidity to inform decisions about screening cessation (Lansdorp-Vogelar et al., 2014). The results suggest that estimates of screening benefits and harms by comorbidity can inform discussions between providers and their older patients about personalizing decisions about when to stop cancer screening. In the analysis, screening 1000 women with average life expectancy at age 74 for breast cancer resulted in 79–96 (range across models) false-positives, 0.5–0.8 overdiagnosed cancers, and 0.7–0.9 breast cancer deaths prevented. While absolute numbers of harms and benefits differed across cancer sites, the ages at which to cease screening were highly consistent across models and cancer sites when based on harm-benefit ratios comparable to screening average-health individuals at age 74. For individuals with no, mild, moderate, and severe comorbidities, screening until ages of 76, 74, 72, and 66, respectively, resulted in similar harms and benefits as for average-health individuals.
We used three Breast Working Group models (E, GE, W) to evaluate race disparities in disease natural history (Batina et al., 2013), mortality (Chang et al., 2012; van Ravesteyn et al., 2011) and how federal policies might affect screening outcomes for different minority groups, including Latinos (van Ravesteyn et al., 2015). In the first of these studies, two simulation models (E and GE) were adapted to model Black and White women (van Ravesteyn et al., 2011). The models used common national race, and age-specific data for incidence, screening and treatment dissemination, stage distributions, survival, and competing mortality from 1975 to 2010 to investigate how much of the mortality disparity can be attributed to racial differences in natural history, uptake of mammography screening, and use of adjuvant therapy. The results suggested that Black women appear to have benefited less from cancer control advances than White women, with a greater race-related gap in the use of adjuvant therapy than screening. However, a greater portion of the disparity in mortality appears to be due to differences in natural history and undetermined factors.
Evaluating the impact of new screening modalities
The CISNET Breast Working Group has studied the impact of adopting new imaging technology. We modeled the observed transitions from plain film to digital mammography and found that digital yielded only modest increases in health while substantially increasing costs (Tosteson et al., 2008; Stout et al., 2014). Models E, GE, and W examined the impact of supplemental screening ultrasound for women with dense breasts (Sprague et al., 2015). The models consistently showed this strategy was most efficient when targeted to the 10-14% of the population with extremely dense breasts, but even this strategy was not cost-effective by most standards. We concluded that the current breast density notification legislation will substantially increase costs while producing relatively small benefits in breast cancer deaths averted and QALYs gained (Sprague et al., 2015). Model W also conducted a preliminary cost-effectiveness of biennial screening for women aged 50-74 with dense breasts using digital mammography plus tomosynthesis (Lee et al., 2015). Assuming improvements in screening sensitivity and specificity for tomosynthesis at current additional out-of-pocket costs, the results suggest that combined screening would cost $53,893 per added QALY gained vs. digital mammography alone. Models E and GE also worked with the CDC to assess the impact of replacing film with digital mammography on health effects (deaths averted, life-years gained); costs (for screening and diagnostics); and number of women reached in the CDC’s National Breast and Cervical Cancer Early Detection Program (van Ravesteyn et al., 2015).
Can the probability of overdiagnosis in early detection programs be estimated?
The probability of overdiagnosis, defined as the situation where a screening exam detects a disease that would have otherwise been undetected in a person's lifetime, is an ongoing concern with early detection of breast cancer. The CISNET Breast Working Group has conducted work to better estimate the amount of overdiagnosis of DCIS in the US population. Model E has incorporated the effect of DCIS grade on natural history into the Dutch MISCAN model using recent data from the Netherlands. Model W has conducted extensive studies of DCIS risk factors (Sprague et al., 2009; Berkman et al., 2014; McLaughlin et al., 2014; Sprague et al., 2013; Sprague et al., 2010; Nichols et al., 2007; Trentham-Dietz et al., 2007; Sprague et al., 2007; Trentham-Dietz et al., 2000) and Model D is in the process of conducting a synthesis of DCIS data.
In closely related work, we have developed methods for estimating overdiagnosis. In an analysis estimating overdiagnosis among older women, all participating models found that screening women after age 74 years resulted in a less favorable balance of benefits and harms than screening women between the ages of 50 and 74 years because of the increasing amount of overdiagnosis at older ages (van Ravesteyn et al., 2015). Model E has examined overdiagnosis in the Dutch screening program (de Koning et al., 2006; de Gelder et al., 2011; de Gelder et al., 2011). Model D has developed a new method to quantify overdiagnosis of invasive breast cancer from mammography screening data in Norway and Model W has analyzed the impact of DCIS treatment patterns on breast cancer mortality (Sprague et al., 2013).
Lastly, we are currently working to refine the DCIS portions of the simulation models.