We simulated and compared the number of deaths that could be prevented by increasing screening from current rates to defined targets by using previously reported model-based estimates. We compared the cumulative numbers of cancer deaths for a single-year age cohort under different scenarios: current level of screening (2016), current level plus 10 percentage points, and increasing screening to 90% and 100% of the eligible population. We also calculated the numbers of adults currently screened and expected to be screened under different scenarios of increased screening. Table 1 provides a summary of key analysis assumptions and model inputs. Current screening estimates are based on 2016 survey data from the Behavioral Risk Factor Surveillance System (BRFSS).
Each of the simulation models on which our calculations are based followed a synthetic cohort from the USPSTF-recommended starting age of screening: 50-year-old women for breast cancer screening, 21-year-old women for cervical cancer screening, and 50-year-old men and women for CRC screening. The simulations followed each cohort through their lifetimes. Screening modalities included mammography for breast cancer and cytology or Pap test for cervical cancer. For CRC, the model assumed a mix of annual fecal occult blood test (FOBT), flexible sigmoidoscopy every 5 years plus FOBT every 3 years, or colonoscopy every 10 years (Table 1).
The estimates of avoidable burden were prepared in 2018 by Health Partners Institute researchers using models that were previously used in peer-reviewed studies to inform the National Commission on Prevention Priorities (NCPP) ranking of clinical preventive services. Specifically, the estimates for avoidable deaths from breast cancer screening were based on results of 5 Cancer Information Surveillance Modeling Network screening models (10) plus an estimate from a sixth model. Estimates for cervical cancer screening and CRC screening were based on results from models to inform the same NCPP ranking.[12,13] These reports provide estimates of cancer deaths that would be prevented either by screening 100% of the target population compared with no screening[8,9] or by screening a portion of the target population who would accept and follow up with screening if recommended by a physician.[10,11,14] Each model estimated cancer deaths prevented by first constructing a natural history of cancer based on progression of lesions through cancer stages and then simulating the potential for screening to interrupt cancer progression and prevent death. Using the estimates from models, we calculated the deaths prevented from each 1% increase in screening uptake in the US eligible population and linearly scaled that estimate from current screening rates up to the screening rates in the scenarios just described. Linear extrapolation should provide a reasonable estimate of the impact of increasing screening rates when capacity exists or is developed to provide additional screening and follow-up of quality equal to existing screening and follow-up, and when the currently screened and unscreened populations have similar risks of lesion development and cancer progression.
Prev Chronic Dis. 2020;17(10):e123 © 2020 Centers for Disease Control and Prevention (CDC)