Colorectal Cancer Screening: An Updated Modeling Study for the US Preventive Services Task Force | Cancer Screening, Prevention, Control | JAMA | JAMA Network
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Figure 1.  Natural History of Colorectal Cancer and the Effects of Screening as Simulated by SimCRC, CRC-SPIN, and MISCAN
Natural History of Colorectal Cancer and the Effects of Screening as Simulated by SimCRC, CRC-SPIN, and MISCAN

The opportunity to intervene in the natural history through screening (adenoma detection and removal and early detection) is noted in red. Screening can either remove a precancerous lesion (ie, adenoma), thus moving a person to the “no lesion” state, or diagnose a preclinical cancer, which, if detected at an earlier stage, may be more amenable to treatment. Each person’s life history is simulated in the absence of screening and in the presence of screening, such that the effect of a given screening strategy on each simulated person’s outcomes are known.

aSimulation Model of CRC (SimCRC) and Microsimulation Screening Analysis (MISCAN) simulate categorical adenoma size (1 to <6 mm; 6 to <10 mm; ≥10 mm), whereas CRC Simulated Population Model for Incidence and Natural History (CRC-SPIN) simulates continuous adenoma size. SimCRC and CRC-SPIN assume that all adenomas have the potential to progress to colorectal cancer, whereas MISCAN assumes that some adenomas are nonprogressive (ie, they do not grow or progress to cancer after reaching a certain size category) and that the likelihood that an adenoma is progressive increases with age. None of the models simulate adenoma histology.

Figure 2.  Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Colonoscopy Screening Strategiesa
Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Colonoscopy Screening Strategiesa

CRC-SPIN indicates CRC Simulated Population Model for Incidence and Natural History; MISCAN, Microsimulation Screening Analysis; SimCRC, Simulation Model of CRC.

aAnalyses assume an increased population incidence of colorectal cancer based on an incidence rate ratio comparing incidence among current 20- to 44-year-olds (ie, in 2012-2016) vs 20- to 44-year-olds in the period used for initial model calibration (ie, 1975-1979) of 1.19. An interactive version of this figure is available at https://resources.cisnet.cancer.gov/projects/#crcr/uspstf2021/explorer

bNear-efficient strategy.

Figure 3.  Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Stool-Based Screening Strategiesa
Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Stool-Based Screening Strategiesa

See Figure 2 legend for expanded abbreviations.

aAnalyses assume an increased population incidence of colorectal cancer based on an incidence rate ratio comparing incidence among current 20- to 44-year-olds (ie, in 2012-2016) vs 20- to 44-year-olds in the period used for initial model calibration (ie, 1975-1979) of 1.19. An interactive version of this figure is available at https://resources.cisnet.cancer.gov/projects/#crcr/uspstf2021/explorer

bNear-efficient strategy.

Figure 4.  Benefits of Colorectal Cancer Screening Strategies Highlighted by the US Preventive Services Task Force in 20163 and the Change in Outcomes When Screening Is Started at Age 45 Years Instead of Age 50
Benefits of Colorectal Cancer Screening Strategies Highlighted by the US Preventive Services Task Force in 2016 and the Change in Outcomes When Screening Is Started at Age 45 Years Instead of Age 50

COL indicates colonoscopy; CRC, colorectal cancer; CRC-SPIN, CRC Simulated Population Model for Incidence and Natural History; CTC, computed tomography colonography; FIT, fecal immunochemical test; MISCAN, Microsimulation Screening Analysis; sDNA-FIT, multitarget stool DNA test with a fecal immunochemical assay; SIG, flexible sigmoidoscopy; SimCRC, Simulation Model of CRC.

aOutcomes expressed over the lifetimes of 1000 40-year-olds who start screening at age 45 or at age 50 and are screened to age 75, assuming an increased population incidence of CRC based on an incidence rate ratio comparing incidence among current 20- to 44-year-olds (ie, in 2012-2016) vs 20- to 44-year-olds in the period used for initial model calibration (ie, 1975-1979) of 1.19.

bCompared with other options for stool-based screening, this strategy was not estimated to provide an efficient balance of the burden (ie, lifetime number of colonoscopies) and the benefit (life-years gained) of screening.

Figure 5.  Complications, Colonoscopy Burden, and Noncolonoscopy Burden of Colorectal Cancer Screening Strategies Highlighted by the US Preventive Services Task Force in 20163 and the Change in Outcomes When Screening Is Started at Age 45 Years Instead of Age 50
Complications, Colonoscopy Burden, and Noncolonoscopy Burden of Colorectal Cancer Screening Strategies Highlighted by the US Preventive Services Task Force in 2016 and the Change in Outcomes When Screening Is Started at Age 45 Years Instead of Age 50

See Figure 4 legend for expanded abbreviations.

aOutcomes expressed over the lifetimes of 1000 40-year-olds who start screening at age 45 or at age 50 and are screened to age 75, assuming an increased population incidence of CRC based on an incidence rate ratio comparing incidence among current 20- to 44-year-olds (ie, in 2012-2016) vs 20- to 44-year-olds in the period used for initial model calibration (ie, 1975-1979) of 1.19.

bCompared with other options for stool-based screening, this strategy was not estimated to provide an efficient balance of the burden (ie, lifetime number of colonoscopies) and the benefit (life-years gained) of screening.

cOther (noncolonoscopy) tests include FIT, sDNA-FIT, CTC, SIG.

Table 1.  Screening Test Characteristics Used in the Analysis
Screening Test Characteristics Used in the Analysis
Table 2.  Screening Strategies Evaluated by the Modelsa
Screening Strategies Evaluated by the Modelsa
Table 3.  Outcomes for Efficient and Near-Efficient Strategies With SimCRC, CRC-SPIN, and MISCANa
Outcomes for Efficient and Near-Efficient Strategies With SimCRC, CRC-SPIN, and MISCANa
Supplement.

Appendix A. Estimation of Increasing Population-Level Risk of CRC

Appendix B. Supplemental Information on Screening Adherence

eTable 1. Model-Predicted Lifetime Outcomes for a Cohort of 40-Year-Olds in the Absence of CRC Screening

eTable 2. Efficient Colonoscopy Screening Strategies

eTable 3. Efficient FIT Screening Strategies

eTable 4. Efficient Colonoscopy Screening Strategies by Scenario for Population Risk of CRC

eTable 5. Efficient FIT and sDNA-FIT Screening Strategies by Scenario for Population Risk of CRC

eTable 6. Efficient Sigmoidoscopy Screening Strategies by Scenario for Population Risk of CRC

eTable 7. Efficient 10-Yearly Sigmoidoscopy plus FIT Screening Strategies by Scenario for Population Risk of CRC

eTable 8. Efficient Computed Tomography Colonography Screening Strategies by Scenario for Population Risk of CRC

eTable 9. Illustration of the Change in Outcomes With Delayed Screening Uptake for Sample Strategies With Screening Beginning at Age 50

eTable 10. Illustration of the Change in Outcomes With Delayed Screening Uptake for Sample Strategies With Screening Beginning at Age 45

eTable 11. Illustration of the Change in Outcomes From Imperfect Adherence to Repeat Screening for Sample Strategies With Screening Beginning at Age 50

eTable 12. Illustration of the Change in Outcomes From Imperfect Adherence to Repeat Screening for Sample Strategies With Screening Beginning at Age 45

eFigure 1. Estimated Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Flexible Sigmoidoscopy Screening Strategies

eFigure 2. Estimated Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for 10-Yearly Flexible Sigmoidoscopy plus Interval FIT Strategies

eFigure 3. Estimated Lifetime Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Computed Tomography Colonography Screening Strategies

eFigure 4. Estimated Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Colonoscopy Screening Strategies, by Race and Sex

eFigure 5. Estimated Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for FIT Screening Strategies, by Race and Sex

eFigure 6. Estimated Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for Colonoscopy Screening Strategies, by Risk Scenario

eFigure 7. Estimated Number of Colonoscopies and Life-Years Gained for a Cohort of 40-Year-Olds for FIT and sDNA-FIT Screening Strategies, by Risk Scenario

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US Preventive Services Task Force
Modeling Study
May 18, 2021

Colorectal Cancer Screening: An Updated Modeling Study for the US Preventive Services Task Force

Author Affiliations
  • 1Institute for Technology Assessment, Massachusetts General Hospital, Boston
  • 2Harvard Medical School, Boston, Massachusetts
  • 3RAND Corporation, Santa Monica, California
  • 4Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
  • 5Now with OPEN Health, Rotterdam, the Netherlands
  • 6Now with the Columbia University School of Nursing, New York, New York
  • 7Kaiser Permanente Evidence-based Practice Center and Center for Health Research, Kaiser Permanente, Portland, Oregon
  • 8American Cancer Society, Atlanta, Georgia
  • 9Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
  • 10Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
  • 11Department of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis
JAMA. 2021;325(19):1998-2011. doi:10.1001/jama.2021.5746
Abstract

Importance  The US Preventive Services Task Force (USPSTF) is updating its 2016 colorectal cancer screening recommendations.

Objective  To provide updated model-based estimates of the benefits, burden, and harms of colorectal cancer screening strategies and to identify strategies that may provide an efficient balance of life-years gained (LYG) from screening and colonoscopy burden to inform the USPSTF.

Design, Setting, and Participants  Comparative modeling study using 3 microsimulation models of colorectal cancer screening in a hypothetical cohort of 40-year-old US individuals at average risk of colorectal cancer.

Exposures  Screening from ages 45, 50, or 55 years to ages 70, 75, 80, or 85 years with fecal immunochemical testing (FIT), multitarget stool DNA testing, flexible sigmoidoscopy alone or with FIT, computed tomography colonography, or colonoscopy. All persons with an abnormal noncolonoscopy screening test result were assumed to undergo follow-up colonoscopy. Screening intervals varied by test. Full adherence with all procedures was assumed.

Main Outcome and Measures  Estimated LYG relative to no screening (benefit), lifetime number of colonoscopies (burden), number of complications from screening (harms), and balance of incremental burden and benefit (efficiency ratios). Efficient strategies were those estimated to require fewer additional colonoscopies per additional LYG relative to other strategies.

Results  Estimated LYG from screening strategies ranged from 171 to 381 per 1000 40-year-olds. Lifetime colonoscopy burden ranged from 624 to 6817 per 1000 individuals, and screening complications ranged from 5 to 22 per 1000 individuals. Among the 49 strategies that were efficient options with all 3 models, 41 specified screening beginning at age 45. No single age to end screening was predominant among the efficient strategies, although the additional LYG from continuing screening after age 75 were generally small. With the exception of a 5-year interval for computed tomography colonography, no screening interval predominated among the efficient strategies for each modality. Among the strategies highlighted in the 2016 USPSTF recommendation, lowering the age to begin screening from 50 to 45 years was estimated to result in 22 to 27 additional LYG, 161 to 784 additional colonoscopies, and 0.1 to 2 additional complications per 1000 persons (ranges are across screening strategies, based on mean estimates across models). Assuming full adherence, screening outcomes and efficient strategies were similar by sex and race and across 3 scenarios for population risk of colorectal cancer.

Conclusions and Relevance  This microsimulation modeling analysis suggests that screening for colorectal cancer with stool tests, endoscopic tests, or computed tomography colonography starting at age 45 years provides an efficient balance of colonoscopy burden and life-years gained.

Introduction

Colorectal cancer remains the second leading cause of US cancer deaths.1 While randomized trials have shown that screening reduces colorectal cancer incidence and colorectal cancer mortality,2 there are many ways to screen, and trial-based evaluation of the long-term outcomes of the full spectrum of screening strategies is not feasible. In 2016, the US Preventive Services Task Force (USPSTF) recommended colorectal cancer screening starting at age 50 years and continuing until age 75 years (A recommendation), offering a number of screening modalities.3 To help inform an update of the colorectal cancer screening recommendations,3,4 the USPSTF requested an updated modeling study to estimate long-term outcomes of screening strategies, along with an updated evidence review.2

Methods

This analysis updated a 2016 modeling analysis estimating how the benefits, burden, and harms of screening average-risk, asymptomatic adults for colorectal cancer vary by modality, interval for repeated testing, and ages to begin and end screening.4 It incorporated recent evidence reporting increasing colorectal cancer incidence rates among recent birth cohorts5 and evaluated whether modeled benefits, burden, and harms of screening vary by race and sex. More information about the models and findings from additional analyses are included in the full modeling report.6

Model Descriptions

This study used 3 independently developed microsimulation models: Simulation Model of CRC (SimCRC), CRC Simulated Population Model for Incidence and Natural History (CRC-SPIN), and Microsimulation Screening Analysis (MISCAN) for colorectal cancer. All were used in the 2016 analysis,4 although they were updated to incorporate observed trends in the population risk of colorectal cancer.5 CRC-SPIN was also updated7 following a validation study.8 Each model has a natural history and a screening component, summarized below. SimCRC was programmed in C++, CRC-SPIN in R, and MISCAN in Delphi.

Natural History Component

All models describe the natural history of colorectal cancer in an average-risk unscreened population, assuming that all colorectal cancer arises from an adenomatous polyp.9 Alternative ways in which colorectal cancer can arise, such as from serrated polyps,10 were not modeled. Simulated persons were assumed to be free of adenomas and colorectal cancer at birth (Figure 1). As they age, they are at risk of developing 1 or more adenomas, each of which may grow in size. Adenomas may progress to preclinical colorectal cancer, after which they may become symptomatic, leading to colorectal cancer diagnosis. Persons may die of other causes at any time11; persons with clinically detected colorectal cancer are also at risk of dying of colorectal cancer.12

Each model’s natural history component was initially calibrated to adenoma prevalence data and colorectal cancer incidence prior to the dissemination of screening in the US.13 The models do not simulate family history of colorectal cancer, although people with a family history are included in the data used to calibrate the models. For this analysis, the natural history components were adjusted to reflect increasing colorectal cancer incidence among birth cohorts since the 1950s.5 The magnitude of the increase was estimated by the ratio of colorectal cancer incidence (excluding tumors that are not the target of colorectal cancer screening) among 20- to 44-year-olds in 2012-2016 (the 5 most recent years of Surveillance, Epidemiology, and End Results [SEER] data available) relative to 1975-1979 (the years of SEER data used for initial model calibration).14 Base-case analyses assumed this incidence rate ratio (IRR) to be 1.19. Scenario analyses were also simulated assuming IRRs of 1.00 (no change in incidence from the prescreening era) and 1.52 (the approximate upper bound of the 95% CI for the IRR estimated from age-period-cohort modeling of SEER incidence rates; see the Supplement for details on estimation of IRRs). In all scenarios, the increase in incidence was assumed to arise from an increase in adenoma risk that is carried forward with age.

Screening Component

The models’ screening components allow for interruption of the adenoma-carcinoma sequence through detection and removal of adenomas and for the detection of preclinical cancer, potentially at an earlier stage (Figure 1). The ability of a test to detect lesions is a function of its sensitivity and, for endoscopic tests, whether the lesion is within reach of the scope (Table 1). All persons with an abnormal noncolonoscopy screening test result were assumed to undergo follow-up colonoscopy. Patients with a history of adenoma(s) were assumed to undergo surveillance with colonoscopy, with surveillance intervals based on published guidelines.19 The models incorporate the risk of fatal20 and nonfatal21 complications from colonoscopy and have been externally validated.22

Screening Strategies

The following screening modalities were evaluated: a fecal immunochemical test (FIT) representative of the OC-Sensor family of tests (Polymedco) with a cutoff of 20 μg of hemoglobin per gram of feces; a stool DNA test with a FIT assay (sDNA-FIT), marketed as Cologuard (Exact Sciences); flexible sigmoidoscopy, alone or with FIT; colonoscopy; and computed tomography colonography. Multiple ages to begin screening (45, 50, 55 years) and end screening (70, 75, 80, 85 years) and screening intervals were evaluated for each modality, resulting in 163 unique strategies (Table 2). No screening was performed after the stopping age, but surveillance continued through at least age 85. Perfect adherence with all screening, follow-up, and surveillance procedures was assumed, reflecting outcomes among persons with full willingness to participate.

Outcomes

Outcomes were simulated for a hypothetical cohort of average-risk US adults who were unscreened and free of diagnosed colorectal cancer at age 40 years and were tallied through death. The primary benefit of screening was expressed as life-years gained (LYG) from the prevention or delay of colorectal cancer death, accounting for the loss of years from fatal complications. The burden of screening was measured by the lifetime number of required colonoscopies. Numbers of noncolonoscopy tests were also reported. Harms were expressed by the number of colonoscopy complications (serious gastrointestinal events [perforations, bleeding, transfusion], other gastrointestinal events [paralytic ileus, nausea and vomiting, dehydration, abdominal pain], and cardiovascular events [myocardial infarction, angina, arrhythmia, congestive heart failure, cardiac or respiratory arrest, syncope, hypotension, shock]).21 Other outcomes included the number of colorectal cancer cases and colorectal cancer deaths.

Analysis

Model output was analyzed in R version 3.6.1 (R Foundation for Statistical Computing). The relative efficiency of colorectal cancer screening across strategies was assessed by evaluating the trade-off in burden (colonoscopies) vs benefits (LYG). Because of large differences in the number of noncolonoscopy tests across modalities, efficiency analyses were performed by class of screening modality. FIT and sDNA-FIT were grouped together as exclusively stool-based modalities with comparable burden. The remaining modalities each comprised a unique class because of differences in bowel preparation, invasiveness, need for sedation, and number and type of noncolonoscopy tests.

Within each class, the subset of strategies that provide an efficient balance of colonoscopies and LYG were identified. A strategy is efficient if no other strategy, or combination of strategies, within the class is estimated to provide more LYG and require fewer colonoscopies. Strategies were compared by plotting their estimated LYG (relative to no screening) on the horizontal axis and their estimated number of lifetime colonoscopies on the vertical axis. The “efficient frontier” is the line connecting the efficient strategies.23 Strategies on the efficient frontier were compared using an efficiency ratio, which represents the estimated number of additional colonoscopies required for each additional LYG. Because an inefficient strategy providing outcomes similar to an efficient strategy may be a reasonable option on other grounds23 (eg, for consistency of screening ages across modalities), efficiency ratios were also calculated for “near-efficient” strategies, defined a priori as being within 3 days of life gained per person of the efficient frontier. For the strategies highlighted by the USPSTF in 2016, this absolute distance of 3 days is generally consistent with the relative measure used in the 2016 decision analysis (ie, LYG within 2% of the efficient frontier).4 Hereafter, “efficient” refers to both efficient and near-efficient strategies.

Analyses by Race and Sex

In consultation with the USPSTF, analyses were conducted by race and sex for the 2 most commonly-used screening modalities in the US: colonoscopy and FIT.24 Prior to performing these analyses, we conducted a comprehensive review of the literature on race and colorectal cancer.25 We concluded that the primary driver of differences in colorectal cancer incidence by race is access to screening and subsequent care, rather than biological differences in natural history. This review found that Black-White differences in colorectal cancer incidence began only after the dissemination of screening, that there is strong evidence that Black adults have had lower rates of colorectal cancer screening than White adults, and that there is limited evidence for Black-White differences in findings at screening, including detection of adenomas, advanced adenomas, and colorectal cancer.25 Accordingly, the modeling analyses assumed no differences in the natural history of colorectal cancer by race (ie, development of clinically detected colorectal cancer in the absence of screening), incorporating only Black-White differences in all-cause mortality11 and in stage-specific relative survival after diagnosis.12

Results

In the absence of screening, the models simulated identical life expectancy among 40-year-olds with no prior diagnosis of colorectal cancer, 40.2 years, and estimated that 77 to 85 per 1000 40-year-olds would be diagnosed with colorectal cancer in their lifetimes and 32 to 34 would die of colorectal cancer (eTable 1 in the Supplement). Compared with no screening, all colorectal cancer screening strategies were estimated to yield substantial increases in life expectancy (171-381 LYG per 1000) and reductions in the lifetime number of colorectal cancer cases and colorectal cancer deaths (16-74 and 15-32 cases and deaths averted per 1000, respectively). In the models, lifetime colonoscopies ranged from 624 to 6817 per 1000, and the lifetime number of colonoscopy complications ranged from 5 to 22 per 1000 (ranges are across strategies and models; see the full report6 for detailed outcomes).

Efficiency Analysis

Figure 2, Figure 3, and eFigures 1 through 3 in the Supplement show the estimated number of lifetime number of colonoscopies and LYG per 1000 for each screening strategy within a class. Although absolute estimation differed by model, general patterns were similar. Within each class, estimated LYG from screening increased when screening was initiated in the models at an earlier age, although the increase was smaller for MISCAN compared with SimCRC and CRC-SPIN.

Forty-nine unique screening strategies were efficient for all 3 models (Table 3). The majority (41/49) were those with screening starting at age 45 years. None of the strategies highlighted in the 2016 USPSTF colorectal cancer screening recommendations,3 which specified screening from ages 50 to 75, were efficient in all 3 models.

Unlike the age to begin screening, no single age to end screening was predominant among efficient strategies. However, for most modalities, the estimated increase in LYG from extending screening beyond age 75 years was small in comparison with the increase in colonoscopies (Table 3). For example, with 10-yearly colonoscopy starting at age 45, extending the age to end screening from 75 to 85 was estimated to increase lifetime colonoscopies by 5% to 6% (from 4212-4300 to 4449-4566 per 1000; ranges are across models), whereas LYG increased by less than 1% (from 301-369 to 302-370 per 1000). With annual FIT from age 45, extending the age to end screening from age 75 to age 80 years was estimated to increase lifetime colonoscopies by 5% to 7% (from 1602-1824 to 1710-1923 per 1000) and LYG by 1% to 3% (from 291-348 to 300-355 per 1000). Extending the age to end FIT screening from age 80 to age 85 was estimated to increase lifetime colonoscopies by 3% to 4% (to 1769-1990 per 1000) and LYG by less than 1% to 1% (to 303-356 per 1000).

With the exception of computed tomography colonography (5-year interval), efficient strategies within each class included multiple screening intervals; no interval was predominant (Table 3). The number of additional colonoscopies required for each additional LYG increased with shorter screening intervals. For colonoscopy from age 45 to 75 years, shortening the interval from 10 to 5 years was estimated to increase the lifetime colonoscopies by 42% to 46% across models (from 4212-4300 to 6016-6235 per 1000), while LYG was estimated to increase by 3% to 7% (from 301-369 to 321-380 per 1000) (Table 3).

Of the 16 efficient stool-based strategies, 11 were those with FIT (Table 3, Figure 3), including triennial, biennial, and annual FIT screening starting at age 45 years. Annual and biennial sDNA-FIT strategies starting at age 45 were also efficient, albeit with a relatively high colonoscopy burden relative to the LYG. For example, compared with annual FIT from ages 45 to 85, screening with biennial sDNA-FIT from ages 45 to 80 was estimated to increase the lifetime number of colonoscopies by 10% to 15% (from 1769-1990 to 2012-2181 per 1000; range is across models), while LYG were estimated to be similar to the FIT strategy (+1% in CRC-SPIN [from 319 to 322 per 1000], unchanged in SimCRC [356 per 1000], and −2% in MISCAN [from 303 to 298 per 1000]). None of the models found the triennial sDNA-FIT strategies to be efficient relative to other stool-based options.

In general, estimated LYG with 5-yearly sigmoidoscopy were comparable to LYG with biennial FIT (Table 3). Sigmoidoscopy with interval FIT was estimated to provide LYG and colorectal cancer deaths averted similar to those estimated for colonoscopy screening over the same age range.

Figure 4 and Figure 5 summarize the model-estimated changes in outcomes if screening were to begin at age 45 years instead of age 50 for strategies highlighted by the USPSTF in 2016.3 Models that lowered the age to begin screening showed 22 to 27 additional LYG (8%-9% increase), 2 to 3 fewer colorectal cancer cases (4%-6% reduction), and 0.9 to 1 fewer colorectal cancer deaths (3%-5% reduction) but also showed 0.1 to 2 additional complications (1%-14% increase) and required 161 to 784 additional colonoscopies (10%-23% increase) and 0 (with colonoscopy) to 3553 additional noncolonoscopy tests (no change to a 24% increase) over the lifetimes of 1000 persons (numbers are mean estimates across models).

Findings by Race and Sex

Compared with the total population, model-estimated life expectancy among unscreened 40-year-olds with no prior colorectal cancer diagnosis was 2 years higher for White women (42.2 vs 40.2 years), similar for Black women (40.1 years), 2 years lower for White men (38.4 years), and 5 years lower for Black men (35.2 years). In the absence of screening, the model-estimated lifetime number of diagnosed colorectal cancer cases was higher for White men compared with the total population (86 vs 81 cases per 1000, respectively; estimates are the mean across models) and lower than the total population for the other groups (77 cases per 1000 White women, 73 cases per 1000 Black men, and 70 cases per 1000 Black women) (eTable 1 in the Supplement).

Among men, the models estimated that the lower lifetime risk of colorectal cancer diagnosis also led to a lower lifetime risk of colorectal cancer death in Black vs White adults in the absence of screening (eTable 1 in the Supplement) despite the worse Black vs White relative survival after colorectal cancer diagnosis.12 The pattern differed for women by race: Black women were estimated to have lower lifetime colorectal cancer incidence than White women in the absence of screening but higher lifetime risk of colorectal cancer death. This implies that the difference in Black vs White lifetime colorectal cancer incidence was offset by the worse Black vs White relative survival after colorectal cancer diagnosis.12

Because of these differences in the estimated lifetime risk of colorectal cancer death among Black vs White adults by sex in the absence of screening, estimated LYG from both colonoscopy and FIT were generally lower for Black vs White men and generally similar for Black vs White women (eFigures 4-5 in the Supplement). However, efficient strategies for each race-sex group were generally the same, and efficiency ratios were similar by race among each sex (eTables 2-3 in the Supplement), suggesting that with equal access to quality care, the relative balance of the colonoscopy burden and the LYG from screening differs slightly by sex but not by race. For example, across models, efficiency ratios for 10-yearly colonoscopy from age 45 to 75 years ranged from 48 to 143 for White men, 46 to 142 for Black men, 57 to 100 for White women, and 51 to 93 for Black women. Among all 4 race-sex groups, efficient screening options for all 3 models were predominantly those beginning at age 45 (eFigures 4-5 and eTables 2-3 in the Supplement).

Scenario Analyses

For each modality, efficient strategies were similar for the 3 colorectal cancer population risk scenarios (eTables 4-8, eFigures 6-7 in the Supplement); efficient strategies in all 3 models were primarily those with screening beginning at age 45 years regardless of risk scenario.

Potential Implications of Adherence

Because this analysis was intended to inform population guidelines, perfect adherence to screening strategies was assumed to estimate achievable benefit for people who adhere to recommendations. However, perfect adherence does not occur in daily life.2 The potential implications of nonadherence can be inferred by comparing model-estimated outcomes with different start ages and screening intervals. These comparisons are described in the Supplement and are highlighted in eTables 9 through 12 in the Supplement. For example, lack of adherence with annual FIT from ages 45 to 75 years, resulting in a 3-year screening interval, was estimated to reduce LYG by 18% to 23% (eTable 12 in the Supplement). Similarly, lack of adherence with 10-yearly colonoscopy, resulting in a 15-year interval, was estimated to reduce LYG by 4% to 7%; if adherent with only the first colonoscopy, LYG would be estimated to decrease by 25% to 44%.

Discussion

This comparative modeling study yielded largely consistent findings across models: assuming perfect adherence, routine screening for colorectal cancer was estimated to reduce the lifetime risk of being diagnosed with and of dying of colorectal cancer, with possibly substantial LYG. Most of the efficient strategies were those with screening beginning at age 45 years. No consistent pattern emerged for the age to end screening, although the estimated LYG from continued screening after age 75 were small. For most modalities, no screening interval was predominant. Efficient strategies were robust to assumptions about population risk of colorectal cancer.

The findings from this analysis are consistent with those of the 2016 decision analysis of colorectal cancer screening for the USPSTF.4 However, in 2016, there was limited evidence to support screening before age 50 years. While no trials have reported on the effect of screening among asymptomatic adults aged 45 to 49 and data on the findings at screening at these ages remain sparse,26-28 there is clearer evidence that colorectal cancer incidence in the US is increasing before age 50.5,29

The SimCRC and CRC-SPIN models estimated that most (and often, nearly all) efficient strategies begin at age 45 years. The estimated LYG and colonoscopy burden of screening were lowest for MISCAN, and MISCAN found strategies with screening beginning at older ages to be efficient, in addition to strategies beginning at age 45 identified by SimCRC and CRC-SPIN. Prior work examining differences in model estimation found that these differences are primarily attributable to assumptions about the nature of adenomas.13 With SimCRC and CRC-SPIN, all adenomas have the potential to progress to colorectal cancer. With MISCAN, some adenomas are assumed to be nonprogressive, and the probability that an adenoma is nonprogressive decreases with age at onset. Because of this age dependency, which is the result of model structure and calibration, MISCAN estimates a smaller benefit from removing adenomas present at age 45 compared with SimCRC or CRC-SPIN.

A key change from the 2016 analysis is inclusion of analyses examining the benefits, harms, and colonoscopy burden of colorectal cancer screening strategies by race and sex. Based on a recent review of the literature on race and colorectal cancer,25 these analyses assumed that the risk of developing adenomas and the progression of adenomas to colorectal cancer did not differ by Black vs White race. However, the analyses incorporated race- and sex-specific all-cause mortality rates11 and relative survival probabilities after colorectal cancer diagnosis.12 Because Black adults have a shorter life expectancy than White adults,11 the model-estimated lifetime incidence of colorectal cancer diagnosis in the absence of screening was lower in Black vs White adults by sex (eTable 1 in the Supplement). While this lower lifetime incidence in the absence of screening may appear to conflict with current SEER data demonstrating higher colorectal cancer incidence among Black adults,14 SEER incidence rates are observed in the presence of screening. Racial disparities in screening have been well documented. Black adults are less likely than White adults to be up to date with screening recommendations, to receive colonoscopy after an abnormal noncolonoscopy test result, and to be screened by endoscopists with higher adenoma detection rates.25

Unlike the current analysis, MISCAN and SimCRC analyses to inform the 2018 American Cancer Society (ACS) colorectal cancer screening guidelines30 assumed differences in natural history by Black vs White race. These analyses were carried out before publication of recent reviews demonstrating limited evidence of Black vs White differences in findings at colorectal cancer screening.25,31 Accordingly, differences in outcomes by race were larger in analyses for the ACS than in this analysis. However, both analyses found that starting screening at age 45 years provided an efficient balance of colonoscopies and LYG for the asymptomatic average-risk population as a whole and by race.

Findings from this analysis for the total population are also largely consistent with MISCAN analyses for the ACS for the total population,32 despite use of different estimates of the magnitude of the observed increase in colorectal cancer incidence among recent birth cohorts. Analyses for the ACS were based on (larger) published estimates5 of the increase in incidence, which included tumors that are not the target of colorectal cancer screening (eg, carcinoid tumors). These tumors were excluded from analysis of SEER data used to inform risk scenarios for the USPSTF. Because the incidence of adenocarcinoma is increasing more slowly than that of carcinoid tumors of the colon or rectum,29 the estimated increase in population risk of colorectal cancer is lower when these tumors are excluded (see the Supplement for details). However, despite the different assumptions about colorectal cancer incidence rates in the absence of screening, analyses for the USPSTF described here and analyses for the ACS both suggest that starting colorectal cancer screening at age 45 years provides an efficient balance of the colonoscopy burden and the LYG from screening.

With many recommended options for colorectal cancer screening, deciding which strategy to adopt may be overwhelming for patients and their clinicians. In addition to helping patients decide whether to begin screening at age 45 years or at age 50, the estimated long-term outcomes from the models may help inform the trade-offs of screening with one approach over another. For example, if choosing among the screening strategies recommended by the USPSTF in 2016, a patient who wishes to maximize LYG and minimize the lifetime risk of colorectal cancer diagnosis may opt for 10-yearly colonoscopy screening, while a patient who wishes to minimize the lifetime number of colonoscopies required may opt for screening with either 3-yearly sDNA-FIT or annual FIT. For whichever option is chosen, model findings may also help illustrate to patients the potential implications of failure to adhere with recommended screening ages and intervals.

A strength of this study is that it used 3 distinct simulation models to estimate the LYG and colonoscopy burden of screening. Each model is based on different assumptions about the natural history of colorectal cancer, although all are calibrated to similar end points.13 Their differences—in dwell times, location of adenomas, progressive vs nonprogressive adenomas, among others—provide a range of outcomes that reflect a sensitivity analysis on underlying model assumptions. The similar relative estimations across classes of screening modalities and similar rankings of strategies within classes of modalities across the 3 models demonstrate the robustness of the findings.

Limitations

This study has several limitations. First, although the modeling results provide a lifetime framework for evaluating benefits and burdens from a screening program, empirical data on test sensitivity and specificity are based on a single screen. Outcomes at repeated rounds of stool testing have been reported in 2 small studies33,34; findings suggest that test results are correlated. While all 3 models assumed independence of repeated tests, an analysis using MISCAN showed that incorporation of correlated results across repeated screening tests would have only a modest effect on model-estimated outcomes.33

Second, the models did not simulate the serrated polyp pathway to colorectal cancer10 because of insufficient evidence on the prevalence of serrated polyps by age, their malignant potential, and the ability of screening tests to detect them. A modeling study by Greuter et al35 estimated the effect of incorporating the serrated pathway on screening effectiveness and found little difference in model results when assuming that 0% vs 30% of cancers arise from this pathway. Exploratory analyses with MISCAN also showed that inclusion of the serrated pathway had limited effect on optimal screening strategies.36

Third, the models assumed that the increased population risk of colorectal cancer arises from an increase in adenoma risk. Whether the observed increase in colorectal cancer incidence among younger adults is caused (instead or in addition) by faster progression to malignancy is unknown. In prior analyses with MISCAN32 the effects of each of these assumptions were evaluated; findings with respect to screening initiation at age 45 years were robust.

Fourth, models can only approximate reality. While the models described here have been extensively calibrated and validated, they are limited by uncertainty in the data used to inform inputs and assumptions. Nevertheless, model-based estimates are important because they provide patients and their clinicians with information they can use to make decisions about when and how to screen for colorectal cancer. Such decisions would otherwise be left to individual judgement, as that information cannot feasibly be obtained from clinical studies. Modeling studies are not a substitute for empirical evidence. Instead they synthesize, build from, and extend empirical evidence to provide insights into questions about screening practices.

Conclusions

This microsimulation modeling analysis suggests that screening for colorectal cancer with stool tests, endoscopic tests, or computed tomography colonography starting at age 45 years provides an efficient balance of colonoscopy burden and life-years gained.

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Article Information

Corresponding Author: Amy B. Knudsen, PhD, Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, Ste 1010, Boston, MA 02114 (amy.knudsen@mgh.harvard.edu).

Accepted for Publication: March 30, 2021.

Author Contributions: Drs Knudsen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Zauber, Kuntz, and Lansdorp-Vogelaar contributed equally to this work.

Concept and design: Knudsen, Rutter, Peterse, Perdue, Doria-Rose, Feuer, Zauber, Lansdorp-Vogelaar.

Acquisition, analysis, or interpretation of data: Knudsen, Rutter, Peterse, Lietz, Seguin, Meester, Lin, Siegel, Feuer, Zauber, Kuntz, Lansdorp-Vogelaar.

Drafting of the manuscript: Knudsen, Rutter, Seguin.

Critical revision of the manuscript for important intellectual content: Knudsen, Rutter, Peterse, Lietz, Meester, Perdue, Lin, Siegel, Doria-Rose, Feuer, Zauber, Kuntz, Lansdorp-Vogelaar.

Statistical analysis: Knudsen, Rutter, Peterse, Lietz, Seguin, Siegel, Zauber, Kuntz.

Obtained funding: Knudsen, Rutter, Zauber, Lansdorp-Vogelaar.

Administrative, technical, or material support: Rutter, Lietz, Perdue, Zauber.

Supervision: Rutter, Lansdorp-Vogelaar.

Conflict of Interest Disclosures: Ms Siegel is Scientific Director of Surveillance Research at the American Cancer Society. Her contributions to the report are solely the responsibility of the author and do not represent the official view of the American Cancer Society. No other authors reported disclosures.

Funding/Support: This research was funded under contract HHSA-290-2015-00007-I-EPC5, Task Order 8, from the Agency for Healthcare Research and Quality (AHRQ), US Department of Health and Human Services, via the Kaiser Permanente Evidence-based Practice Center (EPC), under a contract to support the US Preventive Services Task Force (USPSTF). Drs Knudsen, Rutter, Peterse, Meester, Zauber, Kuntz, and Lansdorp-Vogelaar and Mss Lietz and Seguin were also supported in part by grants U01CA199335 and U01CA253913 from the National Cancer Institute.

Role of the Funder/Sponsor: Investigators worked with USPSTF members, AHRQ staff, and the EPC review team to define the scope of the project and key questions to be addressed. AHRQ staff provided project oversight, reviewed the report to ensure that the analysis met methodological standards, and distributed the draft for peer review. Otherwise, AHRQ had no role in the conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript findings. The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ, the US Department of Health and Human Services, or the National Cancer Institute.

Additional Contributions: We gratefully acknowledge the following individuals for their contributions to this project: Tina Fan, MD, MPH, Tracy Wolff, MD, MPH, and Quyen Ngo-Metzger, MD, MPH (AHRQ); members of the USPSTF who contributed to the decision analysis work plan; Douglas Corley, MD, PhD, MPH (Kaiser Permanente), Jennifer Croswell, MD, MPH (National Cancer Institute), and Jason A. Dominitz, MD, MHS (Veterans Affairs Puget Sound Health Care System and University of Washington), for their content expertise and review of the draft report; the Multi-Society Task Force on Colorectal Cancer for sharing surveillance recommendations; Deborah Schrag, MD, MPH (Dana-Farber Cancer Institute), for assistance with histology codes for colorectal cancer; John Clarke, MS, Zachary Dodge, MS, and Lon Sherritt, MPH (Cornerstone Systems Northwest), for development of interactive online versions of key figures; and Caitlin Senger, MPH (Kaiser Permanente EPC), for assistance and guidance. USPSTF members, EPC members, peer reviewers and those commenting on behalf of partner organizations did not receive financial compensation for their contributions.

Additional Information: A draft version of this modeling report underwent external peer review from 3 content experts (Douglas Corley, MD, PhD, MPH [Kaiser Permanente], Jennifer Croswell, MD, MPH [National Cancer Institute], and Jason A. Dominitz, MD, MHS [Veterans Affairs Puget Sound Health Care System and University of Washington]). Comments from reviewers were presented to the USPSTF during its deliberation of the evidence and were considered in preparing the final report.

Editorial Disclaimer: This modeling study is presented as a document in support of the accompanying USPSTF Recommendation Statement. It did not undergo additional peer review after submission to JAMA.

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