2020
DOI: 10.1186/s12913-020-05883-2
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Development of a multivariable prediction model to identify patients unlikely to complete a colonoscopy following an abnormal FIT test in community clinics

Abstract: Background Colorectal cancer (CRC) is the 3rd leading cancer killer among men and women in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data availa… Show more

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Cited by 6 publications
(7 citation statements)
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References 23 publications
(19 reference statements)
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“…Our findings are consistent with prior studies that showed older patients are less likely to receive a follow-up colonoscopy after an abnormal FIT. 11 , 30 , 31 Some studies have found that patient behaviors (refusal) or physician behaviors (inappropriate screening) may explain some of these differences in follow-up rates by age. 31 Insurance status/type are markers for other social needs (such as housing or transportation), 36 which may be particularly important for colonoscopy completion.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Our findings are consistent with prior studies that showed older patients are less likely to receive a follow-up colonoscopy after an abnormal FIT. 11 , 30 , 31 Some studies have found that patient behaviors (refusal) or physician behaviors (inappropriate screening) may explain some of these differences in follow-up rates by age. 31 Insurance status/type are markers for other social needs (such as housing or transportation), 36 which may be particularly important for colonoscopy completion.…”
Section: Discussionmentioning
confidence: 99%
“…We also found that Hispanic ethnicity, Asian race, and a non-English/Spanish language preference were associated with a lower risk of failing to receive a follow-up colonoscopy. While some studies found populations of color are less likely to receive a follow-up colonoscopy after an abnormal FIT, 30 studies conducted within systems where insurance and access may be similar (e.g., Veterans Health Affairs or safety-net systems) have found racial/ethnic minorities are more likely than non-Hispanic White populations to acquire a colonoscopy after an abnormal FIT. 7 , 31 While few studies have specifically assessed the impact of language preference on abnormal FIT follow-up, our team has shown patients with non-English language preference are more likely to adhere to follow-up recommendations.…”
Section: Discussionmentioning
confidence: 99%
“…Provider- and system-level strategies that identify, report, and resolve abnormal findings may be better suited for diagnostic colonoscopy than patient-level interventions [ 36 , 37 ]. These strategies may include prioritizing scheduling of diagnostic vs. screening colonoscopy; implementing standard tracking and reporting procedures; administrative algorithms that identify the appropriate follow-up needs of individual patients based on test results; and automated phone calls linked to test results, progressing to personal phone calls, as needed [ 38 , 39 ]. More intensive navigation may also be needed to reduce disparities in follow-up among underserved patients [ 40 ].…”
Section: Discussionmentioning
confidence: 99%
“…The methods of the STOP CRC model are described elsewhere. 6 Briefly, the STOP CRC cohort included patients aged 50-75 with an abnormal FIT result and one year of follow-up time. The original STOP CRC model was created using the data from 2014 to 2016 from eight FQHCs, including 26 clinics and 1596 patients.…”
Section: Recalibration Of the Modelmentioning
confidence: 99%
“…In Strategies and Opportunities to Increase CRC Screening in Priority Populations (STOP CRC), a risk prediction model was created to identify patients unlikely to complete a colonoscopy following an abnormal FIT test. 6 The original risk prediction model was developed using data from 26 federally qualified health center (FQHC) clinics in Oregon and California. This model showed adequate separation of patients across risk levels (bootstrap-corrected c-statistic > 0.63, R 2 = 14.03), and included eight variables.…”
Section: Introductionmentioning
confidence: 99%