N Joseph Leishman1, Renda S Wiener2, Angela Fagerlin3, Rodney A Hayward4, Julie Lowery5, Tanner J Caverly6. 1. Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; University of Michigan School of Public Health, Ann Arbor, Michigan. 2. The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts; Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts. 3. Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah; Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, Utah. 4. Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; Departments of Learning Health Sciences and Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan. 5. Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan. 6. Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; Departments of Learning Health Sciences and Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan. Electronic address: tcaverly@med.umich.edu.
Abstract
INTRODUCTION: Little is known about how clinicians make low-dose computed tomography lung cancer screening decisions in practice. Investigators assessed the factors associated with real-world decision making, hypothesizing that lung cancer risk and comorbidity would not be associated with agreeing to or receiving screening. Though these factors are key determinants of the benefit of lung cancer screening, they are often difficult to incorporate into decisions without the aid of decision tools. METHODS: This was a retrospective cohort study of patients meeting current national eligibility criteria and deemed appropriate candidates for lung cancer screening on the basis of clinical reminders completed over a 2-year period (2013-2015) at 8 Department of Veterans Affairs medical facilities. Multilevel mixed-effects logistic regression models (conducted in 2019-2020) assessed predictors (age, sex, lung cancer risk, Charlson Comorbidity Index, travel distance to facility, and central versus outlying decision-making location) of primary outcomes of agreeing to and receiving lung cancer screening. RESULTS: Of 5,551 patients (mean age=67 years, 97% male, mean lung cancer risk=0.7%, mean Charlson Comorbidity Index=1.14, median travel distance=24.2 miles), 3,720 (67%) agreed to lung cancer screening and 2,398 (43%) received screening. Lung cancer risk and comorbidity score were not strong predictors of agreeing to or receiving screening. Empirical Bayes adjusted rates of agreeing to and receiving screening ranged from 22% to 84% across facilities and from 19% to 85% across clinicians. A total of 33.7% of the variance in agreeing to and 34.2% of the variance in receiving screening was associated with the facility or the clinician offering screening. CONCLUSIONS: Substantial variation was found in Veterans agreeing to and receiving lung cancer screening during the Veterans Affairs Lung Cancer Screening Demonstration Project. This variation was not explained by differences in key determinants of patient benefit, whereas the facility and clinician advising the patient had a large impact on lung cancer screening decisions. Published by Elsevier Inc.
INTRODUCTION: Little is known about how clinicians make low-dose computed tomography lung cancer screening decisions in practice. Investigators assessed the factors associated with real-world decision making, hypothesizing that lung cancer risk and comorbidity would not be associated with agreeing to or receiving screening. Though these factors are key determinants of the benefit of lung cancer screening, they are often difficult to incorporate into decisions without the aid of decision tools. METHODS: This was a retrospective cohort study of patients meeting current national eligibility criteria and deemed appropriate candidates for lung cancer screening on the basis of clinical reminders completed over a 2-year period (2013-2015) at 8 Department of Veterans Affairs medical facilities. Multilevel mixed-effects logistic regression models (conducted in 2019-2020) assessed predictors (age, sex, lung cancer risk, Charlson Comorbidity Index, travel distance to facility, and central versus outlying decision-making location) of primary outcomes of agreeing to and receiving lung cancer screening. RESULTS: Of 5,551 patients (mean age=67 years, 97% male, mean lung cancer risk=0.7%, mean Charlson Comorbidity Index=1.14, median travel distance=24.2 miles), 3,720 (67%) agreed to lung cancer screening and 2,398 (43%) received screening. Lung cancer risk and comorbidity score were not strong predictors of agreeing to or receiving screening. Empirical Bayes adjusted rates of agreeing to and receiving screening ranged from 22% to 84% across facilities and from 19% to 85% across clinicians. A total of 33.7% of the variance in agreeing to and 34.2% of the variance in receiving screening was associated with the facility or the clinician offering screening. CONCLUSIONS: Substantial variation was found in Veterans agreeing to and receiving lung cancer screening during the Veterans Affairs Lung Cancer Screening Demonstration Project. This variation was not explained by differences in key determinants of patient benefit, whereas the facility and clinician advising the patient had a large impact on lung cancer screening decisions. Published by Elsevier Inc.
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