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Showing content from https://pubmed.ncbi.nlm.nih.gov/32681828/ below:

Predictors of Lung Cancer Screening Utilization in a Population-Based Survey

. 2020 Dec;17(12):1591-1601. doi: 10.1016/j.jacr.2020.06.015. Epub 2020 Jul 16. Predictors of Lung Cancer Screening Utilization in a Population-Based Survey

Affiliations

¹ Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina.
² Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina.
³ Associate Director, Clinical and Translational Science Institute, Wake Forest School of Medicine; Director, KL2 Training Program, Wake Forest School of Medicine; Department of Internal Medicine and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
⁴ Professor, Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine; Scientific Director, Behavioral Oncology, Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado; University of Colorado Cancer Center, Aurora, Colorado.
⁵ Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Director, Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina. Electronic address: jmeberth@mailbox.sc.edu.

PMID: 32681828
DOI: 10.1016/j.jacr.2020.06.015

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Predictors of Lung Cancer Screening Utilization in a Population-Based Survey

Anja Zgodic et al. J Am Coll Radiol. 2020 Dec.

. 2020 Dec;17(12):1591-1601. doi: 10.1016/j.jacr.2020.06.015. Epub 2020 Jul 16. Affiliations

¹ Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina.
² Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina.
³ Associate Director, Clinical and Translational Science Institute, Wake Forest School of Medicine; Director, KL2 Training Program, Wake Forest School of Medicine; Department of Internal Medicine and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
⁴ Professor, Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine; Scientific Director, Behavioral Oncology, Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado; University of Colorado Cancer Center, Aurora, Colorado.
⁵ Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Director, Rural and Minority Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina. Electronic address: jmeberth@mailbox.sc.edu.

PMID: 32681828
DOI: 10.1016/j.jacr.2020.06.015

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Abstract

Purpose: Annual low-dose CT (LDCT) screening in high-risk individuals has been recommended to detect lung cancer earlier and reduce mortality. The objective of this study was to identify demographic, financial, and health care factors associated with screening uptake in a population-based survey.

Methods: Data from the Lung Cancer Screening Module and core modules of the 2017 Behavioral Risk Factor Surveillance System, a population-based survey administered via cell phone and landline, were analyzed to examine demographic, health, and financial factors associated with screening uptake among the 10 states that administered the screening module. Weighted frequencies and confidence intervals (CIs) were produced, and weighted Wald χ² tests were used to compare differences in screening utilization by patient characteristics. A multivariate logistic mixed-effects model was constructed, in which participant clustering by state was accounted for with a random intercept.

Results: The uninsured were less likely to undergo LDCT screening (odds ratio [OR], 0.28; 95% CI, 0.12-0.65). LDCT screening uptake was higher for participants with chronic respiratory conditions (OR, 4.14; 95% CI, 2.33-7.35); those who were divorced, separated, widowed, or refused to answer (OR, 1.41; 95% CI, 1.05-1.86); those who had previous cancer diagnoses (OR, 1.90; 95% CI, 1.40-2.56); and those aged 65 to 69 years (OR, 1.23; 95% CI, 1.06-1.44) or 70 to 74 years (OR, 1.17; 95% CI, 1.00-1.37). Utilization also varied significantly across states.

Conclusions: Having a related health condition whereby participants were sensitized to the benefits of early screening (ie, another cancer diagnosis, presence of chronic respiratory conditions) and having insurance coverage were associated with higher LDCT screening uptake. Providers should engage LDCT-eligible patients through informed and shared decision making to increase preference-sensitive screening decisions.

Keywords: Lung cancer; computed tomography; early detection of cancer; screening; survey research.

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