Comorbidity Profiles and Lung Cancer Screening among Older Adults: U.S. Behavioral Risk Factor Surveillance System 2017–2019
Bibliographic record
Abstract
Abstract Rationale Although lung cancer screening (LCS) with low-dose computed tomography (LDCT) is now recommended for those meeting standard risk factor–based eligibility criteria, the role of comorbidity in the uptake of LCS with LDCT in an older real-world U.S. population is not well established. Objectives To examine the relationships between comorbidity, functional status, and LCS utilization in the United States. Methods Using population-based data from the 2017–2019 Behavioral Risk Factor Surveillance System, we examined the association of comorbid conditions and functional limitations regarding activities of daily living with LCS utilization among participants that met the LCS criteria based on the U.S. Preventive Service Taskforce guidelines. We employed multivariable weighted logistic regression models to evaluate these associations, both overall and within subgroups defined by age (<65 yr vs. ⩾65 yr), sex, and smoking history. Results Of 11,214 participants who met the eligibility criteria for LCS, 1,731 (16%) underwent LCS with LDCT. The majority were White (90%), male (55%), former smokers (52%), and living with at least one chronic comorbid condition (77%). More than 28% had three or more comorbid conditions, and approximately 40% of participants reported having some form of functional limitations. In the multivariable models, the likelihood of undergoing LCS with LDCT within the past year was positively associated with higher amount of comorbidity (⩾5 vs. 0: adjusted odds ratio, 2.34; 95% confidence interval [CI], 1.22–4.48) but not with functional limitations (⩾3 vs. 0: adjusted odds ratio, 1.00; 95% CI, 0.66–1.50). Conclusions The presence of comorbid conditions is associated with a higher likelihood of undergoing LCS with LDCT. Because poor health status may diminish the benefits of screening, future research is needed to precisely characterize the health status of LCS-eligible individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".