Population-Based Screening Using Low-Dose Chest Computed Tomography: A Systematic Review of Health Economic Evaluations
Bibliographic record
Abstract
BACKGROUND: Chest low-dose computed tomography (LDCT) is a promising technology for population-based screening because it is non-invasive, relatively inexpensive, associated with low radiation and highly sensitive to lung cancer. To improve the cost-effectiveness of lung cancer screening, simultaneous screening for other diseases could be considered. This systematic review was conducted to analyse studies that published evidence on the cost-effectiveness of chest LDCT screening programs for different diseases. METHODS: Scopus and PubMed were searched for English publications (1 January 2011-22 July 2022) using search terms related to screening, computed tomography and cost-effectiveness. An additional search specifically searched for the cost-effectiveness of screening for lung cancer, chronic obstructive pulmonary disease or cardiovascular disease. Included publications should present a full health economic evaluation of population screening with chest LDCT. The extracted data included the disease screened for, model type, country context of screening, inclusion of comorbidities or incidental findings, incremental costs, incremental effects and the resulting cost-effectiveness ratio amongst others. Reporting quality was assessed using the 2022 Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. RESULTS: The search yielded 1799 unique papers, of which 43 were included. Most papers focused on lung cancer screening (n = 40), and three were on coronary calcium scoring. Microsimulation was the most commonly applied modelling type (n = 16), followed by life table analysis (n = 10) and Markov cohort models (n = 10). Studies reflected the healthcare context of the US (n = 15), Canada (n = 4), the UK (n = 3) and 13 other countries. The reported incremental cost-effectiveness ratio ranged from US$10,000 to US$90,000/quality-adjusted life year (QALY) for lung cancer screening compared to no screening and was US$15,900/QALY-US$45,300/QALY for coronary calcium scoring compared to no screening. DISCUSSION: Almost all health economic evaluations of LDCT screening focused on lung cancer. Literature regarding the health economic benefits of simultaneous LDCT screening for multiple diseases is absent. Most studies suggest LDCT screening is cost-effective for current and former smokers aged 55-74 with a minimum of 30 pack-years of smoking history. Consequently, more evidence on LDCT is needed to support further cost-effectiveness analyses. Preferably evidence on simultaneous screening for multiple diseases is needed, but alternatively, on single-disease screening. REGISTRATION OF SYSTEMATIC REVIEW: Prospective Register of Ongoing Systematic Reviews registration CRD42021290228 can be accessed https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=290228 .
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".