Assessing the value of screening tools: reviewing the challenges and opportunities of cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Screening is an important part of preventive medicine. Ideally, screening tools identify patients early enough to provide treatment and avoid or reduce symptoms and other consequences, improving health outcomes of the population at a reasonable cost. Cost-effectiveness analyses combine the expected benefits and costs of interventions and can be used to assess the value of screening tools. OBJECTIVE: This review seeks to evaluate the latest cost-effectiveness analyses on screening tools to identify the current challenges encountered and potential methods to overcome them. METHODS: A systematic literature search of EMBASE and MEDLINE identified cost-effectiveness analyses of screening tools published in 2017. Data extracted included the population, disease, screening tools, comparators, perspective, time horizon, discounting, and outcomes. Challenges and methodological suggestions were narratively synthesized. RESULTS: Four key categories were identified: screening pathways, pre-symptomatic disease, treatment outcomes, and non-health benefits. Not all studies included treatment outcomes; 15 studies (22%) did not include treatment following diagnosis. Quality-adjusted life years were used by 35 (51.4%) as the main outcome. Studies that undertook a societal perspective did not report non-health benefits and costs consistently. Two important challenges identified were (i) estimating the sojourn time, i.e., the time between when a patient can be identified by screening tests and when they would have been identified due to symptoms, and (ii) estimating the treatment effect and progression rates of patients identified early. CONCLUSIONS: To capture all important costs and outcomes of a screening tool, screening pathways should be modeled including patient treatment. Also, false positive and false negative patients are likely to have important costs and consequences and should be included in the analysis. As these patients are difficult to identify in regular data sources, common treatment patterns should be used to determine how these patients are likely to be treated. It is important that assumptions are clearly indicated and that the consequences of these assumptions are tested in sensitivity analyses, particularly the assumptions of independence of consecutive tests and the level of patient and provider compliance to guidelines and sojourn times. As data is rarely available regarding the progression of undiagnosed patients, extrapolation from diagnosed patients may be necessary.
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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 | 0.206 | 0.529 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".