A Systematic Review of Cost-Effectiveness Studies on Gastric Cancer Screening
Bibliographic record
Abstract
Gastric cancer (GC) poses notable economic and health burdens in settings where the incidence of disease is prevalent. Some countries have established early screening and treatment programs to address these challenges. The objectives of this systematic review were to summarize the cost-effectiveness of gastric cancer screening presented in the literature and to identify the critical factors that influence the cost-effectiveness of screening. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Economic evaluation studies of gastric cancer screening were reviewed from SCOPUS and PubMed. The Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) was used to assess the quality of reporting presented in the selected articles. Only primary economic evaluation studies addressing the cost-effectiveness, cost–utility, and cost–benefit of gastric cancer screening were selected. Two reviewers scrutinized the selected articles (title, abstract, and full text) to determine suitability for the systematic review based on inclusion and exclusion criteria. Authors’ consensus was relied on where disagreements arose. The main outcome measures of concern in the systematic review were cost, effectiveness (as measured by either quality-adjusted life years (QALY) or life-years saved (LYS)), and incremental cost-effectiveness ratio (ICER) of screening versus either no screening or an alternative screening method. Thirty-one studies were selected for the final review. These studies investigated the cost-effectiveness of GC screening based on either primary, secondary, or a combination of primary and secondary interventions. The main primary intervention was Helicobacter pylori (Hp) screening with eradication, while the main secondary intervention was endoscopic screening. Cost-effectiveness was evaluated against no screening or screening using an alternative method in both observational and model-based studies. Screening was mainly cost-effective in Asian countries or their diasporas where the prevalence of GC was high. GC screening was generally not cost-effective among Western countries. GC screening can be cost-effective, but cost-effectiveness is dependent on context-specific factors, including geographical location, the prevalence of GC in the local population, and the screening tool adopted. However, there is benefit in targeting high-risk population groups in Asian countries and their diaspora for GC screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".