Back

Dysplasia-Stratified Surveillance Identifies Optimal Strategies for Preventing Esophageal Adenocarcinoma in Barrett's Esophagus: An Incidence-Based Cost-Effectiveness Model

Kowada, A.

2026-02-22 health economics
10.64898/2026.02.17.26346313 medRxiv
Show abstract

The risk of esophageal adenocarcinoma (EAC) in Barretts esophagus (BE) varies substantially by segment length and dysplasia grade. This study evaluated the cost-effectiveness and health impacts of dysplasia-stratified EAC surveillance strategies for the Japanese BE population. A state-transition model was developed comparing endoscopy, sponge test, breath test, and miRNA test with no surveillance from a healthcare payer perspective over a lifetime. Non-invasive strategies were assessed as primary surveillance tools, with positive results triggering confirmatory endoscopy, and a scenario analysis evaluated AI-assisted endoscopy. Five BE populations of 50-year-old individuals were modeled: ultra-short segment BE (USSBE), short-segment BE (SSBE), long-segment nondysplastic BE (LSBE-NDBE), LSBE with low-grade dysplasia (LSBE-LGD), and LSBE with high-grade dysplasia (LSBE-HGD). Each modality was evaluated at surveillance intervals of 1, 2, 3, 4, 5, or 10 years. Primary outcomes included net monetary benefits, costs, quality-adjusted life-years, incremental cost-effectiveness ratios, and EAC deaths, with sensitivity analyses assessing parameter uncertainty. Surveillance was not cost-effective for USSBE, SSBE, or LSBE-NDBE. For LSBE-LGD, annual endoscopy was most cost-effective, averting 83 EAC deaths per 10,000 individuals, while for LSBE-HGD, annual breath testing was most cost-effective, averting 295 deaths. These findings support dysplasia-specific surveillance in LSBE with implications for global surveillance practice.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.