BACKGROUND: Estimates for the annual progression rate from Barrett's esophagus (BE) to esophageal adenocarcinoma (EAC) vary widely. In this explorative study, we quantified how this uncertainty affects the estimates of effectiveness and efficiency of screening and treatment for EAC. DESIGN: We developed 3 versions of the University of Washington / Microsimulation Screening Analysis-EAC model. The models differed with respect to the annual progression rate from BE to EAC (0.12% or 0.42%) and the possibility of spontaneous regression of dysplasia (yes or no). All versions of the model were calibrated to the observed Surveillance, Epidemiology, and End Results esophageal cancer incidence rates from 1998 to 2009. To identify the impact of natural history, we estimated the incidence and deaths prevented as well as numbers needed to screen (NNS) and treat (NNT) of a one-time perfect screening at age 65 years that detected all prevalent BE cases, followed by a perfect treatment intervention. RESULTS: Assuming a perfect screening and treatment intervention for all patients with BE, the maximum EAC mortality reduction (64%-66%) and the NNS per death prevented (470-510) were similar across the 3 model versions. However, 3 times more people needed to be treated to prevent 1 death (24 v. 8) in the 0.12% regression model compared with the 0.42% progression model. Restricting treatment to those with dysplasia or only high-grade dysplasia resulted in smaller differences in NNT (2-3 to prevent one EAC case) but wider variation in effectiveness (mortality reduction of 15%-24%). CONCLUSION: The uncertainty in the natural history of the BE to EAC sequence influenced the estimates of effectiveness and efficiency of BE screening and treatment considerably. This uncertainty could seriously hamper decision making about implementing BE screening and treatment interventions.
BACKGROUND: Estimates for the annual progression rate from Barrett's esophagus (BE) to esophageal adenocarcinoma (EAC) vary widely. In this explorative study, we quantified how this uncertainty affects the estimates of effectiveness and efficiency of screening and treatment for EAC. DESIGN: We developed 3 versions of the University of Washington / Microsimulation Screening Analysis-EAC model. The models differed with respect to the annual progression rate from BE to EAC (0.12% or 0.42%) and the possibility of spontaneous regression of dysplasia (yes or no). All versions of the model were calibrated to the observed Surveillance, Epidemiology, and End Results esophageal cancer incidence rates from 1998 to 2009. To identify the impact of natural history, we estimated the incidence and deaths prevented as well as numbers needed to screen (NNS) and treat (NNT) of a one-time perfect screening at age 65 years that detected all prevalent BE cases, followed by a perfect treatment intervention. RESULTS: Assuming a perfect screening and treatment intervention for all patients with BE, the maximum EAC mortality reduction (64%-66%) and the NNS per death prevented (470-510) were similar across the 3 model versions. However, 3 times more people needed to be treated to prevent 1 death (24 v. 8) in the 0.12% regression model compared with the 0.42% progression model. Restricting treatment to those with dysplasia or only high-grade dysplasia resulted in smaller differences in NNT (2-3 to prevent one EAC case) but wider variation in effectiveness (mortality reduction of 15%-24%). CONCLUSION: The uncertainty in the natural history of the BE to EAC sequence influenced the estimates of effectiveness and efficiency of BE screening and treatment considerably. This uncertainty could seriously hamper decision making about implementing BE screening and treatment interventions.
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Authors: Daysha Ferrer-Torres; Derek J Nancarrow; Rork Kuick; Dafydd G Thomas; Ernest Nadal; Jules Lin; Andrew C Chang; Rishindra M Reddy; Mark B Orringer; Jeremy M G Taylor; Thomas D Wang; David G Beer Journal: Oncotarget Date: 2016-08-23
Authors: Elena Lastraioli; Tiziano Lottini; Jessica Iorio; Giancarlo Freschi; Marilena Fazi; Claudia Duranti; Laura Carraresi; Luca Messerini; Antonio Taddei; Maria Novella Ringressi; Marianna Salemme; Vincenzo Villanacci; Carla Vindigni; Anna Tomezzoli; Roberta La Mendola; Maria Bencivenga; Bruno Compagnoni; Mariella Chiudinelli; Luca Saragoni; Ilaria Manzi; Giovanni De Manzoni; Paolo Bechi; Luca Boni; Annarosa Arcangeli Journal: Oncotarget Date: 2016-09-13