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Cost-effectiveness of Helicobacter pylori screening followed by eradication treatment for employees in Japan

Published online by Cambridge University Press:  30 July 2018

A. Kowada*
Affiliation:
General Affairs Department, Ota City Office, Tokyo, Japan
*
Author for correspondence: A. Kowad, E-mail: kowadaa@gmail.com
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Abstract

Gastric cancer is the third leading cause of cancer death worldwide. Gastric cancer screening using upper gastrointestinal series, endoscopy and serological testing has been performed in population-based (employee-based and community-based) and opportunistic cancer screening in Japan. There were 45 531 gastric cancer deaths in 2016, with the low screening and detection rates. Helicobacter pylori (H. pylori) screening followed by eradication treatment is recommended in high-risk population settings to reduce gastric cancer incidence. The aim of this study was to evaluate the cost-effectiveness of H. pylori screening followed by eradication treatment for a high-risk population in the occupational health setting. Decision trees and Markov models were developed for two strategies; H. pylori antibody test (HPA) screening and no screening. Targeted populations were hypothetical cohorts of employees aged 20, 30, 40, 50 and 60 years using a company health payer perspective on a lifetime horizon. Per-person costs and effectiveness (quality-adjusted life-years) were calculated and compared. HPA screening yielded greater benefits at the lower cost than no screening. One-way and probabilistic sensitivity analyses using Monte-Carlo simulation showed strong robustness of the results. H. pylori screening followed by eradication treatment is recommended to prevent gastric cancer for employees in Japan, on the basis of cost-effectiveness.

Information

Type
Original Paper
Copyright
Copyright © Cambridge University Press 2018 
Figure 0

Fig. 1. Simplified decision trees. HPA, Helicobacter pylori antibody test; H. pylori, Helicobacter pylori. A square node represents the decision node. A circle node represents a chance node. Branches from a chance node represent possible outcomes. A node represents a Markov node.

Figure 1

Fig. 2. Cohort simulation in a state-transition Markov model. H. pylori, Helicobacter pylori.

Figure 2

Table 1. Baseline estimates for selected variables

Figure 3

Fig. 3. Cost-effectiveness acceptability curves. HPA, Helicobacter pylori antibody test.

Figure 4

Table 2. Results of base-case analyses