title: High-resolution microendoscopy for esophageal cancer screening in China: A cost-effectiveness analysis.
abstract: OBJECTIVE
  To study the cost-effectiveness of high-resolution microendoscopy (HRME) in an esophageal squamous cell carcinoma (ESCC) screening program in China.
  
  
  METHODS
  A decision analytic Markov model of ESCC was developed. Separate model analyses were conducted for cohorts consisting of an average-risk population or a high-risk population in China. Hypothetical 50-year-old individuals were followed until age 80 or death. We compared three different strategies for both cohorts: (1) no screening; (2) standard endoscopic screening with Lugol's iodine staining; and (3) endoscopic screening with Lugol's iodine staining and an HRME. Model parameters were estimated from the literature as well as from GLOBOCAN, the Cancer Incidence and Mortality Worldwide cancer database. Health states in the model included non-neoplasia, mild dysplasia, moderate dysplasia, high-grade dysplasia, intramucosal carcinoma, operable cancer, inoperable cancer, and death. Separate ESCC incidence transition rates were generated for the average-risk and high-risk populations. Costs in Chinese currency were converted to international dollars (I$) and were adjusted to 2012 dollars using the Consumer Price Index.
  
  
  RESULTS
  The main outcome measurements for this study were quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER). For the average-risk population, the HRME screening strategy produced 0.043 more QALYs than the no screening strategy at an additional cost of I$646, resulting in an ICER of I$11808 per QALY gained. Standard endoscopic screening was weakly dominated. Among the high-risk population, when the HRME screening strategy was compared with the standard screening strategy, the ICER was I$8173 per QALY. For both the high-risk and average-risk screening populations, the HRME screening strategy appeared to be the most cost-effective strategy, producing ICERs below the willingness-to-pay threshold, I$23500 per QALY. One-way sensitivity analysis showed that, for the average-risk population, higher specificity of Lugol's iodine (> 40%) and lower specificity of HRME (< 70%) could make Lugol's iodine screening cost-effective. For the high-risk population, the results of the model were not substantially affected by varying the follow-up rate after Lugol's iodine screening, Lugol's iodine test characteristics (sensitivity and specificity), or HRME specificity.
  
  
  CONCLUSIONS
  The incorporation of HRME into an ESCC screening program could be cost-effective in China. Larger studies of HRME performance are needed to confirm these findings.
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Chin
authors.initials: C
authors.lastname: Hur
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Sung Eun
authors.initials: SE
authors.lastname: Choi
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Gui-Qi
authors.initials: GQ
authors.lastname: Wang
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Hong
authors.initials: H
authors.lastname: Xu
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Alexandros D
authors.initials: AD
authors.lastname: Polydorides
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Li-Yan
authors.initials: LY
authors.lastname: Xue
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Katherine E
authors.initials: KE
authors.lastname: Perzan
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Angela C
authors.initials: AC
authors.lastname: Tramontano
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Rebecca R
authors.initials: RR
authors.lastname: Richards-Kortum
authors.affiliation: Chin Hur, Sung Eun Choi, Chung Yin Kong, Katherine E Perzan, Angela C Tramontano, Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Sharmila
authors.initials: S
authors.lastname: Anandasabapathy
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002294:Carcinoma, Squamous Cell / Q000191:economics* / Q000401:mortality / Q000473:pathology*; D002681:China / Q000453:epidemiology; D004396:Coloring Agents / Q000191:economics; D003362:Cost-Benefit Analysis; D055088:Early Detection of Cancer / Q000191:economics* / Q000379:methods; D004938:Esophageal Neoplasms / Q000191:economics* / Q000401:mortality / Q000473:pathology*; D000077277:Esophageal Squamous Cell Carcinoma; D004945:Esophagoscopy / Q000191:economics* / Q000379:methods; D005260:Female; D017048:Health Care Costs; D006801:Humans; D007089:Image Enhancement; D007454:Iodides / Q000191:economics; D008297:Male; D008390:Markov Chains; D008853:Microscopy / Q000191:economics* / Q000379:methods; D008875:Middle Aged; D018803:Models, Economic; D011237:Predictive Value of Tests; D011379:Prognosis; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012189:Retrospective Studies; D013997:Time Factors
chemical_list: D004396:Coloring Agents; D007454:Iodides; C010389:Lugol's solution
keywords: Cost-effectiveness analysis; Diagnostic imaging; Endoscopy; Esophageal squamous cell cancer; Simulation disease model