4.5 Article

Cost-effectiveness analysis of ultrasonography screening for nonalcoholic fatty liver disease in metabolic syndrome patients

Journal

MEDICINE
Volume 96, Issue 17, Pages -

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MD.0000000000006585

Keywords

cost-effectiveness analysis; metabolic syndrome; nonalcoholic fatty liver disease; ultrasonography screening

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Background: Nonalcoholic fatty liver disease (NAFLD) can be diagnosed early by noninvasive ultrasonography; however, the cost-effectiveness of ultrasonography screening with intensive weight reduction program in metabolic syndrome patients is not clear. This study aims to estimate economic and clinical outcomes of ultrasonography in Thailand. Methods: Cost-effectiveness analysis used decision tree and Markov models to estimate lifetime costs and health benefits from societal perspective, based on a cohort of 509 metabolic syndrome patients in Thailand. Data were obtained from published literatures and Thai database. Results were reported as incremental cost-effectiveness ratios (ICERs) in 2014 US dollars (USD) per quality-adjusted life year (QALY) gained with discount rate of 3%. Sensitivity analyses were performed to assess the influence of parameter uncertainty on the results. Results: The ICER of ultrasonography screening of 50-year-old metabolic syndrome patients with intensive weight reduction program was 958USD/QALY gained when compared with no screening. The probability of being cost-effective was 67% using willingness-to-pay threshold in Thailand (4848USD/QALY gained). Screening before 45 years was cost saving while screening at 45 to 64 years was cost-effective. Conclusions: For patients with metabolic syndromes, ultrasonography screening for NAFLD with intensive weight reduction program is a cost-effective program in Thailand. Study can be used as part of evidence-informed decision making. Translational Impacts: Findings could contribute to changes of NAFLD diagnosis practice in settings where economic evidence is used as part of decision-making process. Furthermore, study design, model structure, and input parameters could also be used for future research addressing similar questions.

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