Shared-care models are highly effective and cost-effective for managing chronic hepatitis B in China: reinterpreting the primary care and specialty divide

在中国,共享医疗模式在慢性乙型肝炎管理中高效且经济:重新解读基层医疗与专科医疗的分野

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Abstract

BACKGROUND: We evaluate the impact and cost-effectiveness of shared primary-specialty chronic hepatitis B (CHB) care models in China. METHODS: We constructed a decision-tree Markov model to simulate hepatitis B virus (HBV) disease progression in a cohort of 100,000 CHB individuals aged ≥18 years over their lifetime (aged 80). We evaluated the population impacts and cost-effectiveness in three scenarios: (1) status quo; (2) shared-care model with HBV testing and routine CHB follow-ups in primary care and antiviral treatment initiation in specialty care; and (3) shared-care model with HBV testing, treatment initiation and routine CHB follow-up in primary care and treatment for predetermined conditions in specialty care. We evaluated from a healthcare provider's perspective with 3% discounting rate and a willingness-to-pay (WTP) threshold of 1-time China's GDP. FINDINGS: Compared with status quo, scenario 2 would result in an incremental cost of US$5.79-132.43m but a net gain of 328-16,993 quality-adjusted life years (QALYs) and prevention of 39-1935 HBV-related deaths over cohort's lifetime. Scenario 2 was not cost-effective with a WTP of 1-time GDP per capita, but became cost-effective when treatment initiation rate increased to 70%. In contrast, compared with status quo, secnario 3 would save US$144.59-192.93m in investment and achieve a net gain of 23,814-30,476 QALYs and prevention of 3074-3802 HBV-related deaths. Improving HBV antiviral treatment initiation among eligible CHB individuals substantially improved the cost-effectiveness of the shared-care models. INTERPRETATION: Shared-care models with HBV testing, follow up and referring of predetermined conditions to specialty care at an appropriate time, especially antiviral treatment initiation in primary care, are highly effective and cost-effective in China. FUNDING: National Natural Science Foundation of China.

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