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Published on: August 24, 2019
Cost-effectiveness of chronic obstructive pulmonary disease population screening in China: based on individual data
Tiantian Zhang1,2, Jiahuan Ai1, Ke Huang3
1College of Pharmacy/Southern Institute of Pharmacoeconomics and Health Technology Assessment, Jinan University, Guangzhou, China.
Background:
Chronic obstructive pulmonary disease (COPD) imposes a significant and growing burden on China and the world. Early detection and diagnosis may be an effective way to alleviate this severe pressure on public health. The Enjoying Breathing Program (the Program), a nationwide one-time and two-step COPD screening and management program with long-term follow-up initiated by the World Health Organization Collaborating Centre (WHO CC), has demonstrated its significant clinical benefit. However, the cost-effectiveness of the Program remains unknown.
Methods:
A lifetime Markov model was developed to compare the cost-effectiveness of the Program of COPD screening to no screening from a Chinese healthcare perspective. Patient-level data, including treatment rate, medication cost, transition probability, etc., were sourced from the Program. Other parameter data were sourced from published literature.
Results:
Enjoying Breathing Program for COPD screening was proved probably cost-effective compared with no screening in China with an incremental cost of $118, and incremental health benefit gain of 0.021 quality-adjusted life years (QALYs), resulting in an incremental cost-effectiveness ratio (ICER) of $5,679/QALY which was much less than willingness-to-pay (WTP) of 1×Gross Domestic Product (GDP) per capita in 2022 ($11,814). Sensitivity analysis proved the robustness of the results and subgroup analysis demonstrated health benefits varied among different subgroups. Annual screening and higher compliance may further enhance the cost-effectiveness.
Conclusions:
Despite the underlying uncertainty, annual two-step COPD population screening in China may probably be cost-effective compared with no screening and deserves further large-scale implementation.
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