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Estimating Taiwan's QALY league table for catastrophic illnesses: Providing real-world evidence to integrate
Jung-Der Wang1, Wu-Wei Lai2, Szu-Chun Yang3
1Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan.
Background/Purpose:
Curative technologies improve patient's survival and/or quality of life but increase financial burdens. Effective prevention benefits all three. We summarize estimation methods and provide examples of how much money is spent per quality-adjusted life year (QALY) or life year (LY) on treating a catastrophic illness under a lifetime horizon and how many QALYs/LYs and lifetime medical costs (LMC) could be potentially saved by prevention.
Methods:
We established cohorts by interlinkages of Taiwan's nation-wide databases including National Health Insurance. We developed methods to estimate lifetime survival functions, which were multiplied with the medical costs and/or quality of life and summed up to estimate LMC, quality-adjusted life expectancy (QALE) and lifetime average cost per QALY/LY for catastrophic illnesses. By comparing with the age-, sex-, and calendar year-matched referents simulated from vital statistics, we obtained the loss-of-QALE and loss-of-life expectancy (LE).
Results:
The lifetime cost-effectiveness ratios of ventilator-dependent comatose patients, dialysis, spinal cord injury, major trauma, and cancers were US$ 96,800, 16,200-20,000, 5500-5,900, 3400-3,600, and 2900-11,900 per QALY or LY, respectively. The successful prevention of lung, liver, oral, esophagus, stomach, nasopharynx, or ovary cancer would potentially save US$ 28,000-97,000 and > 10 QALYs; whereas those for end-stage kidney disease, stroke, spinal injury, or major trauma would be US$ 55,000-300,000 and 10-14 QALYs. Loss-of-QALE and loss-of-LE were less confounded indicators for comparing the lifetime health benefits of different technologies estimated from real-world data.
Conclusions:
Integration of prevention with treatment for resources allocation seems feasible and would improve equity and efficiency.
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