Prices and Clinical Benefit of National Price-Negotiated Anticancer Medicines in China.

BACKGROUND: High prices of anticancer medicines have increased the economic burden for both patients and health insurance systems. Since 2017, China has implemented national price negotiations for medicines, relying on evidence from health technology assessments. We aim to assess the relation between negotiated price and value of anticancer medicines listed in China's National Reimbursement Drug List (NRDL). METHODS: For all price-negotiated anticancer medicines and corresponding indications listed in the latest NRDL between 2017 and 2020, we collected their clinical outcomes data, including overall survival (OS) and progression-free survival (PFS), in supporting trials. Pearson correlation coefficient was calculated to estimate the association between the daily cost and clinical benefit of each indication. RESULTS: In total, 75 indications of 46 branded anticancer medicines were included for analysis. The median daily costs for the anticancer therapies that had gone through negotiation in 2017-2020 were US$87.6, US$71.8, US$58.9, and US$39.7, respectively. For indications supported by randomized trials, no correlation between daily costs and OS and PFS benefit of the price-negotiated cancer therapies was observed (N = 41, r = -0.05, and N = 49, r = 0.04, respectively). For cancer indications newly listed in NRDL in 2020, the association between their daily cost and OS benefit was -0.78 (N = 4, p = 0.221) and 0.01 (N = 8, p = 0.986) before and after the price negotiation. CONCLUSION: Though the negotiation policy decreased prices of anticancer medicines in China, no statistically significant correlation was observed between their daily costs and clinical benefits. A more transparent and credible pricing approach needs to be established to promote value-based anticancer medicines and healthcare system efficiency.

Neural Representation of Costs and Rewards in Decision Making.

Decision making is crucial for animal survival because the choices they make based on their current situation could influence their future rewards and could have potential costs. This review summarises recent developments in decision making, discusses how rewards and costs could be encoded in the brain, and how different options are compared such that the most optimal one is chosen. The reward and cost are mainly encoded by the forebrain structures (e.g., anterior cingulate cortex, orbitofrontal cortex), and their value is updated through learning. The recent development on dopamine and the lateral habenula's role in reporting prediction errors and instructing learning will be emphasised. The importance of dopamine in powering the choice and accounting for the internal state will also be discussed. While the orbitofrontal cortex is the place where the state values are stored, the anterior cingulate cortex is more important when the environment is volatile. All of these structures compare different attributes of the task simultaneously, and the local competition of different neuronal networks allows for the selection of the most appropriate one. Therefore, the total value of the task is not encoded as a scalar quantity in the brain but, instead, as an emergent phenomenon, arising from the computation at different brain regions.