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Background: Tyrosine kinase inhibitors combined with conventional chemotherapy (CC) in treating Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph-positive ALL) has achieved promising efficacy and safety outcomes. The study was conducted to compare the cost-effectiveness between imatinib (HANSOH Pharma, Jiangsu, China) and dasatinib (CHIATAI TIANQING Pharma, Jiangsu, China) in treating pediatric Ph-positive ALL when combined with CC from the perspective of the health system in China. Methods: A Markov model was established to simulate a hypothetical cohort of pediatric Ph-positive ALL patients receiving imatinib or dasatinib, combined with CC. The model was designed using a 10-year horizon, a 3- month cycle, and a 5% discount rate. Three health states were included: alive with progression-free survival, progressed disease, and death. Patient characteristics and transition probabilities were estimated based on clinical trials. Other relevant data, such as direct treatment costs and health utility data were extracted from published literature and Sichuan Province's centralized procurement and supervision platform. One-way sensitivity analysis and probabilistic sensitivity analysis were performed to assess the robustness of the results. The willingness-to-pay (WTP) was set as three times China's GDP per capita in 2021. Results: In the base-case analysis, the total medical costs were $89,701 and $101,182, and the quality-adjusted life years (QALYs) gained were 1.99 and 2.70, for imatinib and dasatinib regimens, respectively. The incremental cost-effectiveness ratio for dasatinib versus imatinib was $16,170/QALY. The probabilistic sensitivity analysis indicated that treatment with dasatinib combined with CC achieved a 96.4% probability of cost-effectiveness at a WTP threshold of $37,765/QALY. Conclusions: Dasatinib combined with CC is likely to be a cost-effective strategy compared to imatinib combination therapy for pediatric Ph-positive ALL in China at a WTP threshold of $37,765/QALY. [ABSTRACT FROM AUTHOR]

With frequent extreme rainfall events caused by rapid changes in the global climate, many cities are threatened by urban flooding. Timely issuance of flood warnings can help prepare for disasters and minimize losses caused by floods. In this study, we propose a method based on a convolutional neural network-bidirectional long short-term memory-difference analysis (CNN-BiLSTM-DA) model for water level prediction analysis and flood warning. The method calculates and analyzes the difference sequence between water level monitoring values and water level prediction values, compares historical flood data to determine the alarm threshold for abnormal water level data, and achieves real-time flood warnings to provide technical references for flood prevention and mitigation. Taking Yancheng city, a low-lying city located in the plain area of Jiangsu Province in China, as an example, this study verifies the accuracy of the CNN-BiLSTM model in water level prediction, which can achieve an accuracy rate above 95%. This provides a reliable data basis for further determination of warning thresholds using the DA model. The CNN-BiLSTM-DA model achieves an accuracy rate of 85.71% in flood warnings without any missed reports, demonstrating that this method has scientific, practical, and accurate features in addressing flood warning issues. [ABSTRACT FROM AUTHOR]