中文摘要
慢性病危害大,疾病负担重,已成为我国重大公共卫生问题。中医防治慢性病具有较好效益,但疾病负担评估方法亟待建立。慢性病病程长,病情复杂呈动态变化,针对中医辨证治疗,评估更为困难。Markov决策模型能模拟慢性病动态发展过程,估计疾病发展结局及疾病负担。基于"Markov决策模型适宜中医干预慢性病疾病负担评估"的假说,本项目拟开展如下研究:以COPD为示范,根据自然病程和中医辨证治疗设立Markov状态及转归途径;基于既往数据,计算循环周期及转移概率;以360例中医干预临床试验为载体,采用量表法计算效用值(质量调整生命年)、设计问卷进行成本测量,估算各阶段费用和效用等步骤建立Markov决策模型,分析中医干预的疾病负担。通过灵敏度分析和专家咨询,评价模型适用性,探索建立中医干预慢性病疾病负担评估的适宜模型。
英文摘要
Chronic diseases are a major public health problem with serious damaging and huge economic burden throughout China. The remarkable longevity of traditional Chinese medicine (TCM) for chronic diseases implies its potential advantages, however,the methods for evaluating the reduction burdens of chronic diseases need to be established desperately. For TCM treatment is more difficult to assess due to the chronic disease is long course and complicated dynamic change of disease condition. Markov decision model can simulate development process of chronic disease and estimate the prognostic outcome and burden of chronic disease. We point out the hypothesis that Markov decision model is proper for evaluating the burdens of chronic diseases treated by TCM therapies. We will carry out the following study. Take TCM clinical trial for example, within 360 COPD patients, establish the status of Markov decision model and its outcomes prognosis approach through the natural disease course; calculate the cycle and transition probability based on the previous research data; calculate the value of quality adjusted life years (QALY) utility by questionnaire; measure the costing by designing the scale; estimate the costing and utility in each stage,and finally establish the Markov decision model,make cost-utility analysis of TCM therapies.Finally,evaluate the applicability of the established model by sensitivity analysis and expert consultation, and initially establish the model for COPD disease burden. The established model will provide the inspirations and methods model for explore the establishment of chronic disease burden TCM intervention model to assess the suitability.
