镇江市医疗保险微观模拟政策模型的设计和建立

政策模型是微观模拟模型的重要组成部分,用来模拟政策实施的整个过程。根据医疗保险微观模拟型的模拟目标,重点探讨了政策模型的整体框架、政策参数的选择及政策模型的构建等问题。     

镇江市医疗保险微观模拟模型中的事件模块

状态模型是微观模拟模型的核心部分,是由一系列相互关联的事件模块构成的。镇江市医疗保险微观模拟模型的状态模型主要包括人事变动、门诊、住院3个事件模块,着重介绍了各个事件模块的构建思路和模拟过程。     

微观模拟模型在流行病学筛查成本效果分析中的应用

微观模拟模型通过使用个体水平的数据估计状态转换概率, 模拟人群中个体的疾病发展过程。这种方法可以处理人群异质性或疾病史等个体特征的影响, 从而解决流行病学中复杂疾病筛查的成本效果问题。本文介绍了微观模拟模型的基本原理、构建步骤、分析方法和相关注意事项。结合在美国人群中开展的一项慢性肾脏病微量蛋白尿筛查的成本效果分析的研究实例, 从模型构建、模型分析和结果解读等方面, 详细讨论了微观模拟模型在筛查成本效果分析中应用的要点。微观模拟模型通过估计广泛的个体特征, 并考虑复杂疾病的动态发展过程, 越来越多地用于解决马尔科夫模型假设受限的复杂问题。为了更好地支持公共卫生领域的循证决策, 后续研究应注意决策模型参数的准确性和研究结果的透明度, 并且需要按照相应的报告规范开展流行病学筛查的成本效果分析。     

基于微观模拟的慢性肾脏病疾病负担演化模型构建初探

文章以慢性肾脏病发生发展演化规律为基础，结合微观模拟技术探讨了在防治策略干预下，慢性肾脏病疾负担演化微观模拟模型构建与分析过程，为慢性肾脏病防治策略干预效果评价提供理论支持。     

军队医院医疗消费改革中的微观模拟模型应用研究

目的 正确分析评价军队医疗保障制度改革过程中的各种改革措施。方法 以军队人员为微观单位,应用微观分析模拟理论,研究军队卫生经济政策。结果 结合军队医院信息管理的特点,采用静态模型的模拟方式,构建了面向用户,通过性强的军队医院门诊消费微观分析模拟模型。结论 在当前医疗保障制度的改革过程中,如何保障不同身份人员的门诊基本医疗消费,并能公平公理,额度适宜地计算出门诊医疗经费挂卡比例和经费挂卡额度,是当前     

镇江市城镇人员医疗保险政策的模拟与预测

利用微观模拟模型对镇江市现行的城镇人员医疗保险(以下简称医保)政策进行了5年的模拟预测研究。模拟结果显示,参保人员的年龄结构呈现向老龄化发展的趋势,医疗消费的增速远远高于基金的增速,而且统账结构比例失调,个人账户的沉淀金额过多,统筹基金面临越来越大的超支压力。研究表明,镇江市现行医保政策尚不足以应对个人老龄化所带来的挑战,急需采取相应的应对措施。     

基于LabVIEW的模拟心电信号发生器的研制

目的：设计一个方便实验室使用的模拟心电信号发生器。方法：利用处理后数据文件,采用NI公司的PXI-6221 DAQ数据采集卡,在LabVIEW环境下研制一个模拟心电信号发生器。结果：研制的模拟心电信号发生器能产生与数据文件对应的心电波形,且幅值、采样率等参数可调。结论：利用NI的虚拟仪器技术能比较简单地研制模拟心电信号发生器,使为教学实验室提供低价位、高利用率的仪器成为可能。     

微观社会资本对农转非居民主客观健康认知一致性的影响研究

目的探讨微观社会资本对农转非居民自评健康与客观健康认知一致性的影响。方法两层logistic回归模型分析微观社会资本和人口学因素对健康认知一致性的影响。结果自评健康和客观健康的一致率为63.89%,个体社会资本和年龄对主客观健康一致性产生影响。结论使用自评健康作为健康指标时,应考虑个体社会资本的影响,提高自评健康的预测准确性。     

政府规制卫生的基本问题探讨

政府对卫生的管理可以分为宏观调控和微观规制两个方面。宏观调控是指政府通过利用宏观经济政策和手段,如计划、预算分配、税收、转移支付、金融措施、价格政策以及卫生人力政策对卫生保健市场或卫生经济主体(卫生机构和个人)实施的调节和控制,以达到一定的目标。这种调控主要是一种间接管理方式。微观规制是指政府利用行政命令、规章制度、法律约束等手段直接作用于微观主体(尤其     

Haddon模型在突发公共卫生事件应对中的探讨

围绕如何构建适合我国国情的突发公共卫生事件应急体系,作者借鉴在伤害预防控制中实践了20多年,并起到巨大作用的Haddon模型来探讨突发公共卫生事件应对策略及措施。在模拟四川省“人-猪链球菌”疫情应对中,Haddon模型体现了一定灵活性和应用价值,既可以纵揽全局,又可以聚焦于某一微观,甚至包括个体。作为一个对突发公共卫生事件处理进行理解及应对的工具,Haddon模型证明其自身是一个对于处理突发公共卫生事件具有重要应用价值的模型。     

Using geographical information systems and spatial microsimulation for the analysis of health inequalities

The paper presents a spatial microsimulation approach to the analysis of health inequalities. A dynamic spatial microsimulation model of Britain, under development at the Universities of Leeds and Sheffield, uses data from the censuses of 1971, 1981 and 1991 and the British Household Panel Survey to simulate urban and regional populations in Britain. Geographical information systems and spatial microsimulation are used for the analysis of health inequalities in British regions in a 30 year simulation. The interdependencies between socio-economic characteristics and health variables such as limiting long-term illness are discussed. One of the innovative features of the model is the estimation of variables such as household income at the small area level, which can then be used to classify individuals. The health situation of different simulated individuals in different areas is investigated and the role of socio-economic characteristics in determining health is evaluated.     

Adding noise to Markov cohort state-transition model in decision modeling and cost-effectiveness analysis

Following its introduction over 30 years ago, the Markov cohort state-transition model has been used extensively to model population trajectories over time in health decision modeling and cost-effectiveness analysis studies. We recently showed that a cohort model represents the average of a continuous-time stochastic process on a multidimensional integer lattice governed by a master equation, which represents the time-evolution of the probability function of an integer-valued random vector. By leveraging this theoretical connection, this study introduces an alternative modeling method using a stochastic differential equation (SDE) approach, which captures not only the mean behavior but also the variance of the population process. We show the derivation of an SDE model from first principles, describe an algorithm to construct an SDE and solve the SDE via simulation for use in practice, and demonstrate the two applications of an SDE in detail. The first example demonstrates that the population trajectories, and their mean and variance, from the SDE and other commonly used methods in decision modeling match. The second example shows that users can readily apply the SDE method in their existing works without the need for additional inputs beyond those required for constructing a conventional cohort model. In addition, the second example demonstrates that the SDE model is superior to a microsimulation model in terms of computational speed. In summary, an SDE model provides an alternative modeling framework which includes information on variance, can accommodate for time-varying parameters, and is computationally less expensive than a microsimulation for a typical cohort modeling problem.     

Assessing uncertainty in microsimulation modelling with application to cancer screening interventions

Microsimulation is fast becoming the approach of choice for modelling and analysing complex processes in the absence of mathematical tractability. While this approach has been developed and promoted in engineering contexts for some time, it has more recently found a place in the mainstream of the study of chronic disease interventions such as cancer screening. The construction of a simulation model requires the specification of a model structure and sets of parameter values, both of which may have a considerable amount of uncertainty associated with them. This uncertainty is rarely quantified when reporting microsimulation results. We suggest a Bayesian approach and assume a parametric probability distribution to mathematically express the uncertainty related to model parameters. First, we design a simulation experiment to achieve good coverage of the parameter space. Second, we model a response surface for the outcome of interest as a function of the model parameters using the simulation results. Third, we summarize the variability in the outcome of interest, including variation due to parameter uncertainty, using the response surface in combination with parameter probability distributions. We illustrate the proposed method with an application of a microsimulator designed to investigate the effect of prostate specific antigen (PSA) screening on prostate cancer mortality rates. © 1998 John Wiley & Sons, Ltd.     

A New Type 2 Diabetes Microsimulation Model to Estimate Long-Term Health Outcomes,Costs, and Cost-Effectiveness

ObjectivesThis study aimed to develop a microsimulation model to estimate the health effects, costs, and cost-effectiveness of public health and clinical interventions for preventing/managing type 2 diabetes.MethodsWe combined newly developed equations for complications, mortality, risk factor progression, patient utility, and cost—all based on US studies—in a microsimulation model. We performed internal and external validation of the model. To demonstrate the model’s utility, we predicted remaining life-years, quality-adjusted life-years (QALYs), and lifetime medical cost for a representative cohort of 10 000 US adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing hemoglobin A1c from 9% to 7% among adults with type 2 diabetes, using low-cost, generic, oral medications.ResultsThe model performed well in internal validation; the average absolute difference between simulated and observed incidence for 17 complications was < 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of US adults with type 2 diabetes was projected to have an average of 19.95 remaining life-years (from mean age 61), incur $187 729 in discounted medical costs, and accrue 8.79 discounted QALYs. The intervention to reduce hemoglobin A1c increased medical costs by $1256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9103 per QALY.ConclusionsUsing equations exclusively derived from US studies, this new microsimulation model achieves good prediction accuracy in US populations. The model can be used to estimate the long-term health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States.     

Improving spatial microsimulation estimates of health outcomes by including geographic indicators of health behaviour: The example of problem gambling

Gambling is an important public health issue, with recent estimates ranking it as the third largest contributor of disability adjusted life years lost to ill-health. However, no studies to date have estimated the spatial distribution of gambling-related harm in small areas on the basis of surveys of problem gambling. This study extends spatial microsimulation approaches to include a spatially-referenced measure of health behaviour as a constraint variable in order to better estimate the spatial distribution of problem gambling. Specifically, this study allocates georeferenced electronic gaming machine expenditure data to small residential areas using a Huff model. This study demonstrates how the incorporation of auxiliary spatial data on health behaviours such as gambling expenditure can improve spatial microsimulation estimates of health outcomes like problem gambling.     

Incorporating biological natural history in simulation models: empirical estimates of the progression of end-stage liver disease.

OBJECTIVE: To develop an empiric natural-history model that can predict quantitative changes in the laboratory values and clinical characteristics of patients with end-stage liver disease (ESLD), to be used to calibrate an individual microsimulation model. METHODS: The authors report the development of a stochastic model that uses cubic splines to interpolate between observed laboratory values over time in a cohort of 1997 patients with ESLD awaiting liver transplantation at the University of Pittsburgh Medical Center. The splines were recursively sampled to provide a stochastic, quantitative natural history of each candidate's disease. RESULTS: The model was able to simulate the types of erratic disease trajectories that occur in individual patients and was able to preserve the statistical properties of the natural history of ESLD in cohorts of real patients. Moreover, the model was able to predict pretransplant survival rates (87% at 1 year), which were statistically similar to rates observed in the authors' local cohort (92%). CONCLUSIONS: Cubic splines can be used to generate quantitative natural histories for individual patients with ESLD and may be useful for developing clinically robust microsimulation models of other diseases.