Discrete Event Simulation of Mental Health Disorders in Medicaid Populations

描述（由申请人提供）：1期项目的目标是开发一个定量模型，使医疗保健决策者能够分析州医疗补助人群中精神分裂症患者干预措施的有效性、成本和成本效益。此外，离散事件操作规划子模型将评估精神分裂症和相关干预措施如何影响社会层面的资源需求和利用，反之亦然，干预资源的可用性如何影响精神分裂症患者的健康结果。如果第一阶段证明可行，第二阶段项目将开发一个模型，适应任何州的医疗补助人口，分析许多流行的心理健康状况（增加抑郁症，焦虑症，恐慌症和双相情感障碍）。精神疾病对健康和生产力的负担是巨大的，而且在未来几十年可能会大幅增加。精神疾病非常普遍，并与相当大的残疾有关，然而，相当大比例的精神疾病患者得不到任何医疗保健。为此，MDM提出了以下具体目标：1.确定精神分裂症疾病进展操作计划模型的概念框架、数据需求和数据来源。2.提取模型所需的数据并为模型输入创建参数化分布。3.逐步开发、验证和确认疾病进展和运营计划子模型。4.通过分析增加关键精神分裂症治疗资源对主要产出的影响（例如，费用、无复发天数比例）。公共卫生相关性：美国每年在精神卫生服务上花费数百亿美元，美国支付者，特别是公共支付者，在提供有效和具有成本效益的精神卫生服务方面存在相当大的压力。在这种资源有限，成本意识的环境中，建议的离散事件模拟模型可以用来协助制定有效的和具有成本效益的精神卫生资源分配计划。

DESCRIPTION (provided by applicant): The Phase 1 project goal is to develop a quantitative model that allows healthcare decision makers to analyze the effectiveness, costs, and cost-effectiveness of interventions for patients with schizophrenia in a state Medicaid population. Furthermore, a discrete-event operational planning submodel will evaluate how schizophrenia and associated interventions affect resource demand and utilization on a societal level, and conversely, how the availability of intervention resources affects the health outcomes in persons with schizophrenia. If Phase 1 proves feasible, the Phase 2 project will develop a model that adapts to any state Medicaid population, analyzing many prevalent mental health conditions (adding depression, anxiety, panic disorder, and bi-polar disorder). The burden of mental illness on health and productivity is substantial and likely to increase considerably in the decades to come. Mental illness is highly prevalent and associated with considerable disability, however, a substantial percentage of individuals with mental illness do not receive any health care for their condition. To this end, MDM proposes the following specific aims: 1. Determine conceptual framework, data needs, and data sources of the schizophrenia disease progression-operational planning model. 2. Extract required data for the model and create parameterized distributions for model inputs. 3. Incrementally develop, verify, and validate disease progression and operational planning submodels. 4. Demonstrate the value of the disease progression-operational planning model by analyzing the effects of increasing key schizophrenia treatment resources on primary outputs (e.g., costs, proportion of days without relapse). PUBLIC HEALTH RELEVANCE: The U.S. spends tens of billions of dollars per year on mental health services, and considerable pressure exists on U.S. payers, especially public payers, to deliver efficient and cost-effective mental health services. In such resource-constrained, cost-conscious environments the proposed discrete-event simulation model can be used to assist in the development of efficient and cost-effective mental health resource allocation plans.