ADVANCING RISK ADJUSTMENT FOR SCHIZOPHRENIA

本研究申请的目的是为患有严重精神疾病精神分裂症的贫困人群开发一种新的风险调整措施。更具体地说，研究人员打算基于处方药、诊断代码和行政数据库中常见的人口统计/资格信息的综合信息，开发并验证一种精神分裂症特定风险调整指数。潜在地，使用行政索赔数据中发现的诊断代码和药物标记的疾病特定风险调整指数将优于仅基于人口统计学与基于诊断的信息相结合的索赔衍生指数。该模型的理论基础来源于Elixhauser、Deyo和Charlson开发的ICD-9-CM代码和药物暴露数据中基于共病的风险调整技术，这些数据可以补充共病信息，并描述由VonKorf最初组织的疾病和共病严重程度。精神分裂症的其他候选变量也被提出。对1995年至1998年间与格鲁吉亚精神卫生机构住院患者数据相关的行政医疗补助索赔数据进行回顾性纵向审查，该数据涉及约15,000名精神分裂症患者，将用于建立基于前瞻性诊断和综合风险调整模型，包括诊断、人口统计和基于药物的信息。风险调整模型将根据两种相关结果进行调整，即一年的总资源利用情况和专门针对精神健康和药物滥用的资源利用情况。风险调整模型本质上是前瞻性的，其中一年获得的信息将用于预测下一年的支出。模型将使用分割半验证程序进行开发和验证，其中候选变量将被筛选并使用随机一半样本的bootstrap方法进行内部验证，并使用验证样本计算辨别和验证的措施。专家临床判断将用于补充统计候选变量筛选。本研究的结果将为心理健康支付者提供一种调整支付率的手段，并使研究人员能够在准实验回顾性比较评估中对不同水平的风险进行分层。最终，这项研究可以扩展到开发所有精神障碍的风险调整模型。该应用程序响应了2000年健康人在精神疾病预防方面经济评估的优先领域。

The goal of this research application is to develop a new risk adjustment measure for indigent persons suffering from severe mental illness, schizophrenia. More specifically, the researchers intend to develop and validate a schizophrenia specific risk adjustment index based on combined information from prescription drugs, diagnoses codes, and demographic/eligibility information commonly found in administrative data bases. Potentially, a diseases specific risk adjustment index using diagnoses codes and drug markers found in administrative claims data will outperform claims derived indices based on demographics combined with diagnosis based information alone. The theoretical basis of the proposed model is derived from previous risk adjustment techniques based on comorbidities derived from ICD-9-CM codes developed by Elixhauser, Deyo, and Charlson and drug exposure data that can compliment comorbity information and delinieate disease and comorbidity severity initially organized by VonKorf. Other candidate variables specific to schizophrenia are also proposed. A retrospective longitudinal review of administrative Medicaid claims data linked with Georgia mental health institutional inpatient data for approximatley 15,000 persons suffering from schizophrenia between 1995 and 1998 will be used to build a prospective diagnosis based and a combined risk adjustment model including the diagnosis, demographic, and drug based information. The risk adjustment models will adjust for two outcomes of interest, total one year resource utilization and resource utilization specific to mental health and substance abuse. The risk adjustment models will be prospective in nature, where information obtained in one year will be used to predict expenditures the year after. The models will be developed and validated using a split halves validation procedure, where candidate variables will be screened and internally validated using bootstrap methods on a random half of the sample and measures of discrimination and validation will be calculated using the validation sample. Expert clinical judgement will used to supplement statistical candidate variable screening. The results of this research will provide payers of mental health a means to adjust capitated payment rates and enable researchers to stratify varying levels of risk in quasi-experimental retrospective comparitive evaluations. Ultimately this research could be extended to develop a risk adjustment model for all mental disorders. This application is responsive to Healthy People 2000's priority area for economic evaluation in Mental Disorders Prevention.