Time Series Modeling of Trends in Medication Prescribing

描述：医生关于药物处方的决定有一个 对医疗保健成本和临床结果有很大影响。少得惊人 然而，已知一系列因素的相对影响， 影响医生开药的行为。推而广之 解释和预测药物处方趋势能力 目前有限。这项研究将开发一个新的经济模型， 多个决定因素对用药趋势的同时影响 开处方计量经济学时间序列回归技术将用于 估计时间变化预测变量的相对影响， 关于所观察到的国家处方趋势的不连续时间事件 治疗高血压、抑郁症、哮喘和充血性心脏病的药物 失败关于1978年至2000年趋势的全国代表性季度数据 这些疾病的药物处方将来自国家疾病 和治疗指数调查可通过IMS健康。季度病人 1998年的样本量为抑郁症约1，700人，充血性心脏病约550人 5,100人死于高血压，2,000人死于哮喘。潜力数据 将从各种来源制定趋势预测指标。 关于药品促销和药品批发的药物特定趋势信息 价格也将从IMS健康。其他预测因素包括 关于药物剂量、其他药物特性、临床试验结果的信息 出版物、媒体对药物的报道和临床指南 出版物通过构建一个全面的时间模型， 处方行为的决定因素，这项研究将评估相对 这些因素的影响。建立计量经济学时间序列回归模型 对差异化产品的需求理论将被用来检验 关于这些因素作用的具体假设。一个关键假设 宣传的影响力比科学的影响力更强大 影响。此外，本研究还将评估与 药品价格的影响，科学和推广的相互作用， 品牌名称过期的影响。分析结果将具有相关性 努力设计和实施临床和卫生政策，以鼓励 有效和具有成本效益的药物使用。

DESCRIPTION: Physician decisions regarding medication prescribing have a large impact on health care costs and clinical outcomes. Surprisingly little is known, however, about the relative impact of a range of factors that may influence physician behavior in the prescribing of medications. By extension, the ability to explain and project trends in medication prescribing is currently limited. This study will develop a novel economic model of the simultaneous influence of multiple determinants on trends in medication prescribing. Econometric time-series regression techniques will be used to estimate the relative influence of temporally varying predictor variables and discrete temporal events on observed national trends in the prescribing of pharmaceuticals for hypertension, depression, asthma, and congestive heart failure. Nationally representative quarterly data on 1978 through 2000 trends in drug prescribing for these conditions will come from the National Disease and Therapeutic Index survey available through IMS Health. Quarterly patient sample sizes in 1998 are about 1,700 for depression, 550 for congestive heart failure, 5,100 for hypertension and 2,000 for asthma. Data on potential predictors of trends will be developed from a variety of sources. Drug-specific trend information on pharmaceutical promotion and wholesale drug prices also will be available from IMS Health. Other predictors will include information on drug dosing, other drug characteristics, clinical trial results publication, media reporting on medications, and clinical guideline publication. By constructing a comprehensive model of the temporal determinants of prescribing behavior, this study will assess the relative influence of these factors. Econometric time series regression models built on the theory of demand for differentiated products will be employed to test specific hypotheses regarding the role of these factors. A key hypothesis will be that promotional influences are more potent than scientific influences. In addition, the research will evaluate hypotheses related to the influence of drug prices, the interaction of science and promotion, and the impact of brand name expiration. Results from the analysis will be relevant to efforts to design and implement clinical and health policies to encourage the effective and cost-effective use of medications.