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A spatiotemporal analysis of public pharmaceutical expenditure

机译:公共药品支出的时空分析

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摘要

A regression model for per capita public pharmaceutical expenditure, based on aggregate data from fifty Spanish provinces, observed annually for the period 1996-2002 is analyzed. The necessity of simultaneously controlling for dynamic patterns and spatial autocorrelation is demonstrated. As the aim of the present and related studies of small-area variation is to control for spatial association rather than to formulate it as an explicit part of a model, the traditional application of parametric spatial autocorrelation or spatial autoregression specifications seems unnecessarily restrictive and superfluous. The present study analyzes the effects of spatial association using a non-parametric spatial filtering approach. The importance of adjusting for spatial association is confirmed, but it is further shown that the parametric and the non-parametric approaches may lead to substantially different conclusions regarding explanation of pharmaceutical expenditure variations. Thus, the need for further evidence on the implications of spatial association-and the recognition that this is more than just spatial autocorrelation and/or spatial autoregression-when analyzing complex large area behavior using small area data is demonstrated.
机译:基于1996-2002年期间每年观测的西班牙五十个省的汇总数据,分析了人均公共药品支出的回归模型。证明了同时控制动态模式和空间自相关的必要性。由于目前和有关小面积变化的研究的目的是控制空间关联而不是将其表述为模型的显式部分,因此参数化空间自相关或空间自回归规范的传统应用似乎不必要地具有局限性和多余性。本研究使用非参数空间滤波方法分析了空间关联的影响。确认了调整空间关联的重要性,但进一步表明,参数方法和非参数方法可能会导致有关药物支出变化解释的基本不同的结论。因此,证明了在使用小面积数据分析复杂的大面积行为时,需要更多证据来说明空间关联的含义,并认识到不仅仅是空间自相关和/或空间自回归。

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  • 来源
    《The Annals of regional science》 |2010年第2期|299-314|共16页
  • 作者单位

    Institute of Public Health - Health Economics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark;

    Institute of Public Health - Health Economics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark Odense University Hospital, Odense, Denmark;

    Department of Quantitative and Computering Methods, Polytechnieal University of Cartagena, Cartagena, Spain;

    Department of Quantitative and Computering Methods, Polytechnieal University of Cartagena, Cartagena, Spain;

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