Health Economics and Policy Group, Vienna University of Economics and Business (WU), Welthandelsplatz 1, Building D4, 1020, Vienna, Austria.
Eur J Health Econ. 2020 Feb;21(1):85-104. doi: 10.1007/s10198-019-01113-7. Epub 2019 Sep 9.
Despite generous universal social health insurance with little formal restrictions of outpatient utilisation, Austria exhibits high rates of avoidable hospitalisations, which indicate the inefficient provision of primary healthcare and might be a consequence of the strict regulatory split between the Austrian inpatient and outpatient sector. This paper exploits the considerable regional variations in acute and chronic avoidable hospitalisations in Austria to investigate whether those inefficiencies in primary care are rather related to regional healthcare supply or to population characteristics. To explicitly account for inter-regional dependencies, spatial panel data methods are applied to a comprehensive administrative dataset of all hospitalisations from 2008 to 2013 in the 117 Austrian districts. The initial selection of relevant covariates is based on Bayesian model averaging. The results of the analysis show that supply-side variables, such as the number of general practitioners, are significantly associated with decreased chronic and acute avoidable hospitalisations, whereas characteristics of the regional population, such as the share of population with university education or long-term unemployed, are less relevant. Furthermore, the spatial error term indicates that there are significant spatial dependencies between unobserved characteristics, such as practice style or patients' utilization behaviour. Not accounting for those would result in omitted variable bias.
尽管奥地利实行了慷慨的全民社会保险，对门诊利用几乎没有正式限制，但仍存在高比例的可避免住院治疗，这表明初级保健服务效率低下，可能是奥地利住院和门诊部门严格监管分离的结果。本文利用奥地利急性和慢性可避免住院治疗的显著区域差异，研究这些初级保健服务中的低效问题是否与区域医疗保健供应有关，或者与人口特征有关。为了明确考虑区域间的相关性，采用空间面板数据方法对 2008 年至 2013 年奥地利 117 个区所有住院治疗的综合行政数据集进行了分析。相关协变量的初始选择基于贝叶斯模型平均。分析结果表明，供给侧变量，如全科医生数量，与慢性和急性可避免住院治疗的减少显著相关，而区域人口特征，如具有大学学历或长期失业人口的比例，则不太相关。此外，空间误差项表明，未观察到的特征（如实践方式或患者的利用行为）之间存在显著的空间相关性。不考虑这些因素将导致遗漏变量偏差。
