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Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach

机译:评估社区药房的空间不平等:混合地域加权方法

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Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km2, and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita ( I =0.082) and per km 2 ( I =0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population , mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km 2 . The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.
机译:地理上的可及性是使用社区药房的重要决定因素。本研究探讨了意大利利古里亚(一个具有特殊地理和人口特征的地区)与药房相关的空间可及性模式。以人均和每平方公里的药房数量为代表的药房城市密度,进行空间自相关分析以识别空间群。非空间和空间模型都可以预测研究结果。空间自相关分析显示人均(I = 0.082)和每km 2(I = 0.295)的药房密度具有高度显着的聚类模式。潜在供不应求的地区大多位于山区腹地。普通最小二乘(OLS)回归在高海拔地区的市政当局之间建立了药房密度与收入之间的显着正相关关系,而在较低地区未观察到这种相关性。但是,OLS模型的残差在空间上是自动相关的。最适合的混合地理加权回归(GWR)模型优于相应的OLS模型。人均药房最好由两个本地预测因素(移民的海拔和比例)和两个全球预测因素(老年人的比例和收入)预测,而本地术语人口,平均海拔和农村状况以及全球术语收入作为自变量预测每公里药房2。发现利古里亚的药房密度与社会经济因素和景观因素有关。绘制GWR混合结果的结果将有助于决策者。

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