Chaix Basile, Merlo Juan, Chauvin Pierre
Research Team on Social Determinants of Health and Healthcare (INSERM U707), National Institute of Health and Medical Research, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75571 Paris Cedex 12, France.
J Epidemiol Community Health. 2005 Jun;59(6):517-26. doi: 10.1136/jech.2004.025478.
Most studies of place effects on health have followed the multilevel analytical approach that investigates geographical variations of health phenomena by fragmenting space into arbitrary areas. This study examined whether analysing geographical variations across continuous space with spatial modelling techniques and contextual indicators that capture space as a continuous dimension surrounding individual residences provided more relevant information on the spatial distribution of outcomes. Healthcare utilisation in France was taken as an illustrative example in comparing the spatial approach with the multilevel approach.
Multilevel and spatial analyses of cross sectional data.
10,955 beneficiaries of the three principal national health insurance funds, surveyed in 1998 and 2000 on continental France.
Multilevel models showed significant geographical variations in healthcare utilisation. However, the Moran's I statistic showed spatial autocorrelation unaccounted for by multilevel models. Modelling the correlation between people as a decreasing function of the spatial distance between them, spatial mixed models gave information not only on the magnitude, but also on the scale of spatial variations, and provided more accurate standard errors for risk factors effects. The socioeconomic level of the residential context and the supply of physicians were independently associated with healthcare utilisation. Place indicators better explained spatial variations in healthcare utilisation when measured across continuous space, rather than within administrative areas.
The kind of conceptualization of space during analysis influences the understanding of place effects on health. In many contextual studies, viewing space as a continuum may yield more relevant information on the spatial distribution of outcomes.
大多数关于地点对健康影响的研究都采用了多水平分析方法，该方法通过将空间分割为任意区域来研究健康现象的地理差异。本研究考察了使用空间建模技术以及将空间作为个体住所周围连续维度进行捕捉的背景指标，分析连续空间中的地理差异，是否能提供关于结果空间分布的更相关信息。在将空间方法与多水平方法进行比较时，以法国的医疗保健利用情况作为一个示例。
对横断面数据进行多水平和空间分析。
10955名三大主要国家健康保险基金的受益人，于1998年和2000年在法国大陆进行了调查。
多水平模型显示医疗保健利用存在显著的地理差异。然而，莫兰指数显示存在多水平模型未考虑到的空间自相关性。将人与人之间的相关性建模为他们之间空间距离的递减函数，空间混合模型不仅给出了空间差异的大小信息，还给出了其尺度信息，并为危险因素效应提供了更准确的标准误差。居住环境的社会经济水平和医生供应与医疗保健利用独立相关。当在连续空间而非行政区内进行测量时，地点指标能更好地解释医疗保健利用的空间差异。
分析过程中对空间的概念化方式会影响对地点对健康影响的理解。在许多背景研究中，将空间视为一个连续体可能会产生关于结果空间分布的更相关信息。
