Merlo Juan, Yang Min, Chaix Basile, Lynch John, Råstam Lennart
Department of Community Medicine, Lund University Hospital, S-205 02 Malmö, Sweden.
J Epidemiol Community Health. 2005 Sep;59(9):729-36. doi: 10.1136/jech.2004.023929.
(1) To provide a didactic and conceptual (rather than mathematical) link between multilevel regression analysis (MLRA) and social epidemiological concepts. (2) To develop an epidemiological vision of MLRA focused on measures of health variation and clustering of individual health status within areas, which is useful to operationalise the notion of "contextual phenomenon". The paper shows how to investigate (1) whether there is clustering within neighbourhoods, (2) to which extent neighbourhood level differences are explained by the individual composition of the neighbourhoods, (3) whether the contextual phenomenon differs in magnitude for different groups of people, and whether neighbourhood context modifies individual level associations, and (4) whether variations in health status are dependent on individual level characteristics.
Simulated data are used on systolic blood pressure (SBP), age, body mass index (BMI), and antihypertensive medication (AHM) ascribed to 25 000 subjects in 39 neighbourhoods of an imaginary city. Rather than assessing neighbourhood variables, the paper concentrated on SBP variance between individuals and neighbourhoods as a function of individual BMI.
The variance partition coefficient (VPC) showed that clustering of SBP within neighbourhoods was greater for people with a higher BMI. The composition of the neighbourhoods with respect to age, AHM use, and BMI explained about one fourth of the neighbourhood differences in SBP. Neighbourhood context modified the individual level association between BMI and SBP. Individual level differences in SBP within neighbourhoods were larger for people with a higher BMI.
Statistical measures of multilevel variations can effectively quantify contextual effects in different groups of people, which is a relevant issue for understanding health inequalities.
（1）在多水平回归分析（MLRA）与社会流行病学概念之间建立一个教学性和概念性（而非数学性）的联系。（2）形成一种以健康差异测量和区域内个体健康状况聚类为重点的MLRA流行病学视角，这对于将“背景现象”的概念付诸实践很有用。本文展示了如何调查：（1）邻里之间是否存在聚类；（2）邻里层面的差异在多大程度上可由邻里的个体构成来解释；（3）背景现象在不同人群中的程度是否不同，以及邻里背景是否会改变个体层面的关联；（4）健康状况的差异是否取决于个体层面的特征。
使用虚拟城市39个邻里中25000名受试者的收缩压（SBP）、年龄、体重指数（BMI）和抗高血压药物（AHM）的模拟数据。本文没有评估邻里变量，而是将重点放在个体间和邻里间SBP的差异上，将其作为个体BMI的函数。
方差分配系数（VPC）表明，BMI较高的人群中，邻里内SBP的聚类情况更明显。邻里在年龄、AHM使用情况和BMI方面的构成解释了约四分之一的邻里间SBP差异。邻里背景改变了BMI与SBP之间的个体层面关联。BMI较高的人群中，邻里内SBP的个体层面差异更大。
多水平变异的统计测量可以有效地量化不同人群中的背景效应，这是理解健康不平等的一个相关问题。
