Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case in a didactic rather than a mathematical way. Design and participants: Data were used from the health survey conducted in 2000 in the county of Scania, Sweden, that comprised 10 723 persons aged 18-80 years living in 60 areas. Conducting multilevel logistic regression different techniques were applied to investigate whether the individual propensity to consult private physicians was statistically dependent on the area of residence (that is, intraclass correlation (ICC), median odds ratio (MOR)), the 80% interval odds ratio (IOR-80), and the sorting out index). Results: The MOR provided more interpretable information than the ICC on the relevance of the residential area for understanding the individual propensity of consulting private physicians. The MOR showed that the unexplained heterogeneity between areas was of greater relevance than the individual variables considered in the analysis (age, sex, and education) for understanding the individual propensity of visiting private physicians. Residing in a high education area increased the probability of visiting a private physician. However, the IOR showed that the unexplained variability between areas did not allow to clearly distinguishing low from high propensity areas with the area educational level. The sorting out index was equal to 82%. Conclusion: Measures of variation in logistic regression should be promoted in social epidemiological and public health research as efficient means of quantifying the importance of the context of residence for understanding disparities in health and health related behaviour. I n the study of contextual determinants of health, considering the extent to which individual health phenomena cluster within areas is not only necessary for obtaining correct estimates in regression analysis. It also provides relevant information that permits assessment of the importance that the context has for different individual health outcomes. 2In multilevel linear regression analysis it is easy to partition the variance between different levels and compute measures of clustering that provide intuitive information for capturing contextual phenomena.3-5 However, for binary outcomes, the partition of variance between different levels does not have the intuitive interpretation of the linear model. Despite these difficulties several methods have been developed in logistic regression to obtain suitable epidemiological information on area level variance and clustering within areas. [6][7][8][9] This paper represents the last of a series of four included in a project aimed to explain in a conceptual rather than a mathematical way how to calculate and interpret multilevel measures of variance and clustering. [3][4][5] This study is focused at measures of variation in...

Study objective: This didactical essay is directed to readers disposed to approach multilevel regression analysis (MLRA) in a more conceptual than mathematical way. However, it specifically develops an epidemiological vision on multilevel analysis with particular emphasis on measures of health variation (for example, intraclass correlation). Such measures have been underused in the literature as compared with more traditional measures of association (for example, regression coefficients) in the investigation of contextual determinants of health. A link is provided, which will be comprehensible to epidemiologists, between MLRA and social epidemiological concepts, particularly between the statistical idea of clustering and the concept of contextual phenomenon. Design and participants: The study uses an example based on hypothetical data on systolic blood pressure (SBP) from 25 000 people living in 39 neighbourhoods. As the focus is on the empty MLRA model, the study does not use any independent variable but focuses mainly on SBP variance between people and between neighbourhoods. Results: The intraclass correlation (ICC = 0.08) informed of an appreciable clustering of individual SBP within the neighbourhoods, showing that 8% of the total individual differences in SBP occurred at the neighbourhood level and might be attributable to contextual neighbourhood factors or to the different composition of neighbourhoods. Conclusions: The statistical idea of clustering emerges as appropriate for quantifying ''contextual phenomena'' that is of central relevance in social epidemiology. Both concepts convey that people from the same neighbourhood are more similar to each other than to people from different neighbourhoods with respect to the health outcome variable.

Recent environmental changes play a role in the dramatic increase in the prevalence of cardiometabolic risk factors (CMRFs) such as obesity, hypertension, type 2 diabetes, dyslipidemias and the metabolic syndrome in industrialized countries. Therefore, identifying environmental characteristics that are associated with risk factors is critical to develop more effective public health interventions. We conducted a systematic review of the literature investigating relationships between characteristics of geographic life environments and CMRFs (131 articles). Most studies were published after 2006, relied on cross-sectional designs, and examined whether sociodemographic and physical environmental characteristics, and more recently service environment characteristics, were associated with obesity or, to a lesser extent, hypertension. Only 14 longitudinal studies were retrieved; diabetes, dyslipidemias and the metabolic syndrome were rarely analysed; and aspects of social interactions in the neighbourhood were critically underinvestigated. Environmental characteristics that were consistently associated with either obesity or hypertension include low area socioeconomic position; low urbanization degree; low street intersection, service availability and residential density; high noise pollution; low accessibility to supermarkets and high density of convenience stores; and low social cohesion. Intermediate mechanisms between environmental characteristics and CMRFs have received little attention. We propose a research agenda based on the assessment of underinvestigated areas of research and methodological limitations of current literature.

Objective: Through a literature review, we investigated the geographic information systems (GIS) methods used to define the food environment and the types of spatial measurements they generate. Design: Review study. Setting: Searches were conducted in health science databases, including Medline/ Pubmed, PsycINFO, Francis and GeoBase. We included studies using GIS-based measures of the food environment published up to 1 June 2008. Results: Twenty-nine papers were included. Two different spatial approaches were identified. The density approach quantifies the availability of food outlets using the buffer method, kernel density estimation or spatial clustering. The proximity approach assesses the distance to food outlets by measuring distances or travel times. GIS network analysis tools enable the modelling of travel time between referent addresses (home) and food outlets for a given transportation network and mode, and the assumption of travel routing behaviours. Numerous studies combined both approaches to compare food outlet spatial accessibility between different types of neighbourhoods or to investigate relationships between characteristics of the food environment and individual food behaviour. Conclusions: GIS methods provide new approaches for assessing the food environment by modelling spatial accessibility to food outlets. On the basis of the available literature, it appears that only some GIS methods have been used, while other GIS methods combining availability and proximity, such as spatial interaction models, have not yet been applied to this field. Future research would also benefit from a combination of GIS methods with survey approaches to describe both spatial and social food outlet accessibility as important determinants of individual food behaviours.

Study objective: (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. Design and participants: 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. Results: 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. Conclusions: Statistical measures of multilevel variations can effectively quantify contextual effects in different groups of people, which is a relevant issue for understanding health inequalities.

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As their most critical limitation, neighborhood and health studies published to date have not taken into account nonresidential activity places where individuals travel in their daily lives. However, identifying low-mobility populations residing in low-resource environments, assessing cumulative environmental exposures over multiple activity places, and identifying specific activity locations for targeting interventions are important for health promotion. Daily mobility has not been given due consideration in part because of a lack of tools to collect locational information on activity spaces. Thus, the first aim of the current article is to describe VERITAS (Visualization and Evaluation of Route Itineraries, Travel Destinations, and Activity Spaces), an interactive web mapping application that can geolocate individuals' activity places, routes between locations, and relevant areas such as experienced or perceived neighborhoods. The second aim is to formalize the theoretic grounds of a contextual expology as a subdiscipline to better assess the spatiotemporal configuration of environmental exposures. Based on activity place data, various indicators of individual patterns of movement in space (spatial behavior) are described. Successive steps are outlined for elaborating variables of multiplace environmental exposure (collection of raw locational information, selection/exclusion of locational data, defining an exposure area for measurement, and calculation). Travel and activity place network areas are discussed as a relevant construct for environmental exposure assessment. Finally, a note of caution is provided that these measures require careful handling to avoid increasing the magnitude of confounding (selective daily mobility bias).