Basile Chaix1, Juan Merlo, Pierre Chauvin. 1. 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. chaix@u707.jussieu.fr
Abstract
STUDY OBJECTIVE: 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. DESIGN: Multilevel and spatial analyses of cross sectional data. PARTICIPANTS: 10,955 beneficiaries of the three principal national health insurance funds, surveyed in 1998 and 2000 on continental France. MAIN RESULTS: 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. CONCLUSIONS: 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.
STUDY OBJECTIVE: 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. DESIGN: Multilevel and spatial analyses of cross sectional data. PARTICIPANTS: 10,955 beneficiaries of the three principal national health insurance funds, surveyed in 1998 and 2000 on continental France. MAIN RESULTS: 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. CONCLUSIONS: 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.
Authors: Sabriya L Linton; Hannah L F Cooper; Mary E Kelley; Conny C Karnes; Zev Ross; Mary E Wolfe; Yen-Tyng Chen; Samuel R Friedman; Don Des Jarlais; Salaam Semaan; Barbara Tempalski; Catlainn Sionean; Elizabeth DiNenno; Cyprian Wejnert; Gabriela Paz-Bailey Journal: Ann Epidemiol Date: 2016-08-08 Impact factor: 3.797
Authors: Sandi L Pruitt; Matthew J Shim; Patricia Dolan Mullen; Sally W Vernon; Benjamin C Amick Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-10 Impact factor: 4.254