A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale.

Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO2 following two steps: (i) retrospective reconstitution of NO2 concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs.

An ecological study to identify census blocks supporting a higher burden of disease: infant mortality in the lille metropolitan area, france.

In France, reducing social health inequalities has become an explicit goal of health policies over the past few years, one of its objectives is specifically the reduction of the perinatal mortality rate. This study investigates the association between infant mortality and social deprivation categories at a small area level in the Lille metropolitan area, in the north of France, to identify census blocks where public authorities should prioritize appropriate preventive actions. We used census data to establish a neighbourhood deprivation index whose multiple dimensions encompass socioeconomic characteristics. Infant mortality data were obtained from the Lille metropolitan area municipalities to estimate a death rate at the census tract level. We used Bayesian hierarchical models in order to reduce the extra variability when computing relative risks (RR) and to assess the associations between infant mortality and deprivation. Between 2000 and 2009, 668 cases of infant death occurred in the Lille metropolitan area (4.2 per 1,000 live births). The socioeconomic status is associated with infant mortality, with a clear gradient of risk from the most privileged census blocks to the most deprived ones (RR = 2.62, 95 % confidence interval [1.87; 3.70]). The latter have 24.6 % of families who were single parents and 29.9 % of unemployed people in the labor force versus 8.5 % and 7.7 % in the former. Our study reveals socio-spatial disparities in infant mortality in the Lille metropolitan area and highlights the census blocks most affected by the inequalities. Fine spatial analysis may help inform the design of preventive policies tailored to the characteristics of the local communities.