Abstract
BackgroundMany Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale. At the same time, the conceptualization of place at the local scale is one of the weakest theoretical aspects in health geography. The modifiable areal unit problem (MAUP) raises issues when census tracts are used as neighbourhood proxies, and no other alternate spatial structure is used for sensitivity analysis. In the literature, conclusions on the relationship between NO2 and health outcomes are divided, and this situation may in part be due to the selection of an inappropriate spatial structure for analysis. Here, we undertake an analysis of NO2 and respiratory health in Ottawa, Canada using three different spatial structures in order to elucidate the effects that the spatial unit of analysis can have on analytical results.ResultsUsing three different spatial structures to examine and quantify the relationship between NO2 and respiratory morbidity, we offer three main conclusions: 1) exploratory spatial analytical methods can serve as an indication of the potential effect of the MAUP; 2) OLS regression results differ significantly using different spatial representations, and this could be a contributing factor to the lack of consensus in studies that focus on the relation between NO2 and respiratory health at the area-level; and 3) the use of three spatial representations confirms no measured effect of NO2 exposure on respiratory health in Ottawa.ConclusionsArea units used in population health studies should be delineated so as to represent the a priori scale of the expected scale interaction between neighbourhood processes and health. A thorough understanding of the role of the MAUP in the study of the relationship between NO2 and respiratory health is necessary for research into disease pathways based on statistical models, and for decision-makers to assess the scale at which interventions will have maximum benefit. In general, more research on the role of spatial representation in health studies is needed.
Highlights
Many Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale
Since the main objective of this research is to determine the impact of the modifiable areal unit problem (MAUP) on the study of the relationship between exposure to NO2 and respiratory health, three different spatial structures are incorporated into our framework: First, census tracts from the 2006 Canadian Census of population are used as small-scale basic administrative units; second, coarser natural neighbourhoods are delineated based on a homogeneity criteria in order to represent an optimal zoning design for the socioeconomic processes under consideration, and; third, an automated zoning structure is created through a continuity based aggregation of census tracts in order to present a different zoning structure with a scale equivalent to the natural neighbourhoods
Preliminary data analysis was first conducted to determine the role of spatial representation on summary statistics using global spatial autocorrelation and bivariate Moran’s I
Summary
Many Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale. The modifiable areal unit problem (MAUP) raises issues when census tracts are used as neighbourhood proxies, and no other alternate spatial structure is used for sensitivity analysis. Standard geographical units from the Canadian Census, especially the census tract, are often used to operationalize the neighbourhood concept. This method finds benefit in the readily available Census data for this zoning system [19]. From an analytical viewpoint, using census tracts as the only spatial unit of measure is questionable when no other alternative spatial structure is used for sensitivity analysis, in which case, there can be no assessment of MAUP effects on results [16,21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
