Métodos para la suavización de indicadores de mortalidad: aplicación al análisis de desigualdades en mortalidad en ciudades del Estado español (Proyecto MEDEA)

Abstract

Although there is some experience in the study of mortality inequalities in Spanish cities, there are large urban centers that have not yet been investigated using the census tract as the unit of territorial analysis. The coordinated project <<Socioeconomic and environmental inequalities in mortality in Spanish cities. The MEDEA project>> was designed to fill this gap, with the participation of 10 groups of researchers in Andalusia, Aragon, Catalonia, Galicia, Madrid, Valencia, and the Basque Country. The MEDEA project has four distinguishing features: a) the census tract is used as the basic geographical area; b) statistical methods that include the geographical structure of the region under study are employed for risk estimation; c) data are drawn from three complementary data sources (information on air pollution, information on industrial pollution, and the records of mortality registrars), and d) a coordinated, large-scale analysis, favored by the implantation of coordinated research networks, is carried out. The main objective of the present study was to explain the methods for smoothing mortality indicators in the context of the MEDEA project. This study focusses on the methodology and the results of the Besag, York and Mollié model (BYM) in disease mapping. In the MEDEA project, standardized mortality ratios (SMR), corresponding to 17 large groups of causes of death and 28 specific causes, were smoothed by means of the BYM model; however, in the present study this methodology was applied to mortality due to cancer of the trachea, bronchi and lung in men and women in the city of Barcelona from 1996 to 2003. As a result of smoothing, a different geographical pattern for SMR in both genders was observed. In men, a SMR higher than unity was found in highly deprived areas. In contrast, in women, this pattern was observed in more affluent areas.

Social differences in avoidable mortality between small areas of 15 European cities: an ecological study

“…Credibility intervals are large and in the cause-specific analysis presented in the appendix, several cases had to be excluded due to too many areas with zero deaths [29]. This makes it difficult to systematically compare cause-specific results between cities and it was the reason to let the main part of the study focus on the 14 aggregated causes of death in order to draw general conclusions on avoidable mortality.…”

Poor housing and stroke mortality in population over 64 years old at the parish level, in mainland Portugal

“…Moreover, the variability of observed cases can be much greater than expected for a Poisson distribution. This situation, designated as overdispersion, also lowers the estimation accuracy of mortality (Barceló et al 2008).…”

“…Revisiting the issue of sparsely populated areas, these can be understood as small areas, although there is no universally accepted definition, since it depends on the study context and number of occurrences of the disease under study (Barceló et al 2008). In this sense, some authors consider as small any area whose sample size, in the study context, is too small to be able to produce estimates with an acceptable level of accuracy (Ghosh and Rao 1994).…”

“…This effect causes the SMR i of closest or even contiguous units to be more similar than the SMR i of more distant units, although part of this dependence can be caused by the existence of variables (also bearing spatial structure) that were not included in the analysis. The second potential source is due to the existence of independent and spatially uncorrelated excessive variability (or non-spatial heterogeneity), also caused by the existence of unobserved variables, but without spatial structure (Barceló et al 2008).…”

“…One effect absorbs spatial dependency and the other collects overdispersion of non-spatial origin, in other words, the one without spatial structure. The BYM model's ability to deal with overdispersion resulting from spatial dependence represents an advantage over the empirical Bayesian model, thus making the BYM model a more frequent choice to smooth SMR i values (Barceló et al 2008).…”