Inequalities in mortality in small areas of eleven Spanish cities (the multicenter MEDEA project)
Introduction
Within European countries, there are many examples of systematic inequalities in health between social classes, the most deprived population groups having worse health and higher mortality (Whitehead and Dahlgren, 2006, Mackenbach et al., 2008, Marmot et al., 2008). Moreover, most of these inequalities have increased over time (Shaw et al., 1999, Mackenbach et al., 2003, Borrell et al., 2008).
In the last two decades, the number of studies treating geographical area as a health determinant has increased, probably due to re-awakened interest in social and environmental determinants of health (Krieger, 2008, Jerrett et al., 2005), availability of data at the small area level and development of methodology and software to analyse the spatial distribution of health based on Geographical Information Systems (Krieger, 2003, Rushton, 2003). The analysis of inequalities in health in geographical areas is important for at least three reasons. First, there are contextual factors at the area level that explain health outcomes, such as the physical environment, urban sprawl, the labour market, leisure facilities, educational facilities, health care and social institutions, just to name a few (Macintyre et al., 2002, Macintyre and Ellaway, 2003). Second, the identification of geographical areas with worse health and socioeconomic conditions facilitates the implementation of interventions and policies to tackle inequalities in health (Kjellstrom, 2008). And third, monitoring health inequalities can be more feasible and routinely done using ecological data (Borrell and Pasarin, 2004).
An understanding of the processes occurring in urban areas is a key factor to understand the economic, cultural, political and health transformations in a given country since today the majority of the world’s population lives in urban areas (Kjellstrom, 2008, UN-Habitat, 2006, Galea and Vlahov, 2005). In addition, socioeconomic inequalities in health tend to be larger in urban areas with deprived and poor populations being concentrated in marginalized neighbourhoods and urban slums located at the centre or peripheral areas of these cities (Diez Roux, 2007, van Lenthe et al., 2005, Borrell and Pasarin, 2004).
Small area analysis permits gaining a deeper understanding of geographic patterns and clusters of inequalities in health and has proved to be essential in uncovering local-level inequalities often masked by health estimates from large areas such as states, regions or cities. Moreover, the rise of Bayesian methodologies and other powerful new small-area techniques have provided better statistical tools to carry out these analyses (Lawson et al., 2000, Clayton and Bernardinelli, 1992). For all these reasons, the description of health inequalities in small areas of cities has importance for researchers, policy makers and the general population.
Intra-urban inequalities in mortality have not been analysed a great deal in Europe (van Lenthe et al., 2005, Stafford et al., 2004) and have only been studied in a small number of cities in Spain (Dominguez-Berjon et al., 2005; Ocana-Riola et al., 2008; Nolasco et al., 2009). Moreover, when studying inequalities within cities, the areas usually analysed are neighbourhoods, mainly because of the difficulties which an analysis of smaller areas entails. Therefore, the objectives of this study are to identify inequalities in total mortality, and in cause-specific mortality, among census tracts of eleven large Spanish cities, as well as to analyse the relationship between these geographical inequalities and socioeconomic deprivation at the turn of the 21st century.
Section snippets
Design
This study was carried out in the framework of a project known as MEDEA (Socioeconomic and environmental inequalities in mortality in small areas of Spanish cities—http://www.proyectomedea.org/) conducted jointly by 10 Spanish research groups. This study uses a cross-sectional ecological design whose goal is to analyse mortality inequalities at the small area level in Spanish cities. The units of analysis were the census tracts of the 11 largest cities included in the study according to the
Results
Table 1 describes the total population, number of census tracts, and distributions of the population and socioeconomic indicators by census tract, for each city. The number of census tracts varies from 95 (Castellón) to 2358 (Madrid), this number being related with city population. The median population by census tract is around 1000 inhabitants.
In general, Córdoba had the worst, and Barcelona and Bilbao the best levels of socioeconomic indicators. Table 2 shows the number of deaths by cause of
Principal findings and strengths of the study
In the majority of the 11 chosen Spanish cities total mortality presented geographical patterns, in both sexes, that were similar to patterns in the index of socioeconomic deprivation. These geographical mortality inequalities could be explained, at least partly, by socioeconomic deprivation among men and women in the majority of cities. Among men, four specific causes of death (lung cancer, ischemic heart diseases, respiratory diseases and cirrhosis) were positively associated with deprivation
Acknowledgements
This article was partially funded by FIS projects, nos. PI042013, PI040041, PI040170, PI040069, PI042602, PI040388, PI040489, PI042098, PI041260, PI040399, by the CIBER Epidemiología y Salud Pública (CIBERESP), Spain and by the program of “Intensificación de la Actividad Investigadora (Carme Borrell)” funded by the “Instituto de Salud Carlos III” and “Departament de Salut. Generalitat de Catalunya”.
This article will be included in the doctoral thesis of one of the authors (Marc Marí-Dell’Olmo),
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Elena Aldasoroe, Maria Antònia Barcelóf,c, Joan Benachg,c,h, Montse Calvoe, Koldo Cambrae, Antonio Dapontei,c, Felicitas Domínguez-Berjonj, Santiago Esnaolae, Ana Gandarillasj, Berta Ibáñezk,c, José Miguel Martínezg,c, Carmen Martosl, Imanol Montoyam, Carles Muntanern, Andreu Nolascoo, M. Isabel Pasarína,b,c, Javier García-Pérezp,c, Rosa Puigpinósa,c, Rebeca Ramisp,c, Maica Rodríguez-Sanza,c, Rosa Ruize, Marc Saezf,c, Pablo Sánchez-Villegasi, Margarita Taracidoq, Oscar Zurriagad,r. [aAgència de Salut Pública de Barcelona, Spain; bUniversitat Pompeu Fabra, Spain; cCIBER Epidemiología y Salud Pública (CIBERESP), Spain; eDepartamento de Sanidad, Gobierno Vasco; fResearch Group on Statistics, Applied Economics and Health (GRECS), University of Girona, Spain; gOccupational Health Research Center, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona (Spain); hHealth Inequalities Research Group. Employment Conditions Knowledge Network (Emconet); iObservatorio de Salud y Medio Ambiente de Andalucía (OSMAN), Escuela Andaluza de Salud Pública, Consejería de Salud, Junta de Andalucía, Granada, Spain; jSubdirección de Promoción de la Salud y Prevención. Consejería de Sanidad. Comunidad de Madrid, Spain; kFundación Vasca de Innovación e Investigación Sanitarias (BIOEF), Spain; lInstituto Aragonés de Ciencias de la Salud; mUnidad de Investigación de Atención Primaria de Bizkaia. Osakidetza-Servicio Vasco de Salud, Spain; nCenter for Addictions and Mental Health, University of Toronto, Canada; oUnidad de Investigación en Análisis de la Mortalidad y Estadísticas Sanitarias, Universidad de Alicante, Alicante, Spain; pDepartment of Environmental Epidemiology and Cancer, National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain; qUniversidad de Santiago de Compostela; rÁrea de Epidemiología. Dirección General de Salud Pública. Valencia (Spain)]