title: Cancer mortality inequalities in urban areas: a Bayesian small area analysis in Spanish cities.
abstract: BACKGROUND
  Intra-urban inequalities in mortality have been infrequently analysed in European contexts. The aim of the present study was to analyse patterns of cancer mortality and their relationship with socioeconomic deprivation in small areas in 11 Spanish cities.
  
  
  METHODS
  It is a cross-sectional ecological design using mortality data (years 1996-2003). Units of analysis were the census tracts. A deprivation index was calculated for each census tract. In order to control the variability in estimating the risk of dying we used Bayesian models. We present the RR of the census tract with the highest deprivation vs. the census tract with the lowest deprivation.
  
  
  RESULTS
  In the case of men, socioeconomic inequalities are observed in total cancer mortality in all cities, except in Castellon, Cordoba and Vigo, while Barcelona (RR = 1.53 95%CI 1.42-1.67), Madrid (RR = 1.57 95%CI 1.49-1.65) and Seville (RR = 1.53 95%CI 1.36-1.74) present the greatest inequalities. In general Barcelona and Madrid, present inequalities for most types of cancer. Among women for total cancer mortality, inequalities have only been found in Barcelona and Zaragoza. The excess number of cancer deaths due to socioeconomic deprivation was 16,413 for men and 1,142 for women.
  
  
  CONCLUSIONS
  This study has analysed inequalities in cancer mortality in small areas of cities in Spain, not only relating this mortality with socioeconomic deprivation, but also calculating the excess mortality which may be attributed to such deprivation. This knowledge is particularly useful to determine which geographical areas in each city need intersectorial policies in order to promote a healthy environment.
authors.affiliation: Servei de Sistemes d'Informació Sanitaria, Agència de Salut Pública de Barcelona, Barcelona, Spain. rpuigpi@aspb.cat
authors.forename: Rosa
authors.initials: R
authors.lastname: Puigpinós-Riera
authors.forename: Marc
authors.initials: M
authors.lastname: Marí-Dell'Olmo
authors.forename: Mercè
authors.initials: M
authors.lastname: Gotsens
authors.forename: Carme
authors.initials: C
authors.lastname: Borrell
authors.forename: Gemma
authors.initials: G
authors.lastname: Serral
authors.forename: Carlos
authors.initials: C
authors.lastname: Ascaso
authors.forename: Montse
authors.initials: M
authors.lastname: Calvo
authors.forename: Antonio
authors.initials: A
authors.lastname: Daponte
authors.forename: Felicitas M
authors.initials: FM
authors.lastname: Domínguez-Berjón
authors.forename: Santiago
authors.initials: S
authors.lastname: Esnaola
authors.forename: Ana
authors.initials: A
authors.lastname: Gandarillas
authors.forename: Gonzalo
authors.initials: G
authors.lastname: López-Abente
authors.forename: Carmen M
authors.initials: CM
authors.lastname: Martos
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Martínez-Beneito
authors.forename: Agustín
authors.initials: A
authors.lastname: Montes-Martínez
authors.forename: Imanol
authors.initials: I
authors.lastname: Montoya
authors.forename: Andreu
authors.initials: A
authors.lastname: Nolasco
authors.forename: Isabel M
authors.initials: IM
authors.lastname: Pasarín
authors.forename: Maica
authors.initials: M
authors.lastname: Rodríguez-Sanz
authors.forename: Marc
authors.initials: M
authors.lastname: Sáez
authors.forename: Pablo
authors.initials: P
authors.lastname: Sánchez-Villegas
mesh_terms: D001499:Bayes Theorem; D016001:Confidence Intervals; D003430:Cross-Sectional Studies; D005260:Female; D005843:Geography; D054624:Health Status Disparities; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000191:economics / Q000453:epidemiology / Q000401:mortality*; D011203:Poverty; D012306:Risk; D018570:Risk Assessment; D012959:Socioeconomic Factors; D013030:Spain / Q000453:epidemiology; D014505:Urban Population / Q000706:statistics & numerical data*