Trends in socioeconomic inequalities in mortality in small areas of 33 Spanish cities

BMC Public Health

Research Article

Carme Borrell¹  Maica Rodríguez-Sanz²  Laia Palència²  Mercè Gotsens²  Marc Marí-Dell’Olmo³  Margarita Taracido⁴  Lluís Cirera⁵  Diego Salmerón⁵  Mónica Ballesta⁵  Montse Calvo⁶  Miguel A. Martinez-Beneito⁷  Carmen Martos⁸  Conchi Moreno-Iribas⁹  Antonio Daponte^1,10  Natividad Izco Goñi^1,11  Ana Gandarillas^1,12  Felicitas Domínguez-Berjón^1,12  Andreu Nolasco^1,13 

[1] Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023, Barcelona, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain;Universitat Pompeu Fabra, Barcelona, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023, Barcelona, Spain;Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023, Barcelona, Spain;Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain;Universitat Rovira i Virgili, Tarragona, Spain;Departamento de Medicina Preventiva y Salud Pública, Universidad de Santiago de Compostela, Santiago de Compostela, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Estudios e investigación Sanitaria, Departamento de Sanidad y Consumo, Gobierno Vasco, Vitoria-Gasteiz, Spain;Fundación para el fomento de la investigación sanitaria y biomédica de la Comunidad Valenciana (FISABIO), Valencia, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Instituto Aragonés de Ciencias de la Salud, Zaragoza, Spain;Instituto de Salud Pública de Navarra, Departamento de Salud, Gobierno de Navarra, Pamplona, Navarra, Spain;Observatorio de Salud y Medio Ambiente de Andalucía (OSMAN), Escuela Andaluza de Salud Pública (EASP), Granada, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Registro de Mortalidad, Consejería de Salud y Servicios Sociales, La Rioja, Spain;Subdirección de Promoción de la Salud y Prevención, Consejería de Sanidad, Comunidad de Madrid, Spain;Unidad de Investigación en Análisis de la Mortalidad y Estadísticas Sanitarias, Universidad de Alicante, San Vicente del Raspeig, Spain;

关键词: Disease mapping;    Multilevel analysis;    Geographical inequalities;    Bayesian methods;    Trends;    Urban areas;    Small areas;    Mortality;    Inequalities in mortality;    Socioeconomic inequalities;   

DOI  :  10.1186/s12889-016-3190-y

 received in 2015-08-28, accepted in 2016-06-08,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIn Spain, several ecological studies have analyzed trends in socioeconomic inequalities in mortality from all causes in urban areas over time. However, the results of these studies are quite heterogeneous finding, in general, that inequalities decreased, or remained stable. Therefore, the objectives of this study are: (1) to identify trends in geographical inequalities in all-cause mortality in the census tracts of 33 Spanish cities between the two periods 1996–1998 and 2005–2007; (2) to analyse trends in the relationship between these geographical inequalities and socioeconomic deprivation; and (3) to obtain an overall measure which summarises the relationship found in each one of the cities and to analyse its variation over time.MethodsEcological study of trends with 2 cross-sectional cuts, corresponding to two periods of analysis: 1996–1998 and 2005–2007. Units of analysis were census tracts of the 33 Spanish cities. A deprivation index calculated for each census tracts in all cities was included as a covariate. A Bayesian hierarchical model was used to estimate smoothed Standardized Mortality Ratios (sSMR) by each census tract and period. The geographical distribution of these sSMR was represented using maps of septiles. In addition, two different Bayesian hierarchical models were used to measure the association between all-cause mortality and the deprivation index in each city and period, and by sex: (1) including the association as a fixed effect for each city; (2) including the association as random effects. In both models the data spatial structure can be controlled within each city. The association in each city was measured using relative risks (RR) and their 95 % credible intervals (95 % CI).ResultsFor most cities and in both sexes, mortality rates decline over time. For women, the mortality and deprivation patterns are similar in the first period, while in the second they are different for most cities. For men, RRs remain stable over time in 29 cities, in 3 diminish and in 1 increase. For women, in 30 cities, a non-significant change over time in RR is observed. However, in 4 cities RR diminishes. In overall terms, inequalities decrease (with a probability of 0.9) in both men (RR = 1.13, 95 % CI = 1.12–1.15 in the 1st period; RR = 1.11, 95 % CI = 1.09–1.13 in the 2nd period) and women (RR = 1.07, 95 % CI = 1.05–1.08 in the 1st period; RR = 1.04, 95 % CI = 1.02–1.06 in the 2nd period).ConclusionsIn the future, it is important to conduct further trend studies, allowing to monitoring trends in socioeconomic inequalities in mortality and to identify (among other things) temporal factors that may influence these inequalities.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

12906_2015_Article_775_TeX2GIF_IEq3.gif

12906_2017_1593_Article_IEq16.gif