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      Construcción de un índice de privación para los barrios de Madrid y Barcelona Translated title: Process and results of constructing a deprivation index for the districts of Madrid and Barcelona, Spain

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          Abstract

          Fundamentos: los indicadores socioeconómicos que toman el barrio como unidad de referencia en nuestro contexto son escasos. Los objetivos de este artículo son describir el proceso de construcción y la validez de un índice de privación a nivel de barrio y analizar su asociación con la mortalidad. Métodos: el esquema conceptual inicial del IP contuvo elementos que caracterizaban teóricamente la privación y para las que se realizó una recogida de variables de segundo nivel. El IP se adaptó a la disponibilidad de variables y a los resultados de sus análisis exploratorios. Finalmente, se realizó un análisis factorial para la validación del IP que se compuso de 5 dimensiones para Madrid (economía, población y territorio, vivienda, parque móvil y demografía) y 4 para Barcelona (las mismas salvo «demografía»). Los barrios fueron agrupados en cuartiles según la puntuación obtenida para el IP (Q4: mayor nivel de privación). Se calcularon tasas de mortalidad prematura estratificadas por sexo y ajustadas por edad y razones de mortalidad para cada cuartil. Resultados: El IP explicó el 55% de la variabilidad observada en los indicadores para Madrid y el 69% para Barcelona. La tasa de mortalidad prematura para el Q1 en Madrid fue 1,65 por 10³ en hombres y 0,92 por 10³ y de 2,81 por 10³ en hombres y 1,22 por 10³ en mujeres residentes en Q4. En Barcelona la tasa de mortalidad fue de 2,33 por 10³ en hombres y de 1,15 por 10³ mujeres en el Q1 y de 3,49 por 10³ en hombres y 1,52 por 10³ en mujeres del Q4. Conclusión: Las tasas de mortalidad mostraron mayor mortalidad prematura en los barrios con un índice de privación mayor.

          Translated abstract

          Background: There are few economic indicators that take the neighbourhood as the unit of reference in our context. The aim of this article is to describe the process and results of secondary data collection and development of a deprivation index (DI) for the neighbourhoods of the cities of Madrid and Barcelona, discussing their utility for research on health inequalities. Methods: initial DI conceptual framework contained different elements that characterize deprivation and for which we collected second-level variables. ID was adapted to the availability of variables and to the results of an exploratory analysis. Finally, a factor analysis was performed to validate the IP. We built a DI based on five dimensions for Madrid (economy, population and territory, housing, cars and demographics) and 4 for Barcelona (all except "demographics"). Neighbourhoods were grouped into quartiles according to their score for the DI (Q4: higher levels of deprivation). Premature mortality rates and premature mortality ratios adjusted by age were calculated for each quartile. Results: The IP explained 55% of the observed variability in the indicators for Madrid and 69% for Barcelona. Premature mortality rate in Madrid for Q1 was 1.65 per 10³ in men and 0.92 per 10³ women and 2.81 per 10³ in men and 1.22 per 10³ in women residing in Q4. In Barcelona, the mortality rate was 2.33 per 10³ men and 1.15 per 10³ women in Q1 and 3.49 per 10³ in men and 1.52 per 103 in women living in Q4. Conclusion: Premature mortality rates showed higher premature mortality in the most deprived districts.

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          Neighborhood of residence and incidence of coronary heart disease.

          Where a person lives is not usually thought of as an independent predictor of his or her health, although physical and social features of places of residence may affect health and health-related behavior. Using data from the Atherosclerosis Risk in Communities Study, we examined the relation between characteristics of neighborhoods and the incidence of coronary heart disease. Participants were 45 to 64 years of age at base line and were sampled from four study sites in the United States: Forsyth County, North Carolina; Jackson, Mississippi; the northwestern suburbs of Minneapolis; and Washington County, Maryland. As proxies for neighborhoods, we used block groups containing an average of 1000 people, as defined by the U.S. Census. We constructed a summary score for the socioeconomic environment of each neighborhood that included information about wealth and income, education, and occupation. During a median of 9.1 years of follow-up, 615 coronary events occurred in 13,009 participants. Residents of disadvantaged neighborhoods (those with lower summary scores) had a higher risk of disease than residents of advantaged neighborhoods, even after we controlled for personal income, education, and occupation. Hazard ratios for coronary events in the most disadvantaged group of neighborhoods as compared with the most advantaged group--adjusted for age, study site, and personal socioeconomic indicators--were 1.7 among whites (95 percent confidence interval, 1.3 to 2.3) and 1.4 among blacks (95 percent confidence interval, 0.9 to 2.0). Neighborhood and personal socioeconomic indicators contributed independently to the risk of disease. Hazard ratios for coronary heart disease among low-income persons living in the most disadvantaged neighborhoods, as compared with high-income persons in the most advantaged neighborhoods were 3.1 among whites (95 percent confidence interval, 2.1 to 4.8) and 2.5 among blacks (95 percent confidence interval, 1.4 to 4.5). These associations remained unchanged after adjustment for established risk factors for coronary heart disease. Even after controlling for personal income, education, and occupation, we found that living in a disadvantaged neighborhood is associated with an increased incidence of coronary heart disease.
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            Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review.

            Interest in the effects of neighbourhood or local area social characteristics on health has increased in recent years, but to date the existing evidence has not been systematically reviewed. Multilevel or contextual analyses of social factors and health represent a possible reconciliation between two divergent epidemiological paradigms-individual risk factor epidemiology and an ecological approach. Keyword searching of Index Medicus (Medline) and additional references from retrieved articles. All original studies of the effect of local area social characteristics on individual health outcomes, adjusted for individual socioeconomic status, published in English before 1 June 1998 and focused on populations in developed countries. The methodological challenges posed by the design and interpretation of multilevel studies of local area effects are discussed and results summarised with reference to type of health outcome. All but two of the 25 reviewed studies reported a statistically significant association between at least one measure of social environment and a health outcome (contextual effect), after adjusting for individual level socioeconomic status (compositional effect). Contextual effects were generally modest and much smaller than compositional effects. The evidence for modest neighbourhood effects on health is fairly consistent despite heterogeneity of study designs, substitution of local area measures for neighbourhood measures and probable measurement error. By drawing public health attention to the health risks associated with the social structure and ecology of neighbourhoods, innovative approaches to community level interventions may ensue.
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              Neighborhood characteristics and availability of healthy foods in Baltimore.

              Differential access to healthy foods may contribute to racial and economic health disparities. The availability of healthy foods has rarely been directly measured in a systematic fashion. This study examines the associations among the availability of healthy foods and racial and income neighborhood composition. A cross-sectional study was conducted in 2006 to determine differences in the availability of healthy foods across 159 contiguous neighborhoods (census tracts) in Baltimore City and Baltimore County and in the 226 food stores within them. A healthy food availability index (HFAI) was determined for each store, using a validated instrument ranging from 0 points to 27 points. Neighborhood healthy food availability was summarized by the mean HFAI for the stores within the neighborhood. Descriptive analyses and multilevel models were used to examine associations of store type and neighborhood characteristics with healthy food availability. Forty-three percent of predominantly black neighborhoods and 46% of lower-income neighborhoods were in the lowest tertile of healthy food availability versus 4% and 13%, respectively, in predominantly white and higher-income neighborhoods (p<0.001). Mean differences in HFAI comparing predominantly black neighborhoods to white ones, and lower-income neighborhoods to higher-income neighborhoods, were -7.6 and -8.1, respectively. Supermarkets in predominantly black and lower-income neighborhoods had lower HFAI scores than supermarkets in predominantly white and higher-income neighborhoods (mean differences -3.7 and -4.9, respectively). Regression analyses showed that both store type and neighborhood characteristics were independently associated with the HFAI score. Predominantly black and lower-income neighborhoods have a lower availability of healthy foods than white and higher-income neighborhoods due to the differential placement of types of stores as well as differential offerings of healthy foods within similar stores. These differences may contribute to racial and economic health disparities.
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                Author and article information

                Journal
                resp
                Revista Española de Salud Pública
                Rev. Esp. Salud Publica
                Ministerio de Sanidad, Consumo y Bienestar social (Madrid, Madrid, Spain )
                1135-5727
                2173-9110
                August 2013
                : 87
                : 4
                : 317-329
                Affiliations
                [01] Madrid orgnameInstituto de Salud Carlos III orgdiv1Centro Nacional de Epidemiología España
                [02] Madrid orgnameCentro de Investigación Biomédica En Red de Epidemiología y Salud Pública (CIBERESP)
                [03] Madrid orgnameUniversidad Complutense de Madrid orgdiv1Departamento de Medicina Preventiva, Salud Pública e Historia de la Ciencia
                Article
                S1135-57272013000400003 S1135-5727(13)08700400003
                10.4321/S1135-57272013000400003
                24100771
                b1d909af-8d43-44a3-9e94-8832ae80f3d0

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International License.

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                SciELO Public Health


                Small-area analysis,Socioeconomic factors,Censuses,Factor analysis,Health inequalities,Mortalidad,Desigualdades en la salud,Análisis de área pequeña,Análisis factorial,Statistical

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