Índice de privación en España por sección censal en 2011 / Deprivation index by enumeration district in Spain, 2011

Objetivo: Presentar la metodología seguida en el diseño y la implementación de un índice de privación por sección censal, y describir la situación socioeconómica en España en 2011. Método: La unidad de análisis fue la sección censal (N=35.960). Los datos proceden del Censo de Población y Viviendas de 2011. Teniendo en cuenta el carácter muestral del censo y las limitaciones normativas de confidencialidad de datos, las variables se calcularon indirectamente usando los complementarios de las variables disponibles. Se efectuaron comprobaciones para asegurar su fiabilidad. En la selección de indicadores se consideró la comparabilidad con el índice MEDEA y se exploró la incorporación de nueva información. Se elaboró un índice de privación mediante análisis de componentes principales. Se realizó un análisis de sensibilidad del índice en ámbitos urbanos y en el resto de los territorios. Resultados: Con la información censal se elaboraron 22 indicadores para 35.917 secciones censales. El índice de privación se basó en seis indicadores: población trabajadora manual, población asalariada eventual, desempleo, personas de 16 y más años y de 16 a 29 años con instrucción insuficiente, y viviendas principales sin acceso a Internet. El mapa de España muestra un eje decreciente de privación de suroeste a nordeste. Conclusiones: Se ha aprovechado sistemáticamente la información socioeconómica del censo de 2011 por sección censal. El índice elaborado, similar al MEDEA, facilitará el estudio actualizado de las desigualdades en salud para toda España después de la crisis económica iniciada en 2008. (AU)

Objective: To present the methodology used in the design and implementation of a deprivation index by enumeration district, and to describe the socioeconomic situation of Spain in 2011.Method: The unit of analysis was the enumeration district (N=35,960). Data came from the 2011 Population and Housing Census of Spain. Given both the sampling nature of the Census and the regulatory limitations of data confidentiality, variables were calculated indirectly by using the complement of the available variables. Checks were made to ensure reliability. The selection of the indicators took into account comparability with the MEDEA index. The inclusion of additional information was explored. A deprivation index was built using Principal Component Analysis. Sensitivity analysis of the index was performed for urban areas and the rest of the regions. Results: Using the census information, 22 indicators were calculated for 35,917 enumeration districts. The deprivation index was based on six indicators: manual and temporary workers, unemployment, insufficient education overall and in young people (aged 16 to 29 years), and dwellings without access to the internet. The map of Spain shows a gradient of decreasing deprivation from south-west to north-east. Conclusions: The socioeconomic information of the 2011 census by enumeration district was used systematically. The drafted index, similar to the MEDEA, will facilitate the updated study of health inequalities for Spain overall following the economic recession that began in 2008. (AU)

[Deprivation index by enumeration district in Spain, 2011]. / Índice de privación en España por sección censal en 2011.

OBJECTIVE: To present the methodology used in the design and implementation of a deprivation index by enumeration district, and to describe the socioeconomic situation of Spain in 2011. METHOD: The unit of analysis was the enumeration district (N=35,960). Data came from the 2011 Population and Housing Census of Spain. Given both the sampling nature of the Census and the regulatory limitations of data confidentiality, variables were calculated indirectly by using the complement of the available variables. Checks were made to ensure reliability. The selection of the indicators took into account comparability with the MEDEA index. The inclusion of additional information was explored. A deprivation index was built using Principal Component Analysis. Sensitivity analysis of the index was performed for urban areas and the rest of the regions. RESULTS: Using the census information, 22 indicators were calculated for 35,917 enumeration districts. The deprivation index was based on six indicators: manual and temporary workers, unemployment, insufficient education overall and in young people (aged 16 to 29 years), and dwellings without access to the internet. The map of Spain shows a gradient of decreasing deprivation from south-west to north-east. CONCLUSIONS: The socioeconomic information of the 2011 census by enumeration district was used systematically. The drafted index, similar to the MEDEA, will facilitate the updated study of health inequalities for Spain overall following the economic recession that began in 2008.

Density of Green Spaces and Cardiovascular Risk Factors in the City of Madrid: The Heart Healthy Hoods Study.

The aim of this study is to evaluate the relationship between the density of green spaces at different buffer sizes (300, 500, 1000 and 1500 m) and cardiovascular risk factors (obesity, hypertension, high cholesterol, and diabetes) as well as to study if the relationship is different for males and females. We conducted cross-sectional analyses using the baseline measures of the Heart Healthy Hoods study (N = 1625). We obtained data on the outcomes from clinical diagnoses, as well as anthropometric and blood sample measures. Exposure data on green spaces density at different buffer sizes were derived from the land cover distribution map of Madrid. Results showed an association between the density of green spaces within 300 and 500 m buffers with high cholesterol and diabetes, and an association between the density of green spaces within 1500 m buffer with hypertension. However, all of these associations were significant only in women. Study results, along with other evidence, may help policy-makers creating healthier environments that could reduce cardiovascular disease burden and reduce gender health inequities. Further research should investigate the specific mechanisms behind the differences by gender and buffer size of the relationship between green spaces and cardiovascular risk factors.

Understanding urban health inequalities: methods and design of the Heart Health Hoods qualitative project / Comprensión de las desigualdades en salud urbana: métodos y diseños del proyecto cualitativo Heart Health Hoods

Objective: Qualitative methods may help to understand features related to health urban inequalities as a way to include citizens' perceptions of their neighbourhoods in relation to their health-related behaviours. The aim of this article is to describe the methods and design of a qualitative urban health study. Methods: The Heart Healthy Hoods (HHH) analyses cardiovascular health in an urban environment using mixed methods: electronic health records, quantitative individual questionnaires, physical examination, semi-structured Interviews (SSIs), focus groups (FGs) and participatory technics such as photovoice. This article focuses on the HHH qualitative methods and design. A case study was used to select three neighbourhoods in Madrid with different socioeconomic levels: low, medium, and high. The selection process for these three neighbourhoods was as follows: classification of all Madrid's neighbourhoods (128) according to their socioeconomic level; after ranking this classification, nine neighbourhoods, three by socioeconomic level, were short-listed; different urban sociology criteria and non-participant observation were used for the final selection of three neighbourhoods. After selecting the three neighbourhoods, thirty SSIs were held with residents and six SSIs were held with key informants. Finally, twenty-nine FGs will be conducted over the course of 8 months, between May and December of 2018. Conclusions: Systematization in the selection of neighbourhoods and the use of adequate techniques are essential for the qualitative study of urban health inequalities

Objetivo: La metodología cualitativa puede ayudar a entender aspectos relacionados con las desigualdades en salud urbana, incluyendo la percepción de los ciudadanos de su barrio en relación con los comportamientos relacionados con su salud. El objetivo de este artículo es describir los métodos y el diseño de un estudio cualitativo sobre salud urbana. Método: Heart Healthy Hoods (HHH) es un estudio que analiza la salud cardiovascular en un ámbito urbano, utilizando métodos mixtos: registros electrónicos de salud, cuestionarios individuales cuantitativos, exploraciones físicas, entrevistas semiestructuradas (ESE), grupos de discusión (GD) y técnicas participativas como el fotovoz. Este artículo se centra en los métodos y el diseño de la fase cualitativa del HHH. Se aplicó un estudio de caso, seleccionando tres barrios de Madrid con diferente nivel socioeconómico: bajo, medio y alto. El proceso de selección para estos tres barrios fue el siguiente: clasificación de todos los barrios de Madrid (128) según su nivel socioeconómico; tras graduar la clasificación se seleccionaron nueve barrios de Madrid, tres por nivel socioeconómico; se utilizaron diferentes criterios de sociología urbana y observación no participante para la selección final de tres barrios. Tras la elección de los tres barrios, se realizaron 30 ESE a vecinos y 6 ESE a informantes clave. Finalmente, se están organizando 29 GD con vecinos durante 8 meses, entre mayo y diciembre de 2018. Conclusiones: La sistematización en la selección de barrios y la utilización de las técnicas adecuadas son fundamentales para el estudio cualitativo de las desigualdades en el entorno urbano

Assessing the Retail Food Environment in Madrid: An Evaluation of Administrative Data against Ground Truthing.

Previous studies have suggested that European settings face unique food environment issues; however, retail food environments (RFE) outside Anglo-Saxon contexts remain understudied. We assessed the completeness and accuracy of an administrative dataset against ground truthing, using the example of Madrid (Spain). Further, we tested whether its completeness differed by its area-level socioeconomic status (SES) and population density. First, we collected data on the RFE through the ground truthing of 42 census tracts. Second, we retrieved data on the RFE from an administrative dataset covering the entire city (n = 2412 census tracts), and matched outlets using location matching and location/name matching. Third, we validated the administrative dataset against the gold standard of ground truthing. Using location matching, the administrative dataset had a high sensitivity (0.95; [95% CI = 0.89, 0.98]) and positive predictive values (PPV) (0.79; [95% CI = 0.70, 0.85]), while these values were substantially lower using location/name matching (0.55 and 0.45, respectively). Accuracy was slightly higher using location/name matching (k = 0.71 vs 0.62). We found some evidence for systematic differences in PPV by area-level SES using location matching, and in both sensitivity and PPV by population density using location/name matching. Administrative datasets may offer a reliable and cost-effective source to measure retail food access; however, their accuracy needs to be evaluated before using them for research purposes.

Socioeconomic Inequalities in the Retail Food Environment around Schools in a Southern European Context.

Across Europe, excess body weight rates are particularly high among children and adolescents living in Southern European contexts. In Spain, current food policies appeal to voluntary self-regulation of the food industry and parents' responsibility. However, there is no research (within Spain) assessing the food environment surrounding schools. We examined the association between neighborhood-level socioeconomic status (NSES) and the spatial access to an unhealthy food environment around schools using both counts and distance measures, across the city of Madrid. We conducted a cross-sectional study citywide (n = 2443 census tracts). In 2017, we identified all schools (n = 1321) and all food retailers offering unhealthy food and beverages surrounding them (n = 6530) using publicly available data. We examined both the counts of retailers (within 400 m) and the distance (in meters) from the schools to the closest retailer. We used multilevel regressions to model the association of neighborhood-level socioeconomic status (NSES) with both measures, adjusting both models for population density. Almost all schools (95%) were surrounded by unhealthy retailers within 400 m (median = 17 retailers; interquartile range = 8-34). After adjusting for population density, NSES remained inversely associated with unhealthy food availability. Schools located in low-NSES areas (two lowest quintiles) showed, on average, 29% (IRR (Incidence Rate Ratio) = 1.29; 95% CI (Confidence Interval) = 1.12, 1.50) and 62% (IRR = 1.62; 95% CI = 1.35, 1.95) more counts of unhealthy retailers compared with schools in middle-NSES areas (ref.). Schools in high-NSES areas were farther from unhealthy food sources than those schools located in middle-NSES areas (ß = 0.35; 95% CI = 0.14, 0.47). Regulating the school food environment (within and beyond school boundaries) may be a promising direction to prevent and reduce childhood obesity.

Access to and availability of exercise facilities in Madrid: an equity perspective.

BACKGROUND: Identifying socioeconomic determinants that are associated with access to and availability of exercise facilities is fundamental to supporting physical activity engagement in urban populations, which in turn, may reduce health inequities. This study analysed the relationship between area-level socioeconomic status (SES) and access to, and availability of, exercise facilities in Madrid, Spain. METHODS: Area-level SES was measured using a composite index based on seven sociodemographic indicators. Exercise facilities were geocoded using Google Maps and classified into four types: public, private, low-cost and sessional. Accessibility was operationalized as the street network distance to the nearest exercise facility from each of the 125,427 residential building entrances (i.e. portals) in Madrid. Availability was defined as the count of exercise facilities in a 1000 m street network buffer around each portal. We used a multilevel linear regression and a zero inflated Poisson regression analyses to assess the association between area-level SES and exercise facility accessibility and availability. RESULTS: Lower SES areas had a lower average distance to the closest facility, especially for public and low-cost facilities. Higher SES areas had higher availability of exercise facilities, especially for private and seasonal facilities. CONCLUSION: Public and low-cost exercise facilities were more proximate in low SES areas, but the overall number of facilities was lower in these areas compared with higher SES areas. Increasing the number of exercise facilities in lower SES areas may be an intervention to improve health equity.

Understanding Urban Health Inequalities: Methods and Design of the Heart Health Hoods Qualitative Project.

OBJECTIVE: Qualitative methods may help to understand features related to health urban inequalities as a way to include citizens' perceptions of their neighbourhoods in relation to their health-related behaviours. The aim of this article is to describe the methods and design of a qualitative urban health study. METHODS: The Heart Healthy Hoods (HHH) analyses cardiovascular health in an urban environment using mixed methods: electronic health records, quantitative individual questionnaires, physical examination, semi-structured Interviews (SSIs), focus groups (FGs) and participatory technics such as photovoice. This article focuses on the HHH qualitative methods and design. A case study was used to select three neighbourhoods in Madrid with different socioeconomic levels: low, medium, and high. The selection process for these three neighbourhoods was as follows: classification of all Madrid's neighbourhoods (128) according to their socioeconomic level; after ranking this classification, nine neighbourhoods, three by socioeconomic level, were short-listed; different urban sociology criteria and non-participant observation were used for the final selection of three neighbourhoods. After selecting the three neighbourhoods, thirty SSIs were held with residents and six SSIs were held with key informants. Finally, twenty-nine FGs will be conducted over the course of 8 months, between May and December of 2018. CONCLUSIONS: Systematization in the selection of neighbourhoods and the use of adequate techniques are essential for the qualitative study of urban health inequalities.

A multicomponent method assessing healthy cardiovascular urban environments: The Heart Healthy Hoods Index.

Previous studies have examined the built environment mostly focusing on a single exposure construct (e.g. walkability) to examine its association with health outcomes. This study developed a multicomponent Heart Healthy Hoods Index to characterize heart-healthy urban environments and examined its relationship with the prevalence of cardiovascular disease (CVD) in Madrid, Spain. Using spatial methods, we generated two index models (model 0 unweighted and model 1 weighted) using the percentage of deaths for the main behavioral risk factors for CVD (diet, physical activity, alcohol, and tobacco environments). We performed global (Ordinal Least Square) and local (Geographically Weighed Regression) regression analyses to assess the relationship between both index models and CVD prevalence, and to identify the best index model. In the global analysis, both models showed a significant negative relationship with CVD prevalence. In the local analysis, Model 1 removed the spatial autocorrelation of residuals and showed the lowest values for the Akaike information criterion. This study provides evidence of a non-stationary relationship between the heart-healthy urban environment and CVD prevalence. The HHH index may be an effective tool to identify and prioritize geographical areas for CVD prevention.

Development and evaluation of the OHCITIES instrument: assessing alcohol urban environments in the Heart Healthy Hoods project.

OBJECTIVES: To describe the development and test-retest reliability of OHCITIES, an instrument characterising alcohol urban environment in terms of availability, promotion and signs of consumption. DESIGN: This study involved: (1) developing the conceptual framework for alcohol urban environment by means of literature reviewing and previous alcohol environment research experience; (2) pilot testing and redesigning the instrument; (3) instrument digitalisation; (4) instrument evaluation using test-retest reliability. SETTING: Data for testing the reliability of the instrument were collected in seven census sections in Madrid in 2016 by two observers. PRIMARY AND SECONDARY OUTCOME MEASURES: We computed per cent agreement and Cohen's kappa coefficients to estimate inter-rater and test-retest reliability for alcohol outlet environment measures. We calculated interclass coefficients and their 95% CIs to provide a measure of inter-rater reliability for signs of alcohol consumption measures. RESULTS: We collected information on 92 on-premise and 24 off-premise alcohol outlets identified in the studied areas about availability, accessibility and promotion of alcohol. Most per cent-agreement values for alcohol measures in on-premise and off-premise alcohol outlets were greater than 80%, and inter-rater and test-retest reliability values were generally above 0.80. Observers identified 26 streets and 3 public squares with signs of alcohol consumption. Intraclass correlation coefficient between observers for any type of signs of alcohol consumption was 0.50 (95% CI -0.09 to 0.77). Few items promoting alcohol unrelated to alcohol outlets were found on public spaces. CONCLUSIONS: The OHCITIES instrument is a reliable instrument to characterise alcohol urban environment. This instrument might be used to understand how alcohol environment associates with alcohol behaviours and its related health outcomes, and can help in the design and evaluation of policies to reduce the harm caused by alcohol.

Intersection of neighborhood dynamics and socioeconomic status in small-area walkability: the Heart Healthy Hoods project.

BACKGROUND: Previous studies found a complex relationship between area-level socioeconomic status (SES) and walkability. These studies did not include neighborhood dynamics. Our aim was to study the association between area-level SES and walkability in the city of Madrid (Spain) evaluating the potential effect modification of neighborhood dynamics. METHODS: All census sections of the city of Madrid (n = 2415) were included. Area-level SES was measured using a composite index of 7 indicators in 4 domains (education, wealth, occupation and living conditions). Two neighborhood dynamics factors were computed: gentrification, proxied by change in education levels in the previous 10 years, and neighborhood age, proxied by median year of construction of housing units in the area. Walkability was measured using a composite index of 4 indicators (Residential Density, Population Density, Retail Destinations and Street Connectivity). We modeled the association using linear mixed models with random intercepts. RESULTS: Area-level SES and walkability were inversely and significantly associated. Areas with lower SES showed the highest walkability. This pattern did not hold for areas with an increase in education level, where the association was flat (no decrease in walkability with higher SES). Moreover, the association was attenuated in newly built areas: the association was stronger in areas built before 1975, weaker in areas built between 1975 and 1990 and flat in areas built from 1990 on. CONCLUSION: Areas with higher neighborhood socioeconomic status had lower walkability in Madrid. This disadvantage in walkability was not present in recently built or gentrified areas.

Characterizing physical activity and food urban environments: a GIS-based multicomponent proposal.

BACKGROUND: Healthier urban environments influence the distribution of cardiovascular risk factors. Our aim was to design and implement a multicomponent method based on Geographic Information Systems to characterize and evaluate environmental correlates of obesity: the food and the physical activity urban environments. METHODS: Study location comprised a socio-demographically average urban area of 12 contiguous census sections (≈16,000 residents), in Madrid, Spain. We conducted on-field audits on all food stores and street segments. We designed a synthetic index integrating continuous measures of both environments, by kernel density analyses. Index ranges from 0 to 100 (least-most healthy). RESULTS: We found a heterogeneous distribution with 75 and 50 % of the area scoring less than 36.8 and 25.5, respectively. Census sections of study area were categorized by Jenks intervals as high, medium-high, medium-low and low. 41.0 % of residents lived in an area with a low score, 23.6 % medium-low and 31.1 % medium-high and 4.2 % in a high. CONCLUSION: The proposed synthetic index may be a relevant tool to inform urban health interventions, providing a feasible way to integrate different measures of barriers and facilitators of healthy urban environments in terms of food and physical activity.

Understanding differences in the local food environment across countries: A case study in Madrid (Spain) and Baltimore (USA).

Places where we buy food influence dietary patterns, making local food environments a good example of a mass influence on population diets. Cross-cultural studies, using reliable methods, may help understanding the relationship between food environments and diet-related health outcomes. We aimed to understand cross-national differences in the local food environment between Madrid and Baltimore by comparing an average neighborhood in each city in terms of food store types, healthy food availability, and residents' pedestrian access. During 2012-2013, we assessed one neighborhood (~15,000 residents) in each city selecting median areas in terms of socio-demographic characteristics (segregation, education, aging, and population density). We collected on-field data on (a) number and types of all food stores, (b) overall healthy food availability and (c) specific availability of fruits & vegetables. Throughout a street network analysis (200m, 400m and 800m) of food stores with high healthy food availability, we estimated residents' pedestrian accessibility. We found 40 stores in Madrid and 14 in Baltimore. Small food stores carrying fresh foods in Madrid contrasted with the high presence of corner and chain convenience stores in Baltimore. In Madrid, 77% of the residents lived within less than 200m from a food store with high healthy food availability. In contrast, 95% of Baltimore's residents lived further than 400m from these stores. Our results may help promoting interventions from local city agencies to allocate resources to existing small-sized food stores, and to improve walkable urban environments. These actions may influence food choices, especially for those residents lacking access to private vehicles.

Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits.

Audit tools are useful for exploring the urban environment and its association with physical activity. Virtual auditing options are becoming increasingly available potentially reducing the resources needed to conduct these assessments. Only a few studies have explored the use of virtual audit tools. Our objective is to test if the Madrid Systematic Pedestrian and Cycling Environment Scan (M-SPACES) discriminates between areas with different urban forms and to validate virtual street auditing using M-SPACES. Three areas (N = 500 street segments) were selected for variation in population density. M-SPACES was used to audit street segments physically and virtually (Google Street View) by two researchers in 2013-2014. For both physical and virtual audits, all analyzed features score significantly different by area (p < 0.05). Most of the features showed substantial (ICC = 0.6-0.8) or almost perfect (ICC ≥ 0.8) agreement between virtual and physical audits, especially neighborhood permeability walking infrastructure, traffic safety, streetscape aesthetics, and destinations. Intra-rater agreement was generally acceptable (ICC > 0.6). Inter-rater agreement was generally poor (ICC < 0.4). Virtual auditing provides a valid and feasible way of measuring residential urban environments. Comprehensive auditor training may be needed to guarantee good inter-rater agreement.