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Objective: Obtaining reliable health estimates at the small area level (such as neighbourhoods) using survey data usually poses the problem of small sample sizes. To overcome this limitation, we explored smoothing techniques in order to estimate poor mental health prevalence at the neighbourhood level and analyse its profile by income in Barcelona city (Spain). Method: A Bayesian smoothing model with a logit-normal transformation was applied to four repeated cross-sectional waves of the Barcelona health survey for 2001, 2006, 2011 and 2016. Mental health status was identified from the 12-item General Health Questionnaire. Income inequalities were analysed with neighbourhood income in quantiles for each year and trends in the pooled analysis. Results: The prevalence of poor mental health ranged from 14.6% in 2001 to 18.9% in 2016. The yearly difference between neighbourhoods was 12.4% in 2001, 16.7% in 2006, 14.2% in 2011, and 20.0% in 2016. The odds ratio and 95% credible interval (95%CI) of experiencing poor mental health was 1.40 times higher (95%CI: 1.02-1.91) in less advantaged neighbourhoods than in more advantaged neighbourhoods in 2001, 1.61 times higher (95%CI: 1.01-2.59) in 2006 and 2.31 times higher (95%CI: 1.57-3.40) in 2016. Conclusions: This study shows that the Bayesian smoothed techniques allows detection of inequalities in health in neighbourhoods and monitoring of interventions against them. In Barcelona, mental health problems are more prevalent in low-income neighbourhoods and raised in 2016. (AU)

Objetivo: Obtener estimadores de salud en áreas pequeñas (como los barrios) utilizando datos de encuestas supone hacer frente al problema de insuficiente tamaño muestral. Para superar esta limitación exploramos técnicas de alisado con el fin de estimar la prevalencia de mala salud mental a nivel de barrio y analizar su patrón por renta en la ciudad de Barcelona (España). Método: Se aplicó un modelo de alisado bayesiano con transformación logística-normal a cuatro muestras transversales repetidas de la Encuesta de Salud de Barcelona para los años 2001, 2006, 2011 y 2016. La salud mental fue identificada con el Cuestionario General de Salud de 12-items. Las desigualdades de ingreso se analizaron por cuantiles de la renta por barrio para cada año y las tendencias en el análisis conjunto. Resultados: La prevalencia de mala salud mental oscila entre el 14,6% en 2001 y el 18,9% en 2016. La diferencia entre barrios fue del 12,4% en 2001, del 16,7% en 2006, del 14,2% en 2011 y del 20,0% en 2016. La odds ratio y el intervalo creíble al 95% (IC95%) de experimentar mala salud mental fue 1,4 veces superior (IC95%: 1,02-1,91) en los barrios menos aventajados respecto de los más aventajados en 2001, de 1,61 (IC95%: 1,01-2,59) en 2006 y de 2,31 (IC95%: 1,57-3,40) en 2016. Conclusiones: Este estudio muestra que las técnicas de alisado bayesiano permiten la detección de desigualdades en salud a nivel de barrios para su monitorización e intervención con el fin de reducirlas. En Barcelona, los problemas de salud mental son más prevalentes en los barrios de menor renta y se incrementaron en 2016. (AU)

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OBJECTIVE: Obtaining reliable health estimates at the small area level (such as neighbourhoods) using survey data usually poses the problem of small sample sizes. To overcome this limitation, we explored smoothing techniques in order to estimate poor mental health prevalence at the neighbourhood level and analyse its profile by income in Barcelona city (Spain). METHOD: A Bayesian smoothing model with a logit-normal transformation was applied to four repeated cross-sectional waves of the Barcelona health survey for 2001, 2006, 2011 and 2016. Mental health status was identified from the 12-item General Health Questionnaire. Income inequalities were analysed with neighbourhood income in quantiles for each year and trends in the pooled analysis. RESULTS: The prevalence of poor mental health ranged from 14.6% in 2001 to 18.9% in 2016. The yearly difference between neighbourhoods was 12.4% in 2001, 16.7% in 2006, 14.2% in 2011, and 20.0% in 2016. The odds ratio and 95% credible interval (95%CI) of experiencing poor mental health was 1.40 times higher (95%CI: 1.02-1.91) in less advantaged neighbourhoods than in more advantaged neighbourhoods in 2001, 1.61 times higher (95%CI: 1.01-2.59) in 2006 and 2.31 times higher (95%CI: 1.57-3.40) in 2016. CONCLUSIONS: This study shows that the Bayesian smoothed techniques allows detection of inequalities in health in neighbourhoods and monitoring of interventions against them. In Barcelona, mental health problems are more prevalent in low-income neighbourhoods and raised in 2016.

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Health surveys are a key tool for decision-making in health policies and health services planning. The use of small-area statistical methods that use information at different geographic levels may be useful for estimating health indicators at a lower geographical level than originally considered in the survey design.

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OBJECTIVE: To analyse variations in the diagnostic confirmation process between screening units, variations in the outcome of each episode and the relationship between the use of the different diagnostic confirmation tests and the lesion detection rate. METHOD: Observational study of variability of the standardised use of diagnostic and lesion detection tests in 34 breast cancer mass screening units participating in early-detection programmes in three Spanish regions from 2002-2011. RESULTS: The diagnostic test variation ratio in percentiles 25-75 ranged from 1.68 (further appointments) to 3.39 (fine-needle aspiration). The variation ratio in detection rates of benign lesions, ductal carcinoma in situ and invasive cancer were 2.79, 1.99 and 1.36, respectively. A positive relationship between rates of testing and detection rates was found with fine-needle aspiration-benign lesions (R(2): 0.53), fine-needle aspiration-invasive carcinoma (R(2): 0 28), core biopsy-benign lesions (R(2): 0.64), core biopsy-ductal carcinoma in situ (R(2): 0.61) and core biopsy-invasive carcinoma (R(2): 0.48). CONCLUSIONS: Variation in the use of invasive tests between the breast cancer screening units participating in early-detection programmes was found to be significantly higher than variations in lesion detection. Units which conducted more fine-needle aspiration tests had higher benign lesion detection rates, while units that conducted more core biopsies detected more benign lesions and cancer.

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INTRODUCTION: Occasionally, primary care pediatricians notice the presence of small clusters of pediatric cancer (PC), but are often frustrated by the findings after statistical analysis. The study of small areas in spatial epidemiology has led to advances in identifying clusters and the environmental risk factors involved. The purpose of this study was to describe the PC incidence and the spatial distribution at the minimum level of disaggregation possible in Murcia, presenting the first urban municipality map of PC in Spain. MATERIALS AND METHODS: A population-based descriptive study was conducted on the PC cases diagnosed in children younger than 15 years, between 1998 and 2013 in the municipality of Murcia. Cases were classified by sex, age group, and tumor type. Coordinates of home addresses at the time of diagnosis were assigned to each case, and spatial and spatio-temporal analyses were carried out at the level of census tracts, using FleXScan and SatScan. RESULTS: A total of 155 cases of PC were diagnosed during this period. The overall incidence of PC (138/10(6) of children under the age of 15) and the incidence for individual tumor types were within the expected ranges for Europe. A spatio-temporal cluster of Hodgkin lymphoma was identified. CONCLUSIONS: Small area analysis of PC cases may be a useful tool for the identification of PC clusters, which would allow for the generation of hypotheses regarding disease etiology, as well as developing urban models for environmental surveillance of PC.

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OBJECTIVE: To analyze medical practice variation in breast cancer surgery (either inpatient-based or day-case surgery), by comparing conservative surgery (CS) plus radiotherapy vs. non-conservative surgery (NCS). We also analyzed the opportunity costs associated with CS and NCS. METHODS: We performed an observational study of age- and sex-standardized rates of CS and NCS, performed in 199 Spanish healthcare areas in 2008-2009. Costs were calculated by using two techniques: indirectly, by using All-Patients Diagnosis Related Groups (AP-DRG) based on hospital admissions, and directly by using full costing from the Spanish Network of Hospital Costs (SNHC) data. RESULTS: Standardized surgery rates for CS and NCS were 6.84 and 4.35 per 10,000 women, with variation across areas ranging from 2.95 to 3.11 per 10,000 inhabitants. In 2009, 9% of CS was performed as day-case surgery, although a third of the health care areas did not perform this type of surgery. Taking the SNHC as a reference, the cost of CS was estimated at 7,078 € and that of NCS was 6,161 €. Using AP-DRG, costs amounted to 9,036 € and 8,526 €, respectively. However, CS had lower opportunity costs than NCS when day-case surgery was performed frequently-more than 46% of cases (following SNHC estimates) or 23% of cases (following AP-DRG estimates). CONCLUSIONS: Day-case CS for breast cancer was found to be the best option in terms of opportunity-costs beyond a specific threshold, when both CS and NCS are elective.

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