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      Patrón espacial de la legionelosis en España, 2003-2007 Translated title: Spatial pattern of legionellosis in Spain, 2003-2007

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          Abstract

          Objetivos: Analizar el patrón espacial de la legionelosis en España para hombres y mujeres durante el periodo 2003-2007, e identificar agrupamientos espaciales del riesgo. Métodos: Se identificó el patrón espacial de la distribución de las tasas de legionelosis a partir del cálculo de las tasas por municipio por el método directo. Se realizó el suavizado de estas tasas por el método Empirical Bayes para estudiar el patrón espacial de la enfermedad, para ambos sexos. Se utilizó el índice de correlación espacial de Moran para analizar la autocorrelación global de las tasas. Localmente se utilizó el índice local de Moran (LISA) para analizar los agrupamientos (clusters) de municipios con mayor riesgo. Resultados: Una vez suavizado el riesgo, las mayores tasas (más de 50 por 100.000 habitantes) se agrupan en las zonas costeras del Mediterráneo oriental y en el norte de la Península, así como en los territorios insulares del Mediterráneo. El índice de Moran de las tasas suavizadas es 0,15 para los hombres y 0,23 para las mujeres. Las agrupaciones espaciales de las tasas más altas estadísticamente significativas calculadas mediante el LISA se distribuyen en el eje norte-levante para ambos sexos. Conclusiones: Estos métodos de análisis espacial permiten identificar los patrones de distribución de la enfermedad. Los métodos empleados presentan resultados similares. Estas técnicas son una herramienta complementaria para la vigilancia epidemiológica de las enfermedades infecciosas.

          Translated abstract

          Objectives: To analyze the spatial pattern of legionellosis in Spain for men and women during the period 2003-2007 and to identify spatial clustering of risk. Methods: We identified the spatial pattern of the distribution of legionellosis rates based on calculation of rates by municipality through the direct method. Smoothing of these rates was performed by the Empirical Bayes method for studying the spatial pattern of disease for both sexes. We used Moran´s index to analyze spatial autocorrelation rates globally. To calculate local rates, the Local Moran's Index [known as local indicators of spatial association (LISA)], was used to analyze the clusters of municipalities with the highest risk. Results: After smoothing the risk, the highest rates (over 50 per 100,000 inhabitants) were grouped in the eastern Mediterranean coastal areas and the north of the mainland, as well as in the Mediterranean islands. Moran's index smoothed rates were 0.15 for men and 0.23 for women. The spatial clusters of statistically significant higher rates calculated by the LISA index were distributed in the north and east for both sexes. Conclusions: These methods of spatial analysis allow patterns of disease distribution to be identified. All the methods used yielded similar results. These techniques are a complementary tool for epidemiological surveillance of infectious diseases.

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          Most cited references28

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          Legionnaires' disease: description of an epidemic of pneumonia.

          An explosive, common-source outbreak of pneumonia caused by a previously unrecognized bacterium affected primarily persons attending an American Legion convention in Philadelphia in July, 1976. Twenty-nine of 182 cases were fatal. Spread of the bacterium appeared to be air borne. The source of the bacterium was not found, but epidemiologic analysis suggested that exposure may have occurred in the lobby of the headquarters hotel or in the area immediately surrounding the hotel. Person-to-person spread seemed not to have occurred. Many hotel employees appeared to be immune, suggesting that the agent may have been present in the vicinity, perhaps intermittently, for two or more years.
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            Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies

            There is currently much interest in conducting spatial analyses of health outcomes at the small-area scale. This requires sophisticated statistical techniques, usually involving Bayesian models, to smooth the underlying risk estimates because the data are typically sparse. However, questions have been raised about the performance of these models for recovering the “true” risk surface, about the influence of the prior structure specified, and about the amount of smoothing of the risks that is actually performed. We describe a comprehensive simulation study designed to address these questions. Our results show that Bayesian disease-mapping models are essentially conservative, with high specificity even in situations with very sparse data but low sensitivity if the raised-risk areas have only a moderate ( 50 per area). Semiparametric spatial mixture models typically produce less smoothing than their conditional autoregressive counterpart when there is sufficient information in the data (moderate-size expected count and/or high true excess risk). Sensitivity may be improved by exploiting the whole posterior distribution to try to detect true raised-risk areas rather than just reporting and mapping the mean posterior relative risk. For the widely used conditional autoregressive model, we show that a decision rule based on computing the probability that the relative risk is above 1 with a cutoff between 70 and 80% gives a specific rule with reasonable sensitivity for a range of scenarios having moderate expected counts (~ 20) and excess risks (~1.5- to 2-fold). Larger (3-fold) excess risks are detected almost certainly using this rule, even when based on small expected counts, although the mean of the posterior distribution is typically smoothed to about half the true value.
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              It's not the heat, it's the humidity: wet weather increases legionellosis risk in the greater Philadelphia metropolitan area.

              Legionella species are abundant in the environment and are increasingly recognized as a cause of severe pneumonia. Increases in cases of community-acquired legionellosis in the greater Philadelphia metropolitan area (GPMA) led to concern that changing environmental factors could influence occurrence of disease. We evaluated the association between weather patterns and occurrence of legionellosis in the GPMA, using both traditional Poisson regression analysis and a case-crossover study approach. The latter approach controls for seasonal factors that could confound the relationship between weather and occurrence of disease and permits the identification of acute weather patterns associated with disease. A total of 240 cases of legionellosis were reported between 1995 and 2003. Cases occurred with striking summertime seasonality. Occurrence of cases was associated with monthly average temperature (incidence rate ratio [IRR] per degree Celsius, 1.07 [95% confidence interval [CI], 1.05-1.09]) and relative humidity (IRR per 1% increase in relative humidity, 1.09 [95% CI, 1.06-1.12]) by Poisson regression analysis. However, case-crossover analysis identified an acute association with precipitation (odds ratio [OR], 2.48 [95% CI, 1.30-3.12]) and increased humidity (OR per 1% increase in relative humidity, 1.08 [95% CI, 1.05-1.11]) 6-10 days before occurrence of cases. A significant dose-response relationship for occurrence of cases was seen with both precipitation and increased humidity. Although, in the GPMA, legionellosis occurred predominantly during summertime, the acute occurrence of disease is best predicted by wet, humid weather. This finding is consistent with the current understanding of the ecological profile of this pathogen and supports the contention that sporadic legionellosis occurs through contamination of water sources.
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                Author and article information

                Journal
                gs
                Gaceta Sanitaria
                Gac Sanit
                Ediciones Doyma, S.L. (Barcelona, Barcelona, Spain )
                0213-9111
                August 2011
                : 25
                : 4
                : 290-295
                Affiliations
                [01] orgnameCentro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) España
                [03] Paris orgnameInstitut de Veille Sanitaire orgdiv1European Programme for Intervention Epidemiology Training (EPIET) France
                [05] Porto orgnameInstituto de Engenharia Biomédica (INEB) Portugal
                [02] Madrid orgnameInstituto de Salud Carlos III orgdiv1Centro Nacional de Epidemiología España
                [04] Porto orgnameUniversidades do Porto orgdiv1Faculdade de Medicina orgdiv2Serviço de Higiene e Epidemiologia Portugal
                [06] orgnameUniversidad Rey Juan Carlos orgdiv1Facultad de Ciencias de la Salud orgdiv2Departamento de Ciencias de la Salud I España
                Article
                S0213-91112011000400005 S0213-9111(11)02500400005
                a523260e-1598-4619-8fae-68fa1a0d9e44

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 17 February 2011
                : 23 November 2010
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 28, Pages: 6
                Product

                SciELO Public Health

                Categories
                Originales

                Legionelosis,Análisis espacial,Empirical Bayes,Índice de Moran,Índice local LISA,Legionellosis,Spatial analysis,Moran's index,LISA index

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