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      Protocolo EMECAM: análisis del efecto a corto plazo de la contaminación atmosférica sobre la mortalidad Translated title: Short-term effect of air pollution on mortality: the EMECAM project protocol

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          Abstract

          El objetivo del presente trabajo es mostrar el protocolo de análisis elaborado dentro del proyecto EMECAM, ilustrando su aplicación en el efecto de la contaminación en la mortalidad en Valencia ciudad. Se consideran como variables respuesta la mortalidad diaria para todas las causas, excepto las externas Las variables explicativas son las series diarias de diversos contaminantes (humos negros, SO2, NO2, CO, O3). Como posibles variables de confusión se consideran factores metereológicos, factores estructurales y casos semanales de gripe. Se construye un modelo de regresión Poisson para cada una de las cuatro series de mortalidad en dos fases. En la primera se construye un modelo basal con las posibles variables de confusión. En una segunda se incluyen las variables de contaminación o sus retardos, controlando la autocorrelación residual con la inclusión de retardos de mortalidad. El proceso de construcción del modelo basal sigue el siguiente proceso: 1º) Incluir los términos sinusoidales significativos hasta orden 6. 2º) Incluir los términos significativos de temperatura o temperatura al cuadrado con sus retardos hasta orden 15. 3º) Repetir el proceso con la humedad relativa.4º) Introducir los términos significativos de años del calendario, tendencia diaria y tendencia al cuadrado. 5º) Los días de la semana como variables "dummy" se incluyen siempre en el modelo. 6º) Incluir los días festivos, y de los retardos hasta 15 días de gripe aquellos que fueron significativos. Tras la reevaluación del modelo, se prueba cada uno de los contaminantes y sus retardos hasta orden 5. Se analiza el efecto por semestres incluyendo términos de interacción.

          Translated abstract

          The aim of this study is to show the protocol of analysis which was set out as part of the EMECAM Project, illustrating the application thereof to the effect of pollution has on the mortality in the city of Valencia. The response variables considered will be the daily mortality resulting from all causes, except external ones. The explicative variables are the daily series of different pollutants (black smoke, SO2, NO2, CO, O3). As possible confusion variables, weather factors, structural factors and weekly cases of flu are taken into account. A Poisson regression model is built up for each one of the four deaths series in two stages. In the first stage, a baseline model is fitted using the possible confusion-causing variables. In the second stage, the pollution variables or the time lags thereof are included, controlling the residual autocorrelation by including mortality time lags. The process of fitting the baseline model is as follows: 1) Include the significant sinusoidal terms up to the sixth order. 2) Include the significant temperature or temperature squared terms with the time lags thereof up to the 7th power. 3) Repeat this process with the relative humidity. 4) Add in the significant terms of calendar years, daily tendency and tendency squared. 5) The days of the week as dummy variables are always included in the model. 6) Include the holidays and the significant time lags of up to two weeks of flu. Following the reassessment of the model, each one of the pollutants and the time lags thereof up to the fifth order are proven out. The impact is analyzed by six-month periods, including interaction terms.

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          Methodological issues in studies of air pollution and daily counts of deaths or hospital admissions.

          To review the issues and methodologies in epidemiologic time series studies of daily counts of mortality and hospital admissions and illustrate some of the methodologies. This is a review paper with an example drawn from hospital admissions of the elderly in Cleveland, Ohio, USA. The central issue is control for seasonality. Both over and under control are possible, and the use of diagnostics, including plots, is necessary. Weather dependence is probably non-linear, and adequate methods are necessary to adjust for this. In Cleveland, the use of categorical variables for weather and sinusoidal terms for filtering season are illustrated. After control for season, weather, and day of the week effects, hospital admission of persons aged 65 and older in Cleveland for respiratory illness was associated with ozone (RR = 1.09, 95% CI 1.02, 1.16) and particulates (PM10 (RR = 1.12, 95% CI 1.01, 1.24), and marginally associated with sulphur dioxide (SO2) (RR = 1.03, 95% CI = 0.99, 1.06). All of the relative risks are for a 100 micrograms/m3 increase in the pollutant. Several adequate methods exist to control for weather and seasonality while examining the associations between air pollution and daily counts of mortality and morbidity. In each case, care and judgement are required.
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            Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol.

            Results from several studies over the past five years have shown that the current levels of pollutants in Europe and North America have adverse short term effects on health. The APHEA project aims to quantifying these in Europe, using standardised methodology. The project protocol and analytical methodology are presented here. Daily time series data were gathered for several air pollutants (sulphur dioxide; particulate matter, measured as total particles or as the particle fraction with an aerodynamic diameter smaller than a certain cut off, or as black smoke; nitrogen dioxide; and ozone) and health outcomes (the total and cause specific number of deaths and emergency hospital admissions). The data included fulfilled the quality criteria set by the APHEA protocol. Fifteen European cities from 10 different countries with a total population over 25 million. The APHEA collaborative group decided on a specific methodological procedure to control for confounding effects and evaluate the hypothesis. At the same time there was sufficient flexibility to allow local characteristics to be taken into account. The procedure included modelling of all potential confounding factors (that is, seasonal and long term patterns, meteorological factors, day of the week, holidays, and other unusual events), choosing the "best" air pollution models, and applying diagnostic tools to check the adequacy of the models. The final analysis used autoregressive Poisson models allowing for overdispersion. Effects were reported as relative risks contrasting defined increases in the corresponding pollutant levels. Each participating group applied the analyses to their own data. This methodology enabled results from many different European settings to be considered collectively. It represented the best available compromise between feasibility, comparability, and local adaptibility when using aggregated time series data not originally collected for the purpose of epidemiological studies.
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              Métodos de series temporales en los estudios epidemiológicos sobre contaminación atmosférica

              Se revisan los métodos de series temporales en los estudios epidemiológicos sobre contaminación atmosférica, ilustrándolo mediante una regresión de Poisson autoregresiva, la cual ha sido utilizada en los proyectos APHEA y EMECAM. Se relacionan las variaciones en el número diario de muertos mayores de 70 años (todas las causas, CIE-9:001-799) en Barcelona, 1991-1995, con las variaciones en los niveles diarios promedio de contaminación por humos negros. Se utiliza una regresión de Poisson por cuanto la variable aleatoria dependiente sigue presumiblemente tal distribución de probabilidad. Como confusores se consideran variables meteorológicas (promedios diarios de temperatura y de humedad), comportamientos tendenciales, estacionales y efectos de calendario presentes en la mortalidad (todos ellos aproximados de forma determinista) así como cualquier otra variable que tenga un comportamiento que pueda relacionarse con la variable dependiente (ocurrencia de epidemias de gripe por ejemplo). La relación entre la mortalidad y las variables confusoras se modeliza de forma no lineal y se tienen en cuenta además los previsibles periodos de latencia (utilizando retardos de variables explicativa por ejemplo). Sin embargo, y debido a que el control no es perfecto, se opta por estimar un modelo de Poisson autoregresivo (introduciendo como variables explicativas diversos retardos de la mortalidad) corrigiendo la autocorrelación residual. La principal ventaja del método de análisis descrito es la de permitir un control de variables confusoras desde un punto determinista, con un software al alcance de todos los grupos que participan en el proyecto. Además, permite que el método se pueda aplicar de una formar protocolizada y estandarizada que facilite la comparación de resultados y permita la realización de un meta-análisis.
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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
                March 1999
                : 73
                : 2
                : 177-185
                Affiliations
                [10] orgnameAyuntamiento de Vitoria-Gasteiz orgdiv1 Departamento de Salud y Consumo
                [07] orgnameEscuela Andaluza de Salud Publica
                [03] orgnameGobierno Vasco orgdiv1 Departamento de Sanidad
                [09] orgnameComunidad de Murcia orgdiv1 Centro Area Cartagena. Consejería de Sanidad
                [02] orgnameUniversitat de Girona orgdiv1 Departament d'Economia
                [12] Aragón orgnameDirección General de Salud Pública
                [08] Castelló orgnameConselleria de Sanitat orgdiv1 Centro Salud Pública Area 2
                [05] orgnameComunidad de Madrid orgdiv1 Dirección General de Prevención y Promoción de Salud
                [04] orgnameUniversidad de Santiago orgdiv1 Facultad de Medicina
                [06] orgnameAyuntamiento de Pamplona orgdiv1 Area de Sanidad y Medioambiente
                [11] Asturias orgnameDirección Regional de Salud Pública
                [01] orgnameGeneralitat Valenciana orgdiv1 Institut Valencià d'Estudis en Salut Pública (IVESP)
                Article
                S1135-57271999000200007 S1135-5727(99)07300200007
                10.1590/s1135-57271999000200007
                6a279bfd-54fc-4730-b53e-19e9fd1766ef

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

                History
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 6, Pages: 9
                Product

                SciELO Public Health


                Contaminación,Mortalidad,Series temporales,Pollution,Poisson regression,Times series,Regresión Poisson,Mortality

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