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        "resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Se comparan dos metodolog&#237;as para el an&#225;lisis de la relaci&#243;n entre contaminaci&#243;n atmosf&#233;rica y salud&#46; Una de las metodolog&#237;as &#40;ME&#41; est&#225; basada en el modelo lineal generalizado&#44; mientras que la otra &#40;MN&#41; incorpora el modelo aditivo generalizado &#40;GAM&#41;&#46; Aparte del modelo estad&#237;stico utilizado&#44; las dos metodolog&#237;as presentan discrepancias adicionales en cuanto al tipo y n&#250;mero de variables empleadas en el control de la confusi&#243;n&#46;</p> <span class="elsevierStyleSectionTitle">M&#233;todo</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Se realiz&#243; un an&#225;lisis para cada retardo de humos negros &#40;de 0 a 5&#41; y diversos indicadores de salud con datos relativos a la ciudad de Valencia y siguiendo ambas metodolog&#237;as&#46; Los resultados obtenidos se compararon mediante el examen de la secuencia y retardo de mayor efecto&#44; el c&#225;lculo de la raz&#243;n de las amplitudes de los intervalos de confianza y el c&#225;lculo de la diferencia porcentual en las estimaciones&#46;</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">El cambio en los resultados seg&#250;n la metodolog&#237;a fue peque&#241;o&#46; La forma de la secuencia y&#44; en general&#44; el retardo de mayor efecto permanecieron invariables&#46; Los coeficientes se mantuvieron estables &#40;la diferencia porcentual de MN metodol&#243;gicas con respecto a ME no super&#243; el 10&#37;&#41; y los intervalos de confianza resultaron m&#225;s precisos con MN&#44; aunque la disminuci&#243;n de la amplitud de los intervalos de confianza no fue dr&#225;stica &#40;la raz&#243;n de amplitudes se situ&#243; en torno al 90&#37;&#41;&#46;</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">La escasa variaci&#243;n de los resultados ofrece garant&#237;as en cuanto a la aplicabilidad de ambas metodolog&#237;as&#44; MN y ME&#44; si bien la reducci&#243;n sistem&#225;tica del error est&#225;ndar con MN apoya el uso de este &#250;ltimo m&#233;todo como el procedimiento m&#225;s eficiente en el control de la confusi&#243;n&#46;</p>"
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        "resumen" => "<span class="elsevierStyleSectionTitle">Objective</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">We compare two methods to analyse the relationship between air pollution and health&#46; One of them &#40;ME&#41; is based on a generalized linear model&#44; while the other one &#40;MN&#41; incorporates a generalized additive model &#40;GAM&#41;&#46; Besides the statistical model used&#44; both methods schow additional discrepancies for the type and number of variables used in the control of confounding&#46;</p> <span class="elsevierStyleSectionTitle">Method</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">An analysis was carried out for each lag of black smoke &#40;0 to 5&#41; and several health indicators from the city of Valencia &#40;Spain&#41; following both methods&#46; Results were compared examining the sequence and the lag of the higher coefficient&#46; The ratio between confidence intervals width and the percent difference in the estimates were also obtained&#46;</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The discrepancies in results according to the methodology were small&#46; The sequence shaped and the lag of higher coefficient generally remained invariant&#46; Coefficients were similar &#40;percent difference of MN over ME was not greater than 10&#37;&#41;&#46; Confidence intervals were more precise for MN&#44; although the reduction was not very strong &#40;the width ratio was around 90&#37;&#41;&#46;</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">The short variation of the results guarantees the applicability of both MN and ME methods&#44; although the systematic reduction of the standard error of MN supports its use as a more efficient procedure to control for confounding&#46;</p>"
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Vol. 17. Núm. 4.
Páginas 283-288 (julio - agosto 2003)
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Vol. 17. Núm. 4.
Páginas 283-288 (julio - agosto 2003)
Open Access
Comparación de dos métodos en el análisis del efecto a corto plazo de la contaminación atmosférica en la salud
Comparison of two methods in the analysis of the short-term effect of air pollution on health
Visitas
5159
C. Iñígueza, S. Pérez-Hoyosa, F. Ballestera,
Autor para correspondencia
ballester_fer@gva.es

Correspondencia: Escuela Valenciana de Estudios para la Salud. Joan de Garay, 21. 46017 Valencia. España.
, M. Sáezb
a Escuela Valenciana de Estudios para la Salud (EVES). Valencia
b Grup de Recerca en Estadística, Economia Aplicada i Salut (GRECS). Departament d'Economia. Universitat de Girona. Girona. España
Este artículo ha recibido

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Información del artículo
Resumen
Objetivo

Se comparan dos metodologías para el análisis de la relación entre contaminación atmosférica y salud. Una de las metodologías (ME) está basada en el modelo lineal generalizado, mientras que la otra (MN) incorpora el modelo aditivo generalizado (GAM). Aparte del modelo estadístico utilizado, las dos metodologías presentan discrepancias adicionales en cuanto al tipo y número de variables empleadas en el control de la confusión.

Método

Se realizó un análisis para cada retardo de humos negros (de 0 a 5) y diversos indicadores de salud con datos relativos a la ciudad de Valencia y siguiendo ambas metodologías. Los resultados obtenidos se compararon mediante el examen de la secuencia y retardo de mayor efecto, el cálculo de la razón de las amplitudes de los intervalos de confianza y el cálculo de la diferencia porcentual en las estimaciones.

Resultados

El cambio en los resultados según la metodología fue pequeño. La forma de la secuencia y, en general, el retardo de mayor efecto permanecieron invariables. Los coeficientes se mantuvieron estables (la diferencia porcentual de MN metodológicas con respecto a ME no superó el 10%) y los intervalos de confianza resultaron más precisos con MN, aunque la disminución de la amplitud de los intervalos de confianza no fue drástica (la razón de amplitudes se situó en torno al 90%).

Conclusiones

La escasa variación de los resultados ofrece garantías en cuanto a la aplicabilidad de ambas metodologías, MN y ME, si bien la reducción sistemática del error estándar con MN apoya el uso de este último método como el procedimiento más eficiente en el control de la confusión.

Palabras clave:
Series temporales
Modelo aditivo generalizado
Modelo lineal generalizado
Contaminación atmosférica
Abstract
Objective

We compare two methods to analyse the relationship between air pollution and health. One of them (ME) is based on a generalized linear model, while the other one (MN) incorporates a generalized additive model (GAM). Besides the statistical model used, both methods schow additional discrepancies for the type and number of variables used in the control of confounding.

Method

An analysis was carried out for each lag of black smoke (0 to 5) and several health indicators from the city of Valencia (Spain) following both methods. Results were compared examining the sequence and the lag of the higher coefficient. The ratio between confidence intervals width and the percent difference in the estimates were also obtained.

Results

The discrepancies in results according to the methodology were small. The sequence shaped and the lag of higher coefficient generally remained invariant. Coefficients were similar (percent difference of MN over ME was not greater than 10%). Confidence intervals were more precise for MN, although the reduction was not very strong (the width ratio was around 90%).

Conclusions

The short variation of the results guarantees the applicability of both MN and ME methods, although the systematic reduction of the standard error of MN supports its use as a more efficient procedure to control for confounding.

Key words:
Time series
Generalized linear model
Generalized additive model
Ar pollution
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Copyright © 2003. Sociedad Española de Salud Pública y Administración Sanitaria
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