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        "titulo" => "Resumen"
        "resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Identificar&#44; mediante an&#225;lisis de regresi&#243;n m&#250;ltiple&#44; las variables de influencia en el tiempo de acceso a atenci&#243;n especializada &#40;listas de espera&#41;&#44; y analizar las zonas de salud con tiempo de acceso elevado&#44; en funci&#243;n de las mencionadas variables&#46;</p> <span class="elsevierStyleSectionTitle">Dise&#241;o</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Estudio descriptivo transversal&#44; retrospectivo&#44; del tiempo de acceso a atenci&#243;n especializada&#44; durante los a&#241;os 1997 y 1998&#46;</p> <span class="elsevierStyleSectionTitle">Emplazamiento</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">&#193;rea 20 de la Conselleria de Sanitat de la Comunidad Valenciana&#59; 12 zonas de salud&#59; 204&#46;424 habitantes&#46;</p> <span class="elsevierStyleSectionTitle">Intervenciones</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Se recogen variables de demanda &#40;tipo de municipio&#44; envejecimiento e &#237;ndices de dependencia poblacional&#44; porcentaje de pensionistas&#41;&#44; de oferta &#40;edad&#44; sexo&#44; formaci&#243;n y situaci&#243;n laboral del m&#233;dico&#44; tama&#241;o del cupo&#41; y de consumo &#40;porcentaje de derivaci&#243;n a especializada por mil habitantes&#44; tiempo medio de acceso a especializada &#40;en d&#237;as naturales&#41; por zona y a&#241;o&#44; frecuentaci&#243;n y presi&#243;n asistencial&#46; Se construye un modelo de regresi&#243;n m&#250;ltiple por eliminaci&#243;n &#40;hacia atr&#225;s&#41;&#44; tomando como variable dependiente el tiempo medio de espera &#40;TE&#41; y como independiente el resto&#46; La ecuaci&#243;n resultante permiti&#243; calcular el TE esperado por zona de salud y la desviaci&#243;n de su TE real sobre el esperado&#46; Se consider&#243; zona con TE elevado cuando su desviaci&#243;n superaba la media m&#225;s una desviaci&#243;n est&#225;ndar de dicha distribuci&#243;n&#46;</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">El tiempo de espera medio para acceder a especializada fue de 37 d&#237;as en 1997 y 34 d&#237;as en 1998&#46; Existe una correlaci&#243;n significativa &#40;p &#60; 0&#44;005&#41; entre el TE y el porcentaje de poblaci&#243;n menor de 14 a&#241;os &#40;r &#61; &#8211;0&#44;693&#41;&#44; el porcentaje de poblaci&#243;n entre 14 y 65 a&#241;os &#40;r &#61; 0&#44;517&#41;&#44; la frecuentaci&#243;n &#40;r &#61; 0&#44;689&#41; y la poblaci&#243;n de costa &#40;r &#61; 0&#44;470&#41;&#46; Nuestro modelo final incluy&#243;&#58; el porcentaje de poblaci&#243;n menor de 14 a&#241;os&#44; la frecuentaci&#243;n y la poblaci&#243;n de costa &#40;F &#61; 41&#44;803&#59; p &#60; 0&#44;000&#59; r &#61; 0&#44;945&#59; r<span class="elsevierStyleSup">2</span> &#61; 0&#44;893&#41;&#46; Se identificaron tres zonas &#40;37&#44;5&#37;&#41; con TE elevado&#46;</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">La frecuentaci&#243;n&#44; el porcentaje de poblaci&#243;n pedi&#225;trica y la proximidad a la costa guardan una estrecha correlaci&#243;n con el tiempo de acceso a atenci&#243;n especializada&#44; lo cual condiciona las listas de espera&#46;</p>"
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        "titulo" => "Abstract"
        "resumen" => "<span class="elsevierStyleSectionTitle">Objective</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">To identify the variables influencing waiting time for specialized care &#40;waiting lists&#41; through multiple regression analysis and to analyze the health districts with long waiting times according to these variables&#46;</p> <span class="elsevierStyleSectionTitle">Design</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Descriptive&#44; cross sectional and retrospective study of waiting times for access to specialized care between 1997 and 1998&#46;</p> <span class="elsevierStyleSectionTitle">Setting</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Area 20 of the Health Department of the Autonomous Community of Valencia &#40;Spain&#41; consisting of 12 health districts with 204&#44;424 inhabitants&#46;</p> <span class="elsevierStyleSectionTitle">Interventions</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The following variables were gathered&#58; variables influencing demand&#58; type of municipality&#44; aging and indexes of dependent population&#44; and percentage of pensioners&#59; variables influencing supply&#58; age&#44; sex&#44; training and professional stability of the doctor&#44; and size of the patient list&#59; variables influencing resource consumption&#58; percentage of referrals to specialized care per thousand inhabitants&#44; mean WT for access to specialized care &#40;in natural days&#41; by district and year&#44; number of consultations&#44; and workload&#46; A multiple regression model was constructed through &#40;backward&#41; elimination&#44; taking the mean WT as the dependent variable and the remaining variables as independent variables&#46; The resulting equation enabled calculation of the &#171;expected&#187; WT per health district and the deviation of the real WT from the expected WT&#46; A district was considered to have a high WT when its deviation was above the mean plus one standard deviation of the distribution&#46;</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">The mean WT for access to specialized care was 37 days in 1997 and 34 days in 1998&#46; A significant correlation &#40;p &#60; 0&#46;005&#41; was found between WT and the percentage of the population aged less than 14 years &#40;r &#61; &#8211;0&#46;693&#41;&#44; the percentage of the population aged between 14-65 years &#40;r &#61; 0&#46;517&#41;&#44; the number of consultations &#40;r &#61; 0&#46;689&#41;&#44; and coastal population &#40;r &#61; 0&#46;470&#41;&#46; Our final model included&#58; percentage of the population aged less than 14 years&#44; number of consultations&#44; and coastal population &#40;F &#61; 41&#46;803&#59; p &#60; 0&#46;000&#59; r &#61; 0&#46;945&#59; r<span class="elsevierStyleSup">2</span> &#61; 0&#46;893&#41;&#46; Three districts &#40;37&#46;5&#37;&#41; with high WTs were identified&#46;</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">The number of consultations&#44; the percentage of the pediatric population&#44; and proximity to the coast were closely correlated with WT for specialized care&#44; with a consequent influence on waiting lists&#46;</p>"
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Identificación de las variables de influencia en los tiempos de espera en atención especializada
Identification of variables influencing waiting times for specialized care
J.G. Canoa,
Autor para correspondencia
jcanom@meditex.es

Correspondencia: Coordinador Médico. Centro de Salud Orihuela. Plaza de la Salud, s/n. 03300 Orihuela (Alicante). España.
, E. Medinab, J. Custardoyc, D. Orozcod, F. Quincee
a Centro de Salud de Orihuela. Grupo de Investigación Clínica del Sureste (GICS). Orihuela. Alicante
b Unidad Docente de MF y C de Alicante. Grupo de Investigación Clínica del Sureste (GICS). Alicante
c Servicio de Medicina Interna. Hospital Vega Baja. Grupo de Investigación Clínica del Sureste (GICS). Orihuela. Alicante
d Centro de Salud de Petrer. Departamento de Medicina. Universidad Miguel Hernández. Campus de San Juan. Alicante
e Centro de Salud de La Florida. Departamento de Medicina. Universidad Miguel Hernández. Campus de San Juan. Alicante. España
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        "resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Identificar&#44; mediante an&#225;lisis de regresi&#243;n m&#250;ltiple&#44; las variables de influencia en el tiempo de acceso a atenci&#243;n especializada &#40;listas de espera&#41;&#44; y analizar las zonas de salud con tiempo de acceso elevado&#44; en funci&#243;n de las mencionadas variables&#46;</p> <span class="elsevierStyleSectionTitle">Dise&#241;o</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Estudio descriptivo transversal&#44; retrospectivo&#44; del tiempo de acceso a atenci&#243;n especializada&#44; durante los a&#241;os 1997 y 1998&#46;</p> <span class="elsevierStyleSectionTitle">Emplazamiento</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">&#193;rea 20 de la Conselleria de Sanitat de la Comunidad Valenciana&#59; 12 zonas de salud&#59; 204&#46;424 habitantes&#46;</p> <span class="elsevierStyleSectionTitle">Intervenciones</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Se recogen variables de demanda &#40;tipo de municipio&#44; envejecimiento e &#237;ndices de dependencia poblacional&#44; porcentaje de pensionistas&#41;&#44; de oferta &#40;edad&#44; sexo&#44; formaci&#243;n y situaci&#243;n laboral del m&#233;dico&#44; tama&#241;o del cupo&#41; y de consumo &#40;porcentaje de derivaci&#243;n a especializada por mil habitantes&#44; tiempo medio de acceso a especializada &#40;en d&#237;as naturales&#41; por zona y a&#241;o&#44; frecuentaci&#243;n y presi&#243;n asistencial&#46; Se construye un modelo de regresi&#243;n m&#250;ltiple por eliminaci&#243;n &#40;hacia atr&#225;s&#41;&#44; tomando como variable dependiente el tiempo medio de espera &#40;TE&#41; y como independiente el resto&#46; La ecuaci&#243;n resultante permiti&#243; calcular el TE esperado por zona de salud y la desviaci&#243;n de su TE real sobre el esperado&#46; Se consider&#243; zona con TE elevado cuando su desviaci&#243;n superaba la media m&#225;s una desviaci&#243;n est&#225;ndar de dicha distribuci&#243;n&#46;</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">El tiempo de espera medio para acceder a especializada fue de 37 d&#237;as en 1997 y 34 d&#237;as en 1998&#46; Existe una correlaci&#243;n significativa &#40;p &#60; 0&#44;005&#41; entre el TE y el porcentaje de poblaci&#243;n menor de 14 a&#241;os &#40;r &#61; &#8211;0&#44;693&#41;&#44; el porcentaje de poblaci&#243;n entre 14 y 65 a&#241;os &#40;r &#61; 0&#44;517&#41;&#44; la frecuentaci&#243;n &#40;r &#61; 0&#44;689&#41; y la poblaci&#243;n de costa &#40;r &#61; 0&#44;470&#41;&#46; Nuestro modelo final incluy&#243;&#58; el porcentaje de poblaci&#243;n menor de 14 a&#241;os&#44; la frecuentaci&#243;n y la poblaci&#243;n de costa &#40;F &#61; 41&#44;803&#59; p &#60; 0&#44;000&#59; r &#61; 0&#44;945&#59; r<span class="elsevierStyleSup">2</span> &#61; 0&#44;893&#41;&#46; Se identificaron tres zonas &#40;37&#44;5&#37;&#41; con TE elevado&#46;</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">La frecuentaci&#243;n&#44; el porcentaje de poblaci&#243;n pedi&#225;trica y la proximidad a la costa guardan una estrecha correlaci&#243;n con el tiempo de acceso a atenci&#243;n especializada&#44; lo cual condiciona las listas de espera&#46;</p>"
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        "resumen" => "<span class="elsevierStyleSectionTitle">Objective</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">To identify the variables influencing waiting time for specialized care &#40;waiting lists&#41; through multiple regression analysis and to analyze the health districts with long waiting times according to these variables&#46;</p> <span class="elsevierStyleSectionTitle">Design</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Descriptive&#44; cross sectional and retrospective study of waiting times for access to specialized care between 1997 and 1998&#46;</p> <span class="elsevierStyleSectionTitle">Setting</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Area 20 of the Health Department of the Autonomous Community of Valencia &#40;Spain&#41; consisting of 12 health districts with 204&#44;424 inhabitants&#46;</p> <span class="elsevierStyleSectionTitle">Interventions</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The following variables were gathered&#58; variables influencing demand&#58; type of municipality&#44; aging and indexes of dependent population&#44; and percentage of pensioners&#59; variables influencing supply&#58; age&#44; sex&#44; training and professional stability of the doctor&#44; and size of the patient list&#59; variables influencing resource consumption&#58; percentage of referrals to specialized care per thousand inhabitants&#44; mean WT for access to specialized care &#40;in natural days&#41; by district and year&#44; number of consultations&#44; and workload&#46; A multiple regression model was constructed through &#40;backward&#41; elimination&#44; taking the mean WT as the dependent variable and the remaining variables as independent variables&#46; The resulting equation enabled calculation of the &#171;expected&#187; WT per health district and the deviation of the real WT from the expected WT&#46; A district was considered to have a high WT when its deviation was above the mean plus one standard deviation of the distribution&#46;</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">The mean WT for access to specialized care was 37 days in 1997 and 34 days in 1998&#46; A significant correlation &#40;p &#60; 0&#46;005&#41; was found between WT and the percentage of the population aged less than 14 years &#40;r &#61; &#8211;0&#46;693&#41;&#44; the percentage of the population aged between 14-65 years &#40;r &#61; 0&#46;517&#41;&#44; the number of consultations &#40;r &#61; 0&#46;689&#41;&#44; and coastal population &#40;r &#61; 0&#46;470&#41;&#46; Our final model included&#58; percentage of the population aged less than 14 years&#44; number of consultations&#44; and coastal population &#40;F &#61; 41&#46;803&#59; p &#60; 0&#46;000&#59; r &#61; 0&#46;945&#59; r<span class="elsevierStyleSup">2</span> &#61; 0&#46;893&#41;&#46; Three districts &#40;37&#46;5&#37;&#41; with high WTs were identified&#46;</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">The number of consultations&#44; the percentage of the pediatric population&#44; and proximity to the coast were closely correlated with WT for specialized care&#44; with a consequent influence on waiting lists&#46;</p>"
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Información del artículo
ISSN: 02139111
Idioma original: Español
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