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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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Vol. 17. Núm. 5.
Páginas 368-374 (septiembre - octubre 2003)
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Vol. 17. Núm. 5.
Páginas 368-374 (septiembre - octubre 2003)
Open Access
Identificación de las variables de influencia en los tiempos de espera en atención especializada
Identification of variables influencing waiting times for specialized care
Visitas
5593
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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Información del artículo
Resumen
Objetivo

Identificar, mediante análisis de regresión múltiple, las variables de influencia en el tiempo de acceso a atención especializada (listas de espera), y analizar las zonas de salud con tiempo de acceso elevado, en función de las mencionadas variables.

Diseño

Estudio descriptivo transversal, retrospectivo, del tiempo de acceso a atención especializada, durante los años 1997 y 1998.

Emplazamiento

Área 20 de la Conselleria de Sanitat de la Comunidad Valenciana; 12 zonas de salud; 204.424 habitantes.

Intervenciones

Se recogen variables de demanda (tipo de municipio, envejecimiento e índices de dependencia poblacional, porcentaje de pensionistas), de oferta (edad, sexo, formación y situación laboral del médico, tamaño del cupo) y de consumo (porcentaje de derivación a especializada por mil habitantes, tiempo medio de acceso a especializada (en días naturales) por zona y año, frecuentación y presión asistencial. Se construye un modelo de regresión múltiple por eliminación (hacia atrás), tomando como variable dependiente el tiempo medio de espera (TE) y como independiente el resto. La ecuación resultante permitió calcular el TE esperado por zona de salud y la desviación de su TE real sobre el esperado. Se consideró zona con TE elevado cuando su desviación superaba la media más una desviación estándar de dicha distribución.

Resultados

El tiempo de espera medio para acceder a especializada fue de 37 días en 1997 y 34 días en 1998. Existe una correlación significativa (p < 0,005) entre el TE y el porcentaje de población menor de 14 años (r = –0,693), el porcentaje de población entre 14 y 65 años (r = 0,517), la frecuentación (r = 0,689) y la población de costa (r = 0,470). Nuestro modelo final incluyó: el porcentaje de población menor de 14 años, la frecuentación y la población de costa (F = 41,803; p < 0,000; r = 0,945; r2 = 0,893). Se identificaron tres zonas (37,5%) con TE elevado.

Conclusiones

La frecuentación, el porcentaje de población pediátrica y la proximidad a la costa guardan una estrecha correlación con el tiempo de acceso a atención especializada, lo cual condiciona las listas de espera.

Palabras clave:
Atención primaria
Listas de espera
Atención especializada
Abstract
Objective

To identify the variables influencing waiting time for specialized care (waiting lists) through multiple regression analysis and to analyze the health districts with long waiting times according to these variables.

Design

Descriptive, cross sectional and retrospective study of waiting times for access to specialized care between 1997 and 1998.

Setting

Area 20 of the Health Department of the Autonomous Community of Valencia (Spain) consisting of 12 health districts with 204,424 inhabitants.

Interventions

The following variables were gathered: variables influencing demand: type of municipality, aging and indexes of dependent population, and percentage of pensioners; variables influencing supply: age, sex, training and professional stability of the doctor, and size of the patient list; variables influencing resource consumption: percentage of referrals to specialized care per thousand inhabitants, mean WT for access to specialized care (in natural days) by district and year, number of consultations, and workload. A multiple regression model was constructed through (backward) elimination, taking the mean WT as the dependent variable and the remaining variables as independent variables. The resulting equation enabled calculation of the «expected» WT per health district and the deviation of the real WT from the expected WT. A district was considered to have a high WT when its deviation was above the mean plus one standard deviation of the distribution.

Results

The mean WT for access to specialized care was 37 days in 1997 and 34 days in 1998. A significant correlation (p < 0.005) was found between WT and the percentage of the population aged less than 14 years (r = –0.693), the percentage of the population aged between 14-65 years (r = 0.517), the number of consultations (r = 0.689), and coastal population (r = 0.470). Our final model included: percentage of the population aged less than 14 years, number of consultations, and coastal population (F = 41.803; p < 0.000; r = 0.945; r2 = 0.893). Three districts (37.5%) with high WTs were identified.

Conclusions

The number of consultations, the percentage of the pediatric population, and proximity to the coast were closely correlated with WT for specialized care, with a consequent influence on waiting lists.

Key words:
Primary care
Waiting lists
Specialized care
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