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"titulo" => "Identification of variables influencing waiting times for specialized care"
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"resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">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.</p> <span class="elsevierStyleSectionTitle">Diseño</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Estudio descriptivo transversal, retrospectivo, del tiempo de acceso a atención especializada, durante los años 1997 y 1998.</p> <span class="elsevierStyleSectionTitle">Emplazamiento</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Área 20 de la Conselleria de Sanitat de la Comunidad Valenciana; 12 zonas de salud; 204.424 habitantes.</p> <span class="elsevierStyleSectionTitle">Intervenciones</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">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.</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">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; r<span class="elsevierStyleSup">2</span> = 0,893). Se identificaron tres zonas (37,5%) con TE elevado.</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">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.</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 (waiting lists) through multiple regression analysis and to analyze the health districts with long waiting times according to these variables.</p> <span class="elsevierStyleSectionTitle">Design</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Descriptive, cross sectional and retrospective study of waiting times for access to specialized care between 1997 and 1998.</p> <span class="elsevierStyleSectionTitle">Setting</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Area 20 of the Health Department of the Autonomous Community of Valencia (Spain) consisting of 12 health districts with 204,424 inhabitants.</p> <span class="elsevierStyleSectionTitle">Interventions</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">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.</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. 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; r<span class="elsevierStyleSup">2</span> = 0.893). Three districts (37.5%) with high WTs were identified.</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">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.</p>"
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