Print version ISSN 0213-9111
CANO, J.G. et al. Identification of variables influencing waiting times for specialized care. Gac Sanit [online]. 2003, vol.17, n.5, pp. 368-374. ISSN 0213-9111. http://dx.doi.org/10.1590/S0213-91112003000500005.
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.
Keywords : Primary care; Waiting lists; Specialized care.