De Pablos Escobar Laura, García-Centeno María-Carmen
Economía Aplicada, Pública y Política. Facultad de CC Económicas y Empresariales. Universidad Complutense. Madrid. España.
Departamento de Matemática Aplicada y Estadística. Facultad de CC Económicas y Empresariales. Universidad San Pablo CEU (CEU Universidades). Madrid. España.
Rev Esp Salud Publica. 2021 Mar 3;95:e202103035.
In Spain, the number of persons that are in a surgery waiting list as well as the available surgery resources, differ across autonomous communities. The pandemic generated by COVID-19 has increased these waiting lists. In this study two objectives were pursued: on the one hand, to determine which are the resources that are determining the number of persons that are in a surgery waiting list per 1,000 inhabitants; on the other hand, to estimate the impact that the current pandemic has on the latter.
To estimate which are the resources that are having a greater impact on the waiting lists and to forecast the effect that the COVID-19 has on them, we use dynamic panel data models. The data on the surgery resources and on the waiting lists by autonomous communities is obtained from the Surveys on Health, Hospital Statistics and reports on waiting lists of the Ministry of Health, Consumption and Social Well Being and the Counsels. The sample period is 2012-2017 (last published year for surgery resources). In addition, a literature review is conducted and it shows the important and complexity of waiting list like a gestion tool of health system (Science, SciELO and Dialnet web data bases).
COVID-19 will increase the waiting lists by approximately 7.6% to 19.14%, depending on the autonomous community. Not all the available surgery resources have the same relevance nor an equal effect on the reduction of the waiting lists. The most significant resources are the beds and operating rooms per 1,000 inhabitants. The hospital expenditure is not so relevant.
The panel data models estimate the relation between the surgery resources and the waiting list. The latter is deemed complex and different across autonomous communities. In addition, these models allow to predict the expected increase in the waiting lists and are, thus, a useful instrument for their management.
