Use of artificial neural networks in applying methodology for allocating health resources.

Objective: To describe the construction of a factor of allocation of financial resources, based on the population's health needs.

Methods: Quantitative study with data collected from public databases referring to the state of Pernambuco, Northeastern Brazil, between 2000 and 2010. Variables which reflected epidemiological, demographic, socio-economic and educational processes were selected in order to create a factor of allocation which highlighted the health needs of the population. The data sources were: SUS (Brazilian Unified Health System) Department of Computer Science, Atlas of Human Development in Brazil, IBGE (Brazilian Institute of Geography and Statistics), Information System on Public Health Budgets, National Treasury and data from the Pernambuco Health Secretariat between 2000 and 2010. Pearson's coefficient was used to assess linear correlation and the factor of allocation was calculated using analysis by artificial neural networks. The quartiles of the municipalities were defined according to their health needs.

Results: The distribution shown here highlights that all the coastal region, a good part of the Mata Norte and Mata Sul regions and the Agreste Setentrional and Agreste Central regions are in Quartile 1, that which has the largest number of municipalities. The Agreste Meridional region had municipalities in all of the quartiles. In the Pajeú/Moxotó region, many of the municipalities were in Quartile 1. Similar distribution was verified in the Sertão Central region. In the Araripe region, the majority of the municipalities were in Quartiles 3 or 4 and the São Francisco region was divided between Quartiles 1, 2 and 3.

Conclusions: The factor of allocation grouped together municipalities of Pernambuco according to variables related to public health needs and separated those with extreme needs, requiring greater financial support, from those with lesser needs.