Proposta de metodologia para estimar a área de cobertura potencial por equipes de atenção primária

• Autor(a) principal: Thiago Augusto Hernandes Rocha
• Data de Publicação: 2019
• Outros Autores: Dante Grapiuna de Almeida, Pedro Vasconcelos Maia do Amaral, Núbia Cristina da Silva, Erika Bárbara Abreu Fonseca Thomaz, Rejane Christine de Sousa Queiroz, Allan Claudius Queiroz Barbosa, João Ricardo Nickenig Vissoci
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: https://doi.org/10.26633/RPSP.2019.47; http://hdl.handle.net/1843/58038; https://orcid.org/0000-0003-1266-5168
• topic: Atenção primária à saúde; Atenção à saúde; Diagnóstico de situação de saúde; Sistemas de informação geográfica; Análise espacial; Cuidados primários de saúde; Diagnósticos; Sistemas de informação geográfica; Análise espacial (Estatística)

Objective. To present a methodology for the empirical evaluation of primary health care (PHC) through the construction of digital representations of potential PHC coverage areas. Methods. In this methodological study, potential areas were constructed by combinatorial analysis between census tracts and the location of basic health units with working PHC teams in Brazil. Six rules were used to parameterize the algorithm for the construction of potential areas. Thus, six restrictions were applied to enable the model: the selection of census tracts near the basic health unit; contiguous sectors; mutually exclusive sectors; sectors located in the same municipality of basic health units; sum of 4 500 users per health team in each unit; and volume of population ascribed proportional to the number of PHC teams allocated to the unit. Based on 316 594 census tracts and 39 758 basic health units, a neighborhood matrix was developed. To that matrix, a graph algorithm was applied to test combinations of sectors that simultaneously met the stipulated rules. Results. A total of 1 901 114 arcs were defined, connecting 30 351 census tracts, allowing the construction of 26 907 potential areas. Based on these results, intra-municipal analyses can be performed to monitor PHC indicators. Customizable algorithm parameters can be adjusted to accommodate different sets of rules which may be adapted to different countries. Conclusions. The use of geoprocessing approaches creates conditions for the assessment of PHC impact, based on secondary databases at various levels, such as intra-municipal, basic health unit, and even at the team level.