Modelo decisório espacial para a redução da mortalidade infantil: uma discussão no contexto da ruralidade na Paraíba

Abstract

Infant mortality is a health indicator that reveals the quality of life and health services provided to the maternal and child population. Brazil is one of the most unequal societies, a situation also reproduced in infant mortality. Despite the gradual decreasing the rates of infant mortality in Brazil are still high in the Northeast and North regions, especially in population groups of high vulnerability, such as the countryside, forest, and water populations, also known as rural population. Aim: develop a spatial decision support model for the definition of priority levels of public intervention and reduction in infant mortality in the state of Paraíba. Methods: we performed a population-based epidemiological study comprising two phases: an exploratory study of the epidemiological aspects of infant deaths, according to the rural population of Paraiba, and an ecological study. Infant deaths from 2007 to 2016 were used, and 22 health indicators to characterize the municipalities. The spatial decision support model was based on the results from the statistical, spatial and spatial-temporal analyzes of infant deaths and took into account the psychometric approach to define the priority level classification for intervention in municipalities. All mappings are available in a SIGWeb. Results: IBGE typology was the criterion of rurality adopted. The main profile of infant deaths was characterized as male, brown, early neonatal, with low birth weight and gestational age less than 37 weeks. Avoidable death by the Unified Health System (SUS) interventions was the primary death cause defined, and the hospital as the place of death. Postneonatal deaths were more frequent in neighboring rural districts (32%). Infant mortality rate decreased independently of the rural or urban typology. We identified spatial and spatiotemporal conglomerates concentrated in Borborema and Semi-arid (“Sertão”) regions. The decision map showed 44 municipalities in a very high level of priority, from which 81.8% are adjacent rural areas. Final remarks: The Decision Map provides an easily understand result. The decision maps enable to identify the municipalities that should be prioritized by health managers, contributing to the monitoring and evaluation of MI.