Spatial patterns of prematurity and its determinants in the metropolitan region of São Paulo, Brazil, 2010–2019 - Rodríguez, Elias Carlos Aguirre [UNESP]; Rodríguez, Elen Yanina Aguirre [UNESP]; Marins, Fernando Augusto Silva [UNESP]; da Silva, Aneirson Francisco [UNESP]; Nascimento, Luiz Fernando Costa [UNESP] (FapUNIFESP (SciELO), 2024)
Objective: To analyze spatial distribution of preterm births and their association with maternal, social, and health services indicators in the metropolitan region of São Paulo, Brazil, 2010–2019. Methods: Ecological study using data on preterm newborns from 39 municipalities in the metropolitan region of São Paulo. Univariate global Moran’s index (Im) was used to evaluate spatial association of prematurity, and univariate local Moran’s index by using the cluster map (LISA) to identify spatial patterns and clusters. Bivariate global Moran’s index was also used to analyze spatial autocorrelation with maternal, social, and health services indicators. Results: A total of 3,103,898 live births were registered in period 2010-2019, of which 331,174 (10.7%) were preterm. The global Moran’s index showed spatial independence (Im=0.05; p-value=0.233) of the proportion of preterm births between municipalities. However, in the local spatial analysis it was possible to identify a statistically significant spatial cluster between the municipalities of Biritiba Mirim, Guararema and Salesópolis, with high proportions of preterm births. In the bivariate analysis, a significant positive spatial association was identified with proportions of mothers under 20 years old (Im=0.17; p-value=0.024) and mothers with low schooling (Im=0.17; p-value=0.020), and a significant negative spatial association with HDI (Im=-0.14; p-value=0.039). Conclusions: The local spatial approach identified a spatial cluster located in the far east of the metropolitan region of São Paulo, where actions by health managers are needed to minimize occurrence of preterm births.
Padrões espaciais da prematuridade e seus determinantes na região metropolitana de São Paulo, Brasil, 2010–2019
Keywords: Geographic information systems; Geographic mapping; Premature birth; Prematurity; Spatial analysis