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. 2024 Feb 26:27:e240008.
doi: 10.1590/1980-549720240008. eCollection 2024.

Spatial patterns of prematurity and its determinants in the metropolitan region of São Paulo, Brazil, 2010-2019

[Article in English, Portuguese]
Elias Carlos Aguirre Rodríguez  1 , Elen Yanina Aguirre Rodríguez  1 , Fernando Augusto Silva Marins  1 , Aneirson Francisco da Silva  1 , Luiz Fernando Costa Nascimento  1   2
Affiliations

Spatial patterns of prematurity and its determinants in the metropolitan region of São Paulo, Brazil, 2010-2019

[Article in English, Portuguese]
Elias Carlos Aguirre Rodríguez et al. Rev Bras Epidemiol. .

Abstract
in English, Portuguese

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.

Objetivo:: Analisar a distribuição espacial dos nascimentos prematuros e sua associação com indicadores maternos, sociais e de serviços de saúde na região metropolitana de São Paulo, Brasil, 2010-2019.

Métodos:: Estudo ecológico utilizando dados sobre recém-nascidos pré-termo dos 39 municípios da região metropolitana de São Paulo. Utilizou-se o índice de Moran (Im) global univariado para avaliar a associação espacial da prematuridade, e o índice de Moran local univariado por meio do mapa de clusters (LISA) para a identificação de padrões e aglomerados espaciais. Também foi utilizado o índice de Moran global bivariado para analisar a autocorrelação espacial com os indicadores maternos, sociais e de serviços de saúde.

Resultados:: Foram registrados 3.103.898 nascidos vivos no período 2010-2019, dos quais 331.174 (10,7%) foram prematuros. O índice de Moran global mostrou independência espacial (Im=0,05; p-valor=0,233) da proporção dos nascimentos prematuros entre municípios. No entanto, na análise espacial local foi possível identificar aglomerado espacial estatisticamente significativo entre os municípios de Biritiba Mirim, Guararema e Salesópolis, com proporções altas de nascimentos pré-termo. Na análise bivariada, identificou-se associação espacial significativa positiva com proporções de mães menores de 20 anos (Im=0,17; p-valor=0,024) e mães com baixa escolaridade (Im=0,17; p-valor=0,020), e associação espacial significativa negativa com IDH (Im=-0,14; p-valor=0,039).

Conclusão:: A abordagem espacial local identificou agrupamento espacial situado no extremo leste da região metropolitana de São Paulo, onde ações dos gestores de saúde são necessárias para minimizar a ocorrência de partos prematuros.

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Conflict of interest statement

Conflict of interests: nothing to declare

Figures

Figure 1.
Figure 1.. Map of the political-administrative division of municipalities in the metropolitan region of São Paulo.
Figure 2.
Figure 2.. Annual distribution of live births and proportion of preterm births in the metropolitan region of São Paulo, 2010-2019.
Figure 3.
Figure 3.. Spatial distribution of the proportion of preterm births in the municipalities of the metropolitan region of São Paulo, 2010-2019.
Figure 4.
Figure 4.. Cluster map (LISA) of the proportion of preterm births in the municipalities of the metropolitan region of São Paulo, 2010-2019.
See this image and copyright information in PMC

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