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Clusters of preterm live births and respiratory distress syndrome-associated neonatal deaths: spatial distribution

Ana Sílvia Scavacini Marinonio1, Daniela Testoni Costa-Nobre2, Milton Harumi Miyoshi2

  • 1Departamento de Pediatria, Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), Rua Marselhesa 630, São Paulo, Vila Clementino, 04020-060, Brazil. anascavacini@gmail.com.

BMC Public Health
|June 20, 2022
PubMed

Insights

Spatial analysis revealed distinct clusters of preterm births and respiratory distress syndrome (RDS) deaths in Brazil. Identifying these patterns can guide improvements in perinatal care for vulnerable newborns.

Area of Science:

  • Neonatal Health
  • Spatial Epidemiology
  • Public Health

Background:

  • Respiratory distress syndrome (RDS) significantly contributes to neonatal mortality, particularly in low- and middle-income countries.
  • Prematurity is a major risk factor for RDS, highlighting the need to understand their spatial relationship.
  • São Paulo State, Brazil, presents a relevant context for studying these neonatal health challenges.

Purpose of the Study:

  • To identify spatial clusters of preterm live births and RDS-associated neonatal deaths in São Paulo State.
  • To analyze the co-occurrence patterns between preterm births and RDS mortality.
  • To inform targeted perinatal healthcare interventions.

Main Methods:

  • Population-based study analyzing preterm live births (≥22 weeks gestation, ≥400g birthweight) and RDS-associated neonatal deaths (<28 days, ICD-10 codes P22.0 or P28.0) from 2004-2015.
  • Spatial analysis (first- and second-order) with local Bayes estimates smoothing was applied to municipality-level rates.
  • Spearman correlation was used to assess the relationship between preterm birth and RDS mortality rates.

Main Results:

  • Analysis of 645,276 preterm births and 11,078 RDS deaths revealed non-random spatial distributions for both preterm birth rates (I=0.78, p=0.001) and RDS mortality rates (I=0.73, p=0.001).
  • Local Indicator of Spatial Association (LISA) maps identified significant clusters for both outcomes.
  • A negative correlation (r=-0.24, p=0.0000) was observed, with clusters of high RDS mortality overlapping areas of low preterm birth rates.

Conclusions:

  • The asymmetric spatial distribution of preterm births and RDS-associated neonatal deaths provides valuable insights.
  • These findings can guide the allocation of resources and improvement of perinatal healthcare services in identified high-risk areas.
  • Targeted interventions based on spatial patterns are crucial for reducing neonatal mortality from RDS.
Abstract

Keywords:
Epidemiological StudiesInfantNeonatal MortalityNewbornPopulationPrematureRespiratory Distress SyndromeSpatial Distribution

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