Spatial clusters of child lower respiratory illnesses associated with community-level risk factors

Paloma I Beamer1,2,3, Nathan Lothrop2, Zhenqiang Lu3

  • 1Arizona Respiratory Center, University of Arizona, Tucson, Arizona.

Pediatric Pulmonology
|October 6, 2015
PubMed

Insights

Geographic clustering of lower respiratory illnesses (LRIs) in children reveals distinct risk factors by LRI type. Air pollution and housing conditions are linked to specific LRI phenotypes, informing community-level interventions.

Area of Science:

  • Pediatric Respiratory Medicine
  • Environmental Epidemiology
  • Spatial Analysis

Background:

  • Lower respiratory illnesses (LRIs) are a leading cause of childhood mortality and morbidity.
  • Identifying geographic patterns of LRIs can help understand community-level risk factors.
  • Previous research has not fully explored how spatial patterns and associated risks differ by LRI phenotype.

Purpose of the Study:

  • To assess the geographic clustering of four lower respiratory illness (LRI) phenotypes in children.
  • To evaluate if spatial patterns and associated risk factors differ among these LRI phenotypes.
  • To identify community-level social and environmental factors contributing to LRI incidence.

Main Methods:

  • Spatial clustering analysis was performed using birth addresses of 812 children from the Tucson Children's Respiratory Study.
  • Principal component analysis generated indices for socioeconomic status (SES), housing conditions, and air pollution at the census tract level.
  • Multivariable regression analyses examined associations between LRI clusters and environmental/social factors.

Main Results:

  • Spatial clustering patterns and risk factors varied significantly by LRI phenotype.
  • Wheezing LRIs were associated with increased air pollution (OR=1.18, P=0.01).
  • Viral LRIs were linked to poorer housing (OR=1.28, P=0.01), and RSV LRIs to air pollution (OR=1.14, P=0.006), poorer housing (OR=1.54, P=0.003), and higher SES (OR=0.77, P=0.001).

Conclusions:

  • Distinct geographic clusters and associated risk factors exist for different LRI phenotypes.
  • Environmental factors like air pollution and housing conditions, alongside socioeconomic status, play a role in LRI incidence.
  • Multifaceted, community-level interventions are necessary to reduce LRI incidence.

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