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Spatial variation of pneumonia hospitalization risk in Twin Cities metro area, Minnesota

P Y Iroh Tam1, B Krzyzanowski2, J M Oakes3

  • 1Division of Pediatric Infectious Diseases and Immunology,Department of Pediatrics,University of Minnesota Medical School,Minneapolis,MN,USA.

Epidemiology and Infection
|October 18, 2017
PubMed

Insights

Pneumonia hospitalization risk varies spatially, linked to age, income, race/ethnic diversity, air quality (nitrogen dioxide), and solar insolation. Fine spatial analysis helps target prevention efforts for pneumonia.

Area of Science:

  • Public Health
  • Epidemiology
  • Environmental Health

Background:

  • Pneumonia hospitalization rates exhibit spatial variations.
  • Understanding these variations can reveal links to socioeconomic, demographic, and environmental factors.
  • Fine-resolution spatial analysis is crucial for identifying localized risk factors.

Purpose of the Study:

  • To analyze the spatial variability of pneumonia hospitalization risk.
  • To identify socioeconomic, demographic, and environmental correlates of pneumonia hospitalization.
  • To explore the utility of fine spatial analysis for targeted pneumonia prevention.

Main Methods:

  • Retrospective study of pneumonia hospitalizations (2011-2015) within the Fairview Health System, Minnesota.
  • Geocoding of 17,840 patient hospital encounters to census block groups.
  • Spatial analysis using ArcGIS and multivariate Poisson regression to assess risk factors.

Main Results:

  • Increased pneumonia hospitalization risk associated with older populations (⩾65 years), lower median household income, higher nitrogen dioxide emissions, and greater solar insolation.
  • Higher risk observed with increased race/ethnic diversity, even after adjusting for income.
  • Significant associations found between pneumonia hospitalization risk and census block group characteristics.

Conclusions:

  • Pneumonia hospitalization risk at the census block level is influenced by age, income, race/ethnic diversity, air quality, and solar insolation.
  • Region-specific factors contribute to the observed spatial variations in risk.
  • Fine spatial analysis facilitates the identification of correlates for targeted pneumonia prevention and control strategies.
Keywords:
Spatial modelinginfectious diseases epidemiologylower respiratory tract infectionspneumonia

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