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The exposure of infants and children to carbon monoxide from biomass fuels in The Gambia: a measurement and modeling study.

Authors
  • Dionisio, Kathie L1
  • Howie, Stephen R C
  • Dominici, Francesca
  • Fornace, Kimberly M
  • Spengler, John D
  • Donkor, Simon
  • Chimah, Osaretin
  • Oluwalana, Claire
  • Ideh, Readon C
  • Ebruke, Bernard
  • Adegbola, Richard A
  • Ezzati, Majid
  • 1 Department of Global Health and Population, Harvard School of Public Health, Boston, Massachusetts, USA.
Type
Published Article
Journal
Journal of Exposure Science & Environmental Epidemiology
Publisher
Springer Nature
Publication Date
Jan 01, 2012
Volume
22
Issue
2
Pages
173–181
Identifiers
DOI: 10.1038/jes.2011.47
PMID: 22166810
Source
Medline
License
Unknown

Abstract

Smoke from biomass fuels is a risk factor for pneumonia, the leading cause of child death worldwide. Although particulate matter (PM) is the metric of choice for studying the health effects of biomass smoke, measuring children's PM exposure is difficult. Carbon monoxide (CO), which is easier to measure, can be used as a proxy for PM exposure. We measured the exposure of children ≤ 5 years of age in The Gambia to CO using small, passive, color stain diffusion tubes. We conducted multiple CO measurements on a subset of children to measure day-to-day exposure variability. Usual CO exposure was modeled using a mixed effects model, which also included individual and household level exposure predictors. Mean measured CO exposure for 1181 children (n=2263 measurements) was 1.04 ± 1.46 p.p.m., indicating that the Gambian children in this study on average have a relatively low CO exposure. However, 25% of children had exposures of 1.3 p.p.m. or higher. CO exposure was higher during the rainy months (1.33 ± 1.62 p.p.m.). Burning insect coils, using charcoal, and measurement done in the rainy season were associated with higher exposure. A parsimonious model with fuel, season, and other PM sources as covariates explained 39% of between-child variation in exposure and helped remove within-child variability.

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