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Evaluation of a spatially resolved forest fire smoke model for population-based epidemiologic exposure assessment.

Yao J, Eyamie J, Henderson SB - J Expo Sci Environ Epidemiol (2014)

Bottom Line: Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory.We then used meta-regression to estimate the overall effects.Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days.

View Article: PubMed Central - PubMed

Affiliation: Environmental Health Services, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada.

ABSTRACT
Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03-1.06), and physician visits for asthma (1.06, 1.04-1.08), COPD (1.02, 1.00-1.03), lower respiratory infections (1.03, 1.00-1.05), and otitis media (1.05, 1.03-1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.

No MeSH data available.


Related in: MedlinePlus

Meta-regression results for the associations between physician visits and measured and modeled PM2.5 in the 29 LHAs with monitors, modeled PM2.5 in 60 LHAs without monitors, and all 89 LHAs, for all days of fire seasons and extreme fire days. Varying numbers (indicated in Figure 5) of LHAs were excluded from the analyses for 60 LHAs and all LHAs in extreme fire days.
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fig4: Meta-regression results for the associations between physician visits and measured and modeled PM2.5 in the 29 LHAs with monitors, modeled PM2.5 in 60 LHAs without monitors, and all 89 LHAs, for all days of fire seasons and extreme fire days. Varying numbers (indicated in Figure 5) of LHAs were excluded from the analyses for 60 LHAs and all LHAs in extreme fire days.

Mentions: There was generally good agreement between meta-regression estimates for measured and modeled PM2.5 across the 29 LHAs with monitoring stations for all types of physician visits. One exception was a significant effect of measured PM2.5 on upper respiratory infections on all fire season days (RR=1.03; 95% CI=1.02–1.05), with no similar effect observed with the modeled estimates. Both measured and modeled PM2.5 were consistently associated with increased physician visits for asthma, lower respiratory infections, and otitis media on extreme fire days, and not associated with increased visits for upper respiratory infections. The consistency of the associations for COPD and cardiovascular visits was less clear. For COPD, the effects of measured PM2.5 were the same on all days and extreme fire days, whereas the effects of modeled PM2.5 were elevated on the extreme fire days. The effects of PM2.5 were small for cardiovascular visits, and significantly protective for measured PM2.5 in the 29 LHAs on all fire season days. Effects were slightly greater for modeled PM2.5 in those LHAs, and greater still for modeled PM2.5 in the 60 LHAs without monitors, and in all 89 LHAs (Figure 4). Effects of modeled PM2.5 were small but marginally significant on the most extreme fire days (RR=1.01; 95% CI=1.00–1.02) for all 89 LHAs.


Evaluation of a spatially resolved forest fire smoke model for population-based epidemiologic exposure assessment.

Yao J, Eyamie J, Henderson SB - J Expo Sci Environ Epidemiol (2014)

Meta-regression results for the associations between physician visits and measured and modeled PM2.5 in the 29 LHAs with monitors, modeled PM2.5 in 60 LHAs without monitors, and all 89 LHAs, for all days of fire seasons and extreme fire days. Varying numbers (indicated in Figure 5) of LHAs were excluded from the analyses for 60 LHAs and all LHAs in extreme fire days.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835685&req=5

fig4: Meta-regression results for the associations between physician visits and measured and modeled PM2.5 in the 29 LHAs with monitors, modeled PM2.5 in 60 LHAs without monitors, and all 89 LHAs, for all days of fire seasons and extreme fire days. Varying numbers (indicated in Figure 5) of LHAs were excluded from the analyses for 60 LHAs and all LHAs in extreme fire days.
Mentions: There was generally good agreement between meta-regression estimates for measured and modeled PM2.5 across the 29 LHAs with monitoring stations for all types of physician visits. One exception was a significant effect of measured PM2.5 on upper respiratory infections on all fire season days (RR=1.03; 95% CI=1.02–1.05), with no similar effect observed with the modeled estimates. Both measured and modeled PM2.5 were consistently associated with increased physician visits for asthma, lower respiratory infections, and otitis media on extreme fire days, and not associated with increased visits for upper respiratory infections. The consistency of the associations for COPD and cardiovascular visits was less clear. For COPD, the effects of measured PM2.5 were the same on all days and extreme fire days, whereas the effects of modeled PM2.5 were elevated on the extreme fire days. The effects of PM2.5 were small for cardiovascular visits, and significantly protective for measured PM2.5 in the 29 LHAs on all fire season days. Effects were slightly greater for modeled PM2.5 in those LHAs, and greater still for modeled PM2.5 in the 60 LHAs without monitors, and in all 89 LHAs (Figure 4). Effects of modeled PM2.5 were small but marginally significant on the most extreme fire days (RR=1.01; 95% CI=1.00–1.02) for all 89 LHAs.

Bottom Line: Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory.We then used meta-regression to estimate the overall effects.Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days.

View Article: PubMed Central - PubMed

Affiliation: Environmental Health Services, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada.

ABSTRACT
Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03-1.06), and physician visits for asthma (1.06, 1.04-1.08), COPD (1.02, 1.00-1.03), lower respiratory infections (1.03, 1.00-1.05), and otitis media (1.05, 1.03-1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.

No MeSH data available.


Related in: MedlinePlus