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. 2016 Jun;124(6):745-53.
doi: 10.1289/ehp.1409495. Epub 2015 Dec 8.

A Time-Stratified Case-Crossover Study of Ambient Ozone Exposure and Emergency Department Visits for Specific Respiratory Diagnoses in California (2005-2008)

Brian J Malig  1 , Dharshani L Pearson, Yun Brenda Chang, Rachel Broadwin, Rupa Basu, Rochelle S Green, Bart Ostro
Affiliations

A Time-Stratified Case-Crossover Study of Ambient Ozone Exposure and Emergency Department Visits for Specific Respiratory Diagnoses in California (2005-2008)

Brian J Malig et al. Environ Health Perspect. 2016 Jun.

Abstract

Background: Studies have explored ozone's connection to asthma and total respiratory emergency department visits (EDVs) but have neglected other specific respiratory diagnoses despite hypotheses relating ozone to respiratory infections and allergic responses.

Objective: We examined relationships between ozone and EDVs for respiratory visits, including specifically acute respiratory infections (ARI), asthma, pneumonia, chronic obstructive pulmonary disease (COPD), and upper respiratory tract inflammation (URTI).

Methods: We conducted a multi-site time-stratified case-crossover study of ozone exposures for approximately 3.7 million respiratory EDVs from 2005 through 2008 among California residents living within 20 km of an ozone monitor. Conditional logistic regression was used to estimate associations by climate zone. Random effects meta-analysis was then applied to estimate pooled excess risks (ER). Effect modification by season, distance from the monitor and individual demographic characteristics (i.e., age, race/ethnicity, sex, and payment method), and confounding by other gaseous air pollutants were also investigated. Meta-regression was utilized to explore how climate zone-level meteorological, demographic, and regional differences influenced estimates.

Results: We observed ozone-associated increases in all respiratory, asthma, and ARI visits, which were slightly larger in the warm season [asthma ER per 10-ppb increase in mean of same and previous 3 days ozone exposure (lag03) = 2.7%, 95% CI: 1.5, 3.9; ARI ERlag03 = 1.4%, 95% CI: 0.8, 1.9]. EDVs for pneumonia, COPD, and URTI were also significantly associated with ozone exposure over the whole year, but typically more consistently so during the warm season.

Conclusions: Short-term ozone exposures among California residents living near an ozone monitor were positively associated with EDVs for asthma, ARI, pneumonia, COPD, and URTI from 2005 through 2008. Those associations were typically larger and more consistent during the warm season. Our findings suggest that these outcomes should be considered when evaluating the potential health benefits of reducing ozone concentrations.

Citation: Malig BJ, Pearson DL, Chang YB, Broadwin R, Basu R, Green RS, Ostro B. 2016. A time-stratified case-crossover study of ambient ozone exposure and emergency department visits for specific respiratory diagnoses in California (2005-2008). Environ Health Perspect 124:745-753; http://dx.doi.org/10.1289/ehp.1409495.

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Conflict of interest statement

The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
Maps of California CZs and locations of ozone monitors and EDV ZCTA centroids used in this study. CZ boundaries designated by California Energy Commission (2009). Ozone monitor locations provided by CARB (2011).
Figure 2
Figure 2
Excess risks (95% CI) per 10-ppb ozone for respiratory outcomes by lag, with I2statistic, for (A) full year, and (B) warm season. l = lag of best fit, as identified as the lowest sum of AICs over all CZ analyses. Lag0 = same-day exposure, lag1 = exposure 1 day prior, etc. Lag01 = mean of lag0 and lag1, lag03 = mean of lags 0 through 3, etc. Models adjusted for apparent temperature (lag0 and lag13) and influenza visits. I2 = [(Q – df)/Q] × 100. Full year = all months available; warm = limited to May through October. Reported risks [(OR – 1) × 100] are pooled estimates using random effects meta-analysis from CZ-specific estimates obtained using conditional logistic regression comparing exposures on visit days with others of the same day of the week within the same month, adjusting for apparent temperature (lag0 and lag13) and county influenza visits.
Figure 3
Figure 3
Excess risks (95% CI) per 10-ppb ozone for respiratory outcomes in one- and two-pollutant analyses restricted to the population where another pollutant metric was available for (A) full year, (B) warm season (May–October). O3 (subset w/NO2) = models restricted to population with nitrogen dioxide exposures available; O3 (adj. for NO2) = models with same restricted population but also adjusted for nitrogen dioxide. O3 (subset w/CO) = models restricted to population with carbon monoxide exposures available; O3 (adj. for CO) = models with same restricted population but also adjusted for nitrogen dioxide. O3 (subset w/SO2) = models restricted to population with carbon monoxide exposures available; O3 (adj. for SO2) = models with same restricted population but also adjusted for sulfur dioxide. Reported risks [(OR – 1) × 100] are pooled estimates using random effects meta-analysis from climate zone-specific estimates obtained using conditional logistic regression comparing exposures on visit days with others of the same day of the week within the same month, adjusting for apparent temperature (lag0 and lag13) and county influenza visits.
Figure 4
Figure 4
Warm season (May–October) excess risks (95% CI) of EDV per 10-ppb ozone for different demographic/location categories for (A) respiratory (lag03), (B) ARI (lag03), (C) asthma (lag03), (D) pneumonia (lag03), (E) COPD (lag3), and (F) URTI (lag3) types. Models adjusted for apparent temperature (lag0 and lag13) and influenza outbreaks. Lags based on best fitting lag in non-subset models. Reported risks [(OR – 1) × 100] are pooled estimates using random effects meta-analysis from CZ- and category-specific estimates obtained using conditional logistic regression comparing exposures on visit days with others of the same day of the week within the same month, adjusting for apparent temperature (lag0 and lag13) and county influenza visits. Note: C, comparison group; km = kilometer; NH = Non-Hispanic; pdiff = p-value of the difference between the estimates; yo = year olds.
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