Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis
Abstract
BACKGROUND: Results from time-series epidemiologic studies evaluating the relationship between ambient ozone concentrations and premature mortality vary in their conclusions about the magnitude of this relationship, if any, making it difficult to estimate public health benefits of air pollution control measures. We conducted an empiric Bayes metaregression to estimate the ozone effect on mortality, and to assess whether this effect varies as a function of hypothesized confounders or effect modifiers. METHODS: We gathered 71 time-series studies relating ozone to all-cause mortality, and we selected 48 estimates from 28 studies for the metaregression. Metaregression covariates included the relationship between ozone concentrations and concentrations of other air pollutants, proxies for personal exposure-ambient concentration relationships, and the statistical methods used in the studies. For our metaregression, we applied a hierarchical linear model with known level-1 variances. RESULTS: We estimated a grand mean of a 0.21% increase (95% confidence interval = 0.16-0.26%) in mortality per 10-microg/m increase of 1-hour maximum ozone (0.41% increase per 10 ppb) without controlling for other air pollutants. In the metaregression, air-conditioning prevalence and lag time were the strongest predictors of between-study variability. Air pollution covariates yielded inconsistent findings in regression models, although correlation analyses indicated a potential influence of summertime PM2.5. CONCLUSIONS: These findings, coupled with a greater relative risk of ozone in the summer versus the winter, demonstrate that geographic and seasonal heterogeneity in ozone relative risk should be anticipated, but that the observed relationship between ozone and mortality should be considered for future regulatory impact analyses.
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In a meta-analysis, Levy et al. (2005) found that effect estimates for ozone and mortality were slightly higher in communities with higher annual or summer correlations between PM2.5 and ozone levels; however, the relationship was not statistically significant.
“In a meta-analysis of previously conducted time-series studies of ozone and mortality, Levy et al. (2005) found that effect estimates for ozone and mortality were slightly higher in communities with higher annual or summer correlations between PM2.5 and ozone levels; however, the relationship was not statistically significant.”
From Potential confounding of particulate matter on the short-term association between ozone and mortality in multisite time-series studies (2007) · cites this paper as Levy et al. 2005
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- Authors
- Levy, J. I., Chemerynski, S. M., & Sarnat, J. A.
- Affiliations
- Environmental Protection Agency (US)
- Corresponding author
- Levy, Jonathan I.
- Venue
- Epidemiology
- Published
- 2005-07
- Volume
- 16
- Issue
- 4
- Pages
- 458-468
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2063602828
- Cited by
- 302
- References
- 94
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 4 · 2021: 4 · 2020: 14 · 2019: 20 · 2018: 16 · 2017: 13 · 2016: 24 · 2015: 18 · 2014: 17 · 2013: 24 · 2012: 16
- FWCI
- 1.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Indoor Air Quality and Microbial Exposure
- Keywords
- ozone, ambient air pollution, premature mortality, all-cause mortality, effect estimation, effect modification, time delay, PM2.5, seasonal variation, regulatory impact assessment
- MeSH terms
- Air Pollutants, Air Pollution, Bayes Theorem, Humans, Mortality, Ozone, Particle Size, Regression Analysis, Seasons, Urban Population, Weather, Confounding Factors, Epidemiologic, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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