Clearly, our regression findings could be sensitive to many factors. First, to determine the influence of the assumed conversion factors, we evaluated whether indicator variables for reported units (ppb vs. μg/m3) or measurement time (1-hour maximum, 8-hour maximum, 24-hour average) were significant if added to the final multivariate model. Only the 8-hour dummy variable was statistically significant (P = 0.01), indicating that studies using 8-hour maxima had slightly lower estimates than studies using 1-hour maxima or 24-hour averages.
Our final sensitivity analysis considers only studies with number of deaths above the median to determine whether studies with limited statistical power might be influential. Applying the regression model defined in Table 3, the coefficients for lag time and air-conditioning prevalence are similar, whereas the coefficient for the ozone–NO2 regression coefficient decreases from 0.77 to 0.22. Only the lag time enters into the forward regression model (P = 0.08).
Results from our primary model imply that between-study variability in ozone-related mortality can be partially explained by differences in the lag time, air-conditioning prevalence, and relationship between ambient ozone and nitrogen dioxide concentrations. For lag time and air conditioning, the results are robust and intuitive, and suggest that same-day ozone effects exceed lagged effects and that the ambient ozone–mortality relationship might be lower in cities with greater prevalence of residential central air conditioning (and therefore lower personal exposure to ozone).
Beyond the regression findings, we can reach some broad conclusions about the ozone–mortality relationship. Fewer than half of the studies in our analysis reported “statistically significant” findings, which is largely a function of the statistical power of the studies. This observation provides justification for a meta-analytic approach, which helps to combine evidence from individual studies lacking statistical power. In addition, we documented a substantial difference in the ozone–mortality relationship between the summer and winter (Fig. 2).
Clearly, our metaregression has many limitations. Although we attempted to capture the crucial dimensions of methodologic heterogeneity, there are many factors either difficult to quantify or unreported by the authors that could influence effect estimates. This is exemplified by the fact that estimates sometimes differed for studies conducted within the same city, although many of the regression covariates were identical (a factor that limited the predictive power of our regressions). More complex terms reflecting the degrees of freedom used in temperature spline models, for example, might capture some of this uncertainty. More broadly, our pooled estimates depend on the statistical methods applied in the past. For example, recent time-series studies have applied distributed lag models to evaluate the influence of longer time windows,7,102,103 something that cannot be done in a metaregression if the original studies did not follow this approach.
Furthermore, if the ozone–mortality relationship varies geographically, then studies included in the metaregression must be spatially representative to yield generalizable results. Although air conditioning appeared to modify the ozone effect, it is difficult to evaluate potential effect modification given few studies in settings and time periods with high central air-conditioning prevalence. Of our 46 estimates, only 4 were in settings with air-conditioning prevalence above 50%.33,47,59 All 4 of these estimates lacked statistical power, with 3 based on only 1 year of data.33,47 It is therefore difficult to make definitive conclusions about the influence of residential air conditioning on the ozone–mortality relationship. Further time-series studies should be conducted in warm settings with high air-conditioning prevalence to determine the importance of this factor, and studies such as NMMAPS should examine potential effect modification by air-conditioning prevalence. Given the growth of air-conditioning use in many locations, especially in the United States, understanding this influence would be crucial in developing concentration–response functions for prospective regulatory impact analyses. Furthermore, air-conditioning prevalence is only a rough surrogate of residential ventilation and personal exposure patterns, and so more refined indicators should be investigated.
Another limitation is the fact that multiple studies found that ozone was “statistically insignificant” without reporting quantitative estimates.53,56,71,72,82 Other time-series mortality studies may not have mentioned small ozone effects because the results did not reach statistical significance, and in general, such findings may be less likely to be published. Omitting all 3 categories of studies potentially biases our pooled estimates. To bound the influence of this factor, we follow an approach adopted previously19 and calculate the grand mean with no covariates, assuming that omitted studies have a central estimate of zero and the minimum variance among included studies. Adding 5 such estimates would reduce our pooled estimate to 0.17% (95% CI = 0.12% to 0.21%). Although the addition of more studies with no ozone effects would further reduce the central estimate, it would take 161 studies of minimum variance (and many more studies of greater variance) before the pooled estimate became statistically insignificant.
Finally, even if our metaregression accurately captures the relationship between ambient ozone concentrations and mortality risk, this may not reflect the actual exposure–risk relationship. Each of the studies in the current analysis used ambient ozone measurements as surrogates of personal ozone exposures. However, personal ozone exposures have consistently been much lower than corresponding ambient ozone levels of 12- and 24-hour durations.14,105–110 Hence, observed relative risk estimates from studies using ambient concentrations may be underestimating true risks associated with exposure to ozone, presuming that these are the health-relevant averaging times or that these relationships hold for shorter time periods.
Furthermore, exposure assessment studies have not provided conclusive evidence that ambient ozone concentrations are, in fact, strongly correlated with personal ozone exposures. Although only a limited number of exposure assessment studies have examined personal–ambient ozone associations, results suggest that the associations are stronger during the summer, when people spend more time outdoors and within well-ventilated indoor environments.14,105,106,110 The only study to examine hourly relationships between ambient and personal ozone showed weak personal–ambient ozone correlations indoors (r = 0.05) and stronger correlations outdoors (r = 0.8).111 Thus, the amount of exposure error between ambient ozone measurements and corresponding personal exposures may be greater during the winter as compared with the summer, which may contribute to the observed differences in the season-specific risk estimates. Together these results provide some evidence that ambient ozone monitors serve as better surrogates of actual exposure to ozone during warm seasons than during cold seasons.
Despite these limitations, we can draw some conclusions that are useful for public policy. First, our grand mean estimate appears comparable to estimates from previous meta-analyses. Thurston and Ito20 concluded that 6 studies with appropriate temperature characterization had a pooled relative risk of 1.056 per 100 ppb of 1-hour maximum ozone, corresponding to a 0.27% increase in daily mortality per 10-μg/m3 increase of 1-hour maximum ozone. The U.S. EPA estimated a 2.9% increase in deaths per 100-ppb increase in 1-hour maximum ozone4 or an approximate 0.15% increase in daily mortality per 10-μg/m3 increase of 1-hour maximum ozone. These values bound our central estimate of a 0.21% increase.
In addition, the relationship between ozone and mortality appears lower in settings with high residential central air-conditioning prevalence, in agreement with past ozone exposure studies12,13,107 and PM epidemiology.112 Finally, the robustness of the ozone–mortality relationship, even when controlling for key confounders and effect modifiers, indicates that inclusion of ozone-related mortality in future regulatory impact analyses may be warranted, although further investigation is needed into potential PM2.5 confounding in the summer and the personal exposure–ambient concentration relationships by season. Future studies should also explicitly incorporate air-conditioning prevalence or other personal exposure surrogates into the estimation of an appropriate national average ozone–mortality relationship.
We thank Michelle Bell, Francesca Dominici, Kaz Ito, and their colleagues for their participation in this joint effort.
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