Multicity Studies and Meta-Analyses

In reviewing the range of mortality risk estimates, multicity studies provided the most useful information because they analyzed multiple cities data in a consistent method, avoiding potential

publication bias. Risk estimates from multicity studies usually are reported for consistent lag days, further reducing potential bias caused by choosing the “best” lag in individual studies. There have been several multicity studies from the U.S., Canada, and Europe. Meta-analysis studies also provided useful

information on describing heterogeneity of risk estimates across studies, but unlike multicity studies, the heterogeneity of risk estimates seen in meta-analysis may also reflect the variation in analytical

approaches across studies. Thus, we focused our review mainly on the results from multicity studies, and effect estimates from these studies were summarized. Discussion focused on the studies that were not affected by generalized additive models (GAMs) with convergence issues (Dominici et al., 2002; Ramsay et al., 2003) unless otherwise noted when the studies raised relevant issues.

3.3.1.1. National Morbidity, Mortality, and Air Pollution Study (NMMAPS)

The time-series analysis of the largest 90 U.S. cities (Samet et al., 2000; reanalysis Dominici et al., 2003) in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was by far the largest multicity study conducted to date to investigate the mortality effects of air pollution, but its primary interest was PM (i.e., PM10), and NO2 was not measured in 32 of the 90 cities. This study’s model adjustment for weather effects employed more terms than other time-series studies in the literature, showing that the model adjusted for potential confounders more aggressively than the models in other studies. PM10 and O3 (in summer) appeared to be more strongly associated with mortality than the other gaseous pollutants. Regarding NO2, SO2, and CO, the authors stated, “The results did not indicate associations of these pollutants with total mortality.” PM10, NO2, SO2, and CO showed the strongest association at lag 1 day (for O3, it was lag 0 day), and the addition of other copollutants in the model at lag 1 day hardly affected the mortality risk estimates for PM10 or the gaseous pollutants. Figure 3.3-1 shows the total mortality risk estimates for NO2 from Dominici et al. (2003). The NO2 risk estimates in the multipollutant models were about the same or larger. Thus, these results do not indicate that the NO2-mortality association was confounded by PM10 or other pollutants (and vice versa).

Source: Dominici et al. (2003). Figure 3.3-1. Posterior means and 95% posterior intervals of national average estimates for NO2 effects on

total mortality from nonexternal causes at lags 0, 1, and 2 within sets of the 90 cities with pollutant data available. Models A=NO2 alone; B=NO2+PM10; C=NO2+PM10+O3;

D=NO2+PM10+SO2; E=SO2+PM10+CO.

3.3.1.2. Canadian Multicity Studies

There have been four Canadian multicity studies conducted by the same group of investigators (Burnett et al., 1998, 2000, 2004; Brook et al., 2007). This section focuses on Burnett et al. (2004) and Brook et al. (2007), as these studies are most extensive both in terms of the length and coverage of cities.

Total (nonaccidental), cardiovascular, and respiratory mortality were analyzed in the Burnett et al. (2004) study of 12 Canadian cities from 1981 to 1999. Daily 24-h avg as well as 1-h max values were analyzed for all the gaseous pollutants and coefficient of haze (CoH). For PM , coarse PM (PM ),

the 3-day moving average (i.e., average of 0-, 1-, and 2-day lags), and for O3, it was the 2-day moving average. Of the single-day lag estimates for NO2,

Lag 1 day showed the strongest associations, which was consistent with the NMMAPS result, but its risk estimate was more than 4 times larger than that for the NMMAPS study. The 24-h avg values showed stronger associations than the 1-h max values for all the gaseous pollutants and CoH except for O3. The pooled NO2 mortality risk estimate in a single-pollutant model (for all available days) was 2.0% (95% CI: 1.1, 2.9) per 20-ppb increase in the 3-day moving average of NO2. The magnitudes of the effect estimates were similar for total, cardiovascular, and respiratory mortality. Larger risk estimates were observed for warmer months. NO2 was most strongly correlated with CoH (r=0.60), followed by PM2.5 (r=0.48). The NO2-mortality association was not sensitive to adjustment for these or any of other pollutants in the two-pollutant models. However, Burnett et al. (2004) noted that simultaneous inclusion of daily PM2.5 data (available for 1998 and 2000; sample size comparable to the main analysis [every 6th day from 1981 to 1999]) and NO2 in the model resulted in a considerable reduction of the NO2 risk estimates. Authors discussed that reducing combustion would result in public health benefits because NO2 or its products originate from combustion sources, but cautioned that they could not implicate NO2 as a specific causal pollutant.

Brook et al. (2007) further examined data from 10 Canadian cities with a special focus on NO2 and the role of other traffic-related air pollutants. Again, NO2 showed the strongest associations with mortality among the pollutants examined including NO, and none of the other pollutants substantially reduced NO2 risk estimates in multipollutant models. The analysis also confirmed the Burnett et al. (2004) study results that NO2 risk estimate was larger in the warm season. Generally, NO was more strongly correlated with the primary VOCs (e.g., benzene, toluene, xylenes) than NO2 or PM2.5. NO2 was more strongly correlated with the organic compounds than it was with the PM mass indices or trace metals in PM2.5. Brook et al. (2007) concluded that the strong NO2 effects seen in Canadian cities could be a result of it being the best indicator, among the pollutants monitored, of fresh combustion as well as photochemically processed urban air.

In summarizing the Canadian multicity studies, NO2 was most consistently associated with

mortality among the air pollutants examined, especially in the warm season. Adjustments for PM indices and its components generally did not reduce NO2 risk estimates. NO2 also was shown to be associated with volatile organic compounds that are indicative of combustion products (traffic-related air pollution) and photochemical reactions.

3.3.1.3. Air Pollution and Health: A European Approach (APHEA) Studies

The APHEA project was a European multicity effort, which analyzed data from multiple studies using a standardized methodology. This section focuses on the more recent APHEA2 studies which included 29 European cities.

Samoli et al. (2006) analyzed 29 APHEA2 cities to estimate NO2 associations for total,

cardiovascular, and respiratory deaths. The average of lags 0-1 days was chosen a priori to avoid potential bias with the “best” lag approach. In addition, the association of total mortality with NO2 over 6 days (lags 0-5) were summarized over all cities using a cubic polynomial distributed lag model. Results from this model showed multiday effects, with the strongest association shown at lag 1 day, which was consistent with the results from NMMAPS and Canadian multicity studies. The risk estimates for total, cardiovascular, and respiratory causes were comparable. In the two-pollutant models with black smoke, PM10, SO2, and O3, the risk estimates for total and cardiovascular mortality were not affected. The second-stage analysis examined possible effect modifiers. For total and cardiovascular mortality, the geographical area (defined as western, southern, and central eastern European cities) was the most important effect modifier (estimates were lower in eastern cities), followed by smoking prevalence (NO2

results showed effects of NO2 on mortality, but that the role of NO2 as a surrogate of other unmeasured pollutants could not be completely ruled out.

In an earlier study, Katsouyanni et al. (2001; reanalysis, 2003) analyzed data from 29 European cities and reported risk estimates for PM10 and not for NO2, but found that the cities with higher NO2 levels tended to have larger PM10 risk estimates. Furthermore, simultaneous inclusion of PM10 and NO2 reduced the PM10 risk estimate by half. An analysis of the elderly mortality in 28 of the same cities (Aga et al., 2003) also found a similar effect modification of PM by NO2. Thus, PM and NO2 risk estimates in these European cities may be reflecting the health effects of the same air pollution source and/or act as effect modifiers of each other.

3.3.1.4. The Netherlands Study

While the Netherlands studies for the 1986 to 1994 data (Hoek et al., 2000, 2001; reanalysis in Hoek, 2003) are not multicity studies and the Netherlands data were also analyzed as part of APHEA2 (Samoli et al., 2006), the results from the reanalysis (Hoek, 2003) are discussed here, because the database comes from a large population (14.8 million for the entire country) and a more extensive analysis was conducted than in the multicity studies. PM10, black smoke, O3, NO2, SO2, CO, sulfate (SO42–), and nitrate (NO3–) were analyzed at lags 0, 1, and 2 days and the average of lags 0-6 days. All the pollutants were associated with total mortality, and for single-day models, lag 1 day showed strongest associations for all the pollutants. NO2 was most highly correlated with black smoke (r=0.87), and the simultaneous inclusion of NO2 and black smoke reduced both pollutants’ risk estimates (the NO2 estimate was reduced by more than 50%). PM10 was less correlated with NO2 (r=0.62), and the simultaneous inclusion of these pollutants resulted in an increase in the NO2 risk estimate.

3.3.1.5. Other Multicity Studies

Other European multicity studies, conducted in eight Italian cities (Biggeri et al., 2005), nine French cities (Le Tertre et al., 2002) and seven Spanish cities (Saez et al., 2002) provide evidence for a short-term NO2 effect on mortality. An additional multicity study was conducted in Australian cities (Simpson et al., 2005b). The studies by Biggeri et al. (2005) and Simpson et al. (2005b) are summarized in this section. The studies by Le Tertre et al. (2002) and Saez et al. (2002), conducted using GAM methods with the default convergence setting, are presented in Annex Table AX6.3-19.

Biggeri et al. (2005) analyzed eight Italian cities (Turin, Milan, Verona, Ravenna, Bologna, Florence, Rome, and Palermo) from 1990 to 1999. Only single-pollutant models were examined in this study. Statistically significant positive associations were observed between NO2 and total, cardiovascular, and respiratory mortality, with the largest effect estimate observed for respiratory mortality. Since all the pollutants showed positive association and the correlations among the pollutants were not presented, it was not clear how much of the observed associations are shared or confounded. The mortality risk estimates were not heterogeneous across cities for all the gaseous pollutants.

Simpson et al. (2005b) analyzed data from four Australian cities (Brisbane, Melbourne, Perth, and Sydney) using methods similar to the APHEA2 approach. They also examined sensitivity of results to alternative regression models. Associations between mortality and NO2, O3, and nephelometer readings (a measure of PM) were examined at single-day lag 0, 1, 2, and 3 days and using the average of 0- and 1-day lags. Among the three pollutants, correlation was strongest between NO2 and nephelometer readings, ranging from (r ~0.62 among the four cities). Of the three pollutants, NO2 showed the largest mortality risk estimates per interquartile range. Similar to the study by Biggeri et al. (2005), the strongest association was observed between NO2 and respiratory mortality, compared to total or cardiovascular mortality. The three alternative regression models yielded similar results. The NO risk estimates were not

sensitive to the addition of nephelometer readings in the two-pollutant models for total mortality, but the nephelometer risk estimate was greatly reduced in the model with NO2.

3.3.1.6. Meta-Analyses of NO

Mortality Studies

Stieb et al. (2002) reviewed time-series mortality studies published between 1985 and 2000, and conducted a meta-analysis to estimate combined effects for each of PM10, CO, NO2, O3, and SO2. Since many of the studies reviewed in that analysis were affected by the GAM convergence issue, Stieb et al. (2003) updated the estimates by separating the GAM versus non-GAM studies and by single- versus multipollutant models. There were more GAM estimates than non-GAM estimates for all the pollutants except SO2. For NO2, there were 11 estimates from single-pollutant models and only 3 estimates from multipollutant models. The lags and multiday averaging used in these estimates varied. The combined estimate for total mortality was 0.8% (95% CI: 0.2, 1.5) per 20-ppb increase in the daily average NO2 from the single-pollutant models and 0.4% (95% CI: -0.2, 1.1) per 20-ppb increase in the 24-h average from the multipollutant models. Note that, although the estimate from the multipollutant models was smaller than that from the single-pollutant models, the number of the studies for the multipollutant models was small (3), also, the data extraction procedure of this meta-analysis for the multipollutant models was to extract from each study the multipollutant model that resulted in the greatest reduction in risk estimate compared with that observed in single-pollutant models. It should be noted that all the multicity studies whose combined estimates have been discussed above were published after this meta-analysis.