Review of the Air Quality Criteria Document for Particulate Matter

dominant, one criticism is that “best lags” were reported, yet this use of best lags isjustified later (6-238). Almost all studies explore “data driven” lag structures.6-131 It is bizarre that the mortality data are brought up in this section dealing withhospitalizations to “shore up” the argument for PM & cardiovascular effects.6-134 Why does this summary only include the US studies, which incidentally are all “positive”studies, when important international studies, many of which are “negative” studies, arenot included?To summarize, rather than attempting to shore up favored hypotheses, and through doing so,revealing a bias, it would be preferable to do less editorializing during presentation of studies,and stand back for a more objective look at the studies as a whole. This is what we expect froma CD.3. ConfoundingConfounding remains an issue of concern. In the time-series studies, concerns regardingmeteorology, in the absence of more innovative approaches to specifying the form ofmeteorology in the time series regression models, can probably be put to rest given the manyattempts to incorporate alternative specifications without significant impacts on the PMestimates of effect. The CD is probably correct in this regard.Confounding by co-pollutants, a perennial concern, has also been addressed in the CD.Several points should be noted. First, it is correctly noted that effects based on attempts tocontrol for confounding in two-pollutant or multi-pollutant models are often difficult to interpretbecause of the typically strong between-pollutant correlations that are present in the time-seriesstudies. However, this does not imply that effects from single-pollutant models of PM areunconfounded estimates. The findings regarding PM effects, as well as estimates of PM effect inthe CD, are largely reported only from single-pollutant models (as one example, p.6-142, line17). Second, results from various alternatives to the use of multi-pollutant models inestimating PM effects unconfounded by co-pollutants are presented. These approaches aremotivated by frustration at interpreting PM effects from multi-pollutant models. In NMMAPS IIgaseous pollutant effects were controlled in a second stage (multiple city) analysis after theindividual-city single-pollutant PM effects were estimated. This is probably justified in thissetting given the relatively large number of cities included, although it seems difficult to imaginethat adequate control for co-pollutants could be adequately accomplished without attending tothe seasonal variation in co-pollutant concentrations, variation that itself differs from region toregion across the country. A different approach to addressing potential confounding by gaseouspollutants is exemplified by the multi-city hospitalization studies, including NMMAPS II(Schwartz 2000, Zanobetti 2000). Firstly, the description of these methods is difficult to followin the CD narrative (6.223-225). Descriptions of these alternative approaches to accounting forco-pollutant effects are difficult to follow. I still cannot figure out the rationale behind some ofthese approaches from reading this section, which may mean that others cannot either. Clearerrationale for the specific approaches taken is needed. Parenthetically, I wonder whether thecorrect correlation (r) between PM and the co-pollutants should be the correlation after adjustingfor long-term trends and meteorology (that is, correlations between the effect estimates ratherthan raw correlations). Secondly, we have much less confidence in the success of this approachgiven the much smaller number of cities ( and often smaller size of cities [e.g., Boulder,Youngstown) used for these analyses. The CD seems to uncritically accept this approach tocontrolling confounding by the gaseous pollutants (6-126, line 4, for example).There has been discussion of the potential for the gaseous pollutants to confound theassociation between PM and health effects from the perspective of the definition of confounding.It is argued that some of the co-pollutants cannot be viewed as confounders since, based onbiomedical knowledge, they should not affect the outcomes of interest. Neither SO 2 , sulfate norCO can reasonably be argued to cause many of the effects with which they are often associated.It would be true that these pollutants could not confound if in fact the ambient co-pollutantconcentrations were truly measuring exposure to these specific pollutants. Realistically,however, they do not. The co-pollutants are likely measuring various aspects of the pollutionmeteorologymix and acting as surrogate measures of important exposures that we do not nowA - 12