## Comments to the CASAC Ozone Review Panel from Mark Nicolich, Independent Consultant, Sponsored by the American Petroleum Institute

I thank the Panel for reading my submission for this meeting.

BACKGROUND I am asking CASAC to consider in its deliberati ons the journal article “Cumulative effects and threshold levels in air pollution mortality: data analysis of nine large US cities using the NMMAPS dataset,” by my co-author Mario Stylianou and me that was published in

Environmental Pollution in 2009 (Stylianou and Nicolich, 2009). This paper relates to several points used to develop the current and proposed ozone standards, and points referred to in the EPA charge questions. The specific points are the assumption of a linear no threshold concentration-response (CR) model for ozone exposure, the assumption that cities can be combined because they have similar CR func tions, and the modeling of ozone without consideration of other pollutants. I’ll provide a brief overview of the paper, and then discuss the points of the paper in some detail to support the conclusions.

ABSTRACT OF JOURNAL PAPER Briefly, in the journal article we examined the existence of thresholds, cumulative effects and the homogeneity of five air pollutants (simultaneous ly) on the relative risk of three mortality outcomes using data from nine major US c ities using data from NMMAPS. Overall, PM 10 (usually 200 day accumulation) and ozone (3 day accumulation) were the two important predictors of outcome but their effect was not the same across the nine cities. Many of the models for different cities and endpoi nts exhibited thresholds (25-45 μg/m 3 for PM 10 , and 10-45 ppb for O 3 ). Our analyses suggested that the use of a linear, no threshold model for pollution
studies is not consistent with the observed data. Also, the heterogeneity in the risk estimates across the nine cities suggested that the practice of combining individual city or location risk estimates to obtain a national risk estimate may not be justifiable and the resulting estimate is likely to be confounded.

DETAILS OF JOURNAL PAPER Although most of the air pollution literature deals with the short-term effects of air pollutants on mortality, it seems reasonable to assume that pollu tants may increase the risk for adverse events over longer duration of exposure because a narro w moving window may not adequately capture larger effect variations of pollutants under study. Several researchers have considered longerterm effects; Schwartz (2000) studied the effect of PM 2.5 and weather variables using moving averages of up to 60 days. Janes et al. (2007) considered mean monthly exposures over the previous 12 months with an approximate 30-day window. More recently, Schwartz (2008) explored the effect of PM 2.5 exposure to mortality for up to the previous five years. However, there is no consensus in the literature regarding th e lag or cumulative time needed to evaluate the pollutants effect.

Another consideration in air pollution models is the combined effect of several pollutants. Bell et al. (2004) estimated same day non-accidental mort ality using cumulative ozone exposure during the previous week considering PM 10 and the usual confounders of w eather, day of the week, and calendar time. They report that ozone is associ ated with daily non-accidental mortality (and the subsets of cardiovascular and respiratory mortality) and these results are independent of adjustment for PM 10 , weather, seasonality and long-term trends. However, PM 10 was considered only with a 1-day lag which is different from most published models and may explain why PM 10



### 1had no effect in the model with ozone. Bell et al. (2005) replicated their results in a subsequent meta-analysis of ozone mortality studies, again with short lag times for exposures to ozone and

PM 10 or PM 2.5 . It is possible that a PM effect on the death count may be more pronounced when it is cumulated over longer times.

Many of the models assessing the effect of several pollutants on death rates produce relatively poor fitting or biased estimates of exposure-r esponse association (Zeka and Schwartz, 2004). One explanation is that these pollutants are usually correlated in the short term and the correlation leads to unstable estimates of the pollution effects. One approach to the problem of correlated short-term data is to use long-term average or cumulative pollution data so the effect of correlation among long-term cumulative pollutants in the modeling process would be minimized.
We have based our models on the work of Xia and Tong (2006) who suggested a method to account for the cumulative effects of longer-term exposures to air pollution by using a weighted cumulative sum for each pollutant without the rest riction of a linear dose-response function. The weights are derived by a restricted minimization method where the weights are non-increasing (or decreasing) the further we go into the past. Th eir analysis of data from Hong Kong, El Paso, Chicago, and Pittsburgh suggests that cardiovascular disease (CVD) mortality is affected by a
cumulative effect of PM 10 for about 200 days, with roughly unifo rm weights, and that thresholds exist for all pollutants, noticeably for PM 10 and ozone. Importantly, their study suggests that different pollutants may require different cumulative periods.

The objective of our paper was to investigate the simultaneous cumulative effects of O 3 and PM on non-accidental mortality and the existence of thresholds for these pollutants. We considerd the assumption of monotonicity between the risk func tion and pollutant levels. We thought that a monotonic (but not necessarily linear) assumption for a pollutant effect was reasonable in the absence of plausible mechanisms for beneficial effects and their likelihood that these effects at low concentrations are possibly due to chance. In addition, we explored the cumulative effect of other pollutants, such as NO 2 , SO 2 , and CO on death outcomes. Finally we questioned the usefulness of combining the city specific ri sk estimates to form a national average.

This investigation was based on daily time-series data extracted from NMMAPS for 1987 to 2000 from nine major U.S. cities; Baltimore, Chicago, Dallas/Fort Worth, Los Angeles, Miami, New York, Philadelphia, Pittsburgh, and Seattle, whic h have a combined population of more than 35 million people. The response variables consider ed were total non-injury related mortality, cardiovascular disease (CVD) mortality and respiratory disease mortality.

The following is a brief outline of the statistical m odels used and the full statistical details are in the journal article. The models we used were based on the research of Xia and Tong (2006). For each cause of death and for each city we first fit a model that considers calendar variables (day, time) and meteorological variables (temperature, dewpoint). Only those variables that are significantly associated with the outcome (at p ≤ 0.05) remained in the model to form the “basic model”. We added each pollutant individually to assess its importance in predicting the number of events. Each pollutant associated with th e response in the basic model would be further considered for a multivariate model. We modeled the effects of pollutants on each of the daily causes of death using the generalized additive model. For example, a “full” model in a multivariate setting with a basic model plus all five pollutants could be:



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+ ++ ⎜ ⎟⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠⎝ ⎠ ⎛ ⎞⎛ ⎞

+ + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠ 

# ∑∑ ∑

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(1)

where μ is the intercept term, the subscript t represents study time, the summations are over the cumulative period of interest ( τ ), Y t is the daily number of deaths at time t

, D k,t is the indicator function for day of the week, β k is the mortality adjustment for the k th day of the week, T t is the average daily temperature, DP t is the average daily dew point, and Time t is calendar time. PM t and O t are the PM 10 and ozone levels at time t , while, NO 2t , SO 2t and CO t are the corresponding pollutants at time t

. are unknown smooth functions, 1 8 

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term at τ days prior to the current day,

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model fit was assessed both quantitatively and gra phically using residual and partial residuals plots. Autocorrelation and partial autocorrelation plots were used to assess the existence of any autocorrelation remaining in the residuals.

Estimates of the RR were derived using the final model. The RR at exposure x is the risk from being exposed at level x relative to the risk at the base level. When the effect-function is linear any base can be used because the effect change is the same at all points along the line. We have chosen to report risk relative to the lowest observed exposure as do Leitens torfer and Tutz (2007):



### exp( ( ( )) ( | ( )) ( ) ( | (min)) exp( ( (min)) g Pt E event P t RR t E event P g P = = (2)

where P(t) is the pollutant level at time t

, P(min) is the minimum pollutant level observed, and g is a smooth function.

In order to compare and contrast the individual c ity models with a model that is the same across cities, we have used a unified model that incor porated the same predictors for all cities. These predictors were time and meteorological variables and pollutants that are associated with death events across many cities.

Of the three types of deaths assessed (total, CVD, and respiratory), respiratory deaths have the weakest pollutant associations and total deaths outcome has the strongest associations. We found that 3-day cumulative ozone is the most likely pollu tant to have a threshold with an average value of 32 ppb using the non restricted models for the six cities that appear to have a threshold with a total death outcome. Using a monotonic assumption we obtain an average threshold level at 21 ppb. Using CVD death as an outcome, the averag e threshold with a non-restricted and monotonic models is at 28 and 31ppb. With the 200-day cumulative PM 10, a threshold is suggested only for Chicago using the unconstrained model, 29 and 30 μ g/m 3 for total and CVD deaths. Under monotonic model the average threshold is 31 and 30 μ g/m 3 for total and CVD deaths. The basic model analyses suggest that PM 10 and ozone are the most likely pollutants predictive of different accountings of mortality. We evaluated th e consistency of the pollutant effect across the different cities using a model that adjusts for the same variables, that is, the basic model plus



### 3PM 10 and ozone. Figure 1 graphically displays the statistical significance levels of PM 10 and
ozone in the above model. Neither PM 10 nor ozone is a predictor of total deaths in Miami; CVD deaths in Pittsburgh, Baltimore and Miami; or respiratory deaths in Pittsburgh, Miami and Dallas/Fort Worth. However, both PM 10 and ozone are associated with the total deaths in Chicago, NY, Pittsburgh, and LA; CVD deaths in Ch icago; and respiratory deaths in NY and LA. Please note that we use p-values not as a strict decision value, but in the sense of Weinberg (2001) and Poole (2001) to provide a useful scale to evaluate the relative usefulness of terms in
the models of different cities.

The risk functions of PM 10 on total deaths as shown in the journal article are mostly flat near a RR of 1, except Chicago where the RR for PM 10 increases and flattens around 35 μ g/m 3 . Some risk estimates are not increasing with the PM 10 level, such as NY or LA. Some even inexplicably decrease, as in Pittsburgh or Baltimore. Th is behavior could justify the monotonic (nondecreasing) assumption for the effect or risk function. The association and shape of the adjusted

PM 10 RR function (adjusted for ozone, day, time, temp erature, and dp) are inconsistent across the nine cities; the only risk function significantly larger than one is for Chicago at 29 μ g/m 3 . Over half the functions appear linear but are either flat or declining. Chicago clearly appears to be nonlinear. The observed range of PM 10 average cumulative levels and variability differ considerably between cities. Likewise, the shape of the ozone risk function is also inconsistent; thresholds appear to occur in the range of 20 to 40 ppb for all cities but Miami and LA. The estimated ozone risk function is not always a non-decreasing function (LA, Philadelphia, Pittsburgh, and Seattle).

Overall, our analyses suggest the effect of th ese pollutants across the nine cities studied is inconsistent. Associations are more likely for C VD or total death in older large industrialized cities such as Chicago and NY. A possible r eason as to why PM10 and ozone were not as strongly associated with respiratory deaths coul d be the low daily events. While high levels of
ozone and PM 10 are associated with increased non-accidental mortality, our analyses suggest that these effects are not uniform across the U.S., that dose-response relationships are not necessarily linear, and that some relationship shapes are suggestive of no-effect thresholds. We believe that a monotonic (but not necessarily linear) assumption for a pollutant effect is reasonable in the absence of plausible mechanisms for beneficial effects and the likelihood that effects at low concentrations are likely due to chance.

The RR is often assumed or reported to be linear (Bell et a. 2004; Schwartz et al. 2001), implying that a specific increase of a given pollutant has a fixed risk. In contrast, our cumulative pollutant community-specific analyses indicate that the pollu tant RR is often non-linear and the rate of risk increase is not constant. Bell et al.(2006) ha ve explored the possibility of non-linearity or thresholds but they only reported the combined national average, leaving the reader wondering about the shape of the community-specific risk es timates. Even with the combined risk estimate they did find the possibility of thresholds at lo w levels of ozone. However, they dismissed the threshold models because the improvement in the model AIC was minimal (<1%). We have demonstrated that the inclusion of statistically significant PM10 or ozone in the basic model does not provide a considerable model improvement either (<0.4%).

Our analyses suggest that thresholds exist for some of the cities. We found that PM 10 riskfunction for Chicago tends to increase and th en flatten at about a 200-day mean of 30 μ g/m 3 in
contrast to ozone risk-functions for most cities where the risk is flat at low levels and then starts to increase at about a 3-day mean of 20 to 30 ppb. These conclusions remain the same, even when we run the same basic model plus both PM 10 and ozone. In another sensitivity analysis, we have compared the model R 2 adj and AIC with an ozone term as e ither linear or with a threshold at



### 430ppb. For most cities, the threshold model perfo rms slightly better than the model using the
linear ozone term.

Many authors attempted to combine city-specific RRs into a single national estimate (Bell et al 2004 and 2006; Dominici et al. 2002). This approach can increase the statistical power and give an overall RR perspective. However, several underlying assumptions must be true for the
combined results to be valid. Since RR estimat es are conditional on other predictors in the model, it is necessary to include the same variables in order to have the same risk interpretation.Our analyses suggest that even with the same variables in the model, the shapes of the riskfunctions from such models are inconsistent acro ss the different cities. This indicates that a rigorous examination of the results from each city is necessary before including specific cities
into combined estimates of the RR. Similar conclusions regarding a ssociations between 12-
month PM 2.5 exposure and mortality were recently reached by Janes et al. (2007), that is, the
association between trends in PM 2.5 and mortality at the national scale is more likely to be
confounded. On the other hand our conclusions about linearity and th resholds are in contrast to those of Schwartz et al. (2008) using long term PM 2.5 exposure and mortality from the Harvard Six Cities Study.

CONCLUSION EPA’s ozone acute mortality risk assessment for the entire U.S. uses a fixed value based on a uniform linear no threshold model developed from 12 cities. Our work clearly shows that the assumption of a uniform linear no-threshold model across cities is inconsistent with the observed data. We have shown that each city has a different model form w ith different thresholds, and the threshold can change depending on the inclusion of other atmospheric pollutants in the model. In light of our results, ignoring location differences and combining the results into a single nationwide standard result appears to be unjustified. We believe that continuing to estimate risks, specifically at lower exposures, based on a singl e pollutant linear no threshold model is not technically supported and will add greatly to the uncertainty of the resulting estimated risks.

.

Mark Nicolich, PhD
Statistician
24 Lakeview Rd Lambertville, NJ 0850 mark.nicolich@gmail.com

609.397.4089

7 February 2011



### 5REF: Bell ML, Peng RD, Dominici F., 2006. The expos ure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environ Health Perspect Apr;114(4), 532-6

.

Bell ML, Dominici F, Samet JM ., 2005. A meta -analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology 16(4), 436-45.

Bell ML, McDermott A, Zeger SL, et al. 2004. Ozone and short-term mortality in 95 US urban communities, 1987-2000. JAMA 292, 2371-2378.

Dominici F, McDermott A, Zeger SL, et al., 2002. On the use of generalized additive models in time-series studies of air pollution and health. Am J Epidemiol 156(3), 193.

Janes H, Dominici F, Zeger SL.,2007. Trends in air pollution mortality, an approach to the assessment of unmeasured confounding. Epidemiology 18(4), 416-423.

Leitenstorfer F and Tutz G., 2007. Generalized monotonic regression based on B-splines with an application to air polluti on data. Biostatistics 8(3), 654-673.

Poole, C., 2001. Low P-Values or Narrow Conf idence Intervals: Which are More Durable? Epidemiology 12(3), 291-294.

Schwartz J., Coull B., Laden F., and Ryan L., 2008. Environmental Health Perspectives 116 (1), 64-69.

Schwartz J, Ballester F, Saez M, et al., 2001. The concentration-response relation between air pollution and daily deaths. Environ Health Perspect 109(10), 1001-1006.

Schwartz J. , 2000. Mortality displacement and long-term exposure effects related to air pollution and mortality. Health Effects Ins titute research report 94, Part I, 51-59.

Stylianou, M and Nicolich, MJ, 2009. Cumula tive effects and threshold levels in air pollution mortality: data analysis of nine large US cities using the NMMAPS dataset. Environ
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Weinberg, CR., 2001. It’s Time to Rehabilitate the P-Value. Epidemiology 12(3), 288-290.

Xia Y and Tong H., 2006. Cumulative effects of air pollution on public health. Stat Med 25(20), 3548-3559.

Zeka A and Schwartz J, 2004. Estimating the inde pendent effects of multiple pollutants in the presence of measurement error: An applicati on of a measurement-error-resistant technique. Environ Health Perspect 112(17), 1686-1690.



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