OAR Box 1112 

Prepped by Ryan Dugan 

Document Number: 

16) ll-B-5 

Docket Number: 

A-83-22 

Printed 4/16/2008 9:12:06 AM Sheet 16 of 97 

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, _ _ ^ UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 'H_-<IS>-S^ 

I ^SH%Z. ? WASHINGTON, D.C. 20460 

^ U 3 7-'^ 5 

OFFICE OF 

POLICY, PLANNING AND EVALUATION 

SUBJECT: LAG TIME IN THE RESPONSE OF BLOOD LEAD TO AIR LEAD AND 

ITS IMPLICATIONS FOR AVERAGING TIME 

FROM: Joel Schwartz 

Office of Policy Analysis 

TO: Jeff Cohen 

Office of Air Quality Planning and Standards 

This memo discusses the results of our analysis of the lag 

time for changes in air emissions to show up as significant 

changes in blood lead. In our analysis of the NHANES and other 

data we found that there was a detectable lag structure between 

changes in air lead and changes in blood lead levels. This lag 

was also found in the Chicago and New York screening program 

data, in the CDC screening program data, and was also found by 

Rabinowitz et al. in their analysis of cord lead levels in 

Boston. We also used logistic regression analysis of the 

probability of children having high blood lead levels, and found 

the same lag structure. 

In the NHANES analysis, variables for gasoline lead use in 
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the month that the individual was examined, the previous month, 

the second previous month, and the third previous month were 

considered. When only one of those variables was used in a 

regression, along with the demographic factors, the previous 

months gasoline lead had the highest R2 an d th e highest tstatistic. The third previous month's gasoline lead was not 

significant. When all three significant variables were entered 

simultaneously the regression did not become ill conditioned, 

and the ratio's of the regression coefficients therefore 

indicated the relative contributions of the three months gasoline 

lead variables to the blood lead. Again, the previous months 

gasoline lead was most significant, and had the largest 

coefficient. The current month's and second previous month's 

coefficients were approximately equal in size, and about half 

that of the previous month's coefficient. We therefore tried a 

distributed lag, creating a variable equal to 1/2 the previous 

months gasoline lead plus 1/4 the current and second previous 

months gasoline lead, and it gave an even better fit, confirming 

the fact the the ratio's we found were showing the distribution 

of the real lags. 

To confirm whether the one month lag for a significant 

change in blood lead showing up was in fact correct, we examined 

the New York and Chicago screening programs. We only had 

quarterly data on blood lead levels for these programs so the 

distributed lag analysis was unlikely to work, but, since we had 

monthly gasoline lead use in the SMSA's, we could see whether the 
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•V 

we obtained a better fit using the same three month's gasoline 

lead, or by lagging the gasoline lead by a month (i.e. by using 

December, January, and February gaslead in a regression for the 

average blood lead in January, February, and March). The one 

month lagged gasoline lead gave a higher R2 the n th e concurren t 

quarterly gasoline lead in both cities. 

Rabinowitz et al., in their analysis of cord lead and 

gasoline lead in Boston, also found that the one month lagged 

gasoline lead fit their data better. Thus four different data 

sources seem to confirm the same effect. 

To determine whether the prevalence of high blood lead 

levels is also responsive to month to month changes in air lead 

we performed logistic regression analysis of the probability of 

being above 30 ug/dl versus gasoline lead using the NHANES data. 

Once again, one month lagged gasoline lead explained a higher 

portion of the variance, and the distributed lag model gave an 

even better fit. To confirm these results in another data set we 

used the CDC reports of the number of children screened each 

quarter for lead toxicity, and the proportion that were found to 

have lead toxicity. Again, one month lagged gasoline lead gave a 

better fit than concurrent gasoline lead in explaining the 

proportion of children that had lead toxicity. Thus changes in 

air lead emissions produce changes in the number of children with 

high blood lead levels, with the greatest effect showing up 

about a month latter. This indicates that there will be 
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significant changes in the number of children with high blood 

lead levels due to month to month changes in the air lead 

concentration. Setting a monthly standard would protect against 

these increases. Obviously, these are children who will only 

spend a month or two with high blood lead levels, so the health 

risk is less than for children who spend a considerable time at 

high levels, and therefore the health benefits are smaller; 

however the cost of running the compliance monitors more often 

can't be that expensive, and that seems to be the only thing 

counterbalancing the epidemiological evidence that these changes 

will occur in a month. Using a monthly standard will also de 

facto reduce the average air lead level, since controls will be 

installed to meet standards in the high emission months, and that 

will both increase costs to industry and permanently reduce the 

number of children at high blood lead levels. That is a 

separate issue, since on average, the same reduction in the 

average number of children with high blood lead levels can be 

achieved with a lower quarterly standard.