Summary
Average and maximum ground-level ozone measurements were not correlated with emergency admissions for asthma at a large Los Angeles hospital from 2009 through the first quarter of 2012.
Introduction
The U.S. Environmental Protection Agency (EPA) asserts that ambient ozone (smog or O3) exposures increase hospital emergency admissions for asthma.
The EPA claims, for example, that reducing the 8-hour ozone standard from the current 75 parts per billion (ppb) to 65 ppb would reduce the number of asthma exacerbations by 38,000 and related hospitalizations by 11,000 annually.
So we undertook to compare O3 measurements with hospital admissions for asthma in Los Angeles, which is often said to have some of the “worst” air quality in the U.S.
Methods
Through the Freedom of Information Act, we obtained the daily tally of hospital admissions for asthma from the Veterans Administration West Los Angeles Medical Center for the period January 1, 2009 to March 31, 2012, a total of 778 admissions.
We correlated these tallies with the highest average and maximum daily O3 levels as measured at the by the California Air Resources Board for the South Coast Air Basin, the air quality management district that includes the Los Angeles area.
We correlated the O3 and hospital admissions data on a same day, 1-day lag, 2-day lag and 3-day lag bases.
Results
The Pearson correlations and 95% confidence intervals between O3 measurements in the Los Angeles’ area and hospital admissions are presented in the table below.
A Pearson’s correlation of 1.0 indicates perfect correlation (i.e., higher ozone measurements are correlated with more asthma hospital admissions), a −1.0 indicates perfect inverse correlation (higher ozone measurements are correlated with fewer hospital admissions) and zero indicates no correlation between ozone measurement and hospital admissions.
All the correlations presented below are slightly negative and their 95 percent confidence intervals bound the no-correlation level.
| Pollutant | Pearson’s correlation | 95% Confidence Interval |
| Average O3 (No lag) | -0.01 | (-0.07, 0.04) |
| Average O3 (1-day lag) | 0 | (-0.05, 0.06) |
| Average O3 (2-day lag) | -0.01 | (-0.06, 0.05) |
| Average O3 (3-day lag) | -0.05 | (-0.1, 0.01) |
| Maximum O3 (No lag) | -0.03 | (-0.09, 0.03) |
| Maximum O3 (1-day lag) | -0.02 | (-0.08, 0.04) |
| Maximum O3 (2-day lag) | -0.03 | (-0.09, 0.02) |
| Maximum O3 (3-day lag) | -0.04 | (-0.1, 0.02) |
Discussion
These results indicate that average and maximum O3 measurements in the Los Angeles area were not correlated with admissions for asthma at the VA West Los Angeles Medical Center for the three-year period January 1, 2009 to December 31, 2011 — despite that the Los Angeles metropolitan area is said to have some of the worst air quality in America.
If ambient O3 measurements were in fact associated with hospital admissions for asthma, one could reasonably expect — and, in fact — ought to find a correlation in these data. But we did not.
Note: The study data are available upon request.
One more question:
Since you have the data on admissions already in a spread sheet, is there a reason you did not calculate correlations on PM 2.5?
I would be interested in examining the data on this. Normally, one would show a scatter plot, but from the Table, it’s rather clear what the plot should look like. Studies supporting the establishment of the Air Quality Index have indicated positive correlations with far more than asthma triggers, as I understand it, but are also linked to increased rates of other cardiopulmonary disease. I’m curious about why, if you used FOIA to obtain hospital records, you would not investigate other closely related issues. A sample size of 778 should certainly be sufficient to show a correlation, although in medical data, confounding variables can be troublesome.
Has this study been presented to the authors of any of the studies supporting EPA? If so, has there been any response?
Believe the observations, not the people.
Nature never lies – she is just a little inscrutable from time to time.