Air quality and acute deaths in California, 2000–2012
Creators: name: S. Stanley Young
name: Smith, Richard L.
name: Lopiano, Kenneth K.
Publisher: name: F1000Research
Year: 2016

description: B1: The abstract for the study is given. Note that our analysis data set is posted on the web.<br><br>B2: A very large data set was assembled for California: 13 years, over 37,000 exposure days, over 2 million electronic death certificates, daily ozone and PM2.5, and weather variables.<br><br>B3: California is divided into air basins where air flows are similar. We statistically analyze the 8 air basins with the highest populations. The data set covers about 90% of the population.<br><br>B4: Three types of analysis were computed, case crossover, time series regression and a sensitivity analysis.<br><br>B5: On the y-axis we have deviations of daily deaths from a time series smoother. On the x-axis we have daily deviations from time series smoothers for PM2.5 or ozone. The data is from the South Coast air basin which includes Los Angeles. The currently prevailing paradigm is that as air quality decreases that acute deaths should increase. If the prevailing paradigm held for this dataset we should see the point cloud (each dot represents one day) spread from lower left to upper right. We actually see that the cloud centers on zero/zero and that as either PM2.5 or ozone increases that there is no apparent effect on deaths. Similar images are found for the other seven air basins. This simple analysis is similar to a case crossover analysis.<br><br>B6: It is helpful to give an outline of the steps in a Time Series Regression Analysis, TSG. First there is the building of the data set. Variables are constructed for the response, in our case daily deaths, the predictor of interest, in our case either PM2.5 or ozone, and various covariates, lag variables, weather variables etc. A more detailed description of the variables is given in panel B7. The TSR is computed for each air basin. Under the prevailing paradigm, each air basin should have a significant air quality effect and the air basins can be considered replications. We also analyzed California data from the national NMMAPS dataset. The California was subjected to TSR analysis.<br><br>B7: The statistical model is given. It is quite complex, taking into account lag effects, day of week effects, time trends in mortality, weather, etc.<br><br>B8: Given here is a two-way scatter graph of the residuals from the TSR model. On the y-axis we have deaths and on the x-axis we have PM2.5. Each point is for a day. The results are one of the air basins. If the prevailing paradigm held, we should see points spreading from lower left to upper right. What we see is that there is no apparent effect. Several reference lines are included in the figure. The horizontal black line at zero represents no dose effect. The red solid line is a spline fit to the data. It tracks the black no-effect line. The dashed red lines are confidence bounds on the spline fit. The vertical black line is at the current declared save level. There is no apparent effect across the entire exposure levels.<br><br>B9: Given here is a two-way scatter graph of the residuals from the TSR model. On the y-axis we have deaths and on the x-axis we have ozone. Each point is for a day. The results are one of the air basins. If the prevailing paradigm held, we should see points spreading from lower left to upper right. What we see is that there is no apparent effect. The horizontal black line at zero represents no dose effect. The red solid line is a spline fit to the data. It tracks the black no-effect line. The dashed red lines are confidence bounds on the spline fit. The vertical black line is at the current declared save level. There is no apparent effect across the entire exposure levels.<br><br>B10: There are many analysis choices when executing a Time Series Regression. We designed and ran a large factorial experiment to vary the design choices. Using cloud computing we computed a total of 78,624 analyses.<br><br>B11: As part of the sensitivity analysis we left each year out and use the remaining data to fit a model and predict the holdout year. We found that the various models were visually indistinguishable; note the red lines on top of one another. Adding PM2.5 or ozone to the model did not improve the predictive power of the models.<br><br>B12: We give Box plots of the sensitivity analysis results for the South Coast air basin. We formed the ratio of each model to a model including only day of year. Results for each hold out year are given. Note that the ratios are very close to one. The empirical evidence is that a simple model including only day of year is adequate to explain the variability of the observed mortality data.<br><br>B13: Our analysis results are consistent with no effect of air quality, PM2.5 or ozone, on acute mortality in California.<br><br>B14:There is considerable literature supporting the current paradigm. Given here is selected literature that supports no effect of air quality on acute or chronic mortality.<br><br>B15: If air quality is causal of acute deaths, then any large, well-designed study should detect an association. That we and others do not detect an association implies that where there are observed associations, they are not causal. A technical report is available at http://arxiv.org/abs/1502.03062.<br><br> <br><br>