  Many studies have sought to determine if there is an association between air
quality and acute deaths. Many consider it plausible that current levels of air
quality cause acute deaths. However, several factors call causation and even
association into question. Observational data sets are large and complex.
Multiple testing and multiple modeling can lead to false positive findings.
Publication, confirmation and other biases are also possible problems.
  Moreover, the fact that most data sets used in studies evaluating the
relationships among air quality and public health outcomes are not publicly
available makes reproducing the claims nearly impossible. Here we have built
and made publicly available a dataset containing daily air quality levels,
PM2.5 and ozone, daily temperature levels, minimum and maximum and daily
relative humidity levels for the eight most populous California air basins. We
analyzed the dataset using a moving median analysis, a standard time series
analysis, and a prediction analysis within the following analysis strategy. We
examine the eight air basins separately to see if estimates replicate across
locations. We use leave one year out cross validation analysis to evaluate
predictions. Both the moving medians analysis and the standard time series
analysis found little evidence for association between air quality and acute
deaths. The prediction analysis process was a run as a large factorial design
using different models and holding out one year at a time. Among the variables
used to predict acute death, most of the daily death variability was explained
by time of year or weather variables. In summary, the empirical evidence is
that current levels of air quality, ozone and PM2.5, are not causally related
to acute deaths for California. An empirical and logical case can be made air
quality is not causally related to acute deaths for the rest of the United
States.
