The Health Effects Institute (HEI) has recently released an announcement that Johns Hopkins University investigators of the National Morbidity, Mortality and Air Pollution Study (NMMAPS) have updated their previous estimates of the mortality effects of acute exposure to particulate air pollution.
Below find frequently asked questions and answers provided by the Hopkins NMMAPS team that help to better understand the issues involved. More details are available from the web-site: http://www.biostat.jhsph.edu/biostat/research/update.main.htm
What is the concern?
The team carrying out the National Morbidity, Mortality and Air Pollution Study or NMMAPS has published estimates of the health effects of air pollution that were obtained by application of a method called generalized additive models (GAMs) implemented in the S-plus statistical software. A GAM is a valuable methodology for estimating an effect of one or more variables (here, air pollution, weather and time) on an outcome (here, daily deaths) when we cannot assume that the relationship takes a particular functional (e.g., linear) form. Because of its flexibility, it is widely used in air pollution research and other applications. The GAM estimation procedure, as implemented in standard statistical software (as, for example, in S-PLUS , Stata, R and SAS) relies upon default convergence criteria. We accepted the default convergence criteria in the S-PLUS version 3.4 and recently discovered that they were inadequate to produce optimal estimates and could introduce upward bias. We have re-done our analyses with more stringent convergence criteria for the GAM estimation procedure and found that estimates for individual cities changed by small amounts and that the estimate of the average particulate pollution effect across the 90 largest U.S. cities changed from a 0.41% increase to a 0.27% increase in daily mortality per 10 micrograms per cubic meter of PM10. As an independent verification, we have also used a similar parametric model (GLM) and distinct software (glm in S-PLUS 3.4) and obtained a pooled estimate of 0.22%, similar to the value from GAM. See (http://www.healtheffects.org/Pubs/NMMAPSletter.pdf) for further discussion.
Do the NMMAPS conclusions change?
Each of these findings is unchanged in our re-analysis using the stricter convergence criteria.
But doesn’t the decrease from 0.41% to 0.27% represent a 35% decrease in your assessment of the effect of PM10 on mortality?
Yes, this is one way to look at our result. However, the implications may be less than is implied by a 35% reduction in the relative rate. Although we use relative changes to measure the association of air pollution with mortality, for public health purposes, it is the change in absolute risk associated with pollution that is more important. In our case, the change in risks due to implementing the improved algorithm is 0.41-0.27=0.14% per 10 micrograms per m3 of PM10.
To understand these changes, consider the city of Baltimore where there are roughly 20 deaths per day or 7,300 deaths per year. If we could reduce PM10 in Baltimore from the current average value of 35 down to 25 micrograms per m3, our prior estimate of 0.41% corresponds to saving 30 lives per year from the acute effects alone. Our updated estimate would correspond to 20 deaths, a change of 10 deaths per year.
Having heard so much about the health effects of particulate air pollution from the news, I am surprised that there are only 20 deaths per year attributable to particle exposure in a city the size of Baltimore.
The 20 deaths are attributable to acute exposure only. Time series studies like NMMAPS compare mortality within the same population on higher versus lower pollution days and can therefore only estimate the effect of shorter-term elevations in pollution. But persons in Baltimore and other cities are also chronically exposed, that is exposed day-in and day-out. Other long-term “cohort” studies estimate the combined effects of chronic and acute exposure by comparing rates of death across U.S. cities, statistically controlling for personal characteristics such as age and smoking. The major U.S. cohort studies are the American Cancer Society Study (Pope et al. 2002) and the Six Cities Studies (Dockery et al, 1993). They estimate an increase in total mortality of roughly 4% and 5% per 10 microgram per m3 increase in the long-term level of particles, respectively. This is an order of magnitude higher than found in time series studies. Cohort studies also have been used to predict the number of lives saved from a pollution reduction program since both acute and chronic exposures will change.
Then why are the time series studies useful if they only estimate the acute effects and the cohort studies can estimate the combined acute and chronic effects?
The time series studies like NMMAPS contribute important information in identifying whether particles acutely cause illness and death, presumably because persons with underlying heart and lung disease are at risk. By comparing mortality from day to day within the same population, time series studies are less subject to “ecologic bias”. The time series studies also provide evidence relevant to scientific questions that support a causal relationship of particles with mortality including: the effects of co-pollutants, cause-of-death-specific pollution effects, exposure measurement error, existence of thresholds, and geographic variations in the pollution effects that might point toward the toxic component of the particles.
How did you discover the convergence problem with the GAM function in S-plus?
As a follow up to our main NMMAPS analyses, we continued to explore
the sensitivity of the results to several alternative modeling approaches. In doing so, we came across a situation where the software package unexpectedly calculated the same relative rate estimates for particulate pollution even though we were substantially changing the adjustment for confounding factors. This hint led us to examine the implementation of the algorithm line-by-line and to the finding that the default convergence criteria were not adequate for our problem. Using default settings has been the standard of practice in environmental epidemiology until we started investigating this issue.
Does this mean the all time series studies of air pollution will have biased results?
No. First, the convergence problem only occurs if two or more smooth curves are in the GAM. In our case, we used smooth curves for time, temperature, and dew point temperature. Furthermore, the size of the bias depends on two key factors: (1) the size of the log-relative rate; and (2) the degree of correlation between the pollution variable and the smooth functions of the other confounders. We cannot predict the direction or size of the bias, but it tends to be relatively more important for smaller effects when the correlation above is larger.