Invited Commentary: Timescale-dependent Mortality Effects of Air Pollution

The concept of “harvesting,” by whatever name, dates back to the early days of research on the health effects of air pollution. For example, Martin (1), summarizing the results of several London smog studies from the 1950s, noted the short timescale of response to high-air-pollution events. Referring specifically to the December 1952 event, Martin noted that “no cases of sudden death were found which could not have been explained by previous respiratory or cardiovascular lesions” (1, p. 969). Later in the same article, the author stated, “[S]ome of [those who died] might in the natural course have died within a few days. This hypothesis is supported by the compensatory fall of the mortality curve to subnormal levels often noticed after a pollution incident” (1, p. 974). Evidently, Martin was talking about what we now call harvesting, but in a context where both the air pollution and the consequent mortality were much higher than in modern US and European cities. The question is whether similar effects persist under present-day conditions. The question is important, as I perceive it, not because proof of a harvesting effect would in any way lessen the need to take action against air pollution, but because such an effect, if present, would help us better understand the true meaning of studies such as the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), and could help us determine which pollution control or other kinds of public health strategies are most likely to be effective in reducing deaths due to air pollution. Numerous investigators have looked for a harvesting (or mortality displacement) effect, using a variety of methodologies, but without finding any convincing evidence in favor of this hypothesis. Zeger et al. (2) and Smith et al. (3) independently proposed a compartment model in which people pass from a “healthy” state to a “frail” state in which they eventually die, but this model, even though it is almost certainly too simple to represent any true harvesting phenomenon, is extremely hard to fit. Smith et al. (3) proposed a Markov chain Monte Carlo estimation scheme that appeared to show evidence of harvesting in Chicago, Illinois, but when the same model was fitted to simulated data series without harvesting, in 19 out of 20 cases the method appeared to provide evidence in favor of harvesting. Thus, the Markov chain Monte Carlo method does not seem to be effective in discriminating between a harvesting model and a standard regression model, and other authors have rejected this kind of approach in favor of one that examines timescale effects. Zeger et al. (2) proposed an approach based on frequency decompositions of both the mortality and air pollution series, while Schwartz (4) used nonparametric smoothing techniques to decompose the series into long, intermediate, and short timescale series and then looked for pollution-mortality relations in the intermediate timescale series. Both Zeger et al. and Schwartz concluded that air pollution effects exist on a timescale of weeks rather than days and argued that this was inconsistent with a simple harvesting effect. In the current issue of the Journal, Dominici et al. (5) develop a new approach based on a spectral decomposition of only the pollution series, where different components correspond to effects at different timescales. This is simpler both conceptually and for practical implementation than the procedure of Zeger et al. (2). Dominici et al. have helpfully provided software with which to perform the decomposition, and the rest of the analysis uses Poisson regression techniques that are by now totally standard in this field. They have applied this method to four of the series in the NMMAPS database—the only four for which daily readings of particulate matter with an aerodynamic diameter of 10 μm or less (PM10) are available—as well as some older data from Philadelphia, Pennsylvania. They conclude that the strongest effects are those associated with timescales on the order of 1–2 months. This reinforces the conclusions of the earlier