Invited Commentary: Ripeness Is All

An association between air pollution and hospital admission for heart failure was first noted in the literature in 1995 (1). The association was confirmed in subsequent studies (2, 3), along with many studies showing an association with a more general increase in cardiovascular admissions (4–6). The most consistent associations have been with carbon monoxide and particulate matter, and both Poisson time series (1, 2) and case-crossover analyses (3) have been used. Some of these studies have been quite large—for example, Wellenius et al. (3) examined seven cities and 293,000 emergency admissions. More recently, studies of the health effects of air pollution have improved exposure assessment through better resolution in space within cities, differentiation among particle species indicative of different sources of pollution, and better resolution in time. A key limiting factor for all of these approaches is the need to collect more detailed data, whether on time, on place, or on exposure. This is particularly true for event data, as such information is difficult and expensive to obtain. Better spatial resolution reduces exposure error, which is important for the study of pollutants that are not spatially homogeneous, such as traffic exhaust. For example, Kinney et al. (7) reported large differences in exposure to carbon particles from traffic between streets with bus routes and side streets in a Manhattan neighborhood. Making use of such variation, Kunzli et al. (8) used estimates of particulate matter with a diameter less than 2.5 lm (PM2.5) based on geographic information systems to identify an association between particulate exposure and atherosclerosis in Los Angeles, California. Jerrett et al. (9) recently reported a much larger association between within-city variation in particles and survival in the American Cancer Society cohort than had previously been reported for between-city variation in particles, demonstrating the importance of reducing this type of exposure error. By differentiating particles into components that are tracers for specific sources, we help to identify the relative toxicity of emissions from those sources. Since, in the end, pollution control is done at sources, this can provide valuable information for public health policy. Investigators have begun to differentiate exposure in this as well. For example, O’Neill et al. (10) reported that both sulfate particles from coal-burning power plants and black carbon particles from traffic were associated with approximately 12 percent reductions in flow-mediated dilation of the brachial artery, a noninvasive measure of endothelial function with proven prognostication for cardiovascular endpoints (11). This brings us to time. Previous studies of the association between fluctuations in air pollution and serious events have often been limited because the timing of the events was no better than calendar day. This has also limited studies of nonenvironmental risk factors. The case-crossover design is ideally suited for examining short-term influences on an event, while controlling by design for most potential confounders. Identification of the timing of events has been recognized as crucial for determining the roles of short-term potential triggers, such as anger. The design was first developed to identify triggers of myocardial infarction (12), and it has shown its continued usefulness for this purpose, including when examining effects of short-term exposure to particles (13). It requires data from hospital interviews to establish the time of onset of the myocardial infarction. In case-crossover studies, subjects who experienced an event at a given time are matched with themselves as controls, using another time in which they did not experience an event. This is a powerful approach, since in addition to controlling for personal risk factors by matching, controls can also be