Holstius et al. PM10 levels. Improved analysis with PM10 as a continuous variable or modeling of the PM exposure using satellite data or chemical transport models might reveal a relationship between wildfire-related PM exposure and birth weight. Alternatively, it is also possi- ble that the associations with decreased birth weight were mediated not by air pollution but by some other mechanism, such as stress. Several factors could explain our finding of stronger associations with exposure during the second and third trimesters than exposure during the first trimester. First, there is the issue of exposure misclassification. Given that the date of conception is less certain than the date of delivery, it is possible that some infants were categorized as exposed in the first trimester, when in fact their conception date occurred after the fire was over. This misclassification bias is unlikely to affect exposure assessment in the third or second trimester, but may lead to an underestimate of effects during the first trimester. Overestimation of the length of the wildfire event would also have resulted in some unexposed births being misclassified as first-tri- mester exposures. However, reducing the length of the wildfire event did not substantively alter our main effects. Exposure could also have increased the risk of preterm birth or fetal loss. Because we excluded preterm births and fetal losses, excess preterm delivery or fetal loss among the first-trimester exposed could have differ- entially eliminated the most vulnerable from our sample. When Breton et al. (2011) exam- ined prenatal exposure to high PM2.5 levels from the same wildfires among eight counties in Southern California, using vital statistics records for 2003–2004, they did not estimate a significant effect on preterm birth, but they also did not assess fetal loss. The effects of wildfire exposure on birth weight could also be stronger among those exposed in the sec- ond or third trimesters for reasons that are not yet understood. Further examination of the effects of trimester-specific exposures in other studies may help to resolve this question. Seasonal confounding. Controlling for seasonal variation in time-series studies of air pollution can be a challenge (Slama et al. 2008; Woodruff et al. 2009). For example, both temperature and ambient (non-wildfire) air pollution exhibit seasonal patterns, and these patterns themselves vary geographically due to differences in regional characteristics. When we controlled for seasonality using quarterly indicators, 87.6% of the infants exposed in the third trimester were conceived in Q1 (January–March), and 94.8% of those exposed in the second trimester were con- ceived in Q2 (April–June) (Table 1, Figure 2). This raised the possibility of confounding between trimester-specific wildfire exposure and conception in the first half of the year. 1344 To address this, we conducted a sensitivity analysis, parameterizing the seasonal compo- nent of the model as a smooth, continuous, and periodic function of time: the cosinor (Barnett and Dobson 2010). The general form of the cosinor is sinusoidal, like many natural seasonal phenomena, and has only two degrees of freedom, amplitude and phase [see Supplemental Material, Equations S1–S4 (http://dx.doi.org/10.1289/ehp.1104515)]. As such, it is readily interpretable, and it has been widely applied to the analysis of sea- sonal and circadian rhythms (Barnett and Dobson 2010). The cosinor-based analysis yielded effect estimates consistent with the pattern described by our primary model, increasing our confi- dence in the results. The peak-to-peak ampli- tude (11.6 g) in seasonal variation was similar to the difference between the minimum and maximum seasonal coefficients from the model using indicator terms (11.9 g). The phase was also consistent with the primary model’s seasons of lowest and highest aver- age birth weight [January–March (Q1) and July–September (Q3), respectively]. Other potential confounders. We adjusted for several individual-level covariates known to be associated with birth weight, but data on other potential confounders were not avail- able. For example, maternal smoking is not reported on most California birth records, and its inclusion in our study may have changed our results. However, recent studies suggest that although smoking during pregnancy has a large effect on birth weight, in studies of ambient air pollution it does not significantly confound the association between ambient air pollution exposure and adverse perinatal out- comes such as infant mortality and preterm birth (Basu et al. 2003; Darrow et al. 2006). In previous research of wildfire health effects, few studies have attempted to sepa- rate the fraction of smoke attributable to wildfire from that attributable to background air pollution (for a review, see Dennekamp and Abramson 2011). In areas with signifi- cant sources of other pollution, such as the SoCAB, apportionment can be a challenge. Observations from the nearest monitor, which are often used to characterize background air pollution, can be missing during a wildfire epi- sode, sometimes due to the fire itself. To obtain ecologic or individual-level estimates of smoke exposure, several methods can be employed: satellite imagery, dispersion or chemical trans- port modeling, and/or spatiotemporal interpo- lation. However, each of these has associated difficulties in implementation and interpreta- tion, especially during a short time window with atypical meteorology, such as the strong Santa Ana winds that fanned the 2003 fires. To the extent that variation in wildfire- attributable pollution and background pollution are independent, including back- ground pollution in the model could improve the precision of effect estimates, but should not affect the central tendencies. On the other hand, insofar as background concentrations are correlated in space or time with wildfire smoke concentrations (e.g., to the extent that they are similarly determined by physical geography), including background pollution could induce confounding just as including seasonality can. Without access to detailed measurements of both fractions, we elected to consider a strictly temporal contrast, reserving spatiotemporal refinements of exposure for future work. Previous studies that have attempted to isolate the contribution of wildfire-generated smoke have also compared health effects to a reference period (e.g., Delfino et al. 2009). However, in any interrupted time-series study, there is always the possibility of an unmea- sured confounder with a similar temporal profile to that of the exposure. For example, if a foodborne illness outbreak happened at the same time as the wildfires, and had a negative impact on birth weight, it could conceivably explain part or all of the observed effect. The fact that our unexposed births were drawn from both before and after the exposure win- dow, and from other years at the same time of year, helps to reduce such threats to validity, but cannot eliminate them. Conclusion This study indicates that maternal exposure to wildfire events may result in modestly lower infant birth weight. A small decline in birth weight is unlikely to have clinical relevance for individual infants, and there is debate about whether a small shift in the popula- tion distribution of birth weight has broader health implications (e.g., Wilcox 2001). Although the effects we estimated are much smaller than for many other exposures, such as smoking, the extent of exposures during wildfire events and their increasing frequency suggests potentially important implications for infant health and development. Finally, future research should also assess alternative mechanistic pathways besides air pollution (such as stress) for understanding the adverse health effects of wildfire events. Ref Rencs Barnett AG, Dobson AJ. 2010. Analysing Seasonal Health Data. Berlin:Springer-Verlag. Barregard L, Sällsten G, Andersson L, Almstrand A, Gustafson P, Andersson M, et al. 2008. Experimental expo- sure to wood smoke: effects on airway inflammation and oxidative stress. Occup Environ Med 65(5):319–324. Barregard L, Sällsten G, Gustafson P, Andersson L, Johansson L, Basu S, et al. 2006. Experimental exposure to woodsmoke particles in healthy humans: effects on mark- ers of inflammation, coagulation, and lipid peroxidation. Inhal Toxicol 18(11):845–853. Basu R, Parker J, Saulnier M, Woodruff T. 2003. Does maternal smoking confound the relation between PM2.5 and birth ovlume 120 | number 9 | September 2012 • Environmental Health Perspectives