Giorgis-Allemand et al. Respond to "Ambient Environment and Preterm Birth".

We thank Drs. Ha and Mendola for their thoughtful comments (1) on our study of associations of meteorological conditions and air pollution levels with preterm birth risk (2). Over the last decade, the spatial resolution of exposure models for atmospheric pollution has improved. Ha and Mendola mention, among other models, dispersion models as possibly more relevant alternatives to the land-use regression (LUR) approach used in the European Study of Cohorts for Air Pollution Effects (ESCAPE), in which our study was embedded. It should be noted that, although not explicitly considered by LUR models, meteorological conditions are still indirectly taken into account through their influence on local air pollution levels, which constitute an entry parameter of our seasonalized LUR model (2, 3). A detailed comparison of the yearly estimates of our LUR model and of dispersion models at home addresses, made in the European Study of Cohorts for Air Pollution Effects, showed a median (Pearson’s r) correlation of 0.75 for nitrogen dioxide and 0.29 for fine particulate matter, with stronger agreement in areas where dispersion models were more predictive of the local measurements done to define LUR models (4). These models should not be opposed, and estimates from dispersion models and other models can actually be fed into LUR models to increase their predictive ability (5). Further improving the spatial resolution of models below the 10-m to 100-m values of typical current models is unlikely to significantly improve accuracy in exposure estimates as long as the time-space activity of pregnant women is not considered (6). In a small-scale study of pregnant women carrying Global Positioning System devices in a mid-size French city, incorporating time-space activity in an exposure estimate based on an (outdoor) dispersion model produced little change in exposure estimates for fine particulate matter (a pollutant with limited spatial variability within urban areas) and somewhat larger but still limited changes for nitrogen dioxide exposure estimates (7). More generally, it has been shown that bias in dose-response functions due to ignoring time-space activity is likely to increase as spatial resolution of exposure models becomes finer (6). Taking indoor air pollution levels into account seemed to have a greater impact on exposure estimates (7), which is coherent with the limited correlation reported between personal and outdoor exposures outside the context of pregnancy (8). However, dosimeters cannot easily be carried for more than a few weeks during a pregnancy, thus offering a better consideration of indoor levels at the cost of a decreased ability to test numerous exposure windows during the pregnancy. Modeling indoor infiltration of outdoor pollutants could be a way to better take indoor levels into account without decreasing the temporal resolution of exposure estimates (9). Considering larger areas is an option for increasing the sample size. As Ha and Mendola rightly pointed out (1), there is no consensus as to the best way to correct for bias possibly resulting from the consideration of large study areas (10). The main concern here relates to confounding bias. In the context of a birth cohort, Pedersen et al. (10) showed that as the area considered around the city centers was extended, thus increasing sample size, the heterogeneity of the population increased in terms of disease risk factors, thus increasing the potential for bias in a situation where all confounders cannot be perfectly measured; this is but an illustration of the well-known bias-variance tradeoff. We chose to adjust for the study area using a randomeffect covariate (2). In the case of associations with firsttrimester atmospheric pressure, not adjusting for study center at all did not yield an increased point estimate (odds ratio per 5-mBar increase = 1.04, compared with 1.06 after controlling for study center with a random-effect variable) (2); taking the study center into account might indeed lead to overadjustment, but it can reduce confounding bias due to variations between locations in the risk factors for preterm