Arrandale et al. Objectives This manuscript was motivated as a result of a workshop held in Banff, Canada, 9–10 February 2008, in connection with the launch of the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort (Subbarao et al. 2009). The purpose of this review is to discuss the design of envi- ronmental exposure assessment in longitudinal cohort studies of childhood asthma and aller- gies. This review is not intended to be a system- atic review of the topics presented, but rather a focused discussion of, and a starting point for developing, more effective exposure assess- ment strategies pertaining to the environmental causes of asthma and allergy. The references cited herein guide readers to more in-depth discussions on specific issues raised, whereas this review as a whole provides a broader perspec- tive on decisions that affect the environmental exposure data and ultimately the power of the cohort study under design. This review thus serves as an introduction to the challenges in exposure assessment that environmental epide- miologists continue to face. Results Developing an exposure assessment strategy. The exposures linked with childhood asthma in Table 1 [Supplemental Material (doi:10.1289/ ehp.1002267)] are numerous and include those that may act through different uptake routes: inhalation, ingestion, and dermal. Historically, the research focus has been on inhalation, but this is changing, particularly with respect to occupational asthma (Redlich and Herrick 2008). Relevant exposures include biological, chemical, and physiological agents that can be encountered in a variety of envi- ronments including the home, school, and outdoors, and those that can start in utero. Although not discussed in detail here, parental take-home exposures related to their occupa- tion, or paraoccupational exposures, may also play a role in the development of childhood asthma (Magnusson et al. 2006). Furthermore, as adolescents enter the workforce, they may have their own relevant occupational exposures (Syamlal and Mazurek 2008). Three general strategies for characterizing environmental exposures include questionnaires, environmental samples, and predictive models. Measurement of biomarkers of exposure can also be used, but they are typically not applied in large cohort studies for asthma/allergies and therefore are not included in this review. Questionnaires are a common tool for assessing exposure. Questionnaire data are typically surrogate measures for the exposure(s) of interest. The researcher relies on a conceptual model of how the surrogate is related to the exposure of interest and of how well the surrogate differentiates between dif- ferent exposure levels. 592 Information on a variety of exposure and exposure scenarios, including inhala- tion and ingestion of biological and chemical agents, can be acquired from questionnaires [Supplemental Material, Table 3 (doi:10.1289/ ehp.1002267)]. The main limitation of using questionnaires to assess exposure is recall bias (Le Moual et al. 2000). There are also limits to how much can be asked on a single question- naire, how frequently it can be administered, and how specific it can be made. In addi- tion, asking subjects about specific exposures on questionnaires assumes that the subject will recognize both the exposure agents and their own exposure. These issues can result in the wrong question or too many questions being asked, leading to imprecise data, subject fatigue, and/or a poor response. Environmental samples or measurements are considered the most precise way to quan- tify the exposure of an individual at a point in time. Many exposures of interest can be measured, either directly or through collec- tion of samples for subsequent analysis, at least for a short time period. However, whether measurements are practical with respect to the cost and the burden on subjects differs by exposure. In reality, a single measurement fails to describe the variability of an individual’s exposure over time, and the quantity of a spe- cific agent measured in a child’s environment is, at best, a surrogate for dose. Before deciding to undertake exposure measurements, researchers should consider whether a reliable and validated surrogate could be obtained more easily. Of particular importance is the type of data that will be gen- erated from an environmental measurement versus a questionnaire. In some cases an expo- sure measurement may be unnecessary. For example, the presence of pets can be ascer- tained from questionnaires or from observa- tion. But to determine the exposure–response relationship between pet dander and asthma symptoms, a measurement of cat dander in house dust is more desirable, because many houses without a furry pet have measurable lev- els of dander (Chew et al. 1998; Gehring et al. 2004; Nafstad et al. 2001). Similarly, recent research on thirdhand smoking indicates that residual tobacco smoke contamination remains after the cigarette is extinguished (Winickoff et al. 2009). Although exposures related to first- and secondhand smoking are believed to be assessed reliably through questionnaires, this additional route for tobacco product exposure may require modification to questionnaires or more emphasis on measurements (i.e., air sam- pling or urine cotinine) to develop sufficiently informative exposure measures. In studies of the environment and child- hood asthma, the exposures that tend to be measured quantitatively are indoor dust, house dust mite allergens, pet allergens, indoor air pollutants, and outdoor air pollutants. Many birth cohorts focusing on environmental deter- minants of childhood asthma and allergies have measured and reported these exposures at mul- tiple time points and locations within the child’s environment. Table 1 summarizes the expo- sure assessment in a selection of large cohorts that focused on the development of childhood asthma in relation to environmental exposures other than air pollution. Table 1 highlights not only the similarities in exposures selected for measurement, but also the differences in timing and location of sample collection. These differ- ences between studies complicate interpretation and comparison of results as well as subsequent data-pooling efforts. The reliability of exposure measurements is limited by several factors, including the choice of exposure measured, how it relates to the relevant dose and/or biological pathway, and the method used to collect and analyze the sample, as well as the uncharacterized variabil- ity in exposure over time and place. Each will contribute to the overall error and variability of the measured value. Investigators rarely, if ever, measure the actual exposure that an individual receives; ultimately, exposures are quantified based on surrogate information and a model (or a combi- nation of models) that link this information to exposure based on common assumptions. The model can be simple, such as the presence of a furry pet equals exposure to dander, or more complex—using monitoring data for assigning air pollution exposure. In both cases, surrogates are measured, and an underlying model relates the surrogate to the exposure of interest. Estimating exposure using models: an air pollution example. In previous cohort studies of air pollution and childhood asthma, indicator variables or interpolation methods have been used to assign exposure based on a single piece of information [i.e., distance to major roads (Brunekreef et al. 1997)]. In recent years, com- plex modeling approaches have become com- mon for assigning exposure. Approaches for modeling air pollutants vary; some use emis- sions or monitoring data as inputs, whereas others use measurements collected explicitly for the purposes of constructing the model (Brauer et al. 2003). Land-use regression (LUR) models relate measurement data at multiple sites with readily available spatial information (from geographic information systems, such as road and pollut- ant emission location, to calculate air concen- trations (i.e., estimated exposures) across an unsampled area with high resolution (50 m or better) (Brauer et al. 2003; Hoek et al. 2008). Regional-scale air quality models, such as the grid-based Community Multiscale Air Quality (CMAQ), use emissions and meteo- rologic inputs combined with knowledge on atmospheric chemistry, thermodynamics, and ovlume 119 | number 5 | May 2011 • Environmental Health Perspectives