Arrandale et al. and local-scale dispersion models is a current area of development to improve air quality modeling for exposure estimation. A major limitation of outdoor air pollu- tion information (modeled or measured) is that it does not account for the majority of time people spend indoors (Ozkaynak et al. 2008). Additional models, which combine air quality models and/or measurements with exposure models [Hazardous Air Pollutant Exposure Model (HAPEM)] and dose mod- els [Stochastic Human Exposure and Dose Simulation (SHEDS)], are available. For exam- ple, SHEDS estimates time–activity patterns as well as microenvironment exposure differences for populations based on census data and time– activity studies (Burke et al. 2001; Isakov et al. 2009; Ozkaynak et al. 2009). Models can also be combined with time–activity information so that the movement of a subject over the course of the day and the resulting variation in expo- sure between outdoor microenvironments is accounted for (Nethery et al. 2009; Ryan et al. 2008). To apply these approaches to a cohort study requires subject-specific time–activity information, which is difficult to obtain for long time periods and thus tends to rely on snapshots of information from questionnaires. The amount of detail and temporal resolu- tion that can be obtained regarding exposure over the course of a cohort study is limited. It is impossible to evaluate all exposures continu- ously, and there are limitations to the meas- urement collection. Environmental exposure measures are usually time-averaged environ- mental concentrations relevant to the external exposure of the agent rather then the inter- nal, or absorbed, dose. Furthermore, for some agents, cost-effective sampling and analytical methods are not available. For others it is a struggle to convert questionnaire responses into meaningful exposure metrics because of limited validation information. Prioritizing exposure assessment. There are too many exposures associated, or hypothe- sized to be associated, with childhood asthma to evaluate them all in a single study. Given limited resources, there are tradeoffs between number of exposures to explore and the accu- racy and resolution with which they can be characterized. Additionally, no single exposure is likely to hold the key to childhood asthma [i.e., mono-interventions are ineffective (Maas et al. 2009)], and new hypotheses about envi- ronmental exposures will evolve over the course of a cohort study. How should exposures be prioritized in the context of cohort studies? How many exposures should be studied? At what time point(s) or life stage will exposure be measured? How often within each life stage will exposure be meas- ured? Will the exposure be constant or vary over the exposure window of interest? Can expo- sures with similar mechanisms be combined 594 in analyses? These questions are critical to the design of a rigorous exposure assessment strat- egy. Examples of some general strategies that could help prioritize the exposures of interest in a cohort study are importance for public health, existing measurement capability, biolog- ical mechanisms, and potential for future data pooling. This list is not exhaustive, nor would strategies need to be employed exclusively. When considering the public health impor- tance of an exposure, priority should be given to exposures that are modifiable and prevalent in the population and have been hypothesized to be associated with development of asthma. Under this scheme, exposures of priority might be environmental tobacco smoke, building moisture levels, microflora related to building moisture, air pollution, and pest allergens (e.g. mite, cockroach, rodent). Exposures of second- ary priority under this scheme might include those that are not easily or justifiably modifi- able (e.g., pollen) as well as exposures that are highly speculative (e.g., diet, gut microflora, and phthalates). Modifiable exposures could occur at many levels, from the individual level (e.g., smoking) to the policy level (e.g., local policy on smoking in public areas, or national policy on air pollutants). The ability to reliably characterize the exposures of interest is a critical consideration when prioritizing exposures. The existence of a validated measurement method often influences the allocation of resources nec- essary to collect the desired data. In the case of ambient air pollutants, if a model has been developed for the study area, its application to derive exposure estimates for study participants is straight forward and low cost. However, if this model has not been developed, another method of estimating exposure may be more practical. Similarly, the validity and reliabil- ity of questionnaire items should also be con- sidered if self-reported exposure data are to be collected. The validity and reliability of new questionnaire items can always be tested in a subset of participants, but this requires resources and could delay progress. It is important to maximize the amount of information that can be retrieved from a single sample. Both dust samples and questionnaires can provide a significant amount of informa- tion on multiple exposures from a single sam- ple. Dust samples provide the opportunity for quantifying exposure to a variety of agents (e.g., allergens, phthalates, and fungal mark- ers) and when collected from a reservoir, such as deep carpet, can represent a time-integrated exposure (Roberts et al. 2009). Previous stud- ies have also demonstrated that it is possible to have subjects participate in the collection of dust samples, making it more cost effective to collect multiple samples over time (Arbes et al. 2005; Schram-Bijkerk et al. 2006). The biological mechanism of action can also be used as a method for prioritizing exposures. This can be conceptualized in two ways. First, exposures that act through the same biological pathway as other exposures that have been associated with asthma may be high priority for further study. Alternatively, if sev- eral exposures act through the same biological pathway, methods for quantifying biomarkers of exposure (or effect) within the pathway can be developed to measure dysfunction in the pathway. This dysfunction can then be related to the outcome or the exposure of interest, reducing exposure assessment costs. Both of these approaches require the iden- tification of pathways and mechanisms relevant to childhood asthma. We suggest five pathways, as a starting point, through which exposures could act to ultimately cause childhood asthma: • Oxidative stress (Bhalla et al. 2009; Ciencewicki et al. 2008; MacNee 2001; Yang et al. 2008) • Disruption of epithelial barrier function (Bhalla et al. 2009; Hammad and Lambrecht 2008; Holgate 2008; Knight and Holgate 2003) • Adaptive versus innate immune response (Bhalla et al. 2009; Hammad and Lambrecht 2008) • Disruption of normal airway development or later airway remodeling (Becklake and Kauffmann 1999; Folli et al. 2008; Postma 2007) • Genetic and epigenetic inheritance (Bosse and Hudson 2007; Moffatt 2008; Ober and Hoffjan 2006; Yang et al. 2008). These pathways are not mutually exclusive; often exposures (genetic or environmental) act through multiple pathways simultaneously, and pathways may also interact to cause detri- mental effects in the lung (i.e., genetic control of immune response). Interest in gene–environment interactions has created a need for studies with large sam- ple sizes. Obtaining sufficient power to find a significant interaction is unlikely in any one cohort (because of small sample size) but is more likely with a meta-analysis or pooled analysis of multiple cohorts (Hunter 2005). These approaches could be particularly useful in genomewide association studies (GWAS) (Zeggini and Ioannidis 2009). To successfully pool data across studies, it is ideal to plan for data pooling during study design. Ideally the exposure assessment strat- egy should be the same across all studies to be pooled. In reality, this means the same expo- sures are measured at the same time points using the same methods/protocols, which is difficult to achieve. To the extent that it is scientifically justified, new studies should con- sider harmonizing some target exposures with existing studies. Power for a gene-by-environment study depends on four factors: the true effect size, the exposure prevalence, the genotype prevalence, ovlume 119 | number 5 | May 2011 • Environmental Health Perspectives