et al. 2005), shaping health and susceptibility. Behavioral and physiologic responses to SES and stressors may vary by gender (Seeman et al. 2002); women, on average, may respond more strongly to interpersonal stressors (Davis et al. 1999) and experience different physiologic sequelae (Hermes et al. 2006; Iwasaki-Sekino et al. 2009; Seeman et al. 1995). Women’s behavioral responses may emphasize social support, caregiving, and child tending (Taylor et al. 2000), whereas better known “fight- or-flight” responses emphasize sympathetic– adrenal–medullary enervation and activities linked to traditionally male roles (Powell and Matthews 2002; Taylor et al. 2000). Stress may be a gendered factor (i.e., exposures dif- fer by gender) and a sex-differing factor as well, if physiologic responses to stress differ (e.g., sex-differing epinephrine responses). If stress modifies pollution response, then under- standing gendered stress responses is likely important for accurately characterizing gen- dered pollution responses. Research from social geography may help to better elucidate gendered spatial and behav- ioral exposure patterns. Gendered use of space and exposure patterns in urban communities is evident in the example of fear of violence. One large U.S. survey reported that 26% of women “never” leave home after dark (vs. 9% of men), 51% “always” bring friends for protection (vs. 4% of men), and 71% con- sider safety when parking (vs. 33% of men) (Gordon and Riger 1991). Strong gender dif- ferences in perceived safety shape activity and exercise patterns; parents’ greater restriction of girls’ geographic range in U.S. cities shapes exposure paradigms, exercise, experience, and developmental opportunity (Katz 1993). Better understanding the gendered environ- ment can improve exposure assessment, bet- ter isolate biological responses, and provide a model for examining other social effect modi- fiers (Clougherty and Kubzansky 2009). Analytic approaches for disentangling effects of gender and sex. Because gender and sex are tightly intertwined, their effects can be difficult to distinguish in epidemiologic data. “Gender” and “sex” have commonly been conflated in epidemiologic research (Krieger 2003). Most important, careful use of language distinguishing these constructs will enable researchers to better describe and understand sources of difference in exposure– health relationships. Methodology for gen- der analysis is an evolving field, although the methods described here may help to disen- tangle some effects of sex and gender and may merit further exploration in environmental epidemiology. Reporting sex-stratified results is more informative than is adjustment for sex (Arbuckle 2006) and can identify associations differing broadly between males and females. However, sex stratification often confounds tightly correlated gender and sex effects, obscuring true sources of difference. Preferably, researchers may stratify data separately by mul- tiple sex- and gender-associated factors (e.g., body size, working outside the home, time spent on household tasks) to elucidate sources of difference. Most epidemiologic data sets are not adequately powered to perform multiple stratifications simultaneously, so these mul- tiple stratifications usually need be performed separately. Stratification variables should reflect time–activity patterns or meaningful biologi- cal factors, rather than stereotypical attributes, to identify true factors relevant to the cohort under study. Population-specific exposure modeling may improve culturally and behaviorally spe- cific exposure assessment, clarifying gendered exposure differences. Residential exposure met- rics may be more accurate for women, who spend more time near home on average, espe- cially when caring for children or other family members (Gilliand et al. 2005; Kingsley 2003; Maziak et al. 2005; Payne-Sturges et al. 2004). Residential activities may require microen- vironmental exposure assessment (LaRosa et al. 2002), because gendered activities (e.g., cooking, cleaning, lawn care) produce differ- ent exposure patterns. Exposure measurement may benefit from gendered exposure measure- ment, comparison of gendered activities across communities (Berhane et al. 2004), or foci on temporal exposure characteristics (e.g., diurnal trends in residential exposures and activities, critical life-course periods related to hormonal composition or roles) (Gilliand et al. 2005). Assignment of gendered exposures broadly to sex-stratified groups, however, should be gen- erally avoided, because this practice obscures sources of variability between men and women, further confounding sex effects in subsequent epidemiologic analyses. Temporally refined exposure assessment may elucidate gendered activity distribu- tions. Recent approaches include probabilistic modeling of personal exposures (Zidek et al. 2005). Techniques from the social sciences may be useful; the experience sampling method (Csikszentmihalyi and Larson 1987) uses cell phones or pagers to prompt individ- uals throughout the day to record their loca- tion, activities, and well-being. The technique improves upon diary entries, which suffer recall bias, and allows more detail in activity reports (e.g., cleaning activity with duration and product name) with contemporaneous physiological or psychological conditions that may modify effects. Aggregated, the data pro- vide population-specific activity distributions and capture mean daily activity and exposure differences between men and women. Physiologically based pharmacokinetic (PBPK) modeling may help to distinguish sex Environmental Health Perspectives • volume 118 | number 2 | February 2010 Gender in environmental health research differences in dermal absorption, body size, and toxicity (Arbuckle 2006; Meibohm et al. 2002) from gendered exposures. PBPK mod- els may facilitate analysis of biological pro- cesses across multiple life stages (e.g., infancy, childhood, puberty, adulthood) and, among women, by reproductive cycle and hormonal status (e.g., menarche, pregnancy, lactation, menopause). Better understanding of sex and life-stage aspects of bodily chemical transport may help to elucidate differences in effective dose or chemical interactions in the body. Propensity analysis incorporates predictive modeling for both exposures and responses, enabling researchers to predict subjects’ propensity (likelihood) of exposure, given preexposure characteristics and population exposure distributions. Researchers can then examine health responses among individuals with comparable exposure likelihoods, using propensity matching or propensity strati- fication (Kurth et al. 2006). For example, sex-stratified propensity models can estimate effects of education, work history, SES, fam- ily structure, and home demands on exposure assignment (e.g., job, neighborhood of resi- dence) for men and women. Then researchers can better observe health responses by sex, reasonably isolating effects of mean biological differences from those of gendered exposure assignment. One recent occupational study examined blue-collar status and hypertension among employees of a large U.S. manufac- turing company (Clougherty et al. 2009). Family structure influenced exposure (job) assignment for men and women; single moth- ers were more likely to be blue-collar workers than were other women. Men with partners and children were more likely to be white-col- lar workers than were other men. Blue-collar status increased risks solely among women predicted to be blue collar, suggesting interac- tion effects between SES (which predicted job assignment) and on-the-job exposures. Finally, researchers have proposed variants of multilevel modeling (Phillips 2005) to dis- aggregate variability between and within the sexes. Researchers may differentiate sex-linked biological effects (e.g., target organs, hormonal composition), which can differ substantially between men and women, from gendered exposures, which generally display more vari- ability among men and women. The technique may be applicable, however, only to illnesses directly involving biological parameters (e.g., sex organs, hormonal composition) which dif- fer strongly by sex. A different method for employing multilevel modeling stems from the societal-level construction of gender, whereas sex is an individual-level biological construct. Examining men’s and women’s exposure and disease patterns across and within soci- eties that vary in measures of gender equity (e.g., income disparities, female education, 173