Carozza et al. three-quarters or more of their total acreage in agricultural production. In contrast, of the 515 referent counties, 357 (69%) had < 10% of their total acreage in cropland, and 224 (43%) had < 5% in cropland. These distribu- tions illustrate the heterogeneity of possible exposure across the study area and lend sup- port to our key assumption that children resid- ing in an exposed county had a higher probability of encountering agricultural pesti- cides through pesticide drift than did the chil- dren residing in a referent county. Still, because there were so few counties with no agricultural activity, our unexposed population did include counties with up to 20% of total acreage in crop production as well as those counties with no farming, leaving the potential for misclassiﬁcation of the exposure. This mis- classification would move the risk estimates toward the null, though, and is unlikely to have generated the magnitude of risk seen for most cancer sites. Additionally, we acknowledge there are a variety of ways it would be feasible to use existing agricultural data to attempt to capture any crop-specific effects, each with slightly different advantages and disadvantages. We believe our approach is valid to address the very general question of whether risk of individual cancer types varied according to probable differences in pesticides used, as defined by different crops grown. We evalu- ated this speciﬁcally because we saw uniformly increased risk across all cancer types when considering the main effect of percent crop- land. Epidemiologic case–control studies, in contrast to this ecologic study, would be better suited for creating more speciﬁc exposure deﬁ- nitions based on cropping patterns and could better evaluate questions of dose–response and exposure timing for any speciﬁc pesticide (or pesticide surrogate). We deliberately chose not to use existing urban/rural classification systems such as Rural–Urban Continuum Codes (RUCC; previously termed Beale codes) or Urban Inﬂuence Codes for this analysis because these systems are based largely on economic or pop- ulation density measures, not agricultural pro- duction. Our classification approach was chosen speciﬁcally to capture density of agri- cultural activity at the county level. To evaluate Table 6. Top ﬁve agricultural chemicals applied (by percent treated acres), by crop.a Percent of treated acres Chemical class Chemical Barleyb 2,4-D MCPA Bromoxynil Tribenuron methyl Thifensulfuron Cornc Atrazine Metolachlor Acetochlor Dicamba Nicosulfuron Cotton-uplandc Triﬂuralin Ethephon Tribufos Fluometuron Aldicarb Oatsb 2,4-D MCPA Dicamba Glyphosate Chlorsulfuron Soybeansc Glyphosate Imazethapyr Pendimethalin Triﬂuralin Chlorimuron ethyl Wheat (spring, winter)d 2,4-D MCPA Dicamba Tribenuron methyl Fenoxyprop 564 35 22 14 12 8 70 24 24 21 12 50 33 31 29 23 13 6 3 2 2 45 26 19 16 12 38 24 17 13 12 Chlorphenoxy acid/ester Chlorphenoxy acid/ester Hydroxybenzonitrile Sulfonylurea Sulfonylurea Triazine Chloroacetanilide Chloroacetanilide Benzoic acid Sulfonylurea 2,6-Dinitroaniline Organophosphate Organophosphate Urea N-methyl carbamate Chlorphenoxy acid/ester Chlorphenoxy acid/ester Benzoic acid Phosphonoglycine Sulfonylurea Phosphonoglycine Imidazolinone 2,6-Dinitroaniline 2,6-Dinitroaniline Sulfonylurea Chlorphenoxy acid/ester Chlorphenoxy acid/ester Benzoic acid Sulfonylurea Aryloxyphenoxy propionic acid Carcinogenic potential D-not classiﬁable Not likely C-possible carcinogen C-possible carcinogen Not listed Not likely C-possible carcinogen Likely carcinogen D-not classiﬁable E-evidence of noncarc C-possible carcinogen D-not classiﬁable Likely at high dose only E-evidence of noncarc E-evidence of noncarc D-not classiﬁable Not likely D-not classiﬁable E-evidence of noncarc E-evidence of noncarc E-evidence of noncarc E-evidence of noncarc C-possible carcinogen C-possible carcinogen Not listed D-not classiﬁable Not likely D-not classiﬁable C-possible carcinogen Not listed the effectiveness of this approach, we compared our percent cropland classification with the RUCC for metropolitan (metro) and non- metropolitan areas (nonmetro) in our data. In this comparison, we found that of the 752 counties in our referent (i.e., low percentage of cropland and presumably “urban”) category, 432 (57.4%) were classified as nonmetro by RUCC coding. Further, we found that of the 292 counties classiﬁed by us as high percentage of cropland, 77 (26.4%) were classified as metro. Clearly, these data indicate that our analysis is not equivalent to a standard “urban” versus “rural” comparison. Use of county of residence at time of diagnosis may be considered another limita- tion of this study. If the exposures of interest are most pertinent during gestation, then, if available, mother’s county of residence at time of birth or time of conception would be the preferred measure for assessing the impact of exposure to agricultural chemicals. Because pesticides can act as either initiators or pro- moters, however, it is plausible that some pes- ticides may influence cancer development nearer to time of diagnosis. We also had very few data available to address any potential confounding, always a concern in epidemiologic studies. The evi- dence for most putative risk factors for the different childhood cancers is conflicting, so any effect from potential confounders is likely to be weak, particularly when dispersed across the county, our unit of analysis, and when many different types of cancers are considered in the analysis, as in our study. Although there might be a speciﬁc concern about differ- ential use of residential pesticides in the popu- lations living in counties with low agricultural activity (i.e., more urban counties) versus those with medium or high agricultural activ- ity (i.e., more rural), published reports agree that there is very little difference in household use of pesticides in urban versus nonurban settings (Adgate et al. 2000; U.S. EPA 2007) In addition to general concerns regarding the ability to determine causality that apply to any ecologic study, our approach requires sev- eral assumptions, including a) that mobility of study subjects is not sufﬁcient to substantially affect risk estimates, and b) that the cropland data derived from the 2 years of agricultural census information are consistent across the study years. Finally, we have no ready explana- tion for the lack of an effect seen when evalu- ating all cancer types together compared with our results for individual cancer types. Because the OR is not a linear transformation of these data, we cannot expect that the OR for all cancers would be the average of the ORs for the subgroups. It is possible that we may have experienced some form of Simpson’s paradox in our data set when combining the cancer types into one “super” group (Simpson 1951). Abbreviations: 2,4-D, 2,4-dichlorophenoxyacetic acid; MCPA, 4-chloro-2-methylphenoxyacetic acid; noncarc, noncarcinogenicity. aData from USDA (2006) and U.S. EPA (2004). Multiyear data are means. b1998 data only. c1995–2001 data. d1995–1998, 2000 data; durum wheat excluded because it is grown only in North Dakota. VOLUME 116 | NUMBER 4 | April 2008 • Environmental Health Perspectives