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. 2025 Feb 12;9(2):e2024GH001236. doi: 10.1029/2024GH001236

Exploring the Joint Association Between Agrichemical Mixtures and Pediatric Cancer

Jabeen Taiba 1,✉, Cheryl Beseler 1, Muhammad Zahid 1, Shannon Bartelt‐Hunt 2, Alan Kolok 3, Eleanor Rogan 1
PMCID: PMC11815328  PMID: 39944898

Abstract

Nebraska's age‐adjusted incidence rates for childhood cancers are among the highest in the US. Previous studies indicated associations between agrichemical exposures (atrazine and nitrates) and pediatric cancer rate, assuming single pollutant exposure. We evaluated the joint association between the agricultural mixture and pediatric cancer. Agrichemical exposures at a county scale were quantified using the USGS Pesticide National Synthesis Project for frequently applied pesticides from 1992 to 2014 in 93 Nebraska counties. Outcomes were quantified using pediatric cancer diagnosed among children <20 years of age (1992–2014) from the Nebraska cancer registry. We adjusted for social vulnerability factors such as race, income, employment, and access to care. The associations between 32 agrichemicals and cancer subtypes were assessed using the Generalized Weighted Quantile Sum Regression (gWQS) model. The model was fit assuming a Poisson distribution and using the pediatric population as an offset‐term and social vulnerability factors as covariates. We observed a statistically significant positive association between the 32 agrichemicals and overall pediatric cancer and subtypes. The strength of associations was slightly stronger among brain and CNS cancers (β = 0.36, CI = 0.14, 0.57) compared to overall cancer (β = 0.30, CI = 0.16, 0.44) and leukemia (β = 0.23, CI = 0.09, 0.38). Dicamba, glyphosate, paraquat, quizalofop, triasulfuron, and tefluthrin largely contributed to the joint association. These findings may explain the joint associations of the agrichemical mixture on childhood cancer. Alternative biomarker‐based approaches to measuring human exposure are worth investigating for chemicals of concern, particularly in counties with high agrichemical and cancer rates.

Keywords: pesticides, environmental mixtures, pediatric cancer, rural health

Key Points

  • We observed positive associations between agrichemical mixtures and overall cancer, brain and CNS cancers, and leukemia among children

  • About a 10% increase in pesticide, mixtures was associated with a 23%–36% increase in these cancer rates

  • Among the pesticides considered in the mixture, herbicides contributed the most toward these joint associations

1. Introduction

Cancer has been one of the major causes of death in children globally, and the incidence has been increasing in recent times (Huang et al., 2023; Steliarova‐Foucher et al., 2017; Wu et al., 2022). In the United States, it has been the second most common cause of death among children aged 1–14 years and the fourth most common cause among adolescents (aged 15–19 years), and about 1 in 260 children has been diagnosed with cancer before the age of 20 years (Siegel et al., 2023). The incidence of childhood cancer varies across the U.S., (Scott et al., 2020; Siegel et al., 2018), and the age‐adjusted incidence rates in Nebraska have been relatively higher compared to the U.S. average. For example, the age‐adjusted incidence rate of pediatric cancers in Nebraska has been 173.3 per 1,000,000 compared to 167.1 per 1,000,000 in the U.S. (Farazi et al., 2018). Several studies have explored the environmental, genetic, and social risk factors for childhood cancers, (Kehm et al., 2018; Mancini et al., 2023; Onyije et al., 2024; Spector et al., 2015) as well as the association between pediatric cancer and individual waterborne agrichemicals in Nebraska (Ouattara et al., 2022). However, individuals are exposed to a chemical mixture, and by studying individual chemicals in isolation, we will underestimate the cumulative effects of co‐exposures within the mixture (Savitz & Hattersley, 2023). Additionally, confounding due to correlated co‐pollutants might exist, making it challenging to identify the effect of an individual chemical (Braun et al., 2016). Therefore, there is a pressing need to estimate the combined effects of chemical mixtures on the pediatric cancer rate.

Environmental factors, including pesticide exposure, have been known as risk factors for various childhood cancers, mainly acute leukemias (Bailey et al., 2015; Mancini et al., 2023; Ward et al., 2023) and central nervous system tumors (Lombardi et al., 2021; Patel, Gyldenkærne, et al., 2020; Patel, Jones, et al., 2020). The potential mechanisms of childhood cancer development could be due to the interactions between genetic factors and environmental agents. Though there is limited understanding, the main toxic mechanisms of carcinogenic agents (benzene metabolites and some pesticides) include topoisomerase II inhibition and/or excessive free radical generation, which may induce DNA single‐ and double‐strand breaks, and chromosomal rearrangements (duplications, deletions, and translocations), usually occurring in utero leading to oncogene expression. Later disease progression might occur after birth because of genetic, epigenetic, or immune nature (Hernández & Menéndez, 2016). Children primarily encounter pesticides through ingestion of contaminated products (such as food, drink, and dust for young children), inhalation, or dermal contact (Roberts et al., 2012). They are more vulnerable than adults due to their frequent hand‐to‐mouth activity (Freeman et al., 2001, 2005). Children might get exposed in utero through maternal occupation and domestic exposures. The presence of pesticide particles indoors and outdoors may result from agricultural activities in nearby fields (such as agricultural drift, volatilization, and wind erosion) and the transportation of pesticides into the home by family members who are occupationally exposed (e.g., through clothes and shoes) (Deziel et al., 2017). Several studies have reported higher air and dust pesticide concentrations in households near treated fields, specifically during and shortly after application, although quantifying it has proven difficult (Dereumeaux et al., 2020; Madrigal et al., 2023; Taiba et al., 2023).

In addition to environmental factors, socioeconomic status (SES) may also be associated with pediatric cancer incidence, operating through various mechanisms at individual and area levels (Kehm et al., 2018). SES acts as a confounder of other demographic and social determinants of health (SDoH), including race/ethnicity (Geris & Spector, 2020; Spector et al., 2015), poverty (Pan et al., 2010), housing, education, employment (Bailey et al., 2017), and insurance status (Wang et al., 2022). It is crucial to adjust for these factors as these are also associated with pesticide exposure (Donley et al., 2022). A previous study in Nebraska examined the association between individual pesticides and pediatric cancer (Ouattara et al., 2022). In this study, we have addressed the limitations of evaluating single chemical exposure by estimating the cumulative association of pesticides as a mixture. While the previous study explored only three cancer subtypes, we included all 11 types to assess joint associations.

2. Materials and Methods

2.1. Descriptive Analysis

We assessed the correlation across the 32 agrichemicals, pesticide indices, and scaled average of 32 agrichemicals per county included in this study by estimating the Spearman correlation coefficients. Statistically significant correlations were considered to be the correlation coefficients with a P value less than 0.05, as shown in Figure S1 in Supporting Information S1. We conducted a descriptive analysis to map the spatial distribution of the most applied agrichemicals, using a principal component analysis (PCA) (Jolliffe & Cadima, 2016) and implemented PCA using the prcomp function in R (Bryan & Hanson, 2016). Input pesticide variables were pre‐processed by scaling the values so that each chemical or variable was standardized to have a mean of 0 and a variance of 1. The PCA excluded three pesticides with weaker correlations, leaving 29 agrichemicals for analysis. The first principal component explained a maximum variance of 82% with an eigenvalue of 23.7. We used the PC1 to map the spatial distribution of the most‐applied pesticides among Nebraska counties and loadings of each agrichemical contribution to PC1 are shown in Figure 1, and biplot and the proportion of variance are shown in Figure S2 in Supporting Information S1. Additionally, supplemented PCA analysis by including all 32 agrichemicals. The first principal component (PC1) explained a maximum variance of 75% with an eigenvalue of 23.9, and its spatial distribution and loadings as shown in Figure S3 and Table S1 in Supporting Information S1.

Figure 1.

Figure 1

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(a) Spatial distribution of most‐applied pesticides in Nebraska Counties (1992–2014) using a latent variable (PC1) and (b) the loadings plot showing individual pesticide contribution to PC1.

Furthermore, we identified and mapped the counties with high pesticide application and high cancer rates using the pesticide index (PC1) and pediatric cancer rate calculated per 100,000 children. This bivariate mapping of pesticide index and cancer rate was created based on the Intergovernmental Panel on Climate Change (IPCC) risk‐based conceptual framework (O’Neill et al., 2022). These maps were created by transforming continuous variables to categorical bins (low: 0 to 33.3; medium: 33.4 to 66.6; high: 66.7 to 100th percentile), and bivariate classes are pair‐wise combinations of these categories. Additionally, we conducted an exploratory spatial data analysis (ESDA) with the first principal component using Moran's I test (Bivand & Wong, 2018) to explore global spatial autocorrelation between counties with applied pesticides and their neighbors and their LISA clusters and spatial distribution as shown in Figure S4 in Supporting Information S1. We also included the scaled average of the 32 pesticides and mapped their spatial distribution, as shown in Figure S5 in Supporting Information S1. Statistical analysis for PCA and ESDA was carried out using R version 4.2.2, factoextra, ggfortify, tidycensus, spdep, and tmap packages.

2.2. Agrichemical Mixture Analysis

The joint association between the agrichemical mixture and pediatric cancer rate was evaluated using generalized Weighted Quantile Sum (gWQS) regression (Carrico et al., 2015) with a repeated holdout validation (Tanner et al., 2019). Weighted Quantile Sum (WQS) regression is a statistical approach for multivariate regression in data with complex correlational patterns commonly encountered in environmental exposures. The WQS model constructs an empirically weighted index estimating the overall effect of predictor variables on an outcome and can be used in a regression model with relevant covariates to test the association of the index with an outcome variable. The gWQS package in R extends WQS regression to applications with continuous, categorical, and count outcomes and implements the random subset WQS and repeated holdout WQS. WQS offered an advantage as it could reduce multicollinearity issues and extreme values from highly correlated agrichemicals when estimating the cumulative associations. Different outcomes of interest were considered: total pediatric cancer and the sub‐types of pediatric cancer. The main exposures of interest were the 32 agrichemicals and selected covariates for confounder control, which included the SDoH variables. All of the agrichemical variables were log‐scale transformed and are scored into deciles to mitigate the influence of any outliers. To fit the WQS models, the full data set is divided into training and validation sets. We have used a proportion of 40:60, where 40% of the data set was split into the training set, and the other 60% was split into the holdout or validation set, as suggested by Carrico et al. (2015).

Our main analysis assessed the joint association between 32 pesticides and pediatric cancer outcomes by fitting the WQS regression models. In gWQS, we conducted two calculations. First, WQS estimated a composite index and weights across the bootstrap samples of the training set. In our study, we set the number of bootstrap samples as 100. This index was a weighted sum of each pesticide's quantiles (WQS term) based on each pesticide's relevance to the outcome being examined. We estimated the index in a positive direction, assuming that the agrichemical mixture is positively associated with the outcome of pediatric cancer (β 1 is non‐negative). We estimated the overall mixture effect with and without constraining the model in the direction of β 1. Constraint is a setting available in the gWQS package; when it is set to true, the WQS model constraints to have associations in the same direction for the agrichemical mixture. Constraining helps in selecting the chemicals of concern over model prediction. Model constraining is done using a pre‐specified “signal function” so that the association between the weighted quantile sum and the mean is either nonnegative or nonpositive, constraining the correlation between the response and all the components to be of the same sign (Carrico et al., 2015). In the model, the parameters to be estimated are bs and the weights wi. There are two constraints for the weights. First, each weight is constrained between 0 and 1; second, the sum of all weights equals 1 (Carrico et al., 2015). We used the negative binomial family for the regression models and set the pediatric population as the offset term.

Second, the WQS model estimates the joint association between the mixture and outcome by fitting a multivariable regression model with the WQS term as the exposure metric. This regression model is fitted in the validation set as:

g(Y)=β0+β1WQS+β2ϕ

Y is the outcome, g is the link function, WQS is the WQS index that we already estimated in the first part, Ø are the covariates, and β1 is the main coefficient of interest that demonstrates the cumulative association between agrichemical mixture and the outcome.

We implemented repeated holdout validation to validate the model, which combined cross‐validation and bootstrap resampling techniques (Tanner et al., 2019). Specifically, we randomly partitioned 100 times and repeated the WQS regression on each set to simulate a distribution of validated results. Within each repetition, we still included the bootstrap step (Carrico et al., 2015) to ensure weights within a single training partition were stable and, to improve sensitivity and specificity. With 100 bootstraps per repetition and five repetitions, weights were estimated at 5,000 times. From these simulated distributions, we derived the mean as the final estimate for the agrichemical weights and WQS index β coefficient. Finally, we calculated the 95% confidence intervals (CI) based on the standard deviation (S.D.) of the simulated sampling distribution, corresponding to the standard error (S.E.) calculated for a single sample (Krzywinski & Altman, 2013).

Additionally, the contribution of each pesticide included in the WQS mixture was reported as a median percentage and 95% CI. We could also interpret each pesticide's contribution to the cumulative associations through the weights assigned to them in the WQS term. These estimated weights could vary across different outcomes examined in the WQS model. For the cancer subtypes that did not converge when using a repeated holdout validation due to a lapse in their counts, we also used a zero‐inflation model (Lim et al., 2014). As most cancer sub‐types did not converge due to low prevalence, we used a non‐repeated holdout approach in the direction of β 1, with and without constraining the WQS model to explore the association trends. All of the WQS regression procedures, including index and weight estimation and the final WQS regression, were implemented in the gWQS package in R version 4.2.2.

2.3. Pediatric Cancer Data

Pediatric cancer cases were defined as all children aged 0–19 years diagnosed and recorded in the Nebraska Cancer Registry between 01 January 1993 and 31 December 2015. The diagnostic codes were based on the International Classification of Childhood Cancer, third edition (ICCC‐3) (Steliarova‐Foucher et al., 2005), 11 cancer sub‐types, including leukemias, lymphomas, and reticuloendothelial neoplasms, all brain and central nervous system (CNS) neoplasms, neuroblastomas, and other peripheral nervous cell tumors, retinoblastoma, renal tumors, hepatic tumors, malignant bone tumors, soft tissue, and other extraosseous sarcomas, germ cell tumors, and other malignant epithelial neoplasms. Pediatric cancer data were obtained from the Nebraska Department of Health and Human Services (DHHS) Cancer Registry. Residences at the time of cancer diagnosis were used to compute county‐level cancer incidence rates. The pediatric population's county‐level estimates stratified by age group and sex from the American Community Survey (ACS) 2010 decennial census were used as denominators for estimating cancer rates.

2.4. Pesticide Data

Agricultural pesticide or pesticide product data from 1992 to 2014 were retrieved from the United States Geological Survey (USGS) Pesticide National Synthesis Project (PSNP). The data comprised the estimated annual pesticide use on a county scale for 93 Nebraska counties. The USGS PSNP data was obtained through proprietary surveys of farm operations within crop reporting districts and were used with annual harvested‐crop acreage reported by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) to calculate use rates per harvested crop acre, or an “estimated pesticide use” (EPest) rate, for each crop by year. When data were unavailable for a crop reporting district in a particular year, EPest extrapolated rates were calculated from adjoining or nearby crop reporting districts to ensure that pesticide use was estimated for all counties that reported harvested crop acreage (Thelin & Stone, 2013). EPest‐high and EPest‐low estimates were available in the data set; however, we used EPest‐high estimates applied in kilograms in this study as EPest‐high pesticide use for more counties was reported than EPest‐low (Thelin & Stone, 2013). All the data were organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and amount applied in kilograms per county (kg/county) and were log‐transformed for further analysis.

2.5. Pesticide Selection Criteria

We selected the most‐applied pesticides in Nebraska based on satisfaction of the following conditions: (a) median mass applied based on the temporal coverage was set to 90% of the study period, ensuring that the pesticide mass was applied for at least 20 years over the 22‐ year records, (b) the percentage of county coverage was required to be 100%, indicating that the pesticide or active ingredients should be applied in all the Nebraskan counties. Following these criteria, we identified 32 agrichemicals, which are classified as herbicides and insecticides.

2.6. Social Vulnerability and Spatial Data

Based on the previous studies (Braveman & Gottlieb, 2014; Green et al., 2016; Geris & Spector, 2020) we identified 10 metrics related to SDoH from the 5‐year (2010–2014) ACS that approximate the county scale vulnerability. These SDoH metrics were obtained using the U.S. Census Bureau Application Programming Interface (API) and the “tidycensus” R‐library (Bureau USC, 2022; Walker, 2023). We grouped these vulnerability factors into racial vulnerability (Non‐Hispanic Black, Hispanic/Latino populations), economic stability (poverty, federal assistance, single parent, unemployment), education (school education, English language barriers), and health care access (health insurance, no access to vehicle) based on the previous studies (Puvvula et al., 2023). We then transformed these variables into a percentage scale by dividing the specific variable by the total male or female population and multiplying by 100. The variables ethnicity and health insurance were stratified by gender and were specific to the pediatric age group. Simultaneously, SDoH metrics such as single parent, federal financial assistance, poverty, language barrier, unemployment, education, and access to a vehicle were measured at the household scale. The Nebraska state and county boundary shapefiles were obtained from the U.S. Census Bureau's TIGER/Line database using the “tigris” R package (Walker, 2016) and the “sf” R package (Pebesma, 2018).

3. Results

From 1993 to 2015, the Nebraska cancer registry reported 2,512 pediatric cancer cases in 93 Nebraska counties, encompassing 11 identified pediatric cancer types. Among these, brain and other CNS cancers were the most prevalent, accounting for 26% (n = 640) of all cases, followed by leukemia at 24% (n = 599), lymphoma at 17% (n = 422), germ cell tumor at 8% (n = 191), malignant bone tumor at 6% (n = 132), and other types comprising the remaining 19%.

Based on our selection criteria, we identified 32 agrichemicals that were most commonly applied in 93 Nebraska counties from 1992 to 2014. These chemicals were classified according to their pesticide class. Predominantly applied herbicides included 2,4‐D, atrazine, alachlor, acetochlor, and glyphosate, followed by insecticides such as chlorpyrifos, dimethoate, esfenvalerate, and others. Atrazine, 2,4‐D, trifluralin, picloram, and permethrin were categorized as IARC Group 3 carcinogens or classified as not classifiable as to their carcinogenicity to humans. At the same time, glyphosate was labeled as a type 2A carcinogen by IARC. Despite their classifications, numerous human and animal studies have reported these agrichemicals to be carcinogenic to humans. Most of these agrichemicals exhibited a biodegradable average half‐life of 4 days, except for the insecticide terbufos, which had a half‐life of 347 days. Table S2 in Supporting Information S1 details carcinogenic classification, half‐lives, and relevant citations regarding human or animal studies for the 32 agrichemicals. Furthermore, all of these agrichemicals demonstrated significant correlations within each pesticide class and between different pesticide classes, pesticide indices, and scaled mean of 32 pesticides, as illustrated in Figure S1 in Supporting Information S1, with correlations delineated by a dotted line. Although there is a slight difference in the strength of correlation between the pesticides indices developed using the PCA algorithm and the scaled mean of the pesticides applied per county, overall, the correlations were similar between the three metrics (PC1‐using 29 pesticides, PC1‐using 32 pesticides, and the scaled average of 32 pesticides).

A spatial distribution analysis of the most‐applied pesticides in the Nebraska counties (Figure 1) revealed higher pesticide application concentrations in Holt, Knox, Platte, Butler, Lincoln, and other regions highlighted in yellow. Conversely, Cherry, Grant, Rock, Sioux, Kimball, and other counties exhibited lower pesticide application rates, depicted in violet. We observed high agrichemical application and high overall pediatric cancer rate per 100,000 children in Holt, Platte, Box, Boone, Madison, Dodge, Clay, Thayer, Hamilton, Perkins, and Butler counties (Figure 2 and Table 1). High agrichemical application and high brain and other CNS cancer rate per 100,000 children were seen in Holt, Saline, Box, Butte, Boone, Madison, Gage, Buffalo, York, Clay, Thayer, Hamilton, Knox, and Butler counties. Other predominant cancers, leukemia, and lymphoma, in counties with high pesticide application are shown in the bivariate maps. We created bivariate maps for other predominant cancer types, malignant bone tumors, and germ cell tumors. However, malignant bone tumors were not detected in 57 out of the 93 Nebraskan counties, and germ cell tumors were not detected in 45 out of the 93 Nebraskan counties; hence, we could only detect the counties with high pesticide application and high cancer incidence for these cancer types. The exploratory spatial data analysis showed statistically significant positive (Moran's‐I statistic: 0.56; p‐value <0.05) spatial autocorrelation and high‐high clusters in Butler in yellow and low‐low clusters in Cheryl, Grant, Hooker, Thomas, Brown, Arthur, Garden counties depicted in pink, as shown in Figure S4 and Table S3 in Supporting Information S1.

Figure 2.

Figure 2

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Bivariate maps using agrichemical index and cancer rate by types. Cancer rates represent the annual rate per 100,000 children at a county scale. (a) Overall cancer. (b) Brain and other CNS cancer. (c) Leukemia. (d) Lymphoma.

Table 1.

Summary of Nebraska Counties With High Agrichemical Index and Medium/High Pediatric Cancer Rates

Cancer High agrichemical – High cancer rate High agrichemical – Medium cancer rate
Overall Holt, Platte, Box Butte, Boone, Madison, Dodge, Clay, Thayer, Hamilton, Perkins, Butler Cuming, Lancaster, Saline, Cass, Pierce, Gage, Dawson, Antelope, Lincoln, Saunders, Chase, Buffalo, Fillmore, York, Cedar, Hall
CNS Holt, Saline, Box Butte, Boone, Madison, Gage, Buffalo, York, Clay, Thayer, Hamilton, Knox, Butler Cuming, Lancaster, Cass, Pierce, Platte, Dawson, Antelope, Lincoln, Saunders, Dodge, Chase, Fillmore, Seward, Hall
Leukemia Holt, Cass, Pierce, Platte, Boone, Dawson, Lincoln, Dodge, Chase, Clay, Hall, Perkins Cuming, Lancaster, Saline, Otoe, Custer, Box Butte, Madison, Antelope, Saunders, Buffalo, Fillmore, Cedar, Thayer, Hamilton
Lymphoma Cass, Platte, Box Butte, Madison, Lincoln, Fillmore, York, Clay, Hamilton, Perkins Lancaster, Holt, Otoe, Custer, Boone, Gage, Dawson, Antelope, Saunders, Dodge, Buffalo, Seward, Cedar, Hall, Knox, Butler
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Note. High = 66.7th percentile to 100th percentile; Medium = 33.4th percentile to 66.7th percentile.

3.1. WQS Models on 32 Pesticide Components

In this study, we applied the gWQS regression and observed positive associations between agrichemical mixtures and pediatric cancer outcomes, including overall cancer, brain and other CNS, and leukemia, after adjusting for SDoH. We employed repeated holdouts and assumed a positive direction by constraining and non‐constraining the model. When we conducted analyses without constraining the model in the same direction, we found that every 10% increase in pesticide mixture was associated with a 36% increase (β1 = 0.36, 95% CI 0.14, 0.57) in the rate of brain and other CNS cancers in children. The magnitude of this association was slightly greater for brain and other CNS cancers than for overall cancer (β1 = 0.30, 95% CI 0.16–0.44) and leukemia (β1 = 0.23, 95% CI 0.09–0.38), as indicated in Figure 3 and Table 2.

Figure 3.

Figure 3

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Association between agrichemical mixture and pediatric cancer outcomes by cancer types. The y axis represents the beta coefficient (effect estimate) of agrichemical mixture. The x axis represents the overall pediatric cancer and its sub‐types. Adjusted model includes agrichemicals as a mixture and SDoH as covariates. Effect estimates with statistically significant association were represented using blue and non‐significant using orange. The lines with a dot represent constrained model and with a triangle represent un‐constrained model. The effect estimates and 95% CIs were generated using gQWS regression with 5 repeated holdouts. Overall cancer (β1 = 0.30, 95% CI 0.16, 0.44), brain and other CNS (β1 = 0.36, 95% CI 0.14, 0.57), and leukemia (β1 = 0.23, 95% CI 0.09, 0.38) showed positive associations with the agrichemical mixture. Whereas malignant bone (β1 = 0.33, 95% CI −0.19, 0.86) and germ cell (β1 = 0.19, 95% CI −0.33, 0.42) showed negative associations with the agrichemical mixture. Refer to Table 2. For chemical contributions to the joint associations.

Table 2.

Summary of Joint Associations From gWQS Using Repeated and Non‐Repeated Holdout

Cancer gWQS without constrains with repeated holdout a gWQS with constrains with repeated holdout a gWQS without constrains – non repeated holdout gWQS with constrains – non repeated holdout
Overall cancer 0.30 (0.16, 0.44) 0.25 (0.20, 0.31) 0.32 (0.06)* 0.001 (0.08)
Brain and Other CNS 0.36 (0.14, 0.57) 0.34 (0.16, 0.52) 0.31 (0.05)* 0.05 (0.10)
Leukemia 0.23 (0.09, 0.38) 0.16 (0.01, 0.31) 0.33 (0.09)* 0.06 (0.10)
Lymphoma NA NA 0.26 (0.09)* 0.05 (0.09)
Germ Cell 0.19 (−0.03, 0.42) 0.09 (−0.06, 0.31) 0.27 (0.11)* 0.05 (0.13)
Malignant Bone 0.33 (−0.19, 0.86) NA 0.47 (0.17)* 0.01 (0.15)
Neuroblastoma NA NA 0.37 (0.16)* 0.07 (0.15)
Retinoblastoma NA NA 0.34 (0.32) 0.19 (0.23)
Hepatic NA NA NA 0.21 (0.33)
Renal Tumors NA NA 0.09 (0.13) 0.15 (0.15)
Soft Tissue and other NA NA 0.24 (0.11)* 0.18 (0.13)
Other unspecified NA NA NA 0.63 (0.02)
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Note. NA‐statistical model not converged.

a

Model was set to 100 bootstraps and 5 repeated holdouts; contains beta estimates and 95% confidence intervals. b‐Model was set to 100 bootstraps; contains beta and standard error. *‐p‐value < 0.05 (for trend).

With a repeated holdout approach that constrained the WQS model in the positive direction, we found positive associations between the pesticide components mixture and three pediatric cancer outcomes: total cancer, brain and CNS, and leukemia. The pediatric cancer rate for brain and other CNS cancer type (β1 = 0.34, 95% CI 0.16–0.52 per approximately one decile increase in all pesticide components) was of greater magnitude than total cancer (β1 = 0.25, 95% CI 0.20–0.31) and leukemia (β1 = 0.16, 95% CI 0.01–0.31), as shown in Figure 3 and Table 2. The magnitude of association was slightly higher for the WQS models without constraining than with it. We observed that the agrichemicals dicamba, glyphosate, paraquat, quizalofop, triasulfuron, and tefluthrin largely contributed to the joint association of agrichemical mixture with the rates of overall cancer and cancer subtypes. A detailed summary of the weights of each component contributing to the joint association with pediatric cancer‐type outcomes can be viewed in Table 3. Moreover, we included all 11 pediatric cancer types in our analysis; however, models for certain types did not converge due to the low prevalence of cases in the counties.

Table 3.

Chemical Contributions to the Joint Associations Between Chemical Mixture and Cancer Generated Using gWQS Without Constraints With Repeated Holdout

Chemical Total cancer CNS Leukemia Malignant bone cancer Germ cell
2,4‐D 0.00 (0.00, 0.00) 0.00 (0.00, 0.01) 0.00 (0.00, 0.00) 0.00 (0.00, 0.02) 0.00 (0.00, 0.01)
Acetochlor 0.01 (0.00, 0.03) 0.01 (0.00, 0.02) 0.01 (0.00, 0.02) 0.00 (0.00, 0.00) 0.03 (0.00, 0.08)*
Alachlor 0.00 (0.00, 0.01) 0.00 (0.00, 0.01) 0.01 (0.00, 0.02) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00)
Atrazine 0.02 (0.01, 0.03) 0.01 (0.00, 0.02) 0.01 (0.00, 0.02) 0.02 (0.00, 0.05) 0.00 (0.00, 0.01)
Bromoxynil 0.01 (0.00, 0.02) 0.01 (0.00, 0.01) 0.01 (0.00, 0.01) 0.02 (0.00, 0.06) 0.02 (0.00, 0.05)
Clethodim 0.00 (0.00, 0.01) 0.01 (0.00, 0.01) 0.00 (0.00, 0.01) 0.00 (0.00, 0.01) 0.00 (0.00, 0.00)
Clopyralid 0.01 (0.00, 0.04) 0.00 (0.00, 0.01) 0.01 (0.00, 0.04) 0.00 (0.00, 0.01) 0.01 (0.00, 0.01)
Dicamba a 0.07 (0.02, 0.14)* 0.07 (0.04, 0.09)* 0.05 (0.01, 0.10)* 0.06 (0.00, 0.10)* 0.08 (0.03, 0.17)*
Dimethenamid 0.01 (0.00, 0.04) 0.00 (0.00, 0.00) 0.01 (0.00, 0.03) 0.00 (0.00, 0.00) 0.03 (0.00, 0.14)*
Flumetsulam 0.01 (0.00, 0.02) 0.01 (0.00, 0.01) 0.01 (0.00, 0.01) 0.01 (0.00, 0.04) 0.02 (0.00, 0.07)
Glyphosate a 0.07 (0.00, 0.22)* 0.05 (0.00, 0.16)* 0.07 (0.00, 0.23)* 0.06 (0.00, 0.14)* 0.03 (0.00, 0.08)*
Imazethapyr 0.01 (0.00, 0.04) 0.05 (0.01, 0.15)* 0.01 (0.00, 0.03) 0.00 (0.00, 0.02) 0.02 (0.00, 0.05)
Mcpa 0.03 (0.01, 0.05)* 0.01 (0.00, 0.04) 0.04 (0.00, 0.10)* 0.09 (0.03, 0.14)* 0.03 (0.00, 0.05)*
Metolachlor 0.01 (0.01, 0.03) 0.01 (0.00, 0.02) 0.01 (0.00, 0.03) 0.06 (0.00, 0.26)* 0.02 (0.00, 0.03)
Metribuzin 0.02 (0.00, 0.04) 0.04 (0.00, 0.10)* 0.01 (0.00, 0.04) 0.01 (0.00, 0.03) 0.00 (0.00, 0.01)
Metsulfuron 0.06 (0.01, 0.17)* 0.01 (0.00, 0.03) 0.05 (0.01, 0.13)* 0.04 (0.00, 0.07)* 0.03 (0.00, 0.09)*
Nicosulfuron 0.02 (0.00, 0.06) 0.03 (0.00, 0.10)* 0.01 (0.00, 0.01) 0.03 (0.00, 0.08)* 0.01 (0.00, 0.04)
Paraquat a 0.03 (0.01, 0.05)* 0.04 (0.01, 0.08)* 0.03 (0.01, 0.04)* 0.04 (0.00, 0.14)* 0.06 (0.01, 0.12)*
Pendimethalin 0.01 (0.00, 0.06) 0.03 (0.00, 0.08)* 0.02 (0.00, 0.06) 0.00 (0.00, 0.01) 0.02 (0.00, 0.05)
Picloram 0.02 (0.01, 0.04) 0.02 (0.00, 0.05) 0.03 (0.01, 0.08)* 0.06 (0.00, 0.25)* 0.03 (0.00, 0.07)*
Quizalofop a 0.14 (0.02, 0.21)* 0.12 (0.02, 0.22)* 0.11 (0.01, 0.22)* 0.10 (0.00, 0.25)* 0.24 (0.06, 0.62)*
Sethoxydim 0.03 (0.00, 0.09)* 0.01 (0.00, 0.04) 0.03 (0.00, 0.07)* 0.02 (0.01, 0.04) 0.00 (0.00, 0.01)
Thifensulfuron 0.02 (0.00, 0.03) 0.01 (0.00, 0.01) 0.02 (0.00, 0.06) 0.01 (0.00, 0.02) 0.00 (0.00, 0.00)
Triasulfuron a 0.09 (0.06, 0.13)* 0.11 (0.08, 0.13)* 0.17 (0.11, 0.21)* 0.13 (0.08, 0.17)* 0.03 (0.01, 0.05)*
Trifluralin 0.01 (0.00, 0.05) 0.01 (0.00, 0.01) 0.01 (0.00, 0.04) 0.00 (0.00, 0.01) 0.01 (0.00, 0.02)
Bifenthrin 0.01 (0.01, 0.03) 0.01 (0.00, 0.02) 0.01 (0.00, 0.01) 0.07 (0.03, 0.10) 0.01 (0.00, 0.03)
Chlorpyrifos 0.04 (0.00, 0.12)* 0.03 (0.00, 0.14)* 0.04 (0.00, 0.18)* 0.00 (0.00, 0.01) 0.01 (0.00, 0.02)
Dimethoate 0.02 (0.00, 0.03) 0.03 (0.00, 0.09)* 0.01 (0.00, 0.02) 0.00 (0.00, 0.00) 0.01 (0.00, 0.02)
Esfenvalerate 0.04 (0.01, 0.07)* 0.03 (0.03, 0.04)* 0.08 (0.01, 0.16)* 0.02 (0.00, 0.03) 0.07 (0.03, 0.15)*
Permethrin 0.00 (0.00, 0.01) 0.01 (0.00, 0.02) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.01)
Tefluthrin a 0.15 (0.06, 0.30)* 0.18 (0.07, 0.26)* 0.13 (0.04, 0.26)* 0.12 (0.01, 0.25)* 0.16 (0.01, 0.45)*
Terbufos 0.01 (0.00, 0.03) 0.02 (0.00, 0.04) 0.01 (0.00, 0.02) 0.01 (0.00, 0.01) 0.00 (0.00, 0.01)
Open in a new tab
a

Chemicals consistently contributed to the joint associations with overall cancer and sub‐types.

*Chemicals above a threshold of 0.03 (1/n‐chemicals included as mixture).

Among the 11 cancer sub‐types included in this study, gWQS regression models for 7 sub‐subtypes did not converge. We anticipate the potential reasons that gWQS models not converge for certain cancer subtypes could be due to small sample sizes or low case counts. To further explore association trends, we included a non‐repeated holdout approach and observed an increase in agrichemical mixtures associated with a statistically significant increase in the rate of cancer types such as lymphoma, malignant bone, neuroblastoma, retinoblastoma, renal tumors, soft tissue, and other unspecified cancer under an unconstrained setting. However, under a constrained setting, the majority of these findings did not reach statistical significance, as shown in Table 2.

4. Discussion

We analyzed pesticide application in 93 Nebraska counties over 22 years, from 1992 to 2014. Our findings revealed that herbicides were the most frequently used pesticides, with insecticides following. In parallel, our examination of pediatric cancer cases within Nebraska highlighted that the most common subtypes were brain and other CNS tumors, leukemia, lymphoma, germ cell tumors, and malignant bone tumors. We found positive joint associations between the pesticide component mixture with overall pediatric cancer, brain, and other CNS, and leukemia from the WQS models on pesticide components adjusted for social vulnerability factors. The pediatric cancer rate effect estimates for brain and other CNS cancer types were of greater magnitude than the overall cancer and leukemia. In the WQS models on pesticide components, dicamba, glyphosate, paraquat, quizalofop, triasulfuron, and tefluthrin have the highest weights contributing to these associations for overall cancer, brain and other CNS cancers, and leukemia. The current study expands upon the findings of Ouattara et al. (2022), which focused on the association of individual chemicals with pediatric cancer in Nebraska and, encountered limitations due to the confounding influence of co‐pollutant exposures and investigation of only three cancer subtypes. We have addressed these limitations by exploring the impact of exposure to a mixture of agrichemicals, supporting our initial hypothesis that such exposure is a significant risk factor for the development of pediatric cancer.

Ouattara et al. (2022) identified an association between exposure to the waterborne agrichemicals atrazine and nitrate and increased incidence of pediatric cancers, specifically those affecting the brain and central nervous system, alongside leukemia and lymphoma The herbicide atrazine, prevalently applied to control weeds in cornfields across Nebraska, and nitrate, employed for its nitrogen‐enriching properties to augment crop growth, have been staples in agricultural practice. However, for several decades, large‐scale crop production has relied on the simultaneous application of fertilizers through tank mixtures and has become a preferred method of agrichemical application. The interactions with a mixture of pesticides, including insecticides, fungicides, and herbicides, may result in synergistic, antagonistic, or additive effects, influencing the health of ecosystems and living organisms (Almasri et al., 2020; Van Meter et al., 2019). These agrichemicals and their degradates can occur indoors and outdoors due to agricultural drift, volatilization, and wind erosion (Deziel et al., 2017). In an extensive survey of the U.S. groundwater system, Bexfield et al. (2021) analyzed 1,204 wells, revealing that 41% contained pesticide residues, with a notable majority presenting mixtures including herbicides like atrazine and related degradates, hexazinone, prometon, metolachlor, tebuthiuron, and or their degradates. Considering these findings, the current study aims to evaluate the cumulative effect of co‐pollutant exposure on the incidence of pediatric cancers, thereby addressing the research gap presented by examining single pollutant exposures. The findings of this study are interpreted using the effect estimates (β1) of the agrichemical mixture, which were estimated for each cancer subtype. Additionally, this mixture method also identifies the agrichemical(s) that significantly contributed to the weight of the index.

Some Swedish population‐based studies examined the relationship between parental occupational exposure to herbicides and insecticides and the incidence of childhood cancers. These findings indicated an increased risk of certain childhood cancers, lymphoma, and solid tumors other than CNS, and no associations were observed for leukemia or CNS tumors (Coste et al., 2020; Rossides et al., 2022). In addition, parental exposure to herbicides and insecticides was associated with an increased risk of childhood acute myeloid leukemia (AML) but not acute lymphoblastic leukemia (ALL) or CNS tumors (Patel, Gyldenkærne, et al., 2020; Patel, Jones, et al., 2020). In a Danish study, maternal occupational exposure during pregnancy to specific herbicides, prosulfocarb, thifensulfuron‐methyl, and bentazone, was associated with an increased risk for acute childhood leukemia (Patel, Gyldenkærne, et al., 2020; Patel, Jones, et al., 2020).

In California, case‐control studies showed that higher levels of herbicides, such as glyphosate, were detected in the household dust. However, it did not increase the risk of ALL in children (Ward et al., 2023). Early life exposure to glyphosate and paraquat dichloride is associated with increased odds of childhood ALL and AML, and triflusulfuron‐methyl was associated with increased odds of childhood ALL (Park et al., 2020). Although paraquat is currently not considered a carcinogen, the increased risk of AML might be linked to its mechanism of causing oxidative stress and mitochondrial DNA damage (Sanders et al., 2017). Our results can be compared to Park et al., who estimated the overall effect using a multiple‐pesticide model co‐adjusted for exposure to all other carcinogenic pesticides selected via a hierarchical model and accounting for social vulnerability factors, race, and neighborhood socioeconomic status.

Limited studies have explained the increased risk of childhood cancers with exposure to Glyphosate or Dicamba. However, results from the Agricultural Health Study (AHS) showed an increased risk of AML among the pesticide applicators exposed to glyphosate compared with the unexposed group (Andreotti et al., 2018). Hodgkin lymphoma risk was highest among those 40 years and older who ever used dicamba and lower for glyphosate (Kim et al., 2023). Among the AHS participants, an increased risk of liver and intrahepatic bile duct cancer and chronic lymphocytic leukemia was seen among those exposed to dicamba compared to the never‐user group (Lerro et al., 2020).

Although quizalofop, a selective herbicide, is not directly linked with cancer, it is known to cause developmental toxicity. Exposure of Zebrafish embryos to quizalofop‐P‐ethyl (QpE) significantly increased the mortality rate, decreased the hatching rate, and caused morphological defects during embryonic development. Depending on the concentration, several genes associated with heart development and inflammation were also altered, causing cardiotoxicity by altering gene expression (Zhu et al., 2022). In the cell line studies, when QpE was tested along with other endocrine disrupting chemicals (EDCs), including glyphosate, 2,4‐dichlorophenoxyacetic acid, and dicamba to induce lipid accumulation in murine 3T3‐ L1 adipocytes, only QpE caused a dose‐dependent statistically significant triglyceride accumulation from a concentration of 5–100 μM. Furthermore, this lipid accumulation was partly caused by the peroxisome proliferator‐activated receptor gamma (PPARγ)‐mediated pathway, a nuclear receptor whose modulation influences lipid metabolism (Biserni et al., 2019). Furthermore, QpE acted as an EDC by disrupting the zebrafish endocrine system in a sex‐specific manner, increasing estrogen axis activity in males rather than females, which may account for QpE regulating steroidogenesis or activating estrogen receptors (Zhu et al., 2017).

Triasulfuron, a sulfonylurea herbicide, is often found to co‐occur with breast cancer, causing pesticides atrazine, dichlorvos, diuron, simazine, and azoxystrobin in food, drinking water, groundwater, and surface water (Koval et al., 2022). These pesticides activate pathways related to endocrine disruption by altering steroid synthesis in H295R cells, activating nuclear receptors, or affecting xenobiotic enzymes (Cardona & Rudel, 2020). Exposure to the pyrethroid insecticide tefluthrin was associated with an increased risk of childhood brain tumors. In a Chinese case‐control study, urinary analysis of three non‐specific pyrethroid metabolites showed that children in the highest quartile of exposure had a nearly three‐fold increased risk of these tumors compared with those in the lowest quartile range (Chen et al., 2016). In Cell line studies, Tefluthrin, and structurally related pyrethroids acted on membrane ion currents in pituitary tumor cells, affecting endocrine or neuroendocrine function (Wu et al., 2009). Moreover, pyrethroids are genotoxic, induce genetic rearrangements, alter gene expression, and modify DNA. These biological modifications could cause carcinogenicity in hematopoietic cells, leading to leukemia and lymphoma in children (Navarrete‐Meneses & Perez‐Vera, 2019). Prenatal exposure to pyrethroids can induce adverse neurodevelopmental outcomes in children (Andersen et al., 2022).

This study evaluated the overall effect of agricultural mixtures on all childhood cancer types. In contrast, a previous study (Ouattara et al., 2022) explored the relationship between individual chemical exposure (atrazine or nitrate) and only three pediatric cancer types: brain and other CNS, leukemia, and lymphoma in Nebraska. Our study is the first to estimate the effect of an agrichemical mixture on the pediatric cancer rate in Nebraska. One significant advantage of our study is that we identified the pesticide consistently applied over 22 years in Nebraska counties and then estimated the overall mixture effect of these pesticides on pediatric cancer. Pesticides in the mixture were highly correlated, and residual confounding due to social vulnerability factors for both exposure and outcome could lead to potential confounding bias as it makes identifying the effect of an individual chemical difficult (Braun et al., 2016). However, the WQS model with a repeated holdout validation approach could handle such confounding due to the collinearity of highly correlated pesticides. Additionally, this study highlights that exposure to a mixture of agrichemicals might cause childhood cancer. We anticipate these findings will enhance efforts by public health departments, government agencies, and other relevant pediatric cancer stakeholders to design and implement cancer control programs and interventions in agriculturally dominant states, including Nebraska.

There are some limitations in this study. The racial makeup of our study population is a limitation to the generalizability of the results, as our study population is homogenous, with 90.3% White in Nebraska and 72% White in the entire U.S., according to the 2010 census (Nebraska DHHS, 2020). Different race‐ethnicities could be linked to different exposure patterns (Thelin & Stone, 2013) and pediatric cancer incidence (Geris & Spector, 2020). Hence, there may be vulnerable populations that could not be represented well in this study. Another potential limitation of our study is the sample size; there were low counts in the pediatric cancer cases for some of the sub‐types, so the WQS model did not converge even upon using a zero‐inflated extension for those types, as only 93 counties were used for the training and testing data sets. We had limited access to the pediatric cancer data after 2015. Hence, we could not explore Nebraska's current pediatric cancer trends, though exposure data was available beyond 2015. We could not implement other available mixture methods to estimate the overall effect besides WQS, as our outcome data is on a count scale. Currently, only the WQS model has an extension to set the offset term and for the assumptions of Poisson distribution. The WQS model identified some pesticides as risk factors for pediatric cancer that have not been linked with cancer. It is essential to consider the chemicals of concern carefully by evaluating their underlying biology in the dose‐response relationship or toxicological nature before making any regulatory decisions. As the lower‐concentration components might be more potent than the higher‐concentration ones, toxicological studies are needed for the chemicals lacking such evidence. Our analysis is based on population‐level pesticide application estimates, and so may introduce ecological and exposure misclassification bias. Additionally, due to the aggregate scale analysis this study lacks identifying windows of susceptibility. Future directions should include establishing a human biomonitoring program for pediatric cancer and measuring human exposure using biomarker‐based approaches (e.g., blood, urine, and plasma) in adults and also from special populations (e.g., neonates or toddlers), mainly for those chemicals known to be of concern.

5. Conclusions

We applied weighted quantile sum regression to estimate the association between pediatric cancer and an index of 32 correlated agrichemicals applied in Nebraska counties for 22 years. The WQS method allowed us to make a generalized inference about the agrichemical mixture effect and identify individual chemicals most strongly associated with pediatric cancer types while considering the correlation between compounds. Using WQS regression, we found a positive association between the chemical index and pediatric cancer in different subtypes. Our results demonstrate the importance of evaluating chemical mixtures while studying pediatric cancer risk.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Supporting information

Supporting Information S1

GH2-9-e2024GH001236-s001.docx (47.7MB, docx)

Acknowledgments

We sincerely thank the Buffett Early Childhood Institute at the University of Nebraska and The Daugherty Water for Food Global Institute at the University of Nebraska for their support through graduate fellowships.

Taiba, J. , Beseler, C. , Zahid, M. , Bartelt‐Hunt, S. , Kolok, A. , & Rogan, E. (2025). Exploring the joint association between agrichemical mixtures and pediatric cancer. GeoHealth, 9, e2024GH001236. 10.1029/2024GH001236

Data Availability Statement

The Pediatric Cancer data supporting this research are available from the Nebraska cancer registry and can be requested by contacting the Nebraska Department of Health and Human Services (DHHS). Pesticide usage data can be accessed publicly from the USGS Pesticide National Synthesis Project database at https://water.usgs.gov/nawqa/pnsp/usage/maps/county‐level/. Full reproducible code is available at Taiba (2025).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information S1

GH2-9-e2024GH001236-s001.docx (47.7MB, docx)

Data Availability Statement

The Pediatric Cancer data supporting this research are available from the Nebraska cancer registry and can be requested by contacting the Nebraska Department of Health and Human Services (DHHS). Pesticide usage data can be accessed publicly from the USGS Pesticide National Synthesis Project database at https://water.usgs.gov/nawqa/pnsp/usage/maps/county‐level/. Full reproducible code is available at Taiba (2025).

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