Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States

Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States Jabeen Taiba, Cheryl Beseler, Alan Kolok, Muhammad Zahid, Shannon Bartelt-Hunt, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4719285/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background In the contiguous United States, environmental justice burdens and disparities in pesticide exposure are prevalent among racial and ethnic minorities and low socioeconomic groups. Identifying the counties with high pesticide exposure and social vulnerability is essential to mitigating risk. Methods We created an index for pesticides commonly used in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. We identified the US counties with elevated pesticide exposure and elevated social vulnerability. The USGS Pesticide National Synthesis Project quantified pesticide exposures at a county scale for frequently applied pesticides from 1992 to 2019 in 3069 contiguous US counties. We retrieved social vulnerability data from five-year estimates (2015–2019) of the American Community Survey (ACS) for selected variables: race, income, and educational attainment, and created a social vulnerability index. We implemented the pesticide index and social vulnerability index using a principal component analysis (PCA) approach. We used an Intergovernmental Panel on Climate Change ICCP risk-based approach to identify the counties with both high pesticide exposure and social vulnerability. Results One hundred and forty-three US counties had high pesticide use and social vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In conclusion, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across the contiguous US counties in both rural and urban communities. Conclusions Our study will inform regulatory bodies about areas with both high pesticide exposure and social vulnerability areas, as well as facilitate regulatory and public health decisions. Pesticides social vulnerability environmental pollution environmental justice Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Global pesticide use in the past decade has increased by 20% [ 1 ]. This surge can be attributed to the availability of lower-cost options, generic pesticides, shifts in farming practices, and impact on rural economies [ 2 , 3 ]. Every year, around 899 million pounds of active pesticide ingredients are typically used on agricultural land in the US [ 4 , 5 ]. Such environmental spread of pesticides, endangering human health and the integrity of the ecosystem, has become a concerning issue [ 6 , 7 ]. Pesticides can spread beyond the intended agricultural fields due to wind drift and volatilization, causing their deposition in nearby agricultural and non-agricultural areas [ 8 ]. Exposure to pesticides from agricultural drift poses a significant contamination risk, particularly for individuals residing near agricultural areas [ 9 , 10 , 11 , 12 ]. Environmental injustice also drives disparities in pesticide exposure [ 13 ], and these disparities are disproportionately higher among minority racial/ethnic groups [ 14 , 15 ], those with low income or of lower socio-economic status (SES) [ 16 ], and low educational attainment [ 17 ]. Chronic exposure to low doses of pesticides has been linked to several adverse human health outcomes, including epigenetic changes such as DNA methylation [ 18 ], Parkinson’s disease [ 19 ] [ 20 ], Alzheimer’s disease [ 21 ], neuropsychological functioning [ 22 ], diabetes [ 23 ], obesity [ 24 ], and central nervous system tumors [ 25 ]. Prenatal and young children are at an increased risk of early death and developing disease across their lifespan as they are particularly susceptible to pesticide pollution [ 26 ]. Various perinatal outcomes are associated with prenatal exposure to pesticides, including low birth weight [ 27 ], preterm birth [ 28 ], neurodevelopment problems [ 29 ], behavior problems [ 30 ], birth defects [ 31 ], and childhood leukemias [ 32 ]. Besides human health, pesticide exposure has also impacted honeybee colonies, microbial communities, and ecosystems that support human health [ 33 , 34 ]. Social vulnerability has also shown disparities in diseases associated with pesticide exposure, including obesity and diabetes [ 35 ], due to differences in food availability in more disadvantaged areas [ 36 ]; long telomere length (LTL), a bio-marker for cellular aging, is higher in non-Hispanic Black Americans than non-Hispanic White Americans [ 37 ], Parkinson’s disease [ 38 ], childhood cancer [ 39 ], and birth outcomes [ 40 ] [ 41 ]. It is important to identify the contiguous US counties with disproportionately high pesticide application and social vulnerability due to disparities that exist among vulnerable populations in pesticide exposure and health outcomes. Using the United Nations FAOSTAT pesticide database and USGS Pesticide National Synthesis Project(PSNP) data, previous studies created chemical grids to estimate pesticide usage in the US and globally [ 42 ]. The US EPA reported the twenty-five most frequently used conventional pesticide active ingredients in the agricultural market sector from 2008 to 2012 [ 4 ]. The environmental burden index (EBI) for Idaho was developed by Joseph et al using the 2017 USGS PSNP data and these twenty-five most applied pesticides [ 4 , 43 ]. Nevertheless, that EBI was limited to Idaho, and the pesticide exposure lagged in timepoint; no clear selection criteria were developed to identify these most frequently applied pesticides throughout the contiguous United States. Additionally, validation of the methods used to create the pesticide index is required. A pesticide index gives a comprehensive representation of overall pesticide application across the time frame compared with a few individual pesticides. It serves as an indicator of pesticide exposure to improve our understanding of the relationship between environmental exposures and human health and can benefit policymaking. The pesticide index is a latent variable created using a principal component analysis of selected pesticides after standardizing the data. By implementing policy changes on regulating pesticide usage, conducting toxicological risk assessments, reducing exposure, and mitigating disease prevention, it will be possible to reduce the public health burden. Identifying US counties with high pesticide application and high social vulnerability will aid in estimating the pesticide risk in these areas. Our study aims to create an index for commonly used pesticide ingredients in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. In addition, we will identify the US counties with high pesticide exposure and high social vulnerability. 2. Methods 2.1. Data sources and variable selection We used the USGS Pesticide National Synthesis Project (USGS/PSNP) data within the National Water-Quality Assessment Project [ 44 ] to identify the most commonly applied pesticides for counties of the 48 contiguous United States during a twenty-eight-year period (1992–2019). The “high” and “low” estimated annual pesticide estimates for crop-acreage data by county from 1992 to 2019 and for a total of 526 active ingredients relied on surveys used in conjunction with the USDA National Agricultural Statistics Service (USDA/NASS) [ 45 ] and interpolation and extrapolation methods were initially described when data were not available [ 46 – 50 ]. We took the “EPest-high” estimates for our analysis and the difference between EPest-high and EPest-low as described elsewhere [ 47 ]. Furthermore, we calculated the median mass based on the temporal coverage of the pesticide applied and percentages of the state and county coverage based on the spatial or county coverage of the pesticide applied. Using the USGS/PSNP data, we developed selection criteria to identify the most applied pesticide active ingredients in the 48 contiguous states against the satisfaction of the following conditions: (i) median mass applied based on the temporal coverage was set to 80% of the study period, which means the pesticide mass should be applied at least for 22 years over the 28-year records, (ii) the percentages of state and county coverage were set to 80%, i.e., the pesticide active ingredients should be applied in at least 80% of the 48 contiguous states and counties. In total, 30 active ingredients were found over 28 years (1992–2019) and were grouped based on their pesticide classes into 21 herbicides, six insecticides, and three fungicides, as shown in Table 1. We then plotted a box plot to see the individual distribution of pesticide-active ingredients, as shown in Figure S1 . As discussed above, variables representing the most applied pesticide-active ingredients were obtained. They were included in calculating the pesticide index (PI) to assess the sensitivity of principal component analysis (PCA) to input variables. We tested different variables for inclusion in the PI to represent the proportion of pesticide-active ingredients in each county by calculating Spearman’s correlation coefficients between the 30 pesticide variables, as shown in Fig. 1 . We removed the negatively correlated variables and included the positively correlated variables in the PCA. Social vulnerability (SV) data for 48 contiguous US States were retrieved from the American Community Survey (ACS) 5-year estimates for 2015–2019 [ 51 ] using an application programming interface (API) key. Socially vulnerable populations were defined based on income, educational attainment, and race and ethnicity (Table S1 ). The PCA included all three SV variables to construct an index (SVI). 2.2. Unsupervised PCA and PI Calculation We used PCA [ 52 ] and implemented it using the prcomp function, R version 4.2.2, [ 53 ] to limit the number of variables and create independent factors to include in a PI; PI was calculated using two different approaches, and results were compared. In our first approach, following Reid et al [ 54 ]., we used factor rotation to minimize the number of original variables that load highly on any one factor and increase the variation among factors, making the new factors more statistically independent than the original variables. We retained five factors based on a combination of standard criteria: eigenvalues > 1, a clear break in the values in the Scree test, and a percentage of variance explained by factors, as shown in Fig. 2 a. We normalized the factor scores to have a mean of 0 and a standard deviation of 1. Later, these factors were summarized into a single PI by estimating the magnitude of the selected components [ 43 ]. The magnitude for each observation was calculated by taking the square root of the sum of squares of the five component values, as shown here. Magnitude_i = sqrt(∑(x_ij^2)) for j = 1 to m. Magnitude_i represents the magnitude of row i. x_ij represents the element at row i and column j of first_5_components. The sum symbol (∑) indicates that we calculate the sum of the squared values over the columns for a given row. Sqrt () denotes the square root function, taking the sum of squared values to obtain the final magnitude. In our second approach, to calculate the PI, we used PCA (prcomp, R version 4.2.2) to calculate the factor loadings for the input variables. We then used factor rotation, like in approach one, and extracted the scores. Later, we multiplied each factor by each county variable and created a matrix from the indexed scores. Finally, we calculated the PI by summing the scores and got a summed score for each county. This summed factor scores approach is advantageous as it preserves the variation in the original data [ 55 , 56 ]. In our third approach, we took the first principal component alone with a maximum variance of 44% as an index variable, as shown in Fig. 2 a. We selected PC 1 as an index variable from these three approaches as it showed stronger correlations than PC developed using magnitude or summed score approach and validated it using a Spearman correlation matrix as shown in Fig. 1 . 2.3. Social vulnerability index (SVI) To calculate the SVI, we used a similar approach of factor rotation to the one we used to construct the pesticide index, and we included all the social vulnerability variables in the PCA analysis, as shown in Table S1 . 2.4. Risk-based mapping of PI and SVI Using the pesticide and social vulnerability index based on the Intergovernmental Panel on Climate Change (IPCC) risk-based conceptual framework [ 57 ], we identified and mapped the US counties with high pesticide applications and high social vulnerability. 2.5. Secondary analysis 2.5a. Exploratory Spatial analysis Data for the spatial analysis was prepared by joining the pesticide data with the contiguous state’s Federal Information Processing System code data on a county level. The shape files from the United States Census Bureau were obtained using the R package Tigris. We conducted an exploratory spatial data analysis (ESDA) by converting the neighbors list objects into binary spatial weights and produced neighborhood sums using the R package spdep and function ploy2nb and nb2listw. Where neighbors are given the weight one and non-neighbors take the weight 0. We tested for global spatial autocorrelation using Moran's I to evaluate the presence of spatial clustering, where the data values tend to be similar to the neighboring data values [ 58 ]. The Moran’s I statistic was computed as: $$\:I=\frac{N}{W}\frac{\sum\:i\sum\:jwij(xi-¯x)(xj-¯x)}{\sum\:i(xi-¯x)2}$$ Where w ij represents the spatial weights matrix, N is the number of spatial units denoted by i and j, and W is the sum of the spatial weights. We also calculated the local spatial autocorrelation using local indicators of spatial association statistic (LISA) [ 59 ] to identify clusters and spatial outliers as follows: $$\:Ii=zi\sum\:jwijzj$$ Where z i and z j are expressed as deviations from the mean. The spatial clusters, where observations are surrounded by similar values, and spatial outliers, where dissimilar values surround observations. We computed LISA in R using localmoran() family of functions in the spdep package. We then used localmoran_perm() to calculate a GeoDa-style LISA quadrant plot and a cluster map using ggplot2 in R. 2.5b. Heat Maps We generated heat maps to examine the patterns of commonly used pesticides throughout the study, as shown in Figures S3a – S3e. In this study, we collected and analyzed pesticide data from different states and years. We calculated the median amount of pesticide applied and scaled the data accordingly. Additionally, we grouped the data based on their pesticide class. We generated heat maps for each pesticide category using the ggplot function in R. We have also developed a shiny app that allows users to explore the temporal trends of pesticides applied in the contiguous US counties. 3. Results In our twenty-eight-year (1992–2019) study period of the United States, we consistently observed the application of 30 pesticides in the 48 contiguous states. These pesticides were among the most commonly used based on our selection criteria. We classified these commonly used pesticides into three groups: herbicides, insecticides, and fungicides. Herbicides were the most commonly applied, followed by insecticides and fungicides. The most commonly used herbicides include glyphosate, atrazine, acetochlor, 2,4-D, metolachlor, pendimethalin, metolachlor, trifluralin, and simazine. Insecticides such as carbaryl, chlorpyrifos, dimethoate, and fungicides, mancozeb, and chlorothalonil were the most-applied pesticides in kg/acre (Table 1). There were strong correlations among all the pesticides, while the herbicides triclopyr, picloram, and metsulfuron had weaker or negative correlations (Figure 1). Table 1. Summary Characteristics of Frequently Applied Pesticides (1992 – 2019) Rank Compound Class Amount (Kg/acre) 1 Glyphosate Herbicide 69033923.7 2 Atrazine Herbicide 31812125 3 Acetochlor Herbicide 15019875.1 4 2,4-D Herbicide 13826981 5 Pendimethalin Herbicide 6531314.4 6 Metolachlor Herbicide 5716004.65 7 Trifluralin Herbicide 5061700.15 8 Chlorothalonil Fungicide 3946749.55 9 Chlorpyrifos Insecticide 3902792.5 10 Simazine Herbicide 2855285.35 11 Mancozeb Fungicide 2841390.5 12 Paraquat Herbicide 2609745.1 13 Dicamba Herbicide 2553668.25 14 Metribuzin Herbicide 1047224.75 15 Carbaryl Insecticide 794234 16 Dimethoate Insecticide 717050.9 17 Triclopyr Herbicide 457047.35 18 Picloram Herbicide 402177.55 19 Propiconazole Fungicide 357291.3 20 Permethrin Insecticide 272509.75 21 Clethodim Herbicide 261393.15 22 Imazethapyr Herbicide 245514.7 23 Sethoxydim Herbicide 220518.95 24 Cyhalothrin-Lambda Insecticide 176561.95 25 Esfenvalerate Insecticide 80097 26 Chlorimuron Herbicide 62974.9 27 Nicosulfuron Herbicide 55239.9 28 Thifensulfuron Herbicide 53548.2 29 Tribenuron Methyl Herbicide 29607.1 30 Metsulfuron Herbicide 18851.55 Amount applied was calculated by taking the sum of median pesticide applied by compound across the US counties (1992 – 2019) Unsupervised PCA - Pesticide and Social Vulnerability Indices We used the first principal component with a maximum variance of 44% and an eigenvalue of 12, as shown in the scree plot (Figure 2a) for the pesticide index (PI). We observed that herbicides, insecticides, and fungicides equally contributed to the chemical weight of the first principal component in the loadings plot. The second and third principal components explained a percentage variance of 7 and 5, along with eigenvalues 1.9 and 1.5, respectively, and herbicides contributed to both the positive and negative load (Figure 2b , Table S2). Similarly, in the PCA of the social vulnerability, the first principal component explained a maximum variance of 81% with an eigenvalue of 8.1, and the percentage variance of the second and third components were 8.4 and 5.5 with their eigenvalues 0.84 and 0.55, respectively, as shown in Figure 2c and Table S3. We used the first principal component for the SVI. According to the PI and SVI scores, the geospatial distribution of the most commonly used pesticides in the contiguous US revealed high, medium, and low pesticide application counties, as shown in Figures 3a and 3b. We found high pesticide application rates in the counties of North Dakota, South Dakota, Iowa, Nebraska, Illinois, Indiana, Ohio, Arkansas, California, and Washington, as well as certain areas of Idaho, Florida, Georgia, North and South Carolina, Pennsylvania, and New York. The majority of the US, West, and South showed low to moderate pesticide application (Figure 3a). In a similar way, the main counties with significant social vulnerability were Washington, Oregon, California, Arizona, New Mexico, Florida, and certain areas in Ohio, Michigan, Illinois, New York, Massachusetts, Pennsylvania, North and South Carolina, Texas, New Orleans, and Tennessee. We found low to moderate social vulnerability in the Mid-Western region and the rest of the US, as shown in Figure 3b. Risk-based mapping of PI and SVI The bivariate mapping of the Pesticide Index (PI) and Social Vulnerability Index (SVI) revealed the US counties that exhibited high levels of pesticide exposure and social vulnerability, as well as those with low levels of pesticide exposure and vulnerability, as shown in Figure 4. 143 contiguous counties in the United States reported high pesticide exposure and high socioeconomic vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had noticeably higher numbers of these counties, as shown in Table 2. These counties are represented by the dark brown color on the map. The dark teal color represents US counties with low pesticide exposure and low social vulnerability, as shown in Figure 4. Table 2. Summary of the counties in the U.S. with high pesticide application and high social vulnerability State Counties Count Illinois McLean, Macon, St. Clair, Kankakee, Kane, Winnebago, Sangamon, Tazewell, LaSalle, Vermilion, Will, Madison, Peoria, Champaign, McHenry 15 North Carolina Robeson, Halifax, Sampson, Lenoir, Wilson, Duplin, Johnston, Pitt, Union, Edgecombe, Wayne, Nash, Columbus 13 Michigan Van Buren, Ottawa, Berrien, Calhoun, Kent, Saginaw, Bay, Monroe, Allegan, Lenawee 10 California Yolo, Kern, Fresno, Madera, San Joaquin, Merced, Tulare, Stanislaus, Imperial 9 Ohio Allen, Wayne, Clark, Miami, Greene, Ross, Wood, Fairfield, Licking 9 Indiana Allen, Lake, Tippecanoe, LaPorte, Elkhart, Delaware, Howard, Madison 8 Iowa Black Hawk, Dubuque, Linn, Johnson, Polk, Pottawattamie, Scott, Woodbury 8 Pennsylvania Berks, Chester, York, Adams, Franklin, Lancaster 6 Alabama Limestone, Jackson, Baldwin, Houston, Madison 5 South Carolina Darlington, Florence, Sumter, Orangeburg, Horry 5 Texas Hidalgo, Lubbock, Ellis, Williamson, San Patricio 5 Florida Hendry, Palm Beach, Polk, Jackson 4 New York Steuben, Cayuga, Wayne, Niagara 4 Washington Yakima, Benton, Grant, Franklin 4 Wisconsin Rock, Outagamie, Marathon, Dane 4 Arizona Yuma, Pinal, Maricopa 3 Arkansas Jefferson, Craighead, Crittenden 3 Georgia Bulloch, Colquitt, Coffee 3 Kentucky Daviess, Warren, Christian 3 Louisiana St. Landry, Rapides, Avoyelles 3 Maryland Carroll, Frederick, Wicomico 3 Colorado Adams, Weld 2 Delaware Sussex, Kent 2 Idaho Twin Falls, Canyon 2 Minnesota Dakota, Stearns 2 Oregon Marion, Umatilla 2 Kansas Sedgwick 1 Mississippi Washington 1 Nebraska Lancaster 1 North Dakota Cass 1 Oklahoma Kay 1 South Dakota Minnehaha 1 Total 32 States 143 Secondary Analysis Exploratory Spatial Analysis In addition to identifying the areas with high and low application of the most applied pesticides, we also calculated the Moran’s I static using the PI to observe if there is a spatial autocorrelation between the counties with pesticide applied and their neighboring counties. Based on Moran’s I static (< 0.05), these counties have a positive association and spatial autocorrelation (Table S4). We explored regions with potential clusters and outliers for these most-applied pesticides using LISA and observed significant clusters in the areas of the Mid-western US states, North and South Dakota, Illinois, Iowa, and Indiana; Western states, including California and Washington, and Southern states of Texas and Arkansas (Figure S2). Pesticide application trends During the study period, we noticed a consistent herbicide use pattern across all 48 states. Interestingly, there has been a noticeable increase in the use of herbicides, like atrazine, in the mid-western states of Illinois, Indiana, Iowa, Kansas, and Nebraska. After 2010, there was an increase in glyphosate application in the mid-western states, including North and South Dakota (Figure S3a). In Iowa and Nebraska, 2,4-D application rates increased after 2010, whereas in North Dakota, usage was higher prior to 2000. In Delaware, paraquat applications have seen a noticeable increase in recent years. On the other hand, metolachlor applications were higher in Delaware, Indiana, and Iowa before 2000 but have since seen a decrease in recent years (Figures S3b and S3c). In addition, the use of various insecticides, including permethrin, efsenvalerate, dimethoate, chlorpyrifos, and carbaryl, exhibited a consistent pattern across all 48 US states, with only a few deviations. The use of efsenvalerate in the western state of California has seen a rise since 1992 but experienced a decline after 2015 (Figure S3d). In nearly all US states, the use of fungicides such as propiconazole, mancozeb, and chlorothalonil remained consistent, with the exception of Delaware and North Dakota. In Delaware, there were varying trends in the use of these fungicides, whereas in North Dakota, the application of propiconazole has been on the rise since 2010 (Figure S3e). 4. Discussion We examined the use of pesticides in the contiguous US counties over 28 years, from 1992 to 2019. Our study revealed that herbicides were predominantly applied, compared to insecticides and fungicides. The herbicides, 2,4-D, atrazine, glyphosate, metolachlor, paraquat, pendimethalin, simazine, and trifluralin, and the insecticides, carbaryl, chlorpyrifos, dimethoate, fungicides, mancozeb, and chlorothalonil, were the predominantly applied pesticides across the study period. Using the pesticide and social vulnerability indices, 143 US counties had high levels of social vulnerability and pesticide use. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In an exploratory spatial analysis, a positive association, or spatial autocorrelation, was observed between the counties with applied pesticides and their neighboring counties at Moran's I static < 0.05. In the mid-western states of Illinois, Indiana, Iowa, Kansas, and Nebraska, there has been an observed increase in the application of the herbicide atrazine, according to the findings of our study. After 2010, the application rate of 2,4-D increased in Iowa and Nebraska, while in North Dakota, the application rate was higher before 2000. Pesticide exposure risk, along with social vulnerability, was significantly higher in the counties of Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania compared to other counties in the United States. The incidence of high pesticide exposure is highly associated with race, ethnicity, and socioeconomic status. The majority of these states are the primary cultivators of corn and soybeans. Additionally, Pennsylvania is a large producer of hay, while California produces a diverse range of fruits, nuts, and vegetables [ 60 ]. Individually, these states have elevated levels of pesticide usage and social vulnerability. The California EPA discovered that pesticide use in agriculturally dominant states resulted in a chemical pollution burden that disproportionately affected communities with racial or income disparities. This burden was found to be greater than that caused by several air pollutants and other toxicant releases [ 61 ]. Farmworkers experience a significantly greater level of pesticide exposure due to the fact that the majority of pesticide usage in the United States occurs in the agricultural sector. Farmworkers consistently have a somewhat greater level of exposure compared to the general population [ 13 ]. According to a recent report on farmworkers in the US, approximately 87% of them identify as Hispanic or Latinx. Their average annual income is less than $ 20,000, and one-third of their family incomes fall below the federal poverty line. On average, these farmworkers have completed education up to the ninth grade [ 62 ]. A study conducted in Michigan found that Hispanics had a higher likelihood of experiencing work-related pesticide exposure and subsequent illness compared to non-Hispanics [ 63 ]. A comprehensive US study across twelve states, including California, Iowa, Michigan, and North Carolina, showed the rate of acute occupational pesticide-related illness and injury was 37 times higher for agricultural workers than for non-agricultural workers [ 64 ]. Higher rates of hospitalizations for pesticide-related illness and pesticide exposure events were reported among licensed pesticide applicators [ 65 ], increased risk of Parkinson’s disease on exposure to herbicides and insecticides in an agrarian cohort [ 19 , 20 ], childhood leukemia, and central nervous system tumors [ 25 , 32 ], and certain types of birth defects [ 31 ] due to parental occupational exposures. Pesticide exposure disparities due to race, ethnicity, and income using biomonitoring in the US general population revealed African Americans and Mexican Americans had higher concentrations of pesticide biomarkers in their blood or urine than non-Hispanic Whites who do not live in poverty [ 16 ]. Similarly, non-Hispanic Black women, Mexican American, and Other Hispanic women showed the highest disparities in levels of pesticides compared to non-Hispanic White women [ 66 ]. The exposure levels of organophosphate pesticides and associated disease burden and costs were higher in non-Hispanic Blacks and Mexican Americans than in other groups [ 14 ]. Additionally, racial or ethnic minorities, low-income groups, and other disadvantaged groups are disproportionately exposed to pesticides and are linked to adverse endocrine health effects, including diabetes and obesity [ 35 , 67 ], and women’s reproductive health outcomes [ 15 ]. Also, higher levels of pesticides and their metabolites found in the blood and urine in non-Hispanic Black women compared to non-Hispanic white women were found to have breast cancer-related biological activity [ 68 ]. Lower overall survival rates for cancer were higher among non-Hispanic Blacks and Hispanics than non-Hispanic Whites and non-Hispanic Asians [ 69 ]. Furthermore, in a prospective birth cohort, urinary maternal levels of organophosphate metabolites were more strongly associated with decreased birth weight among Black newborns than white newborns. In contrast, these urinary metabolites were associated with shorter gestation time in white mothers but not in Black mothers [ 70 ]. To our knowledge, this is the first study to create a pesticide index based on the most frequently applied pesticides longitudinally for the contiguous United States using definitive selection criteria. The US EPA has developed an Environmental Quality Index (EQI) using PCA based on five environmental domains: air, water, land, built-in environment, and sociodemography [ 71 ] to enhance our understanding of associations between environmental conditions and public health. Despite including pesticide usage in their land domain, we cannot independently determine the geographies based on pesticide usage alone, as it is merely one component of the index. Maggi et al. developed the PEST- CHEMGRIDS database using the 20 most applied pesticides on dominant crop types along with other environmental indices, including soil physical properties and hydroclimatic variables. Our selection criteria for these frequently used pesticides were stringent compared with Maggi et al and are beneficial for analyzing exposure risk in US counties. However, PEST-CHEMGRID would serve as a great resource to model global environmental issues [ 42 ]. We recognize the increasing importance of addressing the negative impact of pesticide pollution on the environment. We took proactive measures to develop a pesticide index using a risk-based conceptual framework. Our research on identifying high pesticide and social vulnerability aims to contribute to risk analysis and inform data-driven policy decisions. By implementing biomonitoring programs tailored to specific US states, we can address environmental justice issues caused by pesticide contamination and work towards mitigating them. Our study also has limitations as we focused solely on the spatial component and did not fully analyze the temporal aspect of the data. We assumed that the US agricultural land use remained consistent over time and hence only considered spatial elements in our study. Due to this, we could not analyze and make predictions on certain pesticides that were replaced with others during our study period. Also, environmental justice issues due to pesticide exposure could be related to crop type, as in areas with large-scale production of corn and soybeans, there might be limited migrant workers due to machinery usage, in contrast to areas with specialty and field crops. This study does not account for such differences, as we did not use pesticide application data specific to crop type. Additionally, populations living near industrial sites manufacturing agrichemicals or in chemical alleys such as Louisiana are susceptible to increased pesticide exposure. We did not consider these unique circumstances in our study design and included Louisiana State. We did only a preliminary exploratory analysis by creating heat maps to see the trends of these pesticides. Future studies should consider conducting a time-series analysis to better understand the shift in pesticide usage due to replacement chemicals. Furthermore, our study did not consider the toxicologic aspects of our selection criteria. Estimating the toxicology of approximately 500 active pesticide ingredients would be impractical and beyond the scope of our research. According to our findings, environmental justice issues due to higher pesticide exposure among racial and ethnic minorities, as well as those with low socioeconomic status, are prevalent. We observed these disparities in pesticide exposure in US counties regardless of their urban or rural status. Several factors contribute to the disproportionate pesticide exposures among racial minorities and low socioeconomic groups. These factors include the pesticide regulatory framework in the USA, the double standard for pesticide safety due to the Food Quality Protection Act of 1996, which guarantees no harm to individuals exposed to pesticides through food and other non-occupational routes, and a lack of necessary protection for children [ 72 , 73 ]. While eliminating exposure disparities built over hundreds of years due to structural racism is a challenging task, we can mitigate the environmental injustice burden by taking necessary actions. These include adopting the precautionary principle that guides the environmental policy of the European Union [ 74 ], eliminating pesticide safety double standards, adequately protecting children [ 75 ], and incorporating more epidemiological studies into EPA’s pesticide risk assessments as they give more information to make regulatory decisions tailored to specific at-risk populations, expanding CDC’s program on biomonitoring of environmental chemicals among the US general population as it currently measures only select pesticides and their metabolites, atrazine, glyphosate, 2,4-dichlorophenoxyacetic acid, sulfonylurea herbicides, organophosphorus and neonicotinoid insecticides, and carbamates [ 76 ]. Additionally, the longitudinal measurement of certain chemicals, such as neonicotinoids and glyphosate, and the availability of biomonitoring data for subsequent years will aid in making informed regulatory and policy decisions. Implementing biomonitoring programs for certain pesticide classes in states with increased exposure risk might protect farmworkers in those states and improve healthcare access, especially in the high-risk communities of the US. We need future studies evaluating toxicology and the risk of more pesticide ingredients. In conclusion, due to several factors mentioned here, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across contiguous US counties in both rural and urban communities. We can reduce environmental injustice issues by ensuring that regulatory decisions are inclusive of everyone, regardless of their vulnerability. Our study will inform regulatory bodies about high-risk pesticides and socially vulnerable areas, as well as facilitate regulatory and public health decisions. Abbreviations ACS American Community Survey API Application Programming Interface CRD Crop Reporting District EBI Environmental Burden Index EPA Environmental Protection Agency EPest Estimated Pesticide Use ESDA Exploratory Spatial Data Analysis FIPS Federal Information Processing System ICCP Intergovernmental Panel on Climate Change NASS National Agricultural Statistics Service NAWQA National Water-Quality Assessment Project PCA Principal Component Analysis USDA U.S. Department of Agriculture Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare they have no competing interests Availability of data and materials Data used in this manuscript is publicly available through the United States Geological Survey Pesticide National Synthesis Project database and the United States Census Bureau American Community Survey. Funding This work was supported by The Edna Ittner Pediatric Research Support Fund at the University of Nebraska Medical Center Contributions JT contributed to the conceptualization of the study design, acquisition of data, analysis and writing the manuscript. CB, AK, MZ, SBH, and ER made substantial contributions to the conception of the study design, critical review and feedback, and manuscript editing. All authors read and approved the final manuscript. Corresponding author : Jabeen Taiba, University of Nebraska Medical Center, Omaha, NE, email: [email protected] 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 References Shattuck A, et al. Global pesticide use and trade database (GloPUT): New estimates show pesticide use trends in low-income countries substantially underestimated. Glob Environ Change. 2023;81:102693. Shattuck A. Generic, growing, green? The changing political economy of the global pesticide complex. J Peasant Stud. 2021;48(2):231–53. Clapp J. Explaining Growing Glyphosate Use: The Political Economy of Herbicide-Dependent Agriculture. Glob Environ Change. 2021;67:102239. Atwood D, Paisley-Jones C. Pesticides industry sales and usage: 2008–2012 market estimates. US EPA; 2017. Bigelow D, Borchers A. Major uses of land in the United States, 2012. 2017. Landrigan PJ, et al. The Lancet Commission on pollution and health. Lancet. 2018;391(10119):462–512. UNEP. Global chemicals outlook II: From legacies to innovative solutions. Geneva, Switzerland: UN Environment Programme; 2019. Farha W, et al. An overview on common aspects influencing the dissipation pattern of pesticides: a review. Environ Monit Assess. 2016;188(12):693. Dereumeaux C, et al. Pesticide exposures for residents living close to agricultural lands: A review. Environ Int. 2020;134:105210. Deziel NC, et al. Relative Contributions of Agricultural Drift, Para-Occupational, and Residential Use Exposure Pathways to House Dust Pesticide Concentrations: Meta-Regression of Published Data. Environ Health Perspect. 2017;125(3):296–305. Larsen K, et al. Using geographic information systems to estimate potential pesticide exposure at the population level in Canada. Environ Res. 2020;191:110100. Curl CL, et al. The Effect of Pesticide Spray Season and Residential Proximity to Agriculture on Glyphosate Exposure among Pregnant People in Southern Idaho, 2021. Environ Health Perspect. 2023;131(12):127001. Donley N, et al. Pesticides and environmental injustice in the USA: root causes, current regulatory reinforcement and a path forward. BMC Public Health. 2022;22(1):708. Attina TM, et al. Racial/ethnic disparities in disease burden and costs related to exposure to endocrine-disrupting chemicals in the United States: an exploratory analysis. J Clin Epidemiol. 2019;108:34–43. James-Todd TM, Chiu YH, Zota AR. Racial/ethnic disparities in environmental endocrine disrupting chemicals and women's reproductive health outcomes: epidemiological examples across the life course. Curr Epidemiol Rep. 2016;3(2):161–80. Belova A, et al. A method to screen U.S. environmental biomonitoring data for race/ethnicity and income-related disparity. Environ Health. 2013;12:114. Larsen AE, Gaines SD, Deschenes O. Agricultural pesticide use and adverse birth outcomes in the San Joaquin Valley of California. Nat Commun. 2017;8(1):302. Hoang TT, et al. Epigenome-Wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study. Environ Health Perspect. 2021;129(9):97008. Shrestha S, et al. Pesticide use and incident Parkinson's disease in a cohort of farmers and their spouses. Environ Res. 2020;191:110186. Pouchieu C, et al. Pesticide use in agriculture and Parkinson's disease in the AGRICAN cohort study. Int J Epidemiol. 2018;47(1):299–310. Eid A, et al. Effects of DDT on Amyloid Precursor Protein Levels and Amyloid Beta Pathology: Mechanistic Links to Alzheimer's Disease Risk. Environ Health Perspect. 2022;130(8):87005. Munoz-Quezada MT, et al. Chronic exposure to organophosphate (OP) pesticides and neuropsychological functioning in farm workers: a review. Int J Occup Environ Health. 2016;22(1):68–79. Guo X, et al. Association between exposure to organophosphorus pesticides and the risk of diabetes among US Adults: Cross-sectional findings from the National Health and Nutrition Examination Survey. Chemosphere. 2022;301:134471. Czajka M, et al. Organophosphorus pesticides can influence the development of obesity and type 2 diabetes with concomitant metabolic changes. Environ Res. 2019;178:108685. Piel C, et al. Increased risk of central nervous system tumours with carbamate insecticide use in the prospective cohort AGRICAN. Int J Epidemiol. 2019;48(2):512–26. Vrijheid M, et al. Environmental pollutants and child health-A review of recent concerns. Int J Hyg Environ Health. 2016;219(4–5):331–42. Simoes M, et al. Exploring associations between residential exposure to pesticides and birth outcomes using the Dutch birth registry. Environ Int. 2023;178:108085. Lin S, et al. Maternal pesticide exposure and risk of preterm birth: A systematic review and meta-analysis. Environ Int. 2023;178:108043. Wang A, et al. Association of Prenatal Exposure to Organophosphate, Pyrethroid, and Neonicotinoid Insecticides with Child Neurodevelopment at 2 Years of Age: A Prospective Cohort Study. Environ Health Perspect. 2023;131(10):107011. Sagiv SK, et al. Prenatal and Childhood Exposure to Organophosphate Pesticides and Behavior Problems in Adolescents and Young Adults in the CHAMACOS Study. Environ Health Perspect. 2023;131(6):67008. Rappazzo KM, et al. Maternal residential exposure to specific agricultural pesticide active ingredients and birth defects in a 2003–2005 North Carolina birth cohort. Birth Defects Res. 2019;111(6):312–23. Patel DM, et al. Parental occupational exposure to pesticides, animals and organic dust and risk of childhood leukemia and central nervous system tumors: Findings from the International Childhood Cancer Cohort Consortium (I4C). Int J Cancer. 2020;146(4):943–52. Dhuldhaj UP, Singh R, Singh VK. Pesticide contamination in agro-ecosystems: toxicity, impacts, and bio-based management strategies. Environ Sci Pollut Res Int. 2023;30(4):9243–70. Rumschlag SL, et al. Consistent effects of pesticides on community structure and ecosystem function in freshwater systems. Nat Commun. 2020;11(1):6333. Volaco A, et al. Socioeconomic Status: The Missing Link Between Obesity and Diabetes Mellitus? Curr Diabetes Rev. 2018;14(4):321–6. Darmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev. 2015;73(10):643–60. Roberts EK, et al. Persistent organic pollutant exposure contributes to Black/White differences in leukocyte telomere length in the National Health and Nutrition Examination Survey. Sci Rep. 2022;12(1):19960. Xie T, et al. Disparities in diagnosis, treatment and survival between Black and White Parkinson patients. Parkinsonism Relat Disord. 2021;87:7–12. Kehm RD, et al. Socioeconomic Status and Childhood Cancer Incidence: A Population-Based Multilevel Analysis. Am J Epidemiol. 2018;187(5):982–91. Harville EW, et al. Pre-pregnancy cardiovascular risk factors and racial disparities in birth outcomes: the Bogalusa Heart Study. BMC Pregnancy Childbirth. 2018;18(1):339. Schraw JM, et al. Prevalence of congenital anomalies according to maternal race and ethnicity, Texas, 1999–2018. Birth Defects Res. 2024;116(1):e2274. Maggi F, et al. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci Data. 2019;6(1):170. Joseph N, Kolok AS. Assessment of Pediatric Cancer and Its Relationship to Environmental Contaminants: An Ecological Study in Idaho. Geohealth, 2022. 6(3): p. e2021GH000548. NT B. Estimated Annual Agricultural Pesticide Use for counties of the Conterminous United States, 1992–2019. U.S. Geological Survey. 2021 [cited 2023 March 24]; https://water.usgs.gov/nawqa/pnsp/usage/maps/county-level/ . Agriculture USDo. State and county data, Geographic area series parts 1–50, AC–12–A–1—AC–12–A–50: U.S. Department of Agriculture 2012 Census of Agriculture, v. 1. United States Department of Agriculture; 2014. Thelin GP, Stone WW. Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009 . 2013. p. 54. Baker NT, Stone WW. Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12 , in Data Series . 2015. p. 9. Wieben CM. Estimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 2013-17 (ver. 2.0, May 2020): U.S. Geological Survey data release . 2019. Wieben CM. Preliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2018: U.S. Geological Survey data release . 2021. Wieben CM. Preliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2019: U.S. Geological Survey data release . 2021. Bureau USC. American Community Survey 5-Year Data (2009–2022) . 2023. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci, 2016. 374(2065): p. 20150202. Bryan A, Hanson DTH. A Comparison of Functions for PCA . 2024; https://cran.r-project.org/web/packages/LearnPCA/vignettes/Vig_07_Functions_PCA.pdf . Reid CE, et al. Mapping community determinants of heat vulnerability. Environ Health Perspect. 2009;117(11):1730–6. Jacobson KC, et al. Ordered subsets linkage analysis of antisocial behavior in substance use disorder among participants in the Collaborative Study on the Genetics of Alcoholism. Am J Med Genet B Neuropsychiatr Genet. 2008;147B(7):1258–69. DiStefano CZ, Min; and, Mîndrilã D. Understanding and Using Factor Scores: Considerations for the Applied Researcher. Research, and E valuation: Practical Assessment; 2009. p. 14. O'Neill B, van Aalst M, Zaiton Ibrahim Z, Berrang Ford L, Bhadwal S, Buhaug H, Diaz D, Frieler K, Garschagen M, Magnan A, Midgley G, Mirzabaev A, Thomas A. and R. Warren. Figure 1 6.1. 2022: Key Risks Across Sectors and Regions. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . 2022; https://www.ipcc.ch/report/ar6/wg2/figures/chapter-16/figure-16-001 . Bivand RS, Wong DWS. Comparing implementations of global and local indicators of spatial association. TEST. 2018;27(3):716–48. Anselin L. Local Indicators of Spatial Association—LISA. Geographical Anal. 1995;27(2):93–115. NASS US. CroplandCROS . 2024; https://www.nass.usda.gov/Data_and_Statistics/ . Cushing L, et al. Racial/Ethnic Disparities in Cumulative Environmental Health Impacts in California: Evidence From a Statewide Environmental Justice Screening Tool (CalEnviroScreen 1.1). Am J Public Health. 2015;105(11):2341–8. Izaac Ornelas WF, Gabbard S, Carroll D. Findings from the National Agricultural Workers Survey (NAWS) 2017–2018: A Demographic and Employment Profile of United States Farmworkers , U.S.D.o. Labor, Editor. 2021. Stanbury M. R.K., Occupational health disparities: a state public health-based approach. Am J Ind Med, 2014. Calvert GM, et al. Acute Occupational Pesticide-Related Illness and Injury -United States, 2007–2011. MMWR Morb Mortal Wkly Rep. 2016;63(55):11–6. Parks CG, et al. High pesticide exposures events, pesticide poisoning, and shingles: A medicare-linked study of pesticide applicators in the agricultural health study. Environ Int. 2023;181:108251. Nguyen VK, et al. A comprehensive analysis of racial disparities in chemical biomarker concentrations in United States women, 1999–2014. Environ Int. 2020;137:105496. Weiss MC, Wang L, Sargis RM. Hormonal Injustice: Environmental Toxicants as Drivers of Endocrine Health Disparities. Endocrinol Metab Clin North Am. 2023;52(4):719–36. Polemi KM, et al. Identifying the link between chemical exposures and breast cancer in African American women via integrated in vitro and exposure biomarker data. Toxicology. 2021;463:152964. Uprety D et al. Racial and socioeconomic disparities in survival among patients with metastatic Non-Small cell lung cancer. J Natl Cancer Inst, 2024. Rauch SA, et al. Associations of prenatal exposure to organophosphate pesticide metabolites with gestational age and birth weight. Environ Health Perspect. 2012;120(7):1055–60. EPA US. Environmental Quality Index - Technical Report (2006–2010) (Final, 2020) , U.S.E.P. Agency, Editor. 2020: Washington, DC. p. 102. Mott L. The disproportionate impact of environmental health threats on children of color. Environ Health Perspect. 1995;103(Suppl 6):33–5. Weathers A, et al. Access to care for children of migratory agricultural workers: factors associated with unmet need for medical care. Pediatrics. 2004;113(4):e276–82. Policy SfE. The Precautionary Priniple: decision making under uncertainty . Future Brief 18 2017; https://ec.europa.eu/environment/integration/research/newsalert/pdf/precautionary_principle_decision_making_under_uncertainty_FB18_en.pdf . EPA US. US EPA Office of Pesticide Programs’ Re-Evaluation of the FQPA Safety Factor for Pyrethroids: Updated Literature and CAPHRA Program Data Review . 2019. Health NCfE. National Report on Human Exposure to Environmental Chemicals. C.f.D.C.a.P.: Department of Health and Human Services, Editor.; 2022. Additional Declarations No competing interests reported. Supplementary Files Supplementalfigurestables.docx Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 30 Oct, 2024 Reviews received at journal 31 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers invited by journal 11 Aug, 2024 Editor invited by journal 05 Aug, 2024 Editor assigned by journal 11 Jul, 2024 Submission checks completed at journal 11 Jul, 2024 First submitted to journal 10 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4719285","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325992877,"identity":"b7de03e1-146b-4224-8492-efc4a01f0a23","order_by":0,"name":"Jabeen Taiba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxgYeKIsZyPgAZUsQo0UCpIVxBjFaGBhgWoAMZpiNeLUwt589+ODjDoY6/nbeg59t2+5E8zcwH7zNg0cLY09esuHMMwwSEof5kqVz257lzjjAlmyNV0tDjpk0bxvQKYd5DIBaDuduYOAxk8arpf+N+e+/QC3yh3mMf1uCtfB/w69lRo4ZMyNQi8FhoOGMEFvYCGh5lyzZ2yYhufEwX5plz7nDuTMOsxlbzsGjxbA/9+CHn202/HLnzx6+8aPscG5/e/PDG2/waWkAU8gRwYxHOQjIE5AfBaNgFIyCUcDAAABje0gnvEAe4gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nebraska Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Jabeen","middleName":"","lastName":"Taiba","suffix":""},{"id":325992878,"identity":"e8c9a348-9573-489b-8467-3acc9398856e","order_by":1,"name":"Cheryl Beseler","email":"","orcid":"","institution":"University of Nebraska Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Cheryl","middleName":"","lastName":"Beseler","suffix":""},{"id":325992880,"identity":"84779505-39a6-4bda-8270-1f3622e8a218","order_by":2,"name":"Alan Kolok","email":"","orcid":"","institution":"Department of Fish and Wildlife Sciences, University of Idaho","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Kolok","suffix":""},{"id":325992882,"identity":"a12f1dbf-72ce-4126-bc41-637e1480fe02","order_by":3,"name":"Muhammad Zahid","email":"","orcid":"","institution":"University of Nebraska Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Zahid","suffix":""},{"id":325992884,"identity":"0128e9e2-5af1-42ed-b5c7-836c3e4e78ee","order_by":4,"name":"Shannon Bartelt-Hunt","email":"","orcid":"","institution":"Department of Civil and Environmental Engineering, College of Engineering, University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Shannon","middleName":"","lastName":"Bartelt-Hunt","suffix":""},{"id":325992885,"identity":"5fcca99a-57c8-46d7-be58-082e3d64ce33","order_by":5,"name":"Eleanor Rogan","email":"","orcid":"","institution":"University of Nebraska Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Eleanor","middleName":"","lastName":"Rogan","suffix":""}],"badges":[],"createdAt":"2024-07-10 16:27:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4719285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4719285/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25277-5","type":"published","date":"2025-12-12T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61779407,"identity":"178552dd-6aae-4229-aa87-0e1b5d2f981d","added_by":"auto","created_at":"2024-08-05 13:19:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":915960,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heat map showing 30 frequently applied pesticide variables during (1992 – 2019). Triclopyr, Picloram, and Metsulfuron were excluded from the PCA due to negative or weaker correlations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/0391ae44ce73e986e77e4089.png"},{"id":61780218,"identity":"cc8174cc-fb47-4939-81ea-5f5c70e61f6c","added_by":"auto","created_at":"2024-08-05 13:27:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":542966,"visible":true,"origin":"","legend":"\u003cp\u003ea.Scree plot illustrates the principal component breaks and the percentage of variance for the first ten principal components.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eb.The principal component loading plot for the first three PCs explains the weights that each chemical contributes to the component.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ec. Scree plot illustrates the principal component breaks and percentage of variance for the social vulnerability variables, race, income, and educational attainment. Data obtained from ACS 5-year estimates (2015 – 2019)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/48697c9b7bffcd5454178c65.png"},{"id":61779404,"identity":"beb6e3de-9124-48f9-b7ea-1a4fe27ab627","added_by":"auto","created_at":"2024-08-05 13:19:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1727737,"visible":true,"origin":"","legend":"\u003cp\u003ea. Spatial distribution of the pesticide index using a latent variable (PC1)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eb. Spatial distribution of the SVI using a latent variable (PC1)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/e9efafd4fae67f834bb07acd.png"},{"id":61780219,"identity":"cba4aebe-b2a0-44c0-82cb-88768a5ef55c","added_by":"auto","created_at":"2024-08-05 13:27:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":800419,"visible":true,"origin":"","legend":"\u003cp\u003eBivariate mapping of pesticide and social vulnerability indexes using a latent variable (PC1). The map shows areas with a high pesticide and social vulnerability index, which are counties colored dark brown. Areas with low pesticide and social vulnerability\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/bb643e15f1da82cefe827533.png"},{"id":98243674,"identity":"62fe7ca2-d325-4032-b2ef-015a2fa9d580","added_by":"auto","created_at":"2025-12-15 16:09:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4598275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/d7bb405d-6dc3-4986-bcc4-83b934717e55.pdf"},{"id":61779409,"identity":"bfa5139c-9110-42f5-ba66-6449d2a1681e","added_by":"auto","created_at":"2024-08-05 13:19:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38146021,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfigurestables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4719285/v1/fbefe7fc791a6322d3fa0429.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States","fulltext":[{"header":"1. Background","content":"\u003cp\u003eGlobal pesticide use in the past decade has increased by 20% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This surge can be attributed to the availability of lower-cost options, generic pesticides, shifts in farming practices, and impact on rural economies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Every year, around 899\u0026nbsp;million pounds of active pesticide ingredients are typically used on agricultural land in the US [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such environmental spread of pesticides, endangering human health and the integrity of the ecosystem, has become a concerning issue [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pesticides can spread beyond the intended agricultural fields due to wind drift and volatilization, causing their deposition in nearby agricultural and non-agricultural areas [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Exposure to pesticides from agricultural drift poses a significant contamination risk, particularly for individuals residing near agricultural areas [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Environmental injustice also drives disparities in pesticide exposure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and these disparities are disproportionately higher among minority racial/ethnic groups [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], those with low income or of lower socio-economic status (SES) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and low educational attainment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic exposure to low doses of pesticides has been linked to several adverse human health outcomes, including epigenetic changes such as DNA methylation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], neuropsychological functioning [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], obesity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and central nervous system tumors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Prenatal and young children are at an increased risk of early death and developing disease across their lifespan as they are particularly susceptible to pesticide pollution [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Various perinatal outcomes are associated with prenatal exposure to pesticides, including low birth weight [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], preterm birth [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], neurodevelopment problems [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], behavior problems [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], birth defects [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and childhood leukemias [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Besides human health, pesticide exposure has also impacted honeybee colonies, microbial communities, and ecosystems that support human health [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Social vulnerability has also shown disparities in diseases associated with pesticide exposure, including obesity and diabetes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], due to differences in food availability in more disadvantaged areas [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; long telomere length (LTL), a bio-marker for cellular aging, is higher in non-Hispanic Black Americans than non-Hispanic White Americans [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], childhood cancer [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and birth outcomes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It is important to identify the contiguous US counties with disproportionately high pesticide application and social vulnerability due to disparities that exist among vulnerable populations in pesticide exposure and health outcomes.\u003c/p\u003e \u003cp\u003eUsing the United Nations FAOSTAT pesticide database and USGS Pesticide National Synthesis Project(PSNP) data, previous studies created chemical grids to estimate pesticide usage in the US and globally [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The US EPA reported the twenty-five most frequently used conventional pesticide active ingredients in the agricultural market sector from 2008 to 2012 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The environmental burden index (EBI) for Idaho was developed by Joseph et al using the 2017 USGS PSNP data and these twenty-five most applied pesticides [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Nevertheless, that EBI was limited to Idaho, and the pesticide exposure lagged in timepoint; no clear selection criteria were developed to identify these most frequently applied pesticides throughout the contiguous United States. Additionally, validation of the methods used to create the pesticide index is required. A pesticide index gives a comprehensive representation of overall pesticide application across the time frame compared with a few individual pesticides. It serves as an indicator of pesticide exposure to improve our understanding of the relationship between environmental exposures and human health and can benefit policymaking. The pesticide index is a latent variable created using a principal component analysis of selected pesticides after standardizing the data. By implementing policy changes on regulating pesticide usage, conducting toxicological risk assessments, reducing exposure, and mitigating disease prevention, it will be possible to reduce the public health burden. Identifying US counties with high pesticide application and high social vulnerability will aid in estimating the pesticide risk in these areas.\u003c/p\u003e \u003cp\u003eOur study aims to create an index for commonly used pesticide ingredients in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. In addition, we will identify the US counties with high pesticide exposure and high social vulnerability.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources and variable selection\u003c/h2\u003e \u003cp\u003eWe used the USGS Pesticide National Synthesis Project (USGS/PSNP) data within the National Water-Quality Assessment Project [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] to identify the most commonly applied pesticides for counties of the 48 contiguous United States during a twenty-eight-year period (1992\u0026ndash;2019). The \u0026ldquo;high\u0026rdquo; and \u0026ldquo;low\u0026rdquo; estimated annual pesticide estimates for crop-acreage data by county from 1992 to 2019 and for a total of 526 active ingredients relied on surveys used in conjunction with the USDA National Agricultural Statistics Service (USDA/NASS) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and interpolation and extrapolation methods were initially described when data were not available [\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. We took the \u0026ldquo;EPest-high\u0026rdquo; estimates for our analysis and the difference between EPest-high and EPest-low as described elsewhere [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, we calculated the median mass based on the temporal coverage of the pesticide applied and percentages of the state and county coverage based on the spatial or county coverage of the pesticide applied.\u003c/p\u003e \u003cp\u003eUsing the USGS/PSNP data, we developed selection criteria to identify the most applied pesticide active ingredients in the 48 contiguous states against the satisfaction of the following conditions: (i) median mass applied based on the temporal coverage was set to 80% of the study period, which means the pesticide mass should be applied at least for 22 years over the 28-year records, (ii) the percentages of state and county coverage were set to 80%, i.e., the pesticide active ingredients should be applied in at least 80% of the 48 contiguous states and counties. In total, 30 active ingredients were found over 28 years (1992\u0026ndash;2019) and were grouped based on their pesticide classes into 21 herbicides, six insecticides, and three fungicides, as shown in Table\u0026nbsp;1. We then plotted a box plot to see the individual distribution of pesticide-active ingredients, as shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs discussed above, variables representing the most applied pesticide-active ingredients were obtained. They were included in calculating the pesticide index (PI) to assess the sensitivity of principal component analysis (PCA) to input variables. We tested different variables for inclusion in the PI to represent the proportion of pesticide-active ingredients in each county by calculating Spearman\u0026rsquo;s correlation coefficients between the 30 pesticide variables, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We removed the negatively correlated variables and included the positively correlated variables in the PCA.\u003c/p\u003e \u003cp\u003eSocial vulnerability (SV) data for 48 contiguous US States were retrieved from the American Community Survey (ACS) 5-year estimates for 2015\u0026ndash;2019 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] using an application programming interface (API) key. Socially vulnerable populations were defined based on income, educational attainment, and race and ethnicity (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The PCA included all three SV variables to construct an index (SVI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Unsupervised PCA and PI Calculation\u003c/h2\u003e \u003cp\u003eWe used PCA [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and implemented it using the prcomp function, R version 4.2.2, [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] to limit the number of variables and create independent factors to include in a PI; PI was calculated using two different approaches, and results were compared. In our first approach, following Reid et al [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]., we used factor rotation to minimize the number of original variables that load highly on any one factor and increase the variation among factors, making the new factors more statistically independent than the original variables. We retained five factors based on a combination of standard criteria: eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, a clear break in the values in the Scree test, and a percentage of variance explained by factors, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. We normalized the factor scores to have a mean of 0 and a standard deviation of 1. Later, these factors were summarized into a single PI by estimating the magnitude of the selected components [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The magnitude for each observation was calculated by taking the square root of the sum of squares of the five component values, as shown here.\u003c/p\u003e \u003cp\u003eMagnitude_i\u0026thinsp;=\u0026thinsp;sqrt(\u0026sum;(x_ij^2)) for j\u0026thinsp;=\u0026thinsp;1 to m. Magnitude_i represents the magnitude of row i. x_ij represents the element at row i and column j of first_5_components. The sum symbol (\u0026sum;) indicates that we calculate the sum of the squared values over the columns for a given row. Sqrt () denotes the square root function, taking the sum of squared values to obtain the final magnitude.\u003c/p\u003e \u003cp\u003eIn our second approach, to calculate the PI, we used PCA (prcomp, R version 4.2.2) to calculate the factor loadings for the input variables. We then used factor rotation, like in approach one, and extracted the scores. Later, we multiplied each factor by each county variable and created a matrix from the indexed scores. Finally, we calculated the PI by summing the scores and got a summed score for each county. This summed factor scores approach is advantageous as it preserves the variation in the original data [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our third approach, we took the first principal component alone with a maximum variance of 44% as an index variable, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. We selected PC 1 as an index variable from these three approaches as it showed stronger correlations than PC developed using magnitude or summed score approach and validated it using a Spearman correlation matrix as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Social vulnerability index (SVI)\u003c/h2\u003e \u003cp\u003eTo calculate the SVI, we used a similar approach of factor rotation to the one we used to construct the pesticide index, and we included all the social vulnerability variables in the PCA analysis, as shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Risk-based mapping of PI and SVI\u003c/h2\u003e \u003cp\u003eUsing the pesticide and social vulnerability index based on the Intergovernmental Panel on Climate Change (IPCC) risk-based conceptual framework [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], we identified and mapped the US counties with high pesticide applications and high social vulnerability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Secondary analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5a. Exploratory Spatial analysis\u003c/h2\u003e \u003cp\u003eData for the spatial analysis was prepared by joining the pesticide data with the contiguous state\u0026rsquo;s Federal Information Processing System code data on a county level. The shape files from the United States Census Bureau were obtained using the R package Tigris. We conducted an exploratory spatial data analysis (ESDA) by converting the neighbors list objects into binary spatial weights and produced neighborhood sums using the R package spdep and function ploy2nb and nb2listw. Where neighbors are given the weight one and non-neighbors take the weight 0. We tested for global spatial autocorrelation using Moran's I to evaluate the presence of spatial clustering, where the data values tend to be similar to the neighboring data values [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e statistic was computed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:I=\\frac{N}{W}\\frac{\\sum\\:i\\sum\\:jwij(xi-\u0026macr;x)(xj-\u0026macr;x)}{\\sum\\:i(xi-\u0026macr;x)2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere w\u003csub\u003eij\u003c/sub\u003e represents the spatial weights matrix, N is the number of spatial units denoted by i and j, and W is the sum of the spatial weights.\u003c/p\u003e \u003cp\u003eWe also calculated the local spatial autocorrelation using local indicators of spatial association statistic (LISA) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] to identify clusters and spatial outliers as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Ii=zi\\sum\\:jwijzj$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere z\u003csub\u003ei\u003c/sub\u003e and z\u003csub\u003ej\u003c/sub\u003e are expressed as deviations from the mean. The spatial clusters, where observations are surrounded by similar values, and spatial outliers, where dissimilar values surround observations. We computed LISA in R using localmoran() family of functions in the spdep package. We then used localmoran_perm() to calculate a GeoDa-style LISA quadrant plot and a cluster map using ggplot2 in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5b. Heat Maps\u003c/h2\u003e \u003cp\u003eWe generated heat maps to examine the patterns of commonly used pesticides throughout the study, as shown in Figures S3a \u0026ndash; S3e. In this study, we collected and analyzed pesticide data from different states and years. We calculated the median amount of pesticide applied and scaled the data accordingly. Additionally, we grouped the data based on their pesticide class. We generated heat maps for each pesticide category using the ggplot function in R. We have also developed a shiny app that allows users to explore the temporal trends of pesticides applied in the contiguous US counties.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn our twenty-eight-year (1992\u0026ndash;2019) study period of the United States, we consistently observed the application of 30 pesticides in the 48 contiguous states. These pesticides were among the most commonly used based on our selection criteria. We classified these commonly used pesticides into three groups: herbicides, insecticides, and fungicides. Herbicides were the most commonly applied, followed by insecticides and fungicides. The most commonly used herbicides include glyphosate, atrazine, acetochlor, 2,4-D, metolachlor, pendimethalin, metolachlor, trifluralin, and simazine. Insecticides such as carbaryl, chlorpyrifos, dimethoate, and fungicides, mancozeb, and chlorothalonil were the most-applied pesticides in kg/acre (Table 1). \u0026nbsp;There were strong correlations among all the pesticides, while the herbicides triclopyr, picloram, and metsulfuron had weaker or negative correlations (Figure\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e1).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Summary Characteristics of Frequently Applied Pesticides (1992 \u0026ndash; 2019)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmount (Kg/acre)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eGlyphosate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e69033923.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eAtrazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e31812125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eAcetochlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e15019875.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003e2,4-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e13826981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003ePendimethalin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e6531314.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eMetolachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e5716004.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eTrifluralin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e5061700.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eChlorothalonil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eFungicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e3946749.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eChlorpyrifos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e3902792.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eSimazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e2855285.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eMancozeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eFungicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e2841390.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eParaquat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e2609745.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eDicamba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e2553668.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eMetribuzin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e1047224.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eCarbaryl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e794234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eDimethoate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e717050.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eTriclopyr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e457047.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003ePicloram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e402177.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003ePropiconazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eFungicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e357291.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003ePermethrin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e272509.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eClethodim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e261393.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eImazethapyr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e245514.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eSethoxydim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e220518.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eCyhalothrin-Lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e176561.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eEsfenvalerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eInsecticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e80097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eChlorimuron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e62974.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eNicosulfuron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e55239.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eThifensulfuron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e53548.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eTribenuron Methyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e29607.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.486301369863014%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.34246575342466%\" valign=\"top\"\u003e\n \u003cp\u003eMetsulfuron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.171232876712327%\" valign=\"top\"\u003e\n \u003cp\u003e18851.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAmount applied was calculated by taking the sum of median pesticide applied by compound across the US counties (1992 \u0026ndash; 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cu\u003eUnsupervised PCA - Pesticide and Social Vulnerability Indices\u003c/u\u003e\u003c/h2\u003e\n\u003cp\u003eWe used the first principal component with a maximum variance of 44% and an eigenvalue of 12, as shown in the scree plot (Figure 2a) for the pesticide index (PI). We observed that herbicides, insecticides, and fungicides equally contributed to the chemical weight of the first principal component in the loadings plot. The second and third principal components explained a percentage variance of 7 and 5, along with eigenvalues 1.9 and 1.5, respectively, and herbicides contributed to both the positive and negative load (Figure 2b\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eTable S2). Similarly, in the PCA of the social vulnerability, the first principal component explained a maximum variance of 81% with an eigenvalue of 8.1, and the percentage variance of the second and third components were 8.4 and 5.5 with their eigenvalues 0.84 and 0.55, respectively, as shown in Figure 2c and Table S3. We used the first principal component for the SVI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the PI and SVI scores, the geospatial distribution of the most commonly used pesticides in the contiguous US revealed high, medium, and low pesticide application counties, as shown in Figures 3a and 3b. We found high pesticide application rates in the counties of North Dakota, South Dakota, Iowa, Nebraska, Illinois, Indiana, Ohio, Arkansas, California, and Washington, as well as certain areas of Idaho, Florida, Georgia, North and South Carolina, Pennsylvania, and New York. The majority of the US, West, and South showed low to moderate pesticide application (Figure 3a). In a similar way, the main counties with significant social vulnerability were Washington, Oregon, California, Arizona, New Mexico, Florida, and certain areas in Ohio, Michigan, Illinois, New York, Massachusetts, Pennsylvania, North and South Carolina, Texas, New Orleans, and Tennessee. We found low to moderate social vulnerability in the Mid-Western region and the rest of the US, as shown in Figure 3b.\u003c/p\u003e\n\u003ch2\u003e\u003cu\u003eRisk-based mapping of PI and SVI\u0026nbsp;\u003c/u\u003e\u003c/h2\u003e\n\u003cp\u003eThe bivariate mapping of the Pesticide Index (PI) and Social Vulnerability Index (SVI) revealed the US counties that exhibited high levels of pesticide exposure and social vulnerability, as well as those with low levels of pesticide exposure and vulnerability, as shown in Figure 4. 143 contiguous counties in the United States reported high pesticide exposure and high socioeconomic vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had noticeably higher numbers of these counties, as shown in Table 2. These counties are represented by the dark brown color on the map. The dark teal color represents US counties with low pesticide exposure and low social vulnerability, as shown in Figure 4.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Summary of the counties in the U.S. with high pesticide application and high social\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evulnerability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eCounties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eIllinois\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eMcLean, Macon, St. Clair, Kankakee, Kane, Winnebago, Sangamon, Tazewell, LaSalle, Vermilion, Will, Madison, Peoria, Champaign, McHenry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eNorth Carolina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eRobeson, Halifax, Sampson, Lenoir, Wilson, Duplin, Johnston, Pitt, Union, Edgecombe, Wayne, Nash, Columbus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMichigan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eVan Buren, Ottawa, Berrien, Calhoun, Kent, Saginaw, Bay, Monroe, Allegan, Lenawee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eCalifornia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eYolo, Kern, Fresno, Madera, San Joaquin, Merced, Tulare, Stanislaus, Imperial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eOhio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eAllen, Wayne, Clark, Miami, Greene, Ross, Wood, Fairfield, Licking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eIndiana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eAllen, Lake, Tippecanoe, LaPorte, Elkhart, Delaware, Howard, Madison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eIowa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eBlack Hawk, Dubuque, Linn, Johnson, Polk, Pottawattamie, Scott, Woodbury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003ePennsylvania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eBerks, Chester, York, Adams, Franklin, Lancaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eAlabama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eLimestone, Jackson, Baldwin, Houston, Madison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eSouth Carolina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDarlington, Florence, Sumter, Orangeburg, Horry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eTexas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eHidalgo, Lubbock, Ellis, Williamson, San Patricio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eFlorida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eHendry, Palm Beach, Polk, Jackson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eNew York\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eSteuben, Cayuga, Wayne, Niagara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eWashington\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eYakima, Benton, Grant, Franklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eWisconsin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eRock, Outagamie, Marathon, Dane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eArizona\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eYuma, Pinal, Maricopa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eArkansas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eJefferson, Craighead, Crittenden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eGeorgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eBulloch, Colquitt, Coffee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eKentucky\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDaviess, Warren, Christian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLouisiana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eSt. Landry, Rapides, Avoyelles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMaryland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eCarroll, Frederick, Wicomico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eColorado\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eAdams, Weld\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eDelaware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eSussex, Kent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eIdaho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eTwin Falls, Canyon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMinnesota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDakota, Stearns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eOregon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eMarion, Umatilla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eKansas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eSedgwick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMississippi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eWashington\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eNebraska\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eLancaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eNorth Dakota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eCass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eOklahoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eKay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eSouth Dakota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eMinnehaha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.96153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e32 States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e143\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eSecondary Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003ch2\u003e\u003cu\u003eExploratory Spatial Analysis\u0026nbsp;\u003c/u\u003e\u003c/h2\u003e\n\u003cp\u003eIn addition to identifying the areas with high and low application of the most applied pesticides, we also calculated the Moran\u0026rsquo;s \u003cem\u003eI\u0026nbsp;\u003c/em\u003estatic using the PI to observe if there is a spatial autocorrelation between the counties with pesticide applied and their neighboring counties. Based on Moran\u0026rsquo;s I static (\u0026lt; 0.05), these counties have a positive association and spatial autocorrelation (Table S4). We explored regions with potential clusters and outliers for these most-applied pesticides using LISA and observed significant clusters in the areas of the Mid-western US states, North and South Dakota, Illinois, Iowa, and Indiana; Western states, including California and Washington, and Southern states of Texas and Arkansas (Figure S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePesticide application trends\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, we noticed a consistent herbicide use pattern across all 48 states. Interestingly, there has been a noticeable increase in the use of herbicides, like atrazine, in the mid-western states of Illinois, Indiana, Iowa, Kansas, and Nebraska. After 2010, there was an increase in glyphosate application in the mid-western states, including North and South Dakota (Figure S3a). In Iowa and Nebraska, 2,4-D application rates increased after 2010, whereas in North Dakota, usage was higher prior to 2000. In Delaware, paraquat applications have seen a noticeable increase in recent years. On the other hand, metolachlor applications were higher in Delaware, Indiana, and Iowa before 2000 but have since seen a decrease in recent years (Figures S3b and S3c). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the use of various insecticides, including permethrin, efsenvalerate, dimethoate, chlorpyrifos, and carbaryl, exhibited a consistent pattern across all 48 US states, with only a few deviations. The use of efsenvalerate in the western state of California has seen a rise since 1992 but experienced a decline after 2015 (Figure S3d). In nearly all US states, the use of fungicides such as propiconazole, mancozeb, and chlorothalonil remained consistent, with the exception of Delaware and North Dakota. In Delaware, there were varying trends in the use of these fungicides, whereas in North Dakota, the application of propiconazole has been on the rise since 2010 (Figure S3e). \u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe examined the use of pesticides in the contiguous US counties over 28 years, from 1992 to 2019. Our study revealed that herbicides were predominantly applied, compared to insecticides and fungicides. The herbicides, 2,4-D, atrazine, glyphosate, metolachlor, paraquat, pendimethalin, simazine, and trifluralin, and the insecticides, carbaryl, chlorpyrifos, dimethoate, fungicides, mancozeb, and chlorothalonil, were the predominantly applied pesticides across the study period. Using the pesticide and social vulnerability indices, 143 US counties had high levels of social vulnerability and pesticide use. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In an exploratory spatial analysis, a positive association, or spatial autocorrelation, was observed between the counties with applied pesticides and their neighboring counties at Moran's I static\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In the mid-western states of Illinois, Indiana, Iowa, Kansas, and Nebraska, there has been an observed increase in the application of the herbicide atrazine, according to the findings of our study. After 2010, the application rate of 2,4-D increased in Iowa and Nebraska, while in North Dakota, the application rate was higher before 2000.\u003c/p\u003e \u003cp\u003ePesticide exposure risk, along with social vulnerability, was significantly higher in the counties of Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania compared to other counties in the United States. The incidence of high pesticide exposure is highly associated with race, ethnicity, and socioeconomic status. The majority of these states are the primary cultivators of corn and soybeans. Additionally, Pennsylvania is a large producer of hay, while California produces a diverse range of fruits, nuts, and vegetables [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Individually, these states have elevated levels of pesticide usage and social vulnerability. The California EPA discovered that pesticide use in agriculturally dominant states resulted in a chemical pollution burden that disproportionately affected communities with racial or income disparities. This burden was found to be greater than that caused by several air pollutants and other toxicant releases [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFarmworkers experience a significantly greater level of pesticide exposure due to the fact that the majority of pesticide usage in the United States occurs in the agricultural sector. Farmworkers consistently have a somewhat greater level of exposure compared to the general population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to a recent report on farmworkers in the US, approximately 87% of them identify as Hispanic or Latinx. Their average annual income is less than \u003cspan\u003e$\u003c/span\u003e20,000, and one-third of their family incomes fall below the federal poverty line. On average, these farmworkers have completed education up to the ninth grade [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. A study conducted in Michigan found that Hispanics had a higher likelihood of experiencing work-related pesticide exposure and subsequent illness compared to non-Hispanics [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. A comprehensive US study across twelve states, including California, Iowa, Michigan, and North Carolina, showed the rate of acute occupational pesticide-related illness and injury was 37 times higher for agricultural workers than for non-agricultural workers [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Higher rates of hospitalizations for pesticide-related illness and pesticide exposure events were reported among licensed pesticide applicators [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], increased risk of Parkinson\u0026rsquo;s disease on exposure to herbicides and insecticides in an agrarian cohort [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], childhood leukemia, and central nervous system tumors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and certain types of birth defects [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] due to parental occupational exposures.\u003c/p\u003e \u003cp\u003ePesticide exposure disparities due to race, ethnicity, and income using biomonitoring in the US general population revealed African Americans and Mexican Americans had higher concentrations of pesticide biomarkers in their blood or urine than non-Hispanic Whites who do not live in poverty [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, non-Hispanic Black women, Mexican American, and Other Hispanic women showed the highest disparities in levels of pesticides compared to non-Hispanic White women [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The exposure levels of organophosphate pesticides and associated disease burden and costs were higher in non-Hispanic Blacks and Mexican Americans than in other groups [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, racial or ethnic minorities, low-income groups, and other disadvantaged groups are disproportionately exposed to pesticides and are linked to adverse endocrine health effects, including diabetes and obesity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and women\u0026rsquo;s reproductive health outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Also, higher levels of pesticides and their metabolites found in the blood and urine in non-Hispanic Black women compared to non-Hispanic white women were found to have breast cancer-related biological activity [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Lower overall survival rates for cancer were higher among non-Hispanic Blacks and Hispanics than non-Hispanic Whites and non-Hispanic Asians [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore, in a prospective birth cohort, urinary maternal levels of organophosphate metabolites were more strongly associated with decreased birth weight among Black newborns than white newborns. In contrast, these urinary metabolites were associated with shorter gestation time in white mothers but not in Black mothers [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to create a pesticide index based on the most frequently applied pesticides longitudinally for the contiguous United States using definitive selection criteria. The US EPA has developed an Environmental Quality Index (EQI) using PCA based on five environmental domains: air, water, land, built-in environment, and sociodemography [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] to enhance our understanding of associations between environmental conditions and public health. Despite including pesticide usage in their land domain, we cannot independently determine the geographies based on pesticide usage alone, as it is merely one component of the index. Maggi et al. developed the PEST- CHEMGRIDS database using the 20 most applied pesticides on dominant crop types along with other environmental indices, including soil physical properties and hydroclimatic variables. Our selection criteria for these frequently used pesticides were stringent compared with Maggi et al and are beneficial for analyzing exposure risk in US counties. However, PEST-CHEMGRID would serve as a great resource to model global environmental issues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We recognize the increasing importance of addressing the negative impact of pesticide pollution on the environment. We took proactive measures to develop a pesticide index using a risk-based conceptual framework. Our research on identifying high pesticide and social vulnerability aims to contribute to risk analysis and inform data-driven policy decisions. By implementing biomonitoring programs tailored to specific US states, we can address environmental justice issues caused by pesticide contamination and work towards mitigating them. Our study also has limitations as we focused solely on the spatial component and did not fully analyze the temporal aspect of the data. We assumed that the US agricultural land use remained consistent over time and hence only considered spatial elements in our study. Due to this, we could not analyze and make predictions on certain pesticides that were replaced with others during our study period. Also, environmental justice issues due to pesticide exposure could be related to crop type, as in areas with large-scale production of corn and soybeans, there might be limited migrant workers due to machinery usage, in contrast to areas with specialty and field crops. This study does not account for such differences, as we did not use pesticide application data specific to crop type. Additionally, populations living near industrial sites manufacturing agrichemicals or in chemical alleys such as Louisiana are susceptible to increased pesticide exposure. We did not consider these unique circumstances in our study design and included Louisiana State. We did only a preliminary exploratory analysis by creating heat maps to see the trends of these pesticides. Future studies should consider conducting a time-series analysis to better understand the shift in pesticide usage due to replacement chemicals. Furthermore, our study did not consider the toxicologic aspects of our selection criteria. Estimating the toxicology of approximately 500 active pesticide ingredients would be impractical and beyond the scope of our research.\u003c/p\u003e \u003cp\u003eAccording to our findings, environmental justice issues due to higher pesticide exposure among racial and ethnic minorities, as well as those with low socioeconomic status, are prevalent. We observed these disparities in pesticide exposure in US counties regardless of their urban or rural status. Several factors contribute to the disproportionate pesticide exposures among racial minorities and low socioeconomic groups. These factors include the pesticide regulatory framework in the USA, the double standard for pesticide safety due to the Food Quality Protection Act of 1996, which guarantees no harm to individuals exposed to pesticides through food and other non-occupational routes, and a lack of necessary protection for children [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. While eliminating exposure disparities built over hundreds of years due to structural racism is a challenging task, we can mitigate the environmental injustice burden by taking necessary actions. These include adopting the precautionary principle that guides the environmental policy of the European Union [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], eliminating pesticide safety double standards, adequately protecting children [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], and incorporating more epidemiological studies into EPA\u0026rsquo;s pesticide risk assessments as they give more information to make regulatory decisions tailored to specific at-risk populations, expanding CDC\u0026rsquo;s program on biomonitoring of environmental chemicals among the US general population as it currently measures only select pesticides and their metabolites, atrazine, glyphosate, 2,4-dichlorophenoxyacetic acid, sulfonylurea herbicides, organophosphorus and neonicotinoid insecticides, and carbamates [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Additionally, the longitudinal measurement of certain chemicals, such as neonicotinoids and glyphosate, and the availability of biomonitoring data for subsequent years will aid in making informed regulatory and policy decisions. Implementing biomonitoring programs for certain pesticide classes in states with increased exposure risk might protect farmworkers in those states and improve healthcare access, especially in the high-risk communities of the US. We need future studies evaluating toxicology and the risk of more pesticide ingredients.\u003c/p\u003e \u003cp\u003eIn conclusion, due to several factors mentioned here, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across contiguous US counties in both rural and urban communities. We can reduce environmental injustice issues by ensuring that regulatory decisions are inclusive of everyone, regardless of their vulnerability. Our study will inform regulatory bodies about high-risk pesticides and socially vulnerable areas, as well as facilitate regulatory and public health decisions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACS American Community Survey \u003c/p\u003e\n\u003cp\u003eAPI Application Programming Interface \u003c/p\u003e\n\u003cp\u003eCRD Crop Reporting District \u003c/p\u003e\n\u003cp\u003eEBI Environmental Burden Index\u003c/p\u003e\n\u003cp\u003eEPA Environmental Protection Agency\u003c/p\u003e\n\u003cp\u003eEPest Estimated Pesticide Use\u003c/p\u003e\n\u003cp\u003eESDA Exploratory Spatial Data Analysis\u003c/p\u003e\n\u003cp\u003eFIPS Federal Information Processing System\u003c/p\u003e\n\u003cp\u003eICCP Intergovernmental Panel on Climate Change\u003c/p\u003e\n\u003cp\u003eNASS National Agricultural Statistics Service \u003c/p\u003e\n\u003cp\u003eNAWQA National Water-Quality Assessment Project\u003c/p\u003e\n\u003cp\u003ePCA Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eUSDA U.S. Department of Agriculture \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this manuscript is publicly available through the United States Geological Survey Pesticide National Synthesis Project database and the United States Census Bureau American Community Survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The Edna Ittner Pediatric Research Support Fund at the University of Nebraska Medical Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJT contributed to the conceptualization of the study design, acquisition of data, analysis and writing the manuscript. CB, AK, MZ, SBH, and ER made substantial contributions to the conception of the study design, critical review and feedback, and manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eauthor\u003c/strong\u003e: Jabeen Taiba, University of Nebraska Medical Center, Omaha, NE, email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShattuck A, et al. Global pesticide use and trade database (GloPUT): New estimates show pesticide use trends in low-income countries substantially underestimated. Glob Environ Change. 2023;81:102693.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShattuck A. Generic, growing, green? The changing political economy of the global pesticide complex. J Peasant Stud. 2021;48(2):231\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClapp J. Explaining Growing Glyphosate Use: The Political Economy of Herbicide-Dependent Agriculture. Glob Environ Change. 2021;67:102239.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtwood D, Paisley-Jones C. Pesticides industry sales and usage: 2008\u0026ndash;2012 market estimates. US EPA; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigelow D, Borchers A. \u003cem\u003eMajor uses of land in the United States, 2012.\u003c/em\u003e 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandrigan PJ, et al. The Lancet Commission on pollution and health. Lancet. 2018;391(10119):462\u0026ndash;512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNEP. Global chemicals outlook II: From legacies to innovative solutions. Geneva, Switzerland: UN Environment Programme; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarha W, et al. An overview on common aspects influencing the dissipation pattern of pesticides: a review. Environ Monit Assess. 2016;188(12):693.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDereumeaux C, et al. Pesticide exposures for residents living close to agricultural lands: A review. Environ Int. 2020;134:105210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeziel NC, et al. Relative Contributions of Agricultural Drift, Para-Occupational, and Residential Use Exposure Pathways to House Dust Pesticide Concentrations: Meta-Regression of Published Data. Environ Health Perspect. 2017;125(3):296\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen K, et al. Using geographic information systems to estimate potential pesticide exposure at the population level in Canada. Environ Res. 2020;191:110100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurl CL, et al. The Effect of Pesticide Spray Season and Residential Proximity to Agriculture on Glyphosate Exposure among Pregnant People in Southern Idaho, 2021. Environ Health Perspect. 2023;131(12):127001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonley N, et al. Pesticides and environmental injustice in the USA: root causes, current regulatory reinforcement and a path forward. BMC Public Health. 2022;22(1):708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttina TM, et al. Racial/ethnic disparities in disease burden and costs related to exposure to endocrine-disrupting chemicals in the United States: an exploratory analysis. J Clin Epidemiol. 2019;108:34\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames-Todd TM, Chiu YH, Zota AR. Racial/ethnic disparities in environmental endocrine disrupting chemicals and women's reproductive health outcomes: epidemiological examples across the life course. Curr Epidemiol Rep. 2016;3(2):161\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelova A, et al. A method to screen U.S. environmental biomonitoring data for race/ethnicity and income-related disparity. Environ Health. 2013;12:114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen AE, Gaines SD, Deschenes O. Agricultural pesticide use and adverse birth outcomes in the San Joaquin Valley of California. Nat Commun. 2017;8(1):302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoang TT, et al. Epigenome-Wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study. Environ Health Perspect. 2021;129(9):97008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha S, et al. Pesticide use and incident Parkinson's disease in a cohort of farmers and their spouses. Environ Res. 2020;191:110186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePouchieu C, et al. Pesticide use in agriculture and Parkinson's disease in the AGRICAN cohort study. Int J Epidemiol. 2018;47(1):299\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEid A, et al. Effects of DDT on Amyloid Precursor Protein Levels and Amyloid Beta Pathology: Mechanistic Links to Alzheimer's Disease Risk. Environ Health Perspect. 2022;130(8):87005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunoz-Quezada MT, et al. Chronic exposure to organophosphate (OP) pesticides and neuropsychological functioning in farm workers: a review. Int J Occup Environ Health. 2016;22(1):68\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo X, et al. Association between exposure to organophosphorus pesticides and the risk of diabetes among US Adults: Cross-sectional findings from the National Health and Nutrition Examination Survey. Chemosphere. 2022;301:134471.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzajka M, et al. Organophosphorus pesticides can influence the development of obesity and type 2 diabetes with concomitant metabolic changes. Environ Res. 2019;178:108685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiel C, et al. Increased risk of central nervous system tumours with carbamate insecticide use in the prospective cohort AGRICAN. Int J Epidemiol. 2019;48(2):512\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVrijheid M, et al. Environmental pollutants and child health-A review of recent concerns. Int J Hyg Environ Health. 2016;219(4\u0026ndash;5):331\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimoes M, et al. Exploring associations between residential exposure to pesticides and birth outcomes using the Dutch birth registry. Environ Int. 2023;178:108085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin S, et al. Maternal pesticide exposure and risk of preterm birth: A systematic review and meta-analysis. Environ Int. 2023;178:108043.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang A, et al. Association of Prenatal Exposure to Organophosphate, Pyrethroid, and Neonicotinoid Insecticides with Child Neurodevelopment at 2 Years of Age: A Prospective Cohort Study. Environ Health Perspect. 2023;131(10):107011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSagiv SK, et al. Prenatal and Childhood Exposure to Organophosphate Pesticides and Behavior Problems in Adolescents and Young Adults in the CHAMACOS Study. Environ Health Perspect. 2023;131(6):67008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRappazzo KM, et al. Maternal residential exposure to specific agricultural pesticide active ingredients and birth defects in a 2003\u0026ndash;2005 North Carolina birth cohort. Birth Defects Res. 2019;111(6):312\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel DM, et al. Parental occupational exposure to pesticides, animals and organic dust and risk of childhood leukemia and central nervous system tumors: Findings from the International Childhood Cancer Cohort Consortium (I4C). Int J Cancer. 2020;146(4):943\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhuldhaj UP, Singh R, Singh VK. Pesticide contamination in agro-ecosystems: toxicity, impacts, and bio-based management strategies. Environ Sci Pollut Res Int. 2023;30(4):9243\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRumschlag SL, et al. Consistent effects of pesticides on community structure and ecosystem function in freshwater systems. Nat Commun. 2020;11(1):6333.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolaco A, et al. Socioeconomic Status: The Missing Link Between Obesity and Diabetes Mellitus? Curr Diabetes Rev. 2018;14(4):321\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev. 2015;73(10):643\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts EK, et al. Persistent organic pollutant exposure contributes to Black/White differences in leukocyte telomere length in the National Health and Nutrition Examination Survey. Sci Rep. 2022;12(1):19960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie T, et al. Disparities in diagnosis, treatment and survival between Black and White Parkinson patients. Parkinsonism Relat Disord. 2021;87:7\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKehm RD, et al. Socioeconomic Status and Childhood Cancer Incidence: A Population-Based Multilevel Analysis. Am J Epidemiol. 2018;187(5):982\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarville EW, et al. Pre-pregnancy cardiovascular risk factors and racial disparities in birth outcomes: the Bogalusa Heart Study. BMC Pregnancy Childbirth. 2018;18(1):339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchraw JM, et al. Prevalence of congenital anomalies according to maternal race and ethnicity, Texas, 1999\u0026ndash;2018. Birth Defects Res. 2024;116(1):e2274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaggi F, et al. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci Data. 2019;6(1):170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph N, Kolok AS. Assessment of Pediatric Cancer and Its Relationship to Environmental Contaminants: An Ecological Study in Idaho. Geohealth, 2022. 6(3): p. e2021GH000548.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNT B. \u003cem\u003eEstimated Annual Agricultural Pesticide Use for counties of the Conterminous United States, 1992\u0026ndash;2019. U.S. Geological Survey.\u003c/em\u003e 2021 [cited 2023 March 24]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://water.usgs.gov/nawqa/pnsp/usage/maps/county-level/\u003c/span\u003e\u003cspan address=\"https://water.usgs.gov/nawqa/pnsp/usage/maps/county-level/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgriculture USDo. State and county data, Geographic area series parts 1\u0026ndash;50, AC\u0026ndash;12\u0026ndash;A\u0026ndash;1\u0026mdash;AC\u0026ndash;12\u0026ndash;A\u0026ndash;50: U.S. Department of Agriculture 2012 Census of Agriculture, v. 1. United States Department of Agriculture; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThelin GP, Stone WW. \u003cem\u003eEstimation of annual agricultural pesticide use for counties of the conterminous United States, 1992\u0026ndash;2009: U.S. Geological Survey Scientific Investigations Report 2013\u0026ndash;5009\u003c/em\u003e. 2013. p. 54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker NT, Stone WW. \u003cem\u003eEstimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12\u003c/em\u003e, in \u003cem\u003eData Series\u003c/em\u003e. 2015. p. 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieben CM. \u003cem\u003eEstimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 2013-17 (ver. 2.0, May 2020): U.S. Geological Survey data release\u003c/em\u003e. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieben CM. \u003cem\u003ePreliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2018: U.S. Geological Survey data release\u003c/em\u003e. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieben CM. \u003cem\u003ePreliminary estimated annual agricultural pesticide use for counties of the conterminous United States, 2019: U.S. Geological Survey data release\u003c/em\u003e. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureau USC. \u003cem\u003eAmerican Community Survey 5-Year Data (2009\u0026ndash;2022)\u003c/em\u003e. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJolliffe IT, Cadima J. \u003cem\u003ePrincipal component analysis: a review and recent developments.\u003c/em\u003e Philos Trans A Math Phys Eng Sci, 2016. 374(2065): p. 20150202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryan A, Hanson DTH. \u003cem\u003eA Comparison of Functions for PCA\u003c/em\u003e. 2024; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/LearnPCA/vignettes/Vig_07_Functions_PCA.pdf\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/LearnPCA/vignettes/Vig_07_Functions_PCA.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReid CE, et al. Mapping community determinants of heat vulnerability. Environ Health Perspect. 2009;117(11):1730\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobson KC, et al. Ordered subsets linkage analysis of antisocial behavior in substance use disorder among participants in the Collaborative Study on the Genetics of Alcoholism. Am J Med Genet B Neuropsychiatr Genet. 2008;147B(7):1258\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiStefano CZ, Min; and, M\u0026icirc;ndril\u0026atilde; D. Understanding and Using Factor Scores: Considerations for the Applied Researcher. Research, and E valuation: Practical Assessment; 2009. p. 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Neill B, van Aalst M, Zaiton Ibrahim Z, Berrang Ford L, Bhadwal S, Buhaug H, Diaz D, Frieler K, Garschagen M, Magnan A, Midgley G, Mirzabaev A, Thomas A. and R. Warren. Figure 1\u003cem\u003e6.1. 2022: Key Risks Across Sectors and Regions. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. 2022; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ipcc.ch/report/ar6/wg2/figures/chapter-16/figure-16-001\u003c/span\u003e\u003cspan address=\"https://www.ipcc.ch/report/ar6/wg2/figures/chapter-16/figure-16-001\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBivand RS, Wong DWS. Comparing implementations of global and local indicators of spatial association. TEST. 2018;27(3):716\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin L. Local Indicators of Spatial Association\u0026mdash;LISA. Geographical Anal. 1995;27(2):93\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNASS US. \u003cem\u003eCroplandCROS\u003c/em\u003e. 2024; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nass.usda.gov/Data_and_Statistics/\u003c/span\u003e\u003cspan address=\"https://www.nass.usda.gov/Data_and_Statistics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCushing L, et al. Racial/Ethnic Disparities in Cumulative Environmental Health Impacts in California: Evidence From a Statewide Environmental Justice Screening Tool (CalEnviroScreen 1.1). Am J Public Health. 2015;105(11):2341\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzaac Ornelas WF, Gabbard S, Carroll D. \u003cem\u003eFindings from the National Agricultural Workers Survey (NAWS) 2017\u0026ndash;2018: A Demographic and Employment Profile of United States Farmworkers\u003c/em\u003e, U.S.D.o. Labor, Editor. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanbury M. R.K., Occupational health disparities: a state public health-based approach. Am J Ind Med, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvert GM, et al. Acute Occupational Pesticide-Related Illness and Injury -United States, 2007\u0026ndash;2011. MMWR Morb Mortal Wkly Rep. 2016;63(55):11\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParks CG, et al. High pesticide exposures events, pesticide poisoning, and shingles: A medicare-linked study of pesticide applicators in the agricultural health study. Environ Int. 2023;181:108251.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen VK, et al. A comprehensive analysis of racial disparities in chemical biomarker concentrations in United States women, 1999\u0026ndash;2014. Environ Int. 2020;137:105496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss MC, Wang L, Sargis RM. Hormonal Injustice: Environmental Toxicants as Drivers of Endocrine Health Disparities. Endocrinol Metab Clin North Am. 2023;52(4):719\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolemi KM, et al. Identifying the link between chemical exposures and breast cancer in African American women via integrated in vitro and exposure biomarker data. Toxicology. 2021;463:152964.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUprety D et al. Racial and socioeconomic disparities in survival among patients with metastatic Non-Small cell lung cancer. J Natl Cancer Inst, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauch SA, et al. Associations of prenatal exposure to organophosphate pesticide metabolites with gestational age and birth weight. Environ Health Perspect. 2012;120(7):1055\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPA US. \u003cem\u003eEnvironmental Quality Index - Technical Report (2006\u0026ndash;2010) (Final, 2020)\u003c/em\u003e, U.S.E.P. Agency, Editor. 2020: Washington, DC. p. 102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMott L. The disproportionate impact of environmental health threats on children of color. Environ Health Perspect. 1995;103(Suppl 6):33\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeathers A, et al. Access to care for children of migratory agricultural workers: factors associated with unmet need for medical care. Pediatrics. 2004;113(4):e276\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolicy SfE. \u003cem\u003eThe Precautionary Priniple: decision making under uncertainty\u003c/em\u003e. Future Brief 18 2017; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ec.europa.eu/environment/integration/research/newsalert/pdf/precautionary_principle_decision_making_under_uncertainty_FB18_en.pdf\u003c/span\u003e\u003cspan address=\"https://ec.europa.eu/environment/integration/research/newsalert/pdf/precautionary_principle_decision_making_under_uncertainty_FB18_en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPA US. \u003cem\u003eUS EPA Office of Pesticide Programs\u0026rsquo; Re-Evaluation of the FQPA Safety Factor for Pyrethroids: Updated Literature and CAPHRA Program Data Review\u003c/em\u003e. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth NCfE. National Report on Human Exposure to Environmental Chemicals. C.f.D.C.a.P.: Department of Health and Human Services, Editor.; 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pesticides, social vulnerability, environmental pollution, environmental justice","lastPublishedDoi":"10.21203/rs.3.rs-4719285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4719285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e In the contiguous United States, environmental justice burdens and disparities in pesticide exposure are prevalent among racial and ethnic minorities and low socioeconomic groups. Identifying the counties with high pesticide exposure and social vulnerability is essential to mitigating risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We created an index for pesticides commonly used in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. We identified the US counties with elevated pesticide exposure and elevated social vulnerability. The USGS Pesticide National Synthesis Project quantified pesticide exposures at a county scale for frequently applied pesticides from 1992 to 2019 in 3069 contiguous US counties. We retrieved social vulnerability data from five-year estimates (2015–2019) of the American Community Survey (ACS) for selected variables: race, income, and educational attainment, and created a social vulnerability index. We implemented the pesticide index and social vulnerability index using a principal component analysis (PCA) approach. We used an Intergovernmental Panel on Climate Change ICCP risk-based approach to identify the counties with both high pesticide exposure and social vulnerability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e One hundred and forty-three US counties had high pesticide use and social vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In conclusion, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across the contiguous US counties in both rural and urban communities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Our study will inform regulatory bodies about areas with both high pesticide exposure and social vulnerability areas, as well as facilitate regulatory and public health decisions.\u003c/p\u003e","manuscriptTitle":"Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 13:19:38","doi":"10.21203/rs.3.rs-4719285/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T14:25:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T12:27:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-19T14:56:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200620131888105029504835663430517728287","date":"2024-08-13T17:36:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250347365989345339413712766316472286046","date":"2024-08-12T11:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86746016845797027171002205067836470107","date":"2024-08-11T18:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-11T05:32:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-05T12:22:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-11T08:53:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T08:52:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-07-10T16:25:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"699d5357-bcb4-4428-8d6a-bf9752aaee4b","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:01:49+00:00","versionOfRecord":{"articleIdentity":"rs-4719285","link":"https://doi.org/10.1186/s12889-025-25277-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-12-12 15:57:44","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2024-08-05 13:19:38","video":"","vorDoi":"10.1186/s12889-025-25277-5","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25277-5","workflowStages":[]},"version":"v1","identity":"rs-4719285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4719285","identity":"rs-4719285","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}