Disparities in exposure to concentrated animal feeding operations (CAFOs) and other animal feeding operations across multiple states in USA

Abstract

BACKGROUND:

Animal feeding operations (AFOs), including concentrated animal feeding operations (CAFOs), pose significant environmental degradation and health risks. These facilities are often disproportionately located in disadvantaged communities, however, findings are inconsistent.

OBJECTIVE:

We investigated disparities in AFO/CAFO exposure across seven US states, focusing on variables related to environmental justice (EJ) and at-risk populations.

METHODS:

We linked AFO/CAFO data from seven states (Iowa, North Carolina, Pennsylvania, South Carolina, Texas, Virginia, and Wisconsin) to ZIP code-level census variables. We assessed exposure by calculating area-weighted number of AFO/CAFO within 15 km buffers and categorized ZIP codes into no, low, medium, and high exposure groups. Our analysis compared the spatial distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations by exposure intensity.

RESULTS:

We found differences in the distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations among states. In some states (e.g., North Carolina, Pennsylvania), AFOs/CAFOs were densely clustered in specific areas, while in others (e.g., Iowa, Wisconsin), they were more evenly distributed. We found disproportionate exposure to AFO/CAFO in disadvantaged communities such as communities with high percentages of racial/ethnic minority persons and low socioeconomic status in some states, whereas other states showed different patterns. Trends varied by state, with some showing increasing Non-Hispanic Black and Hispanic populations with higher exposure (e.g., North Carolina), while others showed opposite trends (e.g., Pennsylvania). Education, poverty, and income levels also varied, with some states (e.g., North Carolina, South Carolina) showing higher poverty rates, lower education level, and lower incomes in higher exposure groups and other states showing reverse trends (e.g., Wisconsin).

INTRODUCTION

Exposure to animal feeding operations (AFOs), especially concentrated animal feeding operations (CAFOs), can cause significant negative impacts on the environment and human health through multiple pathways such as air, water, soil, odor, and land-use shifts. In particular, the large amount of animal waste produced by livestock in these facilities generates harmful airborne emissions (e.g., ammonia, hydrogen sulfide, particulate matter, bioaerosols) and contaminates surface water, groundwater, and soil with excess nutrients, pathogens, and antibiotics. This contributes to environmental degradation and negatively affects human health for farm workers and populations living near AFOs/CAFOs [1–4]. Studies have found that AFOs/CAFOs may be associated with several health outcomes such as mortality, respiratory diseases, gastrointestinal illnesses, and urinary tract infections [5–8]. AFOs/CAFOs may also induce local land-use shifts from forested and grasslands to croplands used to manage liquid animal waste and feed animals [9]. The rise in croplands can lead to increased fertilizer use, which has significant implications for water quality.

Disparities in environmental exposures and the associated health outcomes by marginalized and at-risk populations (e.g., racial/ethnic minority or low-income persons) are well established for many environmental exposures such as air quality [10–12], and some work has investigated disparities in exposure to AFOs including CAFOs. Previous studies observed disproportionate siting of permitted animal facilities in some subpopulations such as racial/ethnic minority persons and communities with low socioeconomic status (SES) [5, 6, 13, 14]. A recent study found that exposure to NH₃ and H₂S from hog CAFOs differed by race/ethnicity, educational attainment, language proficiency, and SES [15]. They reported that most CAFOs in Duplin County, North Carolina (NC) and surrounding counties were in areas with higher percentages of people of color (POC), low education, linguistically isolated populations, and low-income communities. Another study observed that large swine CAFOs in NC and dairy CAFOs in California are disproportionately located in communities of low-income and POC [16]. However, some studies reported no association or opposite findings regarding race/ethnicity or SES indicators [17, 18]. While more studies on disparities in relation to AFO/CAFO exposure have been conducted recently, the evidence is still limited, and the findings vary by study area and population. Further, most studies investigate a single area such as a state. Our recent study systematically reviewed previous literature on AFO/CAFO focusing on exposure assessment, associated health outcomes, and variables related to environmental justice (EJ) and potentially vulnerable or susceptible populations. Results from the review showed differences in findings by study location, populations, AFO/CAFO exposure assessment, and variables related to EJ and at-risk population used in each study [19]. Heterogeneity in findings across states may be attributed to various factors including differences in types of AFOs/CAFOs and waste management practices, population characteristics, social and physical environment, distribution or composition of these factors and their interactions (e.g., demographic composition regarding age, racial/ethnic diversity in urban/rural areas). Further research is needed to better understand the complex disparity patterns in relation to AFO/CAFO exposure, especially considering the need for comprehensive studies that account for various environments and characteristics across different regions.

This study investigated disparities in exposure to AFO/CAFO in seven states in the US. We evaluated disparities by several variables related to environmental justice and potentially at-risk populations. Findings from this study can add scientific evidence on vulnerabilities and susceptibilities as well as potential differences and similarities in results across different populations and conditions regarding AFO/CAFO exposure.

METHODS

Data

We obtained the most recent version of permitted AFO/CAFO data from each state (Iowa, North Carolina, Pennsylvania, South Carolina, Texas, Virginia, and Wisconsin). Each dataset included data for permitted facilities in operation including facilities operating in previous years maintained by state agencies. The AFO/CAFO data information including characteristics and data sources by state are provided in Supplementary Table 1. The information in the AFO/CAFO databases across states varied. For example, AFO/CAFO data for some states included geocoded coordinates (latitude/longitude) for geographic location, while other states provided address-level geographic location. For states with address-level geographic location (i.e., Pennsylvania, Wisconsin), we geocoded the address of each facility to coordinates using the US Census geocoder (US Census Bureau). For facilities with no exact match, we assigned coordinates based on the ZIP code centroid. Supplementary Table 2 provides information on animal type by state. The main species in Iowa and North Carolina is swine, accounting for approximately 91% and 90%, respectively. In South Carolina and Virginia, poultry is the predominant species, comprising about 80% and 87%, respectively, while cattle is the main species in Texas (89%) and Wisconsin (94%). Although different animal types of AFO/CAFO may have distinct environmental and health impacts due to variations in waste production and management practices, emissions, and exposure pathways, this analysis combined all species as we aimed to evaluate disparities in a comprehensive assessment of overall exposure to AFO/CAFO using a consistent approach across all states (information on animal type was not available for all states). To evaluate the disparities in exposure to AFO/CAFO, we considered various community-level variables related to environmental justice and potentially vulnerable and susceptible populations using ZIP code level variables obtained from 2020 Census data and 2018–2022 American Community Survey (ACS): percentage of the population that is Non-Hispanic White (NHW), percentage Non-Hispanic Black (NHB), percentage Hispanic, percentage of population living below the poverty level, median annual household income, percentage of adults with education less than high school diploma, percentage of NHB adults with education less than high school diploma, median annual household income for NHB, racial isolation (RI) for NHB, racial isolation for Hispanic, and educational isolation (EI) for the population without a college degree. The RI and EI indexes measure the geographic separation of each group from other groups and range from 0 (e.g., RI of 0 for NHB represents no isolation, EI of 0 for the population without a college degree indicates that all residents of the neighborhood are college educated) to 1 (e.g., RI of 1 for NHB means complete isolation of NHB from non-NHB persons, EI of 1 for population without a college degree indicates that no residents of the neighborhood are college educated). The calculation of the indexes and additional details can be found in previous studies [14, 20]. Several studies used these metrics to evaluate environmental and health disparities [21, 22]. We included these variables to reflect various aspects of disadvantaged communities.

AFO/CAFO exposure assessment

We estimated AFO/CAFO exposure separately for each ZIP code. To assign exposure to AFO/CAFOs, we calculated area-weighted number of AFO/CAFOs using a buffer (i.e., 15 km) around the location of AFO/CAFOs for each ZIP code (Fig. 1). We first generated a 15 km buffer around each AFO/CAFO location. The overlap of these buffers indicates that some areas were within 15 km of multiple AFOs/CAFOs. The areas generated by overlapping buffers were designated as having exposure to 0 AFO/CAFO, 1 AFO/CAFO, 2 AFOs/CAFOs, etc. We calculated the percentage of area categorized as being within 15 km of a specified number of AFO/CAFO. Then we calculated the overall weighted number of AFO/CAFO for each ZIP code using area-weighting. We chose this approach to account for exposure to AFO/CAFOs that are nearby but in a different ZIP codes and area covered by the AFO/CAFO in relation to the ZIP code area. This approach has been applied in previous work [14].

Fig. 1.

Example of a hypothetical calculation of area-weighted number of AFOs/CAFOs.

For each state, we categorized each ZIP code into four categories of AFO/CAFO exposure (no exposure, low, medium, and high exposure groups) based on the distribution of the area-weighted number of AFO/CAFO (tertiles) to assign exposure intensity. This categorization was performed separately by state using state-specific exposure groups (low, medium, and high) based on different cutoff number of AFO/CAFOs by state. In other words, a ZIP code designated as high exposure group indicates high exposure for that state and might not have the same level of exposure as a ZIP code designated as high exposure for another state.

Analysis of disparities

We compared the spatial distribution of variables related to EJ and at-risk populations by state-specific exposure intensity groups. We mapped spatial distributions of ZIP code level AFO/CAFO exposure using exposure intensity groups (i.e., no exposure, low, medium, and high exposure) and variables related to EJ and vulnerable or susceptible populations (spatial distribution based on quartiles) by state. To examine exposure disparities by AFO/CAFO exposure intensity group, we compared ratios of low, medium, and high exposure groups to the no exposure group by state. We calculated the ratios of the average value of each variable in a given exposure group (i.e., low, medium, and high) to the corresponding average value in the no exposure group. This approach standardizes the values relative to the no exposure group, providing a clear way to compare relative differences across multiple variables and interpret patterns and trends across exposure groups. We examined correlations among variables related to at-risk populations separately for each state and then summarized overall correlations across states. We performed several statistical analyses: (1) analysis of variance (ANOVA) to compare statistical differences among exposure intensity groups, (2) logistic regression to assess the relationship between binary exposure to AFO/CAFO (exposure vs. no exposure) and disparity variables, and (3) multiple regression to evaluate the association between continuous area-weighted number of AFO/CAFO and disparity variables controlling for potential confounders. To confirm whether the findings are robust, we conducted sensitivity analyses using a smaller buffer size (i.e., 5 km) and same cutoff number of AFO/CAFO across all states rather than separate values across states for categorization of exposure intensity group. We used ArcGIS Pro 10.6.1 (ESRI, Redlands, CA), R 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria), and SAS 9.4 (SAS Institute, Cary, NC, USA) for all analyses and mapping.

RESULTS

Figure 2 shows the location of AFO/CAFOs and spatial distribution of state-specific ZIP code level AFO/CAFO exposure across states. Exposure intensity groups in each state were based on the state-specific thresholds such that the low, medium, and high exposure groups may have different levels of exposure by state. Spatial distributions of ZIP code level AFO/CAFO exposure showed different patterns by state. For some states, such as North Carolina, Pennsylvania, and Virginia, many AFOs/CAFOs were clustered in specific areas (e.g., southeastern NC, southeastern Pennsylvania, northern and middle part of Virginia). AFOs/CAFOs in other states (e.g., Iowa, Wisconsin) were generally located throughout the state, although AFOs/CAFOs were more densely located in some areas than others. Iowa had no ZIP code with exposure value of 0 (i.e., no AFO/CAFO exposure).

Fig. 2. Location of AFO/CAFOs and spatial distribution of state-specific ZIP-code level exposure.

Note: State-specific exposure group (low, medium, and high) was based on state-specific thresholds of AFO/CAFO by state. Low, medium, and high exposure group across states may have different levels of exposure by state.

Supplementary Fig. 1 provides the spatial distribution of ZIP-code level exposure using the same thresholds for all states (e.g., the high exposure group is equivalent exposure across states). Cutoff numbers by exposure intensity group are provided in Supplementary Table 3. The spatial distributions of ZIP code level exposure by state were generally similar with those using state-specific AFO/CAFO exposure levels, except for Iowa. Most ZIP codes in Iowa had high exposure based on the categorization using the same cutoff number across all states, indicating the high concentration of AFO/CAFOs in the state. We used state-specific ZIP code level exposure for subsequent analysis.

Supplementary Table 4 shows the distribution of the area-weighted number of AFO/CAFO by exposure level across states. The percent of ZIP codes with exposure value of 0 (i.e., no AFO/CAFO exposure) ranged from 0% (Iowa) to 68.6% (Texas) across states. For Iowa, all 932 ZIP codes had AFO/CAFO exposure. Considering ZIP codes excluding those with 0 exposure, the average number of area-weighted AFO/CAFO ranged from 1.1 in Texas to 42.0 in Iowa. Exposure intensity group (i.e., low, medium, and high exposure) based on state-specific thresholds may have different levels of exposure by state. For example, the ranges of AFO/CAFO number for a ZIP code in the high exposure group for Texas (0.9–18.0) and Wisconsin (2.0–21.8) are similar with the range for low exposure group in Iowa (0.16–16.0).

Supplementary Table 5 provides median, minimum, and maximum correlations among variables related to EJ and at-risk populations across states. Results by state are provided in Supplementary Table 6. The percentage of the population that is NHW showed a strong negative correlation with the percentage of NHB and was negatively correlated with racial isolation for NHB. The percentages of the population that are NHB or Hispanic were positively correlated with racial isolation for NHB or Hispanic, respectively. Median annual household income showed a strong positive correlation with median household income for NHW across all states. Median household income was negatively correlated with educational isolation for the population without a college degree across all states.

Figure 3 shows the ratio of some variables related to EJ and at-risk populations by AFO/CAFO exposure intensity group across states. We compared the low, medium, and high exposure of AFO/CAFO to the no exposure group for some variables (i.e., percentage of the population that is NHB, Hispanic, percentage with less than a high school education, percentage in poverty, median annual household income, EI for population without a college degree). The percent of population and values for variables related to EJ and potentially vulnerable populations for each state by AFO/CAFO exposure level in Supplementary Table 7. Figure 3 does not include results for Iowa as this state has no ZIP code with exposure value of 0. We found generally increasing trends of higher percentage of the population that is NHB with higher exposure for North Carolina and South Carolina and decreasing trends for Pennsylvania and Virginia. In Wisconsin and Texas, the percentage of the population that is NHB in AFO/CAFO exposure groups, regardless of exposure intensity, was lower than that in the no exposure group. The percentage of the population that is Hispanic increased with AFO/CAFO exposure, except for South Carolina, which has the opposite trend. For Virginia and Wisconsin, we found that the percentage of the population that was Hispanic was lower in ZIP codes with AFO/CAFO exposure than in ZIP codes with no exposure. However, for these states within the ZIP codes with AFO/CAFO exposure, the percentage of the population that was Hispanic was higher for the high exposure group than the low and medium exposure groups. ZIP codes with AFO/CAFO exposure generally had lower education (i.e., higher percentage of population with less than a high school education) than the no exposure group, with trends of lower education with higher exposures for all states except Wisconsin, which has the opposite trend. In Pennsylvania and Wisconsin, ZIP codes with AFO/CAFO exposure had higher median household income than the no exposure group, with a generally trend of higher income with higher AFO/CAFO exposure intensity. Other states showed decreasing median household income with increasing AFO/CAFO exposure. The percentage of the population below the poverty level was higher with increasing AFO/CAFO exposure intensity in North Carolina and South Carolina. In general, we found higher EI for the population without a college degree with increasing AFO/CAFO exposure intensity in all states.

Fig. 3. Ratio of variables related to environmental justice and potentially vulnerable populations across states.

Note: Ratios compared low, medium, and high AFO/CAFO exposure group to the no AFO/CAFO exposure group. Values correspond to Supplementary Table 7.

For Iowa, as there were no ZIP codes without AFO/CAFO exposure, we compared the medium and high exposure groups to the low exposure group. We observed a general decreasing trends in the population that is NHB, median household income, and poverty, as well as increasing trends in the population that is Hispanic, has less than a high school education, and EI for the population without a college degree, with increasing AFO/CAFO exposure intensity.

We mapped spatial distribution of EI for the population without a college degree across states in Fig. 4. The spatial distribution of EI without a college degree was roughly similar to that of AFO/CAFO exposure for most states (Fig. 2). For example, most areas with a high index of EI are located near areas with a high density of AFOs/CAFOs in each state (e.g., eastern coastal region in NC, northeastern South Carolina), although other areas within the states also had high EI.

Fig. 4.

Spatial distribution of EI for the population without a college degree across states.

We assessed the relationship between binary exposure to AFO/CAFO and disparity variables for each state (Supplementary Table 8). We found that some variables were associated with the odds of being in exposure group and these variables varied by state. For example, in North Carolina, areas with higher percentage of the population that is NHB were more likely to be exposed to AFO/CAFO. Specifically, each 1% increase in % NHB was significantly associated with a 4% increase in the odds of exposure. In Pennsylvania, areas with higher percentages of the population that is Hispanic, people with less than a high school education were significantly associated with higher odds of exposure. Similar trends were observed in the associations between continuous AFO/CAFO exposure and disparity variables (Supplementary Table 9).

We conducted sensitivity analyses to assess the robustness of the findings. Results using a smaller buffer size (i.e., 5 km) were consistent with the original findings based on a 15 km buffer, showing similar trends and statistical differences among exposure intensity groups by state (Supplementary Table 10). Results for sensitivity analysis using same cutoffs across all states for categorization of exposure intensity group were generally similar with state-specific findings except for some variables in Iowa (e.g., % Hispanic) (Supplementary Table 11). In Iowa, most ZIP codes were classified as high exposure under this approach, reflecting the state’s high concentration of AFO/CAFOs.

DISCUSSION

In this study, we evaluated disparities in exposure to AFO/CAFOs for several variables related to EJ and potentially at-risk populations as well as the distributions of AFO/CAFO exposure across multiple states in the US. We found that in some states AFO/CAFO exposure was higher in communities with more vulnerable and susceptible populations, but these findings were not consistent across states. The spatial patterns of AFOs/CAFOs varied by location; in some states, they were densely located in specific areas, whereas in other states they were dispersed throughout the state.

We observed that the spatial distribution of AFO/CAFOs showed different patterns across states. For example, most AFOs/CAFOs in NC were clustered in southeastern NC, although some AFOs/CAFOs were located in central and western NC. Similarly, AFOs/CAFOs in Pennsylvania, Texas, and Virginia were more densely located in specific areas within parts of each state. On the other hand, the distributions of AFOs/CAFOs in Iowa, South Carolina, and Wisconsin showed different patterns and were generally located throughout the state.

Findings for disparities by AFO/CAFO exposure varied across states. Regarding race/ethnicity, we found that AFOs/CAFOs were disproportionately located in communities with higher percentages of racial/ethnic minority persons such as the percentage of the population that is NHB and the percentage Hispanic for some states. For the percentage of the population that is NHB, results were heterogeneous across states. We found generally increasing trends in the percentage of the population that is NHB with higher exposure intensity in North Carolina and South Carolina, while Pennsylvania and Virginia showed the opposite pattern. For Wisconsin and Texas, the percentage of the population that is NHB in any AFO/CAFO exposure groups (low, medium, high) was lower than that of the no exposure group. For Iowa, every ZIP code had AFO exposure; within AFO/CAFO intensity groups, we observed lower exposure with a higher percent of the population that is NHB in Iowa. Most states showed generally increasing trends of higher percent of the population that is Hispanic with higher AFO/CAFO exposure intensity, except for South Carolina. In South Carolina, the lowest percentage of the population that is Hispanic was in the highest exposure group, and within the ZIP codes with AFO exposure, the percent Hispanic decreased with higher exposure. For Virginia and Wisconsin, we observed higher percent of the population that is Hispanic with higher AFO/CAFO exposure, although the highest percentage Hispanic was in the no exposure group. Overall, our results indicate complex patterns of disparities in AFO/CAFO exposure that vary by location.

Our findings are broadly consistent with the limited number of studies that investigated environmental justice and related issues for AFOs/CAFOs exposure, with findings varying across studies. A study of Ohio, US reported that Black and Hispanic populations were disproportionately exposed to CAFOs compared to other populations [23]. Another study of Maryland, US examined the relationship between sociodemographic factors and presence of CAFOs and meat processing facilities, observing a positive relationship between CAFOs or meat processing facilities with POC [13]. Other studies conducted for locations included in our study found similar results to our research, reporting disproportionate siting of AFOs/CAFOs in communities with higher percentage of racial/ethnic minority persons for North Carolina [5, 6, 14, 24–26]. On the other hand, some studies reported no association or mixed results. For example, Carrel et al. reported that high densities of swine CAFOs were not associated with racial/ethnic minority populations in Iowa, which is similar with our results in Iowa [17]. Khanjar et al. found no association between communities of color and industrial poultry farms in Mississippi [27]. A study in Delaware investigated potential disparities in exposure to poultry CAFOs with regards to race/ethnicity and found that areas with higher percentage of Hispanic were more likely to have CAFOs, while areas that had higher percentage of POC had fewer industrial poultry farms [28]. Our earlier work in Iowa and Wisconsin found that within the areas with AFO/CAFO exposure, areas with higher exposure had higher percentages of NHB and Hispanic, although AFOs/CAFOs were generally located in areas with lower percentages of racial/ethnic minority persons [29, 30]. Regarding SES, for some states we found that AFOs/CAFOs were disproportionately located in low SES communities based on household income or educational attainment. Except for Wisconsin, AFO/CAFO exposure was higher with higher percentage of population with less than a high school education. AFO/CAFO exposure was higher with higher median household income for Pennsylvania and Wisconsin, however the reverse pattern was observed in other states. We observed higher poverty with increasing AFO/CAFO exposure intensity for North Carolina and South Carolina. Other states generally had lower rates of poverty in areas with AFO/CAFO exposure compared to the no exposure group. Many studies reported low median household income with higher AFO/CAFO exposure in Delaware and North Carolina [5, 6, 14, 25, 28], consistent with our findings for North Carolina, South Carolina, Texas, Virginia. Research on Maryland, US observed that CAFO hot spots were located in low-income counties. They reported that an increase in median household income was associated with a reduction in CAFOs [13]. Another study for Ohio, US reported that low-income households were disproportionately exposed to CAFOs compared to other populations [23]. Other studies reported higher AFO/CAFO exposures with various indicators for low SES (e.g., low education, more deprivation, high percentage of people in poverty, high enrollment of school lunch program, high percentage of uninsured reported) [5, 6, 14, 24, 27, 28, 31]. On the other hand, some studies reported no association between AFO/CAFO exposure and education or poverty [17, 18]. In this study, we found that all states showed higher EI for the population without a college degree in high AFO/CAFO exposure groups with increasing exposure intensity. While residential isolation indexes were not evaluated in most previous studies on disparities in AFO/CAFO exposure, our finding is generally consistent with our earlier work conducted in NC, Iowa, and Wisconsin [14, 26, 30].

This study found heterogeneity in findings of disparities related to AFO/CAFO exposure across states. Possible explanations include differences in AFO/CAFO characteristics and exposure assessment, population characteristics, regional characteristics, and their interactions, as well as a wide range of variables related to EJ and at-risk populations across states. For example, different AFO/CAFO characteristics across states such as animal type, operational history of facilities, and manure management systems may affect exposure patterns. Different physical and social environments, population composition across areas and communities may contribute to complex disparity patterns related to AFO/CAFO exposure. Data on characteristics of AFO/CAFOs, such as detailed information by animal type, are needed [32].

This study has several limitations. We relied on provided information on the location and characteristics of permitted facilities provided by state agencies based on permits. The format and information of AFO/CAFO data varied across states, which hindered our ability to consistently incorporate key information in our assessment of AFO/CAFO exposure. For example, some states provided information on detailed geographic location, while others did not. Additionally, there was no information on critical factors to better assess accurate exposure to AFO/CAFO such as operation history over time, actual size of operations or manure storage facilities, and manure production. Future work would benefit from utilizing detailed information from consistent and systematic database systems across states. Finally, more work is needed on disparities by exposure pathway (e.g., water, air quality, odor) and different types of AFO/CAFOs (e.g., animal type).

This study focuses on AFOs/CAFOs, which are state-permitted animal farms, whereas animal farms that fall outside federal and state permitting requirements can also have detrimental impacts on the environment and local community. Many animal farms and specific farm types are not captured by permitting, and their locations are not reported in state registries. Many environmental NGOs and public interest groups have used satellite data to retrieve the coordinates by manually labeling CAFOs detected in satellite or drone imagery to improve this data gap. More recent scholarship has developed machine learning algorithms to automatically and systematically detect animal farms from recent and high-resolution satellite imagery. A study conducted by Handan-Nader and Ho has shown that using image-learning techniques offers an effective, accurate, and lower-cost approach to environmental monitoring and regulatory enforcement for locating CAFOs [33]. They detected 589 additional poultry CAFOs in North Carolina not located by permits or environmental interest groups. Future work may include emerging techniques to locate and account for environmental exposures from permitted and unpermitted animal farms.

Accurate exposure to AFO/CAFO is complicated due to the complexities of multiple pathways (e.g., air, water, soil, odor) and factors such as topography, wind direction, wind speed, and their interactions. Previous studies have used various exposure metrics such as binary indicators for the presence or absence of facilities, the number of specific animals within certain boundaries, and proximity to facilities, which are useful approaches, yet a more refined exposure assessment is needed to capture the multifaceted nature of AFO/CAFO exposure. To assess AFO/CAFO exposure across states, this study applied a more complex method based on the area-weighted number of AFO/CAFO within a buffer. This approach accounts for the area covered by AFOs/CAFOs in relation to the ZIP code area and exposure based on multiple facilities and can reflect exposure from facilities beyond ZIP code boundaries. Still more sophisticated exposure assessments are needed to capture the various pathways through which AFO/CAFOs impact health, such as through detriments to air quality, water quality, soil, and odor, and to account for AFO/CAFO characteristics such as actual spatial area, including location of waste lagoons and crop fields nearby lagoons, types of animals (e.g., poultry, cattle), and number of animal units.

Although research investigating the environmental exposures, public health impacts, and environmental justice implications of AFOs/CAFOs suggest extensive community impacts, AFOs/CAFOs may directly influence community development and dynamics. This study found evidence of complex disparate exposure patterns among at-risk populations, but CAFOs may potentially influence the spatial distribution of these communities. Bastian investigated the impact of hog CAFOs on Iowa’s local development and water quality [34]. They found that CAFO expansion was related to growth in the proportion of adults without a high school education, out-migration, and population decline. More studies linking CAFO expansion to shifts in community dynamics over time are needed to understand how CAFOs may influence community variables such as those related to environmental justice and potentially at-risk populations.

Another limitation is the inability to account for a wide range of economic, structural, and historical factors. These factors may play a significant role in determining the location of AFO/CAFOs and shaping exposure patterns. While our dataset does not allow us to fully explore these relationships, future research that includes detailed information on these factors is needed to better understand the complex disparity patterns observed in this study.

To the best of our knowledge, this is the first study to evaluate disparities in exposure to AFO/CAFO across multiple states. A recent systematic review evaluated the variables used to investigate EJ and potentially vulnerable or susceptible populations in relation to AFO/CAFO exposure, finding variation in the metrics used to investigate disparities across studies. They found that the most commonly investigated variables were race/ethnicity and socioeconomic status [19], although some studies considered other variables such as urbanicity/rurality, foreign-born status, and age. To incorporate diverse aspects of environmental injustice and disadvantaged communities, we considered multiple variables related to EJ and at-risk populations including racial isolation and educational isolation indexes, which have not been considered in most previous studies evaluating disparities in exposure to AFO/CAFO. More research considering various features of community and individual characteristics in different locations is needed given the complex disparity patterns and interactions among variables related to EJ and at-risk populations (e.g., more detailed information on race/ethnicity rather than the broad categories used here). Research investigating how environmental exposures change over time due to AFO/CAFOs growth can aid understanding of long-term impacts for various vulnerable communities. Finally, additional efforts are needed to further explore the health outcomes associated with exposure to AFOs/CAFOs, including disparities in health response, to add to the existing literature [5, 25, 35].

CONCLUSION

We assessed disparities in AFO/CAFO exposure across multiple states with respect to various variables related to EJ and potentially vulnerable or susceptible populations. We observed suggestive evidence indicating disparities in AFO/CAFO exposure for marginalized and minoritized subpopulations, although these results varied across states indicating that environmental justice issues do exist for AFOs/CAFOs, although with heterogeneity across locations. These findings contribute to understanding of disparities and highlight potential differences and similarities in results across diverse populations and locations. Such research can inform decision-makers and communities in developing more effective environmental policies to protect public health and mitigate exposure disparities.

Supplementary Material

Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41370-025-00783-1.

IMPACT:

• This study investigated disparities in AFO/CAFO exposure across seven US states. We applied an advanced exposure metric and considered multiple variables to capture diverse aspects of environmental injustice and disadvantaged communities. Our findings across multiple states provide valuable insights that can inform policy development and help mitigate exposure disparities across various populations and locations.

DATA AVAILABILITY

All data analyzed in this paper are publicly available with data sources provided in the paper.