Recipient Organization
UNIVERSITY OF CHICAGO
5801 SOUTH ELLIS AVE.
CHICAGO,IL 60637
Performing Department
Harris Sch Pub Policy
Non Technical Summary
This project aims to investigate the social and environmental sustainability of industrial animal farming practices by quantifying the externalities they generate through environmental pathways, and the resulting harm to rural communities and threat to public health. Our empirical strategy captures the environmental channels of exposure to contaminants from animal farming operations using fine-resolution data with a large spatiotemporal coverage to estimate their causal impact on: the local environment; the health, quality of life, and value of assets of surrounding communities; and antimicrobial resistance.Over the last century, U.S. livestock production has undergone a structural transformation towards increasingly large and intensive confined operations, called Animal Feeding Operations (AFOs). The high animal densities concentrated inside these farms have two important implications: (1) they generate, at an industrial scale, dust and animal waste that contain various pollutants known to be detrimental to human health and well-being; and (2) they are sustained by the routine application of large volumes of low-dose antibiotics, fostering the selection of antimicrobial resistance genes in the local pathogen pool. The pollutants generated in the high-density environment (including particulate matter, pathogens and malodor) and the biochemical products of antibiotic use (antibiotic resistance genes and drug residues) are then transported by air and water to surrounding communities.
Animal Health Component
(N/A)
Research Effort Categories
Basic
100%
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
The major goals of this project are to analyze the social and environmental sustainability of dominant animal farming practices by building the most comprehensive database of animal farms and using a scalable quasi-experimental design to quantify the externalities generated through environmental pathways. U.S. livestock farming has greatly intensified over recent decades and is now predominantly composed of industrial ``animal feeding operations'' (AFOs). The confinement of animals in great densities, sustained by the routine application of low-dose antibiotics, generates pollution sources that, through air- and waterborne transmission, expose an increasing share of the population to multiple health threats, decrease quality of life and property values, and promote antimicrobial resistance. While nuisance lawsuits against AFOs are increasing across states, we lack a systematic and causal understanding of the impacts of AFO practices on the rural environment. Our study assembles the most comprehensive geo-coded AFO database, covering major producing states since 1984, and a causal inference design capturing exposure to farm pollutants from weather variation, in order to estimate the impact of environmental exposure to AFO practices on: antimicrobial resistance; key human health outcomes obtained from hospital records, medicine purchase data, and birth certificates; water quality; and neighboring property values. We leverage wind and precipitation variation and model the position of all units in the hydrological network to capture exposure mechanisms.This project addresses key elements of the U.S. animal agricultural system that determine its sustainability, on the local, national, and global scale. It will generate comprehensive and detailed knowledge on a system-wide level of significant externalities, in order to inform policy to address them and improve the sustainability of the system.This project addresses an important and understudied topic of environmental and natural resource economics (ENRE): the impacts of exposure to air and water pollution from animal feeding operations.It aims to estimate the causal relationship between the by-products of AFO practices and measures of environmental health, human health, and rural prosperity, caused by two channels of exposure: water and air transmission.This project combines (i) an identification strategy that captures \textit{causal} effects, (ii) granular data for all variables, which also enable us to capture potential heterogeneity--e.g., across number, size, and type of AFOs--in these relationships, and (iii) an extended spatiotemporal coverage (multi-state, 1984-present), and thereby will generate knowledge on these externalities that are both finely estimated and relevant on a national scale.
Project Methods
We aim to quantify the changes in water quality caused by AFOs, in particular the concentrations of pollutants known to pose a public health risk. We focus first on pathogens that originate exclusively from food animals, and can cause severe gastrointestinal illness: fecal coliforms. These bacteria, which reside in the digestive tracts of food animals and end up in their manure, are pathogens that, under short-term exposure, can induce severe gastrointestinal illness, e.g., diarrhea, vomiting and cramps. The presence of these bacteria in surface waters is an indicator of an extremely likely contamination by AFOs.We extract all existing measurements of fecal coliforms, from each water monitoring station with such measurements, from the Water Quality Portal.This platform comprises all water quality data collected by the U.S. EPA and the USGS since the early 20th century. We then tailor the drainage area and period considered to our data. The outcomes are at the level of water stations, and we know the day of measure. We thus delineate the drainage basin of each water station, and use the exogenous variation from intense precipitation events to estimate the impact of upstream AFOs on counts of fecal coliforms in surface waters.We also analyze the changes in levels of other pollutants related to AFOs that carry a public health threat, notably excess nutrients and general indicators of water quality. Because crop agriculture in the vicinity of feedlots might be an important confounder, we will extract the effect of AFOs by capturing extreme precipitation on the exact locations of (i) the main AFO facility and (ii) its sprayfields, whose coordinates we have for a sample of farms, and control for the presence of different types of crop agriculture using the USDA Cropland Data LayerThe State Inpatient Database (SID) of the Healthcare Cost and Utilization Project (HCUP) is an annual dataset of inpatient discharge records from most, if not all, community hospitals in a state.For each inpatient stay, it provides the patient's month of admission, ZCTA of residence, and the list of diagnoses with ICD-9/10 codes.We identify all ICM-9/10 codes related to antimicrobial resistance, and flag all patient records that present such a diagnosis, at the month-by-ZCTA level. We also extract the monthly total number of inpatient visits to run model specifications with case counts or rates as the dependent variable.We have already acquired data for Iowa (1990-2017) and North Carolina (2000-2016). Through the acquisition of additional data, we will use data in an area responsible for more than 50% of U.S. swine production.The HCUP SID data provide spatio-temporal variation and close to full coverage of cases of antimicrobial resistance in a given state, but do not identify the specific bacteria and antibiotics concerned. Data at the level of drug-bacteria pairs can provide useful information in our context, as certain classes of antimicrobials are used almost exclusively in distinct types of animal production. We use data from antibiograms, aggregated in a dataset available to researchers based on tests performed in hospitals and laboratories. This dataset provides spatial, temporal, bacterial and antibiotic variation down to the hospital-level, for the period 2013-2017. We will compare changes in the prevalence of specific resistant strains, e.g., distinguishing livestock-associated methicillin-resistant S. aureus (MRSA) from community-associated MRSA.Infants exposed to contaminants from crop agriculture pollutants in-utero are at risk of developing severe health conditions; we are concerned that animal farming pollutants may have a similar effect, which has so far been overlooked by the literature. We have applied for and obtained access to the CDC Linked Birth - Infant Death restricted data files for all states in our analysis, for the period 1998-2017. The birth certificates contain measures of perinatal health and background information about the mother; the restricted data files provide the mother's county of residence; and for each infant who died under 1 year of age, the information from the death certificate is linked to that from the birth certificate.We will measure exposure during the period of gestation, as the exposure of the mother may have an effect on fetal development. As detailed above, we measure the mother's cumulative exposure to the variations in weather experienced by upwind/upstream AFOs during that period, and study the effects of that exposure on infant health. Because the outcome is at the county-level, the drainage area considered will be that of the county.Residential property values capitalize the value of long-term outcomes and other disamenities such as odor nuisances. We use CoreLogic Tax and Deeds Data on residential transactions and tax assessments, covering the period 1976-2020, to quantify the change in local property values after the openings or expansions of AFOs in their vicinity.The database contains not only the sale price and location of the property, but also a set of variables on the property characteristics--such as the number of rooms, bathrooms, size of the property--such that we can control for potential changes in the composition of houses built or sold following the change in the local intensity of AFOs. Using the parcel identifiers, we will also conduct an analysis using only properties that were sold multiple times, notably before and after a change in animal production. With transaction-level data we will be able to exclude intra-family property transfers, and to test whether farms buy houses that might otherwise trigger setback distance requirements if occupied.In addition to distance to an AFO, being located downwind from the farm is an important determinant in the level of exposure. Using wind vector data, we will compute prevailing wind patterns and identify each property as downwind or upwind to the AFOs in a given radius, and characterize the extent of airborne pollution by comparing the effects upwind and downwind from a farm and how they dissipate with distance.We also want to analyze the changes in rates of multiple health conditions, identified in prior environmental health research as plausibly associated with AFO exposure, such as respiratory problems and gastrointestinal illness. We identify corresponding ICD-9/10 diagnosis codes, and count the number of total inpatient visits, recorded by HCUP SID, for each health condition, at the ZCTA-month level. We use the same research strategy as for AMR case rates.A category of aforementioned contaminants poses immediate health threats: fecal coliforms. These pathogens come from the digestive tracts of food animals, and can induce severe gastrointestinal illness when ingested through water contamination, including diarrhea, vomiting and cramps.Individuals afflicted with gastrointestinal illness may first turn to purchasing over-the-counter medicine to alleviate their symptoms. OTC sales of anti-diarrheal drugs can therefore inform us about the exposure to bacteria from upstream livestock operations.We use data from the NielsenIQ Retail Scanner and Consumer Panel Data Sets, provided by the Kilts-Nielsen Data Center at the University of Chicago Booth School of Business. The Retail Scanner data contain weekly prices and total sales for all items sold in a network of retail chains across the contiguous U.S. (accounting for 53% of all sales in grocery stores and 55% in drug stores) starting in 2006. We extract the total sales of anti-diarrheal medicine and bottled water for each store in all states for which we have AFO information, and match the store to customers' ZCTAs using the Consumer Panel Data Set. We analyze changes in purchases of anti-diarrheal drugs at the ZCTA level, following extreme precipitation events that hit upstream AFOs, and control for potential averting behavior proxied by bottled water sales.