THE ECONOMIC DISAMENITIES AND HEALTH IMPACTS OF ANIMAL FEEDING OPERATIONS’ PRACTICES IN THE UNITED STATES

<b>Project Methods</b><br> 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.