Concern for the number of veterinarians pursuing careers in food animal practice, and the number of veterinarians remaining in those careers, has been a topic of discussion and debate for the past 2 decades. At a food animal veterinary workforce meeting held in 2002, participants concluded that the veterinary profession was facing a shortage of food animal veterinarians across both the private and public sectors, with the potential to adversely impact food safety, public health, and animal welfare.1 Recognizing a variety of social and economic reasons for the downward trend in food animal veterinarians, the Food Supply Veterinary Medicine Coalition was formed and sponsored a series of studies2–4 to better understand the factors influencing this shortage and identify ways to mitigate its effects.1 The results of these studies, commissioned in 2004,1–4 confirmed that numbers of veterinary students choosing to pursue food supply veterinary practice were insufficient to meet future needs.
In response to the studies, and at the request of veterinary stakeholders, the National Academy of Sciences commissioned a study in 2007 to assess the veterinary workforce. The study committee did not find a widespread shortage of veterinarians but did find that the veterinary profession was losing its presence in food animal production and care. Two factors were found to contribute to this. First, rural areas have fewer and more widely dispersed farms, making it difficult for food animal veterinarians to maintain a practice. Second, challenges exist in developing production medicine workforce and service delivery for changing, increasingly intensive livestock industries.5
In response to the National Academy of Sciences report,5 the AVMA commissioned a veterinary workforce study to inform strategies to ensure the economic viability of the profession. This study showed that in 2012, at the national level, excess veterinary capacity appeared to exist without any changes in demand.6 However, even if overall veterinary capacity were adequate, there may be a maldistribution of the workforce both geographically and by discipline.
Recent regulatory changes affecting the use of antimicrobials for food-producing animals has brought the discussion of the food supply veterinary shortage to the forefront again. On January 1, 2017, the US FDA's GFI213 was fully implemented.7 Prior to implementation of this guidance, some antimicrobials had been labeled for over-the-counter sale at feed mills and other outlets and were available to food animal producers for use in feed or water with no veterinary oversight. In accordance with GFI213, in-feed use of medically important antimicrobials, as defined in that document, for growth promotion was removed from antimicrobial product labels, and conditions for in-feed and water use of medically important antimicrobials for food-producing animals were revised to require veterinary oversight. Now, food animal producers must have a valid veterinary-client-patient relationship to use antimicrobials in feed or water. This additional oversight may be increasing demand for food animal veterinary services as well as the workloads of those already providing food animal services, potentially exacerbating shortages of food animal veterinarians in certain regions of the country.
Estimates of workforce shortages in the human medical profession are based on the medical provider-to-human population ratio for a specified geographic area, population type (eg, low income groups or migrant farmers), or medical facility type. For veterinary medicine, and particularly food animal medicine, an ideal veterinarian-to-animal population ratio has not been determined. This is likely because of differences in veterinarians’ roles based on species and production systems. The purpose of the study reported here was to determine geographic areas where access to veterinary services for livestock might be impacted the most by an increased need for veterinary services associated with VFDs or prescriptions by evaluation of 3 federal datasets related to the distribution and availability of veterinarians.
Materials and Methods
Three federal datasets were used for the study: veterinary shortage situations designated by the USDA's VMLRP for fiscal years 2010 through 2016, results of a 2011 USDA APHIS Veterinary Services NAHMS survey of small-scale US livestock operations, and the USDA's list of federally accredited veterinarians for 2016 as obtained through the NVAP.
VMLRP
Program description—The National Veterinary Medical Service Act of 2003 gives the Secretary of Agriculture authority to determine whether and where veterinary shortages exist in the United States and its insular areas and to enter into loan repayment contracts with veterinarians who agree to provide services to mitigate these shortages. The USDA NIFA, on behalf of the Secretary of Agriculture, designates veterinary shortage situations on the basis of nominations submitted by the chief SAHOs of US states and territories and the federal government. The VMLRP process of designating shortage situations occurs annually. The chief SAHOs submit a standardized nomination form in which they must specify the geographic location of the shortage situation (eg, counties or portions thereof, types of animals in need of veterinary services, and quantitative and qualitative data to describe the shortage situation and the reason it exists). They must also identify the services needed and activities the veterinarian is expected to conduct, describe previous efforts to recruit or retain a veterinarian in the designated area, and outline the risks to the food supply and public health if veterinary services are not secured.
The NIFA limits the maximum number of shortage situation nominations per state to ensure a fair distribution of shortage designations across jurisdictions and to reduce the administrative burden on the chief SAHOs. Two variables from census data collected by the USDAs National Agricultural Statistical Service are used to determine the maximum number of designations (up to 8/jurisdiction): livestock and livestock product total sales in dollars, which indicates the extent of live animal agriculture in a state; and land area in acres, which predicts the need for veterinary services given the positive correlation among state land area, percentage of state area classified as rural, and percentage of land devoted to actual or potential livestock production. Land area also impacts the number of veterinarians needed in a state owing to the practical limitations of distance and time required to travel between points of service. On the basis of these criteria, up to 266 veterinary shortage situations can be nominated for designation.
The chief SAHOs may nominate new situations each year or carry over, without changes, shortages designated the previous year. A merit review panel reviews all new nominations, and recommendations for designated national veterinary shortage situations are provided to NIFA. Between 2010 and 2016, a mean of 185 shortages were designated each year. Once a national veterinary shortage situation is designated, veterinarians may compete for loan repayment in exchange for providing the veterinary services described in the nomination. Applications are reviewed by a peer-review panel and recommended for loan repayment on the basis of the strength of the match between the knowledge, skills, and abilities of the veterinarian, as described in the application, as well as the needs in the shortage situation. Each year, 40 to 50 awards are provided to veterinarians who agree to provide 3 years of veterinary services in a designated shortage situation.
Data collection and analysis—The VMLRP allows veterinarians to provide services in both private and public sectors, although the numbers of public service and discipline-focused awards are limited. Therefore, for the present study, data were used for only shortage situations pertaining to type I food animal predominant practice (32 h/wk dedicated to the shortage situation) and type II rural mixed animal practice (12 h/wk dedicated to the shortage situation) for which no award was granted. Additionally, shortages for which no county-level data were provided or that could not be matched to a Federal Information Processing Standards code were excluded.
Although each designated shortage represents a geographic area at the county level, the shortage may exist in only a portion of a county. So, to facilitate spatial analysis of the data, the entire county was identified as the shortage area even if only part of the county was described on the original shortage nomination form. When the same county was listed on > 1 nomination form in a given year, the county was only counted once for that year. When the same county was listed on ≥ 2 shortage situations in the same year and the award status (received or did not receive) differed on each listing, the county was excluded from the analysis.
Shortage situations were presumed current if designated in 2015 or 2016 because those designated early in the program may have been filled and no longer deemed a shortage by the chief SAHO in sub-sequent years. For the purposes of this analysis, any county in which a shortage area was designated but remained unfilled for at least 4 years was considered a chronic shortage area. Current chronic shortage situations (designated for ≥ 4 years including 2015 or 2016) were assumed to be the areas of greatest veterinary need for VMLRP and were used as a layer for combined analyses. A map of counties representing veterinary shortage situations not filled by a VMLRP awardee was created for each year (2010 through 2016) of the VMLRP program. Each map became a layer and each unfilled county assigned the value of 1. All map layers were weighted equally and combined to create 1 map, with the summed value representing the number of years a county was not filled by a VMLRP awardee. Counties were then removed from the map if not designated in 2015 or 2016 and the summed value was < 4.
NAHMS Small-Scale US Livestock Operations Study
Survey—In 2011, the NAHMS conducted a Small-Scale US Livestock Operations Study,8 which focused on operations that raised livestock and had gross annual sales of $10,000 to $499,999. Livestock were defined as cattle, poultry, goats, sheep, swine, horses, aquaculture, or other farm animals raised for sale or home use. Data were collected from 8,123 operations in all 50 states. Producers’ perceptions of veterinary access were assessed by asking for the distance to the nearest veterinarian who provides services for their type of livestock. Most (82%) respondents reported that the nearest veterinarian was within 30 miles of their operation, whereas < 2% reported that the nearest veterinarian was ≥ 100 miles away or that there was no available veterinarian. Of respondents reporting no availability of veterinary services, 25% raised non-traditional species (ie, rabbits, camelids, cervids, fur-bearing animals, bees, or aquaculture species).
Related data analysis—On the basis of the survey responses, a veterinary shortage area was defined in that study8 as the nearest veterinarian being ≥ 100 miles away or no veterinarian at all. These survey responses were summarized to the county level, and a geospatial hot-spot analysisa was then performed on these data to identify significant clusters of counties where producers reported limited or no veterinary access (ie, where veterinary shortage areas concentrated). The hot spots totaled 346 counties with producer-perceived veterinarian shortages (Figure 1).
Map of hot spots identified in a previously reported analysis8 of small-scale livestock veterinary shortages identified through an NAHMS survey of small-scale food animal producers regarding veterinarian availability. Hot spots represent geographic areas where a condition (here, producers indicating that the nearest veterinarian was > 100 miles away or was lacking entirely) is concentrated relative to the neighboring geographic areas. Overall, 346 counties had producer-perceived veterinarian shortages. (Modified from USDA APHIS Veterinary Services Info Sheet. Veterinarian “Shortage” Areas for Small-Scale US Livestock Operations, 2011.8)
Citation: Journal of the American Veterinary Medical Association 253, 10; 10.2460/javma.253.10.1334
NVAP
Program description—The NVAP is a voluntary program administered by USDA APHIS Veterinary Services that authorizes veterinarians to work cooperatively with federal veterinarians and chief SAHOs to protect and ensure animal health. More than 68,000 veterinarians participate in the program, and these individuals are authorized to perform regulatory testing and issue health certifications to ensure that animals transported between states and exported to other countries meet the requirements to move and do not carry or introduce disease. These veterinarians also help the USDA with surveillance, control, and eradication of diseases that is vital for preventing foreign animal disease incursions or their spread. Veterinarians can only seek accreditation in the states in which they hold an active veterinary license and may elect to be accredited to perform regulatory work for 1 of 2 animal categories. Category I veterinarians cannot perform duties associated with food and fiber animal species, horses, birds, farm-raised aquatic animals, all other livestock species, and zoo animals that can transmit exotic animal diseases to livestock. Therefore, only veterinarians electing category II accreditation can issue official documents or perform regulatory testing on livestock species.
Data collection and analysis—Through the NVAP, the USDA maintains a database of currently accredited veterinarians classified by category and state. Because GFI213 focuses on food-producing animals, data pertaining to all veterinarians with category II accreditation in 2016 (n = 35,716) were extracted from the database. Veterinarians who indicated on their application contact with bovine, porcine, ovine, caprine, or poultry species were retained (n = 20,147), and those who indicated “no species contact” for their practice type (38) were excluded, resulting in 20,109 veterinarians for analysis. The county in which the business address was located was assumed to be the county in which the accredited veterinarian practiced during 2016. Business addresses were geocoded, and then the total number of veterinarians located in each county was summed. A total of 19,857 veterinarians were successfully geocoded to counties in the contiguous US and used for further analysis.
To identify geographic areas that may need veterinarians, the ratio of animal farms (by commodity) to veterinarians was calculated by use of data from the 2012 USDA National Agricultural Statistical Service Agricultural Census.b Six commodities from the census were used for this analysis: beef cattle, dairy cattle, cattle on feed, hogs and pigs, poultry, and sheep and lambs. To calculate a ratio for each commodity, the number of farms in each county was divided by the number of selected category II accredited veterinarians in the same county. Counties with greatest potential for a veterinary shortage were defined as those with ≥ 4 commodities with a farm-to-veterinarian ratio in the top 10th percentile.
Spatial analysis
An assumption of the present study was that identification of veterinary shortages in the same geographic area (county) in more than 1 dataset would provide stronger evidence that producer access to livestock veterinarians may be limited than identification of shortages in 1 dataset alone. So, to better assess and target potential geographic areas of concern for compliance with the requirements for a VFD or prescription, all 3 datasets were analyzed by use of geographic information softwarec to identify the co-occurrence of counties with identified veterinarian shortages.
In this GIS overlay analysis, the final map of each of the 3 datasets became a layer for the combined analysis, wherein each county identified as a shortage area was assigned a value of 1. All map layers were weighted equally and county shortage values were summed, resulting in values from 1 to 3. Higher values indicated more agreement between datasets and an increased likelihood of a veterinary shortage, compared with lower values.
Results
Overall, veterinary shortages were identified in 314, 346 (Figure 1), and 117 counties in the VMLRP, NAHMS Small-Scale Livestock Operations Study, and NVAP datasets, respectively. However, 48 of these counties were identified in either 2 (n = 47) or all 3 (1) datasets. Excluding the replicates, a total of 728 unique counties (or 23.2% of 3,140 US counties) were identified in at least 1 of the 3 datasets as having a veterinary shortage.
VMLRP
From 2010 through 2016, 832 VMLRP shortage situations were not filled by a VMLRP awardee (ie, no award was made, and the shortage area remained unfilled), representing 1,299 unique counties. A total of 818 counties were determined to be current (designated in 2015 or 2016), and 314 of these had been also been designated as a shortage for ≥ 4 years. These 314 areas of greatest veterinary need (designated shortage for ≥ 4 years and active in 2015 or 2016) were used for the combined analysis (Figure 2).
Maps of counties included in VMLRP designated shortage situations, excluding public sector shortage situations, during various periods and conditions. A—Map of shortage situations left unfilled by a veterinarian from 2010 through 2016. B—Map of shortage situations left unfilled by a veterinarian and designated as shortage situations in 2015 or 2016. C—Map of shortage situations left unfilled by a veterinarian, designated as a shortage situation in 2015 or 2016, and left unfilled for ≥ 4 years. In panels A and B, the color scale represents the number of years during the evaluated period during which the shortage remained unfilled by a veterinarian.
Citation: Journal of the American Veterinary Medical Association 253, 10; 10.2460/javma.253.10.1334
NVAP
Several accredited veterinarian shortage areas, defined as those counties where farm-to-veterinarian ratios were in the top 10th percentile for each commodity, were identified (Table 1; Supplementary Figure S1, available at avmajournals.avma.org/doi/suppl/10.2460/javma.253.10.1334). The ratios varied widely across commodities, ranging from 8 to 1,122 farms/veterinarian. Poultry farms (n = 312) had the highest number of counties in the top 10th percentile, whereas farms with cattle on feed had the lowest (180).
Summary of US counties with farm-to-veterinarian ratios in the top 10th percentile in 2016, classified by commodity.
| Commodity | Total No. of counties with commodity | No. of counties with a ratio in the top 10th percentile | Ratio range for the top 10th percentile |
|---|---|---|---|
| Cattle on feed | 1,728 | 180 | 8–103 |
| Dairy cattle | 2,499 | 272 | 11–140 |
| Hogs and pigs | 2,882 | 289 | 12–104 |
| Sheep and lambs | 2,888 | 303 | 13–884 |
| Poultry | 3,036 | 312 | 39–220 |
| Beef cattle | 3,046 | 304 | 154–1,112 |
A total of 846 unique counties were in the top 10th percentile of farm-to-veterinarian ratios for any commodity (Figure 3). One hundred seventeen counties had ≥ 4 commodities in the top 10th percentile, suggesting they had the greatest potential for a veterinary shortage, as predefined, and thus were considered veterinary shortage areas for study purposes.
Maps of counties representing the top 10th percentile of farm-to-veterinarian ratios calculated by use of data on selected veterinarians federally accredited through the NVAP in 2016 and data from the 2012 USDA National Agricultural Statistical Service Agricultural Census. A—Map of counties with farm-to-veterinarian ratios in the top 10th percentile for any commodity type (ie, beef cattle, dairy cattle, cattle on feed, hogs and pigs, poultry, or sheep and lambs). B—Map of counties with ≥ 4 commodities with farm-to-veterinarian ratios in the top 10th percentile.
Citation: Journal of the American Veterinary Medical Association 253, 10; 10.2460/javma.253.10.1334
Combined datasets
The GIS overlay analysis revealed that overall 680, 47, and 1 counties were classified as veterinary shortage areas in 1, 2, and 3 of the datasets, respectively. A map of geographic areas with potential veterinary shortage concerns was produced (Figure 4). The 1 county identified as a shortage area in all 3 datasets was in Arizona. The counties identified as having a veterinary shortage in at least 2 of the datasets, a situation presumed to represent geographic areas at greatest risk of being underserved by livestock veterinarians, were listed (Table 2). Arizona, Kentucky, Missouri, South Dakota, and Virginia had ≥ 3 such counties.
Map of counties with shortages of food animal veterinarians as identified through GIS overlay analysis of 3 independent datasets (VMLRP [2010 to 2016], NAHMS Small-Scale US Livestock Operations Study8 [2011], and NVAP [2016]). A total of 728 shortage areas were identified: 680 in 1 dataset, 47 in 2 datasets, and 1 in 3 datasets.
Citation: Journal of the American Veterinary Medical Association 253, 10; 10.2460/javma.253.10.1334
Counties identified as veterinary shortage areas in at least 2 of the VMLRP dataset (2010 to 2016), the NAHMS Small-Scale US Livestock Operations Study8 (2011), or NVAP list of federally accredited veterinarians (2016).
| State | County |
|---|---|
| Arizona | Apache |
| Navajo | |
| Pinal | |
| Santa Cruz | |
| Colorado | Conejos |
| Florida | Lee |
| Sumter | |
| Georgia | Gordon |
| Idaho | Bonner |
| Illinois | Boone |
| Indiana | Owen |
| Iowa | Louisa |
| Kansas | Haskell |
| Kentucky | Breckinridge |
| Casey | |
| Grayson | |
| Pulaski | |
| Taylor | |
| Wayne | |
| Louisiana | St. Mary |
| Terrebonne | |
| Maine | Cumberland |
| Hancock | |
| Maryland | St. Mary's |
| Michigan | Branch |
| Minnesota | Isanti |
| Missouri | Camden |
| Daviess | |
| Douglas | |
| Perry | |
| New Hampshire | Carroll |
| North Carolina | Gaston |
| Pennsylvania | Forest |
| McKean | |
| South Dakota | Dewey |
| Mellette | |
| Todd | |
| Ziebach | |
| Texas | Hunt |
| Starr | |
| Virginia | Alleghany |
| Bath | |
| Highland | |
| Lee | |
| Russell | |
| Smyth | |
| Wise | |
| Wisconsin | Washburn |
Discussion
Each dataset used in the present study reflected different perspectives on veterinary shortages throughout the United States. The VMLRP dataset included quantitative and qualitative data, taking into account logistics, geography, current animal industries in a geographic area, and the associated trends in those industries. It also included retirement projections and information on whether a need existed to retain current services. Therefore, the VMLRP not only addresses current needs but also aims to mitigate future shortages. In the NAHMS small-scale livestock dataset, identified shortages were based on producer perceptions, and those perceptions were limited to smaller producers, who may have different veterinary needs and access concerns relative to producers with gross annual sales < $10,000 or ≥ $500,000. The USDA dataset of federally accredited veterinarians as obtained through the NVAP was based strictly on numbers of veterinarians and numbers of farms and did not take into account the logistical challenges of servicing farms in those areas caused by geography or management systems. We consequently believe that the overlap among these datasets gave more weight and predictive value to an area's current need than any single data source alone.
Although value existed in using the 3 datasets to identify all geographic areas (728 counties) in the 50 states with potential veterinary shortages, the results of the overlay analysis can be used to help focus targeted outreach and further investigations regarding veterinary shortages in specific geographic areas. This analysis indicated 48 counties that warrant attention and further investigation as areas where veterinary access may limit the ability of food animal producers to use some antimicrobial products in feed or water in accordance with GFI213. Indeed, Arizona, Kentucky, Missouri, South Dakota, and Virginia had ≥ 3 counties identified in ≥ 2 datasets as having potential veterinary shortages (Table 2). Further investigation of these areas could be achieved through a more in-depth assessment of the datasets or solicitation of feedback from food animal producers and veterinarians in these areas about veterinary services and the demands associated with implementing VFDs.
Understanding the factors that influence access to veterinary services is important not only to livestock producers but also for assessing the impact of federal regulations and programs. The present study yielded a map (Figure 4) of geographic areas with potential veterinary shortage concerns. The development of statistical models to determine the amount of veterinary coverage considered adequate would enable policy makers to make more objective assessments of producers’ ability to obtain veterinary care for their livestock. Any model developed would need to account for geography, animal species, production systems, and business models, given that these factors affect how veterinarians are employed and the type of medical care needed at the herd, flock, and individual animal level.
Each of the 3 databases used in the present study had inherent limitations. An important limitation of the VMLRP shortage data was the subjectivity inherent to the shortage nomination process. Although the nomination process is standardized, the methods used by each chief SAHO may differ; therefore, the severity and status (current vs projected) can differ widely across states. Shortage designations are also capped, so states with a greater need may have been underrepresented in the dataset. Inclusion of potential or projected shortages (ie, a shortage designated for retention of services) in the VMLRP dataset would lead to overestimation of current needs. Additionally, veterinary shortage situations as designated through this program typically span > 1 county and often do not include the whole county. For this analysis, each county was treated as a standalone data point when, actually, only a small portion of the county may have had a shortage, thereby resulting in overestimation of the extent of the shortage at the county level.
This limitation also applied to the hot-spot analysis involving the dataset from the NAHMS Small-Scale Livestock Operations Study,8 which included only a sample of food animal producers in each state and therefore not all counties were represented. In that study, the reported distance to the nearest veterinarian was dependent on producer knowledge of the location of veterinary service providers. The survey respondents may have overestimated that distance. Finally, regarding the NVAP data, assumptions could have been incorrect that category II veterinarians routinely serviced the livestock species indicated on their accreditation form, that those veterinarians practiced in the county of the business address provided, or that they only practiced in that county. Consequently, we cannot dismiss the possibility that other veterinary shortages exist in areas other than those identified in our study.
Despite these limitations, each dataset also had unique strengths, and together, all 3 were useful in improving our understanding of geographic areas with greater risks of limited access to livestock veterinarians than others. This information can be used to assess the impact of federal regulations and programs and help understand the factors that influence access to food animal veterinary services in specific geographic areas. Additional research is needed and recommended to identify specific factors that limit access to veterinary services in the United States.
Acknowledgments
The findings and conclusions in this publication have not been formally disseminated by the USDA, NIFA, or the Center for Epidemiology and Animal Health and should not be constituted to represent any agency determination or policy.
The authors thank Todd Behre for his review and direction for this manuscript.
ABBREVIATIONS
| GFI213 | Guidance for Industry No. 213 |
| GIS | Geographic informations system |
| NAHMS | National Animal Health Monitoring System |
| NIFA | National Institute of Food and Agriculture |
| NVAP | National Veterinary Accreditation Program |
| SAHO | State animal health official |
| VFD | Veterinary feed directive |
| VMLRP | Veterinary Medicine Loan Repayment Program |
Footnotes
Getis Ord GI statistics tool, ArcGIS Desktop, Esri, Redlands, Calif.
Desktop Query Tool, version 2.0, 2012 Census of Agriculture, USDA, Washington, DC. Available at: www.agcensus.usda.gov/Publications/2012/Online_Resources/Desktop_Application. Accessed Oct 31, 2017.
ArcGIS Desktop, version 10.5, Esri, Redlands, Calif.
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