The focus of this thesis is the application of data envelopment analysis to understand and evaluate the performance of diverse animal welfare organizations across the United States. The results include identification of the most efficient animal welfare organizations, at least among those that post statistics on their operations, and a discussion of various partnerships that may improve the performance of the more inefficient organizations. The Humane Society of the United States estimates that there are 4000 - 6000 independently-run animal shelters across the United States, with an estimated 6-8 million companion animals entering them each year. Unfortunately, more than half of these animals are euthanized. The methods shared in this research illustrate how data envelopment analysis may help shelters improve these statistics through evaluation and cooperation. Data envelopment analysis (DEA) is based on the principle that the efficiency of an organization depends on its ability to transform its inputs into the desired outputs. The result of a DEA model is a single measure that summarizes the relative efficiency of each decision making unit (DMU) when compared with similar organizations. The DEA linear program defines an efficiency frontier with the most efficient animal shelters that are put into the model that "envelops" the other DMUs. Individual efficiency scores are calculated by determining how close each DMU is to reaching the frontier. The results shared in this research focus on the performance of 15 animal shelters. Lack of standardized data regarding individual animal shelter performance limited the ability to review a larger number of shelters and provide more robust results. Various programs are in place within the United States to improve the collection and availability of individual shelter performance. Specifically, the Asilomar Accords provide a strong framework for doing this and could significantly reduce euthanasia of companion animals if more shelters would adopt the practice of collecting and reporting their data in this format. It is demonstrated in this research that combining performance data with financial data within the data envelopment analysis technique can be powerful in helping shelters identify how to better deliver results. The addition of data from other organizations will make the results even more robust and useful for each shelter involved.

Shelters often make euthanasia or adoption decisions based on the results of FeLV-FIV point-of-care tests but given the low estimated prevalence of these diseases and imperfect test performance, this might not be a good practice because of diagnostic error. The objectives of this study were to determine the true prevalence of FeLV and FIV in apparently healthy and sick shelter cats in Mississippi, estimate predictive value of the Zoetis Witness FeLV-FIV Rapid ImmunoMigration test results at the estimated true prevalences through Bayesian latent class modeling, and formulate testing recommendations for shelters. One chapter will review the literature on FeLV and FIV. The bulk of this thesis will focus on determining the true prevalence of retroviral infection in Mississippi shelter cat populations. The last chapter will use Bayesian modeling to estimate test performance and predictive value of test results in healthy and sick shelter cat populations.

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