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Infectious Disease Management in Animal Shelters


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shifting shelter priorities, or changes to the economy of the community can impact the magnitude of disease rates and their interpretation. For example, in one community, the 2008 economic downturn resulted in the surrender of an increased number of animals requiring veterinary care because owners could not afford treatment. If the nature and timing of those changes are not recorded, people forget, and incorrect conclusions may be drawn from the data. One strategy for capturing these changes is a “Log of Events and Protocol Changes” that is updated as changes occur. An example of a log is provided in Table 3.2.

Date Relevant Events
1‐Jan Animal control contracts from Sunny and Marlow towns discontinued. Owner surrenders will continue to be accepted.
20‐Feb A seizure of 60 dogs from Mary Smith is undertaken. The number of dogs exceeds the housing capacity of the shelter. Crates are set up and dogs are housed in staff offices.
25‐Apr Grant (~$20,000) received to increase Trap Neuter Return (TNR) efforts.
30‐Apr The shelter eliminated the part‐time veterinarian position due to financial concerns.
9‐Jul The intake policy changed. Nuisance feral cats managed by providing traps and spay‐neuter services. Animal Control officers trained to vaccinate stray animals before bringing them to the shelter.
10‐Sept Shelter eliminated two full‐time staff positions due to budget crisis; shelter is understaffed.
23‐Oct Funding restored, and two staff members rehired.
15–23‐Dec Mega adoption event.

      The length of time animals spend in a shelter affects their likelihood of exposure to infectious agents; it also affects their stress levels (which impacts susceptibility to infection). Several studies have linked increased length of stay (LOS) with enhanced risk of feline and canine URTD in shelters (Dinnage et al. 2009; Edinboro and Glickman 2004; Edinboro et al. 1999; Gourkow et al. 2013). The average length of stay (ALOS) is a measure of the average speed with which animals are moved or flow through a shelter. When animal movement is slow (i.e. the ALOS is high), not only does the risk of disease and stress increase, but fewer animals are processed in the same time period compared to when the flow is fast. If intake remains constant or increases, slow movement (or high ALOS) often leads to overcrowding, another important risk factor for many infectious diseases.

      The ALOS is calculated by summing the LOS of all animals of interest and dividing that sum by the total number of animals contributing to LOS values during a specified time period. Time periods of greatest interest are usually by month, season, and year. Monitoring graphs of these averages over time can identify patterns, suggest goals, gauge progress toward minimizing ALOS, and reveal associations between ALOS and disease risks. Many commercially available shelter software systems can calculate ALOS for specific populations and over specific time periods.

      A common initial objective of shelters is to reduce the overall ALOS. Progress can often be accelerated by using data to identify the subgroups, procedures, or areas in the shelter that contribute disproportionately to slowing transit times through the shelter. By evaluating the ALOS of various subgroups, e.g. time spent waiting for common procedures to be performed (e.g. behavior evaluations, spay‐neuter, dentistry), and time spent in specific areas (e.g. holding, adoptions), a shelter can identify and focus its efforts on specific impediments that cause delays in animal transit through the facility. Common subgroups to monitor are those of age group, source (e.g. seized, stray), general health status (e.g. healthy, sick) and in some shelters, breed‐type. Since time‐related factors (e.g. seasonal intake, volunteer availability) often affect flow, regular assessments of ALOS by these factors may highlight opportunities for preemptive interventions.

      A few words of caution are in order regarding ALOS calculations. There are at least three approaches to calculating the ALOS for shelter animals during particular periods of time (Scarlett et al. 2017b). It is beyond the scope of this chapter to describe the differences, but when using ALOS values supplied by shelter software, it is important to understand the method of calculation and the strengths and limitations of the method used.

      In addition, current shelter software only provides ALOS values. In many shelters, the frequency distributions of LOS values do not display a Gaussian or Normal (bell‐shaped) distribution. Instead, data are skewed to the right with a few animals having unusually long LOS. These highest LOS values pull the average upwards, away from the center of the data; this results in more than half of the animals having LOS less than the average. If this is true, the average may not be the optimal metric to monitor, depending on the purpose for which the ALOS is being monitored. Instead, the median, by definition, reflects the true middle of the distribution of age data and may provide a more accurate picture of the true situation for most animals in the shelter. One example of this exists for shelters with a few animals residing in the shelter for very long periods. The overall ALOS is especially problematic in such a scenario and it may appear high, even though the majority of animals have a lower (and more acceptable) LOS. Similarly, since some groups (e.g. those in foster care or awaiting court proceedings) may have LOS dictated by their circumstances, they should be monitored separately from other animals.

Bar chart depicts the average LOS of cats with and without URTD by age group.