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.
The ALOS of senior cats was much longer than staff anticipated in the shelter associated with Figure 3.3. After reviewing this graph, the shelter gave the highest priority to reducing the ALOS of its oldest cats. Also, monthly and seasonal differences in ALOS often become apparent when graphed, raising questions as to why. Evaluating these discrepancies can suggest strategies that will minimize or eliminate them.
Figure 3.3 Average LOS of cats by age group.
Increases in LOS not only enhance risks for disease, but disease also increases the LOS, animal suffering and costs to the shelter. Data such as those in Figure 3.4 are useful in explaining requests for funding to enhance disease control measures, in this case, for controlling feline URTD. The data demonstrated that this disease significantly increased ALOS and the associated costs of care in this shelter.
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.
Figure 3.4 Average LOS of cats with and without URTD by age group.
3.4 Capacity for Care
The ability of shelters to meet the needs of their animals is called a shelter's capacity for care (C4C) (http://www.millioncatchallenge.org/resources/capacity‐for‐care). When shelters have more animals than they can house humanely or too few staff members to provide basic care, conditions adverse to animal well‐being can result (e.g. high disease rates, numerous animals housed in small spaces or in carrying crates, dirty cages). It is often easy to recognize when a shelter is overcapacity or understaffed; quantitatively measuring overcrowding or deficits in staffing may be more difficult depending on the type and quality of data available but is worth the extra effort.
3.4.1 Housing Capacity
Why use metrics for monitoring housing capacity? This can be a valuable tool to reduce or avoid overcrowding. Common strategies for reducing overcrowding include increasing the number of housing units (either through construction or finding alternative housing sites), managing intake, and reducing the LOS. Building new housing is usually impractical and even when possible, will not resolve overcrowding if intake chronically exceeds the outflow of animals. The use of offsite adoption venues is also helpful but is rarely effective as the only approach to eliminating overcrowding; reducing the ALOS and/or managing the number of animals admitted over time are also approaches used by many shelters. The success of efforts to reduce ALOS at the population level is monitored as discussed previously. Similarly, efforts to reduce the number of incoming animals can be monitored over time using intake data.
The relationships between ALOS, intake, and the available housing space can be used to predict the effect of changing any one of these factors on the others over time (See the formula below). These predictions help set goals and motivate staff to achieve them.
During a specified period of time:
It is important to note that using this formula assumes that all humane housing spaces are occupied in the shelter at the beginning of a time period; it is used to calculate how many additional animals the shelter can expect to be able to provide humane housing for based on the number of spaces and the average length of stay.
To use the formula, a shelter must start by estimating the number of animals by species that can be humanely housed at any given time. The Guidelines for Standards of Care in Animal Shelters (Newbury et al. 2010) provide recommendations that may be used to determine which housing is humane. If the shelter experiences wide fluctuations in the number and/or age groups of animals entering by season, then the number that can be housed at any given time should be estimated by species, age group and by season. Counting the number of housing spaces is done making assumptions (e.g. two kittens or one adult cat per humane housing unit). (These numbers should be as accurate as possible but need not be perfect
