Journal of Homeland Security and Emergency ...

Journal of Homeland Security and Emergency Management Volume 5, Issue 1

2008

Article 33

Planning for Pet Evacuations during Disasters Andrew S. Edmonds∗

∗ †

Susan L. Cutter†

University of South Carolina, [email protected] University of South Carolina, [email protected]

c Copyright 2008 The Berkeley Electronic Press. All rights reserved.

Planning for Pet Evacuations during Disasters Andrew S. Edmonds and Susan L. Cutter

Abstract Planning for pets in emergencies is now part of local, state, and federal preparedness efforts as a result of the enactment of the 2006 PETS Act. Yet there is little guidance on how to conduct such planning efforts. This paper provides a procedure for estimating the number and location of pet-owning households. Utilizing behavioral studies of evacuation non-compliance, estimates of the number and location of non-evacuating pet-households are made. The procedures are tested in Horry County, South Carolina and Mercer County, New Jersey. We found that the pet estimation model provided a more detailed (numerically and geographically) estimate than the application of national averages. Furthermore, the two approaches to estimating pet owner evacuation noncompliance yield similar results (roughly within 2% of each other). However, the spatial distribution of these non-compliant households shows considerable variability, suggesting a greater need for animal shelters in some areas of the county than others, especially if the goal is to improve evacuation compliance. The development of such fine-grained tools for estimating pet owning households and likely compliance with evacuation orders is the first step in achieving the planning goals within the 2006 PETS Act. It also highlights where to place emergency animal shelters to maximize evacuation compliance of pet-owning households. KEYWORDS: pets, pet-owning households, PETS Act

Edmonds and Cutter: Pet Evacuations

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Introduction In 1992, as the Florida public was evacuating in response to the advancing Hurricane Andrew, residents were instructed to keep their pets enclosed as they departed. After the hurricane, thousands of animals roamed southern Florida; some were reunited with their owners; many more wore no identification tags and were injured or killed by the storm. Not surprisingly, the public was outraged at the mishandling of the disaster and blamed government officials for the loss of their pets. The story replayed itself during Hurricane Katrina in 2005. The failure to adequately evacuate and shelter the poorest residents of New Orleans was compounded by pet owners who refused to leave without their animals. Since the Superdome and other shelters of last resort would not allow animals inside, tens of thousands of pets were left behind when the busses finally rolled out of New Orleans. By Thanksgiving, the U.S. Humane Society and other animal organizations had rescued 9,000 of these animals (PBS 2006), and many national animal advocacy groups stationed relief workers all along the Gulf Coast to rescue pets and reconnect them with their owners. While local animal shelters were the focal points, the rescue operations were financed by public donations from around the country (Ivry 2005), instead of local governments. More than 63 percent of US households own at least one pet (APPMA 2008), roughly translating to 88 million cats and 75 million dogs. Americans annually spend nearly $40 billion—six times the present yearly FEMA budget— on their pets, purchasing food, toys, training products, boarding services, and veterinary care. An estimated one million cat and dog owners have health insurance for their animals. These strong affections for pets or “companion animals” extend far beyond traditional animal care concerns. For many households, pets are considered members of the family. Every year pet owners decide not to evacuate from disaster events as they believe they have no safe place to take their pet. Red Cross disaster shelters cannot accept pets because of states’ health and safety regulations (American Red Cross, 2002). Consequently, petowning families can and do make difficult evacuation decisions during disasters. The influence of pets on household evacuation decisions has significant impact on emergency and evacuation management by local and state officials. The spectacle of abandoned pets in the aftermath of Hurricane Katrina led to the quick passage of the Pets Evacuation and Transportation Standards (PETS) Act of 2006. The PETS Act amends the Robert T. Stafford Disaster Relief and Emergency Assistance Act to “ensure that state and local emergency preparedness operational plans address the needs of individuals with household pets and service animals prior to, during, and following a major disaster or emergency” (GovTrack.us 2007). There are three important components to the legislation.

Published by The Berkeley Electronic Press, 2009

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JHSEM: Vol. 5 [2008], No. 1, Article 33

The first is a planning element that provides the impetus for the development of local and state plans for individuals with pets. The second element provides funding for states and local authorities for animal emergency preparedness. This includes the construction or renovation of emergency shelter facilities to accommodate people with pets and service animals. The final element requires federal agencies to provide rescue, care, and sheltering of not only individuals with pets and service animals, but also the pets and service animals themselves. This paper addresses the first element in the legislation--planning for pet sheltering needs. The following research questions are posed. First, how do local emergency planners estimate the number of households with pets that might need emergency animal sheltering options? Second, how many households with pets are likely to use such facilities?

Human Behavior in Evacuations Research Despite activism regarding the need for pet owners to prepare for emergencies, an effort largely led by the U.S. Humane Society and the American Humane Society, scholarly research on the topic of pets in emergency situations is wanting. The social science research community provides empirically-based evidence for predicting evacuation compliance. In a study of evacuation responses from hurricanes and floods during the mid-1990’s, Drabek (2001) found more than half of the survey respondents acknowledged the influence of their animal on their sheltering decision. Sorensen (2000) also noted that emergency managers increasingly must consider not only whether the public will go to a shelter, but also whether they will bring their pets. There have been a number of overviews on evacuation decision making and those factors that prompt evacuation behavior and those that retard it (Sorensen 2000; Sorensen and Vogt 2006). Heath et al. (2000; 2001a,b) are among the few studies that explicitly examined the link between pet ownership and evacuation behavior. They found that households with children were most likely to evacuate with their pets or attempt a rescue of a pet left behind by reentering the evacuated area. Heath et al. (2001a,b) also found that few canines were boarded in separate facilities. Owners, whether they stayed with family and friends or went to a hotel when they evacuated, preferred to take their animals with them. In a study of Hurricane Bonnie in North Carolina, Whitehead et al. (2000) were able to accurately predict the evacuation choice of the respondent 80% of the time using a logistic regression analysis with pet ownership as one variable. Pet ownership was the strongest predictive variable of non-evacuation. In contrast to this study, the National Hurricane Study Program (1999a) directly

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asked survey takers why they chose not to evacuate from Hurricane Bonnie; only a few respondents suggested pets were a deciding factor. For decades, residents including pet owners have underestimated the risk of hazards to their homes and have rarely expected evacuations to last longer than a day. In the 1979 Mississauga, Canada train derailment and hazardous materials spill, nearly 250,000 people – and an estimated 40,000 animals (Liverman and Wilson 1981) – were evacuated. Their return was not allowed until three to eight days after the spill, yet by the second day, pet owners who left their animals at home were pressuring the government for help in feeding them. The local police and Ontario Humane Society cooperated and arranged for feeding of the animals. Mississauga is not the only disaster in which emergency personnel were called upon to assist pets left behind. During March 1996, the small town of Weyauwega, Wisconsin was evacuated when a train carrying propane and chlorine derailed and caught fire. Approximately half of the pet owners evacuated without their animals, leaving an estimated 200 pets behind from roughly 1000 households (Heath et al. 2001b). By the third day of the event, pet owners in 56 of the households convinced the authorities to help them retrieve their animals. Under the protection of armored National Guard vehicles and wearing flak jackets and helmets, the pet owners were escorted into the hot zone to gather their pets, again potentially risking the lives of themselves and their escorts. The separation of pet owners from their animals forced responders into unfamiliar roles in Graniteville, South Carolina, as well. As in Weyauwega, a train of tanker cars carrying chlorine derailed and ruptured in the early morning hours of January 2005. Quickly, ninety tons of gas poured into the small community and the town was evacuated (Mitchell et al. 2005, 2007). By the third day, public safety officers were delivering food to any animal that they could see from the road within the one-mile evacuation zone. Later, residents were instructed to call the county animal control office to arrange for the collection (by animal control officers escorted by police) of their animals. Within the evacuation literature there are focused studies on “special needs populations” and their difficulties in reacting to a disaster. Just like other special needs populations such as tourists (Drabek 1995), health care facilities (McGlown 2001), nursing homes (Vogt 1991), the elderly (Morrow 1999), those without cars (Sorensen and Mileti 1988), and the physically and mentally impaired (Van Willigen et al. 2002), pet owners also experience problems in evacuations that may reduce their compliance. For example, like many Americans, battered women also view companion animals as family members (Albert and Bulcroft 1988) who provide emotional support for the women in times of crises. Because the pattern of abuse may also transcend to the pet, battered women often are reluctant to seek safety for themselves if they cannot


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also guarantee the safety of their companion animals. Several case studies chronicle this hesitancy, revealing that 18-48% of battered pet-owning women will delay their entry into a shelter out of concern for their pets (Ascione 2005; Flynn 2000; Faver and Strand 2003). The parallel drawn between the actions of battered women and non-evacuating, pet-owning households may offer significant guidance for local disaster planning. The most salient findings regarding pets in the evacuation behavior literature are as follows: Families evacuate as a unit (including pets); pet owners may refuse mandatory evacuation orders in order to protect their pets; and pet owners may return prematurely to an evacuated area to rescue an animal left behind. In many ways, planning for pet evacuations is similar to planning for special needs populations—both require enumeration (how many people or pets are involved), and some specialized sheltering needs.

Predicting Pet Sheltering Needs A community’s ability to adequately safeguard residents and their companion animals from disasters begins with a count of the special needs population. Information about the number, companion animal species, and geographic distribution of pet ownership would assist officials in forecasting expenditures for planning and constructing animal shelters. A pet census would also enable communities to monitor compliance for rabies licensing and thus assist in public health protection. Despite the need and value, the U.S. Census bureau does not incorporate questions on pet ownership in any of its detailed demographic surveys. Pet ownership statistics are available from the American Veterinary Medical Association (AVMA 2002a), but they represent generalized patterns with little geographic specificity. The American Pet Product Manufacturing Association also develops estimates of national pet populations, but these are often proprietary and used in marketing campaigns or the placement of a new pet supply store (Nassar and Mosier 1991). There are proxy sources that could be used in lieu of an actual pet census. Licensing and vaccination data could be used, but they often have to be adjusted to compensate for owners who evade recording their animals with the local municipal animal health office. For example, New York City health officials estimate that 80% of their one-half million dogs are unlicensed (Saulny 2003). Direct contact with pet owners through telephone, mail, or personal interviews can generate the requisite data, but is very time consumptive and expensive. Estimates using sales of pet food have been used, but some suggest that the data may be skewed by those who use the food for human consumption (NRC 1991). Without accurate pet population figures, the



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emergency management community cannot effectively address the animal evacuation needs of residents.

Methodology Study Area Descriptions This research focused on two study areas, a coastal county in South Carolina and an inland county of New Jersey. The selection of the two study areas was based upon three criteria: local familiarity by the authors; broad cross section of sociodemographic characteristics; and a diversity of hazards. One county is subjected annually to hurricane storms and wind events (SC), the other county’s financial losses from natural hazards are limited primarily to riverine flooding and winter storms (NJ). By examining two study areas with divergent risk patterns, the proposed methodology will be tested in a more rigorous fashion, allowing for the implementation of the model across different hazardscapes (Cutter 2001). Pet-owning Estimation Model and Validation In 2002 U.S. Pet Ownership and Demographics Sourcebook, published by the American Veterinary Medical Association (AVMA 2002a), contains data that help form the foundation for the pet-owning estimation model utilized in this paper. The data were derived from a questionnaire distributed to a representative sample of 80,000 U.S. households based on market size, head of household’s age, household size, and income within each of the nine U.S. Census subdivisions (AVMA 2002a). From the survey the AVMA was able to produce the specific likelihood of pet ownership based upon individual factors like wealth, family size, and residence type. In the aggregate, 58.3% of the respondents indicated that at least one animal lived within the household; however, pet ownership varies noticeably when considering socio-economic factors. For instance, only 34.7% of apartment-based households owned a pet of any kind, versus 64.4% of mobile home households. Though the national average of 58.3% could be used to estimate the number of pet-owning households within any given community, geographic variations in factors like household income and household type between communities will lead to differences in the number of expected petowning households, and thus potential sheltering needs. Five of the socio-economic factors identified by the AVMA were utilized in our spatial refinement of pet ownership patterns. These include: size of household (from one to five or more members); type of residence (house, apartment, and mobile home); ownership status (own or rent); community size (aggregated into four interval classes ranging from fewer than 100,000 to 2


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million or more); and household income (aggregated into several interval classes) (Edmonds 2006). Percentages of household pet ownership were calculated by the AVMA for each class of each factor. These percentages were then used as coefficients in the pet-owning estimation model. Block groups were chosen as the geographic unit of analysis. While the majority of the data required from the U.S. Census are reported at the more geographically specific block level, the household income variable is only defined at the block group level. To compute the number of households with likely pet ownership for each of the five socio-economic factors, the following procedure was used. In the hypothetical example in Table 1, the block group has 198 total occupied housing units. Using the national average ownership rate (58.3%), we would predict that this block group would contain 115 (198 *.583) pet owning households (POHs), irrespective of household size. The refined estimate is slightly higher, showing 123 estimated households owning pets. From the AVMA, the coefficient of pet-ownership likelihood is lowest for a one-person household, at 39.5%, which leads to 12 pet-owning households for this block group (30*0.395). Conversely, higher likelihood of ownership is for five-ormore-person households, at 71.0%, leading to an expected 48 POHs (67*0.710). Table 1. One block group and variable in the pet-owning estimation model Household size

1person

2person

3person

4person

5+ person

Total

Total units AVMA Coefficient

30 0.395

43 0.548

31 0.678

27 0.715

67 0.710

198

Pet Owning Households (POHs)

11.85

23.56

21.02

19.31

47.57

123.31

National average (0.583) with no household size distinction

115.43

This process was repeated for each of the five variables (household size, residence type, income level, property ownership status, and community size) yielding five separate pet-owning estimations per block group. The estimations were summed for each block group and then the median value was used to estimate the overall pet owning households. To validate the results, we initially sought surrogate pet census data from the local community. Unfortunately, Horry County, South Carolina, does not require licensing of any animals, cat, dog, or otherwise. The county does mandate the inoculation of dogs against rabies and issues generic identification tags, but does not account for the dogs or their owners in any geographic manner (be it by street address, zip code, etc.). Attempts to obtain data from veterinarians were unsuccessful as well due to concerns over confidentiality. In any event, these



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estimates would only record animals receiving medical care and not a count of all animals living in the community. More rural communities, like portions of Horry County, are less likely to have access to affordable veterinary care available for their pets and, therefore, less likely to be included on any pet population rolls (AVMA 2002b). We expected our other study area, Mercer County, New Jersey, to have better proxy pet census data given the mandated bi-annual canvass of the domestic dog population by the NJ Department of Health and Senior Services. However, only a small percentage of the state’s 567 municipalities have actually filed their data with the State. None of the thirteen municipalities in Mercer County make pet count data available. Since validation data were not available, we used a spatial validation procedure. First, the national average was applied to all households within each block group to get a total figure of estimated pet ownership for each study area. Next, the five socioeconomic variables were individually examined to determine which had the greatest impact on overestimating or underestimating the national average. This allowed for an easy spatial analysis of the entire county, one that could easily visualize where the patterns of pet-ownership differed from the national average.

Pet-owning Estimation Model Results and Discussion The use of the five Census variables and their impact on the expected number of pet-owning households revealed several trends in the two study areas. When considering the national coefficient of 58.3%, Horry County (SC) was expected to have 48,245 pet-owning households. The five-variable methodology yielded an aggregate of 47,252, or roughly a 2% decrease from the national figure. Much of the decline is in the heavily urbanized areas of the county, especially along the Grand Strand region that stretches from Myrtle Beach to North Myrtle Beach (Fig. 1), where pet ownerships rates are between 50-54%. Conversely, large rural areas of block groups in the middle of the county have a higher than average rate of ownership, although none has more than 60%. The reasons for the fluctuations became more evident when the individual variables were examined for the largest divergence from the national average (Fig. 1). The vacation towns along the coastline contain far fewer single family residences and many more apartments and condominiums, thereby forcing the national coefficient to overestimate the pet-owning population on residence type. Because the block groups also include a higher ratio of the elderly and retirees, the household size variable is similarly overestimated. Household income played a role elsewhere in the county. Pets cost money, and the poorer a household is the less likely it owns any animals (AVMA 2002a). This is apparently true for many block groups in the western and northern portions of the study area; however,


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Figure 1 Modeled Estimates of Pet Ownership by Block Group for Horry County (SC), using national pet ownership average (top) and five-variable method (bottom)



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other block groups – containing greater than average numbers of single family residences and mobile homes (both of which are likely to house pets) – reveal an underestimation of ownership by the national average because of housing type. Mercer County (NJ) was expected to have 73,345 pet-owning households. The five-variable methodology yielded an aggregate of 71,449, or roughly a 2.6% decrease. Mercer County follows many of the same patterns as Horry County (Fig. 2). Again, much of deviation is within the urban core of the county, inside the city of Trenton and radiating up the U.S. Route 1 corridor where ownership rates dip as low as 47.6%. Pet ownership in the block groups of Trenton is negatively influenced by a lack of home ownership and a preponderance of apartment living, as are other areas along the Route 1 corridor. In the affluent suburban communities of Hopewell, Princeton, and East Windsor, the income variable fuels the positive influence on pet ownership, while in West Windsor, it is the larger family sizes that cause expected pet ownership to increase. Some of the suburban communities surrounding the local colleges, and the small villages of Pennington and Hopewell, also yield lower rates, due in part to their large numbers of apartments and smaller households. As in Horry County, more rural areas of the county have a higher than average rate of ownership (61.2%). Table 2 details the over- and underestimation by variable when using the national coefficient versus the methodology described in this paper. For example, smaller household sizes in 22% of Horry County block groups result in an overestimation of pet-owning households when the national average is considered versus the five-variable methodology. Conversely, wealthier households in 28% of Mercer County block groups, influence the underestimation of the pet owning housings based on the national average. The five-variable method provides more precision in determining the number and location of pet-owning households. This is exceedingly important data that has a direct influence on preparedness activities, especially evacuation planning. Table 2 Comparisons using the national coefficient vs. 5-variable method on pet-ownership estimations by block group Variable

Horry – Over

Horry - Under

Mercer – Over

Household size 22 % 1% 4% Income 29 % 2% 6% Ownership 9% 0% 21 % Residence type 25 % 11 % 33 % Community size* * Not computed as there was no variation within the study area.

Mercer - Under 6% 28 % 0% 2%


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Figure 2 Modeled Estimates of Pet Ownership by Block Group for Mercer County, NJ, using national pet ownership average (top) and five-variable method (bottom).



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Pet Evacuation Decision-making Results and Discussion In order to understand pet evacuation decision-making, a review of empirical evacuation literature was conducted (Edmonds 2006). Studies that included some reference to pet ownership or a measure of its impact (whether that was a numerical statistic or a descriptive finding) were selected for analysis. Utilizing these secondary sources, a portrait of evacuation behavior and pet ownership was developed along with a better understanding of how frequently pets are the primary reason for evacuation non-compliance. Twenty nine evacuation studies from 1990-2005 were reviewed, the vast majority of which were evacuations from hurricane events (Table 3). The figures shown under the total non-evacuees column include the number of survey respondents who did not evacuate from the event, while the figures under the non-compliance due to pets column indicate the number of those non-evacuees that did not leave their homes primarily because of the presence of a pet. This percentage is listed in the final column, and ranges from a low of 0.3% during 1998’s Hurricane Bonnie to a high of 9.7% of northern Georgia residents during 1999’s Hurricane Floyd. The mean average for all events listed in these studies is 3.2%, while the median is 2.6%. The median represents the percentage of known households which refused to evacuate specifically because of pets. The maximum demand for emergency pet shelter in any given community is defined as the product of this average, 2.6%, and the total number of households in the community. The National Hurricane Study Program (NHSP) routinely studies the evacuation responses from coastal areas using telephone interview surveys. The studies often pose the same questions to a large population from a variety of subregions in the affected geographic areas: landfall areas receiving the brunt of the storm; non-surge affected coastal areas; and non-coastal areas in the interior. Over the years, the NHSP has researched the behavioral aspects of evacuees through a methodic pattern of phone interview surveys. After Floyd, this meant the interview of 600 respondents systematically chosen from eleven coastal regions (NHSP 2000). Evacuation rates varied considerably across the eleven study areas (from 20% in eastern North Carolina to 76% in northern Georgia), as did the affect of pet ownership on a household’s decision not to evacuate. Residents of northern Georgia were the most likely to stay because of an animal (9.7%); interestingly, those in southern Georgia were among the least likely to stay based on pet safety reasons (1.2%). The format for evacuation reporting by the NHSP remained mostly unchanged until the 2004 hurricane season, when the reports no longer included raw figures of pet data. Instead, the NHSP (2005) introduced two new metrics: one that measured if pet ownership affected the decision of


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Table 3 Reluctance to evacuate due to pet safety concern Source

Total Nonevacuees

Noncompliance due to pets

1 2 3 4 5 6 7 8 8 8 8 8 8 8 8 8 8

215 --------153 --478 320 179 306 172 144 150 290 287 474

9 --------3 --8 13 9 8 2 14 2 11 12 10

Pct. Noncompliance 4.2% ** ~ 3.0% ** ** 2.0%