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The use of deer vehicle accidents as a proxy for measuring the degree of interaction between human and deer populations and its correlation with the incidence rate of Lyme disease.

Introduction

The transmission of Lyme disease is well documented. The spirochete that causes Lyme disease, Borrelia burgdorferi, is transmitted by ticks of the genus Ixodes (Burgdorfer et al., 1982; Spielman, Wilson, Levine, & Piesman, 1985; Steere, Broderick, & Malawista, 1978) specifically Ixodes scapularis in the north central and northeastern U.S., and Ixodes pacificus in the western U.S. (Oliver et al., 1993). In the northeast, white-footed mice (Peromys cus leucopus), the major reservoir for B. burgdorferi, are the host to immature I. scapularis, while white-tailed deer (Odocoileus virginianus) are the primary host to adult I. scapularis (Bosler, Ormiston, Coleman, Hanrahan, & Benach, 1984; Spielman et al., 1985). White-tailed deer are an incompetent reservoir for B. burgdorferi, but serve as the primary blood meal for most adult female I. scapularis in the northeast (Rand et al., 2003; Wilson, Telford, Piesman, & Spielman, 1988).

The incidence and geographical distribution of Lyme disease in humans has been correlated with the distribution, abundance, and annual fluctuations in I. scapularis populations infected with B. burgdorferi (Mather, Nicholson, Donnelly, & Matyas, 1996; Stafford, Cartter, Magnarelli, Ertel, & Mshar, 1998). The nymphal I. scapularis become active during the summer months, and Lyme disease symptoms are seen approximately 7-21 days later in humans following a tick bite (Bacon, Kugeler, Mead, & Centers for Disease Control and Prevention [CDC], 2008). Lyme disease features a common clinical characteristic, a target lesion called erythema chronicum migrans and manifests symptoms of arthritis, myocarditis, uveitis, and meningoneuritis (Steere, 1986, 1994; Steere et al., 1977). The abundance of I. scapularis has been correlated with the number of deer in a geographic region (Wilson, Adler, & Spielman, 1985). As the deer population is a proxy for the presence of tick vectors, the presence of deer among humans has been hypothesized to be a risk of Lyme disease infection (Steere, Taylor, Wilson, Levine, & Spielman, 1986).

Much of New England has the four critical components necessary for human infection of B. burgdorferi: a population of I. scapularis, a population of white-tailed deer, a prevalence of B. burgdorferi in white-footed mice, and a human population that lives in proximity with deer. In areas in which Lyme disease is prevalent, specifically in forested residential areas, exposure to I. scapularis often occurs in the vicinity of people's homes (Lastavica, Wilson, Berardi, Spielman, & Deblinger, 1989; Steere, 1986). The number of deer living near these homes should influence the number of adult I. scapularis and the incidence of Lyme disease in the human population (Lastavica et al., 1989).

An accurate method for quantifying the interaction between humans and deer in a geographic area has been lacking. Interaction between humans and deer simply means the co-residence of deer and humans and the inevitable resulting indirect contact, such as exposure to the same or bordering habitats. Currently, the main methods of documenting human and deer interaction are through hunter and residential deer surveys (Rand et al., 2003; Rand, Lacombe, Smith, Gensheimer, & Dennis, 1996; Wilson, Levine, & Spielman, 1984). Human-to-deer interaction surveys are the only measure that correlates with the incidence of Lyme disease in humans; all other measures found to correlate with Lyme disease are specific to Ixodes spp., and therefore only serve to explain the relationship of Lyme disease within a habitat and do not correspond to the human population. A useful correlative measure of Lyme disease incidence would be significant as it would have many public health implications in relation to the control of Lyme disease. Public health departments could direct Lyme prevention resources toward specific geographic areas based on the degree of correlation of the measure.

Our objectives were to use deer vehicle accidents (DVAs) as another metric to measure the interaction between deer and humans. Recent research has investigated how animal vehicle collision databases can be used to improve pedestrian and vehicle operator safety (Sullivan, 2011). DVAs are automobile accidents in which a deer has been struck by an automobile. We propose that DVAs are a proxy measure of deer and human interaction. It is hypothesized that geographic areas of high DVAs are locations with coexisting dense populations of both humans and deer, while in geographic areas of low DVAs it is hypothesized that few humans or few deer live there and that the interaction between the two is minimal. We evaluated the ecological hypothesis that the incidence rate of DVAs (number of DVAs per human population) strongly correlates with the incidence rate of Lyme disease (number of cases per human population) in humans in specified geographic areas (i.e., an ecological correlation).

In addition, it is hypothesized that a weak ecological correlation would exist between the Lyme disease incidence rate and aerial deer population estimates. Presumably, the disease is affected by deer/human interaction so a measure of only the correlation between the deer population and the Lyme disease incidence rate would be low. The primary ecological correlation of interest in our study has no direct equivalent at the individual level. Note that the "incidence rate" of Lyme disease in a given population is an ecological measure because it is a summary rate for a population. Our study does not hypothesize a link between DVA and Lyme disease at the individual level. DVA is an ecological variable that potentially exerts structural and contextual effects on Lyme disease development in an entire human population, not just the individuals experiencing the DVA. As a result, the ecological fallacy, a typical limitation in studies that use ecological data to measure individual-level correlations, is not a concern in our study (Schwartz, 1994).

Connecticut was chosen as a study site because of its high incidence rate of Lyme disease. Between the years 1992 and 2006, 10 states reported Lyme incidence rates greater than 10/100,000 (Connecticut, 73.6; Rhode Island, 45.8; Delaware, 27.4; New Jersey, 24.6; New York, 24.3; Pennsylvania, 23.0; Massachusetts, 14.5; Wisconsin, 13.5; Maryland, 12.2; and New Hampshire, 10.7) (Bacon et al., 2008).

Methods

Yearly DVA data for each Connecticut town from 1999 to 2008 were obtained from the state of Connecticut's Department of Environmental Protection (DEP). The DVAs database is maintained by the DEP's Wildlife Division. The data are a compilation of deer kill incident reports that are completed by either DEP conservation officers, local police, or state police, and routed to the DEP by the officer within 30 days. The report was expanded to include and differentiate between white-tailed deer, moose, and black bear in 2009 (Connecticut General Statutes, 2009). The typical DVA is reported when a deer is killed by a vehicle, and the database does not include deer who survived an accident or who may have died far from the road (Kilpatrick & LaBonte, 2007).

Connecticut has a total of 169 towns, which in aggregate cover the entire surface of Connecticut and do not overlap. The state is 5,543 square miles and was populated by 3,405,565 people according to the 2000 Census. The DEP has grouped the towns into 13 deer management zones (DMZ) (Figure 1). Because deer populations vary across the state, Connecticut developed different DMZs, and each zone has a unique deer management strategy depending on population objectives. Each DMZ has 6 to 25 towns; the largest zone is 655 square miles and the smallest zone is 151 square miles.

In addition, the DEP conducts aerial deer surveys every three years for each DMZ. Aerial surveys were conducted in the winter after a snowfall to maximize visibility. Surveys were conducted from a height of approximately 70 m (200 ft.) and a speed of approximately 16-24 km/hr (10-15 mph). Attempts were made to place three stratified 16-km transects oriented east-west in each of the 12 DMZs. All transects extend in an easterly direction for 16 km with an approximate width of 0.16 km (0.1 mile). Transect placement was determined by placing three evenly spaced points, which served as the center of each transect, vertically aligned in the center of each zone. All zones except 3 and 7 follow these criteria.

The number of annual Lyme disease cases by town for the state of Connecticut from 1999 to 2008 was supplied by the Connecticut Department of Public Health (DPH) epidemiology and emerging infections program. The data are compiled from reports completed by the patient's physician. Reporting Lyme cases has been mandatory for Connecticut health care providers since 1987. Lyme disease can be difficult to diagnose, and the data do not include unreported or undiagnosed cases. Annual rates in Connecticut were variable because of changes in surveillance practices begun in 2003 (Bacon et al., 2008). The DPH also supplied Connecticut town populations from the 1990 and 2000 national census.

Human Lyme disease incidence rates (number of cases per human population) for each town and each deer management zone were calculated using total Lyme cases by town for 1999 using 1990 census data and for 2000 through 2008 based on 2000 census data. DVAs incidence rates (number of DVAs per human population) for each town and each deer management zone were calculated for 1999 using 1990 census data and for 2000 through 2008 using 2000 census data.

The Pearson correlation coefficient (r) was calculated to assess the linear relationship between DVA incidence rates and human Lyme disease incidence rates in DMZs and towns at the ecological level. All correlation analyses were performed separately for each year, 1999 through 2008. Deer population density per square mile for each DMZ was calculated from aerial survey data for the years 2000, 2003, and 2006. Deer population density versus incidence rate of Lyme disease by town was plotted by DMZ. The Pearson correlation coefficient was also calculated to assess the linear relationship between deer population density and human Lyme disease incidence rates by DMZ at the ecological level. All p-values are two-sided with statistical significance evaluated at the .05 alpha level. All analyses were performed in SPSS v. 18.

Results

The total annual incidence rate of Lyme disease in Connecticut during the study years ranged from 40 in 2004 to 136 in 2002 (per 100,000 population) and ranged from 0 to 5,000 for towns (per 100,000 population). The total annual incidence rate of DVAs in the state ranged from 60 in 2006 to 91 in 2000 (per 100,000 population) and ranged from 0 to 1,200 for towns (per 100,000 population).

For both the towns (Table 1) and the DMZs (Table 2), almost all of the calculated ecological correlation coefficients computed within each year (i.e., correlation between DVA incidence rate and human Lyme disease incidence rate by year) were moderate to strongly positive, and all of the resulting p-values were significant (Tables 1 and 2). This demonstrates that a strong positive correlation exists between DVAs and Lyme disease incidence rates in towns and DMZs at the ecological level. The DMZ data demonstrated stronger correlation coefficients for all years between DVAs and Lyme disease incidence rates as compared to the individual town data. Weak correlation coefficients were observed between deer population density and Lyme disease incidence rate by deer management zone; the Pearson correlation coefficients were calculated for years 2000, 2003 and 2006 (year 2000, r = .30, p = .35; year 2003, r = .07, p = 0.84; year 2006, r = .27, p = .40).

Discussion

The principal finding of our study is that a strong linear ecological correlation appears to exist between DVAs and human incidence rate of Lyme disease by DMZ, although a slightly weaker ecological correlation at the town level. As deer are a reservoir for ticks, and DVAs are a proxy for human/deer interactions, DVAs may be seen as a proxy of human/tick interaction. Our results suggest that DVAs quantify the ecological interaction between coexisting populations of humans and deer: geographic locations that have high DVAs should have a large number of human vehicle operators driving within the range of a concentrated deer population.

Locations with high DVAs would most likely be wooded rural environments that support a deer population as well as a coexisting human population. By contrast, geographic locations that have low DVAs would be expected to be areas that have a high human population but very few deer, such as a suburban community or major city; areas that have a low human population but many deer, such as a state or national forest; or areas that have both low human and low deer populations. DVAs will be influenced by the highway density of a town and the unique road infrastructure that facilitate the crossing of animals. In locations where roadside fencing or other means of physically restricting deer crossing, DVAs may not serve well as a Lyme disease indicator. Deer are notorious for not being restricted by fences due to their jumping ability (Curtis & Richmond, 1996), however, and highway density and road infrastructure influences appear to be negligible in Connecticut, as the correlation between DVAs and human Lyme disease incidence rate was consistently high across all regions of the state (Figure 2) for all years.

Our results demonstrate that the total deer population as measured by aerial surveys does not correlate with the Lyme disease incidence rate within a DMZ. This can be explained by acknowledging that deer population numbers do not provide much insight into whether a coexisting human population is present. For example, a high deer density does not mean that a corresponding human population lives in the same zone, whereas a high DVA rate indicates that humans and deer are interacting. Both a high deer density and a high human population are required for towns to have high DVAs, and this degree of interaction accounts for the strong correlation between high DVAs and a high incidence rate of Lyme disease.

The evidence of the role of deer in Lyme disease, the role of I. scapularis as a vector of Lyme disease, as well as the competence of white-footed mice as hosts for the spirochete B. burgdorferi have been clearly established (Matuschka & Spielman, 1986). Size and microgeographic distributions of I. scapularis have been positively correlated with the density of O. virginianus, and increases in I. scapularis populations have been linked to increases in white-tailed deer populations (Barbour & Fish, 1993; Rand et al., 2003; Wilson, Ducey, Litwin, Gavin, & Spielman, 1990). Following a controlled reduction of deer in a costal Massachusetts site, the I. scapularis population declined by approximately half (Wilson et al., 1988). In another study that conducted a survey of six islands in Rhode Island, I. scapularis was found on two of the islands that supported deer populations, while I. scapularis was absent on four islands that did not have deer populations (Anderson, Johnson, Magnarelli, Hyde, & Myers, 1987).

In another experiment, when O. virginianus was removed from Monhegan island off the coast of Maine, populations of I. scapularis disappeared after four years (Rand, Lubelczyk, Holman, Lacombe, & Smith, 2004). The density of I. scapularis and Lyme disease infection among rodents initially increased for the first two to three years as I. scapularis sought out other large mammalian hosts before becoming extinct from a presumed lack of adequate hosts (Rand et al., 2004). Adult I. scapularis reproduce based on the availability of deer as hosts, and I. scapularis populations increase with deer density (Rand et al., 2003).

In addition, significant evidence exists linking deer population to the incidence rate of Lyme disease (Stafford, 2001), although our data does not support this view. In areas in which Lyme disease is prevalent, specifically in forested residential areas, I. scapularis exposure often occurs in the vicinity of people's homes (Lastavica et al., 1989; Steere, 1986). The number of deer living near these homes should influence the number of adult I. scapularis and consequently Lyme disease incidence rate in the human population (Lastavica et al., 1989). Therefore, a metric that quantifies the interaction between humans and deer in a geographic area would be valuable for predicting the incidence rate of Lyme disease.

Results of our analysis demonstrate that DVAs strongly correlate with the human incidence rate of Lyme disease at the ecological level, and that DVAs can be used as a metric to measure the interaction between deer and humans, in addition to hunter and residential homeowner observation surveys. Further studies are required to calibrate the DVA data, such as defining what upper limit of DVA is high enough to warrant a public health intervention.

Currently, tick identification and serology, the human Lyme disease incidence rate, and animal studies provide information for Lyme disease risk assessment, but these sampling approaches are not ideal. Human case data can be valuable in highlighting Lyme disease hotspots. Case data are inexact, however, because individuals may have been exposed to Lyme disease away from their residence or home, and the source is not always known. In addition, tick or small animal capture, identification, and serology can be time consuming, are limited to the specific locations in which samples are collected, and do not correlate with the estimated risk of Lyme disease (Daniels et al., 1998). Another approach involves white-tailed deer serologies. Serologic analysis for B. burgdorferi using O. virginianus has been shown to be an accurate and sensitive surveillance method for determining whether B. burgdorferi is present in a specific geographic location (Gill, McLean, Shriner, & Johnson, 1994). While serologic analysis of O. virginianus provides information regarding the presence of B. burgdorferi, it does not indicate the risk of Lyme disease in the human population, as the deer population may or may not coexist with a human population. In addition, deer serologies for a specific area are difficult to obtain.

Local deer population reductions in areas that have high DVAs should reduce tick density, which in turn should decrease the risk of Lyme disease. It has already been shown that reducing the deer population, fencing deer from specific areas, or treating deer with acaricidal chemicals controls the population of I. scapularis and the incidence rate of Lyme disease in areas that experience human activity (Daniels, Fish, & Schwartz, 1993; Deblinger, Wilson, Rimmer, & Spielman, 1993; Hoen et al., 2009; Pound, Miller, & George, 2000; Rand et al., 2004; Stafford, 1993; Stafford, Denicola, & Kilpatrick, 2003; Wilson et al., 1988).

Evidence exists that deer population by itself has no predictive power of tick population or the reduction of Lyme disease (Jordan, Schulze, & Jahn, 2007; Ostfeld, Canham, Oggenfuss, Winchcombe, & Keesing, 2006). Several studies have indicated that populations of species that support the I. scapularis in its juvenile stages, such as P leucopus, strongly correlate with tick population and the prevalence of B. burgdorferi while populations of species that support the I. scapularis in its adult stage, such as the white-tailed deer, do not (Buskirk & Ostfeld, 1995; Ostfeld et al., 2006). A recent study has determined that deer populations must be maintained at low densities indefinitely to reduce tick populations; during the first few years following a deer reduction, the adult tick population will seek other large hosts, such as humans, but then collapse within two to three years as the adult tick population crashes (Rand et al., 2004).

Several limitations should be noted about the data used in our study. First, many DVAs are not reported to the Connecticut authorities and therefore are not included in the database. It is not possible to accurately establish the number of DVAs that are not reported, but this missing data could be assumed to affect all 169 towns equally. In Connecticut, it is estimated that for every DVA that is reported an additional five go unreported (Kilpatrick & LaBonte, 2007). Second, within the Lyme disease incidence rate data supplied by the Connecticut DPH, a large increase of Lyme disease cases was reported with a missing location (patient's town of residence) in the years 2007 and 2008. Lyme disease incidence rate data is reported by physicians, and this increase in cases missing a location is due to incomplete data from the reporting physician. Third, aerial deer density estimates are merely a tool used in evaluating trends in deer populations and therefore are not expected to provide an exact count of deer (Kilpatrick, 2010; Kilpatrick, Spohr, & Lima, 2001). A more accurate method of recording DVAs and Lyme disease cases would greatly improve the validity of the datasets and further strengthen the relationship we found between DVA and Lyme disease incidence rates. Finally, as our methodology is using DVA as a proxy for human/ deer interactions, it is important to acknowledge a number of factors that might confound the observed ecological correlations, such as changes in traffic patterns, weather, new construction, physical barriers such as road fencing, and so on.

Conclusion

The results of our study demonstrate that DVAs hold promise to be used as an indicator of where deer populations need to be managed and for determining where Lyme disease risk mitigation should be undertaken. Further investigation is required, however, before changes in public health policy can be implemented, as our study is not sufficient to prescribe changes in public policy. Our results will hopefully encourage additional research to evaluate DVA data in assessing the risk of contracting Lyme disease in specific geographic regions. To start, public health departments can analyze their state's DVA and Lyme disease incidence rate data for correlations.

Acknowledgements: We would like to thank the Weill Cornell Medical College Clinical Translational Science Center for providing access to the Weill Cornell Medical College's Department of Public Health, Biostatistics, and Epidemiology resources. Dr. Paul Christos was partially supported by the following grant: Clinical Translational Science Center (CTSC) (UL1-RR024996). None of the authors have a conflict of interest. We would like to thank the Connecticut Department of Environmental Protection and the Connecticut Department of Public Health for providing access to their databases. We would like to thank Dr. Lewis Drusin, Dr. Jacqueline Ehrlich, Dr. Aran Ron, and Dr. Peter Marzuk for their advice regarding this article.

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Daniel H. Wiznia, MD

Department of Orthopaedics and Rehabilitation

Yale School of Medicine

Paul J. Christos, MS, DrPH

Department of Public Health

Weill Cornell Medical College

Andrew M. LaBonte, MS

Connecticut Department of Environmental Protection

Corresponding Author: Daniel Wiznia, Surgical Intern, Department of Orthopaedic Surgery, Yale Medical School, 800 Howard Avenue, New Haven, CT 06510. E-mail: daniel. wiznia@yale.edu.

TABLE 1

Deer Vehicle Accidents Versus
Lyme Disease Incidence Rates
by Town

Year   Pearson Correlation   p-Value
         Coefficient (r)

1999           .49           <.0001
2000           .45           <.0001
2001           .59           <.0001
2002           .60           <.0001
2003           .61           <.0001
2004           .52           <.0001
2005           .58           <.0001
2006           .28           <.0001
2007           .63           <.0001
2008           .52           <.0001

TABLE 2

Deer Vehicle Accidents Versus
Lyme Disease Incidence Rates
by Deer Management Zone

Year   Pearson Correlation   p-Value
         Coefficient (r)

1999           .82            .001
2000           .67            .02
2001           .86           <.0001
2002           .83           <.0001
2003           .90           <.0001
2004           .71            .007
2005           .78            .002
2006           .66            .01
2007           .90           <.0001
2008           .88           <.0001
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Author:Wiznia, Daniel H.; Christos, Paul J.; LaBonte, Andrew M.
Publication:Journal of Environmental Health
Article Type:Report
Geographic Code:1USA
Date:Apr 1, 2013
Words:5146
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