Open Access

Quantitative factors proposed to influence the prevalence of canine tick-borne disease agents in the United States

  • Roger W Stich1Email author,
  • Byron L Blagburn2,
  • Dwight D Bowman3,
  • Christopher Carpenter4,
  • M Roberto Cortinas5,
  • Sidney A Ewing6,
  • Desmond Foley7,
  • Janet E Foley8,
  • Holly Gaff9,
  • Graham J Hickling10,
  • R Ryan Lash11,
  • Susan E Little6,
  • Catherine Lund12,
  • Robert Lund13,
  • Thomas N Mather14,
  • Glen R Needham15,
  • William L Nicholson16,
  • Julia Sharp13,
  • Andrea Varela-Stokes17 and
  • Dongmei Wang13
Parasites & Vectors20147:417

https://doi.org/10.1186/1756-3305-7-417

Received: 19 August 2014

Accepted: 30 August 2014

Published: 4 September 2014

Abstract

The Companion Animal Parasite Council hosted a meeting to identify quantifiable factors that can influence the prevalence of tick-borne disease agents among dogs in North America. This report summarizes the approach used and the factors identified for further analysis with mathematical models of canine exposure to tick-borne pathogens.

Keywords

Anaplasma Ehrlichia Borrelia burgdorferi Tick-borne infectionsPrevalence map factorsTicksIxodidaeProstriataMetastriata

Background

Dogs in the United States (USA) are hosts to a diverse range of ixodid ticks and can become infected with many of the pathogens transmitted by these vectors. Advances in diagnostic test and recording technologies have led to the creation of a monthly dataset containing county-by-county canine test results from across the USA. The Companion Animal Parasite Council (CAPC) has assembled large datasets of such results from commercial laboratories that provide diagnostic tests for canine exposure to Borrelia burgdorferi, Ehrlichia spp. and Anaplasma spp. [1]. These monthly, county-level CAPC prevalence maps generated interest in the utility of the datasets for assessing seroprevalence norms, forecasting future seroprevalence rates and for identifying trends in canine exposure to this array of tick-borne disease agents. A group of vector ecologists, parasitologists, other biologists and statistical modelers met in Atlanta, GA (June 9–10, 2012) to identify factors that could enhance the accuracy of these predictive models. This report narrates the results of the meeting.

Canine diagnostic test results for exposure to tick-borne pathogens, including B. burgdorferi, Ehrlichia spp. and Anaplasma spp., are of significant interest, not only because canine health is important to pet owners and veterinarians, but also because of the public health importance of many of these infectious disease agents. These tick-borne pathogens are transmitted by two phylogenetically distinct groups of ixodid ticks. Members of the ixodid subfamily Prostriata (Ixodes spp.) transmit agents of granulocytic anaplasmosis (Anaplasma phagocytophilum) and Lyme borreliosis (B. burgdoferi) and are likely to include vectors of a more recently described Ehrlichia muris-like agent in the USA. Members of the subfamily Metastriata (e.g., the genera Amblyomma, Dermacentor and Rhipicephalus) transmit agents of canine and human ehrlichiosis (e.g., E. canis, E. chaffeensis and E. ewingii), canine anaplasmosis (A. platys) and spotted-fever group rickettsiosis (i.e., Rickettsia rickettsii, R. conorii and related Rickettsia spp.).

Large datasets have been assembled from reports of diagnostic test results for canine exposure to B. burgdorferi, Anaplasma spp. and Ehrlichia spp. in the USA. For example, from reports submitted nationwide from 2010–2012, 509,195 (7.2%) of 6,996,197 canine samples were seropositive for B. burgdorferi, 270,168 (4.4%) of 6,192,268 samples were seropositive for Anaplasma, and 111,673 (1.1%) of 6,994,683 samples were seropositive for Ehrlichia[2]. A previous national survey, spanning 2001–2007, reported results from 982,336 diagnostic tests for canine exposure to B. burgdorferi and Ehrlichia spp., and 479,640 tests for canine antibodies to Anaplasma spp., with 5.1%, 0.6% and 4.7% of these samples testing seropositive for B. burgdorferi, Ehrlichia and Anaplasma, respectively [3]. Interestingly, when the canine seroprevalence of B. burgdorferi in the 2001–2007 study was compared to the subsequent prevalence of human Lyme disease, the most commonly reported human vector-borne illness in the USA, canine seroprevalence of B. burgdorferi ≥5.1% was predictive of emergent human Lyme disease in low-incidence counties; a low canine seroprevalence (≤1.0%) was associated with minimal risk for emergent human Lyme disease [4]. A subsequent report, however, underscored the importance of other variables, such as the distribution of competent vector species, for accurate interpretation of these canine diagnostic test data [5].

The overall objective of this CAPC-sponsored workshop was to identify factors that are likely to influence the seroprevalence of canine exposure to tick-borne disease agents in the USA, specifically focusing on the factors and the pathogens for which sufficient data are available, so that these factors could be evaluated for incorporation in mathematical models designed to monitor and to predict spatial and temporal seroprevalence patterns. These preliminary factors provided statisticians some of the critical information needed to begin their model-building procedures.

Approach

Two teams of researchers, from various areas of tick and tick-borne pathogen biology, were assembled and tasked with rational identification of factors thought to be relevant to the canine seroprevalence of pathogens transmitted by prostriate (eight team members) or metastriate (seven team members) ticks (Figure 1). Members of each team were selected based upon diverse areas of expertise in tick biology, tick-borne disease, vector ecology or statistics. Each panel was asked to identify and to rank ten key factors that they considered most likely to affect pathogen seroprevalence, and these preliminary factors were then presented to all of the meeting participants for further discussion. It was understood that the relevance of these factors would be subsequently assessed with mathematical models, and that these models would be adjusted with data that continue to be generated. Thus, the utility of different factors would be continually assessed as the mathematical models are refined over time.
Figure 1

Approach to rational identification of quantitative factors proposed to influence the exposure of dogs to vector-borne pathogens. Background information, meeting objectives and guidelines were presented to participants before they were divided into three separate panels, according to vector taxa, for mosquitoes, prostriate ticks and metastriate ticks. Each panel was asked to identify, discuss and to rank candidate factors for evaluation with statistical prevalence models of pathogens transmitted to US dogs by each vector taxon. All of the participants were subsequently reconvened for further discussion and refinement of the results from each panel.

The working groups for both ixodid subfamilies began by discussing variables categorized as (1) vector, (2) host, (3) abiotic, (4) habitat or (5) social. Both groups independently identified numerous factors. The majority of factors were thought to be associated with canine exposure to pathogens vectored by either ixodid subfamily; however, several factors specifically associated with the different ixodid subfamilies also emerged. Variables were also discussed for which there is little or inconsistent supporting data, but these factors could become useful if the data became available. However, in accordance with the workshop objectives, factors for which sufficient data are currently available were chosen for ranking by consensus of each working group.

The variables independently identified by each panel were categorized into the five groups previously indicated (i.e., vector, host, abiotic, habitat and social). Factors regarding exposure to infectious agents transmitted by prostriate ticks were heavily influenced by the preponderance of research on the phenology of Ixodes scapularis and I. pacificus, which are considered the primary vectors of B. burgdorferi and A. phagocytophilum in North America (Table 1). For metastriate-borne pathogens, host biology and human behavior were second only to vector distribution with regard to factors considered likely to influence seroprevalence (Table 2). Brief explanations and comments regarding these factors are described below.
Table 1

Factors initially considered as potential contributors to canine prevalence of disease agents transmitted by Ixodes scapularis and I. pacificus

Vector factors

 

Distribution

 

Abundance

 

% Infected

 

Canine contact

 

Local phenology

 

Tolerance to temperature and humidity

 

Activity

 

 Focus on adults as primary vector to dogs

 

 Host seeking behavior

 

 Host contact

 

 Feeding preferences and opportunities

 

  Deer population drives tick abundance

 

  Small mammal population drives infection prevalence

 

  Lack of lizards

 

  Diversity/dilution effect

 

 Tick encounters

 

 Questing behavior versus relative humidity

 

 Peridomestic encounters – access to areas

 

 Urbanization/Rate of development

 

Infection status (decreased survival versus increased cold tolerance)

Host factors

 

Presence and abundance (deer, small mammals, lizards)

 

 Dilution effect/host diversity

 

Habitat availability and quality

 

 Mast crop as a surrogate for host reproduction/fitness

 

Migratory bird patterns

 

Reproductive capacity and timing of vertebrate host reproduction

 

Population control programs in place locally

 

Abiotic host survival factors

 

 Temperature, water availability, substrate/nesting material, snow cover

 

Feeding preferences

 

Herd immunity of reservoir host populations

 

Hunting pressure/success

 

 Number of deer killed per county – harvest rates

 

 Hunting license versus hunting harvest – how active hunting is for that area

 

 Hunting limits due to development

Abiotic factors

 

Snow cover – depth, duration

 

Miles of roads – neighborhood roads (non-interstate/parkway/highway), trails

 

Soil type – clay versus sand in Northeastern USA

 

Hydrological features

 

I. scapularis

 

 Maximum temperature, warmest month

 

 Annual precipitation

 

I. pacificus

 

 Minimum temperature, coldest month

 

Daily temperature (high, low and average)

 

Relative humidity (average, high, low, duration)

Habitat factors

 

Land cover classification

 

 Urbanization in 3 categories – low, medium, high

 

 Rate of change

 

Forest cover

 

 Land cover classification (categorical), % canopy cover, NDVI, EVI (canopy structure)

 

 Crop phenology – maximum greening, minimum greening – when greening is happening

 

 Supervised vs unsupervised satellite imagery, derived data not currently off the shelf

 

 Forest type, forest fragmentation, forest edge length, forest composition, forest connectivity

 

 Forest fragments within X distance of road or urban area, close to population centers

 

Understory- could be modeled but is not measured

 

Detritus layers/leaf litter

 

 Targeted for future research but perhaps not currently available dataset

 

Soil maps/soil types

 

 World harmonized soil database

 

 Classification scheme

 

Proximity to rivers/drainage areas

 

Proximity to coast

 

Rain shadows

 

Rivers and streams

 

 Attract hosts

 

 Serve as corridors

 

 Provide humidity

 

Aspect/slope/topo index – derived from digital elevation models, available from hydro dataset

 

 More nymphal deer ticks on north- and east-facing slopes

 

 Effective distance – more ticks on uphill side of a payout

 

Ticks associated with east-facing woodland edges that slope down to water

 

Fire

 

 Eliminates leaf litter, changes food availability, changes microclimate

 

 Depending on timing, burn can increase number of infected ticks, so fewer ticks but higher infection rate

 

Park boundaries – proximity to parks

Social factors

 

Human population centers

 

Dog ownership, dog lifestyle

 

 Hunting styles that use dogs

 

 Breed of dog

 

Dog ownership increase – by region

 

More homes in tick habitat – demographic factors

 

Deer/vehicle collisions – deer crossing signs

 

Acaricide use/quality of care for dogs

 

Average household income

 

 Presence of clinics, proximity to clinics, number of vet clinics in an area, size of clinics

 

Cultural – forest foraging (mushroom hunting in Missouri)

 

Internet use

 

Social media

 

Smartphone use

 

Education level

 

Population density

 

 Housing type (average lot size, median home price, age of house unit, census tract size)

Table 2

Factors discussed as potential contributors to seroprevalence of metastriate tick-borne pathogens among dogs in the USA

Vector factors

 

Biology

 

 Competence (different transmission scenarios)

 

 Host preference

 

Persistence and interhost transfer of male ticks

 

Host seeking behavior (hunt, ambush)

 

Population dynamics

 

 Distribution (established, intermittent or absent)

 

 Relative abundance (species and stages)

 

Seasonality

 

 Different stages

 

 Stage overlap

Host factors

 

Principal host(s) of different tick stages

 

 Susceptibility to pathogen

 

 Distribution

 

 Density-Dynamic

 

Ecologic diversity (dilution effect)

 

 Shannon-Weaver Index

 

 Tick-permissive, non-reservoir hosts

 

Behavior

 

 Host grooming

 

 Gregariousness

 

 Host species

 

Home Range

 

 Migration, dispersal

 

 Anthropogenic translocation

 

Hosts permissive for pathogen

 

 Persistence in reservoir

 

 Prevalence of infection

 

 Density

 

 Other transmission routes

 

 Life cycle/age distribution

 

 Immune response

 

 Amplification vs. reservoir

 

Domestic

 

 Indoor/outdoor

 

 Rural/urban

 

 Relocation

 

Sylvatic vs. Suburban

 

 Opportunistic or natural infection

Abiotic factors

 

Humidity

 

 Maximum, minimum and average

 

Temperature

 

 Maximum, minimum and average

 

 Degree-day

 

 Soil temperature

 

Photoperiod

 

Seasonal precipitation

 

 El Niño effect

 

 Snow and other ground cover

 

Catastrophic disturbance

 

 Fire

 

 Hurricane

 

Wind

 

Altitude

Habitat factors

 

Macrohabitat

 

 Vegetation (density, type and fragmentation)

 

 Elevation

 

 Location of water sources

 

 Rainfall

 

Microhabitat

 

 Soil type

 

 LIDAR data

 

Land use

Social factors

 

Land use

 

Indoor versus outdoor dogs

 

Dog use (e.g., hunting)

 

Canine husbandry

 

Use of tick preventives

 

Nuisance permits

 

Housekeeping

 

Animal welfare violations

 

Socioeconomics

 

 Average household income

 

 Human population

 

 Large-scale economic factors

 

Recreation

 

 Hunting

 

 Parks (rural and urban)

 

Pets per household

Vector factors

Distribution

The geographic distribution of prostriate ticks was focused on the Ixodes spp. thought to most commonly feed on dogs (and people) in the USA: I. scapularis and I. pacificus. Metastriate ticks considered as pathogen vectors (e.g., of Ehrlichia spp. and A. platys) included, in alphabetical order, A. americanum, A. maculatum, D. andersoni, D. variabilis and R. sanguineus. The general distributions of these ticks are relatively well documented in the literature and via voucher specimens in the USA. However, the spatial resolutions of these data vary in different regions, and defining the minimum useful scale can be complicated by discontinuous geographic distributions of tick populations in a given area.

Abundance

Defining permanent values of tick abundance levels is problematic, because tick population levels within a given area are temporally and spatially variable and can change rapidly. Tick abundance depends on host abundance and availability, relative humidity, precipitation and temperature, and can reflect conditions from previous years when immature tick stages or prior generations were active.

Activity

Activity is indicative of questing behavior, host-seeking behavior, host contact and the feeding preferences of different developmental stages. The presence of ticks in an area is not alone indicative of activity. For example, tick activity will depend on temperature, precipitation, relative humidity and photoperiod.

Host factors

Deer

The deer population is a major driver of abundance for certain ticks, such as I. scapularis, I. pacificus and A. americanum. Deer are also a reservoir of E. chaffeensis and could be involved in the maintenance of E. ewingii.

Small mammals

Rodents are an important component of the ecologies of several tick species and some tick-borne infectious agents. Immature stages of several tick species acquire blood meals from small vertebrate hosts. Several tick-borne infectious agents, such as B. burgdorferi, A. phagocytophilum and R. rickettsii are adapted to rodent reservoir hosts.

Lizards

Small vertebrates such as lizards, which are permissive hosts for immature tick stages but are not definitively documented reservoirs of the pathogens under consideration, could dampen transmission of disease agents that are adapted to rodent reservoirs. Conversely, removal of lizards reportedly reduced nymphal tick numbers from an environment but did not affect the percentage of B. burgdorferi-positive ticks, suggesting that increased numbers of lizard hosts might actually increase the risk of pathogen transmission by serving to increase the overall number of ticks in a given area [6].

Migratory bird patterns

Migratory birds can introduce some tick species to new areas [7]. However, ticks that feed on dogs and that are dispersed by birds in the USA may be incapable of maintaining an active population cycle in the absence of larger vertebrate hosts (e.g., white-tailed deer).

Abiotic factors

Different tick species and their natural hosts can be adapted to various environments that are influenced by abiotic factors such as precipitation, temperature, relative humidity and soil composition.

Habitat factors

Factors that influence the life cycles of ticks and their vertebrate hosts include vegetation, urbanization, land use in non-urban settings and detritus layers.

Social factors

Human behavior and population characteristics influence the exposure of dogs to ticks. These include access to preventive care, recreation, socioeconomic status, income, pathogen reservoir control, vector-amplification host control and news media coverage.

Unquantified variables

A number of variables were discussed for which comprehensive, nationwide data did not seem currently available. These variables included vector infection rates, detailed reservoir infection rates, vector abundances, vector efficiency indices, vector survival, vectorial capacities, temperature-dependent development rates of vectors (natural temperature regimes), total number of dogs (by county or zip code) and tick control product sales in each geographic region. Local data may be available for some of these variables in certain areas, but national datasets were not available at the time of this meeting.

Mathematical modeling

Each expert panel was asked to prioritize 10 factors most expected to drive a reliable mathematical predictive model. These lists, summarized in Tables 3 and 4, shared several common abiotic and habitat factors. Several other factors were specific to seroprevalence of the pathogens transmitted by Ixodes spp., R. sanguineus or the other metastriate ticks that were considered. For example, while deer populations and vegetation were considered important factors that affect the majority of these tick populations, social factors were given the highest priority for predicting the seroprevalence of agents transmitted by the brown dog tick, R. sanguineus (Table 4).
Table 3

Ranked factors identified for canine seroprevalence models of infections transmitted by Ixodes spp. in the USA

1.

Forest cover/NDVI or EVIa

2.

Relative humidity

3.

Annual precipitation (including snow cover)a

4.

Human population densitya

5.

Deer/vehicle collisionsa

6.

Topography/altitude/aspect

6.

Temperature – max warmest, min coldesta

7.

Proximity of forest to impervious surfaces or roads/built environment

8.

Human case distribution

8.

Distribution/abundance of I. scapularis and I. pacificusa

9.

Household incomea

10.

Forest fragmentation indexa

aSimilar variables also ranked by the metastriate-borne pathogen panel.

Table 4

Ranked factors for preliminary models of metastriate tick-borne pathogen prevalence among dogs in the USA

Majority of the metastriata:

1.

Vector distribution (established, intermittent or absent)a

2.

Maximum, minimum and average temperatureb

3.

Amount of precipitationa

4.

LiDAR (up to 6 layers)

5.

GAP/categorical analysis of vegetationa

6.

Reservoir host densitiesa

7.

Human population (census)a,b

8.

Median household incomea,b

9.

Fragmentation of vegetationb

10.

Degree-days

11.

Seasonal precipitation (snow cover)a

R. sanguineus:

1.

Median household income a,b

2.

Registered dog breeders (kennels, puppy mills, etc.)

3.

Human population (census)a,b

4.

Tick preventive sales

5.

Animal welfare violations

6.

Latitude

aVariables also ranked by the prostriate-borne pathogen panel.

bVariables shared among all ixodid ticks considered for this report.

The prevalence data at the foundation of this predictive model is largely based on serodiagnostic tests. Although seropositivity is reflective of past exposure, it does not demonstrate recent or active infections. Repeatedly seropositive samples from the same dogs at different times are also to be occasionally expected, because some dogs may have tested seropositive in previous tests and because some tests are conducted to monitor host responses to treatment. Travel histories and certainties of the individual test results are currently unavailable for the dogs reported in this dataset.

An analogous project for mathematical modeling of the prevalence of canine heartworm was simultaneously undertaken by CAPC [8, 9], and each prioritized factor identified by the expert panel had significant predictive power with ≥95% confidence. Overall, the model explained 60%-70% of variability in the CAPC county-by-county dataset from 2011–2013. Similarly, preliminary analysis of canine seroprevalence of Anaplasma spp. indicated that temperature, precipitation, relative humidity, population density, median household income, forestation coverage, elevation and deer/vehicle strike rates were significant with ≥95% confidence, and that the total proportion of variability explained in the 2011–2013 data is around 60-70% [10]. Thus, the prevalence of heartworm and seroprevelance of Anaplasma among dogs appear amenable to quantification that could facilitate monitoring for outbreaks, remediation of vector abundance or for forecasting future seroprevalence levels.

Attempts to fit the seroprevalence of B. burgdorferi and of Ehrlichia spp. among dogs are also underway, with mixed results. The spatial seroprevalence of B. burgdorferi among dogs has been similar to and appears to be as quantifiable as that of Anaplasma spp. Conversely, the canine seroprevalence of Ehrlichia spp. appears to be highly variable, with some neighboring areas reporting antipodal seroprevalence rates that could be reflective of vector ecology or social factors. Future work will address these issues.

Conclusions

This meeting brought together a range of junior and senior scientists engaged in various aspects of research in the biology of ticks and tick-borne infections. The specific objectives were to identify and to prioritize quantifiable factors expected to contribute to canine exposure to organisms transmitted by the two major subfamilies of ixodid ticks. The two panels ranked 12 and 17 factors associated with prostriate and metastriate ixodid ticks, respectively. Eight of these factors were independently prioritized by both panels; four of 12 factors were unique to prostriate-vectored agents, two of 11 factors were unique to metastriate-vectored agents transmitted by ticks other than R. sanguineus, and four of six factors were unique to agents vectored by R. sanguineus. The next phase of this project will move from rational identification of perceived factors to statistical assessment of factors for predictive power. Forecasting issues will also be explored.

Declarations

Acknowledgments

This meeting was supported by the Companion Animal Parasite Council (CAPC), and we are grateful to Sonya Hennessy for assistance in organizing and hosting this meeting. The CAPC is in turn grateful to its sponsors that provide data for Parasite Prevalence Maps: the IDEXX, Antech, Banfield and Abaxis corporations. We are also grateful to the veterinarians across the USA who test their patients for exposure to vector-borne pathogens, especially those who report test results that can eventually be included as data in the CAPC Parasite Prevalence Maps. Robert Lund acknowledges support from the CAPC and from the National Science Foundation Grant DMS-1407480. The opinions and assertions contained herein are those of the authors and are not to be construed as official or reflecting the views of the Department of the Army or the Department of Defense.

Authors’ Affiliations

(1)
Department of Veterinary Pathobiology, University of Missouri
(2)
Department of Pathobiology, Auburn University
(3)
College of Veterinary Medicine, Cornell University
(4)
Companion Animal Parasite Council
(5)
School of Veterinary Medicine and Biomedical Sciences, University of Nebraska
(6)
Department of Veterinary Pathobiology, Oklahoma State University
(7)
Walter Reed Biosystematics Unit, National Museum of Natural History
(8)
Department of Medicine and Epidemiology, University of California
(9)
Department of Biological Sciences, Old Dominion University
(10)
Department of Forestry, Wildlife and Fisheries, University of Tennessee
(11)
Department of Geography, University of Georgia
(12)
City Kitty Veterinary Care for Cats
(13)
Department of Mathematical Sciences, Clemson University
(14)
Center for Vector-Borne Disease, University of Rhode Island
(15)
Department of Entomology, The Ohio State University
(16)
Centers for Disease Control and Prevention
(17)
Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University

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Copyright

© Stich et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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