Perspectives from federal and state public health departments on their participation in and the utility of Ixodes scapularis (Acari: Ixodidae) and Ixodes pacificus tick and tick-borne pathogen surveillance in the United States

Abstract

Introduction

Over the past 2 decades in the United States, the incidence of reported tick-borne disease cases has more than doubled (Eisen et al. 2017, Rosenberg et al. 2018). An increasing number of tick-associated microbes have been recognized as human-disease causing pathogens (Buller et al. 1999, Paddock et al. 2004, Chowdri et al. 2013, Gugliotta et al. 2013, Kosoy et al. 2015, Padgett et al. 2016, Pritt et al. 2016, 2017, Eisen et al. 2017, Brault et al. 2018, Eisen and Paddock 2021), and the geographic ranges of medically important ticks have expanded (Sonenshine 2018, Eisen and Paddock 2021), putting an increased number of communities at risk for tick-associated illnesses. Recognizing these increasing threats, a national strategy to protect the public from vector-borne diseases was developed (DHHS 2024). A component goal of that strategy is to better understand when and where people are exposed to medically important ticks and their associated pathogens. An accurate understanding by the public, health care providers, and public health professionals of when and where people are at risk for exposure to infected ticks aids in prevention and diagnosis of tick-borne diseases. However, national maps showing the geographic distribution of medically important tick species and the pathogens they carry are often incomplete or non-existent.

The majority of reported tick-borne disease cases are caused by pathogens spread by the blacklegged tick (Ixodes scapularis Say) in the eastern United States and to a lesser extent by the western blacklegged tick (Ixodes pacificus Cooley and Kohls) in the Pacific Coast states (Eisen et al. 2017, Eisen and Eisen 2018). In 2018, the US Centers for Disease Control and Prevention (CDC) initiated a national tick surveillance program (NTSP) in response to ongoing changes in the distribution of I. scapularis and its associated pathogens in the eastern United States and a paucity of records documenting tick abundance or pathogen distributions in either tick species (Fleshman et al. 2022, Eisen and Eisen 2023, Foster et al. 2023, Eisen et al. 2024). Programmatic goals included collection and reporting of acarological data at the county spatial scale to monitor changes in the distribution and abundance of ticks and the presence and prevalence of tick-borne pathogens to provide actionable, evidence-based information to clinicians, the public, and public health policy makers (CDC 2018, Eisen and Paddock 2021). Tick surveillance data were intended to provide an independent, complementary data source to traditional case reporting for assessing risk of human encounters with infected ticks. Such information was expected to aid in promoting adoption of tick-borne disease prevention strategies, establishing prior probabilities of exposure to inform recommendations for diagnostic testing and treatment, and explaining and predicting epidemiological trends (Eisen and Paddock 2021). Although the program expanded in later years to include other medically important tick species, we focus this summary on surveillance activities for Ixodes ticks and their associated human pathogens.

A survey conducted prior to the initiation of the NTSP (Mader et al. 2021) found that approximately two-thirds of the 140 vector-borne disease professionals across the United States who responded to the survey were engaged in some form of passive tick surveillance (accepting ticks from the public, or animal, and/or human healthcare providers for identification and possible pathogen testing), but fewer than half were engaged in routine, active tick surveillance (field collection of ticks from vegetation or from host animals). Regions with the largest proportion of programs engaged in active tick surveillance were in the Northeast, Upper Midwest, and Pacific Coast states. A majority of those conducting tick surveillance focused on the most basic objective of describing which tick species were present in their communities; fewer assessed pathogen presence or prevalence or described acarological risk based on densities of host-seeking infected ticks. Respondents to the survey expressed a desire to expand their tick surveillance capacity but cited the following barriers: lack of consistent funding, limited infrastructure and training, and a need for guidance on best practices. Since that survey was conducted, CDC issued guidance for standardization of tick surveillance data collection (CDC 2018), developed a data portal (ArboNET Tick Module) to compile tick surveillance records at the county spatial scale across the country, increased funding to state public health departments (SHDs) to lead and conduct tick surveillance, and built overall entomological capacity through funding several cooperative agreements that provide entomological training to rebuild the public health entomology workforce (Beard et al. 2019, Petersen et al. 2019).

As a result of this concerted effort to build tick surveillance capacity, 36 SHDs have submitted I. scapularis or I. pacificus presence or abundance data or associated pathogen presence or prevalence data from 1,128 (36%) of 3,110 counties in the contiguous US to the ArboNET Tick Module between autumn of 2018 and the end of 2023 (Figs 1 and 2). Combining these records from SHD partners with historical records that were previously published, or ArboNET submissions from other public health partners, recent publications have summarized changes in the county-level distribution of I. scapularis and I. pacificus across the United States (Eisen et al. 2016, 2024, Eisen and Eisen 2023), presence or prevalence of pathogens detected in these ticks (Lehane et al. 2021, Fleshman et al. 2022, Foster et al. 2023) and trends in reported densities of host-seeking I. scapularis nymphs (Foster et al. 2024).

Fig. 1.

Range of counties (a) with established I. scapularis or I. pacificus populations and (b) where host-seeking I. scapularis or I. pacificus nymphal or adult densities were reported per 1,000 m², based on records reported to the Centers for Disease Control and Prevention’s ArboNET Tick Module prior to the National Tick Surveillance Program (pre-NTSP, 2004–2017) and during the NTSP (ArboNET, 2018–2023). Establishment is defined as 6 or more ticks of one life stage, or more than one life stage collected in a county in a calendar year. Density estimates (b) were included if at least 750 m² sampling sites were drag or flag sampled during the expected peak of seasonal activity for each life stage. Unshaded counties represent either a lack of tick collection and/or testing records submitted to the NTSP, or records not meeting inclusion criteria.

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Fig. 2.

Range of counties from which host-seeking I. scapularis or I. pacificus ticks were tested for pathogen presence and where pathogens were detected in ticks (a) or to estimate pathogen prevalence in ticks (b) based on records reported to the Centers for Disease Control and Prevention’s ArboNET Tick Module prior to the National Tick Surveillance Program (pre-NTSP, 2004–2017) and during the NTSP (ArboNET, 2018–2023). Testing for pathogen presence (a) required that at least a single tick was tested using a pathogen-specific molecular assay. Testing for pathogen prevalence (b) required that at least 25 Ixodes sp. ticks (per life stage) were tested for at least a single Ixodes tick-borne pathogen by site and life stage within a single calendar year. Ixodes-borne pathogens of interest included: Borrelia burgdorferi sensu stricto, Borrelia mayonii, Borrelia miyamotoi, Anaplasma phagocytophilum, Ehrlichia muris eauclairensis, Babesia microti, and Powassan virus. Unshaded counties represent either a lack of tick collection and/or testing records submitted to the NTSP, or records not meeting inclusion criteria.

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Partnerships between the CDC and SHDs have been a critical component in the success of the NTSP. To guide other public health programs that are considering engaging in tick surveillance activities or using data generated from this initiative, we share the experiences of SHD partners involved in tick surveillance, primarily in regions where the Lyme disease agent, Borrelia burgdorferi sensu stricto (Spirochaetales: Spirochaetaceae), is established or emerging: the Pacific Coast states, the Upper Midwest, Northeast, and northern states within the Southeast region of the United States. We describe SHD experiences in tick surveillance to include information on why and how long states have been engaged in tick surveillance activities, which tick surveillance objectives have been addressed, and we summarize challenges to maintaining tick surveillance programs within SHDs. We share CDC and SHD experiences in communicating tick surveillance data to stakeholders and explore how the acarological data are used to complement epidemiological data, and we describe challenges in interpreting or communicating data collected through this effort.

Inception and Intent of Tick Surveillance Programs Led by State Public Health Departments

Prior to the inception of the NTSP in 2018, national maps of the county-level distribution of I. scapularis and I. pacificus were compiled based on literature review, museum collections, and unpublished state records (Dennis et al. 1998, Eisen and Eisen 2016). Previously, national maps showing the county level distribution of human pathogens found in ticks were nonexistent. Although many entities (e.g., state, local and tribal health departments; parks and agriculture programs; academic researchers) were conducting tick surveillance to inform such maps at local and regional scales, prior to 2018, the data collected were not standardized and were housed in disparate locations, which contributed to numerous records being overlooked or inadequate for inclusion in national distribution maps (Gilliam et al. 2020). The NTSP was developed, in part, to facilitate efforts across SHDs and their local public health partners to collect field data to improve the quality of acarological risk maps.

A previous survey (Mader et al. 2021) indicated that “a unified community of practice across jurisdictions would help to address barriers to tick surveillance program development.” In an effort to share relevant information across a potential community of tick surveillance practice, in Supplementary Material 1, we provide detailed summaries of the inception and intent of specific SHD tick surveillance programs that contribute data to the NTSP. In addition, these programmatic narratives are included to aid in understanding gaps in collection records over time and space, owing in part to resource limitations, shifting public health priorities, or a historic lack of a consistent national scale tick surveillance strategy.

In summary, these narratives show that in some states, particularly in the Northeast region where Lyme disease was first described in the United States, public health departments have been the leading agency coordinating tick surveillance efforts prior to the launch of the national program. Their primary objectives were to describe risk of exposure to newly described human pathogens and later to assess changing risks for tick-borne diseases in their states. In other states, Ixodes surveillance programs were lacking prior to the development of the NTSP and provision of funding to support such programs.

Public health funding to support tick surveillance has largely been reactionary, rather than anticipatory or proactive. In many situations, vector surveillance was initially focused on species transmitting pathogens causing vector-borne diseases of greatest incidence or concern at the time programs were developed (e.g., plague, tularemia, West Nile virus disease, babesiosis). Such programs provided an infrastructure that could be expanded to include Ixodes ticks as the public health significance of the tick increased locally. Ixodes scapularis was not recognized as a medically important species until the 1970s (Spielman 1976, Piesman and Spielman 1980) and was often not the focus for early vector surveillance programs. Eventually, Lyme disease emerged as the most commonly reported vector-borne disease in the United States, multiple other pathogens associated with Ixodes ticks were described (reviewed by Eisen and Eisen 2018, Rosenberg et al. 2018), and I. scapularis expanded its range to communities that were previously not at risk for I. scapularis-borne infections (Eisen and Eisen 2023). Existing vector surveillance programs reacted by expanding or shifting focus to include Ixodes ticks. In other states, as the threat of Ixodes-borne diseases was imminent, new programs were developed to describe the distribution and abundance of the primary vectors of Lyme disease spirochetes, and to map the distribution and prevalence of B. burgdorferi and other Ixodes-associated human pathogens (including Anaplasma phagocytophilum Rickettsiales: Anaplasmataceae (anaplasmosis), Babesia microti Piroplasmida: Babesiidae (babesiosis), Borrelia miyamotoi Spirochaetales: Spirochaetaceae (hard tick relapsing fever [HTRF]), Ehrlichia muris eauclairensis Rickettsiales: Ehrlichiaceae (ehrlichiosis), and Powassan virus Flaviviridae (Powassan encephalitis or Powassan virus disease). In some states, initial tick surveillance efforts were led by academic institutions with a limited spatial focus intended to support research activities, rather than to provide publicly available surveillance data.

Over time, due in part to coordination and funding provided by CDC, SHDs played an increasing role in leading the coordination of tick surveillance and communication efforts as local tick-borne disease threats grew in significance and funding became available to support such activities. The success of many SHD-led tick surveillance programs has been dependent on building partnerships and acquiring sufficient funding to maintain programs. State-specific programmatic objectives and timing of their enactment relative to the start of the NTSP are described in Table 1. In comparisons between pre- and post-inception of the NTSP time periods, we note an increase in the use of active tick surveillance methods (e.g., drag or flag sampling) in programs led by SHDs after the NTSP was initiated (Table 1).

Overcoming Funding Limitations and the Challenge of Large Geographic Areas to Surveil Through Partnership-building and Implementation of Passive Surveillance Efforts

The greatest challenge cited by SHDs to maintaining and expanding tick surveillance efforts was adequate and reliable funding to support active surveillance (drag/flag sampling and/or pathogen testing) (Supplementary Material 1, Mader et al. 2021). Active surveillance can be used to address each of the stated national surveillance goals (Table 1) and provides geographically precise estimates of tick and pathogen presence, tick abundance, and prevalence of human-disease causing pathogens in ticks (Eisen and Paddock 2021). However, active surveillance is time-consuming and costly, often requiring travel costs to survey distant sites, increased staff time to conduct sampling, and coordination with property owners to obtain permission to access properties to collect ticks. Pathogen testing adds additional costs beyond those associated with specimen collection. As the lead agency for national Ixodes tick surveillance in the United States, the CDC provides funding to SHDs to support their tick surveillance efforts through the CDC’s Epidemiology and Laboratory Capacity cooperative agreement, and offers laboratory support for tick identification and molecular detection of pathogens in Ixodes ticks (Foster et al. 2023, Osikowicz et al. 2023). However, even in the smallest states, the geographic coverage of tick habitat can be daunting; programmatic success is highly dependent on building effective partnerships to extend surveillance coverage.

Most SHDs have built partnerships with local (city, county, or tribal) health departments, academic institutions, vector control programs, or other state or federal agencies to extend the geographic area that can be actively sampled (Supplementary Material 1). Many also have established passive tick surveillance programs to address a subset of surveillance objectives over large coverage areas using limited resources to support the program (Table 1). In several states, state health department staff are actively engaged in field collection activities, as funding and time permits (California, Indiana, New Jersey, New York, West Virginia, Wisconsin) and in some cases extend their reach by partnering with local health departments (Indiana, New York, West Virginia, Wisconsin). Others contract tick collection services to other state agencies (Vermont) or collaborate with academic partners (California, Indiana, New York, North Carolina, Virginia, West Virginia, Wisconsin), while other SHDs partner with vector control districts (California, New Jersey, New York).

Each of the participating states provide(d) tick identification services through their SHD or partnering state agencies (e.g., Vermont Department of Agriculture provides tick identification services), and some offer training on tick identification to their surveillance partners (Indiana, New Jersey New York, West Virginia, Wisconsin). Pathogen detection efforts are costly and require specialized skills and equipment. The majority of SHD that are engaged in active tick surveillance submit their ticks to CDC for pathogen testing in a centralized laboratory, but a few have established laboratory capacity and provide pathogen detection support (California, New Jersey, New York); some states that were not engaged in this report contract tick testing to academic institutions.

In some states, resource limitations preclude ongoing active surveillance activities, and a subset of tick surveillance objectives are addressed through passive surveillance or field investigations of reported human tick-borne disease cases (Virginia and Washington). In other states (Indiana, New York, North Carolina, West Virginia, Wisconsin), passive surveillance is used, or has been used, to complement active surveillance or to achieve a subset of surveillance objectives (Table 1). For example, collecting ticks from hunter-killed deer is an extremely efficient way of documenting established tick populations (Schulze et al. 1984, Keefe et al. 2009, Lee et al. 2013). However, in some jurisdictions, changes in the administration of hunting permits pose challenges to this form of surveillance. Data derived from monitoring deer, however, are not informative for assessing densities of host-seeking ticks or estimating pathogen prevalence in ticks (CDC 2018).

Several states have supported passive surveillance programs that accept ticks submitted by the public, human health care providers, or veterinarians to document tick and pathogen presence. Benefits and drawbacks of such surveillance strategies were reviewed previously (Eisen and Eisen 2021, Holcomb et al. 2023). In summary, spatial certainty of data generated through programs focused on tick submissions from the public or veterinarians (e.g., presence of ticks or pathogens) is often limited by lack of reliable information on travel histories or, in some cases, confidence in tick identifications is tempered if ticks were not identified by trained personnel or if specimen condition precluded identification to species. Nonetheless, passive surveillance programs that accept ticks from the public and report back tick identification information to submitters provides an added opportunity for public outreach. Tick bite prevention recommendations are often returned to submitters along with identification of the ticks (Kopsco et al. 2021). Although such programs can be economical, informative, and provide a unique opportunity to engage with the public, they are limited in the surveillance goals they can adequately address (tick and pathogen presence, with limited spatial certainty unless travel histories are considered) and can become costly and unsustainable as submission volumes increase (Nieto et al. 2018, Porter et al. 2021, Supplement 1).

Tick Surveillance Data Are Used to Complement Epidemiological Surveillance to Improve Monitoring of Risk for Acquiring Tick-borne Diseases

Tick surveillance data are used by SHDs to help increase public and healthcare provider awareness of tick-borne disease risks, promote adoption of prevention strategies, encourage pathogen-specific laboratory testing and reporting, and to identify geographic variation and potential disparities in exposure risks and case reporting. Locations of human exposures to infected ticks are typically not captured in reporting of notifiable tick-borne diseases. Instead, established national surveillance protocols require that cases be reported by county of residence. Documenting the distribution of ticks and tick-borne pathogens improves understanding of areas posing risks for acquiring tick-borne infections. Acarological risk metrics generated from tick surveillance are largely concordant with reported human disease cases. In the eastern United States, the expanding distributions of I. scapularis and B. burgdorferi appear to coincide with changes in the reported distribution of Lyme disease cases (Kugeler et al. 2015, Burtis et al. 2022b, Eisen and Eisen 2023) and at regional scales, the density of B. burgdorferi-infected nymphs often correlates with Lyme disease incidence (Pepin et al. 2012). More locally, in New York, tick-borne pathogen surveillance data have confirmed the range expansion of Ixodes-borne pathogens (Kogut et al. 2005), and pathogen-specific acarological risk index values (e.g., densities of pathogen infected ticks) correlate with the distribution and incidence of human tick-borne diseases at the county- and zip code-levels (O’Connor et al. 2021, Russell et al. 2021). In instances where acarological data are concordant with epidemiological trends, SHDs have reported that tick surveillance data build confidence in assuming local exposures of reported tick-borne disease cases. For example, in Washington, local Lyme disease exposure was assumed but not confirmed until pathogen detection in field-collected ticks occurred in 2015. Together, the epidemiological and acarological surveillance data are useful for communicating risk for tick-borne diseases within jurisdictions.

Occasions when tick surveillance data are lacking, or are discordant with human disease case reporting, present opportunities for health departments to assess the possibilities of under-reporting of tick-borne disease cases. For example, the West Virginia Department of Health and Human Resources was concerned that Lyme disease could be underreported in the southwest region of West Virginia. Compared with other parts of the state, Lyme disease burden has been low in southwestern West Virginia. Tick surveillance was used to assess if cases were being under-reported in that region, or if exposure risks were verifiably lower than in other parts of the state. Unfortunately, no veterinarians in southwestern West Virginia were submitting I. scapularis ticks through the West Virginia Veterinary Tick Submission Project. As a result, relative risk of exposure to B. burgdorferi infected ticks could not be assessed based on passive surveillance. In 2021, the state public health entomologist actively surveyed for host-seeking ticks in southwestern West Virginia and only recovered I. scapularis ticks from 3 of 9 tick surveillance sites. Ixodes scapularis abundance was much lower than other parts of the state that had been sampled previously, building confidence in the assumption that Lyme disease risk was low in the southwestern part of the state and cases were not being missed through traditional epidemiological surveillance. Similarly, the New York State Department of Health (NYSDOH) investigated anomalies in tick-borne pathogen (A. phagocytophilum) and epidemiological (anaplasmosis) surveillance and learned that they were overestimating anaplasmosis risk through tick surveillance efforts in certain regions of New York because initial pathogen screening did not differentiate between pathogenic and nonpathogenic genetic variants of A. phagocytophilum in host-seeking ticks. Thus, the lack of anaplasmosis cases reported from these regions was not the result of misdiagnosis or under-reporting (Prusinski et al. 2023).

Tick surveillance data have been used in conjunction with epidemiological data to recognize disparities in risk among populations. For example, analysis of NYSDOH active tick and tick-borne pathogen surveillance data and human tick-borne disease case surveillance data revealed that anaplasmosis disproportionally impacts rural populations in New York (Russell et al. 2021), and that babesiosis may disproportionally impact certain racial and ethnic groups that are traditionally employed in specific outdoor occupations in areas where Ba. microti-infected ticks are abundant in New York (O’Connor et al. 2021).

Tick-borne pathogen surveillance has proven useful in understanding the geographic distributions of pathogens that cause tick-borne illnesses that are not subject to mandatory reporting. For example, hard tick relapsing fever (HTRF) is not currently classified as a reportable disease under New York State public health law. Tick-borne pathogen surveillance is the only way that the NYSDOH can assess potential risk of exposure to B. miyamotoi-infected ticks across the state. A recent analysis of tick surveillance data revealed heterogeneity in risk for HTRF across New York and concluded that diagnosis of HTRF is rare and cases are likely vastly under-reported because of the lack of case reporting mandates (Foley et al. 2023). Similarly, HTRF is a rare and emerging tick-borne disease with 87 cases reported in New Jersey since 2018. Hard tick relapsing fever is not currently a mandatory reportable disease in New Jersey, but voluntary human case-based surveillance has been implemented since 2017 to bolster knowledge of disease incidence in New Jersey. Because > 95% of communicable disease reports are received via electronic laboratory reporting, it is thought that almost all reports for New Jersey residents are being received. However, with voluntary surveillance, some cases may be missed and as an emerging disease, healthcare providers may not routinely consider HTRF in their differential diagnoses when ordering laboratory testing for suspected tick-borne diseases. Human HTRF cases are clustered in the upper northwest part of the state with very few reported cases in southern New Jersey (NJDOH 2024). Tick surveillance data, however, show similar pathogen prevalence of B. miyamotoi in ticks across the state, even in regions where human case have not been reported (NJDOH 2024). These tick surveillance findings might indicate a lack of healthcare provider awareness of HTRF, particularly in southern New Jersey.

Data generated through tick surveillance efforts are also noted to be useful in tracking risk for local exposure to tick-borne pathogens that cause rare diseases (e.g., HTRF, Powassan encephalitis, or ehrlichiosis caused by E. muris eauclairensis) or for estimating risks for coinfections (Johnson et al. 2018, Lehane et al. 2021, Foley et al. 2023), which are also not tracked through national surveillance for vector-borne diseases. For example, Powassan encephalitis is a rare and emerging arboviral disease, with only 16 cases reported in 4 counties in New Jersey since 2013. However, active tick surveillance efforts in New Jersey have detected Powassan virus in ticks in 7 counties, including 4 counties where no human cases have been reported (NJDOH 2024). The tick data can be used to increase awareness of healthcare providers about Powassan virus disease risk, clinical diagnosis and treatment guidelines, and of available public health clinical testing services. However, detection of rare pathogens in ticks takes considerable effort, and in some cases, rare tick-borne diseases might be detected in clinical samples before pathogens are detected in host-seeking ticks.

In some cases, pathogens are discovered locally in ticks before they are identified in clinical samples from the same regions. For example, E. muris eauclairensis has been detected in the Upper Midwest in ticks and in clinical samples (Pritt et al. 2017). However, ehrlichiosis associated with E. muris eauclairensis has not been described outside of that region. Local transmission of E. muris eauclairensis in Ixodes cookei Packard and I. scapularis was detected in the Northeast (Massachusetts) through passive and active tick surveillance, respectively (Xu et al. 2018, 2023), indicating a risk for human infections in the Northeast. Borrelia miyamotoi, which was only recently identified in a California resident lacking out of state travel history, was detected in I. pacificus ticks from California over a decade prior (Mun et al. 2006, Padgett et al. 2014, Rubio et al. 2023). Tick surveillance data have been used to define the geographic distribution and prevalence of B. miyamotoi across California to provide insights into the risk for HTRF, which is not a notifiable condition in California (Padgett et al. 2014). In Washington State, A. phagocytophilum was detected in tick vectors years prior to the first human case reports (Dykstra et al. 2020). In addition, the first reported alpha-gal syndrome case in a Washington resident was detected through citizen tick submissions to Washington’s passive tick surveillance program; the submission included notes regarding an adverse reaction to consumption of meat following the tick bite, which triggered follow-up from the SHD. In Virginia, Ba. microti was detected in a host-seeking tick in southwestern Virginia 2 yr prior to the first babesiosis cases being reported. In New Jersey, Bourbon and Heartland viruses were detected in tick vectors collected through the NTSP in the absence of any reported human cases (NJDOH 2024).

Pathogen data generated through active tick surveillance have been used to describe the geographic distribution of a recently described Lyme disease-causing spirochete, B. mayonii, in the Upper Midwest (Johnson et al. 2018, Lehane et al. 2021, Fleshman et al. 2022). Although Lyme disease is common and broadly distributed in this region, diagnostic (serological) testing for Lyme disease does not typically differentiate human infections caused by B. burgdorferi s.s. or Borrelia mayonii. Clinical presentation of Lyme disease differs between these infectious agents (Pritt et al. 2016, 2022, McGowan et al. 2023). Tick surveillance provides unique information on where persons are at risk for exposure to these individual Borrelia species (Lehane et al. 2021, Fleshman et al. 2022, Foster et al. 2023), information that may be useful in health care provider education (Rodino and Pritt 2022, McGowan et al. 2023).

Although we have provided several examples of how tick and tick-borne pathogen surveillance data have been used to complement epidemiological surveillance, it is crucial to acknowledge that utility of the data is limited by its completeness. In many states in the United States, tick surveillance data are currently insufficient to identify or communicate the risk for tick-borne diseases.

Challenges in Communicating Tick Surveillance Findings to the Public and Health Care Providers

Public health programs aim to provide the public, health care providers, and public health policy makers with data-driven assessments of risk for acquiring tick-associated illnesses. Data generated through tick surveillance activities are typically communicated through federal and state health department web pages, government reports, outreach to health care providers, “at risk” groups (e.g., patient advocacy groups, outdoor workers, outdoor recreationalists) or the public through press or public inquiries, social media postings, or other community outreach activities. Specifically, tick and pathogen presence data are used to communicate risk for tick-borne diseases and to promote adoption of prevention strategies by the public (Piesman and Eisen 2008, Eisen and Dolan 2016) or to educate health care providers on local risk for tick-borne illness and risk for coinfections to better inform diagnosis of tick-borne diseases (see Supplementary Material 2 for examples from representative state and federal agencies). Although more advanced metrics (e.g., pathogen prevalence, densities of host-seeking ticks by life stage, or densities of pathogen specific infected host-seeking ticks) often correlate better with the distribution and incidence of tick-borne disease cases compared with tick presence alone (Pepin et al. 2012, Burtis et al. 2022a), limited data and high variability leads to challenges in communicating complex and often incomplete surveillance data (e.g., densities of infected ticks) to end users.

Public health agencies share a concern for not misleading stakeholders, particularly by inadvertently conveying a lack of risk in areas where ticks or pathogens were not detected. The national tick surveillance objectives differ in complexity and completeness in records reported to ArboNET, with more complex objectives (density of host-seeking Ixodes ticks, prevalence of tick-borne pathogens in host-seeking Ixodes ticks) achieved in fewer US counties than more simple objectives (presence of Ixodes ticks, presence of specific tick-borne pathogens in host-seeking Ixodes ticks) (Fleshman et al. 2022, Foster et al. 2023, 2024) (Figs 1 and 2). Describing the county level distribution of medically important ticks is the easiest and least costly objective to achieve, and arguably the most straight-forward to communicate. Established populations of I. scapularis have been documented in a majority of counties in the eastern United States (CDC 2024b) (Fig. 1a). Owing to more limited surveillance efforts in the Pacific Northwest, maps of the distribution of I. pacificus are incomplete (CDC 2024b) (Fig. 1a). Normally, tick presence is contrasted with “no records” for counties lacking evidence of established tick populations. This lack of records is often misinterpreted as absence of the tick. Showing density data is useful for demonstrating effort and aids in differentiating counties where no sampling was conducted from those where sampling was conducted but no or few ticks were collected. This information is likely to be most useful at the national scale for differentiating low from high probabilities of tick encounters (Pepin et al. 2012), or in states or regions where the tick has not been confirmed to be established in every county.

Several states (California, Indiana, New York, West Virginia) maintain online communication products (data dashboards, maps, etc.) directed to the public that show the county-scale distribution of Ixodes ticks using data generated through active surveillance (collection of ticks from the environment). Other states (North Carolina, Vermont, Virginia, Washington) maintain public facing products that show tick distribution at the county, regional, and state scale based upon passive surveillance programs (ticks submitted or reported by the public, veterinarians, etc.). Spatial models have been developed to estimate the distribution of suitable habitat for both tick species in the United States (Hahn et al. 2016, 2017, 2021). Such generalized distribution maps are often used to estimate risk in unsampled counties and to simplify risk communication (CDC 2024a). In many instances, state and federal public health programs communicate risk for encounters with ticks by showing the generalized distribution of the tick (where ticks could be found) along with county-specific records of where ticks have been found (Supplement 2).

Notably, I. scapularis and I. pacificus are more broadly distributed than their associated human disease-causing pathogens (Burtis et al. 2022a, 2023), highlighting the importance of incorporating pathogen presence data into risk communication. However, pathogen presence records (Fig. 2a) are far less complete than tick presence records (Fig. 1a) and spatial models estimating ranges have only been developed for a subset of pathogens and tick species. Several states (California, Indiana, New Jersey, New York, Vermont) maintain online communication products directed to the public that show the county-scale presence of select pathogens identified in host-seeking I. scapularis or I. pacificus (Supplementary Material 2). However, as a result of the incompleteness of records, some health departments have noted hesitation in using such data in communications and have opted to delay sharing such information until records are more complete.

More complex metrics that include pathogen prevalence or densities of infected ticks are often shared with the public health and scientific communities through peer-reviewed publications, government reports or publicly available look-up tables (Foster et al. 2023, 2024, CDC 2024b). However, owing to the cost and effort of documenting these metrics, with few exceptions (Indiana, New Jersey, New York, Vermont), these metrics are often incomplete. There is also a high degree of variation observed in tick density sampling values reported to ArboNET (Foster et al. 2024). In the absence of complete records, spatial models can be used to generalize estimates of risk for exposure to ticks or infected ticks. Existing regional models (eastern United States) have focused primarily on the nymphal life stage and a single pathogen, B. burgdorferi (Diuk-Wasser et al. 2012), but such models require updating as distributions change over time and will require modeling risk of encounters with multiple life stages and multiple pathogens. There is a need to translate continuous variables of risk (e.g., percentages of ticks infected or numbers of ticks per area sampled) into interpretable categories (e.g., low or elevated risk), to make such metrics more accessible to varying audiences. At present, CDC and SHDs continue to amass data on more complex metrics, which can be used to update risk maps, models and derive meaningful risk categories.

Conclusions

In summary, we found SHD-led tick surveillance programs started in response to the most immediate vector-borne disease threats at the time. As Lyme disease and other Ixodes-associated illnesses expanded in range and incidence from the 1980s onward, resources shifted to focus on Ixodes ticks. Although not the focus of this discussion, we note a need to increase data collection efforts for other medically important ticks and their associated human pathogens. We suggest that the infrastructure that is already in place for Ixodes tick surveillance could be expanded to define the distributions of other ticks and their associated pathogens. Public health programs use data generated through tick surveillance to assess and communicate risk for tick-borne diseases and to promote adoption of prevention strategies. Tick-borne pathogen surveillance has been useful for identifying the risk for tick-borne diseases that are not notifiable and for recognizing disparities in risk and recognition of tick-borne diseases.

The utility of tick surveillance data is hampered by incompleteness of records, a high degree of variation (particularly in tick density data) and vaguely or incompletely defined associations between acarological metrics and human disease outcomes. Although we have seen increased engagement in tick surveillance and improved coordination of activities with the founding of the NTSP (Figs 1 and 2, Table 1), SHDs face resource limitations that preclude extensive sampling. In some cases, a lack of dedicated funding specifically for tick surveillance results in a lack of engagement or prioritization of these activities. In other cases, the lack of accepted, effective, and accessible environmental control of ticks (Eisen 2020) serves as a deterrent to collect tick surveillance data. Tick surveillance is costly and labor-intensive. Programmatic success has been dependent on building partnerships with federal and local health departments and academic institutions to expand coverage to actively sample for host-seeking ticks across broad risk areas and to test ticks for pathogens. Few programs reported partnering with mosquito control programs to expand surveillance activities, but in some localities, such partnerships have proven to be very productive and beneficial. A recent survey suggested a willingness of publicly funded vector control programs that are currently focused primarily on mosquito surveillance and control to engage in tick surveillance activities (Burtis et al. 2024). Passive surveillance was sometimes used as a less costly alternative to active surveillance, but such programs come with unique advantages and challenges. CDC and state health partners are currently engaged in incorporating passively collected tick and tick-borne pathogen data into ArboNET and resulting public-facing maps. We recognize a need for comparative cost-effectiveness analyses for passive and active tick surveillance strategies.

There is a need for the public health community to think strategically about the most cost-effective means of providing complete and accurate information on tick-borne disease risk. In addition to leveraging funding through partnership building, strategic sampling of populated or high human-use localities that are under-represented in existing surveillance efforts, coupled with statistical modeling may be useful in reducing effort and cost of such programs. A clearer understanding is needed of which of the captured acarological metrics (e.g., tick or pathogen presence, tick densities, pathogen prevalence, densities of infected ticks) correlates best with human disease outcomes; identifying the quantitative relationship between tick densities, infection prevalence, human-encounters with ticks and disease outcomes will likely aid in refining recommendations for tick surveillance protocols. We further recognize a need to optimize communication of complex data such that it is accurate and interpretable for all intended audiences.

Supplementary data

Supplementary data are available at Journal of Medical Entomology online.

Acknowledgments

We thank Robert A. Jordan for helpful comments and providing historical context.

Author contributions

Rebecca Eisen (Conceptualization [lead], Data curation [equal], Methodology [equal], Writing—original draft [lead], Writing—review & editing [lead]), Erik Foster (Data curation [equal], Visualization [lead], Writing—original draft [supporting], Writing—review & editing [supporting]), Anne Kjemtrup (Writing—original draft [equal], Writing—review & editing [equal]), Megan Saunders (Writing—original draft [equal], Writing—review & editing [equal]), Jennifer Brown (Writing—original draft [equal], Writing—review & editing [equal]), Lee Green (Writing—original draft [equal], Writing—review & editing [equal]), Kim Cervantes (Writing—original draft [equal], Writing—review & editing [equal]), Melissa Prusinski (Writing—original draft [equal], Writing—review & editing [equal]), Jennifer White (Writing—original draft [equal], Writing—review & editing [equal]), Alexis Barbarin (Writing—original draft [equal], Writing—review & editing [equal]), Carl Williams (Writing—original draft [equal], Writing—review & editing [equal]), Natalie Kwit (Writing—original draft [equal], Writing—review & editing [equal]), Joshua Bernick (Writing—original draft [equal], Writing—review & editing [equal]), David Gaines (Writing—original draft [equal], Writing—review & editing [equal]), Elizabeth Dykstra (Writing—original draft [equal], Writing—review & editing [equal]), Hanna N. Oltean (Writing—original draft [equal], Writing—review & editing [equal]), Eric Dotseth (Writing—original draft [equal], Writing—review & editing [equal]), Xia Lee (Writing—original draft [equal], Writing—review & editing [equal]), and Rebecca Osborn (Writing—original draft [equal], Writing—review & editing [equal])

Disclaimer: The findings and conclusions of this study are by the authors and do not necessarily represent the views or opinions of the Centers for Disease Control and Prevention, or the state public health departments in California, Indiana, New Jersey, New York, North Carolina, Vermont, Virginia, Washington, West Virginia, or Wisconsin.

References