Assessing Tick-Borne Disease Risk and Surveillance: Toward a Multi-Modal Approach to Diagnostic Positioning and Prediction

Targeted Tick-Borne Disease Recognition: Assessing Risk for Improved Public Health

Tick-borne diseases (TBDs) pose a rapidly growing threat to public health. The incidence of TBDs is on the rise, necessitating a comprehensive understanding of the risk factors beyond demographic considerations. This brief report combines a preliminary review of the literature with geographical case mapping to identify the various factors influencing TBD risk. The report highlights the vulnerability of outdoor workers, the importance of outdoor activities, and the role of education in adopting preventive behaviors. Pet ownership and interactions with animals are also associated with an increased risk. The state of Illinois is used as a case study for this report, revealing regional variations in TBD incidence, and linking them to agricultural practices, forested areas, and park accessibility. These findings inform recommendations for targeted prevention strategies, emphasizing the need for detailed geographical data to enhance public health efforts in curbing TBD incidence and risk.

Self-reported tick exposure as an indicator of Lyme disease risk in an endemic region of Quebec, Canada

BACKGROUND

Lyme disease (LD) and other tick-borne diseases are emerging across Canada. Spatial and temporal LD risk is typically estimated using acarological surveillance and reported human cases, the former not considering human behavior leading to tick exposure and the latter occurring after infection.

OBJECTIVES

The primary objective was to explore, at the census subdivision level (CSD), the associations of self-reported tick exposure, alternative risk indicators (predicted tick density, eTick submissions, public health risk level), and ecological variables (Ixodes scapularis habitat suitability index and cumulative degree days > 0 °C) with incidence proportion of LD. A secondary objective was to explore which of these predictor variables were associated with self-reported tick exposure at the CSD level.

METHODS

Self-reported tick exposure was measured in a cross-sectional populational health survey conducted in 2018, among 10,790 respondents living in 116 CSDs of the Estrie region, Quebec, Canada. The number of reported LD cases per CSD in 2018 was obtained from the public health department. Generalized linear mixed-effets models accounting for spatial autocorrelation were built to fulfill the objectives.

RESULTS

Self-reported tick exposure ranged from 0.0 % to 61.5 % (median 8.9 %) and reported LD incidence rates ranged from 0 to 324 cases per 100,000 person-years, per CSD. A positive association was found between self-reported tick exposure and LD incidence proportion (ß = 0.08, CI = 0.04,0.11, p < 0.0001). The best-fit model included public health risk level (AIC: 144.2), followed by predicted tick density, ecological variables, self-reported tick exposure and eTick submissions (AIC: 158.4, 158.4, 160.4 and 170.1 respectively). Predicted tick density was the only significant predictor of self-reported tick exposure (ß = 0.83, CI = 0.16,1.50, p = 0.02).

DISCUSSION

This proof-of-concept study explores self-reported tick exposure as a potential indicator of LD risk using populational survey data. This approach may offer a low-cost and simple tool for evaluating LD risk and deserves further evaluation.

Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases

BACKGROUND

The extent of tick-borne disease (TBD) risk in the United States is generally unknown. Active surveillance using entomological measures, such as presence and density of infected nymphal Ixodes scapularis ticks, have served as indicators for assessing human risk, but results have been inconsistent and passive surveillance via public health systems suggests TBDs are underreported.

OBJECTIVE

Research using various data sources and collection methods (eg, Google Trends, apps, and tick bite encounters [TBEs] reports) has shown promise for assessing human TBD risk. In that vein, and engaging a One Health perspective, this study used multimodal databases, geographically overlaying patient survey data on TBEs and concomitant reports of TBDs with data drawn from other sources, such as canine serological reports, to glean insights and to determine and assess the use of various indicators as proxies for human TBD risk.

METHODS

This study used a mixed methods research strategy, relying on triangulation techniques and drawing on multiple data sources to provide insights into various aspects of human disease risk from TBEs and TBDs in the United States. A web-based survey was conducted over a 15-month period beginning in December 2020 to collect data on TBEs. To maximize the value of the covariate data, related analyses included TBE reports that occurred in the United States between January 1, 2000, and March 31, 2021. TBEs among patients diagnosed with Lyme disease were analyzed at the county level and compared to I scapularis and I pacificus tick presence, human cases identified by the Centers for Disease Control and Prevention (CDC), and canine serological data. Spatial analyses employed multilayer thematic mapping and other techniques.

RESULTS

After cleaning, survey results showed a total of 249 (75.7%) TBEs spread across 148 respondents (61.9% of all respondents, 81.7% of TBE-positive respondents); 144 (4.7%) counties in 30 states (60%) remained eligible for analysis, with an average of 1.68 (SD 1.00) and median of 1 (IQR 1) TBEs per respondent. Analysis revealed significant spatial matching at the county level among patient survey reports of TBEs and disease risk indicators from the CDC and other official sources. Thematic mapping results included one-for-one county-level matching of reported TBEs with at least 1 designated source of human disease risk (ie, positive canine serological tests, CDC-reported Lyme disease, or known tick presence).

CONCLUSIONS

Use of triangulation methods to integrate patient data on TBE recall with established canine serological reports, tick presence, and official human TBD information offers more granular, county-level information regarding TBD risk to inform clinicians and public health officials. Such data may supplement public health sources to offer improved surveillance and provide bases for developing robust proxies for TBD risk among humans.

Understanding Habitats and Environmental Conditions of White-Tailed Deer Population Density and Public Health Data to Aid in Assessing Human Tick-Borne Disease Risk

The extent of tick-borne diseases (TBDs) in the United States is largely unknown and underreported. Equitable diagnostic and treatment options may vary by geographic location. Triangulating multi-modal data sources informed by a One Health approach provides robust proxies for human TBD risk. Using data from the Indiana Department of Natural Resources collected from hunters during the white-tailed deer (Odocoileus virginianus) hunting season and other sources, we employ a mixed-methods approach based on thematic mapping and mixed effects modelling to determine if deer population density aligns with official disease data at the county level from (1) positive canine serological reports for, anaplasmosis, and Lyme Disease (LD); (2) positive human cases of ehrlichiosis, anaplasmosis, LD, and Spotted Fever rickettsioses; and (3) tick infectivity. We propose the need for multimodal data analysis using a variety of potential proxies to better estimate disease risk and inform public health policy and practice. We find similar spatial distributions between deer population density and human and canine TBDs in northeastern and southern Indiana, which are rural and mixed geographic areas. Overall, LD is more prevalent in the northwest, central-western, and southeastern counties, while ehrlichiosis is more common in the southern counties. These findings hold true across humans, canines, and deer.

Triangulating the New Frontier of Health Geo-Data: Assessing Tick-Borne Disease Risk as an Occupational Hazard among Vulnerable Populations

Determining interventions to combat disease often requires complex analyses of spatial-temporal data to improve health outcomes. For some vulnerable populations, obtaining sufficient data for related analyses is especially difficult, thus exacerbating related healthcare, research, and public health efforts. In the United States (U.S.), migrant and seasonal workers are especially affected in this regard, with data on health interventions and outcomes largely absent from official sources. In response, this study offers a multi-modal approach that involves triangulating geographically specified health data that incorporate reports on canine tick species, Lyme disease (LD) incidence, and patient symptom severity indicating potential subsequent disease burden. Spatial alignment of data at the U.S. county level was used to reveal and better understand tick-borne disease (TBD) incidence and risk among the identified populations. Survey data from migrant and seasonal workers in Texas were employed to determine TBD risk based on symptoms, occupations, and locations. Respondents who were found to have a higher likelihood of a TBD were also considerably more likely to report the most common symptoms of LD and other TBDs on the Horowitz Multiple Systemic Infectious Disease Syndrome Questionnaire. Those in the highly likely scoring group also reported more poor health and mental health days. Overall, a notable number of respondents (22%) were likely or highly likely to have a TBD, with particular relevance attributed to county of residence and living conditions. Also of note, almost a third of those reporting severe symptoms had received a previous Lyme disease diagnosis. These findings underscore the need for further surveillance among vulnerable populations at risk for TBDs.