Rodent species as possible reservoirs of Borrelia burgdorferi in a prairie ecosystem

Beckmann, Sean; Freund, Rhonda; Pehl, Hayden; Rodgers, Ashley; Venegas, Taggart

doi:10.1016/j.ttbdis.2019.06.011

Cited by 7 publications

(7 citation statements)

References 58 publications

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“…If we are to limit the impact of emerging Lyme disease on human health in the U.S., the appropriate prevention measures should be implemented and targeted towards the at-risk populations in the high-risk locations [ 9 ]. Prevention measures include personal protection, environmental management for tick control, and community-based interventions such as rodent-targeted vaccines (RTVs), as many small rodents are carriers of B. Burgdorferi [ 9 ], e.g., P. leucopus [ 5 ], Sciurus griseus [ 34 ], Zapus hudsonius , and Ictidomys tridecemlineatus [ 35 ], among others. The common approach for defining human populations at risk of Lyme disease has been identifying endemic locations and predicting the occurrence of vectors using risk maps.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

Ozdenerol

Bingham-Byrne

Seboly

2021

IJERPH

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The aim of this study was to investigate lifestyles at risk of Lyme disease, and to geographically identify target populations/households at risk based on their lifestyle preferences. When coupled with geographically identified patient health information (e.g., incidence, diagnostics), lifestyle data provide a more solid base of information for directing public health objectives in minimizing the risk of Lyme disease and targeting populations with Lyme-disease-associated lifestyles. We used an ESRI Tapestry segmentation system that classifies U.S. neighborhoods into 67 unique segments based on their demographic and socioeconomic characteristics. These 67 segments are grouped within 14 larger “LifeModes” that have commonalities based on lifestyle and life stage. Our dataset contains variables denoting the dominant Tapestry segments within each U.S. county, along with annual Lyme disease incidence rates from 2000 through 2017, and the average incidence over these 18 years. K-means clustering was used to cluster counties based on yearly incidence rates for the years 2000–2017. We used analysis of variance (ANOVA) statistical testing to determine the association between Lyme disease incidence and LifeModes. We further determined that the LifeModes Affluent Estates, Upscale Avenues, GenXurban, and Cozy Country Living were associated with higher Lyme disease risk based on the results of analysis of means (ANOM) and Tukey’s post hoc test, indicating that one of these LifeModes is the LifeMode with the greatest Lyme disease incidence rate. We further conducted trait analysis of the high-risk LifeModes to see which traits were related to higher Lyme disease incidence. Due to the extreme regional nature of Lyme disease incidence, we carried out our national-level analysis at the regional level. Significant differences were detected in incidence rates and LifeModes in individual regions. We mapped Lyme disease incidence with associated LifeModes in the Northeast, Southeast, Midcontinent, Rocky Mountain, and Southwest regions to reflect the location-dependent nature of the relationship between lifestyle and Lyme disease.

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Section: Discussionmentioning

confidence: 99%

The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

Ozdenerol

Bingham-Byrne

Seboly

2021

IJERPH

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“…For example, Cortinas & Kitron [14] found a pattern of the geographic spread of I. scapularis that correlated with the distribution of relatively scarce forest habitats in central Illinois: continuously forested riparian habitats associated with the Illinois River. More recently, however, established I. scapularis populations and B. burgdorferi transmission have been discovered in other habitats that do not occur in the Northeast, such as prairies [31][32][33]. Therefore, the geographic expansion of Lyme disease may be best understood by examining factors specific to each region [11,34,35].…”

Section: Introductionmentioning

confidence: 99%

Landscape features predict the current and forecast the future geographic spread of Lyme disease

et al. 2020

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Lyme disease, the most prevalent vector-borne disease in North America, is increasing in incidence and geographic distribution as the tick vector, Ixodes scapularis , spreads to new regions. We re-construct the spatial-temporal invasion of the tick and human disease in the Midwestern US, a major focus of Lyme disease transmission, from 1967 to 2018, to analyse the influence of spatial factors on the geographic spread. A regression model indicates that three spatial factors—proximity to a previously invaded county, forest cover and adjacency to a river—collectively predict tick occurrence. Validation of the predictive capability of this model correctly predicts counties invaded or uninvaded with 90.6% and 98.5% accuracy, respectively. Reported incidence increases in counties after the first report of the tick; based on this modelled relationship, we identify 31 counties where we suspect I. scapularis already occurs yet remains undetected. Finally, we apply the model to forecast tick establishment by 2021 and predict 42 additional counties where I. scapularis will probably be detected based upon historical drivers of geographic spread. Our findings leverage resources dedicated to tick and human disease reporting and provide the opportunity to take proactive steps (e.g. educational efforts) to prevent and limit transmission in areas of future geographic spread.

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“…In a recent study of landscape determinants for blacklegged ticks in the Ottawa-Gatineau region, Talbot et al,2019 also found that distance to roads was a significant predictor of B. burgdorferi infection prevalence [22]. The importance of infrastructure and urban development in this model may be explained by local adaptations to urbanization of the white-footed mouse, which is the main reservoir for B. burgdorferi, or the role of other small mammals as competent reservoirs for B. burgdorferi in regions of the province [62][63][64]. Additionally, the importance of water in the B. burgdorferi model may represent possible habitat requirements of key reservoir species or hosts for I. scapularis as well as entry points of infected ticks via migratory birds [24,51,65].…”

Section: Plos Onementioning

confidence: 88%

“…burgdorferi , or the role of other small mammals as competent reservoirs for B . burgdorferi in regions of the province [ 62 – 64 ]. Additionally, the importance of water in the B .…”

Section: Discussionmentioning

confidence: 99%

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada

et al. 2020

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The blacklegged tick, Ixodes scapularis, is established in several regions of Ontario, Canada, and continues to spread into new geographic areas across the province at a rapid rate. This poses a significant public health risk since I. scapularis transmits the Lyme diseasecausing bacterium, Borrelia burgdorferi, and other pathogens of potential public health concern. The objective of this study was to develop species distribution models for I. scapularis and B. burgdorferi to predict and compare the potential distributions of the tick vector and the Lyme disease pathogen as well as the ecological factors most important for species establishment. Ticks were collected via tick dragging at 120 sites across southern, central, and eastern Ontario between 2015 and 2018 and tested for tick-borne pathogens. A maximum entropy (Maxent) approach was used to model the potential distributions of I. scapularis and B. burgdorferi. Two independent datasets derived from tick dragging at 25 new sites in 2019 and ticks submitted by the public to local health units between 2015 and 2017 were used to validate the predictive accuracy of the models. The model for I. scapularis showed high suitability for blacklegged ticks in eastern Ontario and some regions along the shorelines of the Great Lakes, and moderate suitability near Algonquin Provincial Park and the Georgian Bay with good predictive accuracy (tick dragging 2019: AUC = 0.898; ticks from public: AUC = 0.727). The model for B. burgdorferi showed a similar predicted distribution but was more constrained to eastern Ontario, particularly between Ottawa and Kingston, and along Lake Ontario, with similarly good predictive accuracy (tick dragging 2019: AUC = 0.958; ticks from public: AUC = 0.863. The ecological variables most important for predicting the distributions of I. scapularis and B. burgdorferi included elevation, distance to deciduous and coniferous forest, proportions of agricultural land, water, and infrastructure, mean summer/spring temperature, and cumulative annual degree days above 0˚C.

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Rodent species as possible reservoirs of Borrelia burgdorferi in a prairie ecosystem

Cited by 7 publications

References 58 publications

The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

Landscape features predict the current and forecast the future geographic spread of Lyme disease

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada

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