Epidemiology of Lyme Disease

Studies on density and pathogen prevalence of Ixodes ricinus indicate that vegetation and local host community drive much of their variation between green spaces. Contrarily, micro-geographic variation is understudied, although its understanding could reduce disease risk. We studied the density of infectious nymphal Ixodes sp. (“DIN”, proxy for disease hazard), density of questing nymphs (“DON”) and nymphal infection prevalence (“NIP”) near recreational forest infrastructure. Drag sampling within forest stands and at adjacent benches and trails was combined with vegetation surveys, camera trapping hosts and pathogen screening of ticks. We analysed Borrelia burgdorferi s.l. and its genospecies, with complementary analyses on Rickettsia sp., Anaplasma phagocytophilum, Neoehrlichia mikurensis and Borrelia miyamotoi. DIN was highest in forest interior and at trails enclosed by forest. Lower disease hazard was observed at benches and trails at forest edges. This infrastructure effect can be attributed to variation in vegetation characteristics and the habitat use of tick hosts, specifically roe deer, rodents and songbirds. DON is the main driver of DIN at micro-geographic scale and negatively affected by infrastructure and forest edges. A positive association with vegetation cover in understorey and canopy was observed, as were positive trends for local rodent and songbird abundance. NIP of different pathogens was affected by different drivers. Lower B. burgdorferi s.l. prevalence in the interior of forest stands, driven by its most prevalent genospecies B. afzelii, points towards higher density of uninfected hosts there. B. afzelii was positively associated with understorey containing tall species and with high canopy cover, whereas local bird community composition predicts B. garinii prevalence. A positive effect of songbird abundance and a negative effect of pigeons were observed. Our findings support amplification and inhibition mechanisms within forest stands and highlight that the effect of established drivers of DIN may differ based on the considered spatial scale.

Background incidence rates (IRs) of health outcomes in Lyme disease endemic regions are useful to contextualize events reported during Lyme disease vaccine clinical trials or post-marketing. The objective of this study was to estimate and compare IRs of health outcomes in Lyme disease endemic versus non-endemic regions in the US during pre-COVID and COVID era timeframes.

IQVIA PharMetrics® Plus commercial claims database was used to estimate IRs of 64 outcomes relevant to vaccine safety monitoring in the US during January 1, 2017-December 31, 2019 and January 1, 2020-December 31, 2021. Analyses included all individuals aged ≥ 2 years with ≥ 1 year of continuous enrollment. Outcomes were defined by International Classification of Diseases Clinical Modification, 10th Revision (ICD-10-CM) diagnosis codes. IRs and 95 % confidence intervals (CIs) were calculated for each outcome and compared between endemic vs. non-endemic regions, and pre-COVID vs. COVID era using IR ratios (IRR).

The study population included 8.7 million (M) in endemic and 27.8 M in non-endemic regions. Mean age and sex were similar in endemic and non-endemic regions. In both study periods, the IRs were statistically higher in endemic regions for anaphylaxis, meningoencephalitis, myocarditis/pericarditis, and rash (including erythema migrans) as compared with non-endemic regions. Conversely, significantly lower IRs were observed in endemic regions for acute kidney injury, disseminated intravascular coagulation, heart failure, myelitis, myopathies, and systemic lupus erythematosus in both study periods. Most outcomes were statistically less frequent during the COVID-era.

This study identified potential differences between Lyme endemic and non-endemic regions of the US in background IRs of health conditions during pre-COVID and COVID era timeframes to inform Lyme disease vaccine safety monitoring. These regional and temporal differences in background IRs should be considered when contextualizing possible safety signals in clinical trials and post-marketing of a vaccine targeted at Lyme disease prevention.

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.

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.

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.

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).

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.

Tick-borne diseases continue to threaten human health across the United States. Both active and passive tick surveillance can complement human case surveillance, providing spatio-temporal information on when and where humans are at risk for encounters with ticks and tick-borne pathogens. However, little work has been done to assess the concordance of the acarological risk metrics from each surveillance method. We used data on Ixodes scapularis and its associated human pathogens from Connecticut (2019–2021) collected through active collections (drag sampling) or passive submissions from the public to compare county estimates of tick and pathogen presence, infection prevalence, and tick abundance by life stage. Between the surveillance strategies, we found complete agreement in estimates of tick and pathogen presence, high concordance in infection prevalence estimates for Anaplasma phagocytophilum, Borrelia burgdorferi sensu stricto, and Babesia microti, but no consistent relationships between actively and passively derived estimates of tick abundance or abundance of infected ticks by life stage. We also compared nymphal metrics (i.e., pathogen prevalence in nymphs, nymphal abundance, and abundance of infected nymphs) with reported incidence of Lyme disease, anaplasmosis, and babesiosis, but did not find any consistent relationships with any of these metrics. The small spatial and temporal scale for which we had consistently collected active and passive data limited our ability to find significant relationships. Findings are likely to differ if examined across a broader spatial or temporal coverage with greater variation in acarological and epidemiological outcomes. Our results indicate similar outcomes between some actively and passively derived tick surveillance metrics (tick and pathogen presence, pathogen prevalence), but comparisons were variable for abundance estimates.