Modulatory effect of cattle on risk for lyme disease.
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ABSTRACT:
SUBMITTER: Richter D
PROVIDER: S-EPMC3291337 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Approximately 10% to 20% of patients optimally treated for early Lyme disease develop persistent symptoms of unknown pathophysiology termed posttreatment Lyme disease syndrome (PTLDS). The objective of this study was to investigate associations between PTLDS and immune mediator levels during acute illness and at several time points following treatment. Seventy-six participants with physician-documented erythema migrans and 26 healthy controls with no history of Lyme disease were enrolled. Sixty-four cytokines, chemokines, and inflammatory markers were measured at each visit for a total of 6 visits over 1 year. An operationalized definition of PTLDS incorporating symptoms and functional impact was applied at 6 months and 1 year following treatment completion, and clinical outcome groups were defined as the return-to-health, symptoms-only, and PTLDS groups. Significance analysis of microarrays identified 7 of the 64 immune mediators to be differentially regulated by group. Generalized logit regressions controlling for potential confounders identified posttreatment levels of the T-cell chemokine CCL19 to be independently associated with clinical outcome group. Receiver operating characteristic analysis identified a CCL19 cutoff of >111.67 pg/ml at 1 month following treatment completion to be 82% sensitive and 83% specific for later PTLDS. We speculate that persistently elevated CCL19 levels among participants with PTLDS may reflect ongoing, immune-driven reactions at sites distal to secondary lymphoid tissue. Our findings suggest the relevance of CCL19 both during acute infection and as an immunologic risk factor for PTLDS during the posttreatment phase. Identification of a potential biomarker predictor for PTLDS provides the opportunity to better understand its pathophysiology and to develop early interventions in the context of appropriate and specific clinical information.
2016-07-20 | GSE84479 | GEO
Project description:BACKGROUND:Forests in urban landscapes differ from their rural counterparts in ways that may alter vector-borne disease dynamics. In urban forest fragments, tick-borne pathogen prevalence is not well characterized; mitigating disease risk in densely-populated urban landscapes requires understanding ecological factors that affect pathogen prevalence. We trapped blacklegged tick (Ixodes scapularis) nymphs in urban forest fragments on the East Coast of the United States and used multiplex real-time PCR assays to quantify the prevalence of four zoonotic, tick-borne pathogens. We used Bayesian logistic regression and WAIC model selection to understand how vegetation, habitat, and landscape features of urban forests relate to the prevalence of B. burgdorferi (the causative agent of Lyme disease) among blacklegged ticks. RESULTS:In the 258 nymphs tested, we detected Borrelia burgdorferi (11.2% of ticks), Borrelia miyamotoi (0.8%) and Anaplasma phagocytophilum (1.9%), but we did not find Babesia microti (0%). Ticks collected from forests invaded by non-native multiflora rose (Rosa multiflora) had greater B. burgdorferi infection rates (mean?=?15.9%) than ticks collected from uninvaded forests (mean?=?7.9%). Overall, B. burgdorferi prevalence among ticks was positively related to habitat features (e.g. coarse woody debris and total understory cover) favorable for competent reservoir host species. CONCLUSIONS:Understory structure provided by non-native, invasive shrubs appears to aggregate ticks and reservoir hosts, increasing opportunities for pathogen transmission. However, when we consider pathogen prevalence among nymphs in context with relative abundance of questing nymphs, invasive plants do not necessarily increase disease risk. Although pathogen prevalence is greater among ticks in invaded forests, the probability of encountering an infected tick remains greater in uninvaded forests characterized by thick litter layers, sparse understories, and relatively greater questing tick abundance in urban landscapes.
| S-EPMC5781316 | biostudies-literature
Project description:Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease.
| S-EPMC7000681 | biostudies-literature
Project description:Lyme disease (LD) is a commonly cited model for the link between habitat loss and/or fragmentation and disease emergence, based in part on studies showing that forest patch size is negatively related to LD entomological risk. An equivalent relationship has not, however, been shown between patch size and LD incidence (LDI). Because entomological risk is measured at the patch scale, while LDI is generally assessed in relation to aggregate landscape statistics such as forest cover, we posit that the contribution of individual patches to human LD risk has not yet been directly evaluated. We design a model that directly links theoretical entomological risk at the patch scale to larger-scale epidemiological data. We evaluate its predictions for relative LD risk in artificial landscapes with varying composition and configuration, and test its ability to predict countywide LDI in a 12-county region of New York. On simulated landscapes, we find that the model predicts a unimodal relationship between LD incidence and forest cover, mean patch size, and mean minimum distance (a measure of isolation), and a protective effect for percolation probability (a measure of connectivity). In New York, risk indices generated by this model are significantly related to countywide LDI. The results suggest that the lack of concordance between entomological risk and LDI may be partially resolved by this style of model.
| S-EPMC5982087 | biostudies-literature
Project description:Seasonal variation in spatial distribution and pathogen prevalence of Borrelia burgdorferi in blacklegged ticks (Ixodes scapularis) influences human population risk of Lyme disease in peri-urban built environments. Parks, gardens, playgrounds, school campuses and neighborhoods represent a significant risk for Lyme disease transmission. From June 2012 through May 2014, ticks were collected using 1 m² corduroy cloths dragged over low-lying vegetation parallel to walkways with high human foot traffic. DNA was extracted from ticks, purified and presence of B. burgdorferi assessed by polymerase chain reaction amplification. Summer is reported as the time of highest risk for Lyme disease transmission in the United States and our results indicate a higher tick density of 26.0/1000 m² in summer vs. 0.2/1000 m² to 10.5/1000 m² in spring and fall. However, our findings suggest that tick infection rate is proportionally higher during the fall and spring than summer (30.0?54.7% in fall and 36.8?65.6% in spring vs. 20.0?28.2% in summer). Seasonal variation in infected tick density has significant implications for Lyme disease transmission as people are less likely to be aware of ticks in built environments, and unaware of increased infection in ticks in spring and fall. These factors may lead to more tick bites resulting in Lyme infection.
| S-EPMC6163686 | biostudies-literature
Project description:BackgroundIt has been suggested that increasing biodiversity, specifically host diversity, reduces pathogen and parasite transmission amongst wildlife (causing a "dilution effect"), whereby transmission amongst efficient reservoir hosts, (e.g. Peromyscus spp. mice for the agent of Lyme disease Borrelia burgdorferi) is reduced by the presence of other less efficient host species. If so, then increasing biodiversity should inhibit pathogen and parasite invasion.MethodsWe investigated this hypothesis by studying invasion of B. burgdorferi and its tick vector Ixodes scapularis in 71 field sites in southeastern Canada. Indices of trapped rodent host diversity, and of biodiversity of the wider community, were investigated as variables explaining the numbers of I. scapularis collected and B. burgdorferi infection in these ticks. A wide range of alternative environmental explanatory variables were also considered.ResultsThe observation of low I. scapularis abundance and low B. burgdorferi infection prevalence in sites where I. scapularis were detected was consistent with early-stage invasion of the vector. There were significant associations between the abundance of ticks and season, year of study and ambient temperature. Abundance of host-seeking larvae was significantly associated with deer density, and abundance of host-seeking larvae and nymphs were positively associated with litter layer depth. Larval host infestations were lower where the relative proportion of non-Peromyscus spp. was high. Infestations of hosts with nymphs were lower when host species richness was higher, but overall nymphal abundance increased with species richness because Peromyscus spp. mouse abundance and host species richness were positively correlated. Nymphal infestations of hosts were lower where tree species richness was higher. B. burgdorferi infection prevalence in ticks varied significantly with an index of rates of migratory bird-borne vector and pathogen invasion.ConclusionsI. scapularis abundance and B. burgdorferi prevalence varied with explanatory variables in patterns consistent with the known biology of these species in general, and in the study region in particular. The evidence for a negative effect of host biodiversity on I. scapularis invasion was mixed. However, some evidence suggests that community biodiversity beyond just host diversity may have direct or indirect inhibitory effects on parasite invasion that warrant further study.
| S-EPMC3728044 | biostudies-literature
Project description:The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300?000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD.
| S-EPMC5717649 | biostudies-literature
Project description:The abundance of infected Ixodid ticks is an important component of human risk of Lyme disease, and various empirical studies have shown that this is associated, at least in part, to landscape fragmentation. In this study, we aimed at exploring how varying woodland fragmentation patterns affect the risk of Lyme disease, through infected tick abundance. A cellular automata model was developed, incorporating a heterogeneous landscape with three interactive components: an age-structured tick population, a classical disease transmission function, and hosts. A set of simplifying assumptions were adopted with respect to the study objective and field data limitations. In the model, the landscape influences both tick survival and host movement. The validation of the model was performed with an empirical study. Scenarios of various landscape configurations (focusing on woodland fragmentation) were simulated and compared. Lyme disease risk indices (density and infection prevalence of nymphs) differed considerably between scenarios: (i) the risk could be higher in highly fragmented woodlands, which is supported by a number of recently published empirical studies, and (ii) grassland could reduce the risk in adjacent woodland, which suggests landscape fragmentation studies of zoonotic diseases should not focus on the patch-level woodland patterns only, but also on landscape-level adjacent land cover patterns. Further analysis of the simulation results indicated strong correlations between Lyme disease risk indices and the density, shape and aggregation level of woodland patches. These findings highlight the strong effect of the spatial patterns of local host population and movement on the spatial dynamics of Lyme disease risks, which can be shaped by woodland fragmentation. In conclusion, using a cellular automata approach is beneficial for modelling complex zoonotic transmission systems as it can be combined with either real world landscapes for exploring direct spatial effects or artificial representations for outlining possible empirical investigations.
| S-EPMC3382467 | biostudies-literature
Project description:BackgroundLittle is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation.MethodsWe identified Lyme disease cases in 2012-2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status.ResultsAmong 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease.ConclusionsThe associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.
| S-EPMC8686252 | biostudies-literature