Environmental risk factors for Lyme disease identified with geographic information systems

Mapping the distribution of Lyme disease at a mid-Atlantic site in the United States using electronic health data

Lyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.

Patterns of Animal Rabies Prevalence in Northern South Africa between 1998 and 2022

Rabies is endemic in South Africa and rabies cycles are maintained in both domestic and wildlife species. The significant number of canine rabies cases reported by the World Organization for Animal Health Reference Laboratory for Rabies at Onderstepoort suggests the need for increased research and mass dog vaccinations on specific targeted foci in the country. This study aimed to investigate the spatiotemporal distribution of animal rabies cases from 1998 to 2017 in northern South Africa and environmental factors associated with highly enzootic municipalities. A descriptive analysis was used to investigate temporal patterns. The Getis-Ord Gi statistical tool was used to exhibit low and high clusters. Logistic regression was used to examine the association between the predictor variables and highly enzootic municipalities. A total of 9580 specimens were submitted for rabies diagnosis between 1998 and 2022. The highest positive case rates were from companion animals (1733 cases, 59.71%), followed by livestock (635 cases, 21.88%) and wildlife (621 cases, 21.39%). Rabies cases were reported throughout the year, with the majority occurring in the mid-dry season. Hot spots were frequently in the northern and eastern parts of Limpopo and Mpumalanga. Thicket bush and grassland were associated with rabies between 1998 and 2002. However, between 2008 and 2012, cultivated commercial crops and waterbodies were associated with rabies occurrence. In the last period, plantations and woodlands were associated with animal rabies. Of the total number of municipalities, five consistently and repeatedly had the highest rabies prevalence rates. These findings suggest that authorities should prioritize resources for those municipalities for rabies elimination and management.

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.

The Spatial-Temporal Dimension of Oncological Prevalence and Mortality in Romania

The objective of this study was to identify spatial disparities in the distribution of cancer hotspots within Romania. Additionally, the research aimed to track prevailing trends in cancer prevalence and mortality according to a cancer type. The study covered the timeframe between 2008 and 2017, examining all 3,181 territorial administrative units. The analysis of spatial distribution relied on two key parameters. The first parameter, persistence, measured the duration for which cancer prevalence exceeded the 75th percentile threshold. Cancer prevalence refers to the total number of individuals in a population who have been diagnosed with cancer at a specific time point, including both newly diagnosed cases (occurrence) and existing cases. The second parameter, the time continuity of persistence, calculated the consecutive months during which cancer prevalence consistently surpassed the 75th percentile threshold. Notably, persistence of elevated values was also evident in lowland regions, devoid of any discernible direct connection to environmental conditions. In conclusion, this work bears substantial relevance to regional health policies, by aiding in the formulation of prevention strategies, while also fostering a deeper comprehension of the socioeconomic and environmental factors contributing to cancer.

Surgical (over) treatment of pediatric Lyme arthritis: a need for faster Borrelia testing

Pediatric Lyme arthritis is described but not well-characterized in urban populations. Similarities in clinical features between Lyme and septic arthritis also results in some patients with Lyme arthritis undergoing surgical treatment. The aims of this study are to (1) characterize Lyme arthritis in an urban population and (2) determine what factors predispose patients with Lyme arthritis to undergoing surgery. We performed a retrospective review of children with Lyme arthritis at a single academic institution in New York City from 2016 to 2021. Inclusion criteria were age ≤18 years, involvement of a major joint, and positive Lyme serology. Patients treated with irrigation and debridement were compared to those treated non-surgically using Chi-squared tests with a significance of P  < 0.05. A total of 106 children with Lyme arthritis were included. Mean age was 9.5 years; 61.3% were male, and 71.7% were Caucasian. 46.2% lived in regions with an average household income >$100 000; 70.8% had private insurance. Ten patients (9.4%) underwent surgery for suspected septic arthritis. The operative group was more likely to have an elevated heart rate, white blood cell count, C-reactive protein level, erythrocyte sedimentation rate level and synovial cell count ( P  < 0.05). Patients were more likely to undergo surgery if they presented to the emergency department than to the clinic ( P  = 0.03). The average time for a Lyme test to result was 43.5 h, averaging 8.7 h after the surgical start time. Lyme arthritis occurs commonly in an urban pediatric population. Surgery is performed in ~10% of Lyme arthritis patients. More efficient diagnostic tests may reduce this rate.

The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000-2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 "high" incidence states and the District of Columbia for 2000-2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a "u-shaped" association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed "n-shaped" relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.

Models for predicting bulinids species habitats in southwestern Nigeria

Background

Schistosomiasis prevalence is high in southwestern Nigeria and planorbids of the genus Bulinus had been implicated in the transmission of the disease in the area. The knowledge of species distribution in relation to environmental variables will be auspicious in planning control strategies.

Methods

Satellite imagery and geographic information system (GIS) were used to develop models for predicting the habitats suitable for bulinid species. Monthly snail sample collection was done in twenty-three randomly selected water contact sites using the standard method for a period of two years. Remotely sensed variables such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) were extracted from Landsat TM, ETM⁺; Slope and Elevation were obtained from digital elevation model (DEM) while Rainfall was retrieved from European Meteorology Research Program. These environmental factors and snail species were integrated into QGIS to predict the potential habitats of different bulinid species using an exploratory regression model.

Results

The following environmental variables: flat-moderate slope (0.01–15.83), LST (21.1 °C-23.4 °C), NDVI (0.19–0.52), rainfall (> 1569.34 mm) and elevation (1–278 m) contributed to the model used in predicting habitat suitable for bulinids snail intermediate hosts. Exploratory regression models showed that LST, NDVI and slope were predictors of Bulinus globosus and Bulinus jousseaumei; elevation, LST, rainfall and slope were predictors of Bulinus camerunensis; rainfall, NDVI and slope were predictors of B. senegalensis while NDVI and slope were predictors of Bulinus forskalii in the area. Bulinids in the forskalii group showed clustering in middle belt and south. The predictive risk map of B. jousseaumei was similar to the pattern described for B. globosus, but with a high R-square value of 81%.

Conclusion

The predictive risk models of bulinid species in this study provided a robust output for the study area which could be used as base-line for other areas in that ecological zone. It will be useful in appropriate allocation of scarces resources in the control of schistosomiasis in that environment.

Tick bites in different professions and regions: pooled cross-sectional study in the focus area Bavaria, Germany

Background

As the vector-borne diseases tick-borne encephalitis (TBE) and Lyme borreliosis (LB) are common in Germany and transmitted by tick bites, the aim of this study was to assess differences in the number of tick bites in various professions and regions across southern Germany to evaluate the differences in tick-associated risk.

Materials and methods

The analysis is based on three cross-sectional studies that were conducted in 2016 and 2017 in two real-life settings and in one medical setting in Bavaria. All participants filled in a paper-based questionnaire about their history with tick bites. Only adult participants (≥ 18 years) were included in this study.

Results

Overall, 3503 individuals (mean age 50.8 ± 15.2 years, median age 53.0 ± 12.2 years, 54.0% female) were included. Of these, 50% worked in an outdoor profession and 56% lived in environs. Around 70% of participants reported at least one previous tick bite. In comparison to indoor workers, forestry workers (OR = 2.50; 95% CI: 1.10–5.68) had the highest risk for a tick bite followed by farmers (OR = 1.22; 95% CI: 1.01–1.47). Furthermore, people living in rural areas (OR = 1.97, 95% CI:1.49–2.59) and environs (OR = 1.98, 95% CI: 1.54–2.55) were twice as likely to have a previous tick bite than people living in urban areas. In general, slightly more tick bites were reported by people living in eastern Bavaria.

Conclusion

Rising numbers of TBE and LB indicate the need for further prevention strategies, which should focus on outdoor professions with a higher risk and people living in environs and rural areas.

Human Otoacariasis in Two Climatically Diverse Districts in Sri Lanka: Seasonality, Risk Factors, and Case Notes

PURPOSE

Otoacariasis is a parasitic otopathy reported in many parts of the world. This study presents the seasonal pattern, risk factors, and case notes on human otoacariasis in two climatically different districts: Anuradhapura and Kandy in the Dry and Wet/Intermediate zone of Sri Lanka, respectively.

METHODS

Ticks removed from the ear canal of patients were collected. Risk factors of otoacarisis were determined by a case/control follow-up study.

RESULTS

Nymphal Dermacentor auratus (90.8%) was the main tick species associated otoacariasis. In the Kandy District, infestation was year-round, while in the Anuradhapura District, it was seasonal with a peak in December-February. Children < 10 years were a risk group in both districts. Females were a risk group in the Kandy District. Engagement in outdoor activities was a risk factor in both districts. In addition, the presence of wildlife and domesticated animals were risk factors in the Kandy and Anuradhapura districts, respectively. The treatment protocols in the two hospitals were different. An infant with otoacariasis from the Anuradhapura District developed rickettsia.

CONCLUSIONS

Although the tick species was the same, seasonality, risk groups, and risk factors in the two districts were different which could be due to the establishment and persistence of tick populations influenced by biotic and abiotic factors.

Prevalence of Risk and Protective Factors for Tick Exposure and Tick-Borne Disease Among Residents of Indiana

OBJECTIVE

To determine the prevalence of risk and protective factors for tick exposure and tick-borne disease (TBD) based on gender and living in urban or rural areas (urbanicity) among a cross section of Indiana adults.

METHODS

Data were collected from 3003 adults (81% response rate) spread across all 92 Indiana counties. Study participants were recruited from existing online panels maintained by Qualtrics. We calculated prevalence ratio (PR) and 95% confidence interval (CI) of 8 primary outcomes for 2 different exposures: (1) gender (male/female) and (2) urbanicity.

RESULTS

Female participants were 10% less likely to find a tick on themselves than male participants (PR = 0.90; 95% CI, 0.74-0.99) and significantly more likely to worry about their health and safety because of ticks, whereas males avoided the outdoors less because of worry about ticks. Female participants were significantly more likely to adopt various personal protective measures, specifically because of concern for ticks. Female participants were also less likely to have been told by a health care provider that they have a TBD (PR = 0.53; 95% CI, 0.38-0.75). Rural participants were significantly more likely to report seeing a tick at their residential property (PR = 2.40; 95% CI, 2.11-2.76), report finding ticks on themselves (PR = 1.90; 95% CI, 1.68-2.23), and report finding a tick on a child at their residential property within the past 6 months than urban dwellers (PR = 1.37; 95% CI, 1.06-1.77). In addition, we found that 62% of participants have very high trust in government sources of information on TBD and tick prevention.

CONCLUSIONS

In Indiana, the area around people's residences may constitute an important source of tick exposure for humans. Being exposed to ticks is most prevalent in those living in rural areas of Indiana. Finally, risk factors for TBD are higher among males than among females.

Impact of Land Use Changes and Habitat Fragmentation on the Eco-epidemiology of Tick-Borne Diseases

The incidence of tick-borne diseases has increased in recent decades and accounts for the majority of vector-borne disease cases in temperate areas of Europe, North America, and Asia. This emergence has been attributed to multiple and interactive drivers including changes in climate, land use, abundance of key hosts, and people's behaviors affecting the probability of human exposure to infected ticks. In this forum paper, we focus on how land use changes have shaped the eco-epidemiology of Ixodes scapularis-borne pathogens, in particular the Lyme disease spirochete Borrelia burgdorferi sensu stricto in the eastern United States. We use this as a model system, addressing other tick-borne disease systems as needed to illustrate patterns or processes. We first examine how land use interacts with abiotic conditions (microclimate) and biotic factors (e.g., host community composition) to influence the enzootic hazard, measured as the density of host-seeking I. scapularis nymphs infected with B. burgdorferi s.s. We then review the evidence of how specific landscape configuration, in particular forest fragmentation, influences the enzootic hazard and disease risk across spatial scales and urbanization levels. We emphasize the need for a dynamic understanding of landscapes based on tick and pathogen host movement and habitat use in relation to human resource provisioning. We propose a coupled natural-human systems framework for tick-borne diseases that accounts for the multiple interactions, nonlinearities and feedbacks in the system and conclude with a call for standardization of methodology and terminology to help integrate studies conducted at multiple scales.

Surveillance of Ticks and Tick-Borne Pathogens in Suburban Natural Habitats of Central Maryland

Lyme and other tick-borne diseases are increasing in the eastern United States and there is a lack of research on integrated strategies to control tick vectors. Here we present results of a study on tick-borne pathogens detected from tick vectors and rodent reservoirs from an ongoing 5-yr tick suppression study in the Lyme disease-endemic state of Maryland, where human-biting tick species, including Ixodes scapularis Say (Acari: Ixodidae) (the primary vector of Lyme disease spirochetes), are abundant. During the 2017 tick season, we collected 207 questing ticks and 602 ticks recovered from 327 mice (Peromyscus spp. (Rodentia: Cricetidae)), together with blood and ear tissue from the mice, at seven suburban parks in Howard County. Ticks were selectively tested for the presence of the causative agents of Lyme disease (Borrelia burgdorferi sensu lato [s.l.]), anaplasmosis (Anaplasma phagocytophilum), babesiosis (Babesia microti), ehrlichiosis (Ehrlichia ewingii, Ehrlichia chaffeensis, and 'Panola Mountain' Ehrlichia) and spotted fever group rickettsiosis (Rickettsia spp.). Peromyscus ear tissue and blood samples were tested for Bo. burgdorferi sensu stricto (s.s), A. phagocytophilum, Ba. microti, and Borrelia miyamotoi. We found 13.6% (15/110) of questing I. scapularis nymphs to be Bo. burgdorferi s.l. positive and 1.8% (2/110) were A. phagocytophilum positive among all sites. Borrelia burgdorferi s.s. was found in 71.1% (54/76) of I. scapularis nymphs removed from mice and 58.8% (194/330) of captured mice. Results from study on tick abundance and pathogen infection status in questing ticks, rodent reservoirs, and ticks feeding on Peromyscus spp. will aid efficacy evaluation of the integrated tick management measures being implemented.

Using i-tree canopy vegetation cover subtype classification to predict peri-domestic tick presence

While extant literature has generally indicated significant associations between vegetation cover and tick activity, no study has demonstrated the relative association between peri-domestic area vegetation cover subtypes and tick presence. In this study, we seek to determine whether neighborhood wood index and residential tick control practices confound or modify the effect of peri-domestic vegetation cover subtypes on tick presence. We conducted an ecological inventory of vegetation cover distribution using i-Tree Canopy on 210 private residential/peri-domestic properties in Indiana, USA. Results were paired with field obtained tick presence/absence data for each property together with online survey data provided by primary occupant of the property. Amblyomma americanum was the predominant tick species in peri-domestic areas. Higher proportion of vegetation cover in the peri-domestic area was significantly associated with tick presence. Of the four vegetation cover subtypes, (grass, shrubs, understory, and canopy), canopy was the most prevalent vegetation in peri-domestic areas of Indiana, USA. It was also the most significant predictor of tick presence. Among residential tick control processes, frequent leaf litter removal was significantly associated with reduced likelihood of peri-domestic tick presence. Neighborhood Wood Index (NWI) confounded the relationship between canopy and peri-domestic tick presence, while leaf-litter removal confounded the effect of understory vegetation subtype on peri-domestic tick presence. Compared to peri-domestic areas in neighborhoods with sparse NWI, those in neighborhoods with heavy/dense NWI had a 3.5x odd of peri-domestic tick presence (AOR = 3.46; 95 % CI: 1.23-9.65). Compared to peri-domestic areas in the central region, those in the southern region of Indiana were 8.7x more likely to have peri-domestic tick presence. Canopy as a vegetation cover subtype and frequent leaf litter removal represent particularly key peri-domestic variables that have significant implications for peri-domestic tick presence. Beyond parcel-scale landscape features, neighborhood wood index also plays an important role in peri-domestic tick presence. Additionally, i-Tree Canopy represents a promising methodological tool for identifying landscape features that predict tick presence.

Impact of prior and projected climate change on US Lyme disease incidence

Lyme disease is the most common vector-borne disease in temperate zones and a growing public health threat in the United States (US). The life cycles of the tick vectors and spirochete pathogen are highly sensitive to climate, but determining the impact of climate change on Lyme disease burden has been challenging due to the complex ecology of the disease and the presence of multiple, interacting drivers of transmission. Here we incorporated 18 years of annual, county-level Lyme disease case data in a panel data statistical model to investigate prior effects of climate variation on disease incidence while controlling for other putative drivers. We then used these climate-disease relationships to project Lyme disease cases using CMIP5 global climate models and two potential climate scenarios (RCP4.5 and RCP8.5). We find that interannual variation in Lyme disease incidence is associated with climate variation in all US regions encompassing the range of the primary vector species. In all regions, the climate predictors explained less of the variation in Lyme disease incidence than unobserved county-level heterogeneity, but the strongest climate-disease association detected was between warming annual temperatures and increasing incidence in the Northeast. Lyme disease projections indicate that cases in the Northeast will increase significantly by 2050 (23,619 ± 21,607 additional cases), but only under RCP8.5, and with large uncertainty around this projected increase. Significant case changes are not projected for any other region under either climate scenario. The results demonstrate a regionally variable and nuanced relationship between climate change and Lyme disease, indicating possible nonlinear responses of vector ticks and transmission dynamics to projected climate change. Moreover, our results highlight the need for improved preparedness and public health interventions in endemic regions to minimize the impact of further climate change-induced increases in Lyme disease burden.

Modeling the Sensitivity of Blacklegged Ticks (Ixodes scapularis) to Temperature and Land Cover in the Northeastern United States

The prevalence of Lyme disease and other tick-borne diseases is dramatically increasing across the United States. While the rapid rise in Lyme disease is clear, the causes of it are not. Modeling Ixodes scapularis Say (Acari: Ixodidae), the primary Lyme disease vector in the eastern United States, presents an opportunity to disentangle the drivers of increasing Lyme disease, including climate, land cover, and host populations. We improved upon a recently developed compartment model of ordinary differential equations that simulates I. scapularis growth, abundance, and infection with Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) by adding land cover effects on host populations, refining the representation of growth stages, and evaluating output against observed data. We then applied this model to analyze the sensitivity of simulated I. scapularis dynamics across temperature and land cover in the northeastern United States. Specifically, we ran an ensemble of 232 simulations with temperature from Hanover, New Hampshire and Storrs, Connecticut, and land cover from Hanover and Cardigan in New Hampshire, and Windsor and Danielson in Connecticut. Consistent with observations, simulations of I. scapularis abundance are sensitive to temperature, with the warmer Storrs climate significantly increasing the number of questing I. scapularis at all growth stages. While there is some variation in modeled populations of I. scapularis infected with B. burgdorferi among land cover distributions, our analysis of I. scapularis response to land cover is limited by a lack of observations describing host populations, the proportion of hosts competent to serve as B. burgdorferi reservoirs, and I. scapularis abundance.

Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016

BackgroundTick-borne diseases have become increasingly common in recent decades and present a health problem in many parts of Europe. Control and prevention of these diseases require a better understanding of vector distribution.AimOur aim was to create a model able to predict the distribution of Ixodes ricinus nymphs in southern Scandinavia and to assess how this relates to risk of human exposure.MethodsWe measured the presence of I. ricinus tick nymphs at 159 stratified random lowland forest and meadow sites in Denmark, Norway and Sweden by dragging 400 m transects from August to September 2016, representing a total distance of 63.6 km. Using climate and remote sensing environmental data and boosted regression tree modelling, we predicted the overall spatial distribution of I. ricinus nymphs in Scandinavia. To assess the potential public health impact, we combined the predicted tick distribution with human density maps to determine the proportion of people at risk.ResultsOur model predicted the spatial distribution of I. ricinus nymphs with a sensitivity of 91% and a specificity of 60%. Temperature was one of the main drivers in the model followed by vegetation cover. Nymphs were restricted to only 17.5% of the modelled area but, respectively, 73.5%, 67.1% and 78.8% of the human populations lived within 5 km of these areas in Denmark, Norway and Sweden.ConclusionThe model suggests that increasing temperatures in the future may expand tick distribution geographically in northern Europe, but this may only affect a small additional proportion of the human population.

Effect of rodent density on tick and tick-borne pathogen populations: consequences for infectious disease risk

BACKGROUND

Rodents are considered to contribute strongly to the risk of tick-borne diseases by feeding Ixodes ricinus larvae and by acting as amplifying hosts for pathogens. Here, we tested to what extent these two processes depend on rodent density, and for which pathogen species rodents synergistically contribute to the local disease risk, i.e. the density of infected nymphs (DIN).

METHODS

In a natural woodland, we manipulated rodent densities in plots of 2500 m² by either supplementing a critical food source (acorns) or by removing rodents during two years. Untreated plots were used as controls. Collected nymphs and rodent ear biopsies were tested for the presence of seven tick-borne microorganisms. Linear models were used to capture associations between rodents, nymphs, and pathogens.

RESULTS

Investigation of data from all plots, irrespective of the treatment, revealed a strong positive association between rodent density and nymphal density, nymphal infection prevalence (NIP) with Borrelia afzelii and Neoehrlichia mikurensis, and hence DIN's of these pathogens in the following year. The NIP, but not the DIN, of the bird-associated Borrelia garinii, decreased with increasing rodent density. The NIPs of Borrelia miyamotoi and Rickettsia helvetica were independent of rodent density, and increasing rodent density moderately increased the DINs. In addition, NIPs of Babesia microti and Spiroplasma ixodetis decreased with increasing rodent density, which had a non-linear association with DINs of these microorganisms.

CONCLUSIONS

A positive density dependence for all rodent- and tick-associated tick-borne pathogens was found, despite the observation that some of them decreased in prevalence. The effects on the DINs were variable among microorganisms, more than likely due to contrasts in their biology (including transmission modes, host specificity and transmission efficiency). The strongest associations were found in rodent-associated pathogens that most heavily rely on horizontal transmission. Our results draw attention to the importance of considering transmission mode of a pathogen while developing preventative measures to successfully reduce the burden of disease.

Systematic review and meta-analysis of tick-borne disease risk factors in residential yards, neighborhoods, and beyond

BACKGROUND

Exposure to blacklegged ticks Ixodes scapularis that transmit pathogens is thought to occur peri-domestically. However, the locations where people most frequently encounter infected ticks are not well characterized, leading to mixed messages from public health officials about where risk is highest.

METHODS

We conducted a systematic review and meta-analysis on spatial risk factors for tick-borne disease and tick bites in eastern North America. We examined three scales: the residential yard, the neighborhood surrounding (but not including) the yard, and outside the neighborhood. Nineteen eligible studies represented 2741 cases of tick-borne illness and 1447 tick bites. Using random effects models, we derived pooled odds ratio (OR) estimates.

RESULTS

The meta-analysis revealed significant disease risk factors at the scale of the yard (OR 2.60 95% CI 1.96 - 3.46), the neighborhood (OR 4.08 95% CI 2.49 - 6.68), and outside the neighborhood (OR 2.03 95% CI 1.59 - 2.59). Although significant risk exists at each scale, neighborhood scale risk factors best explained disease exposure. Analysis of variance revealed risk at the neighborhood scale was 57% greater than risk at the yard scale and 101% greater than risk outside the neighborhood.

CONCLUSIONS

This analysis emphasizes the importance of understanding and reducing tick-borne disease risk at the neighborhood scale. Risk-reducing interventions applied at each scale could be effective, but interventions applied at the neighborhood scale are most likely to protect human health.

TRIAL REGISTRATION

The study was registered with PROSPERO: CRD42017079169 .

Estimating the Geographic Distribution of Host-Seeking Adult Amblyomma americanum (Acari: Ixodidae) in Florida

The lone star tick, Amblyomma americanum, is the most commonly reported human-biting tick in the southeastern United States and is a vector for several human and livestock pathogens. Although it is endemic to Florida, little is known about the ecological preferences and current spatial distribution within the state. Using occurrence records of adult A. americanum collected between August 2015 and September 2016, a logistic regression model was used to estimate environmental associations, as well as to predict the distribution of the tick at a one hectare resolution. Occurrence of adult lone star ticks was associated with land cover and bioclimatic variables, namely the presence of forested areas and precipitation seasonality. The estimated spatial distribution indicated that central and northern regions show greater suitability than the southern half of the state. Furthermore, areas predicted to be suitable for the species decreases from north to south with very little area deemed suitable in the far southern reaches of the state. High heterogeneity in the distribution of suitable habitat has implications for the distribution of tick-borne disease cases in the state. The subcounty resolution of the estimated distribution is an improvement over distributions currently published and may better inform the public and state or federal agencies of potential risk of exposure to A. americanum and its associated pathogens.

Geographical variations and influential factors in prevalence of cardiometabolic diseases in South Korea

Geographical variations and influential factors of disease prevalence are crucial information enabling optimal allocation of limited medical resources and prioritization of appropriate treatments for each regional unit. The purpose of this study was to explore the geographical variations and influential factors of cardiometabolic disease prevalence with respect to 230 administrative districts in South Korea. Global Moran’s I was calculated to determine whether the standardized prevalences of cardiometabolic diseases (hypertension, stroke, and diabetes mellitus) were spatially clustered. The CART algorithm was then applied to generate decision tree models that could extract the diseases’ regional influential factors from among 101 demographic, economic, and public health data variables. Finally, the accuracies of the resulting model–hypertension (67.4%), stroke (62.2%), and diabetes mellitus (56.5%)–were assessed by ten-fold cross-validation. Marriage rate was the main determinant of geographic variation in hypertension and stroke prevalence, which has the possibility that married life could have positive effects in lowering disease risks. Additionally, stress-related variables were extracted as factors positively associated with hypertension and stroke. In the opposite way, the wealth status of a region was found to have an influence on the prevalences of stroke and diabetes mellitus. This study suggested a framework for provision of novel insights into the regional characteristics of diseases and the corresponding influential factors. The results of the study are anticipated to provide valuable information for public health practitioners’ cost-effective disease management and to facilitate primary intervention and mitigation efforts in response to regional disease outbreaks.

Incidence of Lyme Disease Diagnosis in a Maryland Medicaid Population, 2004-2011

The epidemiology of Lyme disease has been examined utilizing insurance claims from privately insured individuals; however, it is unknown whether reported patterns vary among the publicly insured. We examined trends in incidence rates of first Lyme disease diagnosis among 384,652 Maryland Medicaid recipients enrolled from July 2004 to June 2011. Age-, sex-, county-, season-, and year-specific incidence rates were calculated, and mixed-effects multiple logistic regression models were used to study the relationship between Lyme disease diagnosis and these variables. The incidence rate in our sample was 97.65 cases per 100,000 person-years (95% confidence interval (CI): 91.53, 104.06), and there was a 13% average annual increase in the odds of a Lyme disease diagnosis (odds ratio = 1.13, 95% CI: 1.09, 1.17; P < 0.001). Incidence rates for males and females were not significantly different, though males were significantly more likely to be diagnosed during high-season months (relative risk (RR) = 1.24, 95% CI: 1.06, 1.44) and less likely to be diagnosed during low-season months (RR = 0.63, 95% CI: 0.46, 0.87) than females. Additionally, adults were significantly more likely than children to be diagnosed during low-season months (RR = 1.59, 95% CI: 1.19, 2.12). While relatively rare in this study sample, Lyme disease diagnoses do occur in a Medicaid population in a Lyme-endemic state.

Clinical manifestations of reported Lyme disease cases in Ontario, Canada: 2005-2014

Lyme disease (LD) is the most common vector-borne disease in Ontario, Canada. We describe the epidemiology and clinical manifestations of LD in Ontario and examine trends in the incidence of non-disseminated and disseminated LD. LD surveillance data from the integrated Public Health Information System (iPHIS) from 2005–2014 were mapped to symptoms according to syndrome groups (erythema migrans (EM), flu-like, cardiac, neurologic or arthritic) and disease stages (early localized, early disseminated or late disseminated). During the study period, 1,230 cases due to Borrelia burgdoferi were reported in Ontario with annual incidence rates ranging from 0.32 (2006) to 2.16 (2013) cases per 100,000 population. Seventy percent of cases had EM and the proportion of cases with EM increased over time. Other clinical manifestations included flu-like (75%), arthritic (42%), neurologic (41%) and cardiac (6%) symptoms. Early localized disease (n = 415) manifested with EM (87%) and flu-like (57%) symptoms; early disseminated disease (n = 216) manifested with neurologic (94%), cardiac (10%) and EM (63%) symptoms; and late disseminated disease (n = 475) manifested with EM (62%), neurologic (55%), cardiac (9%), and arthritic symptoms (i.e., arthralgia (93%) and arthritis (7%)). Early localized and early disseminated cases (88% each) occurred primarily from May through September, compared to late disseminated cases (81%). The proportion of cases reported to public health within 30 days of illness onset increased during the study period, while the proportion of cases reported within 1–3 months and >3 months decreased. Geographical variations characterized by higher incidence of early localized disease and earlier public health notification (within 30 days of illness onset) occurred in regions with established or recently established LD risk areas, while later public health notification (>3 months after illness onset) was reported more frequently in regions with recently established or no identified risk areas. This is the first study to describe the clinical manifestations of LD in Ontario, Canada. The observed geographical variations in the epidemiology of LD in Ontario reinforce the need for regionally focused public health strategies aimed at increasing awareness, promoting earlier recognition and reporting, and encouraging greater uptake of preventive measures.

Interactions Between Carnivores in Madagascar and the Risk of Disease Transmission

Introduced carnivores exert considerable pressure on native predators through predation, competition and disease transmission. Recent research shows that exotic carnivores negatively affect the distribution and abundance of the native and endangered carnivores of Madagascar. In this study, we provide information about the frequency and distribution of interactions between exotic (dogs and cats) and native carnivores (Eupleridae) in the Betampona Natural Reserve (BNR), Madagascar, using noninvasive camera trap surveys. Domestic dogs (Canis familiaris) were the most frequently detected carnivore species within the BNR, and we found that indirect interactions between exotic and native carnivores were frequent (n = 236). Indirect interactions were more likely to occur near the research station (incidence rate ratio = 0.91), which may constitute a disease transmission hot spot for carnivores at BNR. The intervals between capture of native and exotic carnivores suggest that there is potential for pathogen transmission between species in BNR. These capture intervals were significantly shorter near the edge of the reserve (P = 0.04). These data could be used to implement biosecurity measures to monitor interactions and prevent disease transmission between species at the domestic animal and wildlife interface.

Amblyomma ticks and future climate: Range contraction due to climate warming

Ticks of the Amblyomma cajennense species complex are important vectors of spotted fever in Latin America. Environmental conditions determine the geographic distribution of ticks, such that climate change could influence the distribution of tick-borne diseases. This study aimed to analyze the potential geographic distribution of A. cajennense complex ticks in a Brazil region under present-day and future climate models, assuming dispersal limitations and non-evolutionary adaptation of these tick populations to climate warming. Records of A. cajennense sensu stricto (s.s.) and Amblyomma sculptum were analyzed. Niche models were calibrated using Maxent considering climate variables for 1950-2000 and projecting models to conditions anticipated for 2050 and 2070 under two models of future climate (CCSM4 and HadGEM2-AO). Broad suitable areas for A. cajennense s.s. and A. sculptum were found in present-day climate models, but suitability was reduced when models were projected to future conditions. Our exploration of future climates showed that broad areas had novel climates not existing currently in the study region, including novel extremely high temperatures. Indeed, predicted suitability in these novel conditions would lead to biologically unrealistic results and therefore incorrect forecasts of future tick-distribution. Previous studies anticipating expansions of vectors populations due to climate change should be considered with caution as they assume that model extrapolation anticipates that species would evolve rapidly for adaptation to novel climatic conditions.

Host Distribution Does Not Limit the Range of the Tick Ixodes ricinus but Impacts the Circulation of Transmitted Pathogens

Ticks, pathogens, and vertebrates interact in a background of environmental features that regulate the densities of ticks and vertebrates, affecting their contact rates and thence the circulation of the pathogens. Regional scale studies are invaluable sources of information about the regulation of these interactions, but a large-scale analysis of the interaction of communities of ticks, hosts, and the environment has been never modeled. This study builds on network analysis, satellite-derived climate and vegetation, and environmental modeling, quantifying the interactions between the tick Ixodes ricinus and the transmitted bacteria of the complex Borrelia burgdorferi s.l. in the Western Palaearctic. We derived the rates of contact of the tick with 162 species of vertebrates recorded as hosts, and the relative importance of each vertebrate in the circulation of the pathogen. We compiled more than 11 millions of pairs of coordinates of the vertebrates, deriving distribution models of each species and the relative faunal composition in the target territory. The results of the modeling of the distribution of the tick and its hosts, weighted by their importance in the circulation of Borrelia captured the spatial patterns of interactions that allow the circulation of the pathogen. Results indicate that both I. ricinus and B. burgdorferi s.l. are supported in the Western Palaearctic by complex communities of vertebrates, which have large distribution ranges. This high functional redundancy results in the pervasiveness of B. burgdorferi s.l., which depends on the gradient of contributions of the large community of vertebrates, instead of relying on a few dominant vertebrates, which was the prevailing paradigm. Most prominent reservoirs of the pathogen are distributed in specific regions of the environmental niche. However, literally dozens of potential reservoirs can colonize many other environmental regions, marginally but efficiently contributing to the circulation of the pathogen. These results consistently point to the need of evaluating the beta-diversity of the community of vertebrates acting as reservoirs of the pathogen to better know the interactions with the vector. They also demonstrate why the pathogen is so resilient to perturbations in the composition of the reservoirs.

A Stochastic Tick-Borne Disease Model: Exploring the Probability of Pathogen Persistence

We formulate and analyse a stochastic epidemic model for the transmission dynamics of a tick-borne disease in a single population using a continuous-time Markov chain approach. The stochastic model is based on an existing deterministic metapopulation tick-borne disease model. We compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in tick-borne disease dynamics. The probability of disease extinction and that of a major outbreak are computed and approximated using the multitype Galton-Watson branching process and numerical simulations, respectively. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that a disease outbreak is more likely if the disease is introduced by infected deer as opposed to infected ticks. These insights demonstrate the importance of host movement in the expansion of tick-borne diseases into new geographic areas.

Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China

Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The impact degree of these factors on TBE risks has the following descending order: temperature, relative humidity, vegetation coverage, precipitation and topography. A high-risk area with a triangle shape was determined in the central part of Hulunbuir; the low-risk area is located in the two belts next to the outside edge of the central triangle. The TBE risk distribution revealed that the impact of the geographic factors changed depending on the heterogeneity.

[Analysis of Distribution of Vector-Borne Diseases Using Geographic Information Systems]

The distribution of vector-borne diseases is changing on a global scale owing to issues involving natural environments, socioeconomic conditions and border disputes among others. Geographic information systems (GIS) provide an important method of establishing a prompt and precise understanding of local data on disease outbreaks, from which disease eradication programs can be established. Having first defined GIS as a combination of GPS, RS and GIS, we showed the processes through which these technologies were being introduced into our research. GIS-derived geographical information attributes were interpreted in terms of point, area, line, spatial epidemiology, risk and development for generating the vector dynamic models associated with the spread of the disease. The need for interdisciplinary scientific and administrative collaboration in the use of GIS to control infectious diseases is highly warranted.

Critical Evaluation of the Linkage Between Tick-Based Risk Measures and the Occurrence of Lyme Disease Cases

The nymphal stage of the blacklegged tick, Ixodes scapularis Say, is considered the primary vector to humans in the eastern United States of the Lyme disease spirochete Borrelia burgdorferi sensu stricto. The abundance of infected host-seeking nymphs is commonly used to estimate the fundamental risk of human exposure to B. burgdorferi, for the purpose of environmental risk assessment and as an outcome measure when evaluating environmentally based tick or pathogen control methods. However, as this tick-based risk measure does not consider the likelihoods of either human encounters with infected ticks or tick bites resulting in pathogen transmission, its linkage to the occurrence of Lyme disease cases is worth evaluating. In this Forum article, we describe different tick-based risk measures, discuss their strengths and weaknesses, and review the evidence for their capacity to predict the occurrence of Lyme disease cases. We conclude that: 1) the linkage between abundance of host-seeking B. burgdorferi-infected nymphs and Lyme disease occurrence is strong at community or county scales but weak at the fine spatial scale of residential properties where most human exposures to infected nymphs occur in Northeast, 2) the combined use of risk measures based on infected nymphs collected from the environment and ticks collected from humans is preferable to either one of these risk measures used singly when assessing the efficacy of environmentally based tick or pathogen control methods aiming to reduce the risk of human exposure to B. burgdorferi, 3) there is a need for improved risk assessment methodology for residential properties that accounts for both the abundance of infected nymphs and the likelihood of human-tick contact, and 4) we need to better understand how specific human activities conducted in defined residential microhabitats relate to risk for nymphal exposures and bites.

Contrasting emergence of Lyme disease across ecosystems

Global environmental changes are causing Lyme disease to emerge in Europe. The life cycle of Ixodes ricinus, the tick vector of Lyme disease, involves an ontogenetic niche shift, from the larval and nymphal stages utilizing a wide range of hosts, picking up the pathogens causing Lyme disease from small vertebrates, to the adult stage depending on larger (non-transmission) hosts, typically deer. Because of this complexity the role of different host species for emergence of Lyme disease remains controversial. Here, by analysing long-term data on incidence in humans over a broad geographical scale in Norway, we show that both high spatial and temporal deer population density increase Lyme disease incidence. However, the trajectories of deer population sizes play an overall limited role for the recent emergence of the disease. Our study suggests that managing deer populations will have some effect on disease incidence, but that Lyme disease may nevertheless increase as multiple drivers are involved.

Environmental change is thought to have driven the recent emergence of Lyme disease in Europe. Using a decade of human disease incidence data across a large area in Norway, Mysterud et al. show that incidence correlates with deer population, but that deer population plays a limited role in recent disease emergence.

Spatial scale modulates the strength of ecological processes driving disease distributions

Humans are altering the distribution of species by changing the climate and disrupting biotic interactions and dispersal. A fundamental hypothesis in spatial ecology suggests that these effects are scale dependent; biotic interactions should shape distributions at local scales, whereas climate should dominate at regional scales. If so, common single-scale analyses might misestimate the impacts of anthropogenic modifications on biodiversity and the environment. However, large-scale datasets necessary to test these hypotheses have not been available until recently. Here we conduct a cross-continental, cross-scale (almost five orders of magnitude) analysis of the influence of biotic and abiotic processes and human population density on the distribution of three emerging pathogens: the amphibian chytrid fungus implicated in worldwide amphibian declines and West Nile virus and the bacterium that causes Lyme disease (Borrelia burgdorferi), which are responsible for ongoing human health crises. In all three systems, we show that biotic factors were significant predictors of pathogen distributions in multiple regression models only at local scales (∼10(2)-10(3) km(2)), whereas climate and human population density always were significant only at relatively larger, regional scales (usually >10(4) km(2)). Spatial autocorrelation analyses revealed that biotic factors were more variable at smaller scales, whereas climatic factors were more variable at larger scales, as is consistent with the prediction that factors should be important at the scales at which they vary the most. Finally, no single scale could detect the importance of all three categories of processes. These results highlight that common single-scale analyses can misrepresent the true impact of anthropogenic modifications on biodiversity and the environment.

Imbalanced presence of Borrelia burgdorferi s.l. multilocus sequence types in clinical manifestations of Lyme borreliosis

In this study we used typing based on the eight multilocus sequence typing scheme housekeeping genes (MLST) and 5S-23S rDNA intergenic spacer (IGS) to explore the population structure of Borrelia burgdorferi sensu lato isolates from patients with Lyme borreliosis (LB) and to test the association between the B. burgdorferi s.l. sequence types (ST) and the clinical manifestations they cause in humans. Isolates of B. burgdorferi from 183 LB cases across Europe, with distinct clinical manifestations, and 257 Ixodes ricinus lysates from The Netherlands, were analyzed for this study alone. For completeness, we incorporated in our analysis also 335 European B. burgdorferi s.l. MLST profiles retrieved from literature. Borrelia afzelii and Borrelia bavariensis were associated with human cases of LB while Borrelia garinii, Borrelia lusitaniae and Borrelia valaisiana were associated with questing I. ricinus ticks. B. afzelii was associated with acrodermatitis chronica atrophicans, while B. garinii and B. bavariensis were associated with neuroborreliosis. The samples in our study belonged to 251 different STs, of which 94 are newly described, adding to the overall picture of the genetic diversity of Borrelia genospecies. The fraction of STs that were isolated from human samples was significantly higher for the genospecies that are known to be maintained in enzootic cycles by mammals (B. afzelii, B. bavariensis, and Borrelia spielmanii) than for genospecies that are maintained by birds (B. garinii and B. valaisiana) or lizards (B. lusitaniae). We found six multilocus sequence types that were significantly associated to clinical manifestations in humans and five IGS haplotypes that were associated with the human LB cases. While IGS could perform just as well as the housekeeping genes in the MLST scheme for predicting the infectivity of B. burgdorferi s.l., the advantage of MLST is that it can also capture the differential invasiveness of the various STs.

An Examination of the Demographic and Environmental Variables Correlated with Lyme Disease Emergence in Virginia

Lyme disease is the United States' most significant vector-borne illness. Virginia, on the southern edge of the disease's currently expanding range, has experienced an increase in Lyme disease both spatially and temporally, with steadily increasing rates over the past decade and disease spread from the northern to the southwestern part of the state. This study used a Geographic Information System and a spatial Poisson regression model to examine correlations between demographic and land cover variables, and human Lyme disease from 2006 to 2010 in Virginia. Analysis indicated that herbaceous land cover is positively correlated with Lyme disease incidence rates. Areas with greater interspersion between herbaceous and forested land were also positively correlated with incidence rates. In addition, income and age were positively correlated with incidence rates. Levels of development, interspersion of herbaceous and developed land, and population density were negatively correlated with incidence rates. Abundance of forest fragments less than 2 hectares in area was not significantly correlated. Our results support some findings of previous studies on ecological variables and Lyme disease in endemic areas, but other results have not been found in previous studies, highlighting the potential contribution of new variables as Lyme disease continues to emerge southward.

The Fungus among Us: Cryptococcus neoformans and Cryptococcus gattii Ecological Modeling for Colombia

The environmental isolation of Cryptococcus spp. is typically a difficult undertaking. Collecting samples in the field is costly in terms of travel, personnel time and materials. Furthermore, the recovery rate of Cryptococcus spp. may be very low, thereby requiring a large number of samples to be taken without any guarantee of success. Ecological niche modeling is a tool that has traditionally been used to forecast the distribution of plant and animal of species for biodiversity and conservation purposes. Here, we use it in a public health application to produce risk area maps for cryptococcal disease in Colombia. The Genetic Algorithm for Ruleset Production (GARP) was used to create models for Cryptococcus neoformans (C. neoformans) and Cryptococcus gattii (C. gattii), based on environmental sampling and clinical records data recorded since 1987. These maps could be used to focus public health messaging related to cryptococcal disease, and it enables us to characterize the ecological niche for Cryptococcus in Colombia. We found that the OPEN ACCESS J. Fungi 2015, 1 333 ecological niche for C. gattii in Colombia is quite diverse, establishing itself in sub-tropical and temperate ecoregions within the country. This suggests that C. gattii is highly adaptive to different ecological conditions in Colombia and different regions of the world.

Lyme disease: Current issues, implications, and recommendations for tourism management

Lyme disease is a bacterial infection spread through the bite of an infected tick. In the last few decades, the number and spatial reach of new cases has increased globally and in the United States, Lyme disease is now the most commonly reported vector-borne disease. Despite this evolving public health crisis, there has been little-to-no discussion of the implications for tourism supply and demand. This paper reviews the scientific literature to identify Lyme disease risk factors and the implications for tourism management are discussed. The major contribution of this paper is a set of recommendations for tourism managers who may be tasked with mitigating the risks for visitors and employees as well as the potential impacts of Lyme disease on destination sustainability.

Spatio-temporal epidemiology of hand, foot and mouth disease in Liaocheng City, North China

Hand, foot and mouth disease (HFMD) has posed a notable threat to public health and become a public health priority in China. This study was based on the reported cases of HFMD between 2007 and 2011. A total of 34,176 HFMD cases were geo-coded at town level (n=134). Firstly, a descriptive analysis was conducted to evaluate the epidemic characteristics of HFMD. Then, the Kulldorff scan statistic based on a discrete Poisson model was used to detect spatial-temporal clusters. Spatial distribution of HFMD in Liaocheng City, China from 2007 to 2011 was mapped at town level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFMD was non-random and clustered with a significant Moran’s I value every year. The local Moran’s I Z-score detected three significant spatial clusters for high incidence of HFMD. The space-time analysis identified one most likely cluster and twenty-five secondary clusters for high incidence of HFMD. We demonstrate evidence of the existence of statistically significant HFMD clusters in Liaocheng City. Our results provide better guidance for formulating regional prevention and control strategies.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context.

Methodological caveats in the environmental modelling and projections of climate niche for ticks, with examples for Ixodes ricinus (Ixodidae)

Interest is increasing in inferring the climate niche of health-threatening arthropods and projecting such inferences onto a territory. This approach is intended to predict the range of tick distribution and to elucidate tick responses to climate scenarios, using so-called correlative models. However, some methodological gaps might prevent achieving an adequate background for hypothesis testing. We explore, describe, and illustrate these procedural inaccuracies with examples focused on the tick Ixodes ricinus and examine how these factors might affect modelling outcomes. Our aim was to develop a backdrop of rules for developing reliable models for these parasites. The use of partial sets of tick occurrences might produce unreliable associations with climate because the algorithms cannot capture the complete niche with which the tick is associated. Reliability measures of the model cannot detect these inaccuracies, and undesirable estimations of the niche will prevail in the chain of further calculations. The use of inadequate environmental variables (covariates) may lead to inflation of the results of the model through two statistical processes, autocorrelation and colinearity. We demonstrate the high colinearity existing in climate products derived from interpolation of climate recording stations. Our explicit advice is to focus on the training of climate models with satellite-derived information of climate, from which colinearity of the time series has been removed through a harmonic regression. We also emphasize the high uncertainty if inference about the climate niche is expanded into different time slices, like projected climate scenarios.

Health Based Geographic Information Systems (GIS) and their Applications

Medical researches as well as the study of the Earth’s surface, better still, geography are interlinked with each other; their relationship dates from antiquity. The science of Geographic Information Systems and, by extension, Geomatics engineering belongs to a discipline which is constantly developing at a global level. This sector has many applications regarding medical / epidemiological research and generally, the social sciences. Furthermore, this discipline may act as a decision making tool in the healthcare sector and it might contribute to the formulation of policies into the healthcare sector. The use of GIS so as to solve public health issues has an exponential increase and has been vital to the understanding and treatment of health problems in different geographic areas. In recent years, the use of various information technology services and software has lead health professionals to work more effectively.

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution.

Lyme disease risk influences human settlement in the wildland-urban interface: evidence from a longitudinal analysis of counties in the northeastern United States

The expansion of human settlement into wildland areas, including forests in the eastern United States, has resulted in fragmented forest habitat that has been shown to drive higher entomological risk for Lyme disease. We investigated an alternative pathway between fragmentation and Lyme disease, namely whether increased risk of Lyme disease results in a reduced propensity to settle in high-risk areas at the interface of developed and undeveloped lands. We used longitudinal data analyses at the county level to determine whether Lyme disease incidence (LDI) influences the proportion of the population residing in the wildland-urban interface in 12 high LDI states in the eastern United States. We found robust evidence that a higher LDI reduces the proportion of a county's population residing in the wildland-urban interface in high-LDI states. This study provides some of the first evidence of human behavioral responses to Lyme disease risk via settlement decisions.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

A global set of Fourier-transformed remotely sensed covariates for the description of abiotic niche in epidemiological studies of tick vector species

BACKGROUND

Correlative modelling combines observations of species occurrence with environmental variables to capture the niche of organisms. It has been argued for the use of predictors that are ecologically relevant to the target species, instead of the automatic selection of variables. Without such biological background, the forced inclusion of numerous variables can produce models that are highly inflated and biologically irrelevant. The tendency in correlative modelling is to use environmental variables that are interpolated from climate stations, or monthly estimates of remotely sensed features.

METHODS

We produced a global dataset of abiotic variables based on the transformation by harmonic regression (time series Fourier transform) of monthly data derived from the MODIS series of satellites at a nominal resolution of 0.1°. The dataset includes variables, such as day and night temperature or vegetation and water availability, which potentially could affect physiological processes and therefore are surrogates in tracking the abiotic niche. We tested the capacities of the dataset to describe the abiotic niche of parasitic organisms, applying it to discriminate five species of the globally distributed tick subgenus Boophilus and using more than 9,500 published records.

RESULTS

With an average reliability of 82%, the Fourier-transformed dataset outperformed the raw MODIS-derived monthly data for temperature and vegetation stress (62% of reliability) and other popular interpolated climate datasets, which had variable reliability (56%-65%). The transformed abiotic variables always had a collinearity of less than 3 (as measured by the variance inflation factor), in contrast with interpolated datasets, which had values as high as 300.

CONCLUSIONS

The new dataset of transformed covariates could address the tracking of abiotic niches without inflation of the models arising from internal issues with the descriptive variables, which appear when variance inflation is higher than 10. The coefficients of the harmonic regressions can also be used to reconstruct the complete original time series, being an adequate complement for ecological, epidemiological, or phylogenetic studies. We provide the dataset as a free download under the GNU general public license as well as the scripts necessary to integrate other time series of data into the calculations of the harmonic coefficients.