Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Seafood Wholesale Market. Here, we analyze environmental qPCR and sequencing data collected in the Huanan market in early 2020. We demonstrate that market-linked severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversity is consistent with market emergence and find increased SARS-CoV-2 positivity near and within a wildlife stall. We identify wildlife DNA in all SARS-CoV-2-positive samples from this stall, including species such as civets, bamboo rats, and raccoon dogs, previously identified as possible intermediate hosts. We also detect animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them with those from farms and other markets. This analysis provides the genetic basis for a shortlist of potential intermediate hosts of SARS-CoV-2 to prioritize for serological and viral sampling.

Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014-2021

BACKGROUND

Ticks are an important driver of veterinary health care, causing irritation and sometimes infection to their hosts. We explored epidemiological and geo-referenced data from > 7 million electronic health records (EHRs) from cats and dogs collected by the Small Animal Veterinary Surveillance Network (SAVSNET) in Great Britain (GB) between 2014 and 2021 to assess the factors affecting tick attachment in an individual and at a spatiotemporal level.

METHODS

EHRs in which ticks were mentioned were identified by text mining; domain experts confirmed those with ticks on the animal. Tick presence/absence records were overlaid with a spatiotemporal series of climate, environment, anthropogenic and host distribution factors to produce a spatiotemporal regression matrix. An ensemble machine learning spatiotemporal model was used to fine-tune hyperparameters for Random Forest, Gradient-boosted Trees and Generalized Linear Model regression algorithms, which were then used to produce a final ensemble meta-learner to predict the probability of tick attachment across GB at a monthly interval and averaged long-term through 2014-2021 at a spatial resolution of 1 km. Individual host factors associated with tick attachment were also assessed by conditional logistic regression on a matched case-control dataset.

RESULTS

In total, 11,741 consultations were identified in which a tick was recorded. The frequency of tick records was low (0.16% EHRs), suggesting an underestimation of risk. That said, increased odds for tick attachment in cats and dogs were associated with younger adult ages, longer coat length, crossbreeds and unclassified breeds. In cats, males and entire animals had significantly increased odds of recorded tick attachment. The key variables controlling the spatiotemporal risk for tick attachment were climatic (precipitation and temperature) and vegetation type (Enhanced Vegetation Index). Suitable areas for tick attachment were predicted across GB, especially in forests and grassland areas, mainly during summer, particularly in June.

CONCLUSIONS

Our results can inform targeted health messages to owners and veterinary practitioners, identifying those animals, seasons and areas of higher risk for tick attachment and allowing for more tailored prophylaxis to reduce tick burden, inappropriate parasiticide treatment and potentially TBDs in companion animals and humans. Sentinel networks like SAVSNET represent a novel complementary data source to improve our understanding of tick attachment risk for companion animals and as a proxy of risk to humans.

Shrinkage estimators of the spatial relative risk function

The spatial relative risk function describes differences in the geographical distribution of two types of points, such as locations of cases and controls in an epidemiological study. It is defined as the ratio of the two underlying densities. Estimation of spatial relative risk is typically done using kernel estimates of these densities, but this procedure is often challenging in practice because of the high degree of spatial inhomogeneity in the distributions. This makes it difficult to obtain estimates of the relative risk that are stable in areas of sparse data while retaining necessary detail elsewhere, and consequently difficult to distinguish true risk hotspots from stochastic bumps in the risk function. We study shrinkage estimators of the spatial relative risk function to address these problems. In particular, we propose a new lasso-type estimator that shrinks a standard kernel estimator of the log-relative risk function towards zero. The shrinkage tuning parameter can be adjusted to help quantify the degree of evidence for the existence of risk hotspots, or selected to optimize a cross-validation criterion. The performance of the lasso estimator is encouraging both on a simulation study and on real-world examples.

Estimating survival in continuous space from mark-dead-recovery data - Towards a continuous version of the multinomial dead recovery model

Understanding spatially varying survival is crucial for understanding the ecology and evolution of migratory animals, which may ultimately help to conserve such species. We develop an approach to estimate an annual survival probability function varying continuously in geographic space, if the recovery probability is constant over space. This estimate is based on a density function over continuous geographic space and the discrete age at death obtained from dead recovery data. From the same density function, we obtain an estimate for animal distribution in space corrected for survival, i.e., migratory connectivity. This is possible, when migratory connectivity can be separated from recovery probability. In this article, we present the method how spatially and continuously varying survival and the migratory connectivity corrected for survival can be obtained, if a constant recovery probability can be assumed reasonably. The model is a stepping stone in developing a model allowing for disentangling spatially heterogeneous survival and migratory connectivity corrected for survival from a spatially heterogeneous recovery probability. We implement the method using kernel density estimates in the R-package CONSURE. Any other density estimation technique can be used as an alternative. In a simulation study, the estimators are unbiased but show edge effects in survival and migratory connectivity. Applying the method to a real-world data set of European robins Erithacus rubecula results in biologically reasonable continuous heat-maps for survival and migratory connectivity.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

Built environment influence on the incidence of elderly pedestrian collisions in a medium-large city in southern Brazil: a spatial analysis

Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.

Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.

Assessing the spatial heterogeneity of tuberculosis in a population with internal migration in China: a retrospective population-based study

Background

Internal migrants pose a critical threat to eliminating Tuberculosis (TB) in many high-burden countries. Understanding the influential pattern of the internal migrant population in the incidence of tuberculosis is crucial for controlling and preventing the disease. We used epidemiological and spatial data to analyze the spatial distribution of tuberculosis and identify potential risk factors for spatial heterogeneity.

Methods

We conducted a population-based, retrospective study and identified all incident bacterially-positive TB cases between January 1st, 2009, and December 31st, 2016, in Shanghai, China. We used Getis-Ord Gi* statistics and spatial relative risk methods to explore spatial heterogeneity and identify regions with spatial clusters of TB cases, and then used logistic regression method to estimate individual-level risk factors for notified migrant TB and spatial clusters. A hierarchical Bayesian spatial model was used to identify the attributable location-specific factors.

Results

Overall, 27,383 bacterially-positive tuberculosis patients were notified for analysis, with 42.54% (11,649) of them being migrants. The age-adjusted notification rate of TB among migrants was much higher than among residents. Migrants (aOR, 1.85; 95%CI, 1.65-2.08) and active screening (aOR, 3.13; 95%CI, 2.60-3.77) contributed significantly to the formation of TB high-spatial clusters. With the hierarchical Bayesian modeling, the presence of industrial parks (RR, 1.420; 95%CI, 1.023-1.974) and migrants (RR, 1.121; 95%CI, 1.007-1.247) were the risk factors for increased TB disease at the county level.

Conclusion

We identified a significant spatial heterogeneity of tuberculosis in Shanghai, one of the typical megacities with massive migration. Internal migrants play an essential role in the disease burden and the spatial heterogeneity of TB in urban settings. Optimized disease control and prevention strategies, including targeted interventions based on the current epidemiological heterogeneity, warrant further evaluation to fuel the TB eradication process in urban China.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND

Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).

METHODS

Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.

RESULTS

We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.

DISCUSSION

The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Neighborhood factors and triple negative breast cancer: The role of cumulative exposure to area-level risk factors

BACKGROUND

Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half of the racial difference in breast cancer mortality can be attributed to triple negative breast cancer (TNBC), an aggressive subtype of invasive breast cancer that disproportionately affects Black women. Recent research has implicated neighborhood conditions in the etiology of TNBC. This study investigated the relationship between cumulative neighborhood-level exposures and TNBC risk.

METHODS

This single-institution retrospective study was conducted on a cohort of 3316 breast cancer cases from New Castle County, Delaware (from 2012 to 2020), an area of the country with elevated TNBC rates. Cases were stratified into TNBC and "Non-TNBC" diagnosis and geocoded by residential address. Neighborhood exposures included census tract-level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards. An overall cumulative risk score was calculated based on tract-level exposures.

RESULTS

Univariate analyses showed each tract-level exposure was associated with greater TNBC odds. In multivariate analyses that controlled for patient-level race and age, tract-level exposures were not associated with TNBC odds. However, in a second multivariate model that included patient-level variables and considered tract-level risk factors as a cumulative exposure risk score, each one unit increase in cumulative exposure was significantly associated with a 10% increase in TNBC odds. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents.

CONCLUSIONS

Cumulative exposure to neighborhood-level risk factors that disproportionately affect Black communities was associated with greater TNBC risk.

Geospatial analysis of firearm injuries in an urban setting: Individual rather than community characteristics affect firearm injury risk

BACKGROUND

The relationship between individual/socioeconomic characteristics and firearm injury risk in an urban center was evaluated.

METHODS

A hospital registry was used to identify individuals in Baltimore City who experienced interpersonal firearm injury in 2019 (FA). Injuries that did not satisfy this criterion were used as a comparison group (NF). Socioeconomic characteristics were linked to home address at the block group level. Regression analysis was used to determine predictors of firearm injury. Clusters of high and low firearm relative to non-firearm injuries were identified.

RESULTS

A total of 1293 individuals were included (FA = 277, NF = 1016). The FA group lived in communities with lower income (p = 0.005), higher poverty (p = 0.007), and more Black residents (p < 0.001). Individual level factors were stronger predictors of firearm injury than community factors on multivariate regression with Black race associated with 5x higher odds of firearm injury (p < 0.001). Firearm injury clustered in areas of low socioeconomic status.

CONCLUSIONS

Individual versus community factors have a greater influence on firearm injury risk. Prevention efforts should target young, Black men in urban centers.

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.

Sheep scab: comparison of spatial and temporal patterns determined by clinical diagnosis or ELISA

Background

Ovine psoroptic mange (sheep scab) is an infectious condition caused by an allergen-induced hypersensitivity response to the mite Psoroptes ovis. Infestation results in clinical disease, economic loss and welfare issues in many sheep-producing countries. The aim of this study was to compare the prevalence and spatial pattern of sheep scab on contiguous farms, using both self-reported clinical outbreak history (2012–2020) and serological testing with an enzyme-linked immunosorbent assay (2021/2022).

Methods

Farms included in the study were located in three regions of known high scab prevalence in North, Central and Southwest England. In total, 254 farms completed both a questionnaire, which provided the clinical scab history of the farm, and submitted results of serological testing with the ELISA.

Results

A scab outbreak was reported by 17.4% (± confidence interval [CI]: 4.6%; n = 48) of farms in 2020 based on clinical diagnosis; scab was diagnosed by the ELISA on 25.6% (± 5.5%; n = 65) of farms in 2021/2022. Comparison of self-reported clinical scab cases with the ELISA test results identified a group of farms (n = 52) that did not report scab in 2020, or in some cases did not report having scab over the previous 8 years (n = 20), but whose flocks were nevertheless seropositive in 2021/2022.

Conclusion

A small number of flocks, particularly those using common grazings in North England, where handling is infrequent, often comprising less susceptible sheep breeds, may have persistent scab infestations that are generally undetected by clinical inspection. The data highlight the advantages of serological testing to identify exposure to scab in flocks where clinical signs are less easily detected.

Graphical Abstract

The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2-susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic.

Spatial heterogeneity of neighborhood-level water and sanitation access in informal urban settlements: A cross-sectional case study in Beira, Mozambique

Rapid urbanization, resulting in population growth within informal settlements, has worsened exclusion and inequality in access to water and sanitation (WASH) services in the poorest and most marginalized communities. In this study, we describe the heterogeneity in water service satisfaction and WASH access in low-income, peri-urban neighborhoods of Beira, Mozambique, and examine whether this heterogeneity can be explained by distance to water distribution mains. Using spatial statistics and regression analyses, we identify spatial heterogeneity in household WASH access, as well as consumer-reported satisfaction with water services (services, pressure, quality, and sufficient quantity). We find that as distance from the water main increased, both access to an improved water source at the household and satisfaction with water pressure decreases, and water supply intermittency increases, controlling for household density and socioeconomic status. The odds of a household having access to a water source at the household or on the compound decreases with every 100-meter increase in distance from a water main pipe (odds ratio [OR] 0.87, 95% confidence interval [CI]: 0.82, 0.92). Satisfaction with water services also decreases with every 100-meter increase in distance from a water main pipe (OR: 0.80; 95% CI: 0.69, 0.94). Days of availability in the past week decreases by a factor of 0.22 for every 100-meter increase in distance from the water main (95% CI: −0.29, −0.15). Findings from this study highlight the unequal household access to water and sanitation in urban informal settlements, even within low-income neighborhoods. Describing this heterogeneity of access to water services, sanitation, and satisfaction—and the factors influencing them—can inform stakeholders and guide the development of infrastructural solutions to reduce water access inequities within urban settings.

Aggregated spatial intensity as a method for estimating point-level exposures within area-level units: The case of tobacco retailer exposure in census tracts

BACKGROUND

Aggregating point-level events to area-level units can produce misleading interpretations when displayed via choropleth maps. We developed the aggregated intensity method to share point-level location information across unit boundaries prior to aggregation. This method was applied to tobacco retailers among census tracts in New Castle County, DE.

METHODS

Aggregated intensity uses kernel density estimation to generate spatially continuous expected counts of events per unit area, then aggregates these results to area-level units. We calculated a relative difference measure to compare aggregated intensity to observed counts.

RESULTS

Aggregated intensity produces estimates of event exposure unconstrained by boundaries. The relative difference between aggregated intensity and counts is greater for units with many events proximal to their borders. The appropriateness of aggregated intensity depends on events' spatial influence and proximity to unit boundaries, as well as computational inputs.

CONCLUSIONS

Aggregated intensity may facilitate more spatially realistic estimates of exposure to point-level events.

p3k14c, a synthetic global database of archaeological radiocarbon dates

Archaeologists increasingly use large radiocarbon databases to model prehistoric human demography (also termed paleo-demography). Numerous independent projects, funded over the past decade, have assembled such databases from multiple regions of the world. These data provide unprecedented potential for comparative research on human population ecology and the evolution of social-ecological systems across the Earth. However, these databases have been developed using different sample selection criteria, which has resulted in interoperability issues for global-scale, comparative paleo-demographic research and integration with paleoclimate and paleoenvironmental data. We present a synthetic, global-scale archaeological radiocarbon database composed of 180,070 radiocarbon dates that have been cleaned according to a standardized sample selection criteria. This database increases the reusability of archaeological radiocarbon data and streamlines quality control assessments for various types of paleo-demographic research. As part of an assessment of data quality, we conduct two analyses of sampling bias in the global database at multiple scales. This database is ideal for paleo-demographic research focused on dates-as-data, bayesian modeling, or summed probability distribution methodologies.

The Association of Laryngeal Position on Videolaryngoscopy and Time Taken to Intubate Using Spatial Point Pattern Analysis of Prospectively Collected Quality Assurance Data

BACKGROUND

During videolaryngoscopy (VL), the larynx appears within the defined area of the video screen, and its location can be measured as a point within this space. Spatial statistics offer methods to explore the relationship between location data and associated variables of interest. The aims of this study were to use spatial point pattern analysis to explore if the position of the larynx on VL is associated with longer times to intubate, increased risk of a needing >1 intubation attempt, or percentage of glottic opening.

METHODS

Quality assurance data and clinical notes from all prehospital intubations using C-MAC Pocket Monitor with CMAC-4 blade (Karl Storz) from January 1, 2018, to July 31, 2020, were reviewed. We extracted 6 measurements corresponding to the time taken to obtain the initial and then best laryngeal view, time to manipulate a bougie, and time to place the endotracheal tube, as well a percentage of glottic opening and a number of intubation attempts. Larynx location was the middle of the base of glottis, in cm from the left and bottom on the C-MAC screen. Two plots were produced to summarize the base of glottis location and time to perform each time component of intubation. Next, a cross mark function and a maximum absolute deviation hypothesis test were performed to assess the null hypotheses that the spatial distributions were random. The association between glottis location and >1 intubation attempt was assessed by a spatial relative risk plot.

RESULTS

Of 619 eligible intubations, 385 had a video for analysis. The following time variables had a nonrandom spatial distribution with a tendency for longer times when the larynx was off-center to the top or right of the screen: laryngoscope passing from teeth to glottis, glottis first view to best view of the larynx, time from bougie appearing to being placed in the cords, and overall time from teeth to endotracheal tube passing through cords. There was no increased relative risk for >1 intubation attempt.

CONCLUSIONS

Spatial point pattern analysis identified a relationship between the position of the larynx during VL and prolonged intubation times. We did not find a relationship between larynx location and >1 attempt. Whether the location of the larynx on the screen is a marker for difficult VL or if optimizing the larynx position to the center of the screen improves intubation times would require further prospective studies.

Spatial Point Pattern Analysis Identifies Mechanisms Shaping the Skin Parasite Landscape in Leishmania donovani Infection

Increasing evidence suggests that in hosts infected with parasites of the Leishmania donovani complex, transmission of infection to the sand fly vector is linked to parasite repositories in the host skin. However, a detailed understanding of the dispersal (the mechanism of spread) and dispersion (the observed state of spread) of these obligatory-intracellular parasites and their host phagocytes in the skin is lacking. Using endogenously fluorescent parasites as a proxy, we apply image analysis combined with spatial point pattern models borrowed from ecology to characterize dispersion of parasitized myeloid cells (including ManR+ and CD11c+ cells) and predict dispersal mechanisms in a previously described immunodeficient model of L. donovani infection. Our results suggest that after initial seeding of infection in the skin, heavily parasite-infected myeloid cells are found in patches that resemble innate granulomas. Spread of parasites from these initial patches subsequently occurs through infection of recruited myeloid cells, ultimately leading to self-propagating networks of patch clusters. This combination of imaging and ecological pattern analysis to identify mechanisms driving the skin parasite landscape offers new perspectives on myeloid cell behavior following parasitism by L. donovani and may also be applicable to elucidating the behavior of other intracellular tissue-resident pathogens and their host cells.

A Population Health Assessment in a Community Cancer Center Catchment Area: Triple negative breast cancer, alcohol use, and obesity in New Castle County, Delaware

BACKGROUND

The National Cancer Institute (NCI) requires designated cancer centers to conduct catchment area assessments to guide cancer control and prevention efforts designed to reduce the local cancer burden. We extended and adapted this approach to a community cancer center catchment area with elevated rates of triple negative breast cancer (TNBC).

METHODS

Cancer registry data for 462 TNBC and 2,987 Not-TNBC cases diagnosed between 2012 and 2020 at the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), located in New Castle County, Delaware, were geocoded to detect areas of elevated risk ('hot spots') and decreased risk ('cold spots'). Next, electronic health record (EHR) data on obesity and alcohol use disorder (AUD) and catchment-area measures of fast-food and alcohol retailers were used to assess for spatial relationships between TNBC hot spots and potentially modifiable risk factors.

RESULTS

Two hot and two cold spots were identified for TNBC within the catchment area. The hot spots accounted for 11% of the catchment area but nearly a third of all TNBC cases. Higher rates of unhealthy alcohol use and obesity were observed within the hot spots.

CONCLUSIONS

The use of spatial methods to analyze cancer registry and other secondary data sources can inform cancer control and prevention efforts within community cancer center catchment areas, where limited resources can preclude the collection of new primary data.

IMPACT

Targeting community outreach and engagement activities to TNBC hot spots offers the potential to reduce the population-level burden of cancer efficiently and equitably.

Spatial distribution of Plasmodium falciparum and P. vivax in northern Ethiopia by microscopy, rapid diagnostic test, laboratory antibody and antigen data

BACKGROUND

Determining malaria transmission within regions of low, heterogenous prevalence is difficult. A variety of malaria tests exist and range from identification of diagnostic infection to testing for prior exposure. This study describes concordance of multiple malaria tests using data from a 2015 household survey conducted in Ethiopia.

METHODS

Blood samples (n= 2,279) from three regions in northern Ethiopia were assessed for Plasmodium falciparum and P. vivax by microscopy, rapid diagnostic test (RDT), multiplex antigen assay, and multiplex assay for IgG antibodies. Geospatial analysis was conducted with spatial scan statistics and kernel density estimation to identify hotspots of malaria by different test results.

RESULTS

Prevalence of malaria infection was low (1.4% by RDT, 1.0% by microscopy, and 1.8% by laboratory antigen assay). For P. falciparum, overlapping spatial clusters for all tests and an additional five unique IgG clusters were identified. For P. vivax, clusters identified for bead antigen assay, microscopy, and IgG with partial overlap.

CONCLUSIONS

Assessing the spatial distribution of malaria exposure using multiple metrics can improve the understanding of malaria transmission dynamics in a region. The relative abundance of antibody clusters indicates that in areas of low-transmission, IgG antibodies are a more useful marker to assess malaria exposure.

Spatiotemporal Patterns of Loco-Regional Recurrence After Breast-Conserving Surgery

Background

Loco-regional recurrences (LRR) following breast-conserving surgery (BCS) remain a heterogeneous class of disease that has significant variation in its biological behavior and prognosis.

Methods

To delineate the spatiotemporal patterns of LRR after BCS, we analyzed the data of 4325 patients treated with BCS from 2006 to 2016. Clinico-pathological and treatment specific factors were analyzed using the Cox proportional hazards model to identify factors predictive for LRR events. Recurrence patterns were scrutinized based on recurrence type and recurrence-free interval (RFI). Annual recurrence rates (ARR) were compared according to recurrence type and molecular subtype.

Results

With a median follow-up of 66 months, 120 (2.8%) LRRs were recorded as the first site of failure. Age, pathologic stage, and molecular subtype were identified as predictors of LRR. The major recurrence type was ipsilateral breast tumor recurrence, which mainly (83.6%) occurred ≤5y post surgery. In the overall population, ARR curves showed that relapse peaked in the first 2.5 years. Patients with regional nodal recurrence, shorter RFI, and synchronous distant metastasis were associated with a poorer prognosis. HER2-positive disease had a higher rate of LRR events, more likely to have in-breast recurrence, and had an earlier relapse peak in the first 2 years after surgery.

Conclusions

LRR risk following BCS is generally low in Chinese ethnicity. Different recurrence patterns after BCS were related to distinct clinical outcomes. Management of LRR should be largely individualized and tailored to the extent of disease, the molecular profile of the recurrence, and to baseline clinical variables.

Definition of Local Recurrence Site in Resected Pancreatic Adenocarcinoma: A Multicenter Study (DOLORES-1)

Simple Summary

Pancreatic cancer remains a disease with a dismal outlook for patients, with high relapse rates after surgery and adjuvant treatments. Thanks to the high conformality achievable with advanced radiotherapy techniques, a more robust definition of clinical target volume (CTV) margins is mandatory. Moreover, a precise CTV definition may affect local control, minimizing radiation-related toxicity and allowing dose escalation. Contrary to two recent studies, RTOG contouring guidelines are not based on a pattern of failure analysis. We provided a local failure risk map in resected pancreatic cancer, validating the results of previous studies. Moreover, according to a new probabilistic approach, we provided new CTV contouring guidelines for the postoperative radiotherapy of pancreatic cancer, modeling targets’ margins on a combination of our validated local failure map (30% of local failures) and RTOG guidelines (70% of local failures).

Abstract

The study aimed to generate a local failure (LF) risk map in resected pancreatic cancer (PC) and validate the results of previous studies, proposing new guidelines for PC postoperative radiotherapy clinical target volume (CTV) delineation. Follow-up computer tomography (CT) of resected PC was retrospectively reviewed by two radiologists identifying LFs and plotting them on a representative patient CT scan. The percentages of LF points randomly extracted based on CTV following the RTOG guidelines and based on the LF database were 70% and 30%, respectively. According to the Kernel density estimation, an LF 3D distribution map was generated and compared with the results of previous studies using a Dice index. Among the 64 resected patients, 59.4% underwent adjuvant treatment. LFs closer to the root of the celiac axis (CA) or the superior mesenteric artery (SMA) were reported in 32.8% and 67.2% cases, respectively. The mean (± standard deviation) distances of LF points to CA and SMA were 21.5 ± 17.9 mm and 21.6 ± 12.1 mm, respectively. The Dice values comparing our iso-level risk maps corresponding to 80% and 90% of the LF probabilistic density and the CTVs-80 and CTVs-90 of previous publications were 0.45–0.53 and 0.58–0.60, respectively. According to the Kernel density approach, a validated LF map was proposed, modeling a new adjuvant CTV based on a PC pattern of failure.

sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application

Background

Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design.

Results

We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection.

Conclusions

sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

Characterizing the spatial relationship between smoking status and tobacco retail exposure: Implications for policy development and evaluation

Tobacco retail density and smoking prevalence remain elevated in marginalized communities, underscoring the need for strategies to address these place-based disparities. The spatial variation of smokers and tobacco retailers is often measured by aggregating them to area-level units (e.g., census tracts), but spatial statistical methods that use point-level data, such as spatial intensity and K-functions, can better describe their geographic patterns. We applied these methods to a case study in New Castle County, DE to characterize the cross-sectional spatial relationship between tobacco retailers and smokers, finding that current smokers experience greater tobacco retail exposure and clustering relative to former smokers. We discuss how analysis at different geographic scales can provide complementary insights for tobacco control policy.

Geographical and socioeconomic disparities in opioid access in Mexico, 2015-19: a retrospective analysis of surveillance data

Background

In 2015, Mexico implemented regulatory changes and an electronic system to improve access to prescription opioids. We aimed to investigate trends in opioid dispensing after the implementation of these changes and assess how opioid dispensing varied geographically and by socioeconomic status.

Methods

In this retrospective analysis of prescription medication surveillance data, we analysed dispensing data for group 1 medications (all opioids, including morphine, methadone, hydromorphone, oxycodone, tapentadol, fentanyl, sufentanil, and remifentanil) obtained from the Federal Commission for the Protection against Sanitary Risk database for 32 states and six large metropolitan areas in Mexico. We calculated crude annual opioid prescriptions per 10 000 people at the national, state, and municipal levels. Adapting methods from the report of the Lancet Commission on Palliative Care and Pain Relief, we calculated the need for palliative opioids by state, and then assessed the observed opioid dispensing rates as a percentage of expected need by geographical socioeconomic status. Within the six major metropolitan areas, we mapped the geocoded location of opioid prescriptions and assessed the association between opioid dispensing and socioeconomic status as well as the association between opioid dispensing and time to US border crossing for areas on the US–Mexico border.

Findings

Between June 25, 2015, and Oct 7, 2019, opioid dispensing rates increased by an average of 13% (95% CI 6·8–19·6) per quarter (3 months). The overall national opioid dispensing rate during the study period was 26·3 prescriptions per 10 000 inhabitants. States with a higher socioeconomic status had higher opioid dispensing rates than states with lower socioeconomic status (rate ratio [RR] 1·88, 95% CI 1·33–2·58, p=0·00016) after controlling for the estimated opioid requirement per state, the presence of methadone clinics, and the presence of tertiary hospitals and cancer centres. The same association between opioid dispensing and socioeconomic status was observed in the metropolitan areas, and in those metropolitan areas on the US–Mexico border a 20% decrease (RR 0·80, 95% CI 0·75–0·86) in opioid dispensation was observed per each SD increase (SD 17·1 min) in travel time to the border.

Interpretation

Measures introduced by the Mexican federal Government to increase opioid access for patients with palliative care needs were only marginally successful in raising opioid prescription rates. Opioid access should be improved for patients with palliative care needs who live in geographical areas of lower socioeconomic status.

Funding

US National Institutes of Health.

Choosing interventions to eliminate forest malaria: preliminary results of two operational research studies inside Cambodian forests

BACKGROUND

Rapid elimination of Plasmodium falciparum malaria in Cambodia is a goal with both national and international significance. Transmission of malaria in Cambodia is limited to forest environments, and the main population at risk consists of forest-goers who rely on forest products for income or sustenance. The ideal interventions to eliminate malaria from this population are unknown.

METHODS

In two forested regions of Cambodia, forest-goers were trained to become forest malaria workers (FMWs). In one region, FMWs performed mass screening and treatment, focal screening and treatment, and passive case detection inside the forest. In the other region, FMWs played an observational role for the first year, to inform the choice of intervention for the second year. In both forests, FMWs collected blood samples and questionnaire data from all forest-goers they encountered. Mosquito collections were performed in each forest.

RESULTS

Malaria prevalence by PCR was high in the forest, with 2.3-5.0% positive for P. falciparum and 14.6-25.0% positive for Plasmodium vivax among forest-goers in each study site. In vectors, malaria prevalence ranged from 2.1% to 9.6%, but no P. falciparum was observed. Results showed poor performance of mass screening and treatment, with sensitivity of rapid diagnostic tests equal to 9.1% (95% CI 1.1%, 29.2%) for P. falciparum and 4.4% (95% CI 1.6%, 9.2%) for P. vivax. Malaria infections were observed in all demographics and throughout the studied forests, with no clear risk factors emerging.

CONCLUSIONS

Malaria prevalence remains high among Cambodian forest-goers, but performance of rapid diagnostic tests is poor. More adapted strategies to this population, such as intermittent preventive treatment of forest goers, should be considered.

Fleas infesting cats and dogs in Great Britain: spatial distribution of infestation risk and its relation to treatment

The spatial pattern of flea (Siphonaptera: Pulicidae) infestation risk in cats and dogs across Great Britain is quantified, using data collected from a national survey undertaken in 2018, with particular attention given to the association between insecticidal treatment and infestation risk. Flea infestation risk declined significantly from south to north. None of the factors: pet breed, sex, neutered status or whether the pet had been abroad, showed any relationship with the underlying geographic distribution, which is most likely to be associated with climatic factors. However, overall, only 23.6% of the cats and 35% of the dogs inspected had been treated with identifiable flea products that were still 'in date' at the point of inspection. The percentage of owners treating their pet broadly followed infestation risk. The insecticide fipronil is a common active in a wide range of flea treatments and was the most frequently applied insecticide class, particularly in cats. However, 62% of cats and 45% of dogs that had been treated with a fipronil-based product that was 'in date' at the point of inspection still had fleas. Persistent flea infestation is likely to be due to a range of factors, including compliance and application failure, but the data provide strong inferential evidence for a lack of efficacy of fipronil-based products. Given the ubiquity of flea infestation, this finding and the relatively low-level of treatment compliance, highlight a clear need for greater owner education about the importance of flea management and a better understanding of the efficacy of different products.

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps

BACKGROUND

In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated.

RESULTS

We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016.

CONCLUSIONS

The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

Spatial relative risk and factors associated with porcine reproductive and respiratory syndrome outbreaks in United States breeding herds

Details of incident cases of porcine reproductive and respiratory syndrome (PRRS) in United States breeding herds were obtained from the Morrison's Swine Health Monitoring Project. Herds were classified as cases if they reported an outbreak in a given season of the year and non-cases if they reported it in a season other than the case season or if they did not report a PRRS outbreak in any season. The geographic distribution of cases and non-cases was compared in each season of the year. The density of farms that had a PRRS outbreak during summer was higher in Southern Minnesota and Northwest-central Iowa compared to the density of the underlying population of non-case farms. This does not mean that PRRS outbreaks are more frequent during summer in absolute terms, but that there was a geographical clustering of herds breaking during summer in this area. Similar findings were observed in autumn. In addition, the density of farms reporting spring outbreaks was higher in the Southeast of the United States compared to that of the underlying population of non-case farms. A similar geographical clustering of PRRS outbreaks was observed during winter in the Southeast of the United States. Multivariable analyses, adjusting for the effect of known confounders, showed that the incidence rate of PRRS was significantly lower during winter and autumn during the porcine epidemic diarrhea (PED) epidemic years (2013-2014), compared to PRRS incidence rates observed during the winter and autumn of PED pre-epidemic years (2009-2012). After 2014, an increase in the incidence rate of PRRS was observed during winter and spring but not during autumn or summer. Pig dense areas were associated with a higher incidence rate throughout the year. However, this association tended to be stronger during the summer. Additionally, herds with ≥2500 sows had an increased incidence rate during all seasons except spring compared to those with <2500 sows. PRRS incidence was lower in year-round air-filtered herds compared to non-filtered herds throughout the year. We showed that not only the spatial risk of PRRS varies regionally according to the season of the year, but also that the effect of swine density, herd size and air filtering on PRRS incidence may also vary according to the season of the year. Further studies should investigate regional and seasonal drivers of disease. Breeding herds should maintain high biosecurity standards throughout the year.

Distribution and prevalence of ticks and tick-borne disease on sheep and cattle farms in Great Britain

INTRODUCTION

The most abundant and widespread tick species in Great Britain, Ixodes ricinus, is responsible for the transmission of a range of pathogens that cause disease in livestock. Empirical data on tick distribution and prevalence are required to inform farm management strategies. However, such data are largely unavailable; previous surveys have been rare and are usually relatively localised.

METHODS

A retrospective questionnaire survey of farmers was used to assess the reported prevalence of ticks on livestock across Great Britain. Spatial scan statistics and kernel density maps were used to assess spatial clustering and identify areas of significantly elevated risk, independent of the underlying distribution of respondents. Logistic regression models were used to identify risk factors for tick presence.

RESULTS

Tick infection risk to livestock is shown to be spatially aggregated, with areas of significantly elevated risk in north Wales, northwest England and western Scotland. Overall, the prevalence of farms reporting tick presence was 13% for sheep farms and 6% for cattle farms, but in "hot spot" clusters prevalence ranged between 48-100%. The prevalence of farms reporting tick-borne disease overall was 6% for sheep and 2% for cattle, but on farms reporting ticks, prevalence was 44% and 33% for sheep and cattle farms, respectively. Upland farming, larger flock sizes, region and the presence of sheep on cattle farms were all significant risk factors for tick presence.

CONCLUSIONS

These data have important implications for assessing both the risk of tick-borne disease in livestock and optimising approaches to disease management. In particular, the study highlights the need for effective livestock tick control in upland regions and the southwest, and provides evidence for the importance of sheep as tick maintenance hosts.

Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data

The Automated Enforcement System (AES) has become the most important traffic enforcement system in China. In this study, a spatio-temporal kernel density estimation (STKDE) model, integrating spatio-temporal statistics and three-dimensional visualization techniques, was applied to reveal the spatial and temporal patterns of traffic violation behavior at urban intersections. The multivariate Gaussian kernel function was selected for space and time density estimation, as it has been shown to be a good arbitrary probability density function for continuous multivariate data. Because the STKDE model builds a space-time cube that adopts different colors of voxels to visualize the density of traffic violations, an optimal bandwidth selector that combines unconstrained pilot bandwidth matrices with a data-driven method was selected for achieving the best visualization result. The raw AES traffic violation data over 200 weekdays from 69 intersections in the city of Wujiang were empirically analyzed. The results show that the STKDE space-time cube made it easier to detect the spatio-temporal patterns of traffic violations than did the traditional hotspots map. An interesting finding was that traffic sign violations and traffic marking violations were primarily concentrated not in regular peak hours, but during the time period of 14:00-16:00, which indicates that these intersections were the most congested during this period. Primarily, the STKDE model identified seven patterns of spatio-temporal traffic violation hotspots and coldspots. These results are important because their prediction of temporal trends of traffic violations may help contribute toward the understanding and improvement of intersection safety problems.

Monitoring of Pseudorabies in Wild Boar of Germany-A Spatiotemporal Analysis

To evaluate recent developments regarding the epidemiological situation of pseudorabies virus (PRV) infections in wild boar populations in Germany, nationwide serological monitoring was conducted between 2010 and 2015. During this period, a total of 108,748 sera from wild boars were tested for the presence of PRV-specific antibodies using commercial enzyme-linked immunosorbent assays. The overall PRV seroprevalence was estimated at 12.09% for Germany. A significant increase in seroprevalence was observed in recent years indicating both a further spatial spread and strong disease dynamics. For spatiotemporal analysis, data from 1985 to 2009 from previous studies were incorporated. The analysis revealed that PRV infections in wild boar were endemic in all German federal states; the affected area covers at least 48.5% of the German territory. There were marked differences in seroprevalence at district levels as well as in the relative risk (RR) of infection of wild boar throughout Germany. We identified several smaller clusters and one large region, where the RR was two to four times higher as compared to the remaining areas under investigation. Based on the present monitoring intensity and outcome, we provide recommendations with respect to future monitoring efforts concerning PRV infections in wild boar in Germany.

Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England

Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed.

Spatial Analysis of a Cat-Borne Disease Reveals That Soil pH and Clay Content Are Risk Factors for Sarcocystosis in Sheep

Cat-borne parasites and their associated diseases have substantial impacts on human, livestock, and wildlife health worldwide. Despite this, large and detailed datasets that allow researchers to study broad-scale trends in the ecology of cat-borne diseases are either difficult to obtain or non-existent. One condition that is easily detected at slaughter is macroscopic sarcocystosis, a cat-borne parasitosis of sheep (Ovis aries). We conducted a cross-sectional study to describe the geographic distribution of sarcocystosis in sheep throughout South Australia and investigate ecosystem characteristics associated with the presence of disease. Data were obtained from two slaughterhouses which processed 3,865,608 sheep from 4,204 farms across 385,468 km² of South Australia's land mass for the period 2007-2017. A Poisson point process model was developed to quantify environmental characteristics associated with higher densities of sarcocystosis-positive farms. Sarcocystosis was highly clustered on a large island off of the Australian coast and the density of sarcocystosis-positive farms increased in areas of low soil pH (intensity ratio: 0.86, 95% CI: 0.78, 0.95) and high clay content. We hypothesize that region was confounded by, and predominately acted as a proxy for, cat density. Our results have broader implications regarding the health, welfare, economic, and conservation impacts of other cat-borne parasitosis, such as toxoplasmosis.

Spatiotemporal distribution of a non-haematophagous bat community and rabies virus circulation: a proposal for urban rabies surveillance in Brazil

In Brazil, rabies surveillance is based on monitoring domestic and wild animals, although the most prevalent lineage of the rabies virus (RABV) currently diagnosed in Brazil is associated with bats, particularly non-haematophagous bats. Disease control is based on the mass vaccination of dogs and cats. We used data collected by the passive surveillance system of the city of Campinas from 2011 to 2015, to describe the temporal and geographic distributions of the bat specimens and RABV and discuss the current rabies surveillance with the advent of the declaration of canine and feline rabies-free areas in Brazil. We described the species, locations and health statuses of the collected bat specimens. Moreover, all samples were submitted for RABV diagnosis. Then, we performed a time series decomposition for each bat family. Additionally, we determined the spatiotemporal relative risk for RABV infection using the ratio of the kernel-smoothed estimates of spatiotemporal densities of RABV-positive and RABV-negative bats. From the 2537 bat specimens, the most numerous family was Molossidae (72%), followed by Vespertilionidae (14%) and Phyllostomidae (13%). The bat families behaved differently in terms of seasonal and spatial patterns. The distribution of bats varied geographically in the urban environment, with Molossidae and Phyllostomidae being observed downtown and Vespertilionidae being observed in peripheral zones. Concurrently, a significant relative risk of RABV infection was observed downtown for Vespertilionidae and in peripheral zones for Molossidae. No RABV-positive sample clusters were observed. As a result of the official declaration of RABV-free areas in southern Brazil, mass dog and cat vaccinations are expected to halt in the near future. This stoppage would make most dog and cat populations susceptible to other RABV lineages, such as those maintained by non-haematophagous bats. In this scenario, all information available on bats and RABV distribution in urban areas is essential. Currently, few studies have been conducted. Some local health authorities, such as that in Campinas, are spontaneously basing their surveillance efforts on bat rabies, which is the alternative in reality scenario of increased susceptibility to bat-associated RABV that is developing in Brazil.