Kim B. Stevens

Spatial Analysis in Epidemiology

This book provides an overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This book brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time. With its focus on application rather than theory, this book includes examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. It also provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling, and decision support.

Changes in the glomerular filtration rate of 27 cats with hyperthyroidism after treatment with radioactive iodine

Hyperthyroidism is a common endocrinopathy of older cats and is associated with an increased glomerular filtration rate (gfr). Renal dysfunction is also common in older cats and may develop after they have been treated for hyperthyroidism. This paper describes the changes in the gfr of 27 hyperthyroid cats in the six months after their treatment with radioactive iodine (131I), and evaluates whether any commonly measured pretreatment parameters (serum biochemistry, systolic blood pressure, urine specific gravity) could predict a clinically significant decline in renal function. The gfr of all the cats had decreased one month after treatment, and the mean gfr was significantly lower. There was no further significant change in gfr between one and six months. The only independent variable associated with the final gfr was the pretreatment plasma glucose concentration (P=0·003).

Influenza at the animal-human interface: A review of the literature for virological evidence of human infection with swine or avian influenza viruses other than A(H5N1)

Factors that trigger human infection with animal influenza virus progressing into a pandemic are poorly understood. Within a project developing an evidence-based risk assessment framework for influenza viruses in animals, we conducted a review of the literature for evidence of human infection with animal influenza viruses by diagnostic methods used. The review covering Medline, Embase, SciSearch and CabAbstracts yielded 6,955 articles, of which we retained 89; for influenza A(H5N1) and A(H7N9), the official case counts of t he World Health Organization were used. An additional 30 studies were included by scanning the reference lists. Here, we present the findings for confirmed infections with virological evidence. We found reports of 1,419 naturally infected human cases, of which 648 were associated with avian influenza virus (AIV) A(H5N1), 375 with other AIV subtypes, and 396 with swine influenza virus (SIV). Human cases naturally infected with AIV spanned haemagglutinin subtypes H5, H6, H7, H9 and H10. SIV cases were associated with endemic SIV of H1 and H3 subtype descending from North American and Eurasian SIV lineages and various reassortants thereof. Direct exposure to birds or swine was the most likely source of infection for the cases with available information on exposure.

Furan-modified oligonucleotides for fast, high-yielding and site-selective DNA inter-strand cross-linking with non-modified complements

Among the various types of DNA damage, inter-strand cross-links (ICL) represent one of the most cytotoxic lesions. Processes such as transcription and replication can be fully blocked by ICLs, as shown by the mechanism of action of some anticancer drugs. However, repair of ICLs can be a possible cause of resistance. To study the mechanisms of cross-link repair stable, site-specifically cross-linked duplexes are needed. We here report on the synthesis of site-specifically cross-linked DNA using an acyclic furan containing nucleoside. Selective in situ oxidation of the incorporated furan moiety generates a highly reactive oxo-enal that instantly reacts with the complementary base in a non-modified strand, yielding one specific stable cross-linked duplex species. Varying sequence context showed that a strong selectivity for cross-linking to either complementary A or complementary C is operating, without formation of cross-links to neighboring or distant bases. Reaction times are very short and high isolated yields are obtained using only one equivalent of modified strand. The formed covalent link is stable and the isolated cross-linked duplexes can be stored for several months without degradation. Structural characterization of the obtained ICL was possible by comparison to the natural mutagenic adducts of cis-2-butene-1,4-dial, a metabolite of furan primarily responsible for furan carcinogenicity.

Spatial modelling of disease using data- and knowledge-driven approaches

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution.

Effect of left-sided valvular regurgitation on mortality and causes of death among a population of middle-aged and older horses.

The effect of left-sided valvular regurgitation (LSVR) on the mortality of middle-aged and older horses was investigated in a prospective cohort study involving 19 yards and 1153 horses. The horses were examined to determine whether they had a cardiac murmur and its type, and their age, sex, breed type and occupation were recorded. They were followed up at intervals of two years by postal questionnaire, and after four years information on 773 horses was available. There was no significant difference in the mortality of the horses with and without LSVR, but small horses had a significantly higher risk of having LSVR than small ponies (odds ratio [OR] 2·33), and older horses were slightly more likely to have LSVR than young horses (OR 1·07). Twenty-nine per cent of the deaths reported by the owners were due to orthopaedic problems, 23·3 per cent to gastrointestinal problems, and only 7·9 per cent to cardiovascular problems. Orthopaedic problems were the main cause of death in the horses, and gastrointestinal problems were the main cause of death in the ponies.

Modeling habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia: A spatial multicriteria decision analysis approach

Risk maps are one of several sources used to inform risk-based disease surveillance and control systems, but their production can be hampered by lack of access to suitable disease data. In such situations, knowledge-driven spatial modeling methods are an alternative to data-driven approaches. This study used multicriteria decision analysis (MCDA) to identify areas in Asia suitable for the occurrence of highly pathogenic avian influenza virus (HPAIV) H5N1 in domestic poultry. Areas most suitable for H5N1 occurrence included Bangladesh, the southern tip and eastern coast of Vietnam, parts of north-central Thailand and large parts of eastern China. The predictive accuracy of the final model, as determined by the area under the receiver operating characteristic curve (ROC AUC), was 0.670 (95% CI 0.667-0.673) suggesting that, in data-scarce environments, MCDA provides a reasonable alternative to the data-driven approaches usually used to inform risk-based disease surveillance and control strategies.

Furan‐Oxidation‐Triggered Inducible DNA Cross‐Linking: Acyclic Versus Cyclic Furan‐Containing Building Blocks—On the Benefit of Restoring the Cyclic Sugar Backbone

Oligodeoxynucleotides incorporating a reactive functionality can cause irreversible cross-linking to the target sequence and have been widely studied for their potential in inhibition of gene expression or development of diagnostic probes for gene analysis. Reactive oligonucleotides further show potential in a supramolecular context for the construction of nanometer-sized DNA-based objects. Inspired by the cytochrome P450 catalyzed transformation of furan into a reactive enal species, we recently introduced a furan-oxidation-based methodology for cross-linking of nucleic acids. Previous experiments using a simple acyclic building block equipped with a furan moiety for incorporation into oligodeoxynucleotides have shown that cross-linking occurs in a very fast and efficient way and that substantial amounts of stable, site-selectively cross-linked species can be isolated. Given the destabilization of duplexes observed upon introduction of the initially designed furan-modified building block into DNA duplexes, we explore here the potential benefits of two new building blocks featuring an extended aromatic system and a restored cyclic backbone. Thorough experimental analysis of cross-linking reactions in a series of contexts, combined with theoretical calculations, permit structural characterization of the formed species and allow assessment of the origin of the enhanced cross-link selectivity. Our experiments clearly show that the modular nature of the furan-modified building blocks used in the current cross-linking strategy allow for fine tuning of both yield and selectivity of the interstrand cross-linking reaction.

Spatial and temporal epidemiological analysis in the Big Data era

Abstract Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

Classical sheep scrapie in Great Britain: spatial analysis and identification of environmental and farm-related risk factors

BackgroundPrevious studies suggest that the spatial distribution of classical sheep scrapie in Great Britain is uneven and that certain flock characteristics may be associated with occurrence of the disease. However, the existence of areas of high and low disease-risk may also result from differences in the spatial distribution of environmental characteristics. In this study we explored the spatial pattern of classical scrapie in Great Britain between 2002 and 2005 and investigated the association between disease occurrence and various environmental and farm-related risk factors.ResultsExploratory spatial analysis: South Wales was found to have a higher density of scrapie-positive farms than the rest of Great Britain. In addition, a small cluster of high-risk farms was identified in the center of this region in which clustering of scrapie-positive farms occurred up to a distance of approximately 40 km.Spatial modelling: A mixed-effects regression model identified flock-size and soil drainage to be significantly associated with the occurrence of scrapie in England and Wales (area under the curve (AUC) 0.71 ± 0.01, 95% CI 0.68 - 0.74). The predictive risk map based on the estimated association between these factors and disease occurrence showed most of Wales to be at risk of being confirmed positive for scrapie with areas of highest risk in central and south Wales. In England, areas with the highest risk occurred mainly in the north and the midlands.ConclusionThe observed distribution of scrapie in Great Britain exhibited a definite spatial pattern with south Wales identified as an area of high occurrence. In addition both flock (flock size) and environmental variables (soil drainage) were found to be significantly associated with the occurrence of the disease. However, the models AUC indicated unexplained variation remaining in the model and the source of this variation may lie in farm-level characteristics rather than spatially-varying ones such as environmental factors.