Investigating the spatial risk distribution of West Nile virus disease in birds and humans in southern Ontario from 2002 to 2005

<p>Abstract</p> <p>Background</p> <p>The West Nile virus (WNv) became a veterinary public health concern in southern Ontario in 2001 and has continued to threaten public health. Wild bird mortality has been shown to be an indicator for tracking the geographic distribution of the WNv. The purpose of this study was to investigate the latent risk distribution of WNv disease among dead birds and humans in southern Ontario and to compare the spatial risk patterns for the period 2002–2005. The relationship between the mortality fraction in birds and incidence rate in humans was also investigated.</p> <p>Methods</p> <p>Choropleth maps were created to investigate the spatial variation in bird and human WNv risk for the public health units of southern Ontario. The data were smoothed by empirical Bayesian estimation before being mapped. Isopleth risk maps for both the bird and human data were created to identify high risk areas and to investigate the potential relationship between the WNv mortality fraction in birds and incidence rates in humans. This was carried out by the geostatistical prediction method of kriging. A Poisson regression analysis was used to model regional human WNv case counts as a function of the spatial coordinates in the east and north direction and the regional bird mortality fractions. The presence of disease clustering and the location of disease clusters were investigated by the spatial scan test.</p> <p>Results</p> <p>The isopleth risk maps exhibited high risk areas that were relatively constant from year to year. There was an overlap in the bird and human high risk areas, which occurred in the central-west and south-west areas of southern Ontario. The annual WNv cause-specific mortality fractions in birds for 2002 to 2005 were 31.9, 22.0, 19.2 and 25.2 positive birds per 100 birds tested, respectively. The annual human WNv incidence rates for 2002 to 2005 were 2.21, 0.76, 0.13 and 2.10 human cases per 100,000 population, respectively. The relative risk of human WNv disease was 0.72 times lower for a public health unit that was 100 km north of another public health unit. The relative risk of human WNv disease increased by the factor 1.44 with every 10 positive birds per 100 tested. The scan statistic detected disease cluster in the bird and human data. The human clusters were not significant, when the analysis was conditioned on the bird data.</p> <p>Conclusion</p> <p>The study indicates a significant relationship between the spatial pattern of WNv risk in humans and birds.</p

Burkholderia pseudomallei Is Spatially Distributed in Soil in Northeast Thailand

Melioidosis is a severe infection caused by the environmental bacterium Burkholderia pseudomallei. Soil sampling is important to identify geographic regions where humans and animals are at risk of exposure. The purpose of this study was to examine a factor that has a major bearing on the accuracy of soil sampling: the spatial distribution of B. pseudomallei in soil of a specified sampling site. Soil sampling was performed using a fixed-interval grid of 100 sampling points in each of two sites (disused land and rice field) in northeast Thailand, and the presence and amount of B. pseudomallei determined using culture. Mapping of the presence and B. pseudomallei count demonstrated that samples taken from areas adjacent to sampling points that were culture positive (negative) for B. pseudomallei were also likely to be culture positive (negative), and samples taken from areas adjacent to sampling points with a high (low) B. pseudomallei count were also likely to yield a high (low) count (spatial autocorrelation). These data were used as the basis for highlighting several pitfalls in current approaches to soil sampling, together with a discussion of the suitability of a range of sampling strategies in different geographical locations and for different study objectives

GIS and spatial data analysis: Converging perspectives

We take as our starting point the state of geographic information systems (GIS) and spatial data analysis 50 years ago when regional science emerged as a new field of enquiry. In the late 1950s and 1960s advances in computing technology were making possible forms of automated cartography that in due course would lead to th

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

(Co)variance structures for linear models in the analysis of plant improvement data

Plant improvement programs involve the evaluation of a large number of genotypes (varieties) in a series of designed experiments known as multi-environment trials (MET). The combined analysis of MET data is a complex statistical problem which requires extensions to the standard linear mixed model. The analysis must accommodate spatial correlation structures for the plot errors from each trial and appropriate genetic covariance structures. ASReml (Gilmour, Cullis, Welham & Thompson, 1998) provides a broad range of variance structures for both the errors and the random effects in a linear mixed model. The gains in statistical efficiency resulting from the use of more complex but more realistic variance structures are large. With ASReml they can be achieved at very little extra cost since the algorithm and use of sparse matrix methods ensures timely analyses. In this paper the computational strategy of ASReml will be described and some of the scope of the program will be demonstrated in the analysis of a MET data set