Bayesian cluster detection via adjacency modelling

Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed solution to this problem is a two-stage approach, which produces a set of potential cluster structures for the data and then chooses the optimal structure via a Bayesian hierarchical model. The first stage uses a spatially adjusted hierarchical agglomerative clustering algorithm. The second stage fits a Poisson log-linear model to the data to estimate the optimal cluster structure and the spatial pattern in disease risk. The methodology was applied to a study of chronic obstructive pulmonary disease (COPD) in local authorities in England, where a number of high risk clusters were identified

Using a spatial filter and a geographic information system to improve rabies surveillance data.

The design and coordination of antirabies measures (e.g., oral vaccine and disease awareness campaigns) often depend on surveillance data. In Kentucky, health officials are concerned that the raccoon rabies epizootic that has spread throughout the east coast since the late 1970s could enter the state. The quality of surveillance data from Kentucky's 120 counties, however, may not be consistent. This article presents a geographic model that can be used with a geographic information system (GIS) to assess whether a county has a lower number of animals submitted for rabies testing than surrounding counties. This technique can be used as a first step in identifying areas needing improvement in their surveillance scheme. This model is a variant of a spatial filter that uses points within an area of analysis (usually a circle) to estimate the value of a central point. The spatial filter is an easy-to-use method of identifying point patterns, such as clusters or holes, at various geographic scales (county, intraurban), by using the traditional circle as an area of analysis or a GIS to incorporate a political shape (county boundary)

A Spatial Analysis of Functional Outcomes and Quality of Life Outcomes After Pediatric Injury

BACKGROUND: Changes in health-related quality of life (HRQoL) are more regularly being monitored during the first year after injury. Monitoring changes in HRQoL using spatial cluster analysis can potentially identify concentrations of geographic areas with injury survivors with similar outcomes, thereby improving how interventions are delivered or in how outcomes are evaluated. METHODS: We used a spatial scan statistic designed for oridinal data to test two different spatial cluster analysis of very low, low, high, and very high HRQoL scores. Our study was based on HRQoL scores returned by children treated for injury at British Columbia Children\u27s Hospital and discharged to the Vancouver Metropolitan Area. Spatial clusters were assessed at 4 time periods - baseline (based on pre-injury health as reported prior to discharge from hospital), and one, four, and twelve months after discharge. Outcome data were measured used the PedsQL™ outcome scale. Outcome values of very low, low, high, and very high HRQoL scores were defined by classifying PedsQL™ scores into quartiles. In the first test, all scores were assessed for clustering without specifying whether the response score was from a baseline or follow-up response. In the second analysis, we built a space-time model to identify whether HRQoL responses could be identified at specific time points. RESULTS: Among all participants, geographic clustering of response scores were observed globally and at specific time periods. In the purely spatial analysis, five significant clusters of \u27very low\u27 PedsQL physical and psychosocial health outcomes were identified within geographic zones ranging in size from 1 to 21 km. A space-time analysis of outcomes identified significant clusters of both \u27very low\u27 and \u27low\u27 outcomes between survey months within zones ranging in size from 3 to 5 km. CONCLUSION: Monitoring patient health outcomes following injury is important for planning and targeting interventions. A common theme in the literature is that future prevention efforts may benefit from identifying those most a risk of developing ongoing problems after injury in effort to target resources to those most in need. Spatial scan statistics are tools that could be applied for identifying concentrations of poor recovery outcomes. By classifying outcomes as a categorical variable, clusters of \u27potentially low\u27 outcomes can also be mapped, thereby identifying populations whose recovery status may decrease

The XMM-LSS Survey: A well controlled X-ray cluster sample over the D1 CFHTLS area

We present the XMM-LSS cluster catalogue corresponding to the CFHTLS D1 area. The list contains 13 spectroscopically confirmed, X-ray selected galaxy clusters over 0.8 deg2 to a redshift of unity and so constitutes the highest density sample of clusters to date. Cluster X-ray bolometric luminosities range from 0.03 to 5x10^{44} erg/s. In this study, we describe our catalogue construction procedure: from the detection of X-ray cluster candidates to the compilation of a spectroscopically confirmed cluster sample with an explicit selection function. The procedure further provides basic X-ray products such as cluster temperature, flux and luminosity. We detected slightly more clusters with a (0.5-2.0 keV) X-ray fluxes of >2x10^{-14} erg/s/cm^{-2} than we expected based on expectations from deep ROSAT surveys. We also present the Luminosity-Temperature relation for our 9 brightest objects possessing a reliable temperature determination. The slope is in good agreement with the local relation, yet compatible with a luminosity enhancement for the 0.15 < z< 0.35 objects having 1 < T < 2 keV, a population that the XMM-LSS is identifying systematically for the first time. The present study permits the compilation of cluster samples from XMM images whose selection biases are understood. This allows, in addition to studies of large-scale structure, the systematic investigation of cluster scaling law evolution, especially for low mass X-ray groups which constitute the bulk of our observed cluster population. All cluster ancillary data (images, profiles, spectra) are made available in electronic form via the XMM-LSS cluster database.Comment: 12 pages 5 figures, MNRAS accepted. The paper with full resolution cluster images is available at http://vela.astro.ulg.ac.be/themes/spatial/xmm/LSS/rel_pub_e.htm

Identifying priority and bright spot areas for improving diabetes care: a geospatial approach.

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran\u27s I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community

Spatial clustering of average risks and risk trends in Bayesian disease mapping

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Developing alternatives for optimal representation of seafloor habitats and associated communities in Stellwagen Bank National Marine Sanctuary

The implementation of various types of marine protected areas is one of several management tools available for conserving representative examples of the biological diversity within marine ecosystems in general and National Marine Sanctuaries in particular. However, deciding where and how many sites to establish within a given area is frequently hampered by incomplete knowledge of the distribution of organisms and an understanding of the potential tradeoffs that would allow planners to address frequently competing interests in an objective manner. Fortunately, this is beginning to change. Recent studies on the continental shelf of the northeastern United States suggest that substrate and water mass characteristics are highly correlated with the composition of benthic communities and may therefore, serve as proxies for the distribution of biological biodiversity. A detailed geo-referenced interpretative map of major sediment types within Stellwagen Bank National Marine Sanctuary (SBNMS) has recently been developed, and computer-aided decision support tools have reached new levels of sophistication. We demonstrate the use of simulated annealing, a type of mathematical optimization, to identify suites of potential conservation sites within SBNMS that equally represent 1) all major sediment types and 2) derived habitat types based on both sediment and depth in the smallest amount of space. The Sanctuary was divided into 3610 0.5 min2 sampling units. Simulations incorporated constraints on the physical dispersion of sampling units to varying degrees such that solutions included between one and four site clusters. Target representation goals were set at 5, 10, 15, 20, and 25 percent of each sediment type, and 10 and 20 percent of each habitat type. Simulations consisted of 100 runs, from which we identified the best solution (i.e., smallest total area) and four nearoptimal alternates. We also plotted total instances in which each sampling unit occurred in solution sets of the 100 runs as a means of gauging the variety of spatial configurations available under each scenario. Results suggested that the total combined area needed to represent each of the sediment types in equal proportions was equal to the percent representation level sought. Slightly larger areas were required to represent all habitat types at the same representation levels. Total boundary length increased in direct proportion to the number of sites at all levels of representation for simulations involving sediment and habitat classes, but increased more rapidly with number of sites at higher representation levels. There were a large number of alternate spatial configurations at all representation levels, although generally fewer among one and two versus three- and four-site solutions. These differences were less pronounced among simulations targeting habitat representation, suggesting that a similar degree of flexibility is inherent in the spatial arrangement of potential protected area systems containing one versus several sites for similar levels of habitat representation. We attribute these results to the distribution of sediment and depth zones within the Sanctuary, and to the fact that even levels of representation were sought in each scenario. (PDF contains 33 pages.