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- 3Geographic epidemiology
- 2Cluster detection
- 2Compound Poisson
- 2Conditional autoregressive
- 2Generalized linear mixed model
- 2Spatial scan
A Spatial Scan Statistic for Compound Poisson Data, Using Negative Binomial Distribution and Accounting for Population StratificationDownload
Since the interest in studying spatial relations in plant populations was raised in the 1950s, much effort has been devoted to the development of methods for spatial data analysis. One such development focused on techniques for detecting spatial clusters of cases and events in the biological...
The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research...
An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, CanadaDownload
Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study...
In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally...
This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing...
To analyze childhood cancer diagnoses in the province of Alberta, Canada during 1983-2004, we construct a generalized linear mixed model for the analysis of geographic and temporal variability of cancer rates. In this model, spatially correlated random e®ects and temporal components are adopted....
Temporal trends in pediatric mental health visits: using longitudinal data to inform emergency department health care planning
Temporal trends in pediatric mental health visits: using longitudinal data to inform emergency department health care planningDownload
OBJECTIVE: Understanding the temporality of mental health presentations to the emergency department (ED) during the 24-hour cycle, day of the week, and month of the year may facilitate strategic planning of ED-based mental health services. METHODS: Data on 30,656 ED presentations for mental...