Browsing by Subject "spatial epidemiology"

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    Monitoring the spatiotemporal patterns of wildlife health using rehabilitation databases
    (2022-04-07) Kanankege, Kaushi; Willette, Michelle; Jenni, Phil; Ponder, Julia; Schott, Renee; Bueno, Irene; Muellner, Ulrich; Muellner, Petra; VanderWaal, Kimberly; kanan009@umn.edu; Kanankege, Kaushi; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota
    Wildlife health surveillance is challenging. An alternative is to use wildlife rehabilitation data as potential sentinels, where anomalies in the pattern of submissions may indicate an underlying event that deviates from the baseline and warrants further investigation. Such anomalies may affect multiple species, leading submissions to be clustered in a certain area or time period. To determine spatiotemporal submission patterns and the feasibility of identifying anomalies potentially associated with underlying events, we aggregated databases from two major wildlife rehabilitation centers in Minnesota, drawing on 66,472 submissions from the 2015 – 2019 period, and pertaining to 29 ”species groups” and 12 “circumstances” associated with submission. The infants and juveniles of wildlife, including fledglings, hatchlings, and after-hatch year birds (raptor-specific), submitted as a group from the same location on the same day were grouped and considered as one submission. Hence, the number of records included in the spatiotemporal cluster analysis was 49,352. The multivariate multinomial space-time model of the scan statistic was used to identify statistically significant spatiotemporal clusters of either wildlife species groups or circumstances, simultaneously (Cluster: an area capturing 10% of the submissions aggregated within a maximum radius of 30km during a maximum temporal window of 3-months). This repository contains the data arranged to be used for the spatial cluster analysis.
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    Social Epidemiology and Spatial Epidemiology: An Empirical Comparison of Perspectives
    (2013-05) McDonald, Kelsey
    Social and spatial epidemiologists each bring a unique perspective to how they examine contextual or neighborhood-level determinants of health. Although both perspectives draw from epidemiology, social epidemiology is additionally grounded in sociology and causal counterfactual frameworks while spatial epidemiology is heavily influenced by medical geography and predictive models. No study to date has compared these two distinct perspectives, along with their corresponding analytical approaches and model results. Yet this comparison may advance contextual effects research in epidemiology by suggesting methodological enhancements, providing insights into the robustness of our conclusions to the perspective taken, and suggesting whether we can truly identify contextual effects from observational data. To facilitate this comparison we used both perspectives to examine a research question: What is the estimated effect of increasing neighborhood education or income on overweight/obesity, type 2 diabetes, and current smoking, independent of individual-level differences? The social epidemiology approach employed propensity score matching while the spatial approach used approximated spatial multilevel models. Data for this study came from the California Health Interview Survey (2005, 2007, 2009) and the American Community Survey (2006-2010). Results revealed minimal to no effect of neighborhood education and income on overweight/obesity, type 2 diabetes, or current smoking, but estimated effects did vary somewhat by approach. This comparison highlighted fundamentally different goals in social and spatial epidemiology: identifying causal factors to intervene compared to predicting potential causal factors to describe reality. Attempts to improve causal inference in observational studies by integrating analytical techniques across subfields will likely be hampered by different objectives and model requirements. This incompatibility for integration, lack of strong evidence of effects, and the overall identification problem cast further doubt on our ability to identify causal contextual effects using observational data. However, this work may help in the design of experiments, which is where we should now focus.