Dataset Information

Individual-level space-time analyses of emergency department data using generalized additive modeling.

ABSTRACT:

Background

Although daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can also be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). This paper describes the methods for adapting GAMs so they can be applied to ED data.

Methods

GAMs are an effective approach for modeling spatial and temporal distributions of point-wise data, producing smoothed surfaces of continuous risk while adjusting for confounders. In addition to disease mapping, the method allows for global and pointwise hypothesis testing and selection of statistically optimum degree of smoothing using standard statistical software. We applied a two-dimensional GAM for location to ED data of overlapping calendar time using a locally-weighted regression smoother. To illustrate our methods, we investigated the association between participants' address and the risk of gastrointestinal illness in Cape Cod, Massachusetts over time.

Results

The GAM space-time analyses simultaneously smooth in units of distance and time by using the optimum degree of smoothing to create data frames of overlapping time periods and then spatially analyzing each data frame. When resulting maps are viewed in series, each data frame contributes a movie frame, allowing us to visualize changes in magnitude, geographic size, and location of elevated risk smoothed over space and time. In our example data, we observed an underlying geographic pattern of gastrointestinal illness with risks consistently higher in the eastern part of our study area over time and intermittent variations of increased risk during brief periods.

Conclusions

Spatial-temporal analysis of emergency department data with GAMs can be used to map underlying disease risk at the individual-level and view changes in geographic patterns of disease over time while accounting for multiple confounders. Despite the advantages of GAMs, analyses should be considered exploratory in nature. It is possible that even with a conservative cutoff for statistical significance, results of hypothesis testing may be due to chance. This paper illustrates that GAMs can be adapted to measure geographic trends in public health over time using ED data.

SUBMITTER: Vieira VM

PROVIDER: S-EPMC3542588 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Publications

Individual-level space-time analyses of emergency department data using generalized additive modeling.

Vieira Verónica M VM Weinberg Janice M JM Webster Thomas F TF

BMC public health 20120822

<h4>Background</h4>Although daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can also be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). This paper describes the methods for adapting GAMs so they can be applied to ED data.<h4>Methods</h4>GAMs are an eff ...[more]

PMID: 22914047

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Project description:Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure. The marsh islands in the center of the bay are currently vulnerable. In the future, these islands will likely be inundated, placing additional areas of the shoreline increasingly at risk. Significant differences in vulnerability exist between stakeholders; the Breezy Point Cooperative and Gateway National Recreation Area had the largest erodible shoreline segments. Significant correlations exist for all vulnerability (exposure/surge) and storm damage combinations except for exposure and distance to artificial debris. Coastal protective features, ranging from storm surge barriers and levees to natural features (e.g. wetlands), have been promoted to decrease future flood risk to communities in coastal areas around the world. Our methods of combining coastal vulnerability results with additional data and across multiple time periods have considerable potential to provide valuable predictions that resource managers can effectively use to identify areas for restoration and protection.

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Dataset Information

Individual-level space-time analyses of emergency department data using generalized additive modeling.

Background

Methods

Results

Conclusions

Publications

Individual-level space-time analyses of emergency department data using generalized additive modeling.

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