Linking health and environmental data in geographical analysis: It’s so much more than centroids

Young, Linda J.; Gotway, Carol A.; Yang, Jie; Kearney, Gregory D.; DuClos, Chris

doi:10.1016/j.sste.2009.07.008

Cited by 36 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kriging is a statistical method widely used in the environmental sciences that produces optimal spatial predictions and in the block kriging version produces statistically optimal aggregate level estimates based on point level (monitored) data [57,58]. To implement block kriging, a fine grid is placed over the study area and concentrations predicted at each grid cell using an ordinary kriging approach.…”

Section: Discussionmentioning

confidence: 99%

Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly

Breysse

McDermott

et al. 2014

IJGI

View full text Add to dashboard Cite

In July 2002, lightning strikes ignited over 250 fires in Quebec, Canada, destroying over one million hectares of forest. The smoke plume generated from the fires had a major impact on air quality across the east coast of the U.S. Using data from the Medicare National Claims History File and the U.S. Environmental Protection Agency (EPA) National air pollution monitoring network, we evaluated the health impact of smoke exposure on 5.9 million elderly people (ages 65+) in the Medicare population in 81 counties in 11 northeastern and Mid-Atlantic States of the US. We estimated differences OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 714 in the exposure to ambient PM 2.5 -airborne particulate matter with aerodynamic diameter of ≤2.5 µm-concentrations and hospitalizations for cardiovascular, pulmonary and injury outcomes, before and during the smoke episode. We found that there was an associated 49.6% (95% confidence interval (CI), 29.8, 72.3) and 64.9% (95% CI, 44.3-88.5) increase rate of hospitalization for respiratory and cardiovascular diagnoses, respectively, when the smoke plume was present compared to before the smoke plume had arrived. Our study suggests that rapid increases in PM 2.5 concentrations resulting from wildfire smoke can impact the health of elderly populations thousands of kilometers removed from the fires.

show abstract

Section: Discussionmentioning

confidence: 99%

Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly

Breysse

McDermott

et al. 2014

IJGI

View full text Add to dashboard Cite

show abstract

“…Instead of showing the variation of the relationships between soil attributes and soil process variables, MLR only can provide an 'average' of the relationships. Recently, an approach named Geographical Weighted Regression (GWR) has been used to estimate the relationships among spatially non-stationary variables (Leung et al 2000;Brunsdon et al 2002;Tu & Xia 2008;Propastin 2009;Young et al 2009). GWR is a 'local' regression procedure that was developed to deal with nonstationarity issues faced by MLR .…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic matter with limited sample data using geographically weighted regression

Wang

Zhang

et al. 2013

Journal of Spatial Science

View full text Add to dashboard Cite

The spatial information of soil organic matter (SOM) is crucial for precision agriculture and environmental modeling. It is, however, difficult to obtain the regional details of SOM by dense sampling due to the high cost. Although a variety of interpolation methods are available for mapping SOM at regional scales, accurate prediction usually needs densely distributed samples and requires the interpolated variable to meet some constraints such as spatial stationarity. This paper introduces the Geographically Weighted Regression (GWR) technique as an alternative approach for SOM mapping. We interpolated the spatial distribution of SOM based on a limited number of samples with the incorporation of multiple independent variables. We also compared GWR with the ordinary least squares regression approach in mapping SOM. Results indicated that GWR could capture more local details and improve the prediction accuracy. However, more attention should be paid to the selection of independent variables.

show abstract

“…To link maximum daily heat index to heat-related morbidity at the county level for analysis, block kriging was used to predict the county-level maximum daily heat index based on observed and imputed data from the 43 FCC weather monitors. Block-kriged predictions spatially average the point level estimates from the individual weather monitors and avoid the bias that arises when using the alternative method of aggregation based on county centroids [16,17]. However, block kriging requires at least two observations for maximum daily heat index.…”

Section: Linking Health and Exposure Datamentioning

confidence: 99%

Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

et al. 2017

Self Cite

View full text Add to dashboard Cite

(1) Background: To demonstrate the potential effects of missing exposure data and model choice on public health conclusions concerning the impact of heat waves on heat-related morbidity.(2) Methods: Using four different methods to impute missing exposure data, four statistical models (case-crossover, time-series, zero-inflated, and truncated models) are compared. The methods are used to relate heat waves, based on heat index, and heat-related morbidities for Florida from 2005-2012. (3) Results: Truncated models using maximum daily heat index, imputed using spatio-temporal methods, provided the best model fit of regional and statewide heat-related morbidity, outperforming the commonly used case-crossover and time-series analysis methods. (4) Conclusions: The extent of missing exposure data, the method used to impute missing exposure data and the statistical model chosen can influence statistical inference. Further, using a statewide truncated negative binomial model, statistically significant associations between heat-related morbidity and regional heat index effects were identified.

show abstract

Linking health and environmental data in geographical analysis: It’s so much more than centroids

Cited by 36 publications

References 24 publications

Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly

Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly

Mapping soil organic matter with limited sample data using geographically weighted regression

Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

Contact Info

Product

Resources

About