Geographic variations of childhood asthma hospitalization and outpatient visits and proximity to ambient pollution sources at a U.S.-Canada border crossing

Oyana, Tonny J.; Rivers, Patrick A.

doi:10.1186/1476-072x-4-14

Cited by 28 publications

(9 citation statements)

References 32 publications

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“…The SOM network and the original data files were mapped and compared to visualize and generate the geographic maps pertaining to the spatial distribution of patient data. These geographic maps were compared with the maps obtained from earlier studies Oyana and Rivers, 2005). The results were consistent with the ones previously obtained, suggesting that these maps from the SOM network clearly illustrated similar spatial patterns and distributions of the disease, thereby resolving the idea that the SOM algorithm captures the data set effectively as well as represents the original data accurately.…”

Section: Data Set IIIsupporting

confidence: 77%

“…I gained significant novel insights into the spatial characteristics of patient data and I identified three main subsets (geographies) of asthma and OSA in the study region that require further evaluation. This approach was also essential for improving interpretations of the previous findings reported in , , and Oyana and Rivers (2005). I further confirmed that asthma is more prevalent in Buffalo's west side which is in close proximity to major roadways, the Peace Bridge Complex and pollution sources.…”

Section: Discussionsupporting

confidence: 53%

“…Indeed, the visual outcomes are indispensable for verifying earlier findings and establishing superior study hypotheses. The methods applied in this study have revealed new interesting spatial patterns and associations from the analysis of subsets (clusters) that were previously unclear or unknown in previous studies Oyana and Rivers, 2005). The study was able to identify and delineate representative subsets of the original data where fundamental insights regarding the spatial patterns of asthma and obstructive sleep apnea were gained leading to the verification and formulation of superior study hypotheses.…”

Section: Rationale For Applying Som Pca Mds Sammon Mapping and Gimentioning

confidence: 85%

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Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Oyana¹

2009

J ENV INFORM

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ABSTRACT. The objective of this study is to visualize high-dimensional data vectors using popular data reduction algorithms. The study reports on the effectiveness and expressiveness of a set of data reduction algorithms in the visualization of geospatial data sets derived from clinical records of patients. The experiments show that when the SOM algorithm is combined with GIS methods together they are even more powerful tools for exploratory analysis than when each is applied separately. The visual approach provides a very useful exploration environment to support the formulation of new and better study hypotheses regarding the spatial distribution of a particular disease. While it was apparent that the spatial distribution and patterns of asthma were predominately located near the major roadways and the Peace Bridge Complex, obstructive sleep apnea is slightly more widespread even in the suburbs and surrounding neighborhoods. The spatial patterns discovered between the original features of adult and childhood asthma are consistent with the SOM-trained data, but a slight difference emerges for the SOM-trained obstructive sleep apnea data set. This study is successful at gaining significant novel insights into the spatial characteristics of patient data in relation to key environmental factors.

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Section: Data Set IIIsupporting

confidence: 77%

Section: Discussionsupporting

confidence: 53%

Section: Rationale For Applying Som Pca Mds Sammon Mapping and Gimentioning

confidence: 85%

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Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Oyana¹

2009

J ENV INFORM

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“…In the past, GIS has been applied to estimate the spatial concentrations of air pollutants [39] and many epidemiologic studies have adopted GIS to explore the health impact of air pollutants on asthma [40,41,42,43]. …”

Section: Methodsmentioning

confidence: 99%

A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA

Gorai

Tuluri

Tchounwou

2014

IJERPH

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Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM2.5), sulfur dioxide (SO2) and ozone (O3)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM2.5, SO2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM2.5, SO2 and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM2.5, and SO2 and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM2.5 and SO2 and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association.

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“…It is also very helpful for air pollution distribution analysis and mapping. In the past, GIS has been applied to estimate the spatial concentrations of air pollutants (Liao et al, 2006) and many epidemiologic studies have adopted GIS to explore the health impact of air pollutants on asthma (English et al, 1999;Oyana, 2004Oyana, , 2005Sahsuvaroglu et al, 2009).…”

Section: Methodsmentioning

confidence: 99%

Establishing the Association between Quarterly/Seasonal Air Pollution Exposure and Asthma using Geospatial Approach

Gorai¹,

Tuluri

Tchounwou

et al. 2015

Aerosol Air Qual. Res.

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Although it is known that air pollution may lead to increased asthma prevalence, no clear scientific evidence of direct association between air pollution and asthma rate has been reported. In the present study, a Geographical Information System (GIS) approach was developed to determine the association between asthma hospital discharge rate (ADR) and seasonal exposure to specific ambient air pollutants in eastern Texas, USA, during the period 2009 to 2011. Quarterly asthma data were obtained from Texas State Department of Health, National Asthma Survey surveillance of Texas State, USA. Quarterly mean concentrations of fine particular matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) were determined from the corresponding measured daily data collected by various air quality monitoring stations distributed in different counties in the study area. Using Pearson correlation analysis, quarterly average of air pollutant concentrations was compared to quarterly Asthma discharge rate (ADR). The results revealed that the association between quarterly exposure of air pollution and ADR was not statistically significant in the study area. During the study period, a negative correlation coefficient was observed between the quarterly mean concentration of ozone and NO 2 with the quarterly ADR. However, in most of the cases a positive correlation coefficient was observed between the quarterly mean concentration of PM 2.5 and the quarterly ADR, indicating a probable association between ambient air pollution exposure and asthma prevalence.

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Geographic variations of childhood asthma hospitalization and outpatient visits and proximity to ambient pollution sources at a U.S.-Canada border crossing

Cited by 28 publications

References 32 publications

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA

Establishing the Association between Quarterly/Seasonal Air Pollution Exposure and Asthma using Geospatial Approach

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