Gis Approaches for Estimation of Residential-Level Ambient Pm Concentrations

Liao, Dandan; Peuquet, Donna J.; Duan, Yinkang; Dou, J; Smith, R L.; Whitsel, Eric A.; Lin, Haixiang; Heiss, Gerhard

doi:10.1097/00001648-200509000-00055

Cited by 30 publications

(49 citation statements)

References 13 publications

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“…With increasing interest in the role of place and space in social science theories, recording spatial contexts of demographic and health data and hence spatial sampling design is becoming increasingly important. Recent literature suggests that place and space matters in our day-to-day lives (Goodchild and Janelle 2004;Longley et al 1999) and the local/neighborhood environment can greatly influence socioeconomic, demographic, economic, and health outcomes (Cerin et al 2006;Gordon-Larsen et al 2006;Liao et al 2006;Schwartz 1996). Given the increasing importance of understanding spatial contexts, this article demonstrates the use of GPS, GIS, and RS for constructing a spatial sampling design for demographic and health surveys.…”

Section: Introductionmentioning

confidence: 89%

Spatial Sampling Design for a Demographic and Health Survey

Kumar

2007

Popul Res Policy Rev

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Section: Introductionmentioning

confidence: 89%

Spatial Sampling Design for a Demographic and Health Survey

Kumar

2007

Popul Res Policy Rev

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“…Recently, geographic information system (GIS) techniques have been applied to estimate the concentrations of air particulate matters (Jacquemin et al 2013;Kim et al 2014;Liao et al 2006;Zou et al 2015). Previous studies have used this method to examine the health effects of air pollutants (Sun et al 2013;Wang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal analysis for the effect of ambient particulate matter on cause-specific respiratory mortality in Beijing, China

Wang

Guo

et al. 2016

Environ Sci Pollut Res

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This study explored the association between particulate matter with an aerodynamic diameter of less than 10 μm (PM10) and the cause-specific respiratory mortality. We used the ordinary kriging method to estimate the spatial characteristics of ambient PM10 at 1-km × 1-km resolution across Beijing during 2008-2009 and subsequently fit the exposure-response relationship between the estimated PM10 and the mortality due to total respiratory disease, chronic lower respiratory disease, chronic obstructive pulmonary disease (COPD), and pneumonia at the street or township area levels using the generalized additive mixed model (GAMM). We also examined the effects of age, gender, and season in the stratified analysis. The effects of ambient PM10 on the cause-specific respiratory mortality were the strongest at lag0-5 except for pneumonia, and an inter-quantile range increase in PM10 was associated with an 8.04 % (95 % CI 4.00, 12.63) increase in mortality for total respiratory disease, a 6.63 % (95 % CI 1.65, 11.86) increase for chronic lower respiratory disease, and a 5.68 % (95 % CI 0.54, 11.09) increase for COPD, respectively. Higher risks due to the PM10 exposure were observed for females and elderly individuals. Seasonal stratification analysis showed that the effects of PM10 on mortality due to pneumonia were stronger during spring and autumn. While for COPD, the effect of PM10 in winter was statistically significant (15.54 %, 95 % CI 5.64, 26.35) and the greatest among the seasons. The GAMM model evaluated stronger associations between concentration of PM10. There were significant associations between PM10 and mortality due to respiratory disease at the street or township area levels. The GAMM model using high-resolution PM10 could better capture the association between PM10 and respiratory mortality. Gender, age, and season also acted as effect modifiers for the relationship between PM10 and respiratory mortality.

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“…The allergic disease prevalence variables were collected at the sub- 41,42 With a spherical model, we log transformed the data as a valid method for estimating levels of air pollutants while allowing "out-of-range predicted values" to be excluded. 43 With the interpolated measurements of PM10 in each sub-district, another OLS regression analysis was performed to scrutinize ecological associations between PM10 and the prevalence of allergic diseases in Seoul. Various sub-district level socioeconomic factors were added to the OLS model to confirm if PM10 remains significantly associated with allergic diseases.…”

Section: Methodsmentioning

confidence: 99%

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy

Seo

Kim

Min

et al. 2016

Allergy Asthma Immunol Res

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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose:The role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts. Methods: PM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts. Results: PM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01). Conclusions: Our findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.

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Gis Approaches for Estimation of Residential-Level Ambient Pm Concentrations

Cited by 30 publications

References 13 publications

Spatial Sampling Design for a Demographic and Health Survey

Spatial Sampling Design for a Demographic and Health Survey

Spatiotemporal analysis for the effect of ambient particulate matter on cause-specific respiratory mortality in Beijing, China

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy

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