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Time-Space Modeling of the Health Effects of Environment
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Time-Space Modeling of the Health Effects of Environment

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 40634

Special Issue Editor

Dr. Naresh Kumar

E-Mail Website
Guest Editor
Associated Professor of Environmetnal Health, Department of Public Health Sciences, University of Miami, Miami, FL 33136, USA
Interests: health effects of environment; time–space modeling of the health effects of air pollution; climate mediated health effects of air pollution; optimal spatiotemporal sampling; personalized real-time time health risk surveillance; personalize real-time air pollution monitoring; time–space Kriging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spatiotemporal mismatch in the resolution/scale and misalignment of environment and health data sets has been a major challenge for conducting epidemiological analyses of the health effects of environment. Because resolving spatiotemporal misalignment and resolution of these data sets often result in “exposure uncertainty” and/or “exposure misclassification”, failing to account for such exposure uncertainty and misclassification can result in biased (health) risk estimates. Therefore, addressing environmental exposure uncertainty and misclassification to quantify environmental disease burden is critically important for guiding policies to protect public health from environmental insults. This Special Issue invites scholarly work to address methodological, as well as application areas of spatiotemporal modeling of the health effects of environmental exposure or novel approaches for capturing and quantifying precise exposure. Example methodological and application focus areas of the issues are below:

Methodology

  • Efficacy of different methods to quantify time-space varying and/or spatiotemporal lagged exposures of any of the environmental conditions,
  • Methodology for estimating uncertainty in exposure assessment and/or exposure misclassification,
  • Methodology for estimating health risk uncertainty with respect to exposure uncertainty,
  • Methods of resolving spatiotemporal scales of environmental data to match with the spatiotemporal scales of health data sets,
  • Methods of improving health risk estimates adjusting for exposure uncertainty and/or errors and/or misclassification.

Applications

  • Quantitative analysis of any health outcome(s) with respect to spatiotemporal exposure to any environmental conditions using at least two different methods/approaches of exposure assessment,
  • Comparison of the risk assessment using personal versus population exposure, i.e. comparison of aggregated versus disaggregated environment and health data sets, and
  • Health effects of hieratical spatiotemporal environmental exposure, e.g., hospital admission due to COPD (chronic obstructive pulmonary disease) due to daily lagged air pollution exposure and seasonal meteorological conditions, and
  • Application of portable and/or mobile sensor to compute precise air pollution exposure and its associated health effects.

Dr. Naresh Kumar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Environmental health
  • Time-space modeling
  • Exposure uncertainty
  • Spatiotemporal autocorrelation
  • Spaiotemporal covariance
  • Spatiotemporal scales
  • Time-space lagged environemntal exposure
  • Hierrachical time-space variance

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Published Papers (5 papers)

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Research

11 pages, 1586 KiB  
Article
Space–Time Relationship between Short-Term Exposure to Fine and Coarse Particles and Mortality in a Nationwide Analysis of Korea: A Bayesian Hierarchical Spatio-Temporal Model
by Dayun Kang, Yujin Jang, Hyunho Choi, Seung-sik Hwang, Younseo Koo and Jungsoon Choi
Int. J. Environ. Res. Public Health 2019, 16(12), 2111; https://doi.org/10.3390/ijerph16122111 - 14 Jun 2019
Cited by 4 | Viewed by 3074
Abstract
Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air [...] Read more.
Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air pollution and cause-specific mortality in South Korea between 2012 and 2015 using a two-stage Bayesian spatio-temporal model. We used 2012–2014 mortality and air pollution data for parameter estimation (i.e., model fitting) and 2015 data for model validation. Our results suggest that the relative risks of total, cardiovascular, and respiratory mortality were 1.028, 1.047, and 1.045, respectively, with every 10-µg/m3 increase in monthly PM2.5 (fine particulate matter) exposure. These findings warrant protection of populations who experience elevated ambient air pollution exposure to mitigate mortality burden in South Korea. Full article
(This article belongs to the Special Issue Time-Space Modeling of the Health Effects of Environment)
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16 pages, 2690 KiB  
Article
Spatial Analysis of Built Environment Risk for Respiratory Health and Its Implication for Urban Planning: A Case Study of Shanghai
by Lan Wang, Wenyao Sun, Kaichen Zhou, Minlu Zhang and Pingping Bao
Int. J. Environ. Res. Public Health 2019, 16(8), 1455; https://doi.org/10.3390/ijerph16081455 - 24 Apr 2019
Cited by 16 | Viewed by 5517
Abstract
Urban planning has been proven and is expected to promote public health by improving the built environment. With a focus on respiratory health, this paper explores the impact of the built environment on the incidence of lung cancer and its planning implications. While [...] Read more.
Urban planning has been proven and is expected to promote public health by improving the built environment. With a focus on respiratory health, this paper explores the impact of the built environment on the incidence of lung cancer and its planning implications. While the occurrence of lung cancer is a complicated and cumulative process, it would be valuable to discover the potential risks of the built environment. Based on the data of 52,009 lung cancer cases in Shanghai, China from 2009 to 2013, this paper adopts spatial analytical methods to unravel the spatial distribution of lung cancer cases. With the assistance of geographic information system and Geo-Detector, this paper identifies certain built environments that are correlated with the distribution pattern of lung cancer cases in Shanghai, including the percentage of industrial land (which explains 28% of the cases), location factors (11%), and the percentages of cultivated land and green space (6% and 5%, respectively). Based on the quantitative study, this paper facilitates additional consideration and planning intervention measures for respiratory health such as green buffering. It is an ecological study to illustrate correlation that provides approaches for further study to unravel the causality of disease incidence and the built environment. Full article
(This article belongs to the Special Issue Time-Space Modeling of the Health Effects of Environment)
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11 pages, 1790 KiB  
Article
Spatially Filtered Multilevel Analysis on Spatial Determinants for Malaria Occurrence in Korea
by Sehyeong Kim and Youngho Kim
Int. J. Environ. Res. Public Health 2019, 16(7), 1250; https://doi.org/10.3390/ijerph16071250 - 8 Apr 2019
Cited by 5 | Viewed by 4624
Abstract
Since its re-emergence in 1993, the spatial patterns of malaria outbreaks in South Korea have drastically changed. It is well known that complicated interactions between humans, nature, and socio-economic factors lead to a spatial dependency of vivax malaria occurrences. This study investigates the [...] Read more.
Since its re-emergence in 1993, the spatial patterns of malaria outbreaks in South Korea have drastically changed. It is well known that complicated interactions between humans, nature, and socio-economic factors lead to a spatial dependency of vivax malaria occurrences. This study investigates the spatial factors determining malaria occurrences in order to understand and control malaria risks in Korea. A multilevel model is applied to simultaneously analyze the variables in different spatial scales, and eigenvector spatial filtering is used to explain the spatial autocorrelation in the malaria occurrence data. The results show that housing costs, average age, rice paddy field ratio, and distance from the demilitarized zone (DMZ) are significant on the level-1 spatial scale; health budget per capita and military base area ratio are significant on the level-2 spatial scale. The results show that the spatially filtered multilevel model provides better analysis results in handling spatial issues. Full article
(This article belongs to the Special Issue Time-Space Modeling of the Health Effects of Environment)
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18 pages, 1509 KiB  
Article
Estimation of the Ecological Fallacy in the Geographical Analysis of the Association of Socio-Economic Deprivation and Cancer Incidence
by Katarina Lokar, Tina Zagar and Vesna Zadnik
Int. J. Environ. Res. Public Health 2019, 16(3), 296; https://doi.org/10.3390/ijerph16030296 - 22 Jan 2019
Cited by 23 | Viewed by 4594
Abstract
Ecological deprivation indices at the level of spatial units are often used to measure and monitor inequalities in health despite the possibility of ecological fallacy. For the purpose of this study, the European Deprivation Index (EDI) was used, which is based on Townsend [...] Read more.
Ecological deprivation indices at the level of spatial units are often used to measure and monitor inequalities in health despite the possibility of ecological fallacy. For the purpose of this study, the European Deprivation Index (EDI) was used, which is based on Townsend theorization of relative deprivation. The Slovenian version of EDI (SI-EDI) at the aggregated level (SI-EDI-A) was calculated to the level of the national assembly polling stations. The SI-EDI was also calculated at the individual level (SI-EDI-I) by the method that represents a methodological innovation. The degree of ecological fallacy was estimated with the Receiver Operating Characteristics (ROC) curves. By calculating the area under the ROC curve, the ecological fallacy was evaluated numerically. Agreement between measuring deprivation with SI-EDI-A and SI-EDI-I was analysed by graphical methods and formal testing. The association of the socio-economic status and the cancer risk was analysed in all first cancer cases diagnosed in Slovenia at age 16 and older in the period 2011–2013. Analysis was done for each level separately, for SI-EDI-I and for SI-EDI-A. The Poisson regression model was implemented in both settings but adapted specifically for aggregated and individual data. The study clearly shows that ecological fallacy is unavoidable. However, although the association of cancer incidence and socio-economic deprivation at individual and aggregated levels was not the same for all cancer sites, the results were very similar for the majority of investigated cancer sites and especially for cancers associated with unhealthy lifestyles. The results confirm the assumptions from authors’ previous research that using the level of the national assembly polling stations would be the acceptable way to aggregate data when explaining inequalities in health in Slovenia in ecological studies. Full article
(This article belongs to the Special Issue Time-Space Modeling of the Health Effects of Environment)
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11 pages, 1164 KiB  
Article
Estimated Residential Exposure to Agricultural Chemicals and Premature Mortality by Parkinson’s Disease in Washington State
by Mariah Caballero, Solmaz Amiri, Justin T. Denney, Pablo Monsivais, Perry Hystad and Ofer Amram
Int. J. Environ. Res. Public Health 2018, 15(12), 2885; https://doi.org/10.3390/ijerph15122885 - 16 Dec 2018
Cited by 31 | Viewed by 22121
Abstract
The aim of this study was to examine the relationship between estimated residential exposure to agricultural chemical application and premature mortality from Parkinson’s disease (PD) in Washington State. Washington State mortality records for 2011–2015 were geocoded using residential addresses, and classified as having [...] Read more.
The aim of this study was to examine the relationship between estimated residential exposure to agricultural chemical application and premature mortality from Parkinson’s disease (PD) in Washington State. Washington State mortality records for 2011–2015 were geocoded using residential addresses, and classified as having exposure to agricultural land-use within 1000 meters. Generalized linear models were used to explore the association between land-use associated with agricultural chemical application and premature mortality from PD. Individuals exposed to land-use associated with glyphosate had 33% higher odds of premature mortality than those that were not exposed (Odds Ratio (OR) = 1.33, 95% Confidence Intervals (CI) = 1.06–1.67). Exposure to cropland associated with all pesticide application (OR = 1.19, 95% CI = 0.98–1.44) or Paraquat application (OR = 1.22, 95% CI = 0.99–1.51) was not significantly associated with premature mortality from PD, but the effect size was in the hypothesized direction. No significant associations were observed between exposure to Atrazine (OR = 1.21, 95% CI = 0.84–1.74) or Diazinon (OR = 1.07, 95% CI = 0.85–1.34), and premature mortality from PD. The relationship between pesticide exposure and premature mortality aligns with previous biological, toxicological, and epidemiological findings. Glyphosate, the world’s most heavily applied herbicide, and an active ingredient in Roundup® and Paraquat, a toxic herbicide, has shown to be associated with the odds of premature mortality from PD. Full article
(This article belongs to the Special Issue Time-Space Modeling of the Health Effects of Environment)
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