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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 August 2021) | Viewed by 22669

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Special Issue Editors

Dr. Darren Wraith

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Chief Guest Editor

School of Public Health & Social Work, and Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology, Victoria Park Road, Kelvin Grove 4059, Queensland, Australia
Interests: environmental health; latent variable modeling; bayesian statistics; spatio-temporal analysis

Dr. Susanna Cramb

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Guest Editor

Australian Centre for Health Services Innovation, Queensland University of Technology, Victoria Park Road, Kelvin Grove, QLD 4059, Australia
Interests: cancer epidemiology; disease mapping; Bayesian statistics; spatio-temporal analysis; health equity
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Special Issue Information

Dear Colleagues,

Recent advances in technology have allowed for the possibility to collect detailed measurements at finer spatial and temporal resolutions, which can provide a clearer and more accurate assessment of environmental exposures and related health outcomes. For example, personal and low-cost sensors are increasingly collecting detailed information from precise locations and measuring individual exposures to particulate matter (air pollution). Detailed electronic health data are also becoming more comprehensive and available to examine spatial and temporal uptake of health care and its impact.

Spatial and spatiotemporal analyses can provide critical insights to the health of populations, including how location interacts with the environment to increase or diminish risk, across these new and diverse datasets. These digital, big data developments have many advantages but also come with methodological and application issues, including accuracy/reliability of measurements and integration with existing data.

This Special Issue will highlight current and emerging trends in the field of spatial and spatiotemporal statistics in the area of environmental health. We welcome papers applying novel methodology as well as interesting applications of spatial analysis. Submissions with a broad environmental health perspective are welcome, including those from the physical or social environment.

Dr. Darren Wraith
Dr. Susanna Cramb
Guest Editors

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.

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Keywords

disease mapping
spatiotemporal modeling
public health
environmental modeling
sensors

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Published Papers (7 papers)

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Research

14 pages, 1716 KiB

Open AccessArticle

Mapping the Morbidity Risk Associated with Coal Mining in Queensland, Australia

by Javier Cortes-Ramirez, Darren Wraith, Peter D. Sly and Paul Jagals

Int. J. Environ. Res. Public Health 2022, 19(3), 1206; https://doi.org/10.3390/ijerph19031206 - 21 Jan 2022

Cited by 6 | Viewed by 2699

Abstract

The populations in the vicinity of surface coal mining activities have a higher risk of morbidity due to diseases, such as cardiovascular, respiratory and hypertensive diseases, as well as cancer and diabetes mellitus. Despite the large and historical volume of coal production in Queensland, the main Australian coal mining state, there is little research on the association of coal mining exposures with morbidity in non-occupational populations in this region. This study explored the association of coal production (Gross Raw Output—GRO) with hospitalisations due to six disease groups in Queensland using a Bayesian spatial hierarchical analysis and considering the spatial distribution of the Local Government Areas (LGAs). There is a positive association of GRO with hospitalisations due to circulatory diseases (1.022, 99% CI: 1.002–1.043) and respiratory diseases (1.031, 95% CI: 1.001–1.062) for the whole of Queensland. A higher risk of circulatory, respiratory and chronic lower respiratory diseases is found in LGAs in northwest and central Queensland; and a higher risk of hypertensive diseases, diabetes mellitus and lung cancer is found in LGAs in north, west, and north and southeast Queensland, respectively. These findings can be used to support public health strategies to protect communities at risk. Further research is needed to identify the causal links between coal mining and morbidity in non-occupational populations in Queensland. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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10 pages, 1978 KiB

Open AccessArticle

Disparities in COPD Hospitalizations: A Spatial Analysis of Proximity to Toxics Release Inventory Facilities in Illinois

by Stacey Brown-Amilian and Yussuf Akolade

Int. J. Environ. Res. Public Health 2021, 18(24), 13128; https://doi.org/10.3390/ijerph182413128 - 13 Dec 2021

Cited by 3 | Viewed by 2796

Abstract

Disproportionate distribution of air pollution is a major burden on the health of people living in proximity to toxic facilities. There are over 1000 Toxics Release Inventory (TRI) facilities distributed across the state of Illinois. This study investigates and spatially analyzes the relationship between chronic obstructive pulmonary disease (COPD) hospitalizations and toxic emissions from TRI facilities. In addition, this study investigates the connection between COPD hospitalizations and socioeconomic variables. Accounting for dispersion of air pollution beyond the TRI facilities source was attained using the inverse distance weighting interpolation approach. Multiple statistical methods were used including principal components analysis, linear regression, and bivariate local indicators of spatial association (BiLISA). The results from the linear regression model and BiLISA clustering maps show there is a strong connection between COPD hospitalizations and socioeconomic status along with race. TRI emissions were not statistically significant, but there are three major clusters of high COPD hospitalizations with high TRI emissions. Rural areas also seem to carry a higher burden of pollution-emitting facilities and respiratory hospitalizations. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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10 pages, 2235 KiB

Open AccessArticle

Spatial Distribution of Hospitalizations for Ischemic Heart Diseases in the Central Region of Asturias, Spain

by Isabel Martínez-Pérez, Verónica González-Iglesias, Valentín Rodríguez Suárez and Ana Fernández-Somoano

Int. J. Environ. Res. Public Health 2021, 18(23), 12320; https://doi.org/10.3390/ijerph182312320 - 24 Nov 2021

Cited by 2 | Viewed by 1840

Abstract

Hospitalizations for ischemic heart disease have an uneven distribution throughout Spain, in which Asturias is the community with the highest rates of acute myocardial infarction (AMI) and angina pectoris (AP). Cardiovascular diseases are related to environmental, socioeconomic and previous medical conditions, which result in geographical differences in the incidence of hospital admissions and mortality. The goal of this study was to describe the spatial distribution of hospital admissions in the central area of Asturias and explore the existence of spatial patterns or clusters. Urgent hospital admissions for AMI and angina AP in the hospitals of the central area of Asturias were registered, geocoded and grouped by census tracts. Standardized admission ratio, smoothed relative risk, posterior risk probability and analysis of spatial clusters between relative risks throughout the study area were calculated and mapped. Geographical differences were found in the distribution of hospital admissions for AMI and AP across the area and between men and women. The cluster analysis indicated contiguous census tracts with high relative risk values in the northwest region of the study area and low relative risk in the east, particularly for men. The geographical analysis shows the existence of patterns and spatial clusters in the incidence of AMI and AP, for both men and women, in the central area of Asturias. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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30 pages, 10896 KiB

Open AccessArticle

Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

by Syed Ali Asad Naqvi, Muhammad Sajjad, Liaqat Ali Waseem, Shoaib Khalid, Saima Shaikh and Syed Jamil Hasan Kazmi

Int. J. Environ. Res. Public Health 2021, 18(22), 12018; https://doi.org/10.3390/ijerph182212018 - 16 Nov 2021

Cited by 8 | Viewed by 5297

Abstract

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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12 pages, 1116 KiB

Open AccessArticle

Residential Area Sociodemographic and Breast Cancer Screening Venue Location Built Environmental Features Associated with Women’s Use of Closest Venue in Greater Sydney, Australia

by Jahidur Rahman Khan, Suzanne J. Carroll, Neil T. Coffee, Matthew Warner-Smith, David Roder and Mark Daniel

Int. J. Environ. Res. Public Health 2021, 18(21), 11277; https://doi.org/10.3390/ijerph182111277 - 27 Oct 2021

Cited by 2 | Viewed by 2471

Abstract

Understanding environmental predictors of women’s use of closest breast screening venue versus other site(s) may assist optimal venue placement. This study assessed relationships between residential-area sociodemographic measures, venue location features, and women’s use of closest versus other venues. Data of 320,672 Greater Sydney screening attendees were spatially joined to residential state suburbs (SSCs) (n = 799). SSC-level sociodemographic measures included proportions of: women speaking English at home; university-educated; full-time employed; and dwellings with motor-vehicles. A geographic information system identified each woman’s closest venue to home, and venue co-location with bus-stop, train-station, hospital, general practitioner, and shop(s). Multilevel logistic models estimated associations between environmental measures and closest venue attendance. Attendance at closest venue was 59.4%. Closest venue attendance was positively associated with SSC-level women speaking English but inversely associated with SSC-level women university-educated, full-time employed, and dwellings with motor-vehicles. Mobile venue co-location with general practitioner and shop was positively, but co-location with bus-stop and hospital was inversely associated with attendance. Attendance was positively associated with fixed venue co-location with train-station and hospital but inversely associated with venue co-location with bus-stop, general practitioner, and shop. Program planners should consider these features when optimising service locations to enhance utilisation. Some counterintuitive results necessitate additional investigation. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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26 pages, 72504 KiB

Open AccessArticle

Multidimensional Urban Exposure Analysis of Industrial Chemical Risk Scenarios in Mexican Metropolitan Areas

by Claudia Yazmin Ortega Montoya, Andrés Osvaldo López-Pérez, Marisol Ugalde Monzalvo and Ma. Loecelia Guadalupe Ruvalcaba Sánchez

Int. J. Environ. Res. Public Health 2021, 18(11), 5674; https://doi.org/10.3390/ijerph18115674 - 26 May 2021

Cited by 1 | Viewed by 3187

Abstract

Risk scenarios are caused by the convergence of a hazard with a potentially affected system in a specific place and time. One urban planning goal is to prevent environmental hazards, such as those generated by chemical accidents, from reaching human settlements, as they can cause public health issues. However, in many developing countries, due to their strategic positioning in global value chains, the quick and easy access to labor pools, and competitive production costs, urban sprawls have engulfed industrial areas, exposing residential conurbations to environmental hazards. This case study analyzes the spatial configuration of accidental chemical risk scenarios in three major Mexican metropolitan areas: Mexico City, Guadalajara, and Monterrey. Spatial analyses use an areal locations of hazardous atmosphere (ALOHA) dispersion model to represent the spatial effects of high-risk industrial activities in conurbations and the potentially affected populations vulnerable to chemical hazards. Complementary geostatistical correlation analyses use population data, marginalization indexes, and industrial clustering sectors to identify trends that can lead to comprehensive environmental justice approaches. In addition, the marginalization degree of inhabitants evaluates social inequalities concerning chemical risk scenarios. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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13 pages, 20208 KiB

Open AccessArticle

Spatiotemporal Dynamics of Scrub Typhus in Jiangxi Province, China, from 2006 to 2018

by Shu Yang, Xiaobo Liu, Yuan Gao, Baizhou Chen, Liang Lu, Weiqing Zheng, Renlong Fu, Chenying Yuan, Qiyong Liu, Guichang Li and Haiying Chen

Int. J. Environ. Res. Public Health 2021, 18(9), 4599; https://doi.org/10.3390/ijerph18094599 - 26 Apr 2021

Cited by 8 | Viewed by 2393

Abstract

Background: Scrub typhus (ST) has become a significant potential threat to public health in Jiangxi. Further investigation is essential for the control and management of the spatiotemporal patterns of the disease. Methods: Time-series analyses, spatial distribution analyses, spatial autocorrelation analysis, and space-time scan statistics were performed to detect spatiotemporal dynamics distribution of the incidence of ST. Results: From 2006 to 2018, a total of 5508 ST cases occurred in Jiangxi, covering 79 counties. The number of ST cases increased continuously from 2006 to 2018, and there was obvious seasonality during the variation process in each year, with a primary peak in autumn (September to October) and a smaller peak in summer (June to August). From 2007 to 2018, the spatial distribution of the ST epidemic was significant heterogeneity, and Nanfeng, Huichang, Xunwu, Anyuan, Longnan, and Xinfeng were hotspots. Seven spatiotemporal clusters were observed using Kulldorff’s space-time scan statistic, and the most likely cluster only included one county, Nanfeng county. The high-risk areas of the disease were in the mountainous, hilly region of Wuyi and the southern mountainous region of Jiangxi. Conclusions: Targeted interventions should be executed in high-risk regions for the precise prevention and control of ST. Full article

(This article belongs to the Special Issue Spatial Analysis of Environmental Health)

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