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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 (30 June 2020) | Viewed by 35192

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Special Issue Editor

Dr. Jesse D. Berman

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Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Interests: air pollution; environmental epidemiology; exposure assessment; GIS; interpolation; spatial analysis; weather

Special Issue Information

Dear colleagues,

Over the past 20 years, a major advancement in the field of environmental epidemiology has been the use of spatial methods and geographic information systems (commonly known as GIS) to evaluate environmental hazards and health. Understanding where events occur is often invaluable for identifying why a disease takes place and who might be at risk. Maps provide unique tools to both generate hypotheses and disseminate information to the public, investigators, and policy makers. With developments in computing, GIS methods and spatial methods have allowed us to explore public health questions in new and innovative fashions. Topics such as the health effects of greenspace and land use regression methods for air pollution prediction would not have been possible without GIS and spatial statistics. Climate and health effects research heavily relies on spatial data to identify regions at greatest risk for future change. GIS has further played a major role in the advancement of health disparities research and understanding inequities in health-related outcomes and exposures. These examples represent just a few of the public health issues that have been advanced through GIS applications.

This Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) will highlight public health research that incorporates or applies GIS and spatial modeling for environmental epidemiology applications. Research papers, reviews, case reports, introduction of applied spatial methods, and conference papers are welcome to this issue. Papers that use GIS or spatial methods to address health disparities and climate change research are welcomed. Other manuscript types accepted include methodological papers, position papers, brief reports, and commentaries.

Dr. Jesse D. Berman
Guest Editor

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Keywords

geographic information systems (GIS)
spatial analysis
environmental epidemiology
mapping
disparities

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Published Papers (10 papers)

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Research

9 pages, 3011 KiB

Open AccessArticle

Malaria Transmission and Spillover across the Peru–Ecuador Border: A Spatiotemporal Analysis

by Annika K. Gunderson, Rani E. Kumar, Cristina Recalde-Coronel, Luis E. Vasco, Andree Valle-Campos, Carlos F. Mena, Benjamin F. Zaitchik, Andres G. Lescano, William K. Pan and Mark M. Janko

Int. J. Environ. Res. Public Health 2020, 17(20), 7434; https://doi.org/10.3390/ijerph17207434 - 13 Oct 2020

Cited by 15 | Viewed by 4432

Abstract

Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon’s river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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17 pages, 2922 KiB

Open AccessArticle

Spatial Analysis of the Neighborhood Risk Factors for Respiratory Health in the Australian Capital Territory (ACT): Implications for Emergency Planning

by Sarah Davies, Paul Konings and Aparna Lal

Int. J. Environ. Res. Public Health 2020, 17(17), 6396; https://doi.org/10.3390/ijerph17176396 - 2 Sep 2020

Cited by 4 | Viewed by 2981

Abstract

The Australian Capital Territory (ACT) experienced the worst air quality in the world for several consecutive days following the 2019–2020 Australian bushfires. With a focus on asthma and Chronic Obstructive Pulmonary Disease (COPD), this retrospective study examined the neighborhood-level risk factors for these diseases from 2011 to 2013, including household distance to hospital emergency departments (ED) and general practices (GP) and area-level socioeconomic disadvantage and demographic characteristics at a high spatial resolution. Poisson and Geographically Weighted Poisson Regression (GWR) were compared to examine the need for spatially explicit models. GWR performed significantly better, with rates of both respiratory diseases positively associated with area-level socioeconomic disadvantage. Asthma rates were positively associated with increasing distance from a hospital. Increasing distance to GP was not associated with asthma or COPD rates. These results suggest that respiratory health improvements could be made by prioritizing areas of socioeconomic disadvantage. The ACT has a relatively high density of GP that is geographically well spaced. This distribution of GP could be leveraged to improve emergency response planning in the future. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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23 pages, 5806 KiB

Open AccessFeature PaperArticle

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

by Ellen J. Kinnee, Sheila Tripathy, Leah Schinasi, Jessie L. C. Shmool, Perry E. Sheffield, Fernando Holguin and Jane E. Clougherty

Int. J. Environ. Res. Public Health 2020, 17(16), 5845; https://doi.org/10.3390/ijerph17165845 - 12 Aug 2020

Cited by 15 | Viewed by 3526

Abstract

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km²), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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17 pages, 4955 KiB

Open AccessArticle

A Geographic Information System-Based Indicator of Waste Risk to Investigate the Health Impact of Landfills and Uncontrolled Dumping Sites

by Lucia Fazzo, Marco De Santis, Eleonora Beccaloni, Federica Scaini, Ivano Iavarone, Pietro Comba and Domenico Airoma

Int. J. Environ. Res. Public Health 2020, 17(16), 5789; https://doi.org/10.3390/ijerph17165789 - 10 Aug 2020

Cited by 14 | Viewed by 2783

Abstract

Uncontrolled and poor waste management practices are widespread. The global health impact of hazardous waste exposure is controversial, but the excess of some diseases appears to be consistent. The Geographic Information System (GIS, ESRI Inc., Rome, Italy) method used to estimate the waste risk exposure, in an area with many illegal waste dumps and burning sites, is described. A GIS geodatabase (ESRI ArcGIS format) of waste sites’ data was built. A municipal GIS-based indicator of waste risk (Municipal Risk Index: MRI) has been computed, based on type and quantity of waste, typology of waste disposal, known or potential environmental contamination by waste and population living near waste sites. 2767 waste sites were present in an area 426 km² large. 38% of the population lived near one or more waste sites (100 m). Illegal/uncontrolled waste dumps, including waste burning areas, constituted about 90% of all sites. The 38 investigated municipalities were categorized into 4 classes of MRI. The GIS approach identified a widespread impact of waste sites and the municipalities likely to be most exposed. The highest score of the MRI included the municipalities with the most illegal hazardous waste dumps and burning sites. The GIS-geodatabase provided information to contrast and to prosecute illegal waste trafficking and mismanagements. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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17 pages, 2168 KiB

Open AccessArticle

The Health Opportunity Index: Understanding the Input to Disparate Health Outcomes in Vulnerable and High-Risk Census Tracts

by Chinonso N. Ogojiaku, JC Allen, Rexford Anson-Dwamena, Kierra S. Barnett, Olorunfemi Adetona, Wansoo Im and Darryl B. Hood

Int. J. Environ. Res. Public Health 2020, 17(16), 5767; https://doi.org/10.3390/ijerph17165767 - 10 Aug 2020

Cited by 15 | Viewed by 4768

Abstract

The Health Opportunity Index (HOI) is a multivariate tool that can be more efficiently used to identify and understand the interplay of complex social determinants of health (SDH) at the census tract level that influences the ability to achieve optimal health. The derivation of the HOI utilizes the data-reduction technique of principal component analysis to determine the impact of SDH on optimal health at lower census geographies. In the midst of persistent health disparities and the present COVID-19 pandemic, we demonstrate the potential utility of using 13-input variables to derive a composite metric of health (HOI) score as a means to assist in the identification of the most vulnerable communities during the current pandemic. Using GIS mapping technology, health opportunity indices were layered by counties in Ohio to highlight differences by census tract. Collectively we demonstrate that our HOI framework, principal component analysis and convergence analysis methodology coalesce to provide results supporting the utility of this framework in the three largest counties in Ohio: Franklin (Columbus), Cuyahoga (Cleveland), and Hamilton (Cincinnati). The results in this study identified census tracts that were also synonymous with communities that were at risk for disparate COVID-19 related health outcomes. In this regard, convergence analyses facilitated identification of census tracts where different disparate health outcomes co-exist at the worst levels. Our results suggest that effective use of the HOI composite score and subcomponent scores to identify specific SDH can guide mitigation/intervention practices, thus creating the potential for better targeting of mitigation and intervention strategies for vulnerable communities, such as during the current pandemic. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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20 pages, 9098 KiB

Open AccessArticle

Spatio-Temporal Dynamics of Tick-Borne Diseases in North-Central Wisconsin from 2000–2016

by Austin Rau, Claudia Munoz-Zanzi, Anna M. Schotthoefer, Jonathan D. Oliver and Jesse D. Berman

Int. J. Environ. Res. Public Health 2020, 17(14), 5105; https://doi.org/10.3390/ijerph17145105 - 15 Jul 2020

Cited by 7 | Viewed by 3306

Abstract

Lyme disease is a well-recognized public health problem in the USA, however, other tick-borne diseases also have major public health impacts. Yet, limited research has evaluated changes in the spatial and temporal patterns of non-Lyme tick-borne diseases within endemic regions. Using laboratory data from a large healthcare system in north-central Wisconsin from 2000–2016, we applied a Kulldorf’s scan statistic to analyze spatial, temporal and seasonal clusters of laboratory-positive cases of human granulocytic anaplasmosis (HGA), babesiosis, and ehrlichiosis at the county level. Older males were identified as the subpopulation at greatest risk for non-Lyme tick-borne diseases and we observed a statistically significant spatial and temporal clustering of cases (p < 0.05). HGA risk shifted from west to east over time (2000–2016) with a relative risk (RR) ranging from 3.30 to 11.85, whereas babesiosis risk shifted from south to north and west over time (2004–2016) with an RR ranging from 4.33 to 4.81. Our study highlights the occurrence of non-Lyme tick-borne diseases, and identifies at-risk subpopulations and shifting spatial and temporal heterogeneities in disease risk. Our findings can be used by healthcare providers and public health practitioners to increase public awareness and improve case detection. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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12 pages, 1190 KiB

Open AccessArticle

Spatial Distribution of Land Surface Temperatures in Kuwait: Urban Heat and Cool Islands

by Barrak Alahmad, Linda Powers Tomasso, Ali Al-Hemoud, Peter James and Petros Koutrakis

Int. J. Environ. Res. Public Health 2020, 17(9), 2993; https://doi.org/10.3390/ijerph17092993 - 26 Apr 2020

Cited by 45 | Viewed by 4860

Abstract

The global rise of urbanization has led to the formation of surface urban heat islands and surface urban cool islands. Urban heat islands have been shown to increase thermal discomfort, which increases heat stress and heat-related diseases. In Kuwait, a hyper-arid desert climate, most of the population lives in urban and suburban areas. In this study, we characterized the spatial distribution of land surface temperatures and investigated the presence of urban heat and cool effects in Kuwait. We used historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8-day composite land surface temperature (LST) from 2001 to 2017. We calculated the average LSTs of the urban/suburban governorates and compared them to the average LSTs of the rural and barren lands. We repeated the analysis for daytime and nighttime LST. During the day, the temperature difference (urban/suburban minus versus governorates) was −1.1 °C (95% CI; −1.2, −1.00, p < 0.001) indicating a daytime urban cool island. At night, the temperature difference (urban/suburban versus rural governorates) became 3.6 °C (95% CI; 3.5, 3.7, p < 0.001) indicating a nighttime urban heat island. In light of rising temperatures in Kuwait, this work can inform climate change adaptation efforts in the country including urban planning policies, but also has the potential to improve temperature exposure assessment for future population health studies. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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20 pages, 2732 KiB

Open AccessArticle

Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example

by Andrew Lawson, Anna Schritz, Luis Villarroel and Gloria A. Aguayo

Int. J. Environ. Res. Public Health 2020, 17(5), 1682; https://doi.org/10.3390/ijerph17051682 - 5 Mar 2020

Cited by 2 | Viewed by 2697

Abstract

Background: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. Conclusions: The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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15 pages, 4585 KiB

Open AccessArticle

A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province

by Shen Zhao, Guanpeng Dong and Yong Xu

Int. J. Environ. Res. Public Health 2020, 17(2), 629; https://doi.org/10.3390/ijerph17020629 - 18 Jan 2020

Cited by 4 | Viewed by 2645

Abstract

Urbanization processes at both global and regional scales are taking place at an unprecedent pace, leading to more than half of the global population living in urbanized areas. This process could exert grand challenges on the human living environment. With the proliferation of remote sensing and satellite data being used in social and environmental studies, fine spatial- and temporal-resolution measures of urban expansion and environmental quality are increasingly available. This, in turn, offers great opportunities to uncover the potential environmental impacts of fast urban expansion. This paper investigated the relationship between urban expansion and pollutant emissions in the Fujian province of China by building a Bayesian spatio-temporal autoregressive model. It drew upon recently compiled pollutant emission data with fine spatio-temporal resolution, long temporal coverage, and multiple sources of remote sensing data. Our results suggest that there was a significant relationship between urban expansion and pollution emission intensity—urban expansion significantly elevated the PM_2.5 and NO_x emissions intensity in Fujian province during 1995–2015. This finding was robust to different measures of urban expansion and retained after controlling for potential confounding effects. The temporal evolution of pollutant emissions, net of covariate effects, presented a fluctuation pattern rather than a consistent trend of increasing or decreasing. Spatial variability of the pollutant emissions intensity among counties was, however, decreasing steadily with time. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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10 pages, 1852 KiB

Open AccessArticle

Spatial Distribution Characteristics and Pollution Evaluation of Soil Iron in the Middle Hanjiang River

by Yuting Cheng, Peng Li, Guoce Xu, Kexin Lu, Feichao Wang, Tiegang Zhang and Zhaohong Feng

Int. J. Environ. Res. Public Health 2019, 16(21), 4075; https://doi.org/10.3390/ijerph16214075 - 23 Oct 2019

Cited by 5 | Viewed by 2254

Abstract

Soil iron has an important impact on the ecological environment and on crop growth. This study selected a typical small watershed basin in the middle reaches of the Han River (Yujiehe) at Ankang City and used geostatistical methods and kriging interpolation to analyze the spatial distribution and structure of soil iron content for different land uses and at different depths, using the single-factor pollution evaluation to evaluate the pollution degree of soil iron. The results showed that soil iron in the Yujie River basin decreased with increasing soil depth, with contents of 8.80 mg/kg, 5.52 mg/kg, and 4.92 mg/kg at depths A1 (0–20 cm), A2 (20–40 cm), and A3 (40–60 cm). According to the classification index of effective trace elements in soil, the average contents of soil iron at these three depths were between 4.5 and 10 mg/kg, which are all considered moderate values. The coefficients of variation of soil iron at the three soil depths were 59%, 75%, and 83%, all of which showed moderate spatial variability, and the coefficient of variation increased gradually with soil depth. With semi-variance calculated at the three soil depths, soil iron optimal theoretical models were all exponential models with nugget coefficients of 9.52%, 47.76%, and 33.93%, indicating that spatial correlation was very strong in the A1 layer and moderate in the A2 and A3 layers. The spatial distribution of soil iron showed some variation in the study area, and the soil content was higher in the midwestern part in the A1 and A2 layers; however, in the A3 layer, the higher content was in the center and lower content was in the southern region. Correlations were significant between soil iron content on the one hand and land-use type and topographic factors on the other. The pollution indices of soil iron at the three soil depths under different land uses were all greater than 1.0, with the A1 layer in farmland being the worst, at 3.34. In the study area, using the background value of soil iron as an evaluation standard, the soil iron content of more than 65% of the Yujiehe region exceeded this standard. Full article

(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)

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