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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (31 May 2017) | Viewed by 71755

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Prof. Dr. Louise Ryan

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Guest Editor

School of Mathematical and Physical Sciences, University of Technology Sydney, NSW 2007, Australia
Interests: applied statistics; especially for applications in medicine and health (biostatistics); survival analysis; risk assessment; spatial statistics

Dr. Craig Anderson

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Guest Editor

School of Mathematical and Physical Sciences, University of Technology Sydney, NSW 2007, Australia
Interests: biostatistics; spatial modelling; bayesian statistics

Special Issue Information

Dear Colleagues,

Advances in Geographical Information Systems (GIS) over the past several decades have facilitated the incorporation of spatial information into many different kinds of epidemiological studies. For example, disease mapping studies might explore how disease rates vary from region to region. Identifying such variations can often be the first step in investigating the underlying social, economic or environmental conditions responsible for regional health variations. Alternatively, GIS technologies and/or models might be used to create new predictor variables, such as distance from a major road or a pollution source, or to predict an exposure such as air pollution. The statistical analysis of data involving geographically indexed data can be quite complicated due to the importance of adjusting appropriately for spatial and other types of correlation.

This Special Issue intends to highlight current and emerging trends in the field of spatial and spatio-temporal statistics. It is our intention to present a blend of methodological and applied topics, and as such we welcome submissions of novel methodology as well as interesting applications of spatial modelling. We encourage submissions with a broad environmental perspective, and welcome research relating to either the physical or social environment.

Prof. Dr. Louise Ryan
Dr. Craig Anderson
Guest Editors

Manuscript Submission Information

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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

geostatistics
disease mapping
spatio-temporal modelling
public health
environmental modelling

Published Papers (13 papers)

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Research

3874 KiB

Open AccessArticle

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM_2.5 Mass and Composition

by Jessica H. Belle, Howard H. Chang, Yujie Wang, Xuefei Hu, Alexei Lyapustin and Yang Liu

Int. J. Environ. Res. Public Health 2017, 14(10), 1244; https://doi.org/10.3390/ijerph14101244 - 18 Oct 2017

Cited by 15 | Viewed by 5511

Abstract

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM_2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM_2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM_2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM_2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM_2.5 concentrations. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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Open AccessArticle

Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa

by Samuel O. M Manda and Nada Abdelatif

Int. J. Environ. Res. Public Health 2017, 14(9), 1072; https://doi.org/10.3390/ijerph14091072 - 15 Sep 2017

Cited by 4 | Viewed by 3995

Abstract

Most mortality maps in South Africa and most contried of the sub-Saharan region are static, showing aggregated count data over years or at specific years. Lack of space and temporral dynamanics in these maps may adversely impact on their use and application for vigorous public health policy decisions and interventions. This study aims at describing and modeling sub-national distributions of age–gender specific all-cause mortality and their temporal evolutions from 1997 to 2013 in South Africa. Mortality information that included year, age, gender, and municipality administrative division were obtained from Statistics South Africa for the period. Individual mortality level data were grouped by three ages groups (0–14, 15–64, and 65 and over) and gender (male, female) and aggregated at each of the 234 municipalities in the country. The six age-gender all-cause mortality rates may be related due to shared common social deprivation, health and demographic risk factors. We undertake a joint analysis of the spatial-temporal variation of the six age-gender mortality risks. This is done within a shared component spatial model construction where age-gender common and specific spatial and temporal trends are estiamted using a hierarchical Bayesian spatial model. The results show municipal and temporal differentials in mortality risk profiles between age and gender groupings. High rates were seen in 2005, especially for the 15–64 years age group for both males and females. The dynamic geographical and time distributions of subnational age-gender all-cause mortality contribute to a better understanding of the temporal evolvement and geographical variations in the relationship between demographic composition and burden of diseases in South Africa. This provides useful information for effective monitoring and evaluation of public health policies and programmes targeting mortality reduction across time and sub-populations in the country. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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3747 KiB

Open AccessArticle

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

by Kimberly A. Kaufeld, Montse Fuentes, Brian J. Reich, Amy H. Herring, Gary M. Shaw and Maria A. Terres

Int. J. Environ. Res. Public Health 2017, 14(9), 1046; https://doi.org/10.3390/ijerph14091046 - 11 Sep 2017

Viewed by 3703

Abstract

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated to the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. The proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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Open AccessArticle

Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada

by Stefania Bertazzon and Rizwan Shahid

Int. J. Environ. Res. Public Health 2017, 14(8), 834; https://doi.org/10.3390/ijerph14080834 - 25 Jul 2017

Cited by 10 | Viewed by 9568

Abstract

An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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Open AccessArticle

Health Impact of PM₁₀, PM_2.5 and Black Carbon Exposure Due to Different Source Sectors in Stockholm, Gothenburg and Umea, Sweden

by David Segersson, Kristina Eneroth, Lars Gidhagen, Christer Johansson, Gunnar Omstedt, Anders Engström Nylén and Bertil Forsberg

Int. J. Environ. Res. Public Health 2017, 14(7), 742; https://doi.org/10.3390/ijerph14070742 - 07 Jul 2017

Cited by 110 | Viewed by 9243

Abstract

The most important anthropogenic sources of primary particulate matter (PM) in ambient air in Europe are exhaust and non-exhaust emissions from road traffic and combustion of solid biomass. There is convincing evidence that PM, almost regardless of source, has detrimental health effects. An important issue in health impact assessments is what metric, indicator and exposure-response function to use for different types of PM. The aim of this study is to describe sectorial contributions to PM exposure and related premature mortality for three Swedish cities: Gothenburg, Stockholm and Umea. Exposure is calculated with high spatial resolution using atmospheric dispersion models. Attributed premature mortality is calculated separately for the main local sources and the contribution from long-range transport (LRT), applying different relative risks. In general, the main part of the exposure is due to LRT, while for black carbon, the local sources are equally or more important. The major part of the premature deaths is in our assessment related to local emissions, with road traffic and residential wood combustion having the largest impact. This emphasizes the importance to resolve within-city concentration gradients when assessing exposure. It also implies that control actions on local PM emissions have a strong potential in abatement strategies. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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422 KiB

Open AccessArticle

Vulnerability Reduction Needed to Maintain Current Burdens of Heat-Related Mortality in a Changing Climate—Magnitude and Determinants

by Christofer Åström, Daniel Oudin Åström, Camilla Andersson, Kristie L. Ebi and Bertil Forsberg

Int. J. Environ. Res. Public Health 2017, 14(7), 741; https://doi.org/10.3390/ijerph14070741 - 07 Jul 2017

Cited by 21 | Viewed by 5297

Abstract

The health burden from heatwaves is expected to increase with rising global mean temperatures and more extreme heat events over the coming decades. Health-related effects from extreme heat are more common in elderly populations. The population of Europe is rapidly aging, which will increase the health effects of future temperatures. In this study, we estimate the magnitude of adaptation needed to lower vulnerability to heat in order to prevent an increase in heat-related deaths in the 2050s; this is the Adaptive Risk Reduction (ARR) needed. Temperature projections under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 from 18 climate models were coupled with gridded population data and exposure-response relationships from a European multi-city study on heat-related mortality. In the 2050s, the ARR for the general population is 53.5%, based on temperature projections under RCP 4.5. For the population above 65 years in Southern Europe, the ARR is projected to be 45.9% in a future with an unchanged climate and 74.7% with climate change under RCP 4.5. The ARRs were higher under RCP 8.5. Whichever emission scenario is followed or population projection assumed, Europe will need to adapt to a great degree to maintain heat-related mortality at present levels, which are themselves unacceptably high, posing an even greater challenge. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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2686 KiB

Open AccessArticle

Spatio-Temporal Analysis of Suicide-Related Emergency Calls

by Miriam Marco, Antonio López-Quílez, David Conesa, Enrique Gracia and Marisol Lila

Int. J. Environ. Res. Public Health 2017, 14(7), 735; https://doi.org/10.3390/ijerph14070735 - 06 Jul 2017

Cited by 13 | Viewed by 4430

Abstract

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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496 KiB

Open AccessArticle

Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors

by Komal Basra, M. Patricia Fabian, Raymond R. Holberger, Robert French and Jonathan I. Levy

Int. J. Environ. Res. Public Health 2017, 14(7), 730; https://doi.org/10.3390/ijerph14070730 - 06 Jul 2017

Cited by 7 | Viewed by 4719

Abstract

Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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Open AccessArticle

A Spatial Data Infrastructure for Environmental Noise Data in Europe

by Andrej Abramic, Alexander Kotsev, Vlado Cetl, Stylianos Kephalopoulos and Marco Paviotti

Int. J. Environ. Res. Public Health 2017, 14(7), 726; https://doi.org/10.3390/ijerph14070726 - 06 Jul 2017

Cited by 15 | Viewed by 5513

Abstract

Access to high quality data is essential in order to better understand the environmental and health impact of noise in an increasingly urbanised world. This paper analyses how recent developments of spatial data infrastructures in Europe can significantly improve the utilization of data and streamline reporting on a pan-European scale. The Infrastructure for Spatial Information in the European Community (INSPIRE), and Environmental Noise Directive (END) described in this manuscript provide principles for data management that, once applied, would lead to a better understanding of the state of environmental noise. Furthermore, shared, harmonised and easily discoverable environmental spatial data, required by the INSPIRE, would also support the data collection needed for the assessment and development of strategic noise maps. Action plans designed by the EU Member States to reduce noise and mitigate related effects can be shared to the public through already established nodes of the European spatial data infrastructure. Finally, data flows regarding reporting on the state of environment and END implementation to the European level can benefit by applying a decentralised e-reporting service oriented infrastructure. This would allow reported data to be maintained, frequently updated and enable pooling of information from/to other relevant and interrelated domains such as air quality, transportation, human health, population, marine environment or biodiversity. We describe those processes and provide a use case in which noise data from two neighbouring European countries are mapped to common data specifications, defined by INSPIRE, thus ensuring interoperability and harmonisation. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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6358 KiB

Open AccessArticle

An Ensemble Spatiotemporal Model for Predicting PM_2.5 Concentrations

by Lianfa Li, Jiehao Zhang, Wenyang Qiu, Jinfeng Wang and Ying Fang

Int. J. Environ. Res. Public Health 2017, 14(5), 549; https://doi.org/10.3390/ijerph14050549 - 22 May 2017

Cited by 27 | Viewed by 5018

Abstract

Although fine particulate matter with a diameter of <2.5 μm (PM_2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM₁₀), measurements of PM_2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM_2.5 pollution levels and the cumulative health effects is difficult because PM_2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM_2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM_2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in prediction, and the variogram for the daily residuals of the ensemble predictions is then simulated to improve our predictions. Shandong Province, China, is the study region, and data from 96 monitoring stations were included. To train and validate the models, we used PM_2.5 measurement data from 2014 with other predictors, including PM₁₀ data, meteorological parameters, remote sensing data, and land-use data. The validation results revealed that the R² value was improved and reached 0.89 when PM₁₀ was used as a predictor and a kriging interpolation was performed for the residuals. However, when PM₁₀ was not used as a predictor, our method still achieved a CV R² value of up to 0.86. The ensemble of spatial characteristics of relevant factors explained approximately 32% of the variance and improved the PM_2.5 predictions. The spatiotemporal modeling approach to estimating PM_2.5 concentrations presented in this paper has important implications for assessing PM_2.5 exposure and its cumulative health effects. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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1324 KiB

Open AccessArticle

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data

by Rachel Carroll, Andrew B. Lawson, Christel Faes, Russell S. Kirby, Mehreteab Aregay and Kevin Watjou

Int. J. Environ. Res. Public Health 2017, 14(5), 503; https://doi.org/10.3390/ijerph14050503 - 09 May 2017

Cited by 8 | Viewed by 3663

Abstract

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing–Tianjin–Hebei Region of China

by Chengdong Xu

Int. J. Environ. Res. Public Health 2017, 14(4), 416; https://doi.org/10.3390/ijerph14040416 - 13 Apr 2017

Cited by 32 | Viewed by 4659

Abstract

Hand, foot and mouth disease (HFMD) in children under the age of five is a major public health issue in China. Beijing–Tianjin–Hebei is the largest urban agglomeration in northern China. The present study aimed to analyze the epidemiological features of HFMD, reveal spatial clusters, and detect risk factors in this region. Reports of HFMD cases in Beijing–Tianjin–Hebei from 1 January 2013 to 31 December 2013 were collected from 211 counties or municipal districts. First, the epidemiological features were explored, and then SaTScan analysis was carried out to detect spatial clusters of HFMD. Finally, GeoDetector and spatial paneled model were used to identify potential risk factors among the socioeconomic and meteorological variables. There were a total of 90,527 HFMD cases in the year 2013. The highest rate was in individuals aged one year, with an incidence of 24.76/10³. Boys (55,168) outnumbered girls (35,359). Temporally, the incidence rose rapidly from April, peaking in June (4.08/10³). Temperature, relative humidity and wind speed were positively associated with the incidence rate, while precipitation and sunshine hours had a negative association. The explanatory powers of these factors were 57%, 13%, 2%, 21% and 12%, respectively. Spatially, the highest-risk regions were located in Beijing and neighboring areas, with a relative risk (RR) value of 3.04. The proportion of primary industry was negatively associated with HFMD transmission, with an explanatory power of 32%. Gross domestic product (GDP) per capita, proportion of tertiary industry, and population density were positively associated with disease incidence, with explanatory powers of 22%, 17% and 15%, respectively. These findings may be helpful in the risk assessment of HFMD transmission and for implementing effective interventions to reduce the burden of this disease. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

by Craig Anderson and Louise M. Ryan

Int. J. Environ. Res. Public Health 2017, 14(2), 146; https://doi.org/10.3390/ijerph14020146 - 03 Feb 2017

Cited by 26 | Viewed by 5513

Abstract

The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD) in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran’s I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly. Full article

(This article belongs to the Special Issue Spatial Modelling for Public Health Research)

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