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Geospatial Information in Public Health
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Geospatial Information in Public Health

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 16922

Special Issue Editors

Dr. Ana Cláudia Teodoro

E-Mail Website
Guest Editor
1. Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169007 Porto, Portugal
2. Institute of Earth Sciences (ICT)-Porto Pole, University of Porto, 4169007 Porto, Portugal
Interests: remote sensing; image processing; environmental applications; geologic applications; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Alberto Freitas

E-Mail Website
Guest Editor
1. Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
2. Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
Interests: medical informatics; health information systems; data quality; data mining in medicine; intelligent data analysis; performance and quality indicators; health services research

Special Issue Information

Dear Colleagues,

Geospatial information derived from Earth’s Observation programs (EO), aerial platforms (e.g., unmanned aerial vehicles) and also from global navigation satellite systems are a valuable source of information and play a critical role in Public Health. For a long time, data from environmental satellite programs has been used to derive several environmental factors and afterwards used to establish a relationship between several human diseases and environmental conditions. Remote sensing data are crucial for estimating and monitoring health-relevant environmental variables. Since the launch of Landsat-1 in 1972, an increasing number of health studies have used remotely sensed data. These data are very helpful to understand the transmission mechanisms of several diseases and could be used to develop satellite data-based early warning systems. Although most of the health studies involving remote sensed data were related to parasitic diseases, recently, several studies have related different environmental factors (e.g., humidity, temperature, PM10, NO2) retrieved by remote sensing data with several diseases, such as asthma exacerbations, preterm birth, neurodegenerative disorders, circulatory mortality, and ischemic heart disease.

Geographic information systems also play an important role in public health research and strategy. In fact, the integration of accurate spatial data can be a vital asset in healthcare systems, for doctors and nurses to increment knowledge about factors possibly affecting their patients, but also for public health professionals and their need to be aware of population specific trends.

This Special Issue of the International Journal of Environmental Research and Public Health solicits papers that present research related to geospatial information to support monitoring and forecasting human health in order to better understand the spatial aspects of health and illness.

Dr. Ana Cláudia Teodoro
Dr. Alberto Freitas
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.

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

  • Earth Observation data
  • Geospatial data
  • Air quality parameters
  • Land use land cover
  • Environmental factors
  • Public health
  • Disease modeling
  • Big data

Published Papers (6 papers)

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Research

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18 pages, 6674 KiB  
Article
Spatial Patterns in Hospital-Acquired Infections in Portugal (2014–2017)
by Hugo Teixeira, Alberto Freitas, António Sarmento, Paulo Nossa, Hernâni Gonçalves and Maria de Fátima Pina
Int. J. Environ. Res. Public Health 2021, 18(9), 4703; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094703 - 28 Apr 2021
Cited by 4 | Viewed by 2986
Abstract
Background: Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide. Aim: To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal. Methods: Data from the Portuguese [...] Read more.
Background: Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide. Aim: To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal. Methods: Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile, nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters. Results: A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs (n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups. Conclusion: The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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20 pages, 3962 KiB  
Article
Comprehensive Risk Assessment of Schistosomiasis Epidemic Based on Precise Identification of Oncomelania hupensis Breeding Grounds—A Case Study of Dongting Lake Area
by Jun Xu, Xiao Ouyang, Qingyun He and Guoen Wei
Int. J. Environ. Res. Public Health 2021, 18(4), 1950; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18041950 - 17 Feb 2021
Cited by 2 | Viewed by 1878
Abstract
Spatio-temporal epidemic simulation, assessment, and risk monitoring serve as the core to establishing and improving the national public health emergency management system. In this study, we investigated Oncomelania hupensis breeding grounds and analyzed the locational and environmental preferences of snail breeding in Dongting [...] Read more.
Spatio-temporal epidemic simulation, assessment, and risk monitoring serve as the core to establishing and improving the national public health emergency management system. In this study, we investigated Oncomelania hupensis breeding grounds and analyzed the locational and environmental preferences of snail breeding in Dongting Lake (DTL), Hunan, China. Using geographic information systems and remote sensing technology, we identified schistosomiasis risk areas and explored the factors affecting the occurrence and transmission of the disease. Several key conclusions were drawn. (1) From 2006 to 2016, the spatial change of potential O. hupensis breeding risk showed a diminishing trend from the eastern and northern regions to southwest DTL. Environmental changes in the eastern DTL region resulted in the lakeside and hydrophilic agglomerations of the O. hupensis populations. The shift in snail breeding grounds from a fragmented to centralized distribution indicates the weakening mobility of the O. hupensis population, the increasing independence of solitary groups, and the growing dependence of the snail population to the local environment. (2) The spatial risk distribution showed a descending gradient from west Dongting area to the east and an overall pattern of high in the periphery of large lakes and low in other areas. The cold-spot areas had their cores in Huarong County and Anxiang County and were scattered throughout the peripheral areas. The hot-spot areas had their center at Jinshi City, Nanxian County, and the southern part of Huarong County. The areas with increased comprehensive risks changed from centralized and large-scale development to fragmented shrinkage with increased partialization in the core area. The risk distribution’s center shifted to the northwest. The spatial risk distribution exhibited enhanced concentricity along the major axis and increased dispersion along the minor axis. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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16 pages, 2213 KiB  
Article
Deeper Spatial Statistical Insights into Small Geographic Area Data Uncertainty
by Daniel A. Griffith, Yongwan Chun and Monghyeon Lee
Int. J. Environ. Res. Public Health 2021, 18(1), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18010231 - 30 Dec 2020
Cited by 7 | Viewed by 2013
Abstract
Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small [...] Read more.
Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small local sample sizes, which tend to inflate local uncertainty and undermine otherwise sound statistical analyses. This condition is the opposite of that afflicting statistical significance in the context of big data. These two definitions can also occur jointly, such as during the standardization of data: small geographic units may contain small populations, which in turn have small counts in various age cohorts. Accordingly, big spatial data can become not-so-big spatial data after post-stratification by geography and, for example, by age cohorts. This situation can be ameliorated to some degree by the large volume of and high velocity of big spatial data. However, the variety of any big spatial data may well exacerbate this situation, compromising veracity in terms of bias, noise, and abnormalities in these data. The purpose of this paper is to establish deeper insights into big spatial data with regard to their uncertainty through one of the hallmarks of georeferenced data, namely spatial autocorrelation, coupled with small geographic areas. Impacts of interest concern the nature, degree, and mixture of spatial autocorrelation. The cancer data employed (from Florida for 2001–2010) represent a data category that is beginning to enter the realm of big spatial data; its volume, velocity, and variety are increasing through the widespread use of digital medical records. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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20 pages, 594 KiB  
Article
Landscapes on Prevention Quality Indicators: A Spatial Analysis of Diabetes Preventable Hospitalizations in Portugal (2016–2017)
by Andre Ramalho, Mariana Lobo, Lia Duarte, Julio Souza, Paulo Santos and Alberto Freitas
Int. J. Environ. Res. Public Health 2020, 17(22), 8387; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228387 - 12 Nov 2020
Cited by 7 | Viewed by 2923
Abstract
Preventable hospitalizations due to complications of diabetes mellitus (DM), represented by the related prevention quality indicators (PQI), are ambulatory care-sensitive conditions that can be prevented and controlled through effective primary health care (PHC) treatment. It is important to reduce mortality and promote the [...] Read more.
Preventable hospitalizations due to complications of diabetes mellitus (DM), represented by the related prevention quality indicators (PQI), are ambulatory care-sensitive conditions that can be prevented and controlled through effective primary health care (PHC) treatment. It is important to reduce mortality and promote the quality of life to diabetic patients in regions with higher hospitalization rates. The study aims to analyze the results of the DM age-sex-adjusted PQI, by groups of health centers (ACES), distributed in the Portuguese territory. The most representative PQI at a national level were identified, and the trends were mapped and analyzed. Also, it presents the ACES with the highest age-adjusted rates of avoidable hospitalizations for DM. The absolute number of preventable hospitalizations for all DM complications in Portugal has decreased by 20%, thus passing from the rate of 79 in 2016 to 65.2/100,000 inhabitants in 2017. Despite the improvement in results for PQI 03, 20 of 48 ACES that were above the national 2017 median rate in 2016, achieved better results the following year, and for the overall preventable diabetes hospitalizations (PQI 93) only 11 out 39, revealing the need for further studies and PHC actions to improve the diabetic quality of life. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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14 pages, 3172 KiB  
Article
Comparison and Impact of Four Different Methodologies for Identification of Ambulatory Care Sensitive Conditions
by Andreia Pinto, João Vasco Santos, Júlio Souza, João Viana, Cristina Costa Santos, Mariana Lobo and Alberto Freitas
Int. J. Environ. Res. Public Health 2020, 17(21), 8121; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17218121 - 03 Nov 2020
Cited by 6 | Viewed by 1905
Abstract
Ambulatory care sensitive conditions (ACSCs) are conditions for which hospitalizations are thought to be avoidable if effective and accessible primary health care is available. However, to define which conditions are considered ACSCs, there is a considerable number of different lists. Our aim was [...] Read more.
Ambulatory care sensitive conditions (ACSCs) are conditions for which hospitalizations are thought to be avoidable if effective and accessible primary health care is available. However, to define which conditions are considered ACSCs, there is a considerable number of different lists. Our aim was to compare the impact of using different ACSC lists considering mainland Portugal hospitalizations. A retrospective study with inpatient data from Portuguese public hospital discharges between 2011 and 2015 was conducted. Four ACSC list sources were considered: Agency for Healthcare Research and Quality (AHRQ), Canadian Institute for Health Information (CIHI), the Victorian Ambulatory Care Sensitive Conditions study, and Sarmento et al. Age–sex-adjusted rates of ACSCs were calculated by district (hospitalizations per 100,000 inhabitants). Spearman’s rho, the intraclass correlation coefficient (ICC), the information-based measure of disagreement (IBMD), and Bland and Altman plots were computed. Results showed that by applying the four lists, different age–sex-adjusted rates are obtained. However, the lists that seemed to demonstrate greater agreement and consistency were the list proposed by Sarmento et al. compared to AHRQ and the AHRQ method compared to the Victorian list. It is important to state that we should compare comparable indicators and ACSC lists cannot be used interchangeably. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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Review

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25 pages, 784 KiB  
Review
Geospatial Analysis of Environmental Atmospheric Risk Factors in Neurodegenerative Diseases: A Systematic Review
by Mariana Oliveira, André Padrão, André Ramalho, Mariana Lobo, Ana Cláudia Teodoro, Hernâni Gonçalves and Alberto Freitas
Int. J. Environ. Res. Public Health 2020, 17(22), 8414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228414 - 13 Nov 2020
Cited by 8 | Viewed by 3654
Abstract
Despite the vast evidence on the environmental influence in neurodegenerative diseases, those considering a geospatial approach are scarce. We conducted a systematic review to identify studies concerning environmental atmospheric risk factors for neurodegenerative diseases that have used geospatial analysis/tools. PubMed, Web of Science, [...] Read more.
Despite the vast evidence on the environmental influence in neurodegenerative diseases, those considering a geospatial approach are scarce. We conducted a systematic review to identify studies concerning environmental atmospheric risk factors for neurodegenerative diseases that have used geospatial analysis/tools. PubMed, Web of Science, and Scopus were searched for all scientific studies that included a neurodegenerative disease, an environmental atmospheric factor, and a geographical analysis. Of the 34 included papers, approximately 60% were related to multiple sclerosis (MS), hence being the most studied neurodegenerative disease in the context of this study. Sun exposure (n = 13) followed by the most common exhaustion gases (n = 10 for nitrogen dioxide (NO2) and n = 5 for carbon monoxide (CO)) were the most studied atmospheric factors. Only one study used a geospatial interpolation model, although 13 studies used remote sensing data to compute atmospheric factors. In 20% of papers, we found an inverse correlation between sun exposure and multiple sclerosis. No consensus was reached in the analysis of nitrogen dioxide and Parkinson’s disease, but it was related to dementia and amyotrophic lateral sclerosis. This systematic review (number CRD42020196188 in PROSPERO’s database) provides an insight into the available evidence regarding the geospatial influence of environmental factors on neurodegenerative diseases. Full article
(This article belongs to the Special Issue Geospatial Information in Public Health)
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