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Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. REMOTE SENSING 2022. [DOI: 10.3390/rs14132996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies.
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Big data analytics in health sector: Theoretical framework, techniques and prospects. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.05.003] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Galetsi P, Katsaliaki K, Kumar S. Values, challenges and future directions of big data analytics in healthcare: A systematic review. Soc Sci Med 2019; 241:112533. [PMID: 31585681 DOI: 10.1016/j.socscimed.2019.112533] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 07/06/2019] [Accepted: 08/30/2019] [Indexed: 01/03/2023]
Abstract
The emergence of powerful software has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. The objective of this study is to systematically review 804 scholarly publications related to big data analytics in health in order to identify the organizational and social values along with associated challenges. Key principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology were followed for conducting systematic reviews. Following a research path, we present the values, challenges and future directions of the scientific area using indicative examples from relevant published articles. The study reveals that one of the main values created is the development of analytical techniques which provides personalized health services to users and supports human decision-making using automated algorithms, challenging the power issues in the doctor-patient relationship and creating new working conditions. A main challenge to data analytics is data management and security when processing large volumes of sensitive, personal health data. Future research is directed towards the development of systems that will standardize and secure the process of extracting private healthcare datasets from relevant organizations. Our systematic literature review aims to provide to governments and health policy-makers a better understanding of how the development of a data driven strategy can improve public health and the functioning of healthcare organizations but also how can create challenges that need to be addressed in the near future to avoid societal malfunctions.
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Affiliation(s)
- P Galetsi
- School of Economics, Business Administration & Legal Studies, International Hellenic University, 14th km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece.
| | - K Katsaliaki
- School of Economics, Business Administration & Legal Studies, International Hellenic University, 14th km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece.
| | - S Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus, 1000 LaSalle Avenue, Schulze Hall 435, Minneapolis, MN 55403, USA.
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Chang HH, Pan A, Lary DJ, Waller LA, Zhang L, Brackin BT, Finley RW, Faruque FS. Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:280. [PMID: 31254082 PMCID: PMC10072932 DOI: 10.1007/s10661-019-7421-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 03/20/2019] [Indexed: 05/10/2023]
Abstract
In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM2.5 concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7-13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m3 increase in the 3-day average of PM2.5 levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM2.5 for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.
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Affiliation(s)
- Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
| | - Anqi Pan
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
| | - David J Lary
- Hanson Center for Space Sciences, University of Texas at Dallas, 800 West Campbell Road Richardson, Dallas, TX, 75080, USA
| | - Lance A Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
| | - Lei Zhang
- Office of Health Data and Research, Mississippi State Department of Health, 570 East Woodrow Wilson, Jackson, MS, 39216, USA
| | - Bruce T Brackin
- Office of Epidemiology, Mississippi State Department of Health, 570 East Woodrow Wilson, Jackson, MS, 39216, USA
| | - Richard W Finley
- Department of Medicine, the University of Mississippi Medical Center, 2500 N. State St., Jackson, MS, 39216, USA
| | - Fazlay S Faruque
- Department of Preventive Medicine, University of Mississippi Medical Center, 2500 N. State St., Jackson, MS, 39216, USA.
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Miralles I, Granell C. Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1197. [PMID: 30987151 PMCID: PMC6479344 DOI: 10.3390/ijerph16071197] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 02/13/2019] [Accepted: 03/29/2019] [Indexed: 12/03/2022]
Abstract
This work identifies major areas of knowledge and proposes a set of relevant dimensions by area that must be taken into account in the design and delivery of context-aware mobile applications for mental health interventions. We argue that much of the related research has focused only on a few dimensions, paying little or no attention to others and, most importantly, to potential relationships between them. Our belief is that the improvement of the effectiveness of mobile interventions to support mental health necessarily implies that developers and therapists comprehensively consider the interaction between the proposed dimensions. Taking as a starting point the three areas of knowledge (Technology, Context, and Mental Health), we re-examine each area to identify relevant dimensions, discuss the relationships between them and finally draw a series of considerations. The resulting considerations can help therapists and developers to devise, design, and generate custom mobile applications in a way that increases the motivation and engagement of patients and, therefore, the effectiveness of psychological treatments.
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Affiliation(s)
- Ignacio Miralles
- Geospatial Technologies Research Group (GEOTEC), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain.
| | - Carlos Granell
- Geospatial Technologies Research Group (GEOTEC), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain.
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Remote Sensing and Geospatial Technologies in Public Health. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7080303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Foote FO, Benson H, Berger A, Berman B, DeLeo J, Deuster PA, Lary DJ, Silverman MN, Sternberg EM. Advanced Metrics for Assessing Holistic Care: The "Epidaurus 2" Project. Glob Adv Health Med 2018; 7:2164957X18755981. [PMID: 29497586 PMCID: PMC5824899 DOI: 10.1177/2164957x18755981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 12/08/2017] [Indexed: 11/16/2022] Open
Abstract
In response to the challenge of military traumatic brain injury and posttraumatic stress disorder, the US military developed a wide range of holistic care modalities at the new Walter Reed National Military Medical Center, Bethesda, MD, from 2001 to 2017, guided by civilian expert consultation via the Epidaurus Project. These projects spanned a range from healing buildings to wellness initiatives and healing through nature, spirituality, and the arts. The next challenge was to develop whole-body metrics to guide the use of these therapies in clinical care. Under the "Epidaurus 2" Project, a national search produced 5 advanced metrics for measuring whole-body therapeutic effects: genomics, integrated stress biomarkers, language analysis, machine learning, and "Star Glyphs." This article describes the metrics, their current use in guiding holistic care at Walter Reed, and their potential for operationalizing personalized care, patient self-management, and the improvement of public health. Development of these metrics allows the scientific integration of holistic therapies with organ-system-based care, expanding the powers of medicine.
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Affiliation(s)
| | - Herbert Benson
- Benson-Henry Institute for Mind Body Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Ann Berger
- National Institutes of Health Clinical Center, Bethesda, Maryland
| | - Brian Berman
- The Institute for Integrative Health, Baltimore, Maryland
- University of Maryland School of Medicine Center for Integrative Medicine, Baltimore, Maryland
| | - James DeLeo
- The NIH Clinical Center Department of Clinical Research Informatics, Bethesda, Maryland
| | | | - David J Lary
- The University of Texas at Dallas, Richardson, Texas
| | - Marni N. Silverman
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
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