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Cushing AM, Khan MA, Kysh L, Brakefield WS, Ammar N, Liberman DB, Wilson J, Shaban-Nejad A, Espinoza J. Geospatial data in pediatric asthma in the United States: a scoping review protocol. JBI Evid Synth 2022; 20:2790-2798. [PMID: 36081367 PMCID: PMC9669090 DOI: 10.11124/jbies-21-00284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The objective of this scoping review is to identify and describe the literature on the use of geospatial data in pediatric asthma research. INTRODUCTION Asthma is one of the most common pediatric chronic diseases in the United States, disproportionately affecting low-income patients. Asthma exacerbations may be triggered by local environmental factors, such as air pollution or exposure to indoor allergens. Geographic information systems are increasingly recognized as tools that use geospatial data to enhance understanding of the link between environmental exposure, social determinants of health, and clinical outcomes. Geospatial data in pediatric asthma may help inform risk factors for asthma severity, and guide targeted clinical and social interventions. INCLUSION CRITERIA This review will consider studies that utilize geospatial data in the evaluation of pediatric patients with asthma, ages 2 to 18 years, in the United States. Mixed samples of adults and children will also be considered. Geospatial data will include any external non-clinical geographic-based data source that uses a patient's environment or context. METHODS The following databases will be searched: PubMed, Embase, Cochrane CENTRAL, CINAHL, ERIC, Web of Science, and IEEE. Gray literature will be searched in DBLP, the US Environmental Protection Agency, Google Scholar, Google search, and a hand search of recent abstracts from relevant conferences. Articles published in English, Spanish, and French from 2010 to the present will be included. Study screening and selection will be performed independently by 2 reviewers. Data extraction will be performed by a trained research team member following pilot testing.
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Affiliation(s)
- Anna M. Cushing
- Division of Emergency and Transport Medicine, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Masrur A. Khan
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Lynn Kysh
- Institute for Nursing and Interprofessional Research, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Whitney S. Brakefield
- Bredesen Center for Data Science and Engineering, University of Tennessee, Knoxville, TN, United States
- Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Nariman Ammar
- Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Danica B. Liberman
- Division of Emergency and Transport Medicine, Children's Hospital Los Angeles, Los Angeles, CA, United States
- Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - John Wilson
- Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, United States
| | - Arash Shaban-Nejad
- Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, United States
- Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms. REMOTE SENSING 2021. [DOI: 10.3390/rs13163222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
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Kane NJ, Wang X, Gerkovich MM, Breitkreutz M, Rivera B, Kunchithapatham H, Hoffman MA. The Envirome Web Service: Patient context at the point of care. J Biomed Inform 2021; 119:103817. [PMID: 34020026 DOI: 10.1016/j.jbi.2021.103817] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 05/13/2021] [Accepted: 05/15/2021] [Indexed: 11/27/2022]
Abstract
Patient context - the "envirome" - can have a significant impact on patient health. While envirome indicators are available through large scale public data sources, they are not provided in a format that can be easily accessed and interpreted at the point of care by healthcare providers with limited time during a patient encounter. We developed a clinical decision support tool to bring envirome indicators to the point of care in a large pediatric hospital system in the Kansas City region. The Envirome Web Service (EWS) securely geocodes patient addresses in real time to link their records with publicly available context data. End-users guided the design of the EWS, which presents summaries of patient context data in the electronic health record (EHR) without disrupting the provider workflow. Through surveys, focus groups, and a formal review by hospital staff, the EWS was deployed into production use, integrating publicly available data on food access with the hospital EHR. Evaluation of EWS usage during the 2020 calendar year shows that 1,034 providers viewed the EWS, with a total of 29,165 sessions. This suggests that the EWS was successfully integrated with the EHR and is highly visible. The results also indicate that 63 (6.1%) of the providers are regular users that opt to maintain the EWS in their custom workflows, logging more than 100 EWS sessions during the year. The vendor agnostic design of the EWS supports interoperability and makes it accessible to health systems with disparate EHR vendors.
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Affiliation(s)
- N J Kane
- Children's Mercy Hospital, Kansas City, MO, United States
| | - X Wang
- University of Missouri-Kansas City, United States
| | | | - M Breitkreutz
- Children's Mercy Hospital, Kansas City, MO, United States
| | - B Rivera
- Children's Mercy Hospital, Kansas City, MO, United States
| | | | - M A Hoffman
- Children's Mercy Hospital, Kansas City, MO, United States; University of Missouri-Kansas City, United States.
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