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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http://dx.doi.org/10.11124/JBIES-21-00284 | DOI Listing |
J Urban Health
December 2024
Department of Surgery, Grossman School of Medicine, New York, NY, USA.
Sexually minoritized men (SMM), transgender women (TW), and particularly Black SMM and Black TW may be disproportionately impacted by alcohol-related problems. Few studies have empirically examined neighborhood factors that may contribute to alcohol use, specifically among these populations. Using data from the N2 longitudinal cohort study in Chicago, IL, survey data from the second wave of longitudinal assessment (n = 126) and GPS mobility data from enrollment were used to evaluate neighborhood alcohol outlet availability, neighborhood disorder, and neighborhood poverty as correlates of individual alcohol use.
View Article and Find Full Text PDFEpidemiol Infect
December 2024
School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia.
In 2022, the largest ever virgin soil outbreak of Japanese encephalitis (JE) occurred in Australia resulting in 45 reported human cases of JE, with seven fatalities. Japanese encephalitis virus (JEV) was detected in 84 piggeries across Australia. In response, states implemented targeted vaccination programs for those individuals at the highest risk of JEV exposure.
View Article and Find Full Text PDFSci Data
December 2024
Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BX, UK.
Traditional models deliberately simplify millions of consumers into a single, homogeneous, representative agent with perfect market knowledge and rational expectations, limiting their capacity to capture real-world complexities. To address this limitation in mainstream models, this article provides global datasets to parametrise energy consumers within climate-energy-economy models considering climate-driven energy demand, socioeconomic and demographic factors. The datasets emerge from applying geospatial artificial intelligence, machine learning and big data analytics on a range of geospatial parameters at 1 km resolution.
View Article and Find Full Text PDFMachine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency. However, the specificity of environmental data introduces biases in straightforward implementations. We identify a streamlined pipeline to enhance model accuracy, addressing issues like imbalanced data, spatial autocorrelation, prediction errors, and the nuances of model generalization and uncertainty estimation.
View Article and Find Full Text PDFInt J Health Geogr
December 2024
Department of Geography and Environmental Studies, Social Science and Humanities, Borana University, P.O. Box 85, Yabello, Ethiopia.
Background: Malaria is a major public health issue in Nekemte City, western Ethiopia, with various environmental and social factors influencing transmission patterns. Effective control and prevention strategies require precise identification of high-risk areas. This study aims to map malaria risk zones in Nekemte City using geospatial technologies, including remote sensing and Geographic Information Systems (GIS), to support targeted interventions and resource allocation.
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