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Miah MM, Chakma B, Hossain K. Analyzing the Prevalence of and Factors Associated with Road Traffic Crashes (RTCs) among Motorcyclists in Bangladesh. ScientificWorldJournal 2024; 2024:7090576. [PMID: 38756481 PMCID: PMC11098599 DOI: 10.1155/2024/7090576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 04/23/2024] [Accepted: 04/25/2024] [Indexed: 05/18/2024] Open
Abstract
Methods A cross-sectional survey was conducted using a structured questionnaire involving 402 motorcyclists from four major southeastern towns, comprising 350 (86.07%) males and 52 (12.93%) females. The chi-square test was applied in bivariate analysis, and binary multivariable logistic regression was performed to determine the risk factors of road traffic crashes. Results This study's findings revealed that the overall reported prevalence of road traffic crashes involving motorcycle drivers over one year was 68.66%. Multivariable logistic regression analysis revealed several factors that significantly impacted road traffic crashes. These factors included driving without a valid driving license, the young age (<20) of motorcyclists, driving in rainy weather, exceeding the speed limit, per-week working hours, smoking status, motorcycle ownership, the brand of motorcycle, and not wearing a helmet while driving. Conclusion The study findings highlight the need for improving motorcycle safety by implementing measures such as imposing per-week work hour limits for riders, enforcing traffic regulations, and promoting helmet use among motorcycle drivers. The results of this study draw attention to the Bangladesh Road Transport Authority (BRTA) and motorcycle drivers in the country to decrease motorcycle crashes and the severity of injuries by implementing efficient guidelines and strategies for driving motorcycles. The findings of this study can assist policymakers and concerned authorities in taking the essential steps to lessen road traffic crashes among motorcyclists in Bangladesh.
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Affiliation(s)
- Md. Mamun Miah
- Department of Statistics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Biton Chakma
- Department of Statistics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Kabir Hossain
- Department of Statistics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
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Islam J, Hu W. Heatwaves and Dengue Outbreak in Bangladesh After the Pandemic- An Urgent Call for Climate-Driven Early Warning Systems. Clin Infect Dis 2024; 78:1075-1076. [PMID: 37815170 DOI: 10.1093/cid/ciad625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
Affiliation(s)
- Jahirul Islam
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
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Zain A, Sadarangani SP, Shek LPC, Vasoo S. Climate change and its impact on infectious diseases in Asia. Singapore Med J 2024; 65:211-219. [PMID: 38650059 PMCID: PMC11132621 DOI: 10.4103/singaporemedj.smj-2023-180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/04/2024] [Indexed: 04/25/2024]
Abstract
ABSTRACT Climate change, particularly increasing temperature, changes in rainfall, extreme weather events and changes in vector ecology, impacts the transmission of many climate-sensitive infectious diseases. Asia is the world's most populous, rapidly evolving and diverse continent, and it is already experiencing the effects of climate change. Climate change intersects with population, sociodemographic and geographical factors, amplifying the public health impact of infectious diseases and potentially widening existing disparities. In this narrative review, we outline the evidence of the impact of climate change on infectious diseases of importance in Asia, including vector-borne diseases, food- and water-borne diseases, antimicrobial resistance and other infectious diseases. We also highlight the imperative need for strategic intersectoral collaboration at the national and global levels and for the health sector to implement adaptation and mitigation measures, including responsibility for its own greenhouse gas emissions.
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Affiliation(s)
- Amanda Zain
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Khoo Teck Puat-National University Children’s Medical Institute, National University Health System, Singapore
| | - Sapna P Sadarangani
- National Centre for Infectious Diseases, Singapore
- Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Lynette Pei-Chi Shek
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Khoo Teck Puat-National University Children’s Medical Institute, National University Health System, Singapore
| | - Shawn Vasoo
- National Centre for Infectious Diseases, Singapore
- Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Williams RJ, Brintz BJ, Ribeiro Dos Santos G, Huang AT, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT. Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity. SCIENCE ADVANCES 2024; 10:eadj9786. [PMID: 38363842 PMCID: PMC10871531 DOI: 10.1126/sciadv.adj9786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.
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Affiliation(s)
- Robert J. Williams
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ben J. Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Angkana T. Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Darunee Buddhari
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, RI, USA
| | - Stephen Thomas
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Aaron Farmer
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Derek A. T. Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Kathryn B. Anderson
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT, USA
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Snyder J, Maglasang G. Dengue in Cebu City, Philippines: A Pilot Study of Predictive Models and Visualizations for Public Health. Am J Trop Med Hyg 2024; 110:179-187. [PMID: 38081048 DOI: 10.4269/ajtmh.23-0250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/25/2023] [Indexed: 01/05/2024] Open
Abstract
Dengue is a global health issue, particularly in the tropical and subtropical regions of the world. Prevention is the most appropriate method to fight the spread of the virus. The objective of this research is to present a model, along with visualizations, that will enable health officials and community leaders to identify when and where potential dengue outbreaks are likely to occur. Armed with this information, local resources can be adequately deployed in an effort to use limited supplies effectively. A mathematical model that uses easily obtainable data, along with visualizations for the 80 barangays of Cebu City, Philippines, is presented. Visualizations are constructed appropriate for a generalist audience to comprehend and use for dengue mitigation. Results of this study include a model that uses readily available data to predict dengue outbreaks one month in advance and visualizations appropriate for decision-makers in public health. Additional items are identified that could enhance the explanatory power of the model, and future directions are discussed.
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Affiliation(s)
- Johnny Snyder
- Davis School of Business, Colorado Mesa University, Grand Junction, Colorado
| | - Gibson Maglasang
- Research Institute for Computational Mathematics and Physics, Cebu Normal University, Cebu City, Philippines
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