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Ayenew T, Gedfew M, Afenigus AD, Amha H, Mulugeta H, Mengist B, Bewket B, Melese YH, Teym A, Bishaw KA, Yitayew M. Familiarity with emergency preparedness and its predictors among nurses and physicians working at public hospitals in east Gojjam zone, northwest Ethiopia. SAGE Open Med 2022; 10:20503121221096532. [PMID: 35600702 PMCID: PMC9118889 DOI: 10.1177/20503121221096532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/07/2022] [Indexed: 11/15/2022] Open
Abstract
Objective: Emergency preparedness and response operations for all types of catastrophes rely heavily on healthcare facilities and their staff. On the other hand, hospital employees suffer significant gaps in emergency preparedness knowledge and skills when it comes to treating mass casualties. The objective of this study was to assess the nurses’ and physicians’ familiarity with emergency preparedness and identify the associated factors. Methods: A facility-based cross-sectional survey was conducted by census utilizing a self-administered questionnaire among all nurses and physicians working in emergency departments in East Gojjam zone public hospitals. The collected data were entered into Epi-data version 4.2 and exported to SPSS 25.0 for further analysis. Frequency, mean, and standard deviation were computed to describe individual and other characteristics of the sample. A simple and multiple linear regression model was fitted to identify factors associated with familiarity with emergency preparedness. An unstandardized adjusted beta (β) coefficient with a 95 % confidence level was used to report the result of the association at a p-value of 0.05 statistical significance. Results: In this study, a total of 237 individuals completed the questionnaire, yielding a response rate of 94 %. The mean score of familiarity with emergency preparedness was 106.1 ± 31.8 (95% CI: 102, 110.1), with approximately 52.3 % scoring higher than the mean score. Self-regulation (B = 3.8, 95% CI: 2.6, 5), health care climate (B = 1.4, 95% CI: 0.4, 2.43) and participation in actual major disaster event (B = 15.5, 95% CI: 7.8, 23.2) were significant predictors of familiarity. Conclusion: According to the findings of this study, nurses’ and physicians’ expertise in emergency and disaster preparedness is inadequate. Previous engagement in actual disaster events, self-regulation, and the healthcare climate were significant predictors of familiarity. As a result, the responsible stakeholders should develop strategy to enhance self-regulation (motivation), job satisfaction of emergency department employees, and drills and hands-on training in mass casualty management.
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Affiliation(s)
- Temesgen Ayenew
- Department of Nursing, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Mihretie Gedfew
- Department of Nursing, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Abebe Dilie Afenigus
- Department of Nursing, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Haile Amha
- Department of Nursing, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Henok Mulugeta
- Department of Nursing, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
- School of Nursing and Midwifery, Faculty of Health, University of Technology Sydney (UTS), Sydney, NSW, Australia
| | - Belayneh Mengist
- Department of Public Health, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Bekalu Bewket
- Department of Nursing, College of Health Sciences, Injibara University, Injibara, Ethiopia
| | - Yidersal Hune Melese
- Department of Human Nutrition, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Abraham Teym
- Department of Environmental Health, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Keralem Anteneh Bishaw
- Department of Midwifery, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Meseret Yitayew
- Department of Nursing, College of Health Sciences, Assosa University, Assosa, Ethiopia
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Bera A, Meraj G, Kanga S, Farooq M, Singh SK, Sahu N, Kumar P. Vulnerability and Risk Assessment to Climate Change in Sagar Island, India. Water 2022; 14:823. [DOI: 10.3390/w14050823] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Inhabitants of low-lying islands face increased threats due to climate change as a result of their higher exposure and lesser adaptive capacity. Sagar Island, the largest inhabited estuarine island of Sundarbans, is experiencing severe coastal erosion, frequent cyclones, flooding, storm surges, and breaching of embankments, resulting in land, livelihood, and property loss, and the displacement of people at a huge scale. The present study assessed climate change-induced vulnerability and risk for Sagar Island, India, using an integrated geostatistical and geoinformatics-based approach. Based on the IPCC AR5 framework, the proportion of variance of 26 exposure, hazard, sensitivity, and adaptive capacity parameters was measured and analyzed. The results showed that 19.5% of mouzas (administrative units of the island), with 15.33% of the population at the southern part of the island, i.e., Sibpur–Dhablat, Bankimnagar–Sumatinagar, and Beguakhali–Mahismari, are at high risk (0.70–0.80). It has been concluded that the island has undergone tremendous land system transformations and changes in climatic patterns. Therefore, there is a need to formulate comprehensive adaptation strategies at the policy- and decision-making levels to help the communities of this island deal with the adverse impacts of climate change. The findings of this study will help adaptation strategies based on site-specific information and sustainable management for the marginalized populations living in similar islands worldwide.
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Kumar L, Chhogyel N, Gopalakrishnan T, Hasan MK, Jayasinghe SL, Kariyawasam CS, Kogo BK, Ratnayake S. Climate change and future of agri-food production. Future Foods 2022. [DOI: 10.1016/b978-0-323-91001-9.00009-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Paul S, Chowdhury S. Investigation of the character and impact of tropical cyclone Yaas: a study over coastal districts of West Bengal, India. Saf Extreme Environ 2021. [PMCID: PMC8482366 DOI: 10.1007/s42797-021-00044-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Tropical cyclones have become more frequent as a result of climate change and the associated temperature rise in the ocean surface, wreaking havoc on both natural and man-made elements. The most recent storm, Yaas, has had a wide-spread impact on coastal areas, with high-intensity wind, rainfall, and, most significantly, inundation in Odisha and West Bengal coastal region. Yaas formed over east central Bay of Bengal as a depression and gradually intensified to VSCS and finally made landfall near Balasore of Odisha coast, with a wind speed of 130–140 km/h. on 26th May, 2021. The present study is, therefore, aimed to characterize the cyclone Yaas and to investigate the expansion of cyclonic inundation in different sector of coastal West Bengal. Several space-borne data sets were employed in this study, including GPM data to illustrate precipitation variability, Sentinel-1 images for inundation mapping, and Sentinel-2 data to determine MNDWI for both pre- and post-cyclonic periods. The results show that during this cyclonic period, hundreds of km2 of land in West Bengal, including blocks of South 24 parganas, East Medinipur and North 24 parganas such as Sagar (37.10 km2), Namkhana (78.12 km2), Pathar Pratima (58.74 km2), Ramnagar I (15.24 km2) and II (19.62 km2), Khejuri (22.27 km2), and other blocks were inundated by cyclonic surge and about a total of 1195 mm of rainfall. Eventually, people have lost their homes, properties have damaged, and many agricultural fields have become barren by salt water accumulation.
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Rahaman M, Esraz-ul-zannat M. Evaluating the impacts of major cyclonic catastrophes in coastal Bangladesh using geospatial techniques. SN Appl Sci 2021; 3. [DOI: 10.1007/s42452-021-04700-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
AbstractCyclonic catastrophes frequently devastate coastal regions of Bangladesh that host around 35 million people which represents two-thirds of the total population. They have caused many problems like agricultural crop loss, forest degradation, damage to built-up areas, river and shoreline changes that are linked to people’s livelihood and ecological biodiversity. There is an absence of a comprehensive assessment of the major cyclonic disasters of Bangladesh that integrates geospatial technologies in a single study. This study aims to integrate geospatial technologies with major disasters and compares them, which has not been tried before. This paper tried to identify impacts that occurred in the coastal region by major catastrophic events at a vast level using different geospatial technologies. It focuses to identify the impacts of major catastrophic events on livelihood and food production as well as compare the impacts and intensity of different disasters. Furthermore, it compared the losses among several districts and for that previous and post-satellite images of disasters that occurred in 1988, 1991, 2007, 2009, 2019 were used. Classification technique like machine learning algorithm was done in pre- to post-disaster images. For quantifying change in the indication of different factors, indices including NDVI, NDWI, NDBI were developed. “Change vector analysis” equation was performed in bands of the images of pre- and post-disaster to identify the magnitude of change. Also, crop production variance was analyzed to detect impacts on crop production. Furthermore, the changes in shallow to deep water were analyzed. There is a notable change in shallow to deep water bodies after each disaster in Satkhira and Bhola district but subtle changes in Khulna and Bagerhat districts. Change vector analysis revealed greater intensity in Bhola in 1988 and Satkhira in 1991. Furthermore, over the years 2007 and 2009 it showed medium and deep intense areas all over the region. A sharp decrease in Aus rice production is witnessed in Barishal in 2007 when cyclone “Sidr” was stricken. The declination of potato production is seen in Khulna district after the 1988 cyclone. A huge change in the land-use classes from classified images like water body, Pasture land in 1988 and water body, forest in 1991 is marked out. Besides, a clear variation in the settlement was observed from the classified images. This study explores the necessity of using more geospatial technologies in disastrous impacts assessment around the world in the context of Bangladesh and, also, emphasizes taking effective, proper and sustainable disaster management and mitigation measures to counter future disastrous impacts.
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Aznar-Crespo P, Aledo A, Melgarejo-Moreno J. Social vulnerability to natural hazards in tourist destinations of developed regions. Sci Total Environ 2020; 709:135870. [PMID: 31884275 DOI: 10.1016/j.scitotenv.2019.135870] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/28/2019] [Accepted: 11/28/2019] [Indexed: 05/28/2023]
Abstract
Tourist destinations in developed regions constitute a complex production model of social vulnerability to natural hazards. On the one hand, the high geographical exposure of tourist areas, the volatility of demand or the tourists' lack of knowledge of the local culture of risk/disaster generate sensitivity. On the other hand, the socio-economic dynamism of the tourism industry, the quality of the urban infrastructure or the protection of the institutional framework generate adaptive capacity. The interaction of these two opposing forces gives rise to highly complex adaptive situations that require far-reaching conceptual frameworks. Several researchers have indicated that the mainstream approach to social vulnerability to natural hazards does not have this quality due to its descriptive, quantitative and synchronous nature. The objective of this study is to propose and apply a methodological approach directed at deciphering the complexity of the processes that generate social vulnerability of tourist destinations in developed regions. We select seismic risk of the coastal area of the province of Alicante (SE Spain) as case study. In order to construct and apply the methodological approach, we carried out desk research on the region of study and consulted local experts. This approach articulates a causal structure able to systematise the deep origin and driving forces of the sensitivity and adaptive capacity of the region. Key factors of sensitivity include: occupation of hazardous areas by tourists, low economic diversification, large residential area without earthquake-resistant regulations, lack of seismic culture or non-compliance of seismic risk management plans. Key factors of adaptive capacity include: cooperative relationships between long-stay tourists, multiplying effect of tourism activities, transport infrastructure, welfare state policies or rapid response mechanisms in emergencies. Findings offer an in-depth and holistic view of the generative process of social vulnerability, which is particularly useful for enhancing risk management tools.
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Affiliation(s)
- Pablo Aznar-Crespo
- University Institute of Water and Environmental Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain.
| | - Antonio Aledo
- University Institute of Water and Environmental Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain.
| | - Joaquín Melgarejo-Moreno
- University Institute of Water and Environmental Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain.
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Sajjad M, Chan JCL. Risk assessment for the sustainability of coastal communities: A preliminary study. Sci Total Environ 2019; 671:339-350. [PMID: 30933790 DOI: 10.1016/j.scitotenv.2019.03.326] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/06/2019] [Accepted: 03/21/2019] [Indexed: 06/09/2023]
Abstract
With communities increasingly concentrated in coastal regions globally, governments and stakeholders call for cohesive risk assessments for future sustainability in the wake of natural hazards. This can only be achieved through risk-based decision-making and smart resources treatment. To provide a basis for such actions, we here propose a risk assessment (RA) framework focusing on the risk-resilience-sustainability nexus. In contrast to focusing on the traditional RA approach, we propose an integrative approach based on hazard, vulnerability, and resilience covering the full spectrum of RA for effective risk reduction. We further explain how the proposed framework can simultaneously provide useful input for resilience management in parallel to achieving certain Sustainable Development Goals (SDGs). We apply this framework for typhoon risk assessment (represented by a Typhoon Risk Index-TRI) of coastal counties of mainland China. Different TRIs e.g. total population, elderly population, non-adult population, and economic status are calculated for each coastal county to supplement multi-objective empirical measures for risk reduction. The RA results show a large spatial heterogeneity in typhoon risk with an increase in the risk from north to south along the coast of mainland China. The comparative results from this study are relevant to the prioritization of different regions for immediate or gradual actions, wise decision-making, and risk reduction through proper treatment of resources-related policy implications. The evaluation of the SDGs achievement status reveals that the overall performance of coastal provinces in mainland China is higher to achieve SDGs 3 and 15 followed by 13 and 8. The study shows that while Guangdong province is in the highest risk category, its achievement status for SDG-13 (climate actions, strengthening resilience) is the lowest relative to other provinces, which is critical. This study represents a major scientific contribution to mainland China's coastal risk management and calls for aligning risk-informed planning and sustainability frameworks.
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Affiliation(s)
- Muhammad Sajjad
- Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region; Department of Civil and Environmental Engineering, Princeton University, USA.
| | - Johnny C L Chan
- Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region
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Yin K, Zhang Y, Li X. Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data. Int J Environ Res Public Health 2017; 14:ijerph14111330. [PMID: 29104262 PMCID: PMC5707969 DOI: 10.3390/ijerph14111330] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 10/18/2017] [Accepted: 10/22/2017] [Indexed: 11/16/2022]
Abstract
Owing to the difference of the sequences’ orders and the surface structure in the current panel grey relational models, research results will not be unique. In addition, individual measurement of indicators and objects and the subjectivity of combined weight would significantly weaken the effective information of panel data and reduce the reliability and accuracy of research results. Therefore, we propose the concept and calculation method of dispersion of panel data, establish the grey relational model based on dispersion of panel data (DPGRA), and prove that DPGRA exhibits the effective properties of uniqueness, symmetry, and normality. To demonstrate its applicability, the proposed DPGRA model is used to research on storm-tide disaster losses in China’s coastal areas. Comparing research results of three models, which are DPGRA, Euclidean distance grey relational model, and grey grid relational model, it was shown that DPGRA is more effective, feasible, and stable. It is indicated that DPGRA can entirely utilize the effective information of panel data; what’s more, it can not only handle the non-uniqueness of the grey relational model’s results but also improve the reliability and accuracy of research results. The research results are of great significance for coastal areas to focus on monitoring storm–tide disasters hazards, strengthen the protection measures of natural disasters, and improve the ability of disaster prevention and reduction.
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Affiliation(s)
- Kedong Yin
- School of Economics, Ocean University of China, Qingdao 266100, China.
- Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China.
| | - Ya Zhang
- School of Economics, Ocean University of China, Qingdao 266100, China.
| | - Xuemei Li
- School of Economics, Ocean University of China, Qingdao 266100, China.
- Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China.
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