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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇喜 | |
dc.contributor.author | Chien-Hao Lin | en |
dc.contributor.author | 林鍵皓 | zh_TW |
dc.date.accessioned | 2021-06-15T07:03:01Z | - |
dc.date.available | 2012-03-03 | |
dc.date.copyright | 2011-03-03 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-01-12 | |
dc.identifier.citation | Attia, M. W. and R. Edward (1998). 'Effect of weather on the number and the nature of visits to a pediatric ED.' Am J Emerg Med 16(4): 374-5.
Banerjee, M. M. and D. F. Gillespie (1994). 'Strategy and organizational disaster preparedness.' Disasters 18(4): 344-54. Belville, J. D. (1987). 'The National Weather Service warning system.' Annals of Emergency Medicine 16(9): 1078-80. Brewer, R. D., P. D. Morris, et al. (1994). 'Hurricane-related emergency department visits in an inland area: an analysis of the public health impact of Hurricane Hugo in North Carolina.' Ann Emerg Med 23(4): 731-6. Centers for Disease Control, C. (1986). 'Hurricanes and hospital emergency-room visits--Mississippi, Rhode Island, Connecticut.' MMWR Morb Mortal Wkly Rep 34(51-52): 765-70. Christoffel, K. K. (1985). 'Effect of season and weather on pediatric emergency department use.' Am J Emerg Med 3(4): 327-30. Ciottone, G. R. (2006). Disaster medicine. Philadelphia, Elsevier/Mosby. CWB (2004). 颱風百問. R. Central Weather Bureau of Ministry of Transportation and Communications. Taipei. Holleman, D. R., Jr., R. L. Bowling, et al. (1996). 'Predicting daily visits to a walk-in clinic and emergency department using calendar and weather data.' J Gen Intern Med 11(4): 237-9. JCAHO, T. J. C. o. A. o. H. (2001). Environment of Care 1.4 standards. Lai, T. I., F. Y. Shih, et al. (2003). 'Strategies of disaster response in the health care system for tropical cyclones: experience following Typhoon Nari in Taipei City.' Academic Emergency Medicine 10(10): 1109-12. Lewis, C. P. and R. V. Aghababian (1996). 'Disaster planning, Part I. Overview of hospital and emergency department planning for internal and external disasters.' Emergency Medicine Clinics of North America 14(2): 439-52. Longmire, A. W., J. Burch, et al. (1988). 'Morbidity of Hurricane Elena.' South Med J 81(11): 1343-6. Longmire, A. W. and R. P. Ten Eyck (1984). 'Morbidity of Hurricane Frederic.' Ann Emerg Med 13(5): 334-8. Mortensen, K., Z. Dreyfuss, et al. (2008). 'How many walked through the door?: the effect of hurricane Katrina evacuees on Houston emergency departments.' Medical Care 46(9): 998-1001. Platz, E., H. P. Cooper, et al. (2007). 'The impact of a series of hurricanes on the visits to two central Florida Emergency Departments.' Journal of Emergency Medicine 33(1): 39-46. Quarantelli, E. L. (1998). Disaster Planning, Emergency Management, And Civil Protection: The Historical Development And Current Characteristics Of Organized Efforts To Prevent And To Respond To Disasters, DRC Preliminary Papers. Quinn, B., R. Baker, et al. (1994). 'Hurricane Andrew and a pediatric emergency department.' Ann Emerg Med 23(4): 737-41. Rincon, E., M. Y. Linares, et al. (2001). 'Effect of previous experience of a hurricane on preparedness for future hurricanes.' Am J Emerg Med 19(4): 276-9. Sheppa, C. M., J. Stevens, et al. (1993). 'The effect of a class IV hurricane on emergency department operations.' American Journal of Emergency Medicine 11(5): 464-7. Smith, C. M. and C. S. Graffeo (2005). 'Regional impact of Hurricane Isabel on emergency departments in coastal southeastern Virginia.' Acad Emerg Med 12(12): 1201-5. Tai, C. C., C. C. Lee, et al. (2007). 'Effects of ambient temperature on volume, specialty composition and triage levels of emergency department visits.' Emerg Med J 24(9): 641-4. Timothy Schott, C. L., Gene Hafele, Jeffrey Lorens, Arthur Taylor, Harvey Thurm, Bill Ward, Mark Willis, and Walt Zaleski (2010). The Saffir-Simpson Hurricane Wind Scale N. H. C. National Centers for Environmental Prediction, National Weather Service. Wargon, M., B. Guidet, et al. (2009). 'A systematic review of models for forecasting the number of emergency department visits.' Emerg Med J 26(6): 395-9. Wylie, T., D. Cheanvechai, et al. (2000). 'Emergency response team: Hurricane Georges in Key West.' Prehosp Emerg Care 4(3): 222-6. 不詳 (2006). 談美國政府在卡翠那颶風後之風險管理. 行政院研究發展考核委員會. 石富元 (2007). 台灣災害之健康危害分析及預防策略之應用. 季瑋珠、林芳郁. Taipei, 國立臺灣大學公共衛生學院預防醫學研究所. 財團法人醫院評鑑暨醫療品質策進會 (2008). 新制醫院評鑑基準及評分說明. 合理的醫院經營管理, 財團法人醫院評鑑暨醫療品質策進會. 2.9. 許漢卿•曾淑汾 (2003). '重建區農業產業振興方案執行成果與檢討.' 農政與農情 131. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48579 | - |
dc.description.abstract | 背景與目的 台灣地區每年因為颱風(熱帶氣旋)造成的災害事件約三到四次,每次造成傷亡的人數大約由數十人到數百人不等,而急診是颱風期間病患就醫的唯一或主要的場所,因此急診於颱風前後的服務能量是否充足,在災害準備、應變與恢復期都扮演相當重要的醫療衛生角色,同時也是醫院訂定緊急應變計畫很重要的一部份。根據過去國外的文獻指出,颱風期間,急診的服務量會在颱風登陸當日顯著減少,但是於颱風過後,則會大量地增加,而其中又以外傷病患為主。本研究企圖探討台灣地區急診醫療服務在颱風期間及其後的變化,以進一步瞭解不同颱風的特性,在不同醫院層級對於急診服務量與及急診疾病型態的影響,以利日後醫院及衛生機關在進行災難應變規劃與準備時,可能所需人力及物資需求的參考。
研究方法 本研究主要以台灣東部地區(宜蘭縣、花蓮縣、台東縣)做為研究區域,針對西元2000年到2008年間,該地區颱風季節(6月到11月)急診服務量進行回溯性研究:資料蒐集來源為全民健保研究資料庫的每日急診申報資料,包含門診處方集治療明細檔(CD)及住院醫療費用清單明細檔(DD),以及醫療機構基本資料檔(HOSB)等,颱風的相關數據則來自中央氣象局的颱風資料庫。本研究利用多元迴歸的統計方法,分析急診服務量與急診疾病型態在颱風期間的變化,與颱風特徵(每日累積雨量、是否為登陸地區、颱風強度、陸上警報發佈警報天數、颱風近中心最大風速、近中心最低氣壓、七級風暴風半徑)及醫院規模的關係,颱風期間設定為颱風登陸前兩日到颱風登陸後五日共八天。 研究結果 每日急診就診人數在強烈颱風登陸當天會增加,在登陸地區影響可達兩天,以一家年每日平均來診人數達100人的醫院為例,於登陸當天每日急診就診人數較一般非颱風日(無颱風且日累積雨量小於50毫米)增加18.1人(p<0.001),於登陸後第一日可增加24.9人(p<0.001);而於未登陸但日累積雨量大於50毫米的僅在登陸當天一天,以一家年平均每日就診人數達100人的醫院為例,於登陸當天每日急診就診人數較一般非颱風日(無颱風且日累積雨量小於50毫米)增加6.3人(p<0.001)。然而創傷與非創傷的就診比例僅在強烈颱風登陸地區,於登陸當天有明顯增加,創傷就診比例以一家年每日平均來診人數達100人的醫院為例,約增加4.5%(p<0.001),其餘颱風影響日次,則無顯著差異。在創傷疾病型態中,因撕裂傷就診的病患比例,明顯在強烈颱風登陸當日上升4.1%(p<0.001),而於強烈颱風登陸後第1日,在未登陸但日累積雨量大於50毫米的地區,撕裂傷病患就醫比例增加6.9%(p=0.02)。 而在輕度颱風登陸當天,每日急診就診人數與非颱風日無顯著差異,在登陸後第一日則顯著減少;在中度颱風登陸前一日每日急診就診人數減少,在登陸後第一日於未登陸但有大雨(日雨量>50公釐)的地區,也是顯著減少,然而在登陸當天,在登陸地區每日急診就診人數則略微上升,以一家年每日平均來診人數達100人的醫院為例,約增加7.2人(p=0.002)。 結論 醫院急診部門應根據醫院的規模,在強烈颱風登陸時,增加急診的人力以因應急診就診人數的增加,在登陸地區更應維持人力支援直到颱風登陸後第一天。至於人力需求的種類,在強烈颱風登陸地區,於登陸當天應增加急診外科方面的人力,以應付較多的創傷病患。 | zh_TW |
dc.description.abstract | Background and objectives A tropical cyclone (typhoon) is a storm system characterized by a large low-pressure center, which could produce strong winds, and heavy rain. Previous studies in the United States have shown that tropical cyclones would increase the healthcare demand, shown by the increase of the emergency department(ED) visits after the landfall day. People would possibly get injured more easily during the impact, and also the recovery period. It was suggested that more manpower should be prepared in the EDs to meet the healthcare needs. Taiwan, located at the west Pacific Ocean, is one of the areas frequently impacted by tropical cyclones with an average of 3 to 4 tropical cyclones per year. From this study, we’d like to know the healthcare impact of tropical cyclones and prepare for the possible increased needs in the EDs of Taiwan. By using the different characteristics of each tropical cyclone, we’d also like to show which the strong predictors of ED visits were and the major diseases presented.
Methods This was a retrospective observational study performed in three counties in east Taiwan, Ilan, Hualiang, and Taitung. After excluding those EDs with yearly average daily ED visits less than 10, total twenty hospitals (1 medical center, 7 regional hospitals, and others were area hospitals) were included. We used the National Health Insurance Research Database (NHIRD) provided by National Health Research Institutes. The claimed data, including sex, birthday, visiting date, three codes of “The International Classification of Diseases, 9th Revision, Clinical Modification' (ICD-9 codes) and treatment codes, of all the patients applied for emergency services in the three counties from June to November from 2000 A.D. to 2008 A.D. were collected. ICD-9 codes were further used for identifying trauma and non-trauma patients and different types of trauma. Hospital profiles, including location, contract types (medical center, regional hospital or area hospital) were retrieved from NHIRD, too. All tropical cyclones’ profiles were subtracted from the Tropical Cyclones Database provided by Central Weather Bureau (CWB) of the Republic of China. Total twenty-two tropical cyclones which came from the eastern of Taiwan and had covered any of the three counties were included in the study. The characteristics of those tropical cyclones were retrieved, including the radius, the minimum pressure recorded, maximum windspeed, grade, landing area and total alarm days. The daily rainfall amounts were also retrieved from CWB. The primary outcome was the daily ED visits of each hospital. Different types of diseases, age group, triage level, sex group were the secondary outcomes. Linear regression was used to evaluate the univariate or multivariate associations between the characteristics of tropical cyclones and all primary and secondary outcomes. The regression models were further adjusted by yearly ED visits, holidays. The stepwise variable selection procedure (with iterations between the forward and backward steps) was applied to obtain the candidate final regression model. A two-tailed p-value, 0.05, was considered significant. Results In the areas the severe typhoon landed directly, the ED visits would increase for two days since the landfall day. For a hospital with a yearly average ED visit of 100 per day, there would be 18.1 visits more than the non-typhoon day (not affected by typhoon and the daily rain amount <50 mm) (p<0.001) on the day of landfall and 24.9 visits more (p<0.001) on the next day after landfall day. In the areas, where the severe typhoon didn’t land directly but brought heavy rain (daily rain amount >50 mm), the ED visits would increase by 6.3 on the day of landfall (p<0.001). The percentage of trauma ED visits would increase in the area landed by severe typhoon on the landfall day. For a hospital with a yearly average ED visit of 100 per day, the trauma ED visits increased 4.5% compared with the non-typhoon day (p<0.001). Among the traumatic ED visits, patients visited ER for skin laceration increased 4.1% (p<0.001). There were no significant differences of trauma ED visits on the other days after landfall day. However, patients with skin laceration still increased 6.9% (p=0.02) in the areas where the severe typhoon brought heavy rain without direct landing. In the areas where tropical storm passed, the ED visits were not significantly different on the day of landfall, but decreased on the next day of landfall day. In the areas where moderate typhoons passed, the ED visits decreased on the day before landfall day, but there were 7.2 more visits per day in the direct landing area on the landfall day, for a hospital with a yearly average ED visit of 100 per day (p=0.002). Discussion The emergency department managers and health authorities should consider preparing more manpower to meet the increased ED visits, for two days in the severe typhoon landfall area since the landfall day of and for one day in the non-landfall area with heavy rain. Doctors and nursing staffs that specialized in trauma care, especially wound management were needed preferentially. Future emergency response planning and prepare of both hospitals and health authorities for tropical cyclones could based on the current findings, in order to meet the health care need. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T07:03:01Z (GMT). No. of bitstreams: 1 ntu-100-R97843006-1.pdf: 1916314 bytes, checksum: 18ee110db0b4e40462340fb34569b4a5 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 論文口試委員審定書 i
中文摘要 ii Abstract iv 目 錄 vii 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機 3 第三節 研究目的 5 第四節 研究流程 6 第二章 文獻探討 7 第一節 醫院緊急應變的發展 7 第二節 急診於災難應變與颱風時的角色 9 第三節 颱風的特性、分級 11 第四節 美國颶風災害與急診服務量之案例分析 14 第五節 台灣地區颱風對醫療運作影響的文獻回顧 18 第六節 急診就診量預測分析與模型 20 第三章 研究方法 21 第二節 研究變數與研究架構 22 第三節 研究假設 26 第四節 資料蒐集與處理 27 第五節 研究資料分析 29 第六節 研究倫理 32 第四章 研究結果 33 第一節 資料描述 33 一、 颱風資料描述 33 二、 病患就醫基本資料描述 37 三、 病患疾病型態描述 38 第二節 單變數迴歸分析結果 41 一、 病患就醫基本資料分析 41 二、 病患疾病類別分析 45 第三節 多變數迴歸分析 49 一、 主作用模型 49 二、 完整模型— 每日急診就診人數(主要依變數) 51 三、 完整模型— 性別比例(次要依變數) 54 四、 完整模型—年齡組成(次要依變數) 55 五、 完整模型—檢傷級數(次要依變數) 57 六、 完整模型— 創傷與非創傷比例(次要依變數) 59 七、 完整模型— 創傷疾病類別(次要依變數) 61 八、 完整模型— 精神疾病與皮膚疾病(次要依變數) 64 九、 次族群分析— 其他颱風特徵變數的多變數迴歸分析 65 第五章 討論 66 第一節 每日急診就診人數在颱風時的變化 66 第二節 每日急診就診人數與颱風特徵的關係 68 第三節 颱風影響時急診就醫型態的影響 70 第四節 颱風影響時急診疾病型態的影響 72 第六章 結論與建議 74 第一節 研究結論 74 第二節 研究貢獻 75 第三節 研究限制 76 第四節 建議 78 第七章 參考文獻 80 | |
dc.language.iso | zh-TW | |
dc.title | 颱風災害對急診服務量及疾病型態影響之分析
-以台灣東部地區(宜蘭縣、花蓮縣和台東縣)為例- | zh_TW |
dc.title | The health impact of the typhoon (tropical cyclone) on emergency department visits and disease patterns
-A study in eastern Taiwan (Ilan, Hualian, and Taitung)- | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 郭人介,石崇良 | |
dc.subject.keyword | 颱風,熱帶氣旋,急診服務量,疾病型態,醫院規模, | zh_TW |
dc.subject.keyword | tropical cyclone,typhoon,emergency department visit,laceration,trauma,emergency response, | en |
dc.relation.page | 81 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-01-12 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 健康政策與管理研究所 | zh_TW |
顯示於系所單位: | 健康政策與管理研究所 |
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