整合異質資料探討車禍死亡人數推估係數與壽命年數損失之研究

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賴靜慧(CHING-HUEI LAI) 查詢紙本館藏

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論文名稱

整合異質資料探討車禍死亡人數推估係數與壽命年數損失之研究
(Integrate Heterogeneous Data to Explore the Number and Lost Years of Crash Fatality)

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摘要(中)

降低死亡率及延長人類壽命，是醫學科學的主要目標之一。依據世界衛生組織估計，由2002年到2030年，全球因損傷而肇致的死亡人數將成長40%，多數是肇因於車禍，而車禍將由目前第8-10名上升為第4-5名主要死亡原因。
本研究透過異質資料的整合方法，藉由淨化、連結我國警察單位的交通事故資料與衛生單位的死亡原因資料，建立車禍死亡者的整合資料庫，並據以衍生國際常用之不同死亡時間定義下的30天死亡人數推估係數及壽命年數損失(Years of Life Lost，YLL)，探討影響車禍之人、車、路及環境三項因素，對死亡危急性的影響，以及對社會所造成的死亡負擔，並與國際資料比較。
研究之主要結果：(1)我國車禍當天的推估係數較歐洲及日本為高，顯示我國有較多晚期死亡者；(2)推估係數揭露胸部受傷的危急性在所探討之風險因子中居冠；(3)年輕人因車禍而產生的平均YLL居各年齡層之冠，但高齡者的平均YLL甚高，遠高於西太平洋地區；(4)在不同性別、年齡別族群中，平均YLL的風險因子排名並不相相同，但頭部受傷、市區道路及乾燥路面對所有族群均為高風險因子。(5)不同死亡指標，可提供更多樣化的車禍風險面貌。
透過異質資料整合的方式，可有效結合既有資料庫，發展出有價值的資訊，本研究經驗顯示此可使我國資料與國際比較，並延伸應用，提供我國經驗，供國際參考。

摘要(英)

Lower death rate and longer life years are two objectives of medicine science. According the estimates published by World Health Organization, the deaths caused by injuries would increase 40% from year 2020 to 2030. Most of them were crash fatalities. By then, the crash would rank 4-5 in the causes of deaths.
The present study linked the heterogeneous police reported crash data with vital registration data to build an integrated database. Then, two common used fatality indexes, 30 days fatality adjustment factors under different fatality time definition and years of life lost (YLL), were used to investigate the influence of various risk factors on crashes. The results were also compared with international data.
The major results were: 1) Taiwan’s adjustment factor at the first day of crash was higher than that in Europe and Japan. It indicated more late deaths in Taiwan. 2) Illustrated by fatality adjustment factors, thorax injuries were the most exigent factor among the investigated risk factors. 3) The YLL rates of the youth were highest across age groups. However, the YLL rates of the elder were visible high, and higher than that in Western Pacific Region B area. 4) The factors on YLL rate rank various among sex and age groups; however, head injuries, injuries involving urban roads and dry road condition were the factors with higher YLL rate in all groups. 5) Different fatality indexes could provide various crashes features.
Integrating heterogeneous data could efficiently combine existing databases to provide valuable information. The experiences in present study demonstrated that it could compare local data with international data and investigate advanced.

關鍵字(中)

★ 推估係數
★ 壽命年數損失
★ 交通事故

關鍵字(英)

★ Years of Life Lost
★ Traffic Accident
★ Adjustment

論文目次

目錄 iv
圖目錄 vii
表目錄 viii
英文縮寫意義 xi
符號說明 xii
一、前言 1
1-1 疾病與意外事故對國人死亡及壽命的影響 3
1-2 車禍死亡的國際指標與評估 7
1-3 我國車禍死亡相關的異質資料庫 10
1-4 研究目的 11
1-5 研究方法 12
1-6 研究流程及各章內容 13
二、文獻回顧 15
2-1 車禍死亡指標的相關研究 15
2-1-1 車禍死亡定義的課題 15
2-1-2 壽命年數損失(YLL)指標 17
2-2 車禍死亡特性的相關研究 20
2-2-1 一般車禍特性分析架構：Haddon Matrix 20
2-2-2 車禍前及車禍當時的死亡風險因素相關研究 21
2-2-3 車禍後的死亡風險因素相關研究 27
2-3 異質資料整合 30
2-3-1 國外資料連結方法 30
2-3-2 國外車禍資料連結的相關研究 33
三、研究方法 37
3-1 我國事故與死因資料整合的處理 37
3-1-1 事故與死因資料背景說明 37
3-1-2 身份證字號及日期資料淨化 38
3-1-3 整合資料的型態分析 44
3-2 壽命年數損失(YLL)指標計算 49
3-2-1 YLL的理論：死因指派、生命模型、年齡權重 49
3-2-2 YLL的計算方法 52
3-3 統計檢定方法 55
3-3-1 比例p檢定方法 55
3-3-2 χ2檢定方法 56
四、車禍死亡資料檢誤與死亡人數推估係數之探討 57
4-1 車禍死亡資料檢誤之探討 57
4-1-1 不同死亡時間差下所登錄之外因碼(E-code)變化分析 57
4-1-2 資料一致性的分析 60
4-1-3 事故日期及死亡日期的資料合理性分析 62
4-1-4 外因碼(E-code)輔助車禍死亡研判的探討 64
4-1-5 以替代性個體資料連結的探討 68
4-2 整合事故與死因資料後之死亡人數推估係數 71
4-2-1 不同「人」因素的死亡人數推估係數 74
4-2-2 不同「車」因素的死亡人數推估係數 82
4-2-3 「路及環境」因素的死亡人數推估係數 86
4-3 推估係數與國際比較 89
4-3-1 與日本之推估係數比較 90
4-3-2 與歐盟ECMT之推估係數比較 92
4-4 討論 95
五、車禍死亡的壽命年數損失(YLL)特性與國際比較之探討 101
5-1 YLL指標重要課題探討 101
5-1-1 死因指派的影響分析 101
5-1-2 生命模型的影響分析 112
5-1-3 年齡權重的影響分析 116
5-2 YLL國際與國內比較 124
5-2-1 我國YLL與WHO公布之太平洋東岸地區資料比較 124
5-2-2 整合事故與死因資料之YLL與死因資料之YLL比較 128
5-3 YLL的車禍特性探討 132
5-3-1 不同「人」因素下的YLL分佈 132
5-3-2 不同「車」因素下的YLL分佈 144
5-3-3 不同「路及環境」因素下的YLL分佈 149
5-4 討論 157
六、結論與建議 164
6-1 結論 164
6-2 未來研究方向 167
6-3 建議 168
參考文獻 169
附錄一：道路交通事故資料欄位說明 181
附錄二：死因資料欄位說明 184
附錄三：事故資料庫中受害者出現在死因資料庫的人數統計(2000-2001) 186
附錄四：時間差小於0的受害者人數統計(2000-2001) 189
附錄五：各種死亡時間定義之推估係數差異檢定結果 192
附錄六：「人」因素統計檢定結果彙整表 194
附錄七：「車」因素統計檢定結果彙整表 200
附錄八：「路及環境」因素統計檢定結果彙整表 203
附錄九：不同資料來源之各年齡層死亡人數(2000-2001) 204
附錄十：死因資料MVA年齡比例及整合資料年齡比例(2000-2001) 206
附錄十一：方法(1)-(5)補正死因資料MVA死亡人數之估測結果(2000-2001) 208
附錄十二：死因資料MVA死亡人數以方法(5)重新分配前後比較(2000-2001) 213

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指導教授

董基良、鄭銘章
(Ji-Liang Doong、Ming-Chang Jeng)

審核日期

2008-7-1

推文