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研究生:莊培宏
研究生(外文):Pei-Hung Chuang
論文名稱:統計模式在疾病監測上的應用
論文名稱(外文):The Applications of Statistical Models in Disease Surveillance
指導教授:林逸芬
指導教授(外文):I-Feng Lin
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:91
中文關鍵詞:疾病監測爆發嚴重急性呼吸道症候群
外文關鍵詞:Disease SurveillanceOutbreakSurveillanceESSENCESARS
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近年來由於台灣各種傳染性疾病的流行,國際上也有新興疾病的爆發,引起世界各國關心與囑目;而戰爭的開啟與恐怖攻擊的威脅,更讓疾病監測系統的概念進入國防計畫;如何有效利用疾病監測系統迅速且精確的掌握及控制疫情,即時識別出疾病的爆發並且迅速診斷可以減少罹病率與死亡率,成為疾病監測最優先考量的課題。
本研究以2000年一月至2003年六月台北市所有醫院健保門診、急診申報資料,內容包含依據International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM)診斷碼為標準的診斷結果,並依照美國Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE)1計畫於2003年所發布的症候群分類標準(附錄A),將相似的診斷碼歸類成九種症候群。本研究以呼吸道症候群(Respiratory Syndrome)的每日就診總人數為主要研究變項。每日就診人數是依照就診醫院所在行政區為就診區域,分區加總計算而成。
本研究的目的在(1)建立統計模式預測呼吸道症候群每日就醫人數並探討影響看診人數的重要因子(2) 以cross-validation 的方式比較不同統計模式的預測能力 (3)分析2003年SARS流行期間利用統計模式偵測疾病爆發的效果。對於疾病爆發警訊的認定,以超出統計模式所計算出的預測值95%信賴區間上界為警戒值上限(Upper Outbreak Alert)。至於低於預測值95%信賴區間下界為警戒值下限(Lower limit Alert)則表示可能有異於預期的就醫行為,本分析亦一併報告。
本研究的初步結果可以發現:
(1)資料的規律性:資料擁有七天為周期的規律,看診人數的高低根據星期而不同,我們可以利用此規律性將資料分類或是控制此項因素
(2)影響看診人數的重要因子:研究顯示星期、月份、假日、行政區皆對看診人數有程度不一的影響,顯示時間性的變數對於此種時間序列資料的解釋不可或缺。
(3)比較不同統計模式對疾病監測的差異:部分的統計模式對於預估就診人數並不良好,造成太多誤判,但是各個變數統計模式參數的方向(性質符號)的影響卻是相當一致,有助於我們針對特定變項進行分析解釋。
(4)對2003年SARS流行期間進行疾病監測分析:發現在2003年一月底、四月初、五月初看診人數出有超出預測值,而五月中旬以後則是低於預測值,根據統計分析結果,我們可以更精確看出疾病爆發的時間點。
疾病監測的是否準確的一大關鍵,在於疾病資料本身是否具有即時性。以時間點越靠近的資料進行分析,則越能準確的進行預測。本研究資料來源為健保申報資料,申報期間有三到六個月不等的延遲期,但是所建立的統計模式適合對於長期的預測能力也不因此而減低,更增加了實際應用於疾病監測的方便性。
而根據不同統計模式的分析結果,我們也提供建議給不同需求的使用者,期望能提供公共衛生人員對於疾病監測系統的參考
我們相信疾病監測系統可以提供疾病爆發的早期偵測能力,使政府能夠快速的集中醫療資源以降低罹病率和死亡率,特別是在新興疾病或是恐怖攻擊的偵測方面。因此在疾病監測系統利用適合的統計模式,將會有效及準確的提供疾病的預測。
Due to the impact of numerous epidemic infective diseases in Taiwan in recent years, and outbreak of emerging infections throughout the world, the issue of disease control has been brought to attention to countries all over the world. Furthermore, the outbreak of the war and threat of biological and terrorism warfare has brought the concept of disease surveillance system into the scope of national defense plan. In order to utilize the disease surveillance system rapidly and effectively to grasp and control the epidemic situation accurately, recognize the outbreak of diseases and diagnose immediately to reduce morbidity and mortality, disease surveillance has become a subject considered as top priority.
This research consists of materials gathered from the declaration of the health insurance of the clinic and emergency call of all hospitals since January 2000 to June 2003 in Taipei. The diagnosis results are standardized using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) standard. And the data is further categorized according to the criteria for disease grouping classification released in 2003 (appendix A) of Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) plan in U.S.A; similar diagnostic codes are grouped in nine different clusters. This research takes the daily count of respiratory syndrome patients as the main research variable. The daily count of patients is calculated by adding up the weighted count of patients of every clinic district.
The purpose of this research is (1) Build statistical models to predict the daily count of respiratory syndrome patients and seek for deciding factors which influence the daily count (2) Compare and evaluate the precision of difference statistical models by using cross-validation (3) Analyze the period which SARS epidemic in 2003, and examine how effective and precise statistical models are in detecting and examining the effect of disease outbreak.
The assertion alert of disease outbreak is defined, upper outbreak alerts are generated if the count for that day exceeds a 95% upper limit; while lower limit alerts are generated if the count for that day undervalues a 95% lower limit, which could possibly indicate some unpredicted behavior involved in seeing a doctor. This research would include both scenarios.
The result of this research can be concluded:
(1) Regularity of the data materials: The data materials are observed in a cycle of seven days, hence any variation of patient count can be observed on a weekly basis. We can utilize this regularity to classify the materials or control this factor.
(2) The important factor which influenced the number of people visiting the clinic: The research showed that the difference in week, month, vacations, and districts would all influence the patient count differently; the parameter which shows the timeliness is indispensable in explanation to this kind of time array materials.
(3) Examine the difference between different statistical models in disease surveillance: The predictions in some statistical models are not very precise, which caused too many false conclusions. But the influence each variable generates is quite identical in different statistical models, which contributes in the determination and analysis of specific variables.
(4) The analysis of patient counts in 2003, while SARS is prevailing, reveals that the number of people visiting a clinic is beyond the predicted values at the end of January, the beginning of April, and the beginning of May; it is lower than the predicted value after mid-April. With the help of statistics analysis result, the time of the disease outbreak can be estimated more accurately.
A crucial factor of preciseness on disease surveillance is time. The materials gathered within a closer time frame can be analyzed and predicted more accurately. This research is based on data gathered from the declaration of the health insurance, which usually has a delay period of three to six months; however, the preciseness of the long-term statistical models is not lowered. It increases the convenience in applying disease surveillance to real-life.
Based on different statistic analytical results, different suggestions are offered to the users of various kinds of demands; in hope that the research could serve as a reference of the disease surveillance system to public health personnel.
We believe that disease surveillance system can be utilized in detecting the disease outbreaks in an early stage, which helps the government in promptly allocating the limited medical resources to decrease morbidity and mortality, especially in the fields of disease outbreak detection and the response to biological and terrorism warfare. Hence, utilizing the appropriate statistical model to observe disease behaviors can produce more effective and accurate predictions of diseases.
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