University of Macau / Institute of Chinese Medical Sciences

University of Macau / Institute of Chinese Medical Sciences

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當越來越多的藥物被發展出來進而治療人類的疾病，人們使用藥物時只知道該藥物對疾病的療效，卻忽略了其中可能隱藏的危機，不只是西藥，連中藥也隱藏相當大的危機。雖然國內有藥品管制局進行藥物交互作用的控管，但是藥物交互作用真的被有效控管嗎？成效又如何呢？有鑑於此，這成為大家都想知道的問題。 利用健保資料庫中龐大的門診資料，嘗試推估國人藥物交互作用的情形，由國人十大死因中得知，其中尤以慢性病病患的情形更為嚴重，例如：惡性腫瘤、腦血管疾病、心臟疾病、糖尿病、肝病和高血壓，本次研究以心血管病患為討論主體，試瞭解在長期服用心血管藥物時，醫生在已知病患病情之下卻無法避免藥物交互作用的情形為何，利用資料採礦的方法，找出可能造成交互作用發生的原因、族群，進而去提醒醫療機構避免其發生。 研究發現，心血管疾病病患分為一些併發症族群，如：心臟病、心血管疾病糖尿病、關節炎、消化潰瘍，從這些病患用藥中發現許多交互作用的用藥與其併發症也許多相關。高血壓疾病病患大多以強心劑、利尿劑為主的交互作用，心血管疾病併發糖尿病病患大多以口服降血糖為主的交互作用，心血管疾病併發關節炎病患大多以解熱鎮痛劑為主的交互作用，而心血管疾病併發消化潰瘍病患大多以制酸劑為主的交互作用。 健保局應當面對且對交互作用之高危險族群作一共同用藥規則，將用藥危機降到最低，讓心血管病患在治療的過程中，得以享有良好的醫療品質，亦不造成藥物濫用、浪費之情形。 / As more medicines are being developed to cope with diseases, most of the users think of the therapeutic effect of the medicine without thinking of the risk that might have associated with drug interactions. The risk of drug interactions could have set chemical reactions thereby causing drug side effects. Although the National Bureau of Controlled Drugs has set a procedure for controlling drug interactions, the issue of validity and efficiency remains an open question. The trend of people contracting chronic diseases is on the rise, one of which is cardiovascular. The attempt of this paper is to observe for the effect of drug interactions if any for the long term usage of the cardiovascular medications under the supervision of the doctors. We adopt data mining techniques to single out the probable variable causes in triggering negative effect of drug interactions and presented them to related medical personnel so that a tightening measure is adopted when administered the medications. The data used in this research comes from the National Health Insurance Research Database. Our research findings have reviewed that cardiovascular disease patients suffering from the complications, such as diabetes, arthritis, peptic ulcer and hear disease are highly related to the drug interactions. Heart disease patients are at risk of cardiac stimultants and diuretics. Diabetic’s patients taking the Sulfonylureas suffer from its interactions. And arthritis patients are at risk of having the side effects of taking Aspirin. Bureau of National Health Insurance should set a standard procedure in monitoring the prescription given by the medical personnel in an effort to reduce the risk of drug interactions and set off the stage of quality medical treatment to keep off the abuse of drug usages.

本文寫作的主要目的，是以求學經驗為主軸了解成骨不全症患者的成長經驗，期望透過對於經驗的注視，檢驗經驗與體系的聲稱之間是否存有落差？落差出現在哪裡？原因為何？又可以由什麼方向對焦並且獲得舒緩？若將玻璃娃娃視為弱勢族群的一份子，則其遭遇與經驗或可呈現臺灣教育體系（以及特殊教育體系）所持的態度及隱含的價值。 首先，研究歸納出成骨不全症患者的共同生活經驗，並發現其在學校場域可能遭遇的問題與困境。由於成骨不全症的低發生率與診斷不易，家庭對於病症的全貌、隨著病症而來的生活與問題、適當的醫療處遇等便容易缺乏認識與問題因應的策略。患者與家庭，往往要依靠自身的經驗與難得的病友資訊交流，慢慢摸索出照護方法與原則。在學校教育方面，成骨不全症患者容易在門檻遭遇拒絕。學校系統在行政上、課程和教學的設計與操作上則傾向被動因應甚至抗拒。升學優惠措施、校園無障礙設施、落實家長參與權與同儕關係的相關經驗也還不盡理想。 於是我們看到體系的聲稱與實際之間的落差。為了尋找困境的解法，本文將個體、家長、學校三者設定為行動者，探究此三者如何受到社會結構化特徵的影響進行結構的邏輯複製；同時，也試著發掘行動者散溢出體系邏輯的行動或觀念。困境的解法就如困境本身一般盤根錯節，透過對結構的觀看，一方面形成體系與個體檢討的基礎，一方面為個體的能動力尋到著力點，藉此尋求改變。所謂的特殊學生或是障礙學生「被製造出」特殊需求，其需求需要也應該被適當地理解並進行滿足，然而經過反覆而深切的思維我們可以發現這些孩子更需要回歸到一般教育宣稱，被當作一個完整的、獨特的、應與所有人擁有相同發展機會的個體看待。唯有超越「相對於一般」的障礙，視他們為完整的、需要被全面瞭解的個體，才可能為這些群體創造合理而有效能的教育。

疾病就診率係針對固定期問內，患有疾病而到全民健康保險特約之各級醫療院所就診之人數予以統計，其分析可供規劃醫療保健服務與修訂全民健保制度之參據，因此應成為經常性之統計工作。本文以適合連續性調查的輪換抽樣設計為原則，運用調查當期及較早期之資訊以建搆複合估計式，並推導該估計式之期望值與變異數，據以此比較與簡單估計式之相對效率。為評估複合估計方法運用於疾病就診率之效益，遂選定某些疾病進行分析，並研究在何種權數組合下，複合估計式能減低變動情形及其相對效率。 關鍵字：疾病就診率、輪換抽樣法、複合估計式、相對效率 / The treatment rate is a statistical analysis of numbers of people who had taken treatment in the fixed period. It can be a reference of planning medical service, revising medical laws and so on. So its analysis is important and should be an occasionally statistical work. In this paper, we adopt rotation sampling method, combine current and earlier information to construct composite estimator, and derive its expected value and variance formula, so as to compare with simple estimator. To evaluate the benefit of adopting composite estimator on the treatment rate, we choose some diseases as objectives, and determine the optimal weighted parameter so that composite estimator can reduce the variance and its effects on the relative efficiency. Key words: treatment rate, rotation sampling, composite estimator, relative efficiency

本研究旨在探討以優勢觀點為基礎之社會工作實施場域中，社會工作者與情感性精神疾病個案助人關係歷程、雙向復元因素與意涵，與不同組織政策與文化對雙向復元之影響，進一步形成實務場域中助人關係與雙向復元之建議與策略。 　　本研究運用質性研究深度訪談法與詮釋現象學觀點，採立意與滾雪球抽樣，使用半結構式訪談大綱與研究者於田野之互動與觀察，再現八名受訪者（五名優勢觀點社工、三位情感性精神疾病個案）之生活經驗，回歸現象本質綜觀與探討。 　　本研究主要發現如下： 一、情感性精神疾病的重新詮釋：社會工作者正視個案憂鬱或躁狂發作而產生生理、心理與社會的排除，將疾病常態化、普同化，並經由多面向的宏觀檢視以打破原本負面框架與預設立場，包含：1.給予自殺的生命解套—給予選擇與自我決定；2.賦予反覆訴說的意義—重新詮釋生命的機會。 二、歷經「隧道」、不斷攀升—助人關係發展之歷程：歸納優勢觀點社會工作者與情感性精神疾病個案發展助人關係之歷程，可分為想像期、建立期、考驗期、合作期與復元期五大階段，形塑「穿越生命隧道之旅」般的助人關係意象，並發現差序格局文化下的助人關係連續體之特色。 三、雙向復元的實質意涵：由個案「置身死地而後生」與社會工作者「從助人意義中回觀自我」的復元經驗中，歸納「雙向復元」之實質意義與內涵，是一種助人關係與個人內、外在情境三者交互作用而成的超越狀態與主體終極目標，影響成因包含個人、人際與環境三大層面和七個項目。 四、組織政策與文化對雙向復元之影響：不同組織政策與文化之對雙向復元具有實質之影響，分析受訪社會工作者所屬的機構內部可發現「考驗」與「支持」的態樣，後者不但創造充分的人際支持與成長環境，更能促進社會工作者的復元、提升個案服務品質與績效、穩定社工流動率。 依據上述研究發現，提出以下建議： 一、情感性精神疾病個案之助人關係策略：1.增強個體權能：擱置並轉化問題、充分尊重與信任、著力優勢與能力、累積成功經驗、創造選擇性。2.善用關係影響：借重家庭的影響力、保持助人關係中的接觸與等待、建立分享與互惠的彈性關係、善用權力落差形成改變。3.導入環境資源：地緣與文化的親近性、開放的會談地點與時間、連結資源與網絡合作。 二、正視雙向復元之目標：回歸自身被忽略的正向經驗，賦予生命及工作狀態之意義，個案或社會工作者復元的同時也使對方進一步昇華和復元，像漣漪效應般能擴及旁人，間接影響家庭與社會系統，啟動整體社會文化的善循環。 三、機構推動優勢觀點模式之建議：瞭解如何有效學習並運用優勢觀點模式、適度激勵並給予充分支持、提供合理的保障與實質誘因。 四、未來研究之建議：瞭解情感性精神疾病之特性並有所應變、具備接近田野之管道並增加研究對象的多元性、學習與受訪對象共創新的復元價值。 / The study aimed to explore the process, elements and meanings of mutual recovery for social workers and the clients with mood disorder, the organizational influences on the mutual recovery, and to propose strategies for mutual recovery for social workers. The methodology adopted in this study was hermeneutic phenomenology. Using purposive/ snowball sampling, the researcher interacted, observed, and in-depth interviewed the participants using a semi-structured interview guide. Though this process, the living experiences of the eight interviewees (five strengths-based social workers and three clients with mood disorder) were represented to reveal the nature of the phenomenon of mutual recovery. The major discoveries of this study include: 1.Re-definition of mood disorder: social workers could recognize the consequence of clients being seriously excluded from the society due to their depressive or manic episode; they helped the clients to normalize their disease, as well as to break their own negative assumptions toward the disorder. The tactics that they used were such as like：a) Providing relief to suicide–personal choice and self-determination, b) giving meaning to repetitive narratives– opportunity to redefine life. 2.“Tunnel” process, continuing to rise, the process of helping relationship: the process of strength-based social workers in developing relationship with clients with mood disorder could be categorized into five stages: imagination stage, development stage, challenge stage, cooperation stage, and recovery stage. Hence, the nature of helping relationship was like “going through the life tunnel” . The characteristics of such a relationship reflected the differential association continuum among Chinese culture. 3.Essential meaning of mutual recovery: For the clients, the recovery experiences were as if being brought back to life. For the social workers, they rediscovered of themselves in the meaning of assisting others. The real meaning and essence of mutual recovery could be summed up as the interaction of within a person, internal and external environment, and in turn further shaping the transcendental force to help acquire the life goal. Moreover, those goal were affected by personal, relational and environmental factors. 4.Organizational influences on mutual recovery: by analyzing the effects of organizational policies and cultures on mutual recovery, we found that “supportive surrounding” creates an environment which was better for the development of interpersonal relationship than an “challenging surrounding”. The former could help facilitate the recovery and performance of social workers, as well as decrease the turnover rate of social workers. Based on these findings, we propose: 1.Strategy for forming the helping relationship with clients with mood disorder: 1) empowering individual: letting go and transform problems into respect, trust, and focusing on advantages, capabilities, and successful experiences to create options. 2) Better utilizing the effects of interpersonal relationship: by using the influences from family, maintaining the contacts in interpersonal relationship, building mutual and flexible relationship, and using the power differentiation to facilitate changes. 3) Introducing resources from surrounding: an open location and timing that connect the clients/social workers with their surrounding and culture, and enabling the linkage of resources and networks. 2.Emphasizing mutual recovery: stressing the positive experiences that were over sighted previously and finding meaning to life and work status. The recovery of the clients and social workers will further influence their families and the whole social system, and create a benign cycle accordingly. 3.Suggestions for organizations in promoting the strength-based model: to learn the effective ways of learning and applying strengths-based models, giving adequate encouragement and support, and providing reasonable assurance and incentives. 4.Recommendations for future research: should understand the characteristics of mood disorder and know how to handle the situations, to expand the sources of participants, and to have the intention of creating the new meaning of recovery with participants.

空間群聚分析應用於流行病學已行之有年，但國內這方面的研究仍較缺乏，尤其在找出哪些地區有較高疾病發生率的群聚偵測。本文針對台灣鄉鎮市資料的特性，提出一套合適的群聚檢測方法，這個方法使用兩階段的電腦模擬，實證上更容易使用；這個方法除了可找出最大顯著群聚外，也能夠偵測出多個群聚的分佈。本文使用電腦模擬比較本文的方法與目前使用較為廣泛的方法(包括Kulldorff(1995)的spatial scan statistic和Tango(2005)的flexible scan statistic)，以型一誤差、型二誤差及錯誤率三種標準衡量方法的優劣。最後套用台灣癌症死亡率與健保就診次數資料，探討台灣癌症空間群聚與就診情形的變化。 / Spatial cluster analyses have applied in epidemiology for many years. In this topic there still are few researches in Taiwan, especially in detecting the areas which have higher disease intensity. In this paper, we proposed a new cluster detection method which is aimed at Taiwan counties’ data. This method which uses two-stage computer simulation procedures is useful in practice. This method can find the most likely cluster. Besides, it can find multiple clusters. We use computer simulations to compare our method with others (Kulldorff’s spatial scan statistic& Tango’s flexible scan statistic). Type-I error, Type-II error and error rate are criterions of measurement. At last, we use Taiwan cancer mortality data and all the people health insurance data to discuss Taiwan cancer spatial clusters and the change of diagnoses.

本文透過國衛院所發行的精神疾病住院病患歸人檔，串連用藥、醫師、承保人，以及醫院檔，對於台灣抗憂鬱劑的使用情形做描述。發現抗憂鬱劑的使用次數是逐年增加的，且新藥成長快速，同時舊藥的使用卻是逐年下降，而國內製藥的使用也是逐年下降。總費用的部份，即使經過消費者物價指數的平減，仍是逐年上升；另外，醫院的特性，特別是醫院層級與醫院權屬別，對於抗憂鬱劑使用的種類與國內製藥使用的情形，都扮演重要的角色。

本文應用攝護腺癌症蛋白質資料庫，是經由表面強化雷射解吸電離飛行質譜技術的血清蛋白質強度資料，藉此資料判斷受測者是否罹患癌症。此資料庫之受測者包含正常、良腫、癌初和癌末四種類別，其中包括兩筆資料，一筆為包含約48000個區間資料(變數)之原始資料，另一筆為經由人工變數篩選後，僅剩餘779區間資料(變數)之人工處理資料，此兩筆皆為高維度資料，皆約有650個觀察值。高維度資料因變數過多，除了分析不易外，亦造成運算時間較長。故本研究目的即探討在有效的維度縮減方式下，找出最小化分錯率的方法。 本研究先比較分類方法－支持向量機、類神經網路和分類迴歸樹之優劣，再將較優的分類方法：支持向量機和類神經網路，應用於維度縮減資料之分類。本研究採用之維度縮減方法，包含離散小波分析、主成份分析和主成份分析網路。根據分析結果，離散小波分析和主成份分析表現較佳，而主成份分析網路差強人意。 本研究除探討以上維度縮減方法對此病例資料庫分類之成效外，亦結合線性維度縮減－主成份分析，非線性維度縮減－主成份分析網路，希望能藉重疊法再改善僅做單一維度縮減方法之病例篩檢分錯率，根據分析結果，重疊法對原始資料改善效果不明顯，但對人工處理資料卻有明顯的改善效果。 / In this paper, we study the serum protein data set of prostate cancer, which acquired by Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) technique. The data set, with four populations of prostate cancer patients, includes both raw data and preprocessed data. There are around 48000 variables in raw data and 779 variables in preprocessed data. The sample size of each data is around 650. Because of the high dimensionality, this data set provokes higher level of difficulty and computation time. Therefore, the goal of this study is to search efficient dimension reduction methods. We first compare three classification methods: support vector machine, artificial neural network, and classification and regression tree. And, we use discrete wavelet transform, principal component analysis and principal component analysis networks to reduce the data dimension. Then, we discuss the dimension reduction methods and propose overlap method that combines the linear dimension reduction method－principal component analysis, and the nonlinear dimension reduction method－principal component analysis networks to improve the classification result. We find that the improvement of overlap method is significant in the preprocessed data, but not significant in the raw data.