本文關(guān)鍵詞：基于空間模型的小地域疾病制圖研究　出處：《武漢大學》2014年博士論文　論文類型：學位論文

  更多相關(guān)文章： 小地域疾病制圖 空間統(tǒng)計 貝葉斯統(tǒng)計 核密度估計 時空建模

【摘要】：隨著全球經(jīng)濟一體化、全球氣候與環(huán)境的變化加劇和人類改造自然能力的不斷提升,人類健康已成為全世界普遍關(guān)注的熱點問題,特別是近三十年,我國人口的迅速增長、經(jīng)濟的迅猛發(fā)展和環(huán)境生態(tài)質(zhì)量的不斷惡化等,人們對公共衛(wèi)生和各類疾病的關(guān)注度也在持續(xù)增強。疾病制圖是空間流行病學的重要研究領(lǐng)域,可對復雜疾病信息進行快速的地理可視化表達,可以識別在表格中難以確定的分布模式。小地域疾病制圖是近年的研究熱點之一,通過運用空間統(tǒng)計和地理計算方法,識別某種疾病在小尺度范圍內(nèi)的高風險區(qū)和爆發(fā)源。本文在空間分析和小地域疾病制圖研究等關(guān)鍵問題的基礎(chǔ)上,結(jié)合疾病數(shù)據(jù)的不同類型,研究了基于空間模型的小地域疾病制圖的基本框架,針對小地域疾病制圖所遇到的問題采用相應的模型方法,并輔以實例來說明。本文主要以理論和實踐兩個層面展開研究。 (一)理論研究 本文從空間自相關(guān)、邊界問題、空間關(guān)系概念化和統(tǒng)計顯著性檢驗等地理數(shù)據(jù)空間效應的角度上,詳細闡述了空間分析面臨的難點和關(guān)鍵問題,針對不同疾病數(shù)據(jù),總結(jié)了現(xiàn)有疾病聚類分析的方法,探討了傳統(tǒng)統(tǒng)計制圖的缺陷,指出由于隨機變化,小地域疾病制圖往往會導致地圖的額外變異,并且傳統(tǒng)疾病制圖沒有考慮空間自相關(guān)、隨機變化和視覺偏差等因素,難以準確描述空間小概率疾病事件的空間變化,需要引入空間模型移除疾病地圖的隨機部分,并運用三種空間模型解決小地域疾病制圖存在的問題。核密度估計用于疾病點數(shù)據(jù),充分考慮了點要素的空間依賴性特征,生成疾病點數(shù)據(jù)的平滑地圖。層次貝葉斯模型用于區(qū)域數(shù)據(jù),該模型考慮相對風險等制圖指標的空間效應,通過引入空間統(tǒng)計單元的空間關(guān)系和概率分布,將數(shù)據(jù)的不確定性和空間自相關(guān)關(guān)系包含在模型之中。貝葉斯時空模型用于時空數(shù)據(jù),將疾病相對風險的空間趨勢、時間趨勢和時空交互進行統(tǒng)一建模,并可探測疾病風險的熱點和冷點及其時空變化趨勢。 (二)實際應用 本文使用核密度估計對深圳市2011年高血壓患者的空間分布進行了探測,采用數(shù)字深圳空間基礎(chǔ)信息平臺的地址匹配服務完成疾病病例的空間化,克服了傳統(tǒng)“人工打點”的缺陷。分析和討論了不同搜索半徑對核密度計算過程的影響,并采用局部Moran's I計算每個街道內(nèi)核密度值均值的局部自相關(guān)指數(shù),嘗試對核密度估計的性能進行評價。實驗結(jié)果證明深圳市2011年高血壓患者存在顯著的空間分布模式,桂園、華強北等街道為高血壓的高發(fā)區(qū)域。針對病例地址信息缺失和定位精度等問題,本文采用層次貝葉斯模型分析深圳市2011年高血壓相對風險的空間變化,并討論了不同結(jié)構(gòu)的空間權(quán)重矩陣對模型性能的影響,研究成果有助于深圳市公共衛(wèi)生部門對高血壓患者的防控與管理。 基于深圳市2010年-2012年肝癌發(fā)病數(shù)據(jù),針對空間統(tǒng)計處理時空問題的困難,本文運用貝葉斯時空模型研究肝癌相對風險的時空變化,采用兩步分類過程識別對風險的熱點和冷點及其時空變化趨勢,討論了不同空間鄰域類型對模型性能的影響,使用時空掃描統(tǒng)計探測肝癌患者的時空聚類,研究結(jié)果表明三年間深圳市肝癌風險存在明顯的東-西劃分的分布格局和顯著的時空變化趨勢,該信息有助于深圳市公共衛(wèi)生服務和肝癌病因?qū)W研究,并可用于其他領(lǐng)域小概率事件的時空建模。 論文的最后,本文根據(jù)研究過程中所遇到的問題,對整個研究工作進行總結(jié)并提出今后研究的重點和方向。
[Abstract]:With the global economic integration, global climate and environment change and continuously improve the ability of the transformation of human nature, human health has become a hot issue all over the world, especially in the past thirty years, the rapid growth of China's population, the rapid development of economy and ecological environment quality worsening, people continue to enhance the public health and various diseases. Attention is also in disease mapping is an important research field of spatial epidemiology, expression of geographic visualization quickly on the information of complex diseases, can be distributed in the form of pattern recognition is uncertain. Small regional disease mapping is one of the research hotspot in recent years, through the use of spatial statistics and geographical calculation method. Identification of a disease in high-risk areas in a small scale and the source of the outbreak. The key issues in spatial analysis and mapping of the small regional disease group Based on the combination of different types of disease data, research the basic frame of the space model of small regional disease mapping based on the corresponding method used for small regional disease mapping problems, and illustrate by examples. This paper mainly focuses on two aspects of theory and practice.
(1) theoretical study
This paper from the spatial autocorrelation, boundary problem, spatial relationship of conceptual and statistical significance test geographical data such as the spatial effect angle, elaborated the key problems and difficulties facing the spatial analysis, according to different disease data, summarizes the method of clustering analysis of existing diseases, discusses the defects of traditional statistical mapping, pointed out that because of random variation, small regional disease mapping often leads to additional variation map, and the traditional disease mapping without considering spatial autocorrelation, random changes and visual bias and other factors, it is difficult to describe the spatial variation of space small probability of disease events, needs to introduce random space model remove disease map, and three space model by solving the existence of small regional disease mapping problem. Kernel density estimation for disease data, considering the dependence of the feature elements of the space, generating disease Smooth map data. Bayesian hierarchical model for the regional data, the model considering the spatial effect of relative risk mapping index, the spatial relation and probability distribution into spatial statistical units, data uncertainty and spatial autocorrelation relations are included in the model. The model for Bayesian spatio-temporal temporal data, spatial trend of relative disease the risk, time trend and temporal interaction of unified modeling and detection of disease risk, hot and cold spots and its change tendency.
(two) practical application
The use of hypertension patients in Shenzhen city in 2011 the spatial distribution of the detection space using kernel density estimation, digital Shenzhen space information platform, service address complete disease cases, overcome the defect of the traditional artificial management ". And discussed different search radius effects on the calculation process of kernel density, local self the correlation index and the local Moran's I kernel density average value calculated for each street, to try to evaluate the performance of kernel density estimation. Experimental results show that there is an obvious spatial pattern of Shenzhen city in 2011, hypertension garden, street Huaqiang North high risk area for hypertension. Aiming at the problem of missing cases address information and the positioning precision in this paper, using a hierarchical Bayesian model analysis of spatial variation in Shenzhen city in 2011, the relative risk of hypertension, and discuss the different structure of space The impact of the weight matrix on the performance of the model is helpful to the prevention and control of the hypertension patients in the public health department of Shenzhen.
2010 -2012 in Shenzhen city based on the data of liver cancer, according to the spatial statistics processing of spatio-temporal problem difficult, using the spatial and temporal variation of liver cancer Bias space-time model of relative risk, the focus of two step classification process in risk identification and cold point and its variation with time and space, the effects of different types of spatial neighborhood model performance is discussed, the use of space and time scan statistics detection temporal clustering of HCC patients, the results showed that there were significant East West Division in Shenzhen during three years the risk of liver cancer the distribution pattern and the temporal and spatial variation of significant trends, the information service and contribute to the study of etiology of public health in Shenzhen, and can be used in other areas of spatio-temporal modeling of small probability events.
At the end of the paper, this paper summarizes the whole research work and puts forward the focus and direction of the future research according to the problems encountered in the study.

【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R188;P208

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