發(fā)布時間：2017-12-28 00:11

  本文關(guān)鍵詞：基于時空聚集面板模型的肺結(jié)核病高危區(qū)域探測及影響因素研究　出處：《山西醫(yī)科大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 肺結(jié)核病 空間自相關(guān) 時空掃描統(tǒng)計 空間截面回歸模型 空間面板數(shù)據(jù)模型

【摘要】：目的:針對疾病發(fā)病水平的監(jiān)測數(shù)據(jù)存在時間、空間自相關(guān)性和空間異質(zhì)性的特點,采用時空統(tǒng)計分析方法和空間計量經(jīng)濟模型,對青海省肺結(jié)核病監(jiān)測數(shù)據(jù)和地區(qū)主要社會經(jīng)濟指標(biāo)及氣象因子數(shù)據(jù),在生態(tài)學(xué)層面開展肺結(jié)核病系統(tǒng)研究,準(zhǔn)確探測發(fā)病高危區(qū)域和定量分析影響發(fā)病率的相關(guān)社會環(huán)境因素,并借助氣象因子的變化,對發(fā)病率進行合理預(yù)測。通過本研究探討空間地理信息系統(tǒng)、時空聚集性分析方法和空間計量經(jīng)濟模型在具有時空屬性的傳染病監(jiān)測數(shù)據(jù)挖掘中的應(yīng)用價值,為類似研究提供分析思路和方法學(xué)參考,也為政府決策提供科學(xué)依據(jù)。方法:通過中國疾病預(yù)防控制信息系統(tǒng)收集青海省肺結(jié)核病發(fā)病資料,《青海省統(tǒng)計年鑒》收集青海省人口學(xué)資料和相關(guān)社會因素及氣象因素資料,開展如下研究:1、采用流行病學(xué)“三間”分布描述、集中度法、圓形分布法、季節(jié)指數(shù)法和三維趨勢分析等方法對2009-2013年青海省肺結(jié)核病流行病學(xué)特征進行分析。2、針對傳染病監(jiān)測數(shù)據(jù)時空不獨立性,采用Moran’s I和Getis-Ord G空間自相關(guān)分析以及Sa TScan時空掃描分析對2009-2013年青海省縣級水平肺結(jié)核病空間、時間以及時空聚集特征進行分析,對發(fā)病高危聚集區(qū)域及范圍進行準(zhǔn)確探測,并評價其風(fēng)險強度。3、針對橫截面數(shù)據(jù)空間分布非獨立,采用Moran’s I空間自相關(guān)分析和空間截面回歸模型,對2011年和2013年青海省各縣級行政區(qū)域肺結(jié)核病年發(fā)病率與政府醫(yī)療衛(wèi)生支出(千元/人)、教育支出(千元/人)、醫(yī)療機構(gòu)床位數(shù)(張/千人)、醫(yī)療機構(gòu)人員配備情況(人/千人)、農(nóng)村居民人均純收入(千元)以及人均GDP(萬元)等6項社會指標(biāo)關(guān)系進行雙變量空間自相關(guān)分析和回歸分析,研究社會因素的空間聚集特征,以及在扣除鄰近地區(qū)發(fā)病率的影響后,定量探討影響肺結(jié)核病年發(fā)病率的社會因素。4、由于肺結(jié)核病發(fā)病具有一定的季節(jié)特征,氣象因素和前期發(fā)病率可能對本地區(qū)發(fā)病率的影響存在時間滯后性,以及鄰近地區(qū)間發(fā)病率的相互影響,以2009-2013年青海省各市(州)肺結(jié)核病月發(fā)病率數(shù)據(jù)為應(yīng)變量,同期到6個月滯后間隔的月平均氣溫(°C)、降水量(mm)、日照總時數(shù)(hours)、平均風(fēng)速(m/s)和發(fā)病率為自變量,進行不同滯后間隔的面板數(shù)據(jù)模型回歸分析,探討氣象因素對發(fā)病率影響的最佳滯后期;利用F檢驗、Hausman檢驗、誤差項Moran’s I檢驗以及拉格朗日乘數(shù)(LM)檢驗確定最優(yōu)分析模型,定量分析影響肺結(jié)核病月發(fā)病率的氣象因素。5、利用上述氣象因素與發(fā)病率的最佳滯后期空間面板數(shù)據(jù)模型進行發(fā)病率預(yù)測時,由于自變量中包含著鄰近地區(qū)同期發(fā)病率,故嘗試采用專家建模器對預(yù)測時段各地區(qū)發(fā)病率進行預(yù)估,再利用空間面板數(shù)據(jù)模型對各地區(qū)發(fā)病率進行預(yù)測,評價預(yù)測精度,比較專家建模器聯(lián)合空間面板數(shù)據(jù)模型的預(yù)測效果;探討聯(lián)合模型進行發(fā)病率短期預(yù)測的可行性和可靠性。結(jié)果:1、青海省肺結(jié)核病年均報告發(fā)病率為98.26/10萬,明顯高于全國平均水平,且近年有略微上升趨勢;中老年人群發(fā)病率最高,其次為青壯年;男性高于女性,新發(fā)病例以農(nóng)牧民為主;具有明顯的周期性和微弱的季節(jié)性,發(fā)病高峰主要集中在3-5月;南北方向呈北低南高的弧形變化趨勢,東西方向呈明顯的倒“U”型。2、全局空間自相關(guān)Moran’s I和General G值均大于期望值,取值范圍分別為0.398-0.581和0.029-0.034,表明肺結(jié)核病年發(fā)病率地區(qū)分布存在明顯的高發(fā)地區(qū)聚集傾向;Sa TScan時空掃描分析結(jié)果顯示青海省肺結(jié)核病在時間、空間以及時空上存在明顯的高發(fā)病風(fēng)險聚集性,時空一類聚集區(qū)域位于青海省西南部,中心位置在囊謙縣(東經(jīng)96.12°,北緯32.17°),覆蓋囊謙縣、玉樹市和雜多縣等8個縣(市),聚集半徑為421.00Km,發(fā)病高峰期為2012年1月到2013年6月,相對危險度(RR)為4.58;疊加分析顯示,以RR2作為判斷標(biāo)準(zhǔn)比較合理,高危聚集區(qū)域主要集中在青海省西南部的玉樹和果洛州所轄12個縣(市)。3、雙變量Moran’s I空間自相關(guān)分析顯示2011年醫(yī)療機構(gòu)床位數(shù)、醫(yī)療機構(gòu)人員配備情況、農(nóng)村居民人均純收入和人均GDP共4項社會指標(biāo)與肺結(jié)核病年發(fā)病率間均具有統(tǒng)計學(xué)意義(P0.05),提示以上社會因素可能影響地區(qū)發(fā)病水平;以發(fā)病率對數(shù)值建立的普通最小二乘回歸顯示回歸殘差不獨立(Moran’s I=0.16,P0.05),而依據(jù)LM檢驗,空間滯后模型為最佳模型,該模型顯示:空間自相關(guān)系數(shù)r=0.4041,說明相鄰區(qū)域的發(fā)病率存在空間外溢現(xiàn)象(空間自相關(guān)性),即當(dāng)其它影響因素固定不變時,相鄰地區(qū)肺結(jié)核病年發(fā)病率每增加9倍,本地區(qū)年發(fā)病率將增加1.54倍;在扣除了發(fā)病率的空間自相關(guān)性后,農(nóng)村居民人均純收入是影響肺結(jié)核病年發(fā)病率的主要社會因素,b=-0.0657,即農(nóng)村居民人均純收入每增加1千元,本地區(qū)肺結(jié)核病年發(fā)病率將降低14%;空間截面回歸模型與普通最小二乘回歸模型相比,回歸系數(shù)絕對值有所下降(-0.0657 vs-0.0883),說明充分考慮了發(fā)病率的空間自相關(guān)性后,估計結(jié)果更為合理,而傳統(tǒng)回歸模型沒有考慮空間自相關(guān)性,夸大了社會因素的作用。2013年分析結(jié)果與2011年結(jié)果一致。4、面板數(shù)據(jù)模型分析結(jié)果顯示氣象因素對發(fā)病率的影響存在3個月的滯后期;發(fā)病率對數(shù)轉(zhuǎn)換構(gòu)建的固定效應(yīng)模型(F=193.90,H=10.41,P0.05)顯示回歸殘差不獨立(Moran’s I=0.20,P0.05),而依據(jù)LM檢驗,空間滯后固定效應(yīng)面板數(shù)據(jù)模型為最佳模型,該模型顯示:不同地區(qū)截距項不同,體現(xiàn)了發(fā)病率的空間異質(zhì)性;空間自相關(guān)系數(shù)r=0.3017,說明相鄰區(qū)域的月發(fā)病率存在空間外溢現(xiàn)象,即相鄰地區(qū)肺結(jié)核病月發(fā)病率每增加9倍,本地區(qū)月發(fā)病率將增加1倍;相比氣象因素而言,當(dāng)前發(fā)病率對滯后3個月的發(fā)病率影響更明顯;在扣除了發(fā)病率的空間自相關(guān)性、空間異質(zhì)性以及前期發(fā)病率的影響后,平均氣溫和降水量是影響滯后3個月發(fā)病率的主要氣象因素;當(dāng)前發(fā)病率每增加9倍,滯后3個月的發(fā)病率將增加36%,平均氣溫每升高10°C,滯后3個月的發(fā)病率將降低9%,降水量每增加2cm,滯后3個月的發(fā)病率將降低3%;與空間截面回歸模型類似,空間面板數(shù)據(jù)模型與傳統(tǒng)回歸模型相比,估計結(jié)果也更為合理。5、2013年10-12月各地區(qū)發(fā)病率時間序列專家建模器預(yù)測相對誤差為0.90%-136.14%,平均相對誤差為28.99%;專家建模器聯(lián)合空間面板數(shù)據(jù)模型預(yù)測相對誤差為0.17%-94.20%,平均相對誤差為21.09%;2014年1-3月平均相對誤差分別為26.60%和19.79%;專家建模器聯(lián)合空間面板數(shù)據(jù)模型的預(yù)測精度明顯提高。結(jié)論:本研究首次采用時空統(tǒng)計分析方法和空間計量經(jīng)濟模型,從生態(tài)學(xué)角度對青海省肺結(jié)核病監(jiān)測數(shù)據(jù)進行了詳細探討,得出如下結(jié)論:1、針對傳染病監(jiān)測數(shù)據(jù)時空非獨立性特點,空間自相關(guān)分析和時空掃描分析是疾病時空聚集特征和高危區(qū)域探測的理想分析方法,準(zhǔn)確探測出了青海省肺結(jié)核病高危聚集區(qū)域主要集中在該省西南部,最大危險區(qū)以玉樹市為中心,r=259Km,覆蓋玉樹、囊謙、稱多、雜多、瑪多和曲麻萊等6縣(市),RR=3.77。2、考慮到發(fā)病率的時空屬性,空間截面回歸模型和空間面板數(shù)據(jù)模型是生態(tài)學(xué)影響因素研究的理想分析模型,在公共衛(wèi)生領(lǐng)域具有廣泛應(yīng)用價值�？鄢l(fā)病率的時空影響后,農(nóng)村居民人均純收入、氣溫以及降水量是影響地區(qū)肺結(jié)核病發(fā)病率的主要社會環(huán)境因素。3、相比單純時間序列專家建模器預(yù)測,專家建模器聯(lián)合空間面板數(shù)據(jù)模型的預(yù)測策略,在考慮了鄰近地區(qū)間發(fā)病率的相互影響以及氣象因子的作用后,預(yù)測精度明顯提高,可應(yīng)用到實際工作中發(fā)揮預(yù)警作用。通過本研究既為青海省肺結(jié)核病防控措施的合理制定提供了理論依據(jù),也為具有時空屬性特征數(shù)據(jù)的研究提供了分析思路和方法學(xué)參考。
[Abstract]:Objective: the temporal and spatial characteristics of autocorrelation and spatial heterogeneity of the disease incidence rate of the monitoring data, the analysis method and the spatial econometric model of temporal statistics, Qinghai province tuberculosis monitoring data and area of main social and economic indicators and meteorological data, in the study of pulmonary tuberculosis ecology system to carry out accurate detection of high risk level. Regional and quantitative analysis of the influence of the social environment related morbidity factors, and with the change of meteorological factors, a reasonable forecast of incidence. Through this study, geographic information system, spatial aggregation value analysis method and spatial econometric model in infectious disease monitoring data with spatial attribute mining, provide analysis ideas and methodology reference for similar research, but also provide a scientific basis for government decision-making. Methods: the Chinese information system for Disease Control and prevention of tuberculosis incidence data collected in Qinghai Province, "Qinghai Province Statistical Yearbook" data collection factors and meteorological factors in Qinghai province and related social demographic data, carry out the research as follows: 1, "three" the epidemiological distribution description, degree method, the method of circular distribution, seasonal index method and focus on three-dimensional trend analysis method to analyze the epidemiological characteristics of pulmonary tuberculosis in Qinghai province 2009-2013. 2, according to the monitoring data of infectious diseases is not independent of time and space, using Moran s I and Getis-Ord G spatial autocorrelation analysis and Sa TScan space-time scan analysis to 2009-2013 at county level in Qinghai province tuberculosis space, time and space aggregation characteristics were analyzed, the accurate detection and scope of the gathering area of high risk, and to evaluate its risk strength. 3, according to the cross-sectional data of spatial distribution of non independent, using Moran s I spatial regression model and spatial autocorrelation analysis section, on 2011 and 2013 in Qinghai Province, county-level administrative region tuberculosis incidence and government health expenditure (1000 yuan / person), education expenditure (1000 yuan / person), medical institutions the number of beds (A / 1000), medical institutions (staffing / 1000), per capita net income of rural residents (thousand dollars), per capita GDP (million) 6 social indicators between the analysis and autocorrelation analysis and bivariate regression, accumulation characteristics of social factors in space, and net affected the incidence of adjacent areas, quantitative discussion on social factors influencing the annual incidence of pulmonary tuberculosis. 4, due to the occurrence of pulmonary tuberculosis has certain seasonal characteristics, meteorological factors and the incidence may be time effect on local incidence of lag, and adjacent areas of incidence between the mutual influence, in the cities of Qinghai Province during 2009-2013 (state) pulmonary tuberculosis incidence data for the month should be variable, the average monthly temperature over the same period to 6 months lag interval (C) and precipitation (mm), the total sunshine hours (hours), mean velocity (m/s) and the incidence rate as independent variables, regression analysis of panel data models with different lag intervals, to investigate the meteorological factors on the optimal lag influence the incidence rate; using F test Hausman test, the error Moran s I test and the Lagrange multiplier (LM) test to determine the optimal analysis model, quantitative analysis of the influence of meteorological factors on the incidence of pulmonary tuberculosis. 5, the incidence of the weather prediction using best lag factors and the incidence of spatial panel data model, the independent variables included in the adjacent regions in the same period of incidence, so try to use to predict the onset of each expert modeling area prediction rate with time, spatial panel data model to forecast the incidence of various regions. To evaluate the prediction accuracy, the prediction effect of modeling device combined with spatial panel data model comparison expert; to study the model of the incidence of the reliability and feasibility of the short-term forecast. Results: 1, Qinghai Province, the annual report of tuberculosis incidence rate of 98.26/10 million, significantly higher than the national average, and in recent years, there is a slight upward trend; the highest incidence in the elderly population, followed by young adults; men and women, new cases to farmers and herdsmen; with obvious periodicity and weak seasonal the peak incidence, mainly concentrated in 3-5 months; the north-south direction curved trend of North South High low, east-west direction is inverted "U" type obviously. 2, the global spatial autocorrelation of Moran 's I and General G values are greater than the expected value, range were 0.398-0.581 and 0.029-0.034, showed that the annual incidence of tuberculosis area distribution is significant in areas of high Sa TScan aggregation tendency; space-time scan analysis showed that Qinghai province tuberculosis in time, space and time are high incidence the risk of significant aggregation, a kind of space gathering area in Qinghai province is located in the southwest, central location in Nangqian county (96.12 degrees east longitude, latitude 32.17 degrees), 8 counties covered in Nangqian, Zaduo County, and Yushu city (city), gathering radius is 421.00Km, the peak incidence from January 2012 to June 2013, the relative risk degree (RR) is 4.58; superposition analysis showed that using RR2 as the judgment standard is reasonable, high accumulation area mainly concentrated in the southwest of Qinghai Province, Yushu and Guoluo Prefecture under the jurisdiction of 12 counties (city). 3, double variable Moran 's I spatial autocorrelation analysis showed that in 2011 the medical institutions beds, personnel, per capita net income of rural residents and the per capita GDP total of 4 social indicators and the incidence of pulmonary tuberculosis among years were statistically significant (P0.05), suggesting that social factors may influence the incidence level in area; the incidence of ordinary least squares regression to establish numerical display regression residuals are not independent (Moran' s I=0.16, P0.05), according to the LM test, the spatial lag model is the best model, the model shows that the spatial autocorrelation coefficient R =0.4041, shows that the incidence rate of adjacent area spatial spillover phenomenon (spatial autocorrelation), i.e. when other factors are fixed, the adjacent areas of TB incidence per year
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：R181.3;R521
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本文編號：1343768