季節性/新型流感和腸病毒在全國與台北市的流行病學、考量假期效應的時間序列統計模式、建置傳染病症候群大眾偵測系統及其在臺北市的推廣之、應用評估與大流行之整備

Epidemiology Study and Time Series Analysis of Seasonal/Pandemic Influenza and Enterovirus in Taiwan and Taipei City to Establish a Better Community-based Syndromic Surveillance System for Emerging Infectious Diseases and Pandemic Influenza Preparedness - A Pilot study in Taipei.

張嘉琨(Chia-Kun Chang)

指導教授 ： 金傳春

國立臺灣大學/公共衛生學院/流行病學與預防醫學研究所/碩士(2012年)

https://doi.org/10.6342/NTU.2012.03387

摘要

鑒於2009年新型流感H1N1大流行對台灣健康與經濟造成重大影響，面對新興傳染病的衝擊，台北市位處政、經、交通要衝，更需完善的防疫及偵測準備。惜傳統的傳染病監測系統無法即時反映社區間的傳染病流行趨勢與特徵，又缺乏未就醫民眾的疫訊，加上特殊節日與社交行為利於傳染病的傳播，為解決此一傳統監測的盲點。因此本研究首度嘗試在臺北市社區建置一市民傳染病症狀通報系統，以衛生資訊結合傳染流行病學特徵、症候群偵測與統計模式，並與病毒偵測資料庫進行與交叉比較分析，明瞭此一社區主動偵測系統未來在公共衛生應用的潛力與應考量處，期達社區中「早期」發掘潛藏的傳染病流行警訊。

本研究主要目的有五：（一）利用全國大型病患資料庫百萬抽樣檔、例行性病毒偵測及急診症候群的「類流感」病例，進行流行病學的因子及時空分佈趨勢分析；（二）考量中西方傳統節日人群聚集對流感時序資料的影響，建立更妥當的統計模型以偵測突發流行(outbreak)；（三）開發網路通報的「大眾傳染病症候群通報系統」（www.eid.url.tw）與簡易化症候群群組，並在台北市擇區推廣評估；（四）分析自2011年8月至2012年3月由大眾傳染病症候群偵測系統所得的疫訊，探討此一新系統之應用潛力與未來改進重點；及（五）串聯臺北市與全國性數據分析，對中央、地方衛生與醫院行政首長的未來傳染病偵測提出剴切建議。

本研究方法包括三大部分：（一）在疾病流行相關因子及時空分析上，先以全國病毒偵測資料庫分析2007-09三年流行的流感病毒型別，再以全國大型資料庫，將病患分成「急診」和「急診加門診」兩群，分析不同病毒流行的相關流行病學因子（年齡、性別）及症狀。此外，再以時間序列分析法的自迴歸移動平均模型（Autoregressive Integrated Moving Average Model, ARIMA），分析此三年流感流行趨勢和特殊節日之影響；（二）利用台北市的人口統計數據及類流感病例，得臺北市此三年各年、各區的類流感侵襲率，再以地理資訊系統（geographic information system, GIS）分析流行季的時空流行趨勢；再進一步用捉放法，配合自病患資料庫擷取台北市各年/區由「地區診所」通報的類流感病例，估計小區域內的「尋醫人口」(health seeking population, HSP)，作為往後大眾傳染病偵測系統估計分母之用；（三）在系統建置上，以網路與五種程式設計技術（Joomla架構、HTML、CSS、PHP和MySQL），開發社區導向的「大眾傳染病症候群偵測系統」，稱為「防疫先鋒」（Epi-Intelligence Frontline for detecting EID, EIF-ID）；另考量不同教育程度而選定台北市四區（萬華、中正、大安、信義），自2011年5月開始宣導防疫先鋒，並自該年8月正式上路，收集數據至次年3月。

結果分析發現2007-2009三年全國主要流感病毒型別各為H3N2、H1N1與新型H1N1，2007和2008年的「急診加門診」類流感病例年齡分布主在5-9 (27.4%, 21.0%)、10-19 (22.6%, 25.0%)和0-4 (21.6%, 21.5%)歲，與2009年集中於10-19 (42.7%)，5-9 (22.7%)和20-29 (10.4%)歲，有明顯差異（p<0.001）；2009年「急診加門診」和「急診」的類流感病例年齡眾數各為9歲和12歲，高於2007和2008年H3N2 與 H1N1季節性流感流行時的5歲和4歲。而時間序列分析顯示自迴歸移動平均模型放入節日後，每日「急診」的類流感病例確實在農曆春節增加(平均上升87例，p<0.001)，但「急診加門診」病例數下滑(平均下降9000例, p<0.001)，清明掃墓節也有類似趨勢(急診：平均升60例，急診加門診：平均降4881例，p<0.001) ；國外重要節日如感恩節、聖誕節、西方新年卻沒有顯示顯著影響。

資料庫分析臺北市的流感，發現季節性或新型流感在臺北市各區的三年侵襲率分佈類似，北投、中山、大同、萬華和中正五區的侵襲率高於臺北市總平均（2007年：6.4, 6.5, 8.8, 5.7, 6.8 vs. 5.6；2008：5.6, 5.6, 7.2, 5.1, 5.8 vs. 4.8；2009：8.2, 8.7, 11.8, 7.0, 8.3 vs. 6.9），大同區更高於平均的1.5-2倍。流感流行季的時空流行趨勢分析，發現在流行季始，北部北投區及西部大同、中正區有較高的侵襲率，繼而往東蔓延至中山、大安、信義、松山等區，爾後再匯聚回中西部。

防疫先鋒的台北市通報率最高（73.4%，160/218），且擇區推廣後，於2011年8月至2012年3月間的症候群偵測，發現在臺北市社區73位類流感病例中，以發燒(35.8%)、咳嗽(70.6%)、鼻塞(31.4%)和流鼻水(30.3%)為主症狀，而通報族群以學生(59.7%)、教職員(8.7%)和服務業(8.1%)較多，且在推廣區與控制區的類流感侵襲率各為54.2%（32/59）和8.5%（5/59）。進一步以使用者流量評量，發現和谷歌流感趨勢（Google Flu Trend）在2011年流感季及2012年初腸病毒疫情期間各達到0.6（5天間隔）和0.83（當日）的相關性，在2011年尾流感季時，且較谷歌流感趨勢早5天偵測到波峰。

綜言之，此一大眾傳染病症候群偵測系統可與傳統監測系統互補，並將偵測概念自醫療體系拓展至社區，利用網路且易近性高的症狀群組，能額外獲得未求醫族群的流行病學資訊，有助於進一步探討傳染病在不同社群環境中傳播的異同，提供地方公共衛生單位進行主動疫情調查的參考依據，彌補原傳染病防疫網在社區中的缺漏。未來研究方向應著重於此一系統於城鄉的不同偵測性，於台灣的中、南、東三區各擇地進行建置與持續偵測，且統計預測模型也應加入環境、氣候及人群動態因子（如工作通勤率），以得更精準的疫情動態預測。此外，更需拓增參與者，並持續評估與改進系統；未來要利用更新的疫訊評估「防疫先鋒」的偵測時效性、敏感度，以提升此一偵測系統的穩定度。大型醫療資料庫提供醫療層級變項，使用生態學捉放法排除大型醫療層級機構，應能更準確估計個小區域的實際尋醫人口，協助防疫先鋒有效計算社區中傳染病侵襲率。另應於高風險社區強化症候群偵測、加強與公家機關配合，由公共衛生管道擴增「防疫先鋒」的觸角深入社區深層，考量重要傳播因素以建立最適統計預測模型，期能發揮此新系統發掘社區潛藏疫情的洞察力，開創偵測的另一嶄新方向。

關鍵字

傳染流行病學 ； 症候群偵測 ； 新興傳染病 ； 醫療資訊 ； 台灣公共衛生 ； 國際疾病分類碼 ； 地理資訊系統 ； 流感 ； 腸病毒 ； 時間序列

並列摘要

The 2009 pandemic A (H1N1) influenza had resulted in considerable health and economic impact in Taiwan. Facing such a great challenge of emerging infectious diseases (EID), public health professionals in Taipei City, a capital metropolitan with political, economic, and transportation significance, need well-preparedness in surveillance network and control of infectious diseases (ID). However, traditional infectious disease surveillance systems have not real-time reflected true epidemic patterns in community, nor epidemiological information of patients without seeking medical-care. Meanwhile, holidays with social gathering may enhance microbial transmission but the impact of different holidays on ID surveillance has not been carefully measured. Therefore, this study firstly established a better community-based and public participated ID syndromic surveillance system (PID-SSS) in Taiwan (www.eid.url.tw) with a pilot site in Taipei to facilitate early detection of microbial transmission at the levels of community. Using health informatics, the integrated data from syndromic surveillance to epidemiological characteristics, virological surveillance and statistical modeling can be quickly cross-analyzed to early detect outbreaks at the rooted level.

Five specific aims were: (1) to analyze epidemiological characteristics and temporal-spatial patterns of seasonal/pandemic influenza；(2) to assess and adjust the effects of Chinese and western holidays on ICD-9 coded syndromic surveillance data using time-series analyses；(3) to set up simplified syndrome groups for establishing a bottom-up web-based PID-SSS and then to evaluate its effectiveness in Taipei City；(4) to analyze data reported from community to novel PID-SSS and to areas to be improved；and (5) to provide solid recommendations to central and local departments of health and hospital administrators.

This study involves three parts obtaining data from national, city and community levels. First, national data from laboratory and school-absenteeism surveillance systems were used to define influenza seasons during 2007–2009. ILI cases using the ICD-9-CM codes randomly extracted from the nationwide patients dataset were used to analyze differences in the distributions of age and symptoms between ED and ED+OPD and to assess the magnitude of different holidays in these two groups with an autoregressive integrated moving average model (ARIMA model). Second, tempo- spatial trends in district-specific attack rates of ILI cases in Taipei City for each year were examined, using geographic information system (GIS) with the denominator of health-seeking population (HSP) estimated by capture-recapture method that will be applied for the denominator of future community-based surveillance. Third, a novel community-based web-accessible PID-SSS called “Epi-Intelligence Frontline for detecting EID” (EIF-ID, 防疫先鋒), using five programming technologies (Joomla framework, HTML, CSS, PHP and MySQL) was established since 2010. Four Districts in Taipei City were chosen for the pilot study to promote EIF-EID from March, 2011 to July, 2011. The data collected from August, 2011 to March, 2012 was compared with those from Google Flu Trend with cross-correlation, and also evaluated its effectiveness in detecting outbreak signals, using Cumulative Sum Control Chart (CUSUM).

National data analyses showed that the dominant subtypes of human influenza viruses were seasonal H3N2, H1N1 and the pandemic H1N1 (pdm H1N1) in 2007, 2008 and 2009, respectively. Age distributions of ILI ED+OPD patients demonstrated that seasonal flu mainly attacked 5-9 (27.43%, 21.04%), 10-19 (22.61%, 25.00%) and 0-4 (21.59%, 21.45%) years in 2007 and 2008. But such patterns were significantly different from the pdm H1N1 flu primarily involved 10-19 (42.65%), 5-9 (22.67%) and 20-29 (10.36%) years in 2009 (p<0.001). The modes of age of ILI cases in ED+OPD vs. ED in the 2009 pdmH1N1 were mostly 9 vs. 12 years, respectively, with higher younger adults than seasonal influenza (5 and 4 years of age, respectively). Time series analysis adding holiday into ARIMA model displayed that Chinese Lunar New Year ranked the highest effect with statistical difference in ED vs. ED+OPD (mean increase of 87 cases in ED vs. mean decrease of 9,000 cases in ED+OPD, p<0.001) and Chin-Ming Festival ranked the second with a similar trend (mean increase of 60 cases in ED vs. mean decrease of 4,881 cases in ED+OPD, p< 0.001). By contrast, western holidays (Thanksgiving, Christmas and western-new-year days) had no significant effect.

In Taipei City, district-specific attack rates (AR) of ILI cases from the NHID in these three studied years were quite similar. ARs of Beitou, Zhongshan, Tatung, Wanhua and Zhongzheng districts were higher than the overall means of Taipei City, with the highest in Tatung (1.5-2.0-fold higher than City mean). Tempo-spatial analysis on the AR of ILI observed similar diffusion patterns in all these three years, beginning from northern, spreading to eastern and finally ending in central- western Taipei.

Community surveillance system of EIF-ID showed that Taipei City had the highest ILI reporting rate [73.4%(160/218)]. Among the 73 ILI reported cases from August, 2011 to March, 2012, fever (35.78%), cough (70.64%), stuffy nose (31.36%) and running nose (30.28%) were major symptoms. Student (59.73%), teachers/ faculty/school staff (8.72%) and service workers (8.05%) were top three main reporting groups. The districts of Taipei City with promotion program showed much higher reporting rate than those without [54.2%(32/59) vs. 8.5%(5/59)]. Further evaluation of aberration detection using web-page view rate of the novel system showed high correlation with Google Flu Trend, with Pearson’s correlation coefficients of 0.6 (time-lag = 5) and 0.83 (time-lag = 0) and even detected peaks of ILI at 5 days earlier than Google Trend at the end of 2011.

In conclusion, public syndromic surveillance system can complement with the traditional ID surveillance systems and extend surveillance network from medical facilities to communities through highly accessible internet. This approach even obtains epidemiological information of those without medical visits and thus increasing better representativeness of community, providing local public health professionals for further epidemiological investigation, and helping decision-makers to understand the similarities and differences of EID transmission in different communities. Future efforts need to increase participants and coverage rates of this novel surveillance system, to extend this pilot study of public syndromic surveillance to other parts of Taiwan, to better estimate the denominator of population at risk using capture-recapture method after excluding the impact of teaching hospitals/medical centers, to continue evaluation efforts, to integrate with environment, meteorological, epidemiologically important risk factors, human dynamic movement factors, and other social factors related to transmission for better predication of future outbreaks. With the application of this “Epi-Intelligence Frontier” at high risk areas to enhance syndromic surveillance using the best statistical modeling considering important factors, more hidden outbreaks can be detected earlier for gaining greater public health effectiveness.

並列關鍵字

Infectious Disease Epidemiology ； Participated Epidemiology ； Syndromic Surveillance ； Emerging Infectious Diseases ； Health Informatics ； Taiwan Public Health ； Geographic Information System ； Influenza ； Enterovirus ； Time Series Analysis

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