登革熱疫情特徵的時空脆弱度因素：多層次模型的分析

Abstract

登革熱是目前傳播最快速的蟲媒傳染病，其傳播媒介為白線斑蚊與埃及斑蚊。為了從登革熱傳播的脆弱根源──病媒暴露性、人口易感特徵、缺乏調適能力的族群加以防治，找出因子是登革熱風險評估重要的研究議題。過去研究分別從登革熱發生率、發生案例數與病媒蚊指數等風險指標，探討影響風險分布的因素，這些研究成果顯示，登革熱的風險評估應同時考量環境暴露與人口條件的差異，才能有效的判別高風險的地區、群體，若能有效掌握這些環境與人口因子的時空分佈，將有利於防疫資源的分配與提升病媒管制的效率。 然而，登革熱的疫情是以年為週期性消長的傳染性疾病，仍較少有研究探討疫情在年內的波動特徵的影響因子，究竟疫情在年內的發生頻率、每一波疫情延燒多久、會有多強的爆發規模，並不完全能被過去研究所探討的發生率與病媒蚊密度指數所解釋。而欲理解這些疫情年內變異的原因，過去在登革熱病媒暴露性因子中的溫度與降雨效果的量測上，也較少考量到溫度、降雨的在時間單位內的變異，以及與這些因子本身與疫情的非線性關係。 本研究採取聯合國國際減災策略組織（UNISDR）與政府間氣候變遷小組（IPCC）的風險模式，視風險為災害潛勢與人類既存狀態下脆弱性的綜合函數。有別於過去多數研究，應用登革熱「流行機會」、「流行延時」、與「流行強度」的風險定義，以脆弱度的架構整合了環境暴露性與社會人口脆弱特性，釐清病媒端與宿主端促成登革熱疫情型態的因果途徑。為捕捉氣象變異與登革熱疫情波動的關係，本研究發展三個登革熱病媒傳播最適溫度(Most Suitable Temperature)的指標來捕捉溫度的年變異，分別為最適溫度的最大連續效果(Max. continuity of MST)、平均延時效果(intermittence of MST)以及累積效果(accumulation of MST)，以及四個降雨變異的指標來捕捉不同時期的降雨效果，分別為流行季前的連續濕潤效果(pre-epidemic continuity of wetness)、連續乾燥效果(pre-epidemic continuity of dryness)、流行季中的暴雨破壞效果(peri-epidemic extreme rainfall)、降雨分散效果(peri-epidemic mild rainfall)。在研究中，由於不同城鄉發展型態、疫區與非疫區、以及不同年之間地疫情規模不同，可能存在不同的脆弱度因子，因此本研究將這些時空條件納入考量。 以臺灣1998-2015年共18年、南臺灣100個鄉鎮市區的時空序列資料，評估登革熱流行機會、流行延時與流行強度的脆弱度因子，重要的研究結果包括：(1) 大規模爆發年的重要氣象因子是溫度，其中MST累積效果反映流行延時與強度的能力，大規模爆發年優於度日，度日又優於平均溫度。中規模爆發年的重要氣象因子則是降雨，尤其流行季中的降雨強度特性，比同時期的累積降雨量更顯著地在中規模爆發年反映疫情動態。(2) 脆弱度因子展在不同時空條件下展現了差異：屋齡較低的新屋在城鎮地區相較於都會區，更能夠調適疫情變異風險；幼年人口數在經常性流行的疫區，更能夠調適風險；易暴露於鄰里地區的幼年與老年人口，在防疫意識不足的非大規模爆發年更為脆弱。 (3) 不同脆弱度因子在流行機會、延時與強度等三個年內變異指標的作用效果不同，因而具備了不同的的疫情型態效果，在持續性上有連環波作用（ex: 人口密度）、長波作用（ex: 屋齡<10年新屋比例）、散波作用（ex: 流行季前連續乾旱效果）之別，在強度上有強化（ex: MST累積效果、流行季前連續濕潤效果）、弱化（ex: MST最大連續效果、流行季中暴雨效果）之別。 這些結果呼應到過去病媒蚊棲地生態、生活史以及病毒外潛伏的相關理論，以及本土性的環境特徵與實務上的防疫經驗。本研究區分了不同時空條件下脆弱度因子的差異，藉由辨識年內疫情變異與疫情型態類別的關鍵脆弱度因子，希冀提供不同的健康風險管理角度作為研究與實務參考。

Dengue fever is a most important vector-borne disease nowadays. To reduce human-vector contact, measuring mosquito-vector distribution is the direct method but it’s not the long-term solution unless we know the reasons. Therefore, interfering the key vulnerability factors is a more cost-effective measure in long term dengue prevention. This study evaluated the vulnerability and its spatial temporal difference of dengue in terms of epidemiological characteristics: epidemic probability, duration, and intensity. These characteristics are more useful for public health management and resource allocation than traditional risk definition, incidence. Furthermore, both environment exposure and socio-demographic factors were investigated in our vulnerability framework, so that the pathway from mosquito-vector to population-at-risk is complete. Rather than average temperature or accumulative precipitation, this study developed variability indices of temperature and rainfall to evaluate how the continuity and intensity of weather events affect the intra-annual variability of epidemic. The indices of intra-annual temperature variability are max. continuity, intermittence, and accumulation of most suitable temperature (MST). And the indices of intra-annual rainfall variability are pre-epidemic continuity of wetness, pre-epidemic continuity of dryness, peri-epidemic extreme rainfall, and peri-epidemic mild rainfall. The empirical data in the southern Taiwan from 1998-2015 was analyzed by a multi-level growth model to identify the factors of vulnerability. The major finding includes: (1) Empirical studies concluded that relationship temperature and precipitation are highly related to dengue incidence. However, this study finds accumulation of MST and max. continuity of MST has more significant effects on dengue epidemics. The effect of pre-epidemic continuity of wetness is significantly more positive than the effect of peri-epidemic accumulative precipitation, while both the effects of extreme high and extreme low intensity of rainfall are negative to epidemics instead. (2) The vulnerability factors exists spatial temporal difference. Between city and town, new house ratio possesses in town higher adaptive capacity. Between usually epidemic areas and not usually ones, population of children in usually epidemic areas are at lower risk. Comparing large-scale outbreak years and middle-scale outbreak years, the most susceptible population is DHF related patients in the former years, while it’s population beyond 65 years old in the latter years. (3) Because the vulnerability factors are not all the same of the three epidemiological indices, these factors can prompt different epidemic patterns. In terms of epidemic continuity, there are factors of continuous wave (e.g. population density), long wave (e.g. population > 65 years old), and intermittent wave (e.g. pre-epidemic continuity of dryness). In terms of epidemic intensity, there are factors of high wave (e.g. accumulation of MST) and low wave (e.g. max. continuity of MST).