研究背景: 登革熱的發生雖然與病媒蚊有關，但是病媒指數與登革熱疫情的相關性研究卻顯示不一致的結論。
研究目的: 探究病媒指數與登革疫情的關係，在不同的地理層級上是否有差異，並就實際的預測價值提出探討。
研究對象與方法: 研究所用的資料來自2006-2009年高雄市前鎮區，並以三種研究方式加以分析各年的資料。第一部分是空間分析，計算該區各年的各里累積登革熱發生率與各里的年平均病媒指數之間的線性關係。第二部分則是時間分析，計算該區各里每週病例數與前數週的病媒指數之間的皮爾森相關係數。第三部分則是利用類似回溯性世代研究法的方式，觀察有發生登革疫情的里與同月份無發生疫情的里，於疫情發生前的一或二個月，各項病媒指數包括成蚊指數、容器指數、家戶指數與布氏指數是否有差別，同時也使用ROC曲線分析，以探究病媒指數在預測疫情地點上之能力。
結果與效應: 由研究方法一只看到2006年的病媒指數與登革發生率有線性相關。研究方法二卻發現2006年、2008年與2009年的各里每週登革發生數與4至6週前的病媒指數有線性相關。而研究方法三則是發現在這四年的入夏期間，布氏指數較高的里下個月會出現疫情的狀況主要僅出現在三個月份，且全集中在大流行年度的疫情初期，暗示布氏指數在疫情初期有較高的預測價值。利用ROC曲線將布氏指數值大於9.5設為閾值，則對於預測登革疫情發生里的靈敏度為60%，特異度為56%。現有的病媒蚊監測資料用於預測疫情地（里）的價值仍有限，改善病媒指數的監測範圍與品質將有助於釐清病媒指數在預測疫情發生地點上的價值。

Background: Although occurrences of dengue epidemics were associated with mosquitoes, studies assessing the relationship between entomologic indices and risk of dengue epidemics had conflicting results.
Objectives: To clarify the relationship between entomologic indices and risk of dengue epidemics in various geographic levels and determine which entomologic indices could be used for prediction of dengue epidemics in the geographic level of neighborhood.
Methods: We conducted our study with three different methods using data from Cianjhen district, Kaohsiung city from 2006 to 2009. The first method is spatial analysis of linear association between cumulative dengue incidences and mean entomologic indices in the geographic level of neighborhood. The second method is time series analysis, measuring the cross correlation between weekly dengue incidence and entomologic indices measured in the preceding weeks in the geographic level of district. The third method was similar to a retrospective cohort design. This cohort consisted of all the neighborhoods in Cianjhen district. Neighborhoods in which dengue epidemics occurred were defined as case neighborhoods. For any month when case neighborhoods occurred, various entomologic indices including household index (HI), container index (CI), Breteau index (BI) and adult index (AI) measured in the preceding two months were compared between case and non-case neighborhoods to determine whether entomologic indices were related to occurrences of dengue epidemics, in favor of its prediction potential of dengue epidemics. ROC curve analysis was used to find the cut-off value of entomologic index of interest in predicting dengue epidemics.
Results: Linear association was found between various entomologic indices and cumulative dengue incidence only in year 2006 by method 1. In method 2, linear association was found between dengue incidence and the entomologic indices with the time lag of four to six weeks in year 2006, 2008 and 2009. With method 3, neighborhoods with higher BI relating to dengue epidemics next month were mainly seen in three months when dengue epidemics just occurred in the year of interest. Using ROC curve analysis, the cut-off value of BI =9.5 was estimated to have sensitivity of 60% and specificity of 56% in predicting dengue epidemics in the geographic level of neighborhood.
Conclusion: All the current commonly used entomologic indices had limited value in predicting dengue epidemics in the geographic level of neighborhood. More comprehensive vector surveillance would be helpful to explore the value of entomologic indices in predicting where dengue epidemics would occur.

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