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研究生： 李欣怡
Lee, Sin-Yee
論文名稱： 登革熱全球疾病負擔的估計與各國通報資料之比較
Comparing dengue burden estimates from the Global Burden of Disease and country-reported data
指導教授： 簡玉雯
Chien, Yu-Wen
呂宗學
Lu, Tsung-Hsueh
學位類別： 碩士
Master
系所名稱： 醫學院 - 公共衛生學系
Department of Public Health
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 118
中文關鍵詞： 登革熱 、全球疾病負擔 、全球疾病負擔資料庫 、流行病學
外文關鍵詞： Dengue, the Global Burden of Disease Study, global burden diseases, epidemiology, global disease burden estimates
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    • 研究背景： 近幾十年來，全球由病媒蚊傳播的病毒性疾病日益嚴重，其中尤以登革熱為甚。全球暖化導致登革熱從熱帶擴散至亞熱帶地區，其臨床症狀與多種疾病相似，容易導致誤診。 此外，許多國家和地區的有限檢測能力使得準確診斷更加困難。 現有研究表明，官方監測和通報系統可能嚴重低估了實際病例數。 全球疾病負擔研究(Global Burden of Disease, GBD)對於全球公衛政策制定及學術研究至關重要，讓各國政府和世界衛生組織等可以明確地理解各種健康問題的相對重要性，從而做出合理的資源分配和決策，然而GBD模型估計值的準確性一直是公衛領域的熱議話題。本研究透過比較不同國家登革熱通報病例數和GBD資料庫的估算值，評估GBD模型的估計值對登革熱疾病負擔估計的準確性。
    分析方法： 本研究以2010年至2019年全球登革熱報告病例數前30名的國家或地區為主，計算2010年至2019年之間通報病例數和GBD估計病例數的10年平均值和每十萬人口的10年平均粗發生率。 為了比較兩種資料來源間的差異，我們也計算了10年平均通報病例數和GBD估計的差異及比率，並計算了變異係數(Coefficient of Variance, CV)以評估這兩種數據來源在絕對數字和發生率方面的變異性。 此外，我們透過計算每個國家或地區從2010年至2019年的估計年度百分比變化(Estimated Annual Percent Change, EAPC)，來描述隨時間變化的趨勢。 我們的分析同時考慮通報病例數和GBD的估計值，並檢視在絕對數字和發生率方面的趨勢。 我們另外使用Joinpoint趨勢分析來評估年度百分比變化(Annual Percent Change, APC)，這種分析方法能使用分段線回歸模型準確地描繪出在時間範圍內觀察到的趨勢轉變。 我們使用散布圖（scatter plot）方式進行了分層分析（按地區、流行和非流行狀態，以及社會人口指數(Socio-Demographic Index, SDI)）。
    結果： 本研究詳細分析了2010至2019年在30個國家或地區的登革熱病例，發現登革熱通報病例數報告數據與GBD估計之間存在顯著差異，尤其是在中國、印度、孟加拉、臺灣與印尼。此外，兩種來源的數據在變異性和EAPC上差異也很大，特別是通報病例數顯示出極高的變異性， 然而GBD估計值卻沒有明顯的年度變化。 Joinpoint迴歸分析揭示了這些國家登革熱病例的趨勢和轉折點，提供了更深入的理解。 研究也發現通報病例數與GBD估計值所計算出之平均發病率的排名差異極大，依GBD估計值所計算的平均發病率台灣排名為全球第一。
    結論：本研究的結果發現2010至2019年間，登革熱的報告案例與全球疾病負擔(GBD)估計之間存在顯著差異，且GBD估計值未能呈現登革熱疫情與案例數爆發的巨大的年度變化，因此GBD目前的模型對全球登革熱的疾病負擔估計並不合理，未來的GBD模型可參考本研究以優化模型。

    Research Background: Over recent decades, the severity of mosquito-borne viral diseases globally, especially dengue, has escalated. Global warming has facilitated the spread of dengue from tropical to subtropical regions, with its clinical symptoms often resembling those of other febrile diseases, leading to misdiagnosis. Moreover, limited diagnostic capabilities in various countries and regions have further complicated accurate diagnosis. Existing research suggests that official surveillance and reporting systems might severely underestimate the actual case numbers. The Global Burden of Disease (GBD) study is critical for global public health policy and academic research, allowing governments and organizations like the World Health Organization (WHO) to understand the relative importance of various health issues, thereby making reasoned resource allocations and decisions. However, the accuracy of GBD model estimates has been a topic of much debate in the public health arena. This study evaluated the accuracy of the GBD model's estimates in assessing the disease burden of dengue by comparing reported cases of dengue in various countries with the estimated values from the GBD database.
    Methods: This study focuses on the top 30 countries and territories with the highest reported dengue cases from 2010 to 2019, to assess the accuracy of dengue burden estimation in the GBD model. We first calculated the 10-year average of reported cases and the GBD estimates between 2010 and 2019. Additionally, we calculated the 10-year average crude incidence rate per 100,000 population for both reported cases and GBD estimates. To compare the discrepancies between these two data sources, we calculated the differences and ratios of the 10-year average reported cases to GBD estimates and the Coefficient of Variance (CV) to assess variability in absolute numbers and incidence rates. Furthermore, we described time-varying trends by calculating the Estimated Annual Percent Change (EAPC) for each country or region from 2010 to 2019 based on the two data sources. We also used Joinpoint trend analysis to evaluate the Annual Percent Change (APC), employing segmented line regression models to depict observed trend changes over time accurately. Stratified analyses were conducted using scatter plots (by region, epidemic status, and the Socio-Demographic Index (SDI)), primarily analyzing descrepancies between averages and crude rates, categorized by SDI, region, and epidemic or non-epidemic status.
    Results: This study meticulously analyzed dengue fever cases in 30 countries or territories from 2010 to 2019, revealing significant discrepancies between reported data and GBD estimates, particularly in China, India, Bangladesh, Taiwan, and Indonesia. The variability and EAPC also differed greatly between the two sources, with reported cases showing extremely high variability. However, GBD estimates did not show significant annual changes. Joinpoint regression analysis revealed trends and turning points in these countries, providing deeper insights. The study also found significant differences in the rankings of average incidence rates calculated from reported cases and GBD estimates, with Taiwan ranked first globally based on the GBD estimated average incidence rate.
    Conclusion: The results of this study indicate significant discrepancies between reported cases of dengue fever and GBD estimates from 2010 to 2019, and the GBD estimates failed to capture the substantial annual changes in dengue epidemics and case numbers. Therefore, the current GBD model's estimation of the global dengue disease burden is not reasonable. Future GBD models could consider this study to optimize the model.

    Table of Contents 摘要 i Abstract iii 誌謝 v Table of Contents vi Chap 1 Introduction 1 1.1 Study Background 1 1.2 Study Aim 2 Chap 2 Literature Review 4 2.1 Overview of Dengue Fever 4 2.2 The Epidemiology of Dengue Fever in Taiwan 7 2.3 Evaluating Health Globally: The Role of GBD and GHE 9 2.4 Methodological Approaches in GBD Studies for Dengue Estimation 12 Chap 3 Methodology 15 3.1 The data sources 15 3.1.1 The country reported case 15 3.1.2 GBD Estimates 16 3.2 The Detailed Process of Data Selection 17 3.3 The Classified of SDI 17 3.4 Classification of Countries or Territories as Dengue Endemic or Non-Endemic 18 3.5 The Ethical Issues 19 3.6 Statistical Methods 19 Chap 4 Result 22 Chap 5 Discussion 29 Chap 6 Conclusion 31 Chap 7 Bibliography 32 Chap 8 List of Tables 40 Table 1 The average number of dengue cases, Coefficient Variance (CV), Estimated Annual Percentage Change (EAPC) and 95% CI of Dengue from the Year 2010 to 2019 in 30 selected countries or territories according to the country reported dengue cases and the GBD estimates 40 Table 2 (B) Annual Percent Change (APC) and 95% Confidence Intervals (CIs) of the Number of Dengue estimate in the GBD estimates 44 Table 3 The average incidence Rates (per 100,000 people), Coefficient of Variance (CV), Estimated Annual Percentage Change (EAPC) and 95% CI of Dengue from the Year 2010 to 2019 in 30 selected countries or territories according to the reported dengue cases and the GBD estimates 46 Table 4 (A) Annual Percent Change (APC) and 95% Confidence Intervals (CIs) of the Rate of Reported Data 48 Table 4 (B) Annual Percent Change (APC) and 95% Confidence Intervals (CIs) of the Rate of Dengue estimates in the GBD estimates 50 Chap 9 List of Figures 52 Figure 1 The average number of dengue cases from the Year 2010 to 2019 in 30 selected countries or territories according to (A) the reported dengue cases and (B)the GBD estimates 52 Figure 2 (A) Differences and (B) Ratios of Dengue from the Year 2010 to 2019 in 30 selected countries and territories according to the reported dengue cases and the GBD estimates 53 Figure 3 The Estimated Annual Percent Change (EAPC) of Dengue from the Year 2010 to 2019 in 30 selected countries or territories according to (A) the reported country cases and (B) the GBD estimates 54 Figure 4 (A) The scatter plot of average reported dengue cases and GBD estimates by Region from the Year 2010 to 2019 55 Figure 4 (B) The scatter plot of average reported dengue cases and GBD estimates by Endemic or non-endemic from the year 2010 to 2019 56 Figure 4 (C) The scatter plot of average reported dengue cases and GBD estimates by Socio-Demographic Index (SDI) from the year 2010 to 2019 57 Figure 5 Ranking of the average number of Dengue from the Year 2010 to 2019 in 30 selected countries or territories between the reported dengue cases and the GBD estimates 58 Figure 6 The average incidence rates (per 100,000 people) of Dengue from the year 2010 to 2019 in 30 selected countries and territories according to (A) the reported data and (B) the GBD estimates 59 Figure 7 (A) Differences and (B) Ratios rates (per 100,000 people) of Dengue from the year 2010 to 2019 in 30 selected countries and territories according to the reported data and the GBD estimates 60 Figure 8 The Estimated Annual Percent Change (EAPC) of Dengue rates from the Year 2010 to 2019 in 30 selected countries or territories according to (A) the reported data and (B) the GBD estimates 61 Figure 9 (A) The scatter plot of dengue average incidence rates (per 100,000 people): Comparison between reported data and GBD estimates by Region from the year 2010 to 2019 62 Figure 9 (B) The scatter plot of dengue average incidence rates (per 100,000 people): Comparison between reported data and GBD estimates by Endemic or non-endemic from the year 2010 to 2019 63 Figure 9 (C) The scatter plot of dengue average incidence rates (per 100,000 people): Comparison between reported data and GBD estimates by Socio-Demographic Index (SDI) from the year 2010 to 2019 64 Figure 10 Ranking of the average incidence rates of Dengue from the Year 2010 to 2019 in 30 selected countries between the reported data and the GBD estimates 65 Chapter 10 Supplementary Material 66 Supplementary 1 R coding for Estimated Annual Percentage Change 66 Supplementary Table 1 Number of cases for Reported Dengue Cases and GBD estimates for 101 Countries (2010-2019) 68 Supplementary Table 2 Classification of the countries or territories 77 Supplementary Table 3 The total population for the countries or territories included in our analysis during 2010-2019 (in thousands) 78 Supplementary Table 4 Annual reported dengue cases from 2010 to 2023 in Taiwan, laboratory-confirmed dengue cases as well as the test positivity rates for dengue virus infection among all cases, both indigenous and imported. 80 Supplementary Table 5 Clinical case definitions for dengue, lab sampling, and testing techniques used for surveillance have been utilized in 30 countries 82 Supplementary Figure 1 Taiwan CDC Data: Dengue Fever, Nationwide, Indigenous and Imported, Year 2010 - 20196 92 Supplementary Figure 2 Trend Analysis of Each Country or Territory from 2010 to 2019: Comparing reported country cases and GBD estimates 93 1. Argentina 93 2. Bangladesh 93 3. Bolivia 94 4. Brazil 94 5. Cambodia 95 6. China 95 7. Colombia 96 8. Costa Rica 96 9. Dominica Republic 97 10. Ecuador 97 11. El Salvador 98 12. Guatemala 98 13. Honduras 99 14. India 99 15. Indonesia 100 16. Laos 100 17. Malaysia 101 18. Mexico 101 19. Myanmar 102 20. Nicaragua 102 21. Paraguay 103 22. Peru 103 23. Philippines 104 24. Puerto Rico 104 25. Singapore 105 26. Sri Lanka 105 27. Taiwan 106 28. Thailand 106 29. Venezuela 107 30. Viet Nam 107

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