臺灣博碩士論文加值系統

不論是在印尼，抑或是在臺灣，登革熱都是一項重大的公共衛生負擔。而在這些地區，登革熱疫情之爆發經常出現於雨季。顯示出降雨是熱帶/亞熱帶地區可能導致登革熱疫情爆發之潛在影響。在此，研究主旨即利用，位於熱帶區印尼萬隆地區及台灣高雄地區之1998年至2011年之資料，之以探討降雨及相關氣候因子對於登革熱發病率的潛在衝擊。
研究彙整1998年至2011年印尼萬隆地區每月平均溫度、平均相對濕度以及累積降雨量資料，及1998年至2011年台灣高雄地區每日平均溫度、相對濕度及累積降雨量等氣象資。並自印尼萬隆市衛生局及台灣疾病管制局收集萬隆每月計數與高雄每囸計數之登革熱登記資料。研究利用廣義加乘模型中之 Poisson 回歸，以評估降雨以及登革熱發病率兩者之間的劑量關係。研究將兩地的乾季與雨季分別建立模式，而估計之相對風險考慮校正溫度、相對濕度、聖嬰年等變異。其中，每一氣象因子在兩地模型中亦考量了一定的延遲時間，在萬隆地區考慮了0 至3個月的延遲，而高雄地區則考慮了0至84天的延遲。
研究發現，高雄地區在濕季時，若56天前日累積雨量每增加30mm、溫度增加1度及相對溼度增加1%時，則將分別增加 9.29 % (95% confidence interval (CI)= 8.59-10.01%; p-value 〈 0.001) 、71.53 % (95% CI= 67.46-75.7%; p-value 〈 0.001) 及5.97 % (95% CI= 2.01-10.09%; p-value= 0.0029) 登革熱發病之風險。若為乾季之時，同樣發現63天前之氣象條件變化，將分別18.51% (95% CI= 6.91-31.37%; p-value = 0.0013)、73.83% (95% CI= 68.89-78.91%; p-value 〈.0001)及1.3% (95 % CI= 0.47-2.14%; p-value = 0.0022) 登革熱發病之風險。而在印尼萬隆地區，在濕季時3個月前的一個月裡每日累積雨量每增加30mm及相對溼度增加1% 時，則會分別增加58.51 % (95% CI= 34.59-86.68%; p-value 〈 0.001)、36.94 % (95% CI= 32.48-41.54%; p-value 〈 0.001)及 4.42 % (95% CI= 3.87-4.97%; p-value 〈 0.001)登革熱之發生，若為乾季之時，雨量每增加30mm 與相對溼度增加1%時，則會分別增加112.16% (95% CI= 85.36-142.80%; p-value 〈 0.001)及 1.89% (95% CI= 1.28-2.53%; p-value 〈 0.001)登革熱之發生。但溫度增加1度則會減少增加5.45% (95% CI= 1.57-9.18%; p-value= 0.0081) 登革熱之發生。
整體而言，無論在印尼萬隆地區與台灣高雄地區，結果皆指向溫度及雨量可能為登革熱發生的主要因子。然而，相對而言，雨量在印尼萬隆地區登革熱發生的影響性高於台,而溫度對印尼萬隆地區登革熱發生的影響性則低於台灣，這可能是因為萬隆地區平均雨量較台灣地區更高，而溫度平均與溫度範圍較台灣地區更涼爽。本研究同時發現，高雄以及萬里等兩地皆顯示相較於雨季時，乾季時降雨的衝擊.更為強烈。.此研究結果可幫助在特殊氣象條件下先進行登革熱的防治，如結合氣象預報以促使預警系統的建立，或在特殊季節中給予登革熱衛生教育，如使用蚊帳或驅蚊知能等，以達最大效益。

Dengue is a significant public health burden in Indonesia and Taiwan. High peaks for dengue outbreak is reported on rainy season which varies in these areas. This suggests that precipitation is likely to exert potential impact on dengue fever outbreak in tropical and/or subtropical regions. This study is aimed for assessing the potential impact of precipitation on dengue fever incidence in two tropical regions: Bandung, Indonesia and Kaohsiung, Taiwan from 1998 to 2011.
Official statistics of dengue incidence was obtained from Bandung Health Bureau and Taiwan Center for Disease Control (CDC), and weather information including average temperature, relative humidity (RH) and precipitation were obtained from Badan Meteorologi dan Geofisika Bandung and Taiwan Central Weather Bureau. Poisson regressions with generalized additive model were utilized to evaluate the associations between weather factors and dengue fever incidence. Estimated relative risks were adjusted for temperature, RH, El Niño year (about three years a cycle) in rainy season (November-March in Indonesia and April-October in Taiwan), and dry season (April-October in Indonesia and November-March in Taiwan). Each weather factor was investigated for month lag of 0-3 in Indonesia and day lag of 0-84 in Taiwan.
In Kaohsiung, during rainy season an increase in precipitation (30 mm), temperature (1°C), and RH (1%) at 56 days prior were associated with 9.29% (95% confidence interval (CI)= 8.59-10.01%; p-value 〈 0.001), 71.53% (95% CI= 67.46-75.7%; p-value 〈 0.001), and 5.97% (95% CI= 2.01-10.09%; p-value= 0.0029) increases of dengue incidence, respectively. Meanwhile, the most significant condition for dry season is at lag-63 days where an increase of 30 mm precipitation, 1°C temperature, and 1% RH were associated with an increase of 18.51% (95% CI= 6.91-31.37%; p-value = 0.0013), 73.83% (95% CI= 68.89-78.91%; p-value 〈 0.0001), and 1.3% (95 % CI= 0.47-2.14%; p-value = 0.0022) of dengue fever incidence. In Bandung at 3-months prior during rainy season, an increase in precipitation (30 mm), temperature (1°C), and RH (1%) at 3-months prior would raise the incidence of dengue by 58.51% (95% CI= 34.59-86.68%; p-value 〈 0.001), 36.94% (95% CI= 32.48-41.54%; p-value 〈 0.001), and 4.42% (95% CI= 3.87-4.97%; p-value 〈 0.001), respectively. In dry season, a 30 mm increase in precipitation and 1% increase in RH at 3-months prior were positively associated with 112.16% (95% CI= 85.36-142.80%; p-value 〈 0.001) and 1.89% (95% CI= 1.28-2.53%; p-value 〈 0.001) increase of dengue incidence, whereas 1°C increase in temperature was associated with 5.45% (95% CI= 1.57-9.18%; p-value= 0.0081) decrease of dengue incidence.
Our findings indicate that precipitation and temperature are trigging factors for dengue incidence in Bandung and Kaohsiung. However, effects of precipitation in Bandung were stronger than those in Kaohsiung; whereas effects of temperature in Bandung were weaker than those in Kaohsiung. This finding may result from that Bandung has higher humidity and cooler compare to Kaohsiung. Study also finds effect of precipitation in dry season is stronger than in rainy season for both areas. These findings may be served as an early warning systems based on weather forecast that can help reinforce preventive measures.

TABLE OF CONTENTS
中文摘要 i
ABSTRACT iii
ACKNOWLEDGEMENTS v
TABLE OF CONTENTS vi
LIST OF TABLES viii
LIST OF FIGURES ix
CHAPTER I INTRODUCTION 1
1.1 Background 1
1.2 Study Objectives 5
CHAPTER II LITERATURE REVIEW 6
2.1 Weather and Disease 6
2.2 Generalities on Dengue Fever 9
2.2.1 Definition and Manifestation 9
2.2.2 Pathogenesis 10
2.2.3 Epidemiology 11
2.2.4 Dengue Fever and Risk Factors 13
2.3 Country Profile 16
2.3.1 Indonesia 16
2.3.2 Taiwan 17
CHAPTER III MATERIALS AND METHODS 19
3.1 Type of Study 19
3.2 Study Area 19
3.3 Study Design and Statistical Analysis 20
3.3.1 Data Collection 20
3.3.2 Statistical Analysis 22
CHAPTER IV RESULTS 25
4.1 General Pattern 25
4.2 Descriptive Statistics 26
4.3 Spearman’s Rank Correlation Coefficient 27
4.4 Statistical Analysis 29
CHAPTER V DISCUSSION 32
CHAPTER VI MAJOR FINDINGS AND CONCLUSIONS 40
REFERENCES 41
TABLES 46
FIGURES 52
Appendixes 66

 
LIST OF TABLES
Table 1. Descriptive Statistics for Daily Weather Factors and Dengue Cases Number in Kaohsiung 46
Table2. Descriptive Statistics for Monthly Weather Factors and Dengue Cases Number in Bandung 47
Table 3. Spearman’s Rank Correlation Coefficient Testing in Bandung 48
Table 4. Spearman’s Rank Correlation Coefficient Testing in Kaohsiung 49
Table 5. Multivariate Analysis for Dengue Incidence in Bandung for Rainy and Dry Season 50
Table 6. Multivariate Analysis for Dengue Incidence in Kaohsiung for Rainy and Dry Season 51
 
LIST OF FIGURES
Figure 1. Schematic Diagram of Pathways by which climate changes affects health (IPCC, 2007) 52
Figure 2. Risk Map of Worldwide Dengue Fever (Map Courtesy of Jane Messina 2013) 52
Figure 3. Trend of Dengue Cases as reported by Indonesia2007- mid July 2010 (WHO, 2009) 53
Figure 4. Trend of dengue case reported by Taiwan Jan 2007 – Dec 2011 (Taiwan CDC, 2011) 53
Figure 5. Annual Temperature Trends: 1976-2000 (Allan et al 2008) 54
Figure 6. Annual Precipitation Trends: 1900-2000 (Allan et al 2008) 54
Figure 7. Global geographical distribution of dengue (Van Kleef E et al. 2011) 55
Figure 8. Aedes aegypti Lifecycle 55
Figure 9. Results of a logistic regression model with vapor pressure as the predictor of dengue fever risk, using weather data from 1961 to 1990 56
Figure 10. Pattern of Monthly Dengue Cases in Kaohsiung and Bandung 1998-2011 57
Figure 11. Pattern of Monthly Precipitation in Kaohsiung and Bandung 1998-2011 57
Figure 12. Pattern of Monthly Temperature in Kaohsiung and Bandung 1998-2011 58
Figure 13. Pattern of Monthly Relative Humidity in Kaohsiung and Bandung 1998-2011 58
Figure 14. Relationship between daily weather factors and dengue incidence in Kaohsiung for rainy season (Univariate) 59
Figure 15. Relationship between daily weather factors and dengue incidence in Kaohsiung for dry season (Univariate) 59
Figure 16. Relationship between daily weather factors and dengue incidence in Bandung for rainy season (Univariate) 60
Figure 17. Relationship between daily weather factors and dengue incidence in Bandung for rainy season for Dry Season (Univariate) 60
Figure 18. Relationship between daily weather factors and dengue incidence in Kaohsiung for rainy season A. Precipitation B. Temperature C. Humidity (Multivariate) 61
Figure 19. Relationship between daily weather factors and dengue incidence in Kaohsiung for dry season A. Precipitation B. Temperature C. Humidity (Multivariate) 62
Figure 20. Relationship between daily weather factors and dengue incidence in Bandung for rainy season A. Precipitation B. Temperature C. Humidity (Multivariate) 63
Figure 21. Relationship between daily weather factors and dengue incidence in Bandung for dry season A. Precipitation B. Temperature C. Humidity (Multivariate) 64
Figure 22. Study Areas of the Research 65
Figure 23. Study Design of the Research 65

REFERENCES
Akbar R.M. , A., R. (2008). PCR Detection of Dengue Transovarial Transmissibility in Aedes aegypti in Bandung, Indonesia. Tropical Medicine Parasitology, 3, 84-89.
Akbar R.M., A. R. (2008). PCR Detection of Dengue Transovarial Transmissibility in Aedes aegypti in Bandung, Indonesia. Tropical Medicine Parasitology, 3, 84-89.
Akhtar, R., ＆ McMichael, A. J. (1996). Rainfall and malaria outbreaks in western Rajasthan. Lancet, 348(9039), 1457-1458.
Allan, R. P., ＆ Soden, B. J. (2008). Atmospheric warming and the amplification of precipitation extremes. Science, 321(5895), 1481-1484.
AN, C. (1999). The Biology of Mosquitoes Egg laying (Vol. II): Cabi, Wallingford.
Balmaseda, A., Hammond, S. N., Perez, M. A., Cuadra, R., Solano, S., Rocha, J., et al. (2005). Short report: assessment of the World Health Organization scheme for classification of dengue severity in Nicaragua. Am J Trop Med Hyg, 73(6), 1059-1062.
Bangs, M. J., Larasati, R. P., Corwin, A. L., ＆ Wuryadi, S. (2006). Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health, 37(6), 1103-1116.
Butler, C. D., ＆ Harley, D. (2010). Primary, secondary and tertiary effects of eco-climatic change: the medical response. Postgrad Med J, 86(1014), 230-234.
Carrington, L. B., Armijos, M. V., Lambrechts, L., ＆ Scott, T. W. (2013). Fluctuations at a Low Mean Temperature Accelerate Dengue Virus Transmission by Aedes aegypti. PLoS Negl Trop Dis, 7(4), e2190.
Cazelles, B., Chavez, M., McMichael, A. J., ＆ Hales, S. (2005). Nonstationary influence of El Nino on the synchronous dengue epidemics in Thailand. PLoS Med, 2(4), e106.
Chen, M. J., Lin, C. Y., Wu, Y. T., Wu, P. C., Lung, S. C., ＆ Su, H. J. (2012). Effects of extreme precipitation to the distribution of infectious diseases in Taiwan, 1994-2008. PLoS One, 7(6), e34651.
Chowell, G., Cazelles, B., Broutin, H., ＆ Munayco, C. V. (2011). The influence of geographic and climate factors on the timing of dengue epidemics in Peru, 1994-2008. BMC Infect Dis, 11, 164.
Costello, A., Abbas, M., Allen, A., Ball, S., Bell, S., Bellamy, R., et al. (2009). Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. Lancet, 373(9676), 1693-1733.
D, D. (2006). Dengue Haemorraghic Fever Epidemiology,Imunopatology, Pathogenesis,Diagnosis and Management. Malang UMM Press (In Indonesia).
Division of Vector-Borne Infectious Diseases Centers for Disease Control and Prevention. (2008). Dengue fever. Retrieved 2 July 2013. from http://www.cdc.gov/ncidod/dvbid/dengue/index.htm.
Focks, D. A., Brenner, R. J., Hayes, J., ＆ Daniels, E. (2000). Transmission thresholds for dengue in terms of Aedes aegypti pupae per person with discussion of their utility in source reduction efforts. Am J Trop Med Hyg, 62(1), 11-18.
Fouque, F., Carinci, R., Gaborit, P., Issaly, J., Bicout, D. J., ＆ Sabatier, P. (2006). Aedes aegypti survival and dengue transmission patterns in French Guiana. J Vector Ecol, 31(2), 390-399.
Fuller, D. O., Troyo, A., ＆ Beier, J. C. (2009). El Nino Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa Rica. Environ Res Lett, 4, 140111-140118.
Githeko, A. K., Lindsay, S. W., Confalonieri, U. E., ＆ Patz, J. A. (2000). Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ, 78(9), 1136-1147.
Gubler, D. J. (2001). Prevention and control of tropical diseases in the 21st century: back to the field. Am J Trop Med Hyg, 65(1), v-xi.
Gubler, D. J., Reiter, P., Ebi, K. L., Yap, W., Nasci, R., ＆ Patz, J. A. (2001). Climate variability and change in the United States: potential impacts on vector- and rodent-borne diseases. Environ Health Perspect, 109 Suppl 2, 223-233.
Hales, S., de Wet, N., Maindonald, J., ＆ Woodward, A. (2002). Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet, 360(9336), 830-834.
Hales, S., ＆ van Panhuis, W. (2005). A new strategy for dengue control. Lancet, 365(9459), 551-552.
Halstead, S. B. (2003). Neutralization and antibody-dependent enhancement of dengue viruses. Adv Virus Res, 60, 421-467.
Jury, M. R. (2008). Climate influence on dengue epidemics in Puerto Rico. Int J Environ Health Res, 18(5), 323-334.
Kalayanarooj, S., Vaughn, D. W., Nimmannitya, S., Green, S., Suntayakorn, S., Kunentrasai, N., et al. (1997). Early clinical and laboratory indicators of acute dengue illness. J Infect Dis, 176(2), 313-321.
Keller, C. F. (2007). Global warming 2007. An update to global warming: the balance of evidence and its policy implications. ScientificWorldJournal, 7, 381-399.
Kelly-Hope, L. A., Purdie, D. M., ＆ Kay, B. H. (2004). Differences in climatic factors between Ross River virus disease outbreak and nonoutbreak years. J Med Entomol, 41(6), 1116-1122.
Koenraadt, C. J., Githeko, A. K., ＆ Takken, W. (2004). The effects of rainfall and evapotranspiration on the temporal dynamics of Anopheles gambiae s.s. and Anopheles arabiensis in a Kenyan village. Acta Trop, 90(2), 141-153.
Koh, B. K., Ng, L. C., Kita, Y., Tang, C. S., Ang, L. W., Wong, K. Y., et al. (2008). The 2005 dengue epidemic in Singapore: epidemiology, prevention and control. Ann Acad Med Singapore, 37(7), 538-545.
Kovats, R. S., Campbell-Lendrum, D., ＆ Matthies, F. (2005). Climate change and human health: estimating avoidable deaths and disease. Risk Anal, 25(6), 1409-1418.
Lin, H. M., Chen, C. S., Hsu, C. C., ＆ Chung, C. L. (1986). [Dengue vector density survey in Liuchiu, Pintung, Taiwan]. Zhonghua Min Guo Wei Sheng Wu Ji Mian Yi Xue Za Zhi, 19(3), 218-223.
McMichael, A. J., ＆ Lindgren, E. (2011). Climate change: present and future risks to health, and necessary responses. J Intern Med, 270(5), 401-413.
McMichael, A. J., ＆ Woodruff, R. E. (2002). Climate change and human health: what do we know? Med J Aust, 177(11-12), 590-591.
Meteorological Services Division National Environment Agency. (2009). Climatology of Singapore. Retrieved 2 July 2013. from http://app2.nea.gov.sg/weather_statistics.aspx.
Moore, C. G., Cline, B. L., Ruiz-Tiben, E., Lee, D., Romney-Joseph, H., ＆ Rivera-Correa, E. (1978). Aedes aegypti in Puerto Rico: environmental determinants of larval abundance and relation to dengue virus transmission. Am J Trop Med Hyg, 27(6), 1225-1231.
Murray, V., ＆ Ebi, K. L. (2012). IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX). J Epidemiol Community Health, 66(9), 759-760.
Nimmannitya, S. (1987). Clinical spectrum and management of dengue haemorrhagic fever. Southeast Asian J Trop Med Public Health, 18(3), 392-397.
Nimmannitya, S., Halstead, S. B., Cohen, S. N., ＆ Margiotta, M. R. (1969). Dengue and chikungunya virus infection in man in Thailand, 1962-1964. I. Observations on hospitalized patients with hemorrhagic fever. Am J Trop Med Hyg, 18(6), 954-971.
Ooi, E. E., Goh, K. T., ＆ Gubler, D. J. (2006). Dengue prevention and 35 years of vector control in Singapore. Emerg Infect Dis, 12(6), 887-893.
Ooi, E. E., ＆ Gubler, D. J. (2009). Dengue in Southeast Asia: epidemiological characteristics and strategic challenges in disease prevention. Cad Saude Publica, 25 Suppl 1, S115-124.
Ooi, E. E., Hart, T. J., Tan, H. C., ＆ Chan, S. H. (2001). Dengue seroepidemiology in Singapore. Lancet, 357(9257), 685-686.
Pham, H. V., Doan, H. T., Phan, T. T., ＆ Minh, N. N. (2011). Ecological factors associated with dengue fever in a Central Highlands province, Vietnam. BMC Infect Dis, 11, 172.
Racloz, V., Ramsey, R., Tong, S., ＆ Hu, W. (2012). Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Negl Trop Dis, 6(5), e1648.
Rigau-Perez, J. G., Clark, G. G., Gubler, D. J., Reiter, P., Sanders, E. J., ＆ Vorndam, A. V. (1998). Dengue and dengue haemorrhagic fever. Lancet, 352(9132), 971-977.
Rowley, W. A., ＆ Graham, C. L. (1968). The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. J Insect Physiol, 14(9), 1251-1257.
Shaman, J., ＆ Day, J. F. (2007). Reproductive phase locking of mosquito populations in response to rainfall frequency. PLoS One, 2(3), e331.
Srikiatkhachorn, A., Krautrachue, A., Ratanaprakarn, W., Wongtapradit, L., Nithipanya, N., Kalayanarooj, S., et al. (2007). Natural history of plasma leakage in dengue hemorrhagic fever: a serial ultrasonographic study. Pediatr Infect Dis J, 26(4), 283-290; discussion 291-282.
Sukri, N. C., Laras, K., Wandra, T., Didi, S., Larasati, R. P., Rachdyatmaka, J. R., et al. (2003). Transmission of epidemic dengue hemorrhagic fever in easternmost Indonesia. Am J Trop Med Hyg, 68(5), 529-535.
Sutherst, R. W. (2004). Global change and human vulnerability to vector-borne diseases. Clin Microbiol Rev, 17(1), 136-173.
Thai, K. T., Cazelles, B., Nguyen, N. V., Vo, L. T., Boni, M. F., Farrar, J., et al. (2010). Dengue dynamics in Binh Thuan province, southern Vietnam: periodicity, synchronicity and climate variability. PLoS Negl Trop Dis, 4(7), e747.
Thu, H. M., Aye, K. M., ＆ Thein, S. (1998). The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitos. Southeast Asian J Trop Med Public Health, 29(2), 280-284.
Tong, S., ＆ Hu, W. (2001). Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis. Environ Health Perspect, 109(12), 1271-1273.
Tong, S., Hu, W., ＆ McMichael, A. J. (2004). Climate variability and Ross River virus transmission in Townsville Region, Australia, 1985-1996. Trop Med Int Health, 9(2), 298-304.
Tun-Lin, W., Burkot, T. R., ＆ Kay, B. H. (2000). Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med Vet Entomol, 14(1), 31-37.
Van Kleef E, B. H., Hales S. (2011). The geographic distribution of dengue fever and the potential influence of global climate change. TropIKA Reviews.
Watts, D. M., Burke, D. S., Harrison, B. A., Whitmire, R. E., ＆ Nisalak, A. (1987). Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg, 36(1), 143-152.
Wilder-Smith, A., ＆ Gubler, D. J. (2008). Geographic expansion of dengue: the impact of international travel. Med Clin North Am, 92(6), 1377-1390, x.
World Health Organization Regional Office for the Western Pacific. (2009). Dengue in the Western Pacific region. Retrieved 2 July 2013. from http://www.wpro.who.int/health_topics/dengue/.
Wu, P. C., Guo, H. R., Lung, S. C., Lin, C. Y., ＆ Su, H. J. (2007). Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Trop, 103(1), 50-57.
Wu, P. C., Lay, J. G., Guo, H. R., Lin, C. Y., Lung, S. C., ＆ Su, H. J. (2009). Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. Sci Total Environ, 407(7), 2224-2233.
Yang, H. M., Macoris, M. L., Galvani, K. C., Andrighetti, M. T., ＆ Wanderley, D. M. (2009). Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue. Epidemiol Infect, 137(8), 1188-1202.