Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010

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• 作者：Anna M Stewart-Ibarra ; ángel G Mu?oz ; Sadie J Ryan…
• 关键词：Dengue fever ; Aedes aegypti ; GIS ; Social ; ecological ; Climate ; Spatial ; Temporal ; Wavelet analysis ; Ecuador ; Early warning system
• 刊名：BMC Infectious Diseases
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：14
• 期：1
• 全文大小：4,235 KB
• 参考文献：1. Dick, OB, Martín, JLS, Montoya, RH, Diego, J, Zambrano, B, Dayan, GH (2012) The history of dengue outbreaks in the Americas. Am J Trop Med Hyg 87: pp. 584-593 jtmh.2012.11-0770" target="_blank" title="It opens in new window">CrossRef
  2. San Martín, JL, Brathwaite, O, Zambrano, B, Solórzano, JO, Bouckenooghe, A, Dayan, GH, Guzmán, MG (2010) The epidemiology of dengue in the Americas over the last three decades: a worrisome reality. Am J Trop Med Hyg 82: pp. 128-135 jtmh.2010.09-0346" target="_blank" title="It opens in new window">CrossRef
  Global Strategy for Dengue Prevention and Control, 2012-020. WHO, Geneva
  Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. WHO, Geneva
  3. Gubler, DJ (2002) Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10: pp. 100-103 CrossRef
  4. Bhatt, S, Gething, PW, Brady, OJ, Messina, JP, Farlow, AW, Moyes, CL, Drake, JM, Brownstein, JS, Hoen, AG, Sankoh, O, Myers, MF, George, DB, Jaenisch, T, Wint, GRW, Simmons, CP, Scott, TW, Farrar, JJ, Hay, SI (2013) The global distribution and burden of dengue. Nature 496: pp. 504-507 CrossRef
  5. Messina, JP, Brady, OJ, Scott, TW, Zou, C, Pigott, DM, Duda, KA, Bhatt, S, Katzelnick, L, Howes, RE, Battle, KE, Simmons, CP, Hay, SI (2014) Global spread of dengue virus types: mapping the 70?year history. Trends Microbiol 22: pp. 138-146 j.tim.2013.12.011" target="_blank" title="It opens in new window">CrossRef
  6. Kyle, JL, Harris, E (2008) Global spread and persistence of dengue. Annu Rev Microbiol 62: pp. 71-92 CrossRef
  7. Kuhn, K, Campbell-Lendrum, D, Haines, A, Cox, J (2005) Using climate to predict infectious disease epidemics. World Health Organization, Geneva
  8. Thomson MC, Garcia-Herrera R, Beniston M (Eds.): / Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. Springer; 2008.
  9. Lowe, R, Bailey, TC, Stephenson, DB, Jupp, TE, Graham, RJ, Barcellos, C, Carvalho, MS (2013) The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in southeast Brazil. Stat Med 32: pp. 864-883 CrossRef
  10. 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: pp. e1648 journal.pntd.0001648" target="_blank" title="It opens in new window">CrossRef
  11. Yu, H-L, Yang, S-J, Yen, H-J, Christakos, G (2011) A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stoch Environ Res Risk Assess 25: pp. 485-494 CrossRef
  12. Grover-Kopec, E, Kawano, M, Klaver, RW, Blumenthal, B, Ceccato, P (2013) An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J 4: pp. 5
  13. Ruiz D, Connor SJ, Thomson MC: A multimodel framework in support of malaria surveillance and control. In / Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. Springer; 2008:101-25.
  14. Thomson, MC, Mason, SJ, Phindela, T, Connor, SJ (2005) Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73: pp. 214-221
  15. Anyamba, A, Chretien, J-P, Small, J, Tucker, CJ, Formenty, PB, Richardson, JH, Britch, SC, Schnabel, DC, Erickson, RL, Linthicum, KJ (2009) Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci 106: pp. 955-959 CrossRef
  16. Almeida, AS, Medronho, RA, Valencia, LIO (2009) Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (southeastern Brazil). Rev Saúde Publica 43: pp. 666-673 CrossRef
  17. Mattos Almeida, MC, Caiaffa, WT, Assun?ao, RM, Proietti, FA (2007) Spatial vulnerability to dengue in a Brazilian urban area during a 7-yea
• 刊物主题：Infectious Diseases; Parasitology; Medical Microbiology; Tropical Medicine; Internal Medicine;
• 出版者：BioMed Central
• ISSN：1471-2334

文摘

Background Dengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS. Methods The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran’s I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak. Results We found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak. Conclusions Our findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.