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

BMC Infectious Diseases

Keytia Rivero¹  G Cristina Recalde-Coronel²  Tania Ordoñez⁸  Raúl Mejía¹  Julia L Finkelstein⁶  Mercy J Borbor-Cordova²  Efraín Beltrán Ayala⁵  Sadie J Ryan⁷  Ángel G Muñoz⁴  Anna M Stewart-Ibarra³ 

[1] National Institute of Meteorology and Hydrology, Guayaquil, Ecuador;Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador;Department of Microbiology and Immunology, Center for Global Health and Translational Science, State University of New York Upstate Medical University, 750 East Adams St, Syracuse 13210, NY, USA;Centro de Modelado Científico (CMC), Universidad del Zulia, Maracaibo, Venezuela;Facultad de Medicina, Universidad Técnica de Machala, Machala, El Oro Province, Ecuador;Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA;School of Life Sciences, College of Agriculture, Engineering, and Science, University of KwaZulu-Natal, Durban, South Africa;The National Service for the Control of Vector-Borne Diseases, Ministry of Health, Machala, El Oro Province, Ecuador

关键词: Early warning system;    Ecuador;    Wavelet analysis;    Temporal;    Spatial;    Climate;    Social-ecological;    GIS;    Aedes aegypti;    Dengue fever;   

Others  :  1121973
DOI  :  10.1186/s12879-014-0610-4

 received in 2014-06-12, accepted in 2014-11-04,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Stewart-Ibarra et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

【 参考文献 】

• [1]Dick OB, Martín JLS, Montoya RH, del Diego J, Zambrano B, Dayan GH: The history of dengue outbreaks in the Americas. Am J Trop Med Hyg 2012, 87:584-593.
• [2]San Martín JL, Brathwaite O, Zambrano B, Solórzano JO, Bouckenooghe A, Dayan GH, Guzmán MG: The epidemiology of dengue in the Americas over the last three decades: a worrisome reality. Am J Trop Med Hyg 2010, 82:128-135.
• [3]Global Strategy for Dengue Prevention and Control, 2012–2020. WHO, Geneva; 2012.
• [4]Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. WHO, Geneva; 2009.
• [5]Gubler DJ: Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 2002, 10:100-103.
• [6]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: The global distribution and burden of dengue. Nature 2013, 496:504-507.
• [7]Messina JP, Brady OJ, Scott TW, Zou C, Pigott DM, Duda KA, Bhatt S, Katzelnick L, Howes RE, Battle KE, Simmons CP, Hay SI: Global spread of dengue virus types: mapping the 70 year history. Trends Microbiol 2014, 22:138-146.
• [8]Kyle JL, Harris E: Global spread and persistence of dengue. Annu Rev Microbiol 2008, 62:71-92.
• [9]Kuhn K, Campbell-Lendrum D, Haines A, Cox J: Using climate to predict infectious disease epidemics. World Health Organization, Geneva; 2005.
• [10]Thomson MC, Garcia-Herrera R, Beniston M (Eds.): Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. Springer; 2008.
• [11]Lowe R, Bailey TC, Stephenson DB, Jupp TE, Graham RJ, Barcellos C, Carvalho MS: The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in southeast Brazil. Stat Med 2013, 32:864-883.
• [12]Racloz V, Ramsey R, Tong S, Hu W: Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Negl Trop Dis 2012, 6:e1648.
• [13]Yu H-L, Yang S-J, Yen H-J, Christakos G: A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stoch Environ Res Risk Assess 2011, 25:485-494.
• [14]Grover-Kopec E, Kawano M, Klaver RW, Blumenthal B, Ceccato P: An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J 2013, 4:5.
• [15]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; 2008:101–125.
• [16]Thomson MC, Mason SJ, Phindela T, Connor SJ: Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 2005, 73:214-221.
• [17]Anyamba A, Chretien J-P, Small J, Tucker CJ, Formenty PB, Richardson JH, Britch SC, Schnabel DC, Erickson RL, Linthicum KJ: Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci 2009, 106:955-959.
• [18]Almeida AS, Medronho RA, Valencia LIO: Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (southeastern Brazil). Rev Saúde Publica 2009, 43:666-673.
• [19]De Mattos Almeida MC, Caiaffa WT, Assunçao RM, Proietti FA: Spatial vulnerability to dengue in a Brazilian urban area during a 7-year surveillance. J Urban Health 2007, 84:334-345.
• [20]Mondini A, de Moraes Bronzoni RV, Nunes SHP, Neto FC, Massad E, Alonso WJ, Lázzaro ES, Ferraz AA, de Andrade Zanotto PM, Nogueira ML: Spatio-temporal tracking and phylodynamics of an urban dengue 3 outbreak in Sao Paulo, Brazil. PLoS Negl Trop Dis 2009, 3:e448.
• [21]da Teixeira GM, da Costa MCN, Ferreira LDA, Vasconcelos PFC, Cairncross S: Dynamics of dengue virus circulation: a silent epidemic in a complex urban area. Trop Med Int Health 2002, 7:757-762.
• [22]Stewart Ibarra AM, Ryan SJ, Beltrán E, Mejía R, Silva M, Muñoz Á: Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control. PloS One 2013, 8:e78263.
• [23]Moore CG, Cline BL, Ruiz-Tiben E, Lee D, Romney-Joseph H, Rivera-Correa E: Aedes aegypti in Puerto Rico: environmental determinants of larval abundance and relation to dengue virus transmission. Am J Trop Med Hyg 1978, 27:1225-1231.
• [24]Barrera R, Amador M, MacKay AJ: Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl Trop Dis 2011, 5:e1378.
• [25][http://www.salud.gob.ec/direccion-nacional-de-vigilancia-epidemiologica/] webcite Ministerio de Salud Publica de Ecuador Dirección Nacional de Vigilancia Epidemiológica: Anuario Epidemiológico, 1994-2014. Quito; 2014. []
• [26]Stewart Ibarra AM, Lowe R: Climate and non-climate drivers of dengue epidemics in southern coastal ecuador. Am J Trop Med Hyg 2013, 88:971-981.
• [27]Muñoz ÁG, Stewart-Ibarra AM, Ruiz Carrascal D: Desarrollo de modelos de pronóstico experimental: análisis socio ecológico de riesgo a dengue y análisis estadístico de patrones climáticos, entomológicos y epidemiológicos en modelos de dengue. In Technical Report for the CLIDEN Project. INAMHI-SENESCYT, Quito; 2013.
• [28]Recalde-Coronel GC, Barnston AG, Muñoz ÁG: Predictability of December-April rainfall in coastal and Andean Ecuador. J Appl Meteorol Climatol 2014, 53(6):1471-1493.
• [29][http://www.ecuadorencifras.gob.ec/] webcite Instituto Nacional de Estadística y Censos (INEC): Censo de Poblacion Y Vivienda. Quito: 2010 []
• [30]Arunachalam N, Tana S, Espino F, Kittayapong P, Abeyewickrem W, Wai KT, Tyagi BK, Kroeger A, Sommerfeld J, Petzold M: Eco-bio-social determinants of dengue vector breeding: a multicountry study in urban and periurban Asia. Bull World Health Organ 2010, 88:173-184.
• [31]Quintero J, Brochero H, Manrique-Saide P, Barrera-Pérez M, Basso C, Romero S, Caprara A, Cunha JCDL, Ayala EB, Mitchell-Foster K, Kroeger A, Sommerfeld J, Petzold M: Ecological, biological and social dimensions of dengue vector breeding in five urban settings of Latin America: a multi-country study. BMC Infect Dis 2014, 14:38. BioMed Central Full Text
• [32]Real J, Mosquera C: Detección del virus dengue en el ecuador. una visión epidemiológica. Periodo, 1988–2012. Instituto Nacional de Higiene y Medicina Tropical del Ministerio de Salud Publica, Guayaquil; 2012.
• [33]Servicio Nacional para el Control de Enfermedades Transmitidas por Artropodos (SNEM), Ministerio de Salud Publica (MSP): Informe general por provincias, cantones/localidades. programa de control y vigilancia del Aedes aegypti. Indices Breteau y Casa por ciclos. Guayaquil.
• [34]Hartter J, Ryan SJ, MacKenzie CA, Parker JN, Strasser CA: Spatially explicit data: stewardship and ethical challenges in science. PLoS Biol 2013, 11:e1001634.
• [35]Beyer HL: Geospatial Modelling Environment v0. 7.0. 2012.
• [36]Beyer HL, Jenness J, Cushman SA: Components of spatial information management in wildlife ecology: software for statistical and modeling analysis. In Spatial Complexity, Informatics, and Wildlife Conservation. Springer, Japan; 2010:245-253.
• [37]Anselin L: Local indicators of spatial association—LISA. Geogr Anal 1995, 27:93-115.
• [38]de Oliveira MA, Ribeiro H, Castillo-Salgado C, de Oliveira MA, Ribeiro H, Castillo-Salgado C: Geospatial analysis applied to epidemiological studies of dengue: a systematic review. Rev Bras Epidemiol 2013, 16:907-917.
• [39]Castillo KC, Körbl B, Stewart A, Gonzalez JF, Ponce F: Application of spatial analysis to the examination of dengue fever in Guayaquil, Ecuador. Procedia Environ Sci 2011, 7:188-193.
• [40]Calcagno V, de Mazancourt C: Glmulti: an R Package for easy automated model selection with (generalized) linear models. J Stat Softw 2010, 34:1-29.
• [41]Cazelles B, Chavez M, McMichael AJ, Hales S: Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med 2005, 2:e106.
• [42]Cazelles B, Chavez M, de Magny GC, Guégan J-F, Hales S: Time-dependent spectral analysis of epidemiological time-series with wavelets. J R Soc Interface 2007, 4:625-636.
• [43]Muñoz ÁG, Ruiz D, Ramírez P, León G, Quintana J, Bonilla A, Torres W, Pastén M, Sánchez O: Risk management at the Latin American Observatory. In Risk Manag - Curr Issues Chall. Edited by Banaitiene N. InTech; 2012:532–556.
• [44]Muñoz ÁG, López P, Velásquez R, Monterrey L, León G, Ruiz F, Recalde C, Cadena J, Mejía R, Paredes M: An environmental watch system for the Andean Countries: El Observatorio Andino. Bull Am Meteorol Soc 2010, 91:1645-1652.
• [45][http://cmc.org.ve/descargas/Cursos/CRRH/Manual_rclimdex_extraQC.r.pdf] webcite Aguilar E, Sigró J, Brunet M: RClimdex con funcionalidades extras de control de calidad. Manual de Uso, version 1.0 2009:12. []
• [46][http://etccdi.pacificclimate.org/RHtest/RHtestsV4_UserManual_20July2013.pdf] webcite RHTestsV4 software []
• [47]Wang XL, Chen H, Wu Y, Feng Y, Pu Q: New techniques for detection and adjustment of shifts in daily precipitation data series. J Appl Meteorol Climatol 2010, 49:2416-2436.
• [48]Wang XL: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or f test. J Appl Meteorol Climatol 2008, 47:2423-2444.
• [49]Wang XL: Penalized maximal f test for detecting undocumented mean shift without trend change. J Atmospheric Ocean Technol 2008, 25:368-384.
• [50]Torrence C, Compo GP: A practical guide to wavelet analysis. Bull Am Meteorol Soc 1998, 79:61-78.
• [51]Grinsted A, Moore JC, Jevrejeva S: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 2004, 11:561-566.
• [52]Lourenço J, Recker M: Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLoS Comput Biol 2013, 9:e1003308.
• [53]Reiner RC, Stoddard ST, Forshey BM, King AA, Ellis AM, Lloyd AL, Long KC, Rocha C, Vilcarromero S, Astete H, Bazan I, Lenhart A, Vazquez-Prokopec GM, Paz-Soldan VA, McCall PJ, Kitron U, Elder JP, Halsey ES, Morrison AC, Kochel TJ, Scott TW: Time-varying, serotype-specific force of infection of dengue virus. Proc Natl Acad Sci 2014, 111:E2694-E2702.
• [54]Cummings DAT, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K, Burke DS: Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 2004, 427:344-347.
• [55]Stewart Ibarra AM, Luzadis VA, Borbor Cordova MJ, Silva M, Ordoñez T, Beltrán Ayala E, Ryan SJ: A social-ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health 2014, 14:1135. BioMed Central Full Text
• [56]Galli B, Chiaravalloti Neto F: Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública 2008, 42:656-663.
• [57]Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA: Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010, 4:e920.
• [58]Hu W, Clements A, Williams G, Tong S: Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011, 139:391-399.
• [59]Endy TP, Nisalak A, Chunsuttiwat S, Libraty DH, Green S, Rothman AL, Vaughn DW, Ennis FA: Spatial and temporal circulation of dengue virus serotypes: a prospective study of primary school children in Kamphaeng Phet, Thailand. Am J Epidemiol 2002, 156:52-59.
• [60]Yoon I-K, Getis A, Aldstadt J, Rothman AL, Tannitisupawong D, Koenraadt CJM, Fansiri T, Jones JW, Morrison AC, Jarman RG, Nisalak A, Mammen MP Jr, Thammapalo S, Srikiatkhachorn A, Green S, Libraty DH, Gibbons RV, Endy T, Pimgate C, Scott TW: Fine scale spatiotemporal clustering of dengue virus transmission in children and Aedes aegypti in rural Thai villages. PLoS Negl Trop Dis 2012, 6:e1730.
• [61]Stoddard ST, Morrison AC, Vazquez-Prokopec GM, Soldan VP, Kochel TJ, Kitron U, Elder JP, Scott TW: The role of human movement in the transmission of vector-borne pathogens. PLoS Negl Trop Dis 2009, 3:e481.
• [62]Vazquez-Prokopec GM, Stoddard ST, Paz-Soldan V, Morrison AC, Elder JP, Kochel TJ, Scott TW, Kitron U: Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. Int J Health Geogr 2009, 8:68. BioMed Central Full Text
• [63]Andrighetti MTM, Galvani KC, da Gracca Macoris ML: Evaluation of Premise Condition Index in the context of Aedes aegypti control in Marília, São Paulo. Brazil. Dengue Bull 2009, 33:167.
• [64]Tun-Lin W, Kay BH, Barnes A: The Premise Condition Index: a tool for streamlining surveys of Aedes aegypti. Am J Trop Med Hyg 1995, 53:591-594.
• [65]Focks DA: A review of entomological sampling methods and indicators for dengue vectors. World Health Organization, Geneva; 2003.
• [66]Pant CP, Yasuno M: Field studies on the gonotrophic cycle of Aedes aegypti in Bangkok, Thailand. J Med Entomol 1973, 10:219-223.
• [67]Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A: Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg 1986, 36:143-152.
• [68]Rueda LM, Patel KJ, Axtell RC, Stinner RE: Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J Med Entomol 1990, 27:892-898.
• [69]Bar-Zeev M: The effect of temperature on the growth rate and survival of the immature stages of Aedes aegypti (L.). Bull Entomol Res 1958, 49:157-163.
• [70]Thu HM, Aye KM, Thein S: The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitos. Southeast Asian J Trop Med Public Health 1998, 29:280-284.
• [71]Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, Scott TW: Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl Acad Sci 2011, 108:7460-7465.
• [72]Chowell G, Cazelles B, Broutin H, Munayco CV: The influence of geographic and climate factors on the timing of dengue epidemics in Perú, 1994-2008. BMC Infect Dis 2011, 11:164. BioMed Central Full Text
• [73]Chowell G, Munayco CV, Escalante AA, McKenzie FE: The spatial and temporal patterns of falciparum and vivax malaria in Perú: 1994-2006. Malar J 2009, 8:142. BioMed Central Full Text
• [74]Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda N, Shanks GD, Snow RW, Rogers DJ: Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci 2000, 97:9335-9339.
• [75]Wearing HJ, Rohani P: Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci 2006, 103:11802-11807.
• [76]Delmelle EM, Zhu H, Tang W, Casas I: A web-based geospatial toolkit for the monitoring of dengue fever. Appl Geogr 2014, 52:144-152.
• [77]Chang AY, Parrales ME, Jimenez J, Sobieszczyk ME, Hammer SM, Copenhaver DJ, Kulkarni RP: Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries. Int J Health Geogr 2009, 8:49. BioMed Central Full Text
• [78]Moreno-Sanchez R, Hayden M, Janes C, Anderson G: A web-based multimedia spatial information system to document Aedes aegypti breeding sites and dengue fever risk along the US–Mexico border. Health Place 2006, 12:715-727.
• [79]Hernández-Ávila JE, Rodríguez M-H, Santos-Luna R, Sánchez-Castañeda V, Román-Pérez S, Ríos-Salgado VH, Salas-Sarmiento JA: Nation-wide, web-based, geographic information system for the integrated surveillance and control of dengue fever in Mexico. PLoS ONE 2013, 8:e70231.