Risk of Dengue Incidence in Children and Adolescents in Zulia, Venezuela, using a Negative Binomial Generalized Linear Mixed Model


Autores/as

  • Maritza Cabrera
  • Gordon Taylor
  • Víctor Saldaña-Núñez
  • Fernando Córdova-Lepe
  • Juan Pablo Escalera-Antezana
  • Lucia Elena Alvarado-Arnez
  • Alfonso J. Rodriguez-Morales

Resumen

Introduction: Dengue is the most important arboviral disease. Its incidence has increased 30-fold over the last 50 years, causing global concerns. Studies have showed children to be the most vulnerable. Methods: Observational study using dengue cases from Zulia state, Venezuela, modelling through a Negative Binomial Generalized Linear Mixed Model (GLMM) accounting for heterogeneity in the variance via a hierarchical Bayesian framework, was done. We assessed risk factors such as age and sex. The Bayesian framework enabled the estimation of Relative Risk (RR) and a Binomial regression was run using the WinBUGS software. Results: During 2002-2008, there were 49,330 cases of dengue in Zulia state, Venezuela. Most of them (18.71%) in 2007. The model revealed that children aged from 5 to 14 y-old had 1.59-higher risk (95%CI 1.41-1.79) compared with those aged from 0-4 y-old. Those aged 25-44 years old and ≥45, have significantly less RR than the baseline category, RR 0.5228 (95%CI 0.46-0.59) and 0.3069 (95%CI 0.27-0.34). Conclusions: The findings confirmed that groups most at risk were children aged 5 to 14 years. Modelling and predicting dengue epidemiology are still a need in multiple countries, especially those at risk of newer epidemics, as is the case of Zulia and Venezuela.

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Oletta JF. Dengue en America Latina y Venezuela. Med Interna 2006; 22(4):247-258.

Brightmer MI, Fantato MG. Human and environmental factors in the increasing incidence of dengue fever: a case study from Venezuela. GeoJournal 1998:44(2):103-109.

SanMartin JL, Brathwaite O, Zambrano B, Solorzano JO, Bouckenooghe A, Dayan GH, Guzman MG. The epidemiology of dengue in the Americas over the last three decades: a worrisome reality. Am J Trop Med Hyg 2010;82(1):128-135.

Anez G, Balza R, Valero N, Larreal Y. Impacto economico del dengue y del dengue hemorragico en el Estado Zulia, Venezuela, 1997 a 2003. Rev Panam Salud Publica 2006;19(5):314-320.

Gelman A, Hill J. Data analysis using regression and Multilevel Hierarchical. Cambridge University Press, 2007.

Zuur I, Walker S, Smith S. Mixed Effects Models and Extensions in Ecology with R. Springer Science, New York, 2009.

Cabrera M, Taylor G, Sim M. Geospatial & temporal modelling of dengue in Zulia state, Venezuela, 2002-2008: TIES 2010- the 21st Anuual Conference of The International Environmetrics Society, Margarita Island, Venezuela, 2010.

Kyle J, Harris E. Global spread and persistence of dengue. Annu Rev Microbiol. 2008;62:71-92.

Lowe R, Bailey T, Stephenson, D, Graham R, Coelho C, Carvalho M, Barcellos Ch. Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil. Computers & Geosciences 2011;37(3):371-381.

Raja NS, Devi S. The incidence of dengue disease in a university teaching hospital in Malaysia in 2002, 2003 and 2004. Infect Dis Pak 2006;15(4):99-102.

Sierra B, Kouri G, Guzman M. Race: a risk factor for dengue hemorrhagic fever. Arch Virol 2007; 152(3):533-542.

Kongsomboon K, Singhasivanon P, Kaewkungwal J, Nimmannitya S, Mammen M.P, Nisalak A, Sawanpanyalert P. Temporal trends of dengue fever/dengue hemorrhagic fever in Bangkok, Thailand from 1981 to 2000: an age-period-cohort analysis. Southeast Asian J Trop Med Public Health 2004;35(4):913-917.

Morrison A, Getis A, Santiago M, Rigau-Perez J, Reiter P. Exploratory space-time analysis of reported dengue cases during an outbreak in Florida, Puerto Rico, 1991-1992. American Journal of Tropical Medicine and Hygiene 1998;58(3):287-298.

Guha-Sapir D, Schimmer B. Dengue fever: new paradigms for a changing epidemiology. Emerg Themes Epidemiol 2005;2(1):1-5.

Hoeff, J, Boveng P. Quasi Poisson vs. Negative Binomial regression. How we should model overdispersed data. Ecology 2007;88(11):2766-2772.

Spiegelhalter D, Thomas A, Best N, Lunn D. WinBUGS User Manual Version 1.4. Institute of Public Health Cambridge and Imperial College School of Medicine, 2003.

Lunn D, Thomas A, Best N, Spiegelhalter D. WinBUGS – A Bayesian modelling framework: Concepts, structure and extensibility. Statistics and Computing 2000;10:325-337.

Chien CK, Chen TH. A Bayesian model for age, period, and cohort effects on mortality trends for lung cancer, in association with gender-specific incidence and case fatality rates. J Thorac Oncol 2009;4(2):1-5.

Spiegelhalter D. Bayesian graphical modelling: a case-study in monitoring health outcomes. Appl. Statist 1998;47:115-133.

Greendland S. Bayesian perspective for epidemiological research: I Foundations and basic methods. International Journal of Epidemiology 2006;35:765-775.

McMahon P, Zaslavsky A, Weinstein M, Kuntz K, Weeks J, Gazelle G. Estimation of mortality rates for disease simulation models using Bayesian evidence synthesis. Med Decis Making 2006;26(5):497-511.

Weiss J. Refit the frequentist and Bayesian analysis of covariance. http://www.unc.edu/courses/2010fall/ecol/563/001/docs/lectures/lecture14.htm. Accessed Jul. 6, 2017.

Tan SB. Introduction to Bayesian methods for medical research. Ann Acad Med Singapore 2001; 30(4):444-446.

Lawson A. Bayesian Disease mapping, Hierarchical modelling in Spatial epidemiology. Chapman & Hall CRC, Boca Raton, U.S., 2009.

Banerjee S, Carlin B, Gelfand A. Hierarchical modeling and analysis for spatial data. Chapman & Hall CRC, Boca Raton, U.S., 2004.

Richardson S, Thomson A, Best N, Elliott P. Interpreting posterior relative risk estimates in disease mapping studies. Environ Health Perspect 2004;112(9):1016-1025.

Weiss J. Predictive simulation. http://www.unc.edu/courses/2008fall/ecol/563/001/docs/lectures/lecture14.htm . Accessed Jul. 6, 2017.

Ntzoufras I. Bayesian Modeling using WINBUGS, An Introduction. John Wiley & Sons, Inc. New Jersey, 2009.

Rodríguez-Morales AJ, Villamil-Gómez WE, Franco-Paredes C. The arboviral burden of disease caused by co-circulation and co-infection of dengue, chikungunya and Zika in the Americas. Travel Medicine & Infectious Disease 2016; 14(3):177-179.

Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S. Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infect Dis. 2014;14:167.

Chan M. Global strategy for dengue prevention and control (num WC-528). WHO, Geneva, Switzerland, 2012.

Bennett SN, Drummond AJ, Kapan DD, Suchard MA, Muñoz-Jordán JL, Pybus OG, Holmes EC, Gubler DJ. Epidemic dynamics revealed in dengue evolution. Mol Biol Evol. 2010;27(4):811-8.

Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006 ;1:1-19.

Chipman H, George E, McCulloch R, Clyde M, Foster D, Stine R. The Practical Implementation of Bayesian Model Selection. Lecture Notes-Monograph Series 2001;38:65-116.

Trevisani M, Torelli N. Hierarchical Bayesian models for small area estimation with count data. Dipartimento di Scienze Economiche e Statistiche, Universita Degli studi di Trieste, 2007.

Gimenez O, Bonner S, Bonner J, King R, Parker R, Brooks S, Jamieson L, Grosbois V, Morgan B, Thomas L. WinBUGS for Population Ecologists Bayesian Modeling Using Markov Chain Monte Carlo methods. Environmental and Ecological Statistics 2009:2:883-915.

Basáñez MG, Marshall C, Carabin H, Gyorkos T, Joseph L. Bayesian statistics for parasitologists. Trends Parasitol. 2004;20(2):85-91.

Achee NL, Gould F, Perkins TA, Reiner RC Jr, Morrison AC, Ritchie SA, Gubler DJ, Teyssou R, Scott TW. A critical assessment of vector control for dengue prevention. PLoS Negl Trop Dis. 2015 May 7;9(5):e0003655.

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(5):864-83.

Rodríguez-Morales AJ, Paniz-Mondolfi AE. Venezuela: far from the path to dengue and chikungunya control. J Clin Virol. 2015;66:60-61.

Rodriguez-Morales AJ, Ruiz P, Tabares J, Ossa CA, Yepes-Echeverry MC, Ramirez-Jaramillo V, Galindo-Marquez ML, García-Loaiza CJ, Sabogal-Roman JA, Parra-Valencia E, Lagos-Grisales GJ, Lozada-Riascos CO, de Pijper CA, Grobusch MP. Mapping the Ecoepidemiology of Zika virus infection in Urban and Rural Areas of Pereira, Risaralda, Colombia, 2015-2016: Implications for Public Health and Travel Medicine. Travel Med Infect Dis 2017; 18(C):57-66.

Rodríguez-Morales AJ, Suárez JA, Risquez A, Delgado-Noguera L, Paniz-Mondolfi A. The current syndemic in Venezuela: measles, malaria and more co-infections coupled with a breakdown of social and healthcare infrastructure. Quo vadis? Travel Med Infect Dis. 2019;27:5-8.

Arenívar C, Rodríguez Y, Rodríguez-Morales AJ, Anaya JM. Osteoarticular manifestations of Mayaro virus infection. Curr Opin Rheumatol. 2019;31(5):512-516.

Cabrera M, Taylor G. Modelling spatio-temporal data of dengue fever using generalized additive mixed models. Spat Spatiotemporal Epidemiol. 2019;28:1-13.

Rodriguez-Morales AJ, Haque U, Ball J, García-Loaiza CJ, Galindo-Marquez ML, Sabogal-Roman JA, Marin-Loaiza S, Ayala AF, Lozada-Riascos CO, Diaz-Quijano FA, Alvarado-Socarras JL. Spatial distribution of Zika virus infection in Northeastern Colombia. Infez Med. 2017;25(3):241-246.

Thahir-Silva S, Betancourt-Trejos ML, García-Loaiza CJ, Villegas-Rojas S, Cardona-Ospina JA, Lagos-Grisales GJ, Soto-Arbelaez A, Rodriguez-Morales AJ. Mapping Zika in the 125 municipalities of Antioquia department of Colombia using Geographic Information System (GIS) during 2015-2016 outbreak. Infez Med. 2018;26(2):178-180.

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2020-02-16

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