Literature DB >> 25564418

Potential impact of climatic variability on the epidemiology of dengue in Risaralda, Colombia, 2010-2011.

Liseth L Quintero-Herrera1, Valeria Ramírez-Jaramillo1, Sergio Bernal-Gutiérrez1, Erika V Cárdenas-Giraldo1, Edwin A Guerrero-Matituy1, Anderson H Molina-Delgado1, Cindy P Montoya-Arias1, Jhon A Rico-Gallego1, Albert C Herrera-Giraldo2, Shirley Botero-Franco3, Alfonso J Rodríguez-Morales4.   

Abstract

Dengue continues to be the most important viral vector-borne disease in the world, particularly in Asia and Latin America, and is significantly affected by climate variability. The influence of climate in an endemic region of Colombia, from 2010 to 2011, was assessed. Epidemiological surveillance data (weekly cases) were collected, and incidence rates were calculated. Poisson regression models were used to assess the influence of the macroclimatic variable ONI (Oscillation Niño Index) and the microclimatic variable pluviometry (mm of rain for Risaralda) on the dengue incidence rate, adjusting by year and week. During the study period, 13,650 cases were reported. In 2010, the rates ranged from 8.6 cases/100,000 pop. up to a peak of 75.3 cases/100,000 pop. for a cumulative rate of 456.2 cases/100,000 pop. in that week. The climate variability in 2010 was higher (ONI 1.6, El Niño to -1.5, La Niña) than in 2011 (ONI -1.4, La Niña to -0.2, Neutral). The mean pluviometry was 248.45mm (min 135.9-max 432.84). During El Niño, cases were significantly higher (mean 433.81) than during the climate neutral period (142.48) and during the La Niña (52.80) phases (ANOVA F=66.59; p<0.001). Regression models showed that the ONI (coefficient 0.329; 95%CI 0.209-0.450) and pluviometry (coefficient 0.003; 95%CI 0.002-0.004) were highly significant independent variables associated with dengue incidence rate, after adjusting by year and week (p<0.001, pseudo r(2)=0.6913). El Niño significantly affected the incidence of dengue in Risaralda. This association with climate change and variability should be considered in the elements influencing disease epidemiology. In addition, predictive models should be developed further with more available data from disease surveillance.
Copyright © 2014 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Keywords:  Climate change; Climate variability; Colombia; Dengue; Ecoepidemiology

Mesh:

Year:  2015        PMID: 25564418     DOI: 10.1016/j.jiph.2014.11.005

Source DB:  PubMed          Journal:  J Infect Public Health        ISSN: 1876-0341            Impact factor:   3.718


  9 in total

1.  Mapping Zika virus disease incidence in Valle del Cauca.

Authors:  Alfonso J Rodriguez-Morales; Maria Leonor Galindo-Marquez; Carlos Julian García-Loaiza; Juan Alejandro Sabogal-Roman; Santiago Marin-Loaiza; Andrés F Ayala; Guillermo J Lagos-Grisales; Carlos O Lozada-Riascos; Esteban Parra-Valencia; Jorge H Rojas-Palacios; Eduardo López; Pío López; Martin P Grobusch
Journal:  Infection       Date:  2016-10-14       Impact factor: 3.553

2.  Epidemiology of Cutaneous Leishmaniasis in a Colombian Municipality.

Authors:  Diego Alejandro Medina-Morales; Manuel E Machado-Duque; Jorge Enrique Machado-Alba
Journal:  Am J Trop Med Hyg       Date:  2017-08-18       Impact factor: 2.345

3.  A prospective cohort study to assess seroprevalence, incidence, knowledge, attitudes and practices, willingness to pay for vaccine and related risk factors in dengue in a high incidence setting.

Authors:  Ruth Aralí Martínez-Vega; Alfonso J Rodriguez-Morales; Yalil Tomás Bracho-Churio; Mirley Enith Castro-Salas; Fredy Galvis-Ovallos; Ronald Giovanny Díaz-Quijano; María Lucrecia Luna-González; Jaime E Castellanos; José Ramos-Castañeda; Fredi Alexander Diaz-Quijano
Journal:  BMC Infect Dis       Date:  2016-11-25       Impact factor: 3.090

4.  Relative risk estimation of dengue disease at small spatial scale.

Authors:  Daniel Adyro Martínez-Bello; Antonio López-Quílez; Alexander Torres Prieto
Journal:  Int J Health Geogr       Date:  2017-08-15       Impact factor: 3.918

5.  The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh.

Authors:  Fazle Rabbi Chowdhury; Quazi Shihab Uddin Ibrahim; Md Shafiqul Bari; M M Jahangir Alam; Susanna J Dunachie; Alfonso J Rodriguez-Morales; Md Ismail Patwary
Journal:  PLoS One       Date:  2018-06-21       Impact factor: 3.240

Review 6.  Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence.

Authors:  Florence Fouque; John C Reeder
Journal:  Infect Dis Poverty       Date:  2019-06-13       Impact factor: 4.520

7.  Time series forecasting for tuberculosis incidence employing neural network models.

Authors:  Alvaro David Orjuela-Cañón; Andres Leonardo Jutinico; Mario Enrique Duarte González; Carlos Enrique Awad García; Erika Vergara; María Angélica Palencia
Journal:  Heliyon       Date:  2022-07-06

8.  Bayesian dynamic modeling of time series of dengue disease case counts.

Authors:  Daniel Adyro Martínez-Bello; Antonio López-Quílez; Alexander Torres-Prieto
Journal:  PLoS Negl Trop Dis       Date:  2017-07-03

9.  Dengue and COVID-19, overlapping epidemics? An analysis from Colombia.

Authors:  Jaime A Cardona-Ospina; Kovy Arteaga-Livias; Wilmer E Villamil-Gómez; Carlos E Pérez-Díaz; D Katterine Bonilla-Aldana; Álvaro Mondragon-Cardona; Marco Solarte-Portilla; Ernesto Martinez; Jose Millan-Oñate; Eduardo López-Medina; Pio López; Juan-Carlos Navarro; Luis Perez-Garcia; Euler Mogollon-Rodriguez; Alfonso J Rodríguez-Morales; Alberto Paniz-Mondolfi
Journal:  J Med Virol       Date:  2020-07-11       Impact factor: 20.693

  9 in total

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