BackgroundDengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach.MethodsWe estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1–20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008–2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen’s Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation.ResultsWe found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair.ConclusionThe study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.
The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015–2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.
We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015–December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.
Introducción: durante la noche del 31 de marzo y la madrugada de abril 1 se presentó creciente súbita y desbordamiento de los ríos Mocoa, Mulato y Sangoyaco sobre la cabecera municipal de Mocoa, ocasionando avalancha, inundaciones, afectando la población, infraestructura y líneas vitales, lo que requirió alojamiento de damnificados en alojamientos temporales. Ante el riesgo de enfermedad se implementaron estrategias de vigilancia. El objetivo fue determinar características socio-demográficas de población albergada, establecer condiciones sanitarias de los albergues y determinar necesidades críticas de salud. Métodos: estudio transversal de las familias afectadas, alojadas en albergues. Fueron realizadas dos encuestas: una para caracterización de las condiciones higiénico-sanitarias del albergue y otra de las condiciones de salud de las familias. Se aplicó la metodología de la Evaluación Comunitaria de Respuesta a Emergencias de Salud Pública (CASPER) de los CDC. Fueron calculadas medidas de frecuencia, tendencia central, dispersión y morbilidad sentida y auto-reportada. Resultados: Fue encuestado el 92,6%(1.959) de los residentes de 13 alojamientos temporales de emergencia y un ancianato. El 54,1%(1.048) eran mujeres; 29,5%(571) menores de 15 años y 19,5%(378) indígenas. El 30,8 % de los albergues contaba con atención médica continua; el 92,3% tenían agua para el consumo humano (embotellada). El 27,5% presentó algún tipo de trastorno o síndrome: 11,7%(227) psicológico, 4,3%(83) febril y 4,1%(79) respiratorio, de predominancia en población de 15- 44 años 15,2%(295) y 95,3%(508) manifestada post-emergencia. El 6,2% de la población refirió morbilidad auto-reportada, siendo hipertensión la más reportada 1,5%(29). Conclusiones: la incidencia de eventos post-desastre y la prevalencia de morbilidad auto-reportada permitió que los mecanismos de respuesta dieran atención oportuna a las necesidades de salud de esta población.
Introducción: en el departamento de Santander el primer caso de COVID-19 se reportó el 15 de marzo de 2020. Objetivo: analizar el comportamiento de la epidemia por COVID-19 en Santander durante 2020 desde un enfoque territorial de provincias y evaluar la asociación de algunas condiciones con la letalidad por COVID-19. Metodología: estudio descriptivo para la caracterización de la epidemia complementado con un abordaje analítico de casos y controles de la letalidad. Se analizaron los casos confirmados en el sistema de vigilancia en salud pública de Santander entre el 15 de marzo de 2020 y el 31 de diciembre de 2020. Se calcularon medidas de incidencia, mortalidad y letalidad por provincia y municipio. Las condiciones asociadas con la letalidad se analizaron mediante un modelo de regresión logística multivariable. Resultados: en Santander se confirmaron 69 190 casos con infección por el virus SARS-CoV-2 y 4210 muertes por COVID-19 durante 2020. Las provincias Metropolitana y Yariguíes presentaron las mayores proporciones de incidencia y mortalidad mientras que las provincias de Soto Norte y García Rovira registraron mayor letalidad. Ser hombre, tener edad mayor a 60 años, tener pertenencia étnica indígena, pertenecer al régimen subsidiado, presentar alguna comorbilidad y tener retraso en el diagnóstico mayor a tres días fueron las condiciones asociadas con la letalidad en Santander. Conclusiones: las tasas de incidencia, letalidad y mortalidad evidenciaron diferentes niveles de afectación en las provincias. Existen condiciones sociodemográficas y de atención en salud asociadas con mayor letalidad por COVID-19 en Santander.
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