Relative risk estimation of dengue disease at small spatial scale

Abstract

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.

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

“…Nine studies used geographic characteristics in their model. Altitude [ 26 – 28 ] and mean vegetation index [ 29 , 46 , 47 ] were the most common features used. Out of nine geographic variables applied, most studies used only one indicator.…”

“…Martínez-Bello et al . [ 46 ] compared CAR BYM (Besag, York and Mollié) [ 62 ] prior and Leroux CAR prior [ 63 ] for spatially structured random effects for estimating relative risk of dengue. They found that the CAR BYM prior was better than the Leroux CAR prior.…”

“…Geographical data were used in nine studies reviewed. The most common indicator was altitude [ 26 – 28 ] and mean vegetation index [ 29 , 46 , 47 ]. Only one study [ 38 ] used three indicators, namely the mean elevation from sea level, elevation range and distance from the CTs to the public health unit.…”

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

“…Relative risk estimation of dengue disease has been developed using spatial and spatio-temporal data at several spatial resolutions. For example, spatial modeling of dengue data has been applied to data from Brazil [ 12 ] and Colombia [ 13 ], while spatio-temporal dengue data have been analyzed using relative risk models in Brazil [ 14 – 16 ], Ecuador [ 17 ], Thailand [ 18 ], Colombia [ 19 ] [ 20 ], and Indonesia [ 21 ]. However, most of these analyses did not fully explore the space-time interaction effect model framework.…”

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

“…Racloz et al ( 6 ) and Louis et al ( 7 ) reviewed the spatial patterns assessment of dengue risk; specifically for RR estimation of dengue, Ferreira and Schmidt ( 8 ) and Martínez-Bello et al ( 9 ) estimated RR for dengue on a local spatial scale; and Restrepo et al ( 10 ) and Martínez-Bello et al ( 11 ) applied methods for the spatiotemporal assessment of dengue risk. Examples for the spatial patterns assessment of Zika risk run from merely descriptive methods to model-based approaches.…”