Relative risk estimation of dengue disease at small spatial scale

Abstract

Background: Dengue 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.

Methods: We 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.

Results: We 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.

Conclusion: The 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.

Keywords: Bayesian modeling; Cohen’s Kappa; Disease mapping; Satellite images.

Fig. 1

a Logarithm of the standardized morbidity rate (log SMR); b logarithm of the mean relative risk (log RR) of dengue disease; c discretized relative risk (DRR${}_i$); and d mean spatial effects (SE) $u_i$, by census section for the data aggregated at global scale for the period 2008–2015

Fig. 2

a Mean spatial effects (SE) $w_i$ (2008) and $u_i$ (2009, 2010, and 2014) from the selected models at annual aggregation scale; b mean space-varying NDVI coefficients (${\mathit{\beta }}_1+b_{i,1}$); and c discretized space-varying (DSV) NDVI coefficients (${\mathit{\beta }}_1+b_{i,1}$) for years 2011, 2012, 2013, 2015, from models at annual aggregation scale

Fig. 3

a Logarithm of the mean relative risk (log RR) of dengue disease , from models at annual aggregation scale 2008–2015; b discretized relative risk (DRR) of dengue disease, 2008–2015