Evaluation of dengue fever reports during an epidemic, Colombia

Effectiveness of mass dengue vaccination with CYD-TDV (Dengvaxia®) in the state of Paraná, Brazil: integrating case-cohort and case-control designs

Background

CYD-TDV (Dengvaxia®) was the first dengue vaccine approved, launched in Brazil in 2015 for individuals aged 9-44 years. We aimed to estimate the effectiveness of CYD-TDV in preventing symptomatic dengue cases during a campaign targeting individuals aged 15-27 years in selected municipalities in Paraná, Brazil. Additionally, we examined whether a history of dengue, as recorded by the surveillance system, modified the vaccine's effectiveness.

Methods

We conducted a case-cohort analysis comparing the frequency of vaccination, with at least one dose of CYD-TDV, in individuals with dengue confirmed by RT-PCR, identified by the surveillance system during 2019 and 2020, with the vaccination coverage in the target population. Moreover, in a case-control design using weighted controls, we assessed the documented history of dengue as a modifier of the vaccine's effectiveness. We used a logistic random-effects regression model, with data clustered in municipalities and incorporating covariates such as the incidence of dengue before the campaign, age, and sex. We calculated vaccine effectiveness (VE) as (1-relative risk) x 100%.

Findings

1869 dengue cases were identified, which had a vaccination frequency significantly lower than the overall vaccination coverage in the target population (50.3% vs. 57.2%, respectively; overall VE: 21.3%; 95% confidence interval [CI]: 13.4%-28.4%). In individuals with a documented history of dengue, vaccination had a VE of 71% (95% CI: 58%-80%) in reducing the incidence of dengue. However, vaccination was not associated with a significant reduction in the overall dengue case risk in individuals without a documented history of dengue (VE: 12%; 95% CI: -21% to 36%). In this last stratum, vaccination was associated with reduced cases due to DENV-1 and DENV-4, but an excess of DENV-2 cases.

Interpretation

Vaccination led to a significant reduction in reported dengue cases within the target population. The case-control design suggested that this reduction was primarily driven by the benefits observed in individuals with a documented history of dengue. In endemic regions with limited serological testing facilities, a previous history of dengue diagnosis recorded by epidemiological surveillance could be used to triage candidates for CYD-TDV vaccination.

Funding

Research supported by Sanofi.

Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia

Background

Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015.

Methodology/Principal findings

We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff’s scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika’s spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department.

Conclusions/Significance

The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions.

Dengue, chikungunya, and Zika are diseases transmitted to humans by the bite of infected Aedes mosquitoes. Between 2014 and 2016 chikungunya and Zika viruses started causing outbreaks in Colombia, one of the countries historically most affected by dengue. We used case counts of the diseases by municipality and week to study the spread trajectory of chikungunya and Zika within Colombia’s territory, and to identify space-time high-risk clusters, i.e., the areas and time periods that dengue, chikungunya, and Zika were more present. Chikungunya and Zika spread similarly in Colombia, but Zika spread faster. The Atlantic coast, a famous touristic destination in the country, was likely the place of entry of chikungunya and Zika in Colombia. The south-western region was identified as a high-risk cluster for all three diseases in separate and simultaneously. This region has a favorable climate for the Aedes mosquitoes and other characteristics that facilitate the diseases’ transmission, such as social deprivation and high population mobility. Our results provide useful information on the locations that should be prioritized for interventions to prevent the entry of new diseases transmitted by Aedes and to reduce the burden of dengue, chikungunya and Zika where they are established.

Endothelial Dysfunction, HMGB1, and Dengue: An Enigma to Solve

Dengue is a viral infection caused by dengue virus (DENV), which has a significant impact on public health worldwide. Although most infections are asymptomatic, a series of severe clinical manifestations such as hemorrhage and plasma leakage can occur during the severe presentation of the disease. This suggests that the virus or host immune response may affect the protective function of endothelial barriers, ultimately being considered the most relevant event in severe and fatal dengue pathogenesis. The mechanisms that induce these alterations are diverse. It has been suggested that the high mobility group box 1 protein (HMGB1) may be involved in endothelial dysfunction. This non-histone nuclear protein has different immunomodulatory activities and belongs to the alarmin group. High concentrations of HMGB1 have been detected in patients with several infectious diseases, including dengue, and it could be considered as a biomarker for the early diagnosis of dengue and a predictor of complications of the disease. This review summarizes the main features of dengue infection and describes the known causes associated with endothelial dysfunction, highlighting the involvement and possible relationship between HMGB1 and DENV.

Assessing the reporting of Dengue, Chikungunya and Zika to the National Surveillance System in Colombia from 2014-2017: A Capture-recapture analysis accounting for misclassification of arboviral diagnostics

BACKGROUND

Chikungunya, dengue, and Zika are three different arboviruses which have similar symptoms and are a major public health issue in Colombia. Despite the mandatory reporting of these arboviruses to the National Surveillance System in Colombia (SIVIGILA), it has been reported that the system captures less than 10% of diagnosed cases in some cities.

METHODOLOGY/PRINCIPAL FINDINGS

To assess the scope and degree of arboviruses reporting in Colombia between 2014-2017, we conducted an observational study of surveillance data using the capture-recapture approach in three Colombian cities. Using healthcare facility registries (capture data) and surveillance-notified cases (recapture data), we estimated the degree of reporting by clinical diagnosis. We fit robust Poisson regressions to identify predictors of reporting and estimated the predicted probability of reporting by disease and year. To account for the potential misclassification of the clinical diagnosis, we used the simulation extrapolation for misclassification (MC-SIMEX) method. A total of 266,549 registries were examined. Overall arboviruses' reporting ranged from 5.3% to 14.7% and varied in magnitude according to age and year of diagnosis. Dengue was the most notified disease (21-70%) followed by Zika (6-45%). The highest reporting rate was seen in 2016, an epidemic year. The MC-SIMEX corrected rates indicated underestimation of the reporting due to the potential misclassification bias.

CONCLUSIONS

These findings reflect challenges on arboviruses' reporting, and therefore, potential challenges on the estimation of arboviral burden in Colombia and other endemic settings with similar surveillance systems.

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

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.

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

Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference.

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

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.

Seroprevalencia en una zona de hiperendemia por dengue, en Barranquilla, Colombia

Objetivo: Estimar seroprevalencia de anticuerpos IgG e IgM para dengue y factores asociados. Método: estudio transversal, mediante la aplicación de encuesta y estimación serológica para IgG e IgM para dengue en mayores de 15 años con representabilidad por localidad y sexo. 478 sujetos encuestados en la ciudad de Barranquilla Colombia Análisis estadístico, mediante el programa SPSS®, versión 22. Se estimó la sero-prevalencia para el análisis univariado y el análisis bivariado mediante la odds ratio de proporciones con una p<0,05. Resultados: la seroprevalencia para IgG fue 88,2% (IC 95% =85,2-91,1) y para IgM de 11,8% (IC 95%= 8,80-14,75). Se encon- tró asociación estadística en edades de 30 a 49 y 50-69 años (OR=7,09 [2,6-19,3] y OR= 8,93 [2,9-27,0] respectivamente), pertenecer al régimen de salud subsidiado (OR=2,56[1,18-5,53]) y ocupación hogar (OR= 3,38[1,31-8,67]. Conclusiones: la alta seroprevalencia para IgG es explicada por la circulación de los cuatro serotipos en el país, los brotes epidémicos cada dos a tres años y los comportamientos y prácticas desfavorables relacionadas con el manejo de agua. Se requiere cambios de conducta frente al manejo de factores predisponen- tes, la atención oportuna de sintomáticos y vigilancia activa comunitaria e institucional ante casos sospechosos.

Co-circulation and simultaneous co-infection of dengue, chikungunya, and zika viruses in patients with febrile syndrome at the Colombian-Venezuelan border

BACKGROUND

In Colombia, the dengue virus (DENV) has been endemic for decades, and with the recent entry of the chikungunya virus (CHIKV) (2014) and the Zika virus (ZIKV) (2015), health systems are overloaded because the diagnosis of these three diseases is based on clinical symptoms, and the three diseases share a symptomatology of febrile syndrome. Thus, the objective of this study was to use molecular methods to identify their co-circulation as well as the prevalence of co-infections, in a cohort of patients at the Colombian-Venezuelan border.

METHODS

A total of 157 serum samples from patients with febrile syndrome consistent with DENV were collected after informed consent and processed for the identification of DENV (conventional PCR and real-time PCR), CHIKV (conventional PCR), and ZIKV (real-time PCR). DENV-positive samples were serotyped, and some of those positive for DENV and CHIKV were sequenced.

RESULTS

Eighty-two patients were positive for one or more viruses: 33 (21.02%) for DENV, 47 (29.94%) for CHIKV, and 29 (18.47%) for ZIKV. The mean age range of the infected population was statistically higher in the patients infected with ZIKV (29.72 years) than in those infected with DENV or CHIKV (21.09 years). Both co-circulation and co-infection of these three viruses was found. The prevalence of DENV/CHIKV, DENV/ZIKV, and CHIKV/ZIKV co-infection was 7.64%, 6.37%, and 5.10%, with attack rates of 14.90, 12.42, and 9.93 cases per 100,000 inhabitants, respectively. Furthermore, three patients were found to be co-infected with all three viruses (prevalence of 1.91%), with an attack rate of 4.96 cases per 100,000 inhabitants.

CONCLUSION

Our results demonstrate the simultaneous co-circulation of DENV, CHIKV, ZIKV and their co-infections at the Colombian-Venezuelan border. Moreover, it is necessary to improve the differential diagnosis in patients with acute febrile syndrome and to study the possible consequences of this epidemiological overview of the clinical outcomes of these diseases in endemic regions.

Relative risk estimation of dengue disease at small spatial scale

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.

Bayesian dynamic modeling of time series of dengue disease case counts

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.

Exploratory wavelet analysis of dengue seasonal patterns in Colombia

INTRODUCTION

Dengue has a seasonal behavior associated with climatic changes, vector cycles, circulating serotypes, and population dynamics. The wavelet analysis makes it possible to separate a very long time series into calendar time and periods. This is the first time this technique is used in an exploratory manner to model the behavior of dengue in Colombia. 

OBJECTIVE

To explore the annual seasonal dengue patterns in Colombia and in its five most endemic municipalities for the period 2007 to 2012, and for roughly annual cycles between 1978 and 2013 at the national level. 

MATERIALS AND METHODS

We made an exploratory wavelet analysis using data from all incident cases of dengue per epidemiological week for the period 2007 to 2012, and per year for 1978 to 2013. We used a first-order autoregressive model as the null hypothesis. 

RESULTS

The effect of the 2010 epidemic was evident in both the national time series and the series for the five municipalities. Differences in interannual seasonal patterns were observed among municipalities. In addition, we identified roughly annual cycles of 2 to 5 years since 2004 at a national level. 

CONCLUSIONS

Wavelet analysis is useful to study a long time series containing changing seasonal patterns, as is the case of dengue in Colombia, and to identify differences among regions. These patterns need to be explored at smaller aggregate levels, and their relationships with different predictive variables need to be investigated.