Brazilian Ministry of Health, Pan American Health Organization, and Enhancing Research Activity in Epidemic Situations.

BackgroundDengue is an increasing public health concern in Brazil. There is a need for an updated evaluation of the economic impact of dengue within the country. We undertook this multicenter study to evaluate the economic burden of dengue in Brazil.MethodsWe estimated the economic burden of dengue in Brazil for the years 2009 to 2013 and for the epidemic season of August 2012- September 2013. We conducted a multicenter cohort study across four endemic regions: Midwest, Goiania; Southeast, Belo Horizonte and Rio de Janeiro; Northeast: Teresina and Recife; and the North, Belem. Ambulatory or hospitalized cases with suspected or laboratory-confirmed dengue treated in both the private and public sectors were recruited. Interviews were scheduled for the convalescent period to ascertain characteristics of the dengue episode, date of first symptoms/signs and recovery, use of medical services, work/school absence, household spending (out-of-pocket expense) and income lost using a questionnaire developed for a previous cost study. We also extracted data from the patients’ medical records for hospitalized cases. Overall costs per case and cumulative costs were calculated from the public payer and societal perspectives. National cost estimations took into account cases reported in the official notification system (SINAN) with adjustment for underreporting of cases. We applied a probabilistic sensitivity analysis using Monte Carlo simulations with 90% certainty levels (CL).ResultsWe screened 2,223 cases, of which 2,035 (91.5%) symptomatic dengue cases were included in our study. The estimated cost for dengue for the epidemic season (2012–2013) in the societal perspective was US$ 468 million (90% CL: 349–590) or US$ 1,212 million (90% CL: 904–1,526) after adjusting for under-reporting. Considering the time series of dengue (2009–2013) the estimated cost of dengue varied from US$ 371 million (2009) to US$ 1,228 million (2013).ConclusionsThe economic burden associated with dengue in Brazil is substantial with large variations in reported cases and consequently costs reflecting the dynamic of dengue transmission.

BackgroundTransmission dynamics, vectorial capacity, and co-infections have substantial impacts on vector-borne diseases (VBDs) affecting urban and suburban populations. Reviewing key factors can provide insight into priority research areas and offer suggestions for potential interventions.Main bodyThrough a scoping review, we identify knowledge gaps on transmission dynamics, vectorial capacity, and co-infections regarding VBDs in urban areas. Peer-reviewed and grey literature published between 2000 and 2016 was searched. We screened abstracts and full texts to select studies. Using an extraction grid, we retrieved general data, results, lessons learned and recommendations, future research avenues, and practice implications. We classified studies by VBD and country/continent and identified relevant knowledge gaps. Of 773 articles selected for full-text screening, 50 were included in the review: 23 based on research in the Americas, 15 in Asia, 10 in Africa, and one each in Europe and Australia. The largest body of evidence concerning VBD epidemiology in urban areas concerned dengue and malaria. Other arboviruses covered included chikungunya and West Nile virus, other parasitic diseases such as leishmaniasis and trypanosomiasis, and bacterial rickettsiosis and plague. Most articles retrieved in our review combined transmission dynamics and vectorial capacity; only two combined transmission dynamics and co-infection. The review identified significant knowledge gaps on the role of asymptomatic individuals, the effects of co-infection and other host factors, and the impacts of climatic, environmental, and socioeconomic factors on VBD transmission in urban areas. Limitations included the trade-off from narrowing the search strategy (missing out on classical modelling studies), a lack of studies on co-infections, most studies being only descriptive, and few offering concrete public health recommendations. More research is needed on transmission risk in homes and workplaces, given increasingly dynamic and mobile populations. The lack of studies on co-infection hampers monitoring of infections transmitted by the same vector.ConclusionsStrengthening VBD surveillance and control, particularly in asymptomatic cases and mobile populations, as well as using early warning tools to predict increasing transmission, were key strategies identified for public health policy and practice.Electronic supplementary materialThe online version of this article (10.1186/s40249-018-0475-7) contains supplementary material, which is available to authorized users.

The aim of the study was to evaluate the temporal patterns of dengue incidence from 2001 to 2014 and forecast for 2015 in two Brazilian cities. We analysed dengue surveillance data (SINAN) from Recife, 1.6 million population, and Goiania, 1.4 million population. We used Auto-Regressive Integrated Moving Average (ARIMA) modelling of monthly notified dengue incidence (2001-2014). Forecasting models (95% prediction interval) were developed to predict numbers of dengue cases for 2015. During the study period, 73,479 dengue cases were reported in Recife varying from 11 cases/100,000 inhab (2004) to 2418 cases/100,000 inhab (2002). In Goiania, 253,008 dengue cases were reported and the yearly incidence varied from 293 cases/100,000 inhab (2004) to 3927 cases/100,000 inhab (2013). Trend was the most important component for Recife, while seasonality was the most important one in Goiania. For Recife, the best fitted model was ARIMA (1,1,3) and for Goiania Seasonal ARIMA (1,0,2) (1,1,2). The model predicted 4254 dengue cases for Recife in 2015; SINAN registered 35,724 cases. For Goiania the model predicted 33,757 cases for 2015; the reported number of cases by SINAN was 74,095, within the 95% prediction interval. The difference between notified and forecasted dengue cases in Recife can be explained by the co-circulation of dengue and Zika virus in 2015. In this year, all cases with rash were notified as "dengue-like" illness. The ARIMA models may be considered a baseline for the time series analysis of dengue incidence before the Zika epidemic.

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