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To the Editor: Dengue is an underreported disease globally. In 2010, the World Health Organization recorded 2.2 million dengue cases (1), but models projected that the number of symptomatic dengue cases might have been as high as 96 million (2). Brazil reports more cases of dengue than any other country (1); however, the degree of dengue underreporting in Brazil is unknown. We conducted a study to evaluate dengue underreporting by Brazil’s Notifiable Diseases Information System (Sistema de Informacao de Agravos de Notificacao [SINAN]). From January 1, 2009, through December 31, 2011, we performed enhanced surveillance for acute febrile illness (AFI) in a public emergency unit in Salvador, Brazil. The surveillance team enrolled outpatients >5 years of age with measured (>37.8°C) or reported fever. Patients or their legal guardians provided written consent. The study was approved by the Oswaldo Cruz Foundation Ethics Committee, Brazil’s National Council for Ethics in Research, and the Yale Institutional Review Board. We collected participants’ blood samples at study enrollment and >15 days later. Acute-phase serum samples were tested by dengue nonstructural protein 1 ELISA and IgM ELISA (Panbio Diagnostics, East Brisbane, Queensland, Australia). Convalescent-phase serum samples were tested by IgM ELISA. In concordance with case-reporting guidelines in Brazil (3), we defined dengue cases by a positive nonstructural protein 1 ELISA result or a positive acute-phase or convalescent-phase IgM ELISA result. All others were classified as nondengue AFI. We then identified which study patients were officially reported to SINAN as having a suspected case of dengue. In Brazil, notification of suspected dengue cases is mandatory. A suspected case is defined as illness in a person from an area of dengue transmission or Aedes aegypti mosquito infestation who has symptoms of dengue (fever of 2 of the following symptoms: nausea/vomiting, exanthema, myalgia, arthralgia, headache, retro-orbital pain, petechiae/positive tourniquet test, or leukopenia). We used Link Plus software (CDC-Link Plus Production 2.0; Centers for Disease Control and Prevention, Atlanta, GA, USA) to perform probabilistic record linkage from our database with official reports in the SINAN database. The records were matched based on the patients’ first names, last names, and dates of birth. We then manually reviewed the matches to confirm the pairs. On the basis of the results, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the national surveillance system. We calculated accuracy measurements with 95% CIs for the overall study period and for each study year, age group (5–14 vs. >15 years), and seasonal prevalence of dengue (months of low vs. high dengue transmission, defined by dengue detection in 20% of the AFI patients, respectively). We estimated multiplication factors by dividing the number of dengue cases in our study by the number of study patients who were reported to SINAN as having dengue. Of the 3,864 AFI patients identified during the 3-year study period, 997 (25.8%) had laboratory evidence of dengue infection, and 2,867 (74.2%) were classified as having nondengue AFI. Of the 997 dengue cases, 57 were reported to SINAN (sensitivity 5.7%) (Table). Of the 2,867 nondengue AFI cases, 26 were reported to SINAN as dengue cases (false-positive ratio 0.9%, specificity 99.1%). None of these 26 cases had laboratory confirmation in the SINAN database. The PPV for reporting to SINAN was 68.7%, and the negative predictive value was 75.1% (Table). PPV was higher among patients >15 years of age, which might be attributable to atypical presentations of dengue in children (4,5). Table Accuracy of a national surveillance system for recording cases of suspected dengue among patients with acute febrile illness who visited an emergency health unit of Salvador, Brazil, January 1, 2009–December 31, 2011* We found that 1 in 4 patients with AFI had laboratory evidence of dengue infection. However, for every 20 dengue patients that we identified, only about 1 had been reported to SINAN as having dengue. During periods of low dengue transmission, only about 1 in 40 dengue cases identified was reported. Conversely, among the patients who were reported as having dengue, 31.2% did not have the disease; this percentage reached 61.5% in low-transmission periods. We estimated that overall, there were 12 dengue cases per reported case in the community, but in months of low dengue transmission, this ratio was >17:1 (Table). Comparable results have been observed in Nicaragua, Thailand, and Cambodia (6–8). By applying the estimated multiplication factor to the study period’s mean annual incidence of 303.8 reported dengue cases/100,000 Salvador residents (9), we estimated that the actual mean annual dengue incidence for Salvador was 3,645.7 cases/100,000 residents. We showed that dengue surveillance substantially underestimated disease burden in Brazil, especially in what are considered low-transmission periods. Dengue underreporting has been attributed to passive case detection, which fails to identify persons with dengue who do not seek health care (1). We also showed that surveillance failed to detect dengue cases among symptomatic patients seeking health care. Novel surveillance tools, such as active syndromic surveillance and point-of-care testing, should be applied to improve estimates of dengue incidence. Furthermore, given the recent emergence of chikungunya and Zika viruses in Brazil (10), improved surveillance and laboratory diagnostics are needed to avert misclassification and mismanagement of cases.

Objective To estimate the time of decision (TD) to look for medical care and the time of arrival (TA) at the health service for men (M) and women (W) suffering from acute myocardial infarction and to analyze the influence of the interpretation of pain and pain resistance behaviors during these times. Methods This is an exploratory research, performed at the university hospital in Salvador/Bahia. 43 W and 54 M were interviewed. To study the dependence among sociodemographic and gender variables, the Fisher Exact Test was used. To analyze times, a geometric mean (GM) was used. In order to verify the association between the GM of TD and TA and the judgment of pain, and between the GM of TD and TA and the behavior of resistance to pain, as well as to test the time of interaction between the gender variable and other variables of interest, the robust regression model was used. The statistical significance adopted was 5%. Results The GM of the TD for M was 1.13 h; for W, 0.74 h. The GM of the TA was 1.74 h for M and 1.47 h for W. Those who did not recognize the symptoms of AMI and presented behavior of resistance to pain had higher TD and TA, being the associations significant. Gender did not change the associations of interest. Conclusion The findings demonstrate the importance of health education aiming at the benefits of early treatment. Objetivo estimar o tempo de decisão (TD) para a procura de atendimento medico e o tempo de chegada a um serviço de saúde (TC) para homens (H) e mulheres (M) que sofreram infarto do miocárdio e analisar a influência da interpretação da dor e de comportamentos de resistência à dor nesse tempo. Métodos trata-se de pesquisa exploratória, realizada em hospital universitário de Salvador/BA. Foram entrevistados 43 M e 54 H. Para estudar a dependência entre as variáveis sociodemográficas e os sexos, empregou-se o Teste Exato de Fisher. Para analisar os tempos, utilizou-se a média geométrica (MG). Para verificar a associação entre a MG dos TD e TC e o julgamento da dor e entre a MG dos TD e TC e os comportamentos de resistência à dor, bem como para testar o termo de interação entre a variável sexo e as variáveis de interesses, utilizou-se o modelo de regressão robusto. A significância estatística adotada foi de 5%. Resultados a MG do TD para H foi de 1,13 h; para M, de 0,74 h. A MG do TC foi de 1,74 h para H e de 1,47 h para M. Aqueles que não reconheceram os sintomas do IAM e apresentaram comportamentos de resistência à dor apresentaram maior TD e TC, sendo as associações significantes. Os sexos não modificaram as associações de interesse. Conclusão os achados assinalam a importância da educação em saúde visando aos benefícios do tratamento precoce.

Background Our understanding of the epidemiology of influenza is limited in tropical regions, which in turn has hampered identifying optimal region-specific policy to diminish disease burden. Influenza-like illness (ILI) is a clinical diagnosis that can be used as a surrogate for influenza. This study aimed to define the incidence and seasonality of ILI and to assess its association with climatic variables and school calendar in an urban community in the tropical region of Salvador, Brazil. Methods Between 2009 and 2013, we conducted enhanced community-based surveillance for acute febrile illnesses (AFI) among patients ≥5 years of age in a slum community emergency unit in Salvador, Brazil. ILI was defined as a measured temperature of ≥37.8 °C or reported fever in a patient with cough or sore throat for ≤7 days, and negative test results for dengue and leptospirosis. Seasonality was analyzed with a harmonic regression model. Negative binomial regression models were used to correlate ILI incidence with rainfall, temperature, relative humidity and the number of days per month that schools were in session while controlling for seasonality. Results There were 2,651 (45.6 % of 5,817 AFI patients) ILI cases with a mean annual incidence of 60 cases/1,000 population (95 % CI 58–62). Risk of ILI was highest among 5–9 year olds with an annual incidence of 105 cases/1,000 population in 2009. ILI had a clear seasonal pattern with peaks between the 35–40th week of the year. ILI peaks were higher and earlier in 5–9 year olds compared with >19 year olds. No association was seen between ILI and precipitation, relative humidity or temperature. There was a significant association between the incidence of ILI in children 5–9 years of age and number of scheduled school days per month. Conclusions We identified a significant burden of ILI with distinct seasonality in the Brazilian tropics and highest rates among young school-age children. Seasonal peaks of ILI in children 5–9 years of age were positively associated with the number of school days, indicating that children may play a role in the timing of seasonal influenza transmission. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1456-8) contains supplementary material, which is available to authorized users.

Background Few studies of dengue have shown group-level associations between demographic, socioeconomic, or geographic characteristics and the spatial distribution of dengue within small urban areas. This study aimed to examine whether specific characteristics of an urban slum community were associated with the risk of dengue disease. Methodology/Principal Findings From 01/2009 to 12/2010, we conducted enhanced, community-based surveillance in the only public emergency unit in a slum in Salvador, Brazil to identify acute febrile illness (AFI) patients with laboratory evidence of dengue infection. Patient households were geocoded within census tracts (CTs). Demographic, socioeconomic, and geographical data were obtained from the 2010 national census. Associations between CTs characteristics and the spatial risk of both dengue and non-dengue AFI were assessed by Poisson log-normal and conditional auto-regressive models (CAR). We identified 651 (22.0%) dengue cases among 2,962 AFI patients. Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. All the four dengue serotypes were identified, but DENV2 predominated (DENV1: 8.1%; DENV2: 90.7%; DENV3: 0.4%; DENV4: 0.8%). Multivariable CAR regression analysis showed increased dengue risk in CTs with poorer inhabitants (RR: 1.02 for each percent increase in the frequency of families earning ≤1 times the minimum wage; 95% CI: 1.01-1.04), and decreased risk in CTs located farther from the health unit (RR: 0.87 for each 100 meter increase; 95% CI: 0.80-0.94). The same CTs characteristics were also associated with non-dengue AFI risk. Conclusions/Significance This study highlights the large burden of symptomatic dengue on individuals living in urban slums in Brazil. Lower neighborhood socioeconomic status was independently associated with increased risk of dengue, indicating that within slum communities with high levels of absolute poverty, factors associated with the social gradient influence dengue transmission. In addition, poor geographic access to health services may be a barrier to identifying both dengue and non-dengue AFI cases. Therefore, further spatial studies should account for this potential source of bias., Author Summary Dengue is influenced by the environment; however, few studies have investigated the relationship between neighborhood characteristics and the spatial distribution of dengue within small urban areas. We examined whether specific characteristics of an urban slum community were associated with dengue risk. From January 2009 to December 2010, we conducted community-based surveillance in a slum in Salvador, Brazil to identify patients with acute febrile illness (AFI) and to test them for dengue. We identified 651 (22.0%) patients with laboratory evidence of dengue infection among 2,962 AFI patients. All the four dengue serotypes were detected, but DENV2 predominated (DENV1 8.1%; DENV2 90.7%; DENV3 0.4%; DENV4 0.8%). Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. We found that neighborhood poverty level and proximity to the health center were associated with higher risk of detection of dengue and other AFI. This study highlights the large burden of dengue in poor urban slums of Brazil and indicates that socioeconomic development could potentially mitigate risk factors for both dengue and non-dengue AFI cases. In addition, we found that residential proximity to a health care facility was associated with improved case detection. Therefore, further studies on disease distribution should consider household proximity to health care facilities when assessing risk.