Evaluating the validity of dengue clinical-epidemiological criteria for diagnosis in patients residing in a Brazilian endemic area.

BACKGROUND: We evaluated the validity of clinical diagnosis compared with laboratory diagnosis of dengue in a retrospective sample of patients in São José do Rio Preto, Brazil. METHODS: Our sample included 148 299 clinically (56.3%) or laboratory-diagnosed (43.7%) dengue cases. We compared the sensitivity, specificity, positive and negative predictive value (PPV and NPV) of dengue patients' demographic and clinical characteristics with laboratory-based diagnosis. We used logistic regressions to estimate the correlation between clinical and laboratory diagnosis of dengue and a full set of dengue signs and symptoms. RESULTS: We found substantial variability in sensitivity and specificity of signs and symptoms ranging from 0.8-81.1 and 21.5-99.6, respectively. Thrombocytopenia exhibited the highest PPV (92.0) and lowest NPV (42.2) and was the only symptom showing agreement with laboratory-confirmed dengue (φ = 0.38). The presence of exanthema and thrombocytopenia led to a greater likelihood of concordant clinical and laboratory diagnoses (exanthema: OR: 4.23; 95% CI: 2.09 to 8.57; thrombocytopenia: OR: 4.02; 95% CI: 1.32 to 12.27). CONCLUSIONS: We found substantial variation in sensitivity, specificity, PPV and NPV of dengue signs and symptoms. For accuracy, clinical and laboratory diagnosis of dengue should be performed concurrently. When laboratory tests are not available, we suggest focusing on the clinical manifestations most associated with dengue.

Clinical, laboratory, and demographic determinants of hospitalization due to dengue in 7613 patients: A retrospective study based on hierarchical models.

In Brazil, the incidence of hospitalization due to dengue, as an indicator of severity, has drastically increased since 1998. The objective of our study was to identify risk factors associated with subsequent hospitalization related to dengue. We analyzed 7613 dengue confirmed via serology (ELISA), non-structural protein 1, or polymerase chain reaction amplification. We used a hierarchical framework to generate a multivariate logistic regression based on a variety of risk variables. This was followed by multiple statistical analyses to assess hierarchical model accuracy, variance, goodness of fit, and whether or not this model reliably represented the population. The final model, which included age, sex, ethnicity, previous dengue infection, hemorrhagic manifestations, plasma leakage, and organ failure, showed that all measured parameters, with the exception of previous dengue, were statistically significant. The presence of organ failure was associated with the highest risk of subsequent dengue hospitalization (OR=5·75; CI=3·53-9·37). Therefore, plasma leakage and organ failure were the main indicators of hospitalization due to dengue, although other variables of minor importance should also be considered to refer dengue patients to hospital treatment, which may lead to a reduction in avoidable deaths as well as costs related to dengue.

Spatiotemporal-based clusters as a method for dengue surveillance.

OBJECTIVES: To develop and demonstrate the use of a new method for epidemiological surveillance of dengue. METHODS: This was a retrospective cohort study using data from the Health Department of São José do Rio Preto (São Paulo, Brazil). The geographical coordinates were obtained using QGIS™ (Creative Commons Corporation, Mountain View, California, United States), based on patient addresses in the dengue notification system of the Government of Brazil. SaTScan™ (Martin Kulldorff, Boston, Massachusetts, United States) was then used to create a space-time scan analysis to find statistically significant clusters of dengue. These results were plotted and visualized using Google Earth™ mapping service (Google Incorporated, Mountain View, California, United States). RESULTS: More clusters were detected when the maximum number of households per cluster was set to 10% (11 statistically significant clusters) rather than 50% (8 statistically significant clusters). The cluster radius varied from 0.18 - 2.04 km and the period of time varied from 6 days - 6 months. The infection rate was more than 0.5 cases/household. CONCLUSIONS: When using SaTScan for space-time analysis of dengue cases, the maximum number of households per cluster should be set to 10%. This methodology may be useful to optimizing dengue surveillance systems, especially in countries where resources are scarce and government programs have not had much success controlling the disease.