Dataset Information

Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic.

ABSTRACT:

Background

The incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.

Methods

Fatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus (DENV) in serum or tissue, immunohistochemical testing of tissue, and immunoassay detection of anti-DENV IgM from serum. Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death.

Results

Among 10,576 reported dengue cases, 40 suspect fatal cases were identified, of which 11 were laboratory-positive, 14 were laboratory-negative, and 15 laboratory-indeterminate. The median age of laboratory-positive case-patients was 26 years (range 5 months to 78 years), including five children aged < 15 years; 7 sought medical care at least once prior to hospital admission, 9 were admitted to hospital and 2 died upon arrival. The nine hospitalized case-patients stayed a mean of 15 hours (range: 3-48 hours) in the emergency department (ED) before inpatient admission. Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock. Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend. Dengue was listed on the death certificate in only 5 instances.

Conclusions

During a dengue epidemic in an endemic area, none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines. Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock, prolonged ED stays, and infrequent patient monitoring.

SUBMITTER: Tomashek KM

PROVIDER: S-EPMC3328431 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic.

Tomashek Kay M KM Gregory Christopher J CJ Rivera Sánchez Aidsa A Bartek Matthew A MA Garcia Rivera Enid J EJ Hunsperger Elizabeth E Muñoz-Jordán Jorge L JL Sun Wellington W

PLoS neglected tropical diseases 20120417 4

<h4>Background</h4>The incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.<h4>Methods</h4>Fatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagn ...[more]

PMID: 22530072

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Project description:BackgroundDengue is a potentially fatal acute febrile illness (AFI) caused by four mosquito-transmitted dengue viruses (DENV-1-4) that are endemic in Puerto Rico. In January 2010, the number of suspected dengue cases reported to the passive dengue surveillance system exceeded the epidemic threshold and an epidemic was declared soon after.Methodology/principal findingsTo characterize the epidemic, surveillance and laboratory diagnostic data were compiled. A suspected case was a dengue-like AFI in a person reported by a health care provider with or without a specimen submitted for diagnostic testing. Laboratory-positive cases had: (i) DENV nucleic acid detected by reverse transcriptase-polymerase chain reaction (RT-PCR) in an acute serum specimen; (ii) anti-DENV IgM antibody detected by ELISA in any serum specimen; or (iii) DENV antigen or nucleic acid detected in an autopsy-tissue specimen. In 2010, a total of 26,766 suspected dengue cases (7.2 per 1,000 residents) were identified, of which 46.6% were laboratory-positive. Of 7,426 RT-PCR-positive specimens, DENV-1 (69.0%) and DENV-4 (23.6%) were detected more frequently than DENV-2 (7.3%) and DENV-3 (<0.1%). Nearly half (47.1%) of all laboratory-positive cases were adults, 49.7% had dengue with warning signs, 11.1% had severe dengue, and 40 died. Approximately 21% of cases were primary DENV infections, and 1-4 year olds were the only age group for which primary infection was more common than secondary. Individuals infected with DENV-1 were 4.2 (95% confidence interval [CI]: 1.7-9.8) and 4.0 (95% CI: 2.4-6.5) times more likely to have primary infection than those infected with DENV-2 or -4, respectively.Conclusions/significanceThis epidemic was long in duration and yielded the highest incidence of reported dengue cases and deaths since surveillance began in Puerto Rico in the late 1960's. This epidemic re-emphasizes the need for more effective primary prevention interventions to reduce the morbidity and mortality of dengue.

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Dataset Information

Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic.

Background

Methods

Results

Conclusions

Publications

Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic.

Similar Datasets

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