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Dengue epidemic in a non-endemic zone of Bangladesh: Clinical and laboratory profiles of patients.

ABSTRACT: BACKGROUNDS:Approximately, half of the population in the world including tropical and sub-tropical climates region is at risk of dengue. Being an endemic country, Bangladesh has experienced the largest dengue epidemic in 2019. The present study aimed at evaluating the clinical and laboratory profile of dengue patients in northern Bangladesh during the epidemic. METHODS:This cross-sectional study included 319 serologically confirmed dengue patients admitted in Shaheed Ziaur Rahman Medical College Hospital in Bogra district. It is one of the main tertiary care hospitals in northern Bangladesh. Data were collected from July to September 2019. Patients' clinical and laboratory data were extracted from clinical records. Patients were classified into two classes according to the WHO 2009 dengue classification such as (i) non-severe dengue and (ii) severe dengue. Chi-square test and independent t-test were used in this study. RESULTS:Of the 319 patients, 94.1% had non-severe dengue and the remaining 5.9% had severe dengue (severe plasma leakage 68.4%, severe organ involvement 68.4%, and severe clinical bleeding 10.5%). Most of the patients were suffering from primary dengue infection. The most common clinical presentation was fever followed by headache and myalgia. Vomiting and abdominal pain were the most prevalent warning signs. The common hematological findings on admission were leukopenia (63.3%), thrombocytopenia (30.4%) and increased hematocrit (26.6%). Raised serum ALT or AST was observed in 14.1% cases whereas raised serum creatinine was observed in 6.6% cases. Signs of plasma leakage (pleural effusion, respiratory distress, and ascites, rise of hematocrit >20% during hospital stay) and hepatic or renal involvement (serum ALT >42UI/L or serum creatinine >1.2 mg/dL) on admission were mostly associated with severe dengue. CONCLUSION:The study provides clinical evidence on presentation as well as hematological and biochemical profile of dengue patients in northern Bangladesh that should be implicated in effective patient management.

SUBMITTER: Rafi A

PROVIDER: S-EPMC7553334 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Dengue epidemic in a non-endemic zone of Bangladesh: Clinical and laboratory profiles of patients.

Rafi Abdur A Mousumi Ashrafun Nahar AN Ahmed Reejvi R Chowdhury Rezwanul Haque RH Wadood Abdul A Hossain Golam G

PLoS neglected tropical diseases 20201013 10

<h4>Backgrounds</h4>Approximately, half of the population in the world including tropical and sub-tropical climates region is at risk of dengue. Being an endemic country, Bangladesh has experienced the largest dengue epidemic in 2019. The present study aimed at evaluating the clinical and laboratory profile of dengue patients in northern Bangladesh during the epidemic.<h4>Methods</h4>This cross-sectional study included 319 serologically confirmed dengue patients admitted in Shaheed Ziaur Rahman ...[more]

PMID: 33048921

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Project description:BackgroundDespite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.Methodology/principal findingsEpidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005-2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1-2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively.Conclusion/significanceThere was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.

Project description:The increasing dengue burden and epidemic severity worldwide have highlighted the need to improve surveillance. In non-endemic areas such as Taiwan, where outbreaks start mostly with imported cases from Southeast Asia, a closer examination of surveillance dynamics to detect cases early is necessary. To evaluate problems with dengue surveillance and investigate the involvement of different factors at various epidemic stages, we investigated 632 laboratory-confirmed indigenous dengue cases in Kaohsiung City, Taiwan during 2009-2010. The estimated sensitivity of clinical surveillance was 82.4% (521/632). Initially, the modified serological surveillance (targeting only the contacts of laboratory-confirmed dengue cases) identified clinically unrecognized afebrile cases in younger patients who visited private clinics and accounted for 30.4% (35/115) of the early-stage cases. Multivariate regression indicated that hospital/medical center visits [Adjusted Odds Ratio (aOR): 11.6, 95% confidence interval (CI): 6.3-21.4], middle epidemic stage [aOR: 2.4 (1.2-4.7)], fever [aOR: 2.3 (2.3-12.9)], and musculo-articular pain [aOR: 1.9 (1.05-3.3)] were significantly associated with clinical reporting. However, cases with pruritus/rash [aOR: 0.47 (0.26-0.83)] and diarrhea [aOR: 0.47 (0.26-0.85)] were underreported. In conclusion, multiple factors contributed to dengue surveillance problems. To prevent a large-scale epidemic and minimize severe dengue cases, there is a need for integrated surveillance incorporating entomological, clinical, serological, and virological surveillance systems to detect early cases, followed by immediate prevention and control measures and continuous evaluation to ensure effectiveness. This effort will be particularly important for an arbovirus, such as Zika virus, with a high asymptomatic infection ratio. For dengue- non-endemic countries, we recommend serological surveillance be implemented in areas with high Aedes mosquito indices or many breeding sites. Syndromic surveillance, spatial analysis and monitoring changes in epidemiological characteristics using a geographical information system, as well as epidemic prediction models involving epidemiological, meteorological and environmental variables will be helpful for early risk communication to increase awareness.

Dataset Information

Dengue epidemic in a non-endemic zone of Bangladesh: Clinical and laboratory profiles of patients.

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Dengue epidemic in a non-endemic zone of Bangladesh: Clinical and laboratory profiles of patients.

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