Imported dengue fever and climatic variation are important determinants facilitating dengue epidemics in Southern Taiwan

Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.

Population-based assessment of factors influencing antibiotic prescribing for adults with dengue infection in Taiwan

Background

Antibiotic treatment for dengue is likely considerable and potentially avoidable but has not been well characterized. This study aimed to assess antibiotic prescribing for confirmed dengue cases in outpatient and inpatient settings and to identify associated patient, physician and contextual factors.

Methods

57,301 adult dengue cases reported in Taiwan between 2008–2015 were analyzed. We assessed both outpatient and inpatient claims data of dengue patients from a week before to a week after their dengue infections were confirmed under Taiwan’s National Health Insurance program. A multivariable logistic regression with generalized estimating equations was used to estimate the probability of antibiotic prescribing in dengue patients.

Results

Overall, 24.6% of dengue patients were prescribed an antibiotic during the 14 day-assessment period. Antibiotics were prescribed in 6.1% and 30.1% of outpatient visits and inpatient admissions, respectively. Antibiotic prescriptions were reduced by ~50% in epidemic years. Among inpatients, advanced age, females, and major comorbidities were risk factors for receipt of an antibiotic; antibiotics were used in 26.0% of inpatients after dengue was diagnosed. Significant differences in antibiotic prescribing practices were observed among physicians in outpatient settings but not in inpatient settings.

Conclusions

In addition to patient and physician demographic characteristics, contextual factors such as care setting and during epidemics significantly influenced prescription of antibiotics. Characterization of prescribing patterns should help direct programs to curb antibiotic prescribing.

Antimicrobial resistance is a growing global public health threat. The non-specific clinical manifestations of dengue overlap with signs and symptoms of other febrile illnesses common to tropical and subtropical zones making differential diagnosis between dengue and bacterial infections difficult, hence, leading to potentially unnecessary antibiotic prescribing. However, our understanding of factors underlying antibiotic prescribing for dengue is rather limited. Taiwan has experienced periodic dengue outbreaks and has a comprehensive national health insurance database including reliable infectious diseases surveillance and prescribing records. The findings in Taiwan show that other than commonly known patient characteristics, provider and contextual factors play a significant role. Physician’s age and practice setting were significant factors influencing the decision to prescribe antibiotics, particularly in outpatient visits. The likelihood of prescribing an antibiotic to dengue patients was reduced by more than 50% in medical visits occurring after the dengue infection was confirmed. Understanding patient, provider and contextual factors in antibiotics prescription for dengue infections can provide insights for improved antibiotic stewardship and unnecessary antibiotic treatment for dengue.

The Global Trends and Regional Differences in Incidence of Dengue Infection from 1990 to 2019: An Analysis from the Global Burden of Disease Study 2019

Introduction

Dengue, as a neglected tropical disease, brings a heavy socioeconomic burden. To provide tailored global prevention strategies, we analyzed the global trends and regional differences in incidence of dengue infection from 1990 to 2019.

Methods

We obtained data on annual dengue episodes and incidence rates, which reflected the epidemic status of dengue infection from the 2019 Global Burden of Disease (GBD) Study. The changes in dengue episodes and estimated annual percentage changes (EAPCs) of the age-standardized incidence rate (ASR) were calculated to quantify the temporal trends of dengue infection.

Results

Globally, dengue ASR increased by 1.70% (95% CI 1.62%–1.78%) per year from 1990 to 2011; subsequently, it decreased by 0.41% (95% CI 0.20%–0.62%) per year from 2011 to 2019. However, the global number of dengue episodes increased steadily by 85.47% from 30.67 million in 1990 to 56.88 million in 2019. Against the global trend of decreasing ASR from 2011 to 2019, an increasing trend was reported in Oceania (EAPC 11.01, 95% CI 8.79–13.27), East Asia (EAPC 4.84, 95% CI 2.70–7.03) and Southeast Asia (EAPC 0.38, 95% CI 0.13–0.62). For socio-demographic index (SDI) regions, ASR continued to have an increasing trend in the middle (EAPC 0.26, 95% CI 0.07–0.45) and high-middle (EAPC 1.70, 95% CI 0.98–2.42) SDI regions from 2011 to 2019. In contrast to the global peak age of dengue incidence rate (10 to 25 years), the dengue incidence rate of older people (> 65 years) was higher than in other age groups in low and low-middle SDI regions. Additionally, the proportions of dengue episodes in the > 70-year-old age group increased in 2019 (using the baseline in 1990 or 2011) in most GBD regions.

Conclusions

Global dengue episodes have increased tremendously in 3 decades. Although global dengue ASR decreased in the last decade, it is still increasing in hyperendemic regions including Oceania, East Asia and Southeast Asia, and also in the middle and high-middle SDI regions. More attention should be paid to the elderly because of the higher dengue incidence rate among them in low and low-middle SDI regions and the increased proportions of dengue episodes among the elderly in most GBD regions. Therefore, more efforts should be undertaken to develop targeted prevention strategies for crucial regions and older populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-021-00470-2.

Importation of SARS-CoV-2 infection leads to major COVID-19 epidemic in Taiwan

OBJECTIVE

COVID-19 has recently become a pandemic affecting many countries worldwide. This study aims to evaluate the current status of COVID-19 in Taiwan and analyze the source of infection.

METHODS

National data regarding SARS-CoV-2 infection were obtained from Taiwan. CDC at the end of April 2020. These data were subjected to analysis of the current status and correlation between indigenous and imported COVID-19 cases. A phylogenetic tree was created to analyze the phylogeny of Taiwanese SARS-CoV-2 isolates.

RESULTS

The first case of SARS-CoV-2 infection in Taiwan was detected on January 21, 2020. Epidemiological data indicate that by April 30, there were a total of 429 COVID-19 confirmed cases with the death rate of 1.3%. Most cases were identified as imported (79.9%; 343/429), with the majority originating from the United States of America (22.1%) and the United Kingdom (17.6%). Results from phylogenetic tree analyses indicate that the Taiwanese SARS-CoV-2 isolates were clustered with the SARS-CoV-2 isolates from other countries (bootstrap value 98%) and sub-clustered with bat SARS-like coronaviruses (bootstrap value 99%).

CONCLUSION

This study suggests that the importation of SARS-CoV-2 infection was the primary risk-factor resulting in the COVID-19 epidemic in Taiwan.

The Impact of Dengue on Economic Growth: The Case of Southern Taiwan

Dengue has long been a public health problem in tropical and subtropical countries. In 2015, a dengue outbreak occurred in Taiwan, where 43,784 cases were reported. This study aims to assess the impact of dengue on Southern Taiwan’s economic growth according to the economic growth model-based regression approach recommended by the World Health Organization (WHO). Herein, annual data from Southern Taiwan on the number of dengue cases, income growth, and demographics from 2010–2015 were analyzed. The percentage of reduction of the average income per capita in 2015 due to the dengue outbreak was estimated. Dengue was determined to have a negative linear economic impact on Southern Taiwan’s economic growth. In particular, a reduction of 0.26% in the average income per capita was estimated in Southern Taiwan due to the 2015 outbreak. If the model is applied alongside other dengue outbreak forecast models, then the forecast for economic reduction due to a future dengue outbreak may also be estimated. Prevention and recovery policies may subsequently be decided upon based on not only the number of dengue cases but also the degree of economic burden resulting from an outbreak.