Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019

Abstract

Background: Dengue has become one of the major vector-borne diseases, which has been an important public health concern. We aimed to estimate the disease burden of dengue in major endemic regions from 1990 to 2019, and explore the impact pattern of the socioeconomic factors on the burden of dengue based on the global burden of diseases, injuries, and risk factors study 2019 (GBD 2019). Methods: Using the analytical strategies and data from the GBD 2019, we described the incidence and disability-adjusted life years (DALYs) of dengue in major endemic regions from 1990 to 2019. Furthermore, we estimated the correlation between dengue burden and socioeconomic factors, and then established an autoregressive integrated moving average (ARIMA) model to predict the epidemic trends of dengue in endemic regions. All estimates were proposed as numbers and age-standardized rates (ASR) per 100,000 population, with uncertainty intervals (UIs). The ASRs of dengue incidence were compared geographically and five regions were stratified by a sociodemographic index (SDI). Results: A significant rise was observed on a global scale between 1990 and 2019, with the overall age-standardized rate (ASR) increasing from 557.15 (95% UI 243.32–1212.53) per 100,000 in 1990 to 740.4 (95% UI 478.2–1323.1) per 100,000 in 2019. In 2019, the Oceania region had the highest age-standardized incidence rates per 100,000 population (3173.48 (95% UI 762.33–6161.18)), followed by the South Asia region (1740.79 (95% UI 660.93–4287.12)), and then the Southeast Asia region (1153.57 (95% UI 1049.49–1281.59)). In Oceania, South Asia, and Southeast Asia, increase trends were found in the burden of dengue fever measured by ASRs of DALY which were consistent with ASRs of dengue incidence at the national level. Most of the countries with the heaviest burden of dengue fever occurred in areas with low and medium SDI regions. However, the burden in high-middle and high-SDI countries is relatively low, especially the Solomon Islands and Tonga in Oceania, the Maldives in South Asia and Indonesia in Southeast Asia. The age distribution results of the incidence rate and disease burden of dengue fever of major endemic regions showed that the higher risk and disease burden are mainly concentrated in people under 14 or over 70 years old. The prediction by ARIMA showed that the risk of dengue fever in South and Southeast Asia is on the rise, and further prevention and control is warranted. Conclusions: In view of the rapid population growth and urbanization in many dengue-endemic countries, our research results are of great significance for presenting the future trend in dengue fever. It is recommended to policy makers that specific attention needs to be paid to the negative impact of urbanization on dengue incidence and allocate more resources to the low-SDI areas and people under 14 or over 70 years old to reduce the burden of dengue fever.

Dengue in Pregnancy: A Southeast Asian Perspective

“…In SEA, 7,700,000 cases of dengue were reported in 2019. This is a 36.5% increase from 1990, with 4,890,000 cases [ 4 ]. Trends and incidences of each country are summarized in Figure 1 .…”

“…Globally, the incidence of dengue increased from 30,668,000 in 1990 to 56,879,000 in 2019 [ 3 ]. In SEA alone, there were 7,700,000 cases of dengue in 2019 [ 4 ].…”

“…Dengue cases were seen more often in females than males, with the age-standardized incidence rate (ASIR) female-to-male ratio of 1.10 in 1990 and 1.08 in 2019 [ 3 ]. In 2019, the ASIR per 100,000 in SEA was 1153.57, the 3rd highest after Oceania and South Asia [ 4 ].…”

Time series analysis and prediction of scarlet fever incidence trends in Jiangsu Province, China: using ARIMA and TBATS models

“…s denotes the seasonal period (s = 12 in this study). The principle is to transform the non-stationary time series into a stationary time series and then regress the dependent variable only on its lagged value and the present and lagged values of the random error term [20] .The ARIMA model can be built in the following steps( ) The establishment of a smooth series, the unstable time series is transformed into a smooth time series by differencing or data transformation ( ) The identi cation of the model, the parameters p, q of the model are initially determined by plotting the autocorrelation coe cient (ACF) and partial autocorrelation coe cient (PACF) of the series, and the parameters p, q of the model are determined by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). (AIC) and Bayesian Information Criterion (BIC) minimum to determine the optimal model, and also through the R software auto.…”

Knowledge, Attitudes and Practices toward Dengue Fever, Vector Control, and Vaccine Acceptance Among the General Population in Countries from Latin America and Asia Pacific: A Cross-Sectional Study (GEMKAP)

“…Dengue is one of the most widespread vector-borne viral diseases and continues to spread rapidly [ 1 ]. It is transmitted by the bite of female mosquitoes of the species Aedes aegypti (primary vector) and Aedes albopictus (secondary vector) [ 2 ] and is associated with a high morbidity rate, causing death in up to 20% of those who contract severe dengue [ 3 ].…”