A scoring scheme prediction model for dengue outbreaks using weather factors in Ho Chi Minh city, Vietnam

Original Research

Abstract

Background: The dengue infection cases are increasing in Ho Chi Minh city (HCMC), Vietnam. Previous studies have demonstrated the correlation between dengue cases and weather factors, which then are used to built prediction models for dengue outbreaks. However, the association between dengue and weather varies greatly between regions and locations. In HCMC, a tropical climate city in Vietnam, there is no such a weather-based prediction model for dengue outbreaks.

Objectives: This study aims to determine the correlation between weather factors and a weekly number of dengue cases and to develop a scoring scheme prediction model for dengue outbreaks using weather factors in HCMC, Vietnam. 

Methods: An ecological study was conducted on the evaluation of weekly time-series data from 1999 to 2017. A Poisson regression model coupled with Distributed Lag Non-Linear Model (DLNM) was constructed to evaluate the effects of weather factors (i.e., temperature, relative humidity, cumulative rainfall, wind speed) and the weekly dengue cases in HCMC with lag 1-12 weeks.

Results: The predictive model was based on the following weather factors: wind speed at lag 5-8 and 9-12 weeks; temperature amplitude and humidity at lag 5-8 weeks; rainfall at lag 1-4, 5-8, and 9-12 weeks. The predictive model using climate predictors explained about 80% of the variance in dengue cases with a small value of the mean absolute percentage error (MAPE= 0.17). The scoring scheme was then developed from the predictive model; it had a good prediction power – with the accuracy rate = 81%, sensitivity = 1, and specificity = 0.80. In summary, our study indicated that weather factors significantly influence and are predictors for the variation of dengue cases in Ho Chi Minh city, Vietnam. We recommend applying this model to improve the prevention of dengue outbreak.

Graphical abstract

The prevalence and related factors of phantom vibration among medical students: A first look in Vietnam

Original Research

Abstract

Background: Phantom vibration (PV) is an illusionary perception in which people perceive their mobile phone vibrates while it actually does not. Recently, PV has attracted attention in psychology and medical field. There are several studies investigating the prevalence and risk factors associated with this phenomenon. However, the findings are inconsistent. The prevalence of PV fluctuates from 21% to 89% among different groups and its mechanism remains unclear. Further understanding is necessary to identify the settings in which PV may harm the population and warrant further exploration.

Objectives: This study aims to explore the prevalence of PV among medical students in Ho Chi Minh City and settings that PV can risk people’s health. Relationships between PV and phone usage habits as well as psychiatric disturbance also are investigated.

Methods: By using online questionnaire on 377 undergraduate medical students in Ho Chi Minh City, Vietnam, the cross-sectional study explored factors associated with PV, including demographic, behavioral phone usage, and mental/emotional factors using the Self Reporting Questionaire - 20 (SRQ-20). The descriptive and association analyses were employed using R software.

Results: The study found a significant association between mental/emotional factors (i.e. mental disturbance and phone attachment) and PV (OR=2.15, 95% CI=1.21-3.81, p value=0.009; OR=1.75, 95% CI=1.02-3.01, p value=0.043 respectively), which suggests an important role of mental/emotional factors in explaining the potential mechanism of PV. A high proportion of participants also experienced PV while driving (55.5%) within the last month. This implies the impact of PV possibly becomes significant, causing an increase in the risk of traffic accident due to distracted driving.

Graphical abstract

Directed Acyclic Graphs: Alternative tool for causal inference in epidemiology and biostatistics research and teaching

Review

Abstract

The issue of causation is one of the major challenges for epidemiologists who aim to understand the association between an exposure and an outcome to explain disease patterns and potentially provide a basis for intervention. Suitably designed experimental studies can offer robust evidence of the causal relationships. The experimental studies, however, are not popular, difficult or even unethical and impossible to conduct; it would be desirable if there is a methodology for reducing bias or strengthening the causal inferences drawn from observational studies. The traditional approach of estimating causal effects in such studies is to adjust for a set of variables judged to be confounders by including them in a multiple regression. However, which variables should be adjusted for as confounders in a regression model has long been a controversial issue in epidemiology. From my observation, the adjustments using only "statistical artifacts" methods such as the p-value<0.2 in univariate analysis, stepwise (forward/backward) are widely used in research and teaching in Epidemiology and Statistics but without appropriated notice on the biological or clinical relationships between exposure and outcome which may induce the bias in estimating causal effects. In this mini-review, we introduce an interesting method, namely Directed Acyclic Graphs (DAGs), which can be used to reduce the bias in estimating causal effects; it is also a good application for Epidemiology and Biostatistics teaching.

Graphical abstract

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