Background: The use of non-invasive ventilation (NIV) in preterm infants is becoming increasingly common. The use of cannula in NIV can cause ulceration of the nasal bridge with the current practices using the thin foam patches. This study aims to evaluate the effectiveness of hydrocolloid nasal dressing pads in preventing nasal ulceration comparing to that of the thin foam patches.

Methods: A prospective cohort study using hydrocolloid dressing pads (1 November to 30 April 2020) was compared to that of a historical control group using thin foam dressing (1 April to 15 October 2019) to evaluate the effectiveness of hydrocolloid dressing pads. All participants were preterm infants (less than 37 weeks of gestational age) and used nasal cannula NIV at the Department of Neonatal Intensive Care (NICU), Children's Hospital 1.

Results: 71 infants used hydrocolloid dressing pads, and 42 used ordinary thin foam nasal dressings. In the hydrocolloid dressings group, two infants (2.8%) had nasal ulcers; among them, one was mild, and the other was moderate. In comparison, ten infants (23.8%) using thin foam dressings developed ulcers, of which seven were mild, two were moderate, and one was severe. Using hydrocolloid nasal dressings significantly reduced nasal ulceration compared to thin foam dressings (OR = 0.09, 95%CI = 0.02 – 0.45).

Conclusion: Using hydrocolloid nasal dressings for preterm infants on nasal cannula NIV significantly reduced nasal ulceration compared to ordinary thin foam dressings.

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.

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.

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.