Publications by authors named "Tran Dac Phu"

3 Publications

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Patterns of Raised Blood Pressure in Vietnam: Findings from the WHO STEPS Survey 2015.

Int J Hypertens 2019 1;2019:1219783. Epub 2019 Dec 1.

Division of Non-Communicable Diseases, General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam.

This study aims to describe the prevalence of raised blood pressure and the situation of management for raised blood pressure among the adult population in Vietnam. It also aims to examine the association between diversified socioeconomic and behavioral factors of raised blood pressure and awareness of raised blood pressure. Data were obtained from the STEPS survey conducted in Vietnam in 2015. Survey sample was nationally representative with a total of 3,856 people aged 18-69 years old. The study outcomes included raised blood pressure and awareness of and control of raised blood pressure. Multiple logistic regression was used to examine the association of socioeconomic and behavior risk factors with the outcome variables. The overall prevalence of raised blood pressure in Vietnam in 2015 was 18.9% (95% CI: 17.4%-20.6%). The prevalence of raised blood pressure was higher among men. Significantly correlated factors with raised blood pressure were age, sex, body mass index, and diabetes status. Levels of awareness of raised blood pressure were higher among the older age group and overweight people and lower among ethnic minority groups. Raised blood pressure in Vietnam is a serious problem due to its magnitude and the unacceptably high unawareness rate in the population. Public health actions dealing with the problems of raised blood pressure are urgent, while taking into account its relationship with sex and socioeconomic status. It is clear that the interventions should address all people in society, with a focus on disadvantaged groups which are the rural and ethnic minority peoples.
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December 2019

Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk.

PLoS One 2019 27;14(11):e0224353. Epub 2019 Nov 27.

National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.

Background: Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.

Methods: The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps.

Results: The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated.

Conclusions: The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.
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April 2020

Sustainable Model for Public Health Emergency Operations Centers for Global Settings.

Emerg Infect Dis 2017 10;23(13)

Capacity to receive, verify, analyze, assess, and investigate public health events is essential for epidemic intelligence. Public health Emergency Operations Centers (PHEOCs) can be epidemic intelligence hubs by 1) having the capacity to receive, analyze, and visualize multiple data streams, including surveillance and 2) maintaining a trained workforce that can analyze and interpret data from real-time emerging events. Such PHEOCs could be physically located within a ministry of health epidemiology, surveillance, or equivalent department rather than exist as a stand-alone space and serve as operational hubs during nonoutbreak times but in emergencies can scale up according to the traditional Incident Command System structure.
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October 2017