The effects of high temperature on cardiovascular admissions in the most populous tropical city in Vietnam.

This study examined the short-term effects of temperature on cardiovascular hospital admissions (CHA) in the largest tropical city in Southern Vietnam. We applied Poisson time-series regression models with Distributed Lag Non-Linear Model (DLNM) to examine the temperature-CHA association while adjusting for seasonal and long-term trends, day of the week, holidays, and humidity. The threshold temperature and added effects of heat waves were also evaluated. The exposure-response curve of temperature-CHA reveals a J-shape relationship with a threshold temperature of 29.6 °C. The delayed effects temperature-CHA lasted for a week (0-5 days). The overall risk of CHA increased 12.9% (RR, 1.129; 95%CI, 0.972-1.311) during heatwave events, which were defined as temperature ≥ the 99th percentile for ≥2 consecutive days. The modification roles of gender and age were inconsistent and non-significant in this study. An additional prevention program that reduces the risk of cardiovascular disease in relation to high temperatures should be developed.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH3 are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO2 are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Temperature as a risk factor for hospitalisations among young children in the Mekong Delta area, Vietnam.

BACKGROUND: The Mekong Delta is the most vulnerable region to climate change in South-East Asia; however, the association between climate and children's health has rarely been studied in this region. OBJECTIVE: We examined the short-term association between daily temperature and hospital admissions for all causes, gastrointestinal and respiratory infection, among young children in the Mekong Delta area in Vietnam. METHODS: Daily paediatric hospital admissions and meteorological data were obtained from January 2008 to December 2012. A time-series approach was used with a combination of a Poisson regression and constrained distributed lag models to analyse the data. The long-term and seasonal trends, as well as other time-varying covariates, were adjusted using spline functions. Temperature--pediatric admission relationship was evaluated by age-specific (0-2 and 3-5-year-olds) and cause of admission groupings. RESULTS: A 1°C increase in the 2-day moving average temperature was significantly associated with a 3.4% (95% CI 1.2% to 5.5%), 4.6% (95% CI 2.2% to 7.3%), 2.6% (95% CI 0.6% to 4.6%), 4.4% (95% CI 0.6% to 8.2%) and 3.8%(95% CI 0.4% to 7.2%) increase in hospital admissions with 0-2-year-old children, 3-5-year-old children, all causes, gastrointestinal infection and respiratory infection, respectively. The cumulative effects from 1-day to 6-day moving average temperature on hospital admissions were greater for 3-5-year-old children and gastrointestinal infection than for 0-2-year-old children and other causes. CONCLUSIONS: Temperature was found to be significantly associated with hospital admissions in young children with the highest association between temperature and gastrointestinal infection. The government agencies of Mekong Delta should implement measures to protect children from the changing temperature conditions related to climate change.

Association between climate factors and diarrhoea in a Mekong Delta area.

The Mekong Delta is vulnerable to changes in climate and hydrological events which alter environmental conditions, resulting in increased risk of waterborne diseases. Research exploring the association between climate factors and diarrhoea, the most frequent waterborne disease in Mekong Delta region, is sparse. This study evaluated the climate-diarrhoea association in Can Tho city, a typical Mekong Delta area in Vietnam. Climate data (temperature, relative humidity, and rainfall) were obtained from the Southern Regional Hydro-Meteorological Centre, and weekly counts of diarrhoea visits were obtained from Can Tho Preventive Medicine Centre from 2004 to 2011. Analysis of climate and health variables was carried out using spline function to adjust for seasonal and long-term trends of variables. A distributed lag model was used to investigate possible delayed effects of climate variables on diarrhoea (considering 0-4 week lag periods), then the multivariate Poisson regression was used to examine any potential association between climate factors and diarrhoea. The results indicated that the diarrhoea incidence peaked within the period August-October annually. Significant positive associations were found between increased diarrhoea and high temperature at 4 weeks prior to the date of hospital visits (IRR = 1.07; 95 % CI = 1.04-1.08), high relative humidity (IRR = 1.13; 95 % CI = 1.12-1.15) and high (>90th percentile) cumulative rainfall (IRR = 1.05; 95 % CI = 1.05-1.08). The association between climate factors and diarrhoea was stronger in rural than urban areas. These findings in the context of the projected changes of climate conditions suggest that climate change will have important implications for residential health in Mekong Delta region.

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system.