The association between high ambient temperature and risk of hospitalization: a time-series study in eight ecological regions in Vietnam

Viet Nam is among the countries most threatened by and vulnerable to climate change and extreme weather events. However, research on the temperature-morbidity relationship at the national scale has been scarce. This study aimed to assess the impact of high temperatures on the risk of hospital admissions for all causes and heat-sensitive diseases across eight ecological regions in Vietnam. The study utilized a longitudinal dataset that included hospitalization and meteorological data from eight provinces representing eight regions in Vietnam. A time series analysis was applied using the generalized linear and distributed lag models with a quasi-Poisson family to examine the temperature-hospitalization association in each province. A random-effects meta-analysis was used to calculate the pooled estimate of risk for the national scale. The country-level pooled effects (%, [95% CI]) indicated that a 1 °C increase above the threshold temperature (19 °C) increased the hospitalization risk for all causes and infectious diseases by 0.8% [0.4%–1.2%] and 2.4% [1.02%–1.03%], respectively at lag 0–3 d. The effects of heat on respiratory diseases and mental health disorders were not significant. At the regional level, the association varied across eight regions, of which the Northern parts tended to have a higher risk than the Southern. This is among very few national-scale studies assessing hospitalization risk associated with high temperatures across eight ecological regions of Vietnam. These findings would be useful for developing evidence-based heat-health action plans

Deep learning models for forecasting dengue fever based on climate data in Vietnam

BackgroundDengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam.ObjectiveThis study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change.MethodsConvolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).Results and discussionLSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features.ConclusionThis study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years