Assessing the impact of stricter drink driving policy on alcohol consumption in a population of Hanoi, Vietnam using wastewater analysis.

INTRODUCTION: On 1 January 2020, Vietnam introduced a new law with harsher fines and penalties for driving under the influence of alcohol. Reports of empty beer restaurants following this implementation suggested the new law has the potential to reduce population-level alcohol consumption. This pilot study aims to quantify short-term changes in alcohol consumption levels after the implementation of the new law and assess whether it could lead to a reduction in total alcohol consumption in the population. METHODS: Wastewater samples were collected from two sites along a sewage canal in Hanoi during two periods: Period 1 (15 December 2018 to 14 January 2019) and Period 2 (15 December 2019 to 14 January 2020). Ethyl sulfate, a specific metabolite of alcohol, was quantified to monitor the trend of alcohol consumption. Both interrupted time series and controlled interrupted time series approaches were utilised, with Period 1 and Period 2 serving as the control and intervention periods, respectively. RESULTS: Our analysis indicated that the implementation of the new law did not result in an immediate and significant reduction in alcohol consumption at the population level. Meanwhile, there was no significant difference in alcohol consumption between weekdays and weekends both before and after the implementation of the new law. DISCUSSION AND CONCLUSIONS: Long-term monitoring is needed to assess the impact of stricter DUI policy on alcohol consumption in the urban areas of Vietnam.

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.