METHODS: A self-administered validated questionnaire was used to obtain data from the nationally representative samples of school-going adolescents aged 11-19 years in Malaysia. Prevalence rates were computed and chi-squared tests and multiple logistic regression were conducted.
RESULTS: Of the participants, 23.3% reported exposure to SHS at least once in the car of their parents/guardians during the last 7 days before the survey. The prevalence and likelihood of SHS exposure were significantly higher in Malays, descendants of natives of Sabah and Sarawak, schools in rural areas, females, and current smokers. However, age group and knowledge on the harmful effects of SHS were not significant after adjusting for confounding effects.
CONCLUSIONS: A substantial proportion of school-going adolescents were exposed to secondhand smoke in the car of their parents/guardians. This highlights the need for effective tobacco control measures to include health promotion and smoke-free car regulations to be introduced to prevent severe health hazards and to reduce smoking initiation among non-smoking adolescents.
METHODS: We analyzed data from the Global Youth Tobacco Survey (GYTS) 2003, GYTS 2009, and the Tobacco and Electronic Cigarette Survey among Malaysia Adolescents (TECMA) 2016. The surveys employed multistage sampling to select representative samples of adolescents attending secondary school in Malaysia. Data were collected using a pre-validated self-administered anonymous questionnaire adopted from the GYTS.
RESULTS: Between 2003 and 2016, major changes occurred in which there were reductions in the prevalence of ever smoking, current smoking, and susceptibility to smoking. Reductions were also observed in exposure to SHS in public places and in the home. The proportion of school-going adolescents who support a ban on smoking in public places increased between 2013 to 2016, and there was a significant reduction in the proportion of respondents that were offered 'free' cigarettes by tobacco company representatives. However, there was no difference in the proportion of adolescents who initiated smoking before the age of 10 years and current smokers seeking advice to quit smoking across the time period.
CONCLUSIONS: Our study indicates that the smoking policies and measures have been effective in reducing smoking prevalence, secondhand smoke exposure, and access to cigarettes, among school-going adolescents in Malaysia. However, measures to reduce smoking initiation and increase smoking cessation need to be strengthened to reduce the burden of smoking-related diseases in Malaysia in the long-term.
OBJECTIVE: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem.
METHODS: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation.
RESULTS: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance.
CONCLUSIONS: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.