MATERIALS AND METHODS: A total of 81 cases of oral cancers were matched with 162 controls in this hospital-based study. Information on sociodemographic characteristics and details of risk habits (duration, frequency and type of tobacco consumption and betel quid chewing) were collected. Association between smoking and betel quid chewing with oral cancer were analysed using conditional logistic regression.
RESULTS: Slightly more than half of the cases (55.6%) were smokers where 88.9% of them smoked kretek. After adjusting for confounders, smokers have two fold increased risk, while the risk for kretek consumers and those smoking for more than 10 years was increased to almost three-fold. Prevalence of betel quid chewing among cases and controls was low (7.4% and 1.9% respectively). Chewing of at least one quid per day, and quid combination of betel leaf, areca nut, lime and tobacco conferred a 5-6 fold increased risk.
CONCLUSIONS: Smoking is positively associated with oral cancer risk. A similar direct association was also seen among betel quid chewers.
METHODS: A quasi experimental interventional study involving 166 non-smokers adolescents, aged 13 to 14 years old were carried out in two schools located in two different suburbs. Both schools had equal number of participants. One school was given the smoking prevention module for intervention while the control school only received the module after the study had been completed. The knowledge on smoking and its harmful effects and smoking refusal skill score were assessed using a set of validated Malay questionnaires at baseline, two weeks and eight weeks after the intervention. Repeated measure ANCOVA was used to analyse the mean score difference of both groups at baseline and after intervention.
RESULT: Baseline analysis shows no significant difference in knowledge score between the study groups (p = 0.713) while post intervention, it shows significant inclination of knowledge score in intervention group and the difference was significant after controlling the gender [F(df) = 15.96(1.5), p <0.001]. The mean baseline for refusal skills score in the control and intervention groups were 30.89(6.164) and 28.02(6.241) respectively (p= 0.003). Post intervention, there is a significant difference in the crude mean and the estimated marginal means for smoking refusal skills score between the two groups after controlling for sex [F(df) = 5.66(1.8), p = 0.005].
CONCLUSION: This smoking prevention module increased the level of knowledge on smoking and its harmful effects and smoking refusal skill among the secondary school students. Thus, it is advocated to be used as one of the standard modules to improve the current method of teaching in delivering knowledge related to harmful effects of smoking and smoking refusal skill to the adolescents in Malaysia.
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METHODS: We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z-scores for an 'energy-dense, high-fat, low-fibre' DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ-score or food intake quartile at 14 and 17 years. Early-life exposures included: maternal age; maternal pre-pregnancy body mass index; parent smoking status during pregnancy; and parent socio-economic position (SEP) at 14 and 17 years. Associations between the DPZ-scores, early-life factors and SEP were analysed using regression analysis.
RESULTS: Dietary tracking was strongest among boys with high DPZ-scores, high intakes of processed meat, low-fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P