METHODS: A study was carried out in 2013, which involved a total of 40 secondary schools. They were randomly selected using a two-stage clustering sampling method. Subsequently, all upper secondary school students (aged 16 to 17 years) from each selected school were recruited into the study. Data was collected using a validated standardised questionnaire.
RESULTS: This study revealed that the prevalence of smoking was 14.6% (95% CI:13.3-15.9), and it was significantly higher among males compared to females (27.9% vs 2.4%, p
METHODS: Electronic databases, including PubMed, EMBASE, Cochrane Library, Science Direct, Google Scholar, were systematically searched from the initiation of the database until 12 December 2020. All relevant studies about smoking and COVID-19 were screened using a set of inclusion and exclusion criteria. The Newcastle-Ottawa Scale was used to assess the methodological quality of eligible articles. Random meta-analyses were conducted to estimate odds ratios (ORs) with 95% confidence interval (CIs). Publication bias was assessed using the funnel plot, Begg's test and Egger's test.
RESULTS: A total of 1248 studies were retrieved and reviewed. A total of 40 studies were finally included for meta-analysis. Both current smoking and former smoking significantly increase the risk of disease severity (OR=1.58; 95% CI: 1.16-2.15, p=0.004; and OR=2.48; 95% CI: 1.64-3.77, p<0.001; respectively) with moderate appearance of heterogeneity. Similarly, current smoking and former smoking also significantly increase the risk of death (OR=1.35; 95% CI: 1.12-1.62, p=0.002; and OR=2.58; 95% CI: 2.15-3.09, p<0.001; respectively) with moderate appearance of heterogeneity. There was no evidence of publication bias, which was tested by the funnel plot, Begg's test and Egger's test.
CONCLUSIONS: Smoking, even current smoking or former smoking, significantly increases the risk of COVID-19 severity and death. Further causational studies on this association and ascertianing the underlying mechanisms of this relation is warranted.
METHODS: This secondary dataset analysis used data from the National Health and Morbidity Survey (NHMS) 2018. Data from 3914 participants were collected on elderly health in the Malaysian population. Sociodemographic characteristics were recorded. Smoking status was grouped as current smokers, former smokers, and non-smokers. A validated Malay language version of the Geriatric Depression Scale (M-GDS-14) was used to screen for depression among the elderly.
RESULTS: There was a significant association between smoking status with location, gender, employment status, marital status, ethnicity, education level, income, and depression. Current smokers are significantly higher in rural than urban areas. Among depressed participants, 65.7%, 17.1% and 17.2% were non-smokers, former smokers and current smokers, respectively. Multiple logistic regression showed that single (unmarried/separated/ divorced/widowed) participants were more likely to be depressed compared to married participants (AOR=1.68; 95% CI: 1.16-2.43). Whilst unemployed participants were more likely to be depressed than those who were employed (AOR=1.72; 95% CI: 1.22-2.44). Other Bumiputras were more likely to have depression compared to Malay, Chinese and Indian participants. Participants without formal education were more likely to be depressed compared to those having tertiary education. These participants have a 2-fold increased risk of depression (AOR=2.13; 95% CI: 1.02-4.45). Participants whose monthly salaries were <2000 MYR (AOR=3.67; 95% CI: 1.84-7.31) and 1000-1999 MYR (AOR=2.71; 95% CI: 1.23-5.94) were more likely to have depression compared with those who had received ≥3000 MYR. Ever smokers were more likely to be depressed than non-smokers (AOR=1.68; 95% CI: 1.23-2.29).
CONCLUSIONS: Elderly Malaysians are indeed at risk of developing depression particularly if they had ever smoked. Public health awareness and campaigning are pertinent to disseminate these outcomes in order to spread the awareness associated with smoking-related depression.
METHODS: Two overlapping cohorts of adults who reported smoking factory- made cigarettes from Malaysia and Thailand were interviewed face-to-face (3189 were surveyed at baseline and 1781 re-contacted at Wave 2; 2361 current smokers were surveyed at Wave 2 and 1586 re-contacted at Wave 3). In Thailand at baseline, large text only warnings were assessed, while at Wave 2 new large graphic warnings were assessed. In Malaysia, during both waves small text only warnings were in effect. Reactions were used to predict interest in quitting, and to predict making quit attempts over the following inter-wave interval.
RESULTS: Multivariate predictors of "interest in quitting" were comparable across countries, but predictors of quit attempts varied. In both countries, cognitive reactions to warnings (adjusted ORs; 1.57 & 1.69 for Malaysia at wave 1 and wave 2 respectively and 1.29 & 1.19 for Thailand at wave 1 and wave 2 respectively), forgoing a cigarette (except Wave 2 in Malaysia) (adjusted ORs; 1.77 for Malaysia at wave 1 and 1.54 & 2.32 for Thailand at wave 1 and wave 2 respectively), and baseline knowledge (except wave 2 in both countries) (adjusted ORs; 1.71 & 1.51 for Malaysia and Thailand respectively) were positively associated with interest in quitting at that wave. In Thailand only, "cognitive reactions to warnings" (adjusted ORs; 1.12 & 1.23 at wave 1 and wave 2 respectively), "forgoing a cigarette" (adjusted OR = 1.55 at wave 2 only) and "an interest in quitting" (adjusted ORs; 1.61 & 2.85 at wave 1 and wave 2 respectively) were positively associated with quit attempts over the following inter-wave interval. Salience was negatively associated with subsequent quit attempts in both Malaysia and Thailand, but at Wave 2 only (adjusted ORs; 0.89 & 0.88 for Malaysia and Thailand respectively).
CONCLUSION: Warnings appear to have common mechanisms for influencing quitting regardless of warning strength. The larger and more informative Thai warnings were associated with higher levels of reactions predictive of quitting and stronger associations with subsequent quitting, demonstrating their greater potency.
FINDINGS: We studied 127 women; and based on their hair nicotine levels measured using gas chromatography-mass spectrometry, 25 of them were categorized as having higher hair nicotine levels, 25 were grouped as having lower hair nicotine and 77 women were grouped into the non-detected group. The non-detected group did not have detectable levels of hair nicotine. Anthropometry, blood pressure (BP), lipid profile and high-sensitivity C-reactive protein (hsCRP) were measured accordingly. Microvascular endothelial function was assessed non-invasively using laser Doppler fluximetry and the process of iontophoresis involving acetylcholine and sodium nitroprusside as endothelium-dependent and endothelium-independent vasodilators respectively. The mean hair nicotine levels for higher and lower hair nicotine groups were 0.74 (1.04) and 0.05 (0.01) ng/mg respectively. There were no significant differences in anthropometry, BP, lipid profile and hsCRP between these groups. There were also no significant differences in the microvascular perfusion and endothelial function between these groups.
CONCLUSION: In this study, generally healthy non-smoking women who have higher, lower and non-detected hair nicotine levels did not show significant differences in their microvascular endothelial function. Low levels of SHS exposure among generally healthy non-smoking women may not significantly impair their microvascular endothelial function.
METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.
RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.
CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.
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.
METHODS: Data were derived from the Global School-Based Student Health Survey (GSHS). Data from 71176 adolescents aged 12-15 years residing in 23 countries were analyzed. The Centers for Disease Control and Prevention (CDC) 2000 growth charts were used to identify underweight, normal weight, and overweight/ obesity. Weighted age- and gender-adjusted prevalence of weight categories and tobacco use was calculated. Multivariate logistic regression analysis was performed to estimate the association between weight categories and tobacco use for each country, controlling for covariates. Pooled odds ratios and confidence intervals were computed using random- or fixed-effects meta-analyses.
RESULTS: A significant association between weight categories and tobacco use was evident in only a few countries. Adolescents reporting tobacco use in French Polynesia, Suriname, and Indonesia, had 72% (95% CI: 0.15-0.56), 55% (95% CI: 0.24-0.84), and 24% (95% CI: 0.61-0.94) lower odds of being underweight, respectively. Adolescents reporting tobacco use in Uganda, Algeria, and Namibia, had 2.30 (95% CI: 1.04-5.09), 1.71 (95% CI: 1.25-2.34), and 1.45 (95% CI: 1.00-2.12) times greater odds of being overweight/obese, but those in Indonesia and Malaysia had 33% (95% CI: 0.50-0.91) and 16% (95% CI: 0.73-0.98) lower odds of being overweight/obese.
CONCLUSIONS: The association between tobacco use and BMI categories is likely to be different among adolescents versus adults. Associating tobacco use with being thin may be more myth than fact and should be emphasized in tobacco prevention programs targeting adolescents.
METHODS: We administered the BM-PTSQ to 669 secondary school students selected through multistage sampling; 60% of respondents were male (n=398), and 69.9% (n=463) were from rural areas. Respondents were aged 13-16 years, 36.4% (n=241) were 13 years, 40.0% (n=265) were 14 years, and 23.6% (n=156) were 16 years old. We used parallel and exploratory factor analysis (EFA) to determine the domains of the questionnaire. In addition, we also employed EFA, confirmatory factor analyses (CFA), and Cronbach's alpha to evaluate the construct validity and reliability of the BM-PTSQ.
RESULTS: EFA and parallel analysis identified two domains in the BM-PTSQ that accounted for 62.9% of the observed variance, and CFA confirmed the two-domain structure. The two domains' internal consistency scores ranged from 0.702 to 0.80, which suggested adequate reliability.
CONCLUSIONS: The BM-PTSQ has acceptable psychometric validity and is appropriate for assessing smoking perception and intention among Malaysian secondary school-aged youth. Researchers should further evaluate this tool's applicability in a more sociodemographically diverse population.