METHODS: Data were derived from a cross-sectional study of 1082 adolescents in 22 welfare institutions located across Peninsular Malaysia in 2009. Using supervised self-administered questionnaires, adolescents were asked to assess their self-esteem and to complete questions on pubertal onset, substance use, family structure, family connectedness, parental monitoring, and peer pressure. SRB was measured through scoring of five items: sexual initiation, age of sexual debut, number of sexual partners, condom use, and sex with high-risk partners. Multivariate logistic regression analysis was used to examine the various predictors of sexual risk behaviour.
RESULTS: The study showed that 55.1% (95%CI = 52.0-58.2) of the total sample was observed to practice sexual risk behaviours. Smoking was the strongest predictor of SRB among male adolescents (OR = 10.3, 95%CI = 1.25-83.9). Among females, high family connectedness (OR = 3.13, 95%CI = 1.64-5.95) seemed to predict the behaviour.
CONCLUSION: There were clear gender differences in predicting SRB. Thus, a gender-specific sexual and reproductive health intervention for institutionalised adolescents is recommended.
METHODS AND RESULTS: Data was sourced from participants in the Western Australian Pregnancy (Raine) Cohort Study. At 14 and 17 y, dietary intake, anthropometric and biochemical data were measured and z-scores for an 'energy dense, high fat and low fibre' DP were estimated using reduced rank regression (RRR). Associations between DP z-scores and cardiometabolic risk factors were examined using regression models. Tracking of DP z-scores was assessed using Pearson's correlation coefficient. A 1 SD unit increase in DP z-score between 14 and 17 y was associated with a 20% greater odds of high metabolic risk (95% CI: 1.01, 1.41) and a 0.04 mmol/L higher fasting glucose in boys (95% CI: 0.01, 0.08); a 28% greater odds of a high-waist circumference (95% CI: 1.00, 1.63) in girls. An increase of 3% and 4% was observed for insulin and HOMA (95% CI: 1%, 7%), respectively, in boys and girls, for every 1 SD increase in DP z-score and independently of BMI. The DP showed moderate tracking between 14 and 17 y of age (r = 0.51 for boys, r = 0.45 for girls).
CONCLUSION: An 'energy dense, high fat, low fibre' DP is positively associated with cardiometabolic risk factors and tends to persist throughout adolescence.
METHODS: A cross-sectional study was carried out at three selected public schools in the state of Selangor. A total of 379 Malaysian adolescents completed the PedsQL 4.0 adolescent self-report and 218 (55.9%) parents completed the PedsQL 4.0 parent proxy-report. Weight and height of adolescents were measured and BMI-for-age by sex was used to determine their body weight status.
RESULTS: There were 50.8% male and 49.2% female adolescents who participated in this study (14.25 ± 1.23 years). The prevalence of overweight and obesity (25.8%) was four times higher than the prevalence of severe thinness and thinness (6.1%). Construct validity was analyzed using Confirmatory Factor Analysis (CFA). Based on CFA, adolescent self-report and parent proxy-report met the criteria of convergent validity (factor loading > 0.5, Average Variance Extracted (AVE) > 0.5, Construct Reliability > 0.7) and showed good fit to the data. The adolescent self-report and parent proxy-report exhibited discriminant validity as the AVE values were larger than the R(2) values. Cronbach's alpha coefficients of the adolescent self-report (α = 0.862) and parent proxy-report (α = 0.922) showed these instruments are reliable. Parents perceived the HRQoL of adolescents was poorer compared to the perception of the adolescent themselves (t = 5.92, p < 0.01). There was no significant difference in total HRQoL score between male and female adolescents (t = 0.858, p > 0.05). Parent proxy-report was negatively associated with the adolescents' BMI-for-age (r = -0.152, p < 0.05) whereas no significant association was found between adolescent self-report and BMI-for-age (r = 0.001, p > 0.05).
CONCLUSION: Adolescent self-report and parent proxy-report of the PedsQL 4.0 are valid and reliable to assess HRQoL of Malaysian adolescents. Future studies are recommended to use both adolescent self-report and parent-proxy report of HRQoL as adolescents and parents can provide different perspectives on HRQoL of adolescents.
MATERIALS AND METHODS: 100 smokers and 100 nonsmokers ages 18-50 years were recruited for this study in Kota Bharu, Malaysia. Oral hygiene (good/fair vs poor) was determined using the Simplified Oral Hygiene Index, and the halitosis level was measured using a Halimeter. Subjects were instructed to refrain from consuming foods containing garlic, onions, strong spices, alcohol and using mouthwashes 48 h prior to the examination. The halitosis levels were quantified by recording volatile sulphur compounds (VSCs) three times at 3-min intervals, resulting in a mean halitosis score. Various statistical analyses were performed, ranging from simple frequency analysis to multivariable modelling.
RESULTS: The proportions of subjects with poor oral hygiene and high halitosis were 24.0% and 41.5%, respectively. According to bivariate analyses, both problems were significantly less frequent among younger adults (halitosis), females, subjects with higher education, those with adequate habits to maintain good oral hygiene, those who had recent dental visits and those self-reporting fewer health problems. The percentages of poor oral hygiene and high halitosis were significantly higher in smokers (p < 0.001). However, almost all these variables failed to show significance in the multivariate analyses, with the exceptions of smoking for both poor oral hygiene and halitosis, education for poor oral hygiene, and age, self-reported health problems and time since the previous dental visit for halitosis.
CONCLUSION: These findings demonstrate a significantly higher level of halitosis and poorer oral hygiene in smokers than nonsmokers.
METHODS: In an international, community-based prospective study, we enrolled individuals from communities in 17 countries between Jan 1, 2005, and Dec 31, 2009 (except for in Karnataka, India, where enrolment began on Jan 1, 2003). Trained local staff obtained data from participants with interview-based questionnaires, measured weight and height, and recorded forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC). We analysed data from participants 130-190 cm tall and aged 34-80 years who had a 5 pack-year smoking history or less, who were not affected by specified disorders and were not pregnant, and for whom we had at least two FEV₁ and FVC measurements that did not vary by more than 200 mL. We divided the countries into seven socioeconomic and geographical regions: south Asia (India, Bangladesh, and Pakistan), east Asia (China), southeast Asia (Malaysia), sub-Saharan Africa (South Africa and Zimbabwe), South America (Argentina, Brazil, Colombia, and Chile), the Middle East (Iran, United Arab Emirates, and Turkey), and North America or Europe (Canada, Sweden, and Poland). Data were analysed with non-linear regression to model height, age, sex, and region.
FINDINGS: 153,996 individuals were enrolled from 628 communities. Data from 38,517 asymptomatic, healthy non-smokers (25,614 women; 12,903 men) were analysed. For all regions, lung function increased with height non-linearly, decreased with age, and was proportionately higher in men than women. The quantitative effect of height, age, and sex on lung function differed by region. Compared with North America or Europe, FEV1 adjusted for height, age, and sex was 31·3% (95% CI 30·8-31·8%) lower in south Asia, 24·2% (23·5-24·9%) lower in southeast Asia, 12·8% (12·4-13·4%) lower in east Asia, 20·9% (19·9-22·0%) lower in sub-Saharan Africa, 5·7% (5·1-6·4%) lower in South America, and 11·2% (10·6-11·8%) lower in the Middle East. We recorded similar but larger differences in FVC. The differences were not accounted for by variation in weight, urban versus rural location, and education level between regions.
INTERPRETATION: Lung function differs substantially between regions of the world. These large differences are not explained by factors investigated in this study; the contribution of socioeconomic, genetic, and environmental factors and their interactions with lung function and lung health need further clarification.
FUNDING: Full funding sources listed at end of the paper (see Acknowledgments).