METHODS: Data are obtained from 2,436 observations from the Malaysia Non-Communicable Disease Surveillance-1. The multi-ethnic sample is segmented into Malay, Chinese, and Indian/other ethnicities. Ordered probit analysis is conducted and marginal effects of sociodemographic and health lifestyle variables on BMI calculated.
RESULTS: Malays between 41 and 58 years are more likely to be overweight or obese than their 31-40 years counterparts, while the opposite is true among Chinese. Retirees of Chinese and Indian/other ethnicities are less likely to be obese and more likely to have normal BMI than those between 31 and 40 years. Primary educated Chinese are more likely to be overweight or obese, while tertiary-educated Malays are less likely to suffer from similar weight issues as compared to those with only junior high school education. Affluent Malays and Chinese are more likely to be overweight than their low-middle income cohorts. Family illness history is likely to cause overweightness or obesity, irrespective of ethnicity. Malay cigarette smokers have lower overweight and obesity probabilities than non-cigarette smokers.
CONCLUSIONS: There exists a need for flexible policies to address cross-ethnic differences in the sociodemographic and health-lifestyle covariates of BMI.
METHODS: Validated translated versions of the Hiroshima University-Dental Behavioural Inventory (HU-DBI) questionnaire were administered to 1,096 final-year dental students in 17 countries. Hierarchical cluster analysis was conducted within the data to detect patterns and groupings.
RESULTS: The overall response rate was 72%. The cluster analysis identified two main groups among the countries. Group 1 consisted of twelve countries: one Oceanic (Australia), one Middle-Eastern (Israel), seven European (Northern Ireland, England, Finland, Greece, Germany, Italy, and France) and three Asian (Korea, Thailand and Malaysia) countries. Group 2 consisted of five countries: one South American (Brazil), one European (Belgium) and three Asian (China, Indonesia and Japan) countries. The percentages of 'agree' responses in three HU-DBI questionnaire items were significantly higher in Group 2 than in Group 1. They include: "I worry about the colour of my teeth."; "I have noticed some white sticky deposits on my teeth."; and "I am bothered by the colour of my gums."
CONCLUSION: Grouping the countries into international clusters yielded useful information for dentistry and dental education.
METHODS: This retrospective case-control study involves 790 cases of cancers of the oral cavity and 450 controls presenting with non-malignant oral diseases, recruited from seven hospital-based centres nationwide. Data on risk habits (smoking, drinking, chewing) were obtained using a structured questionnaire via face-to-face interviews. Multiple logistic regression was used to determine association between risk habits and oral cancer risk; chi-square test was used to assess association between risk habits and ethnicity. Population attributable risks were calculated for all habits.
RESULTS: Except for alcohol consumption, increased risk was observed for all habits; the highest risk was for smoking + chewing + drinking (aOR 22.37 95% CI 5.06, 98.95). Significant ethnic differences were observed in the practice of habits. The most common habit among Malays was smoking (24.2%); smoking + drinking were most common among Chinese (16.8%), whereas chewing was the most prevalent among Indians (45.2%) and Indigenous people (24.8%). Cessation of chewing, smoking and drinking is estimated to reduce cancer incidence by 22.6%, 8.5% and 6.9%, respectively.
CONCLUSION: Ethnic variations in the practice of oral cancer risk habits are evident. Betel quid chewing is the biggest attributable factor for this population.