METHODS: Data are obtained from the Malaysia Non-Communicable Disease Surveillance-1. Logistic regressions are conducted using a multiracial (Malay, Chinese, Indian and other ethnic groups) sample of 2,447 observations to examine the factors affecting individual decisions to consume FV on a daily basis.
RESULTS: Based on the binary outcomes of whether individuals consumed FV daily, results indicate that work hours, education, age ethnicity, income, gender, smoking status, and location of residence are significantly correlated with daily fruit consumption. Daily vegetable consumption is significantly correlated with income, gender, health condition, and location of residence.
CONCLUSIONS: Our results imply the need for programs to educate and motivate consumers to make healthier dietary choices. Interventions to increase FV consumption by changing behaviors should be considered, as should those that increase public awareness of the dietary benefits of FV. These intervention programs should be targeted at and tailored toward individuals who are less educated, younger, less affluent, males, smokers, and metropolitan dwellers.
METHODS: This cross-sectional study utilizes data of adults ≥15 years who completed the Global Adult Tobacco Surveys. Ordered probit analysis is used to account for the smoking statuses of non-smokers, occasional smokers, and daily smokers.
RESULTS: Malaysian and Vietnamese households with more family members face lower smoking likelihoods than otherwise. Urbanites in Philippines and rural residents in Thailand and Indonesia are more likely to smoke on occasional and daily basis than others. Males are consistently more likely to smoke occasionally or daily and less likely to be non-smokers than females across all countries. Younger middle-age (retiree) individuals aged 30-35 (≥60) years in Malaysia and Thailand exhibit higher (lower) likelihoods to smoke occasionally or daily than their younger cohorts aged 15-29 years. Individuals aged 30 years and above in Indonesia, Vietnam, and Philippines display higher daily smoking propensities than others. Higher education levels dampens smoking likelihoods and increases non-smoking propensities in all countries. Non-government or self-employed workers in all countries are more likely to smoke occasionally or daily than unemployed persons. Being married is associated with higher non-smoking likelihoods in Thailand although this association is not evident in Malaysia.
CONCLUSION: These findings suggest that a portfolio of targeted interventions is necessary to meet the needs of specific subpopulations within the various countries.
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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: Data were obtained from the Malaysia Non-Communicable Disease Surveillance-1. Logistic regressions were estimated and odds ratios of exposure variables calculated.
RESULTS: Diabetes awareness was associated with work hours, age, family history of illnesses, and ethnicity. Individuals with diminished hypertension awareness included those who were younger, without family history of illnesses, not obese, working more hours, and not adhering to a healthy diet. Low awareness of hypercholesterolemia was associated with younger age, lower education level, living in rural areas, female gender, no family history of illnesses, non-obesity, and minority ethnic background. Prevalence generally had the same pattern of association with the exposure variables.
CONCLUSIONS: Various sociodemographic and health and lifestyle characteristics were associated with diabetes, hypertension, and hypercholesterolemia awareness in Malaysia, albeit with varying outcomes. Therefore, programs focusing on lifestyle improvements should be targeted at high-risk subgroups, such as individuals working longer hours and young adults, who are less likely to be aware of their health risk factors.
METHODS: Data were obtained from the 2012 Malaysia Global School-based Student Health Survey. Generalized ordered logit regression analysis was conducted on 24 339 adolescents by PA status.
RESULTS: Early- (ages 11-13) and middle-stage (ages 14-16) adolescents were associated with higher overweight and obesity risks than their older peers (ages 17-18). Male adolescents faced higher underweight and obesity likelihoods than females. Hunger due to food shortage at home was associated with higher likelihoods of underweight and normal weight BMI categories. Smokers were more likely to be underweight or normal weight than non-smokers. Segmented-sample analysis by PA status indicated that, while the direction of associations was parallel across PA status, the magnitudes of association between age, hunger and smoking status with BMI status were greater among active than inactive adolescents.
CONCLUSIONS: Male adolescents faced a dual burden of underweight and obesity. Other sociodemographic and dietary-lifestyle factors were associated with adolescent BMI categories. Segmented-sample analysis by PA status uncovered varying associations between factors that would otherwise be masked in pooled sample analysis. Public health authorities should take these factors into consideration when deliberating programs to ensure healthy adolescent body weight.