DESIGN: Data on length/height-for-age percentile values were collected. The LMS method was used for calculating smoothened percentile values. Standardized site effects (SSE) were used for identifying large or unacceptable differences (i.e. $\mid\! \rm SSE \!\mid$ >0·5) between the pooled SEANUTS sample (including all countries) and the remaining pooled SEANUTS samples (including three countries) after weighting sample sizes and excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14202 eligible children were used.
RESULTS: From pair-wise comparisons of percentile values between the pooled SEANUTS sample and the remaining pooled SEANUTS samples, the vast majority of differences were acceptable (i.e. $\mid\! \rm SSE \!\mid$ ≤0·5). In contrast, pair-wise comparisons of percentile values between the pooled SEANUTS sample and WHO revealed large differences.
CONCLUSIONS: The current study calculated length/height percentile values for South East Asian children aged 0·5-12 years and supported the appropriateness of using pooled SEANUTS length/height percentile values for assessing children's growth instead of country-specific ones. Pooled SEANUTS percentile values were found to differ from the WHO growth references and therefore this should be kept in mind when using WHO growth curves to assess length/height in these populations.
Methods: This cross-sectional study was conducted with school-going children from 16 selected schools of a tribal district in Jharkhand using multistage cluster random sampling. In each selected school, 60 students, 30 boys and 30 girls, were chosen randomly, totaling 960 children (full data was for 935 children only). Growth charts were created using Lambda-Mu-Sigma (LMS) chart maker version 2.5 for height, weight and body mass index (BMI). In the charts, the LMS values with Z scores for each age and respective height and weight for boys and girls were recorded.
Results: The 468 boys and 467 girls were in the range of 6-14 years of age. Percentile values obtained for the measured heights in centimetres were evaluated and compared with Indian Academy of Pediatrics reference charts for boys and girls for the same age group, and our values were found to be on the lower side. We were able to plot a growth chart of the data set; as the tribal children's ethnicity is different, this growth chart might be used to assess nutritional status.
Conclusion: We concluded that growth curves for height, weight, and BMI may be used for evaluating children of age 6-14 years in the tribal population. The measures can be a good indicator of their nourishment status and overall growth patterns, which might be indigenous to their ethnicity. A larger sample size of similar tribal populations may give a clearer picture.
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.
DESIGN: Body weight and length/height were measured. The LMS method was used for calculating smoothened body-weight- and BMI-for-age percentile values. The standardized site effect (SSE) values were used for identifying large differences (i.e. $\left| {{\rm SSE}} \right|$ >0·5) between the pooled SEANUTS sample and the remaining pooled SEANUTS samples after excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14 202 eligible children.
RESULTS: The SSE derived from the comparisons of the percentile values between the pooled and the remaining pooled SEANUTS samples were indicative of small/acceptable (i.e. $\left| {{\rm SSE}} \right|$ ≤0·5) differences. In contrast, the comparisons of the pooled SEANUTS sample with WHO revealed large differences in certain percentiles.
CONCLUSIONS: The findings of the present study support the use of percentile values derived from the pooled SEANUTS sample for evaluating the weight status of children in each SEANUTS country. Nevertheless, large differences were observed in certain percentiles values when SEANUTS and WHO reference values were compared.
METHODS: Anonymous questionnaires to assess practices on feeding, nutrition management and post-natal growth monitoring of tSGA infants were distributed among health-care professionals (HCPs) participating in regional/local perinatology symposia in Malaysia, Thailand and Singapore.
RESULTS: Three hundred seventy-seven respondents from Malaysia (37%), Thailand (27%), Singapore (18%) and other Asian countries (19%) participated in the survey. Respondents were neonatologists (35%), paediatricians (25%) and other HCPs (40%) including nurses and midwives. Exclusive human milk feeding was reported the most preferred feeding option for tSGA infants, followed by fortified human milk feeding (60% and 20%, respectively). This was consistent among the different countries. The perceived nutrient requirements of tSGA infants varied between countries. Most respondents from Malaysia and Singapore reported requirements to be similar to preterm infants, while the majority from Thailand reported that it was less than those of preterm infants. The World Health Organization Growth Chart of 2006 and Fenton Growth Charts of 2013 were the most frequently used charts for growth monitoring in the hospital and after discharge.
CONCLUSIONS: Nutrition management and perceived nutrient requirements for tSGA infants among practising HCPs in Southeast Asia showed considerable variation. The impetus to form standardised and evidence based feeding regimens is important as adequate nutritional management and growth monitoring particularly in this population of infants will have long term impact on population health.