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: A total of 2406 Malaysian children aged 5 to 12 years, who had participated in the South East Asian Nutrition Surveys (SEANUTS), were included in this study. Cognitive performance [non-verbal intelligence quotient (IQ)] was measured using Raven's Progressive Matrices, while socioeconomic characteristics were determined using parent-report questionnaires. Body mass index (BMI) was calculated using measured weight and height, while BMI-for-age Z-score (BAZ) and height-for-age Z-score (HAZ) were determined using WHO 2007 growth reference.
RESULTS: Overall, about a third (35.0%) of the children had above average non-verbal IQ (high average: 110-119; superior: ≥120 and above), while only 12.2% were categorized as having low/borderline IQ ( 3SD), children from very low household income families and children whose parents had only up to primary level education had the highest prevalence of low/borderline non-verbal IQ, compared to their non-obese and higher socioeconomic counterparts. Parental lack of education was associated with low/borderline/below average IQ [paternal, OR = 2.38 (95%CI 1.22, 4.62); maternal, OR = 2.64 (95%CI 1.32, 5.30)]. Children from the lowest income group were twice as likely to have low/borderline/below average IQ [OR = 2.01 (95%CI 1.16, 3.49)]. Children with severe obesity were twice as likely to have poor non-verbal IQ than children with normal BMI [OR = 2.28 (95%CI 1.23, 4.24)].
CONCLUSIONS: Children from disadvantaged backgrounds (that is those from very low income families and those whose parents had primary education or lower) and children with severe obesity are more likely to have poor non-verbal IQ. Further studies to investigate the social and environmental factors linked to cognitive performance will provide deeper insights into the measures that can be taken to improve the cognitive performance of Malaysian children.
DESIGN: Cross-sectional survey conducted in 2019-2020.
SETTING: Multistage cluster sampling conducted in Central, Northern, Southern, and East Coast regions of Peninsular Malaysia.
PARTICIPANTS: 2989 children aged 0.5-12.9 years.
RESULTS: Prevalences of stunting, thinness, overweight, and obesity among children aged 0.5-12.9 years were 8.9%, 6.7%, 9.2%, and 8.8%, respectively. Among children below 5 years old, 11.4% were underweight, 13.8% had stunting, and 6.2% wasting. Data on nutritional biomarkers showed a small proportion of children aged 4-12 years had iron (2.9%) and vitamin A deficiencies (3.1%). Prevalence of anaemia was distinctly different between children below 4 years old (40.3%) and those aged 4 years and above (3.0%). One-fourth of children (25.1%) had vitamin D insufficiency, which was twice as prevalent in girls (35.2% vs. boys: 15.6%). The majority of children did not meet the recommended dietary intake for calcium (79.4%) and vitamin D (94.8%).
CONCLUSIONS: Data from SEANUTS II Malaysia confirmed that triple burden of malnutrition co-exists among children in Peninsular Malaysia, with higher prevalence of overnutrition than undernutrition. Anaemia is highly prevalent among children below 4 years old, while vitamin D insufficiency is more prevalent among girls. Low intakes of dietary calcium and vitamin D are also of concern. These findings provide policymakers with useful and evidence-based data to formulate strategies that address the nutritional issues of Malaysian children.
DESIGN AND METHODS: This cross-sectional study was carried out among 254 primary and secondary school adolescents aged 10 to 16 years. Anthropometric measurements and blood pressure were determined through standardized protocols, while participants' birth weight was obtained from birth certificate. Body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and a body shape index (ABSI) were calculated.
RESULTS: Boys had significantly higher weight, height, WC, WHtR and systolic blood pressure (SBP) than girls (p +1SD had higher odds of being prehypertensive or hypertensive (aOR 8.97; 95% CI 3.16, 25.48), followed by participants with WC ≥ 90th percentile (aOR 6.31; 95% CI 2.48, 16.01) and participants with WHtR > 0.5 (aOR 5.10; 95% CI 2.05, 12.69). Multiple linear regression showed BMI was positively associated with both SBP and DBP. No significant association was found between birth weight and BP.
CONCLUSION: BMI had the best predictive ability for SBP and DBP. These findings strongly emphasize the importance of primary prevention of hypertension in adolescents, especially among those with high BMI.
METHODS: We systematically searched for publications in PubMed® and Scopus, manually searched the grey literature and consulted with national health and nutrition officials, with no restrictions on publication type or language. We included low- and middle-income countries in the World Health Organization South-East Asia Region, and the Association of Southeast Asian Nations and China. We analysed the included programmes by adapting the United States Centers for Disease Control and Prevention's public health surveillance evaluation framework.
FINDINGS: We identified 82 surveillance programmes in 18 countries that repeatedly collect, analyse and disseminate data on nutrition and/or related indicators. Seventeen countries implemented a national periodic survey that exclusively collects nutrition-outcome indicators, often alongside internationally linked survey programmes. Coverage of different subpopulations and monitoring frequency vary substantially across countries. We found limited integration of food environment and wider food system indicators in these programmes, and no programmes specifically monitor nutrition-sensitive data across the food system. There is also limited nutrition-related surveillance of people living in urban deprived areas. Most surveillance programmes are digitized, use measures to ensure high data quality and report evidence of flexibility; however, many are inconsistently implemented and rely on external agencies' financial support.
CONCLUSION: Efforts to improve the time efficiency, scope and stability of national nutrition surveillance, and integration with other sectoral data, should be encouraged and supported to allow systemic monitoring and evaluation of malnutrition interventions in these countries.