Methods: We assessed study-related records to determine the pace of data collection, response from potential participants, and feedback following data and sample collection. Overall and stratified measures of data and sample availability were summarised. Crude prevalence of key risk factors was examined.
Results: Approximately half (49.5%) of invited individuals consented to participate in this study, for a final sample size of 203 (161 adults and 42 children). Women were more likely to consent to participate compared with men, whereas children, young adults and individuals of Malay ethnicity were less likely to consent compared with older individuals or those of any other ethnicity. At least one biological sample (blood from all participants - finger-prick and venous [for serum, plasma and whole blood samples], hair or urine for adults only) was successfully collected from all participants, with blood test data available from over 90% of individuals. Among adults, urine samples were most commonly collected (97.5%), followed by any blood samples (91.9%) and hair samples (83.2%). Cardiometabolic risk factor burden was high (prevalence of elevated HbA1c among adults: 23.8%; of elevated triglycerides among adults: 38.1%; of elevated total cholesterol among children: 19.5%).
Conclusions: In this study, we show that it is feasible to create biodata resources using existing HDSS frameworks, and identify a potentially high burden of cardiometabolic risk factors that requires further evaluation in this population.
Methods: We used cross-sectional data on 6759 children and adolescents aged 6-19 years living in Segamat, Malaysia. We compared prevalence estimates for stunting defined using the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) references, using Cohen's κ coefficient. Associations between sociodemographic indices and stunting risk were examined using mixed-effects Poisson regression with robust standard errors.
Results: The classification of children and adolescents as stunted or normal height differed considerably between the two references (CDC v. WHO; κ for agreement: 0.73), but prevalence of stunting was high regardless of reference (crude prevalence: CDC 29.2%; WHO: 19.1%). Stunting risk was approximately 19% higher among underweight v. normal weight children and adolescents (p = 0.030) and 21% lower among overweight children and adolescents (p = 0.001), and decreased strongly with improved household drinking water sources [risk ratio (RR) for water piped into house: 0.35, 95% confidence interval (95% CI) 0.30-0.41, p < 0.001). Protective effects were also observed for improved sanitation facilities (RR for flush toilet: 0.41, 95% CI 0.19-0.88, p = 0.023). Associations were not materially affected in multiple sensitivity analyses.
Conclusions: Our findings justify a framework for strategies addressing stunting across childhood, and highlight the need for consensus on a single definition of stunting in older children and adolescents to streamline monitoring efforts.
STUDY DESIGN: We assessed data from 6414 children aged 6-18 years, collected by the South East Asia Community Observatory. Child underweight, overweight, and obesity were expressed according to 3 internationally used BMI references: World Health Organization 2007, International Obesity Task Force 2012, and Centers for Disease Control and Prevention 2000. We assessed agreement in classification of anthropometric status among the references using Cohen's kappa statistic and estimated underweight, overweight, and obesity prevalence according to each reference using mixed effects Poisson regression.
RESULTS: There was poor to moderate agreement between references when classifying underweight, but generally good agreement when classifying overweight and obesity. Underweight, overweight, and obesity prevalence estimates generated using the 3 references were notably inconsistent. Overweight and obesity prevalence estimates were higher using the World Health Organization reference vs the other 2, and underweight prevalence was up to 8.5% higher and obesity prevalence was about 4% lower when using the International Obesity Task Force reference.
CONCLUSIONS: The choice of reference to express BMI may influence conclusions about child anthropometric status and malnutrition prevalence. This has implications regarding strategies for clinical management and public health interventions.
METHODS: We used data from health and demographic surveillance conducted by the South East Asia Community Observatory in Segamat, Malaysia. Analyses included 9207 individuals (4806 children, 2570 mothers and 1831 fathers). Child obesity was defined based on the World Health Organization 2007 reference. We assessed the relation between parental anthropometric (overweight, obesity and central obesity) and cardiometabolic (systolic hypertension, diastolic hypertension and hyperglycaemia) risk factors and child obesity, using mixed effects Poisson regression models with robust standard errors.
RESULTS: We found a high burden of overweight and obesity among children in this population (30% overweight or obese). Children of one or more obese parents had a 2-fold greater risk of being obese compared with children of non-obese parents. Sequential adjustment for parental and child characteristics did not materially affect estimates (fully adjusted relative risk for obesity in both parents: 2.39, 95% confidence interval: 1.82, 3.10, P