METHODOLOGY: A cross-sectional study with a universal sampling of children and adolescents with special needs aged 2-18 years old, diagnosed with cerebral palsy, down syndrome, autism and attention-deficit/hyperactivity disorder was conducted at Community-Based Rehabilitation in Central Zone Malaysia. Socio-demographic data were obtained from files, and medical reports and anthropometric measurements (body weight, height, humeral length, and mid-upper arm circumference) were collected using standard procedures. Data were analysed using IBM SPSS version 26. The accuracy of the formula was determined by intraclass correlation, prediction at 20% of actual body weight, residual error (RE) and root mean square error (RMSE).
RESULT: A total of 502 children with a median age of 7 (6) years were enrolled in this study. The results showed that the Mercy formula demonstrated a smaller degree of bias than the Cattermole formula (PE = 1.97 ± 15.99% and 21.13 ± 27.76%, respectively). The Mercy formula showed the highest intraclass correlation coefficient (0.936 vs. 0.858) and predicted weight within 20% of the actual value in the largest proportion of participants (84% vs. 48%). The Mercy formula also demonstrated lower RE (0.3 vs. 3.6) and RMSE (3.84 vs. 6.56) compared to the Cattermole formula. Mercy offered the best option for weight estimation in children with special needs in our study population.
MATERIALS AND METHODS: A systematic literature search was performed through SCOPUS database and Google Scholar from January till March 2018. All published articles which developed stature estimation from different types of bone, methods and type of statures (i.e. living stature, forensic stature and cadaveric stature) were included in this study. Risks of biases were also assessed. Population studies with no regression equations were excluded from the study.
RESULTS: Seven studies that met the inclusion criteria were identified. In the South-East Asia region, regression equations for stature estimation were developed in Thailand and Malaysia. In these studies, bone measurements were done either by radiography, direct bone measurement, or palpation on body surface for anatomical bony prominence. All of these studies used various parts of bones for stature estimation.
CONCLUSION: The most widely used regression equations for stature estimation in South-East Asian population were from the Thailand population. Further research is recommended to develop regression equations for other South-East Asian countries.
AIMS: (1) To investigate the association between birth weight and anthropometric measurements during adulthood; (2) to study the genetic and environmental influences on body measures including birth weight, weight and height among twins; and (3) to assess the variation in heritability versus environment among two cohorts of twins who lived in different geographical areas.
SUBJECTS AND METHODS: Twins were collected from two twin registers. Data on birth weight, adult weight and height in 430 MZ and 170 DZ twins living in two geographically distinct parts of the world were collected. A genetic analysis was performed using MX software.
RESULTS: Birth weight was associated with weight, height and BMI. Both MZ and DZ twins with low birth weight had shorter height during their adult life (p = 0.001), but only MZ twins with lower birth weight were lighter at adulthood (p = 0.001). Intra-pair differences in birth weight were not associated with differences in adult height (p = 0.366) or weight (p = 0.796). Additive genetic effects accounted for 53% of the variance in weight, 43% in height and 55% in birth weight. The remaining variance was attributed to unique environmental effects (15% for weight, 13% for height and 45% for birth weight and only 16% for BMI). Variability was found to be different in the two cohorts. The best fitting model for birth weight and BMI was additive genetic and non-shared environment and for weight and height was additive genetic, non-shared environment (plus common Environment).
CONCLUSIONS: Data suggests that the association between weight at birth and anthropometric measures in later life is influenced by both genetic and environmental factors. Living in different environments can potentially relate to variation found in the environment.