OBJECTIVE: Given the unpopularity of the current hard hat, the general perception of workers concerning its use and its measurements are the determining factors in the development of a new hard hat.

METHOD: A cross-sectional study was conducted in which 132 male oil palm harvesters between 19 and 60 years of age were selected from among the employees of the same oil palm harvesting company. A set of questionnaires was developed to collect their socio-demographic information as well as their perceptions of comfort and the prevalence of head injury. In addition, a set of measuring instruments, including Martin's anthropometry set, was used for head measurement and data collection in respect of the current hard hat. In this research, six respondents were randomly selected to attend an interview session for qualitative assessment.RESULTSBased on the questionnaires, the unpopularity in the use of the hard hat was largely influenced by factors related to poor design, in general, and, specifically, poor ventilation (64%), load (67% ), and physical discomfort (42% ). The measurements of the anthropometric parameters and the dimensions of the hard hat also showed a significant mismatch.

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.

INTRODUCTION: To investigate the longitudinal associations of bone mineral measures with antiepileptic drug (AED) use, including enzyme-inducing (EIAED) and non-enzyme-inducing (NEIAED) types, and other predictors of bone loss in a study of 48 same-sex twin/age-matched sibling pairs (40 female, 8 male) discordant for AED use.

METHODS: Using dual-energy X-ray absorptiometry (DXA), areal bone mineral density (aBMD) and content (BMC) at the hip regions, forearm, lumbar spine, and whole body were measured twice, at least 2 years apart. The mean within-pair difference (MWPD), MWPD%, and mean annual rate of aBMD change were adjusted for age, weight, and height. Predictors of bone loss were evaluated.

RESULTS: AED users, compared to non-users, at baseline and follow-up, respectively, had reduced aBMD at the total hip (MWPD% 3.8, 4.4%), femoral neck (4.7, 4.5%), and trochanter regions (4.1, 4.6%) (p  0.05) regions did not differ within pairs. Nevertheless, EIAED users had greater aBMD loss than non-users (n = 20 pairs) at the total hip (1.7 vs. 0.3%, p = 0.013) and whole body regions (0.7% loss vs. 0.1% BMD gain, p = 0.019), which was not found in NEIAED-discordant pairs (n = 16). AED use >20 years predicted higher aBMD loss at the forearm (p = 0.028), whole body (p = 0.010), and whole body BMC (p = 0.031).

MATERIALS AND METHODS: A prospective cohort study was carried out among 684 infants attending goverment health clinics in 2 states in Malaysia. Body weight, length, and clinical assessment were measured on the same day for 9 visits, scheduled every month until 6 months of age and every 2 months until 12 months of age. All of the 3 z-scores for weight for age (WAZ), length for age (HAZ), and weight for length (WHZ) were calculated using WHO Anthro for Personal Computers software.

RESULTS: The average sensitivity and specificity for the visual clinical assessment for the detection of thinness were higher using the WHO 2006 standard as compared with using NCHS 1977. However, the overall sensitivity of the visual clinical assessment for the detection of thin and lean children was lower from 1 month of age until a year as compared with the WHO 2006 standard and NCHS 1977 reference. The positive predictive value (PPV) for the visual clinical assessment versus the WHO 2006 standard was almost doubled as compared with the PPV of visual clinical assessment versus the NCHS 1977 reference. The overall average sensitivity, specificity, PPV, and negative predictive value for the detection of stunting was higher for visual clinical assessment versus the WHO 2006 standard as compared with visual clinical assessment versus the NCHS 1977 reference.

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).

METHODS: A cross-sectional study was conducted among 134 geriatric patients with a mean age of 68.9 ± 8.4 who stayed at acute care wards in Hospital Tuanku Ampuan Rahimah, Klang from July 2017 to August 2017. The SGA, MNA, and GNRI were administered through face-to-face interviews with all the participants who gave their consent. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the GNRI and MNA were analyzed against the SGA. Receiver-operating characteristic (ROC) curve analysis was used to obtain the area under the curve (AUC) and suitable optimal cutoff values for both the GNRI and MNA.

RESULTS: According to the SGA, MNA, and GNRI, 26.9%, 42.5%, and 44.0% of the participants were malnourished, respectively. The sensitivity, specificity, PPV, and NPV for the GNRI were 0.622, 0.977, 0.982, and 0.558, respectively, while those for the MNA were 0.611, 0.909, 0.932, and 0.533, respectively. The AUC of the GNRI was comparable to that of the MNA (0.831 and 0.898, respectively). Moreover, the optimal malnutrition cutoff value for the GNRI was 94.95.