Molecular epidemiology: Issues in study design and statistical analysis

Annals of the Academy of Medicine Singapore25155-63AAMS

Screening for childhood obesity: international vs population-specific definitions. Which is more appropriate?

Aim: The objectives of this study are: (1) to study the relation between body mass index (BMI), percentage-weight-for-height (PWH) and percentage body fat (PBF) in Singaporean Chinese children; (2) to assess the applicability of an international definition of obesity (the International Obesity Task Force (IOTF) BMI) as a screening tool to detect childhood obesity, as compared with the current Singapore population-specific definition using PWH. Methods: A total of 623 Chinese children aged 6-11 y (321 males, 302 females) were recruited from a school by proportionate (40) stratified random sampling. BMI and PWH were calculated from weight and height, while PBF was derived using leg-to-leg bioelectrical impedance analysis. The strength of association among the three indices of obesity was assessed using Spearman's correlation coefficient. Obese children were defined as those above the 95th percentile of PBF in each age gender-specific group. Sensitivity and specificity of IOTF-BMI cutoff values and PWH cutoff values were compared by testing their ability to correctly identify obese children. Results: All three indices correlated well with one another (BMI: PWH r=0.83, BMI: PBF r=0.87, PWH: PBF r=0.76). Prevalence of obesity was lower using IOTF-BMI cutoffs (6.9) than using PWH cutoffs (16.4). The sensitivity and specificity of IOTF-BMI cutoff values were 75.0 and 96.0, respectively, with sensitivity differing between boys (83.3) and girls (66.6) (P=0.35). In comparison, PWH cutoff values had higher sensitivity (91.6) but lower specificity (86.6), with no significant difference between the genders. Conclusion: IOTF-recommended BMI cutoff values had low sensitivity and may underestimate the local prevalence of childhood obesity. For screening purposes, we recommend that population-specific measures rather than international cutoff values be used

Lightweight CNN-Based Deep Neural Networks Application in Safety Measurement

Inspired by the face covering period in the past two years, COVID-19 pandemic has resulted in the mandate of public safety measures such as face mask-wearing in many countries. This paper provides a preliminary feasibility planning on how Artificial Intelligence (AI), Computer Vision (CV) and the Internet of Things (IoT) can work together to implement a face-mask detection system as a public health safety solution. This paper reviews how edge computing can overcome traditional cloud computing issues. This work also examines the current state of computer vision, convolutional neural networks and their potential application in the health and safety domain. This writing serves as an interim report on how the lightweight CNNs and single-shot detectors such as YOLOv5 variants with SSD to train and deploy an object detection system

Biomarkers: A molecular approach to cancer epidemiology

Annals of the Academy of Medicine Singapore25149-54AAMS