RESUMO
BACKGROUND: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model's outcomes with the validated American Diabetes Association's (ADA) risk assessment regarding predicting people with high risk for T2DM. RESEARCH DESIGN AND METHODS: Patients' age, gender, and risk factors that were obtained from the National Health Information Center's dataset were used to build and train the ML model. To evaluate the developed ML model, an external validation study was conducted in three primary health care centers. A random sample (N = 3400) was selected from the non-diabetic individuals. RESULTS: The results showed the plotted data of sensitivity/100-specificity represented in the Receiver Operating Characteristic (ROC) curve with an AROC value of 0.803, 95% CI: 0.779-0.826. CONCLUSIONS: The current study reveals a new ML model proposed for population-level classification that can be an adequate tool for identifying those at high risk of T2DM or who already have T2DM but have not been diagnosed.
RESUMO
Recent national reports have indicated an increasing trend of dental decay among school-aged children. National school-based data are required to guide decision-making to ensure effective public health efforts to manage dental decay. This study aimed to assess the prevalence of dental decay among school-age children in Saudi Arabia and explore the possible link between dental decay and weight status. This was a cross-sectional analysis of a national sample including 1,134,317 Saudi children in the 1st, 4th, 7th, and 10th grades who participated in a national school screening program. Data for weight, height, body mass index (BMI), dental decay prevalence, and decayed, missing, and filled teeth (DMFT) index were analysed. An independent t-test, one-way analysis of variance, and chi-square test were used to compare the means, while Pearson's r correlation and multiple linear regression were used to examine the relationships between the study variables. The prevalence of dental decay was 24.20%, and dental decay was the highest among female students (26.5%), students in primary school (25.9%), students living in the eastern region (35.2%), in the administrative capital (27.6%), and in rural areas (23.4%). Dental decay was the highest among students who were underweight. Female sex and living in rural areas significantly predicted higher DMFT while being overweight/obese significantly predicted lower DMFT. Dental decay is highly prevalent among underweight students, female students, and students living in rural areas and the eastern regions of Saudi Arabia. To reduce the prevalence of dental decay and related health disparities, dental health screening programs should be designed to detect dental decay early among children at high risk due to abnormal BMIs and sociodemographic factors. In addition, dental health screening and management programs should utilize standardized dental decay assessment methods and ethnically representative growth charts.
RESUMO
Background: Recent reports indicated an increasing prevalence of obesity among children and adolescents in Saudi Arabia, making it an impending national epidemic. However, obesity prevalence data in children and adolescents in Saudi Arabia are largely inconsistent. Objectives: This study analyzed and compared the prevalence of obesity among a national sample of children and adolescents across sexes, school grades, regions, and city types in Saudi Arabia using the Growth Charts for Saudi Children and Adolescents. Methods: Weight, height, and body mass index (BMI) data from 1 134 317 children in first, fourth, seventh, and tenth school grades who participated in the national school screening program were analyzed cross-sectionally. BMI values were classified using the Growth Charts for Saudi Children and Adolescents. Results: Nearly 10.4% of students were overweight, 10.7 % were obese, and 4.50% were severely obese. Male students had a higher prevalence of overweight and obesity than their female counterparts. The prevalence of overweight and obesity was the highest among students in intermediate school, the Central region, and administrative capitals. Conclusion: Managing childhood obesity is challenging due to its multifaceted nature Therefore, utilizing clinical and community-based participatory approaches is essential to develop nationwide obesity prevention and management program that is effective and sustainable. This program must utilize dynamic BMI surveillance systems using ethnically representative growth references, conduct national pediatric obesity research with careful consideration for demographic and regional differences, lead targeted pediatric obesity awareness campaigns, provide obesity management interventions in a pediatric multi-disciplinary clinic, and evaluate the program outcomes periodically.