Background: Obesity and its complications are associated with morbidity, mortality and high economic cost in Saudi Arabia. Estimating this impact at the population level and potential benefits to be gained from obesity reduction is vital to underpin policy initiatives to prevent disease risks.
Methods: We combined data in an adapted version of the value of weight loss simulation model, to predict reductions in complication rates and cost savings achievable with 15% weight loss in Saudi Arabia over 10 years. To obtain model inputs, we conducted a systematic literature review (SLR) to identify data on the prevalence of obesity and its complications in Saudi Arabia, and surveyed specialist physicians and hospital administrators in public (governmental) and private healthcare sectors. We used combinations of age, sex, obesity and type 2 diabetes (T2D) rates in Saudi Arabia to sample a United Kingdom (UK) cohort, creating a synthetic Saudi Arabia cohort expected to be representative of the population.
Results: The synthetic Saudi Arabia cohort reflected expected comorbidity prevalences in the population, with a higher estimated prevalence of T2D, hypertension and dyslipidaemia than the UK cohort in all age groups. For 100,000 people with body mass index 30-50 kg/m, it was estimated that 15% weight loss would lead to a 53.9% reduction in obstructive sleep apnoea, a 37.4% reduction in T2D and an 18.8% reduction in asthma. Estimated overall cost savings amounted to 1.026 billion Saudi Arabian Riyals; the largest contributors were reductions in T2D (30% of total cost savings for year 10), dyslipidaemia (26%) and hypertension (19%).
Conclusions: Sustained weight loss could significantly alleviate the burden of obesity-related complications in Saudi Arabia. Adopting obesity reduction as a major policy aim, and ensuring access to support and treatment should form an important part of the transformation of the healthcare system, as set out under 'Vision 2030'.
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http://dx.doi.org/10.1007/s12325-022-02415-8 | DOI Listing |
J Atten Disord
January 2025
Johns Hopkins Aramco Healthcare, Clinical Psychology and Counseling Services Unit, Saudi Arabia.
Objective: This study investigated the psychometric properties of the Arabic version of the Adult Self-Report Scale-5 (the ASRS-5-AR) within a large sample of adults residing in Saudi Arabia.
Methods: This cross-sectional study applied the ASRS-5-AR to a random sample of 4,299 Saudi and non-Saudi adults, aged 19 to 66 years (31.16 ± 9.
SAGE Open Med
January 2025
College of Medicine King Khalid University, Abha, Saudi Arabia.
Background: The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people worldwide, and although it is primarily a respiratory illness, gastrointestinal symptoms have been reported in a significant proportion of patients.
Aim: Prevalence of gastrointestinal symptoms after recovery from COVID-19.
Methodology: A community-based cross-sectional study was conducted in the Aseer region of Saudi Arabia.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFFront Artif Intell
January 2025
RV University, Bengaluru, India.
Introduction: Cyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning.
View Article and Find Full Text PDFNanoscale Adv
December 2024
Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University Taif 21944 Saudi Arabia.
Mesoporous materials have garnered significant interest because of their porous structure, large surface area and ease of surface functionalization to incorporate the functional groups of choice. Herein, chiral mesoporous silica nanoparticles (CMSNPs) were prepared using quaternary amino silane as the template, tetramethyl orthosilicate as the silica source and proline and cellulose as chiral selector. The developed CMSNPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction analysis, BET surface area analysis and BJH pore size/volume analysis.
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