The Burden of Obesity in Saudi Arabia: A Real-World Cost-of-Illness Study
Bibliographic record
Abstract
Background: The rising prevalence of obesity in the Kingdom of Saudi Arabia (KSA) poses a significant public health challenge. Estimates of the economic cost of obesity are crucial for prioritizing healthcare interventions, guiding policy choices, and justifying budget allocations aimed at reducing obesity prevalence. This study aimed to estimate the cost of obesity in the KSA in 2022. Methods: A prevalence-based cost-of-illness approach was used to determine the cost of obesity. This analysis encompasses 29 diseases, namely obesity and twenty-eight diseases attributable to obesity. Both direct and indirect costs were considered. The annual cost of treatment for each obesity-attributable disease was obtained from the hospital records of one tertiary hospital in the KSA. Data on direct non-medical costs were obtained from the patient survey. The human capital approach was used to estimate the indirect costs of morbidity and mortality. Results: The total economic burden of obesity (2022 values) was estimated at US$116.85 billion from a societal perspective and US$109.67 billion from a healthcare system perspective. From a societal perspective, the total direct medical cost accounted for the largest portion of the total cost (94%). In terms of direct medical costs, the cost of treating diseases attributable to obesity was substantially greater than the cost of treating obesity itself. According to the sensitivity analysis, the total cost ranged from 3.4% of the country's Gross domestic product (GDP) when the unit cost of treatment was reduced by 74% to 9.5% of the country's GDP when the prevalence of obesity and its comorbidities was reduced by 5%. Conclusion: Obesity imposes a substantial economic burden on the healthcare system and society in the KSA. Interventions aimed at promoting healthier lifestyles to reduce the prevalence and incidence of obesity and its comorbidities are highly warranted to alleviate the impact of obesity in the country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".