The clinical and economic burden of obesity in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
Obesity has emerged as a critical public health challenge globally, with substantial health and economic repercussions. This study aimed to evaluate the literature on the clinical and economic burdens associated with obesity, specifically in low- and middle-income countries (LMICs). A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was performed. The CINAHL, MEDLINE, PubMed, Web of Science and Scopus databases were systematically searched for studies published from inception to March 28, 2025. The costs of illness for all included studies were converted to 2024 United States (US) dollars, using country-specific gross domestic product inflators. Conversion to US dollars was based on purchasing power parity (PPP). The quality of all included studies was assessed via the Newcastle‒Ottawa Scale (NOS). Of the total of 676 reports identified by the search strategy, six studies were prevalence-based, four studies were survey-based, and three model-based studies (n = 13) were eligible for inclusion on the basis of predefined inclusion criteria. These studies published data from Brazil, Ghana, China, Iran, South Africa, Mexico, and Thailand. Three of the 13 studies reported indirect costs. Two studies reported the clinical impact of obesity. Methodological quality was deemed moderate. The annual direct and indirect costs associated with obesity for a population in LMICs ranged from USD 0.2 billion to USD 12.56 billion and USD 223 million to USD 227.5 million, respectively. Hospitalisation was the main cost driver in five of the included studies. One study reported the total number of hospitalisations/number of person-years for men and women as 803/9207 and 2354/25,173, respectively. This is the first systematic review to summarise the clinical and economic burdens associated with obesity in LMICs. The clinical and economic burden of obesity on individuals and healthcare systems is significant, necessitating effective prevention and management strategies. To increase the accuracy and comparability of findings, future research should adopt a standardised cost-of-illness methodology. This approach will provide clearer insights into the economic impact of obesity and facilitate more effective public health interventions.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".