Economic Burden of Rheumatoid Arthritis in Low‐ and Middle‐Income Countries: Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this systematic review was to synthesize the economic impact of rheumatoid arthritis (RA) on households, health systems, and society in low- and middle-income countries (LMICs). METHODS: Electronic databases such as PubMed, Web of Science, and CINAHL were searched using keywords related to RA and cost of illness. Eligible studies were required to report RA-related costs, be conducted in LMICs, and be published in English. Quality appraisal of the included studies was conducted using the Newcastle-Ottawa Scale for cohort studies. A narrative synthesis and meta-analysis of findings was conducted. RESULTS: A total of 5,134 studies was initially identified for screening. After removing 1,028 duplicates, 50 studies were selected for full-text review, and 15 met the eligibility criteria and were therefore included in the review. These studies, published between 2007 and 2024, were conducted in various countries, including Turkey (n = 3), China (n = 2), and one study each from Thailand, Hungary, Mexico, Colombia, Morocco, Pakistan, India, Romania, Brazil, and Argentina. Nine studies adopted a societal perspective, whereas six used a health care perspective. The total sample size was 218,575 participants, with individual study sizes ranged from 62 to 209,292. Average annual direct costs per patient ranged from US$523 to US$2,837.90, and indirect costs ranged from US$81.80 to US$2,463.40. The pooled average annual costs for outpatients, inpatients, and medical costs were US$517.72 (95% confidence interval [CI] $3.35-$1,032.09), US$543.88 (95% CI US$499.51-US$588.24), and US$3,379.83 (95% CI US$3,137.58-US$3,622.08), respectively. CONCLUSION: RA poses a significant economic challenge in LMICs, where limited health care resources and high treatment costs make care unaffordable for many. This review uniquely underscores that enhancing treatment access and optimizing resource use can reduce both medical and productivity losses, improving patient outcomes and strengthening economic resilience.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.029 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".