The burden of osteoporosis in four Latin American countries: Brazil, Mexico, Colombia, and Argentina
Bibliographic record
Abstract
Objective: Osteoporosis is under-diagnosed and under-treated worldwide. Information on the burden of osteoporosis in Latin American countries is limited. This study aimed to estimate the economic burden of osteoporosis in adults aged 50–89 years in Brazil, Mexico, Colombia, and Argentina.Methods: Analyses were conducted using a burden of illness model. Where possible, country-specific model inputs were informed by a systematic review and expert opinion. Osteoporosis-related fracture costs were calculated for hospitalizations, testing, surgeries, prescription drugs, and patient productivity losses. Costs were expressed in 2018 USD for the annual burden, annual burden per 1,000 at risk, and projected 5-year burden. No discounting was applied.Results: Over 840,000 osteoporosis-related fractures were predicted to occur in 2018, amounting to a total annual cost of ∼1.17 billion USD. The total projected 5-year cost was ∼6.25 billion USD. Annual costs were highest in Mexico (411 million USD), followed by Argentina (360 million USD), Brazil (310 million USD), and Colombia (94 million USD). The average burden per 1,000 at risk was greatest in Argentina (32,583 USD), followed by Mexico (16,671 USD), Colombia (8,240 USD), and Brazil (6,130 USD).Conclusions: Over the next 5 years, ∼4,485,352 fractures are anticipated to occur in Brazil, Mexico, Colombia, and Argentina. To control and prevent these fractures, stakeholders must work together to close the care gap. Efforts to identify individuals at high fracture risk, initiate treatment, and improve long-term treatment persistence will be essential in minimizing the financial and patient burden of osteoporosis in Latin America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".