Cost-of-illness study of type 2 diabetes mellitus in Colombia
Bibliographic record
Abstract
OBJECTIVE: To determine the per patient and overall cost of illness of type 2 diabetes mellitus (T2DM) in Colombia from Ministry of Health and societal perspectives. METHODS: A published Markov transition model was adapted for Colombia, using the clinical expertise of a Colombian endocrinologist. Transition probabilities for the model were derived from an international literature review. A model was run for a time horizon of 42 years. Direct resources (drugs, laboratory, medical, hospital, other health care) were identified and cost was ascertained by using national price lists, international health care guidelines, and other Colombian studies or data from other countries. Indirect costs (work time lost) were calculated by using the human capital approach. Annual and lifetime direct and indirect costs, in 2007 U.S. dollars with a 5% discount rate, were determined on a per patient basis and projected to the overall Colombian population. Costs were clustered according to treatments and outcomes. RESULTS: The estimated annual cost was $2.7 billion from the societal perspective and $921 million from the Ministry of Health perspective. The annual direct cost per patient was $288, and the indirect cost was $559 (total = $847). This cost was distributed across disease outcomes as follows: diabetes treatment (drugs), 47%; cardiac and coronary disease, 24%; stroke, 15%; amputation, 9%; nephropathy, 3%; retinopathy, 2%. Macrovascular complications made up 86% of the annual direct costs and 95% of the annual indirect costs of T2DM. CONCLUSIONS: We estimated the annual cost of T2DM for Colombia from societal, Ministry of Health, and Colombian Health System perspectives. We also estimated annual direct cost per patient and the cost of treating diabetes and macrovascular complications. The economic burden is substantial and comparable to results for other countries. The model showed a logical disease progression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".