Cost-effectiveness and cost-utility analysis of type-2 diabetes screening in pharmacies in Iran
Bibliographic record
Abstract
Background and purpose: Several studies have shown the effectiveness of screening programs in decreasing the costs and disutility of type-2 diabetes and related complications. As there is a growth in the incidence of type-2 diabetes amongst the Iranian population, the cost-effectiveness of performing type-2 diabetes screening tests in community pharmacies of Iran was evaluated in this study from the payer's perspective. The target population consisted of two hypothetical cohorts of 1000 people 40 years of age without a prior diagnosis of diabetes, for the intervention (screening test) and no-screening groups. Experimental approach: A Markov model was developed to evaluate the cost-effectiveness and cost-utility of a type-2 diabetes screening test in community pharmacies in Iran. A 30-year time horizon was considered in the model. Three screening programs with 5-year intervals were considered for the intervention group. The evaluated outcomes were quality-adjusted life-years (QALYs) for cost-utility-analysis and life-years-gained (LYG) for cost-effectiveness-analysis. To examine the robustness of the results, one-way and probabilistic-sensitivity analyses were applied to the model. Findings/Results: The screening test represented both more effects and higher costs. The incremental effects in the base-case scenario (no-discounting) were estimated to be 0.017 and 0.0004 (approximately 0) for QALYs and LYG, respectively. The incremental cost was estimated to be 2.87 USD/patient. The estimated incremental-cost-effectiveness ratio was 164.77 USD/QALY. Conclusion and implications: This study indicated that screening for type-2 diabetes in community pharmacies of Iran could be considered highly cost-effective, as it meets the WHO criteria of the annual GDP per capita ($2757 in 2020).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".