Supplementary materials: Budget impact analyses of hemoglobin A1c and lipid panel point-of-care testing with Afinion™ 2 in Canada and Italy
Bibliographic record
Abstract
These are peer-reviewed supplementary materials for the article 'Budget impact analyses of hemoglobin A1c and lipid panel point-of-care testing with Afinion™ 2 in Canada and Italy' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: Market SharesSupplementary Table 2: Patient Flow Subcategorization of Monitoring Patients with Diagnosed Diabetes or DyslipidemiaSupplementary Table 3: Afinion™ 2 POC Device Costs and PCP Practice ParametersSupplementary Table 4: HbA1c POC Healthcare Resource Use Costs in the Monitoring PopulationSupplementary Table 5: Lipid Panel POC Healthcare Resource Use Costs in the Monitoring PopulationSupplementary Table 6: Indirect Costs in the Monitoring PopulationAppendix ASupplementary Table 7: Indirect Costs in the Screening PopulationSupplementary Table 8: Scenario Analyses: Incremental Budget Impact of Afinion™ 2 POC HbA1c Testing – CanadaSupplementary Table 9: Scenario Analyses: Incremental Budget Impact of Afinion™ 2 POC HbA1c TestingAppendix BSupplementary Table 10: Scenario Analyses: Incremental Budget Impact of Afinion™ 2 POC Lipid Panel Testing – Canada – ItalySupplementary Table 11: Scenario Analyses: Incremental Budget Impact of Afinion™ 2 POC Lipid Panel Testing – Italy Baseline (2024) YearReferencesAbstract Aim: Screening and monitoring of diabetes or dyslipidemia frequently involves a multi-step processrequiring patients to obtain test requisitions from their primary care physician (PCP), followed by a laboratory visit and re-consultation. Point-of-care testing (POCT) for hemoglobin A1c (HbA1c) and lipid panel can streamline the patient care pathway. This study assessed the budget impact of introducing Afinion™ 2 POCT (Abbot Rapid Diagnostics) from the Canadian and Italian societal perspectives. Methods: Budget impact models were developed for Canada and Italy over a 5-year time horizon (2025 to 2029). The analyses considered the screening and monitoring of diabetes or dyslipidemia for patients utilizing the public healthcare system and attending primary care, and included direct costs (testing, consultations) and indirect costs (productivity loss, transportation) based on published sources. The budget impact (BI) was calculated by comparing scenarios with and without POCT. All costs were adjusted to Canadian dollars ($) or 2024 Euros (€). Scenario analyses were conducted to explore the impact of alternative assumptions. Results: The 5-year cumulative BI was -$758,006,692 (-$50,709,964 direct, -$707,296,728 indirect) for HbA1c POCT and -$726,452,755 ($2,684,011 direct, -$729,136,766 indirect) for lipid panel POCT in Canada and -€1,380,658,764 (-€6,391,954 direct, -€1,374,266,809 indirect) for HbA1c POCT and -€851,792,115 (€55,962,879 direct, -€907,754,993 indirect) for lipid panel POCT in Italy. In both countries, cost savings for both the healthcare payer and patients were observed for HbA1c POCT, while costs savings were derived from patient indirect costs for lipid panel POCT. The analyses estimated that 1,558,062 and 1,501,260 PCP consultations in Canada, 4,962,338 and 1,951,026 PCP consultations in Italy were avoided with implementation of POCT for HbA1c and lipid panel, respectively. Scenario analyses demonstrated potential further cost savings with implementation of POCT in pharmacies. Conclusion: This study demonstrates that the adoption of Afinion 2 POCT for HbA1c and lipid panel can provide efficiencies to different types of healthcare systems through reducing PCP consultations, saving time and money for patients and providing cost savings for payers.
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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 | 0.003 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.290 | 0.017 |
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".