Supplementary materials: Budget impact analyses of hemoglobin A1c and lipid panel point-of-care testing with Afinion™ 2 in Canada and Italy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,290 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».