MO518<i>INSIDE CKD</i>: MODELLING THE ECONOMIC BURDEN OF CHRONIC KIDNEY DISEASE IN THE AMERICAS AND THE ASIA-PACIFIC REGION USING PATIENT-LEVEL MICROSIMULATION
Notice bibliographique
Résumé
Abstract Background and Aims Chronic kidney disease (CKD) is a debilitating and costly condition, with an estimated global prevalence of approximately 10%. Progression of CKD is associated with end-stage renal disease, cardiovascular events and premature mortality, as well as increased requirement for renal replacement therapies (RRTs), which are associated with significant healthcare costs and resource use. Furthermore, patients with CKD often have additional comorbidities, which are associated with CKD progression and increased costs. The trajectories of CKD prevalence, progression, outcomes and the related costs are therefore critical considerations for public health and policy planning. Using country-specific, patient-level microsimulation, Inside CKD aims to model the global clinical and economic burden of CKD from 2020 to 2025. Method We used the Inside CKD microsimulation to model the economic burden of CKD in the Americas and Asia-Pacific region. We developed a virtual general population for each country using national survey data and relevant data from published literature. Data inputs included country demographics, the prevalence of CKD and RRT, comorbidities and complication rates as well as associated healthcare costs. CKD stages were defined according to Kidney Disease Improving Global Outcomes (KDIGO) 2012 recommendations and patients were categorized according to estimated glomerular filtration rate and albuminuria status. We calibrated the RRT modelling against historical trends from country-specific renal registries. We conducted model validation and calibration using established methods for health economic modelling. Here, we report the results from the US and Canada analyses, with further analyses currently underway for additional countries in the Americas and Asia-Pacific region. Results Initial results for the US and Canada revealed that, between 2020 and 2025, annual healthcare costs associated with CKD will increase linearly from US$232.3B to US$376.2B in the US and from C$21.4B to C$34.1B in Canada (this figure does not include complication costs). In the US, the largest absolute increase in cost was observed in CKD stage 3a ($98.4B); however, CKD stage 4 had the largest relative increase in cost with an approximately three-fold increase (US$7.30B to US$23.3B). In Canada, the largest absolute increase in cost was observed in CKD stage 3a (C$5.84B); whereas CKD stage 5 had the largest relative increase in cost with an approximately five-fold increase (C$0.27B to C$1.41B). By 2025, costs associated with CKD will increase across all age categories (18–34, 35–64 and 65+ years) in both countries. In the US, the 35–64 age group had the largest absolute increase in costs with an increase of $74B (US$58.3B to US$132B). The largest relative increase in cost was observed in the 18–34 age category, with approximately a three-fold increase in costs (US$3.76B to US$10.2B). In Canada, the 65+ age group had the largest absolute increase in costs with an increase of C$7.9B (C$16.4B to C$24.3B). Both the 18–34 and 35–64 age categories had the largest relative increase in costs, with an approximately two-fold increase (C$0.25B to C$0.49B and C$4.77B to C$9.31B, respectively). Conclusion Initial results from Inside CKD demonstrate that CKD poses a significant economic burden over the next 5 years. CKD stage 3a was associated with the most pronounced cost increases in both the US and Canada, likely due to the increased prevalence of this stage. In the US, the largest increase in CKD costs was observed in the 35–64-year-old ‘working’ population, whereas the largest increase in Canada was observed in the 65 years old and over population. Further policy interventions aimed at early diagnosis and proactive management should be considered to slow disease progression, improve patient outcomes and reduce the economic burden associated with CKD.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».