MO545INSIDE ANEMIA OF CKD: ESTIMATING THE IMPACT OF POLICY INTERVENTIONS ON ANAEMIA OF CKD IN THE USA BY MICROSIMULATION MODELLING
Notice bibliographique
Résumé
Abstract Background and Aims Anaemia is a common complication in patients with chronic kidney disease (CKD) and is associated with increased mortality, cardiovascular complications, reduced quality of life and increased use of healthcare resources. Mathematical modelling based on robust epidemiological and clinical data is a useful approach for predicting the future burden of disease and the impact of different intervention scenarios; this is important for health service planning. This analysis uses a microsimulation model, Inside ANEMIA of CKD, to predict the effects of a hypothetical intervention scenario that reduces the prevalence of anaemia of CKD on related healthcare costs in the USA from 2020 to 2025. Method A virtual cohort representing the US population was created within the Inside ANEMIA of CKD microsimulation model framework using demographics and epidemiological data drawn from the US Census Bureau, the Centers for Disease Control and Prevention, and the National Health and Nutrition Examination Survey. In the cohort, virtual individuals were ascribed an age–sex-stratified CKD status (defined by estimated glomerular filtration rate and albuminuria levels, as per international guidelines) and anaemia status (defined by haemoglobin level as mild, moderate or severe, as per WHO criteria) based on US prevalence data. Key comorbidities (type 2 diabetes, heart failure and hypertension) were also assigned, reflecting US-specific population statistics. Healthcare costs related to CKD and anaemia of CKD were taken from the published literature. The study modelled the effects on healthcare costs of a hypothetical intervention scenario in which the prevalence of moderate and severe anaemia is reduced by 20% per year from 2020 to 2025 compared with no intervention (baseline). In each scenario (i.e. intervention or baseline), the modelling analysis estimated healthcare costs related to CKD and anaemia (including inpatient, outpatient, pharmacy costs) for patients with moderate or severe anaemia of CKD. The model did not adjust for the potential costs of the intervention. Results Preliminary results predict that, with the hypothetical intervention, there could be 1.40 million fewer patients with moderate or severe anaemia of CKD in the USA in 2025 compared with no intervention (1.45 million versus 2.85 million). This represents a 49% reduction in cases of moderate or severe anaemia of CKD in 2025 with the intervention versus no intervention. The intervention is projected to lead to a reduction of approximately US$18 billion in annual direct healthcare costs in 2025 for patients with moderate or severe anaemia of CKD compared with no intervention (US$26 billion versus US$44 billion). Conclusion The Inside ANEMIA of CKD microsimulation model predicts that a hypothetical intervention which reduces the prevalence of moderate and severe anaemia of CKD would reduce direct healthcare costs. This suggests that interventions effective at reducing the prevalence of anaemia of CKD would help to reduce the economic burden on healthcare services.
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».