Incidence and Early Outcomes of Heart Failure in Commercially Insured and Medicare Advantage Patients, 2006 to 2014
Notice bibliographique
Résumé
H eart failure (HF) affects >5.7 million individuals in the United States 1 with 870 000 individuals newly diagnosed each year. 2 Previous epidemiological studies have demonstrated that the incidence of HF varies by race and ethnicity, with the highest incidence in blacks. [3][4]4][5] However, this association is partially mediated by differences in socioeconomic factors, 3 such as access to care, and whether differences extend to commercially insured populations requires examination.Furthermore, although the incidence of HF is known to increase with age, 6,7 younger individuals with HF remain understudied because several large epidemiological cohorts 8 and claims-based studies 6 are restricted to older individuals.Our goal was to address these gaps in knowledge by leveraging a large US insurance claims database, containing information from >100 million individuals enrolled in private and Medicare Advantage health plans.The objectives of this study were to evaluate the incidence of HF by age, sex, and race/ ethnicity and to examine differences in the rate of hospitalizations and office visits in the year after diagnosis. Methods and Results Data SourceWe conducted a retrospective analysis using the OptumLabs Data Warehouse, a large US commercial insurance database. 9The database comprises medical claims for individuals in all 50 states and of all ages and ethnic and racial groups. 10Medical claims include claims for professional (eg, physician), facility (eg, hospital), and outpatient prescription medication services.Pursuant to the Health Insurance Portability and Accountability Act, the use of de-identified data does not require Institutional Review Board approval. Study PopulationWe included adult enrollees for whom a diagnosis of HF (International Classification of Diseases, Ninth Revision, Clinical Modification codes 428.XX, 402.X1, 404.X1, or 404.X3 6 appeared on a single inpatient claim between January 1, 2006 and April 1, 2014.We also included individuals with an HF diagnosis on at least 3 physician or outpatient claims on different days within 20 consecutive months. 6The incidence date was defined as the earliest discharge date of the qualifying inpatient claim or the latest service date of the qualifying outpatient claim.To ensure that enrollees had newly diagnosed HF, we required them to have at least 2 previous years of continuous medical coverage with no claim listing HF as a diagnosis.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».