County-Level Enrollment in Medicare Advantage Plans Offering Expanded Supplemental Benefits
Notice bibliographique
Résumé
Importance: Since 2019 and 2020, Medicare Advantage (MA) plans have been able to offer supplemental benefits that address long-term services and supports (LTSS) and social determinants of health (SDOH). Objective: To examine the temporal trends and geographic variation in enrollment in MA plans offering LTSS and SDOH benefits. Design, Setting, and Participants: This cross-sectional study used publicly available data to examine changes in beneficiary enrollment and plan offerings of LTSS and SDOH benefits from the benefits data from the second quarter of each year and other data from April of each year except 2024, for which the first quarter was the latest for benefits data and January the latest for other data at the time of analysis. Multivariable linear regression models for each type of benefit were used to investigate associations between county characteristics and enrollment in 2024. Analyses were stratified for (1) Dual Eligible Special Needs Plans (D-SNPs) that exclusively enroll dual-eligible beneficiaries and (2) non-D-SNPs. Main Outcomes and Measures: The percentage of MA enrollees in plans offering LTSS or SDOH benefits at the county level. Results: This study included 2 631 697 D-SNP and 20 114 506 non-D-SNP enrollees in 2020, which increased to 5 494 426 and 25 561 455, respectively, in 2024. From 2020 to 2024, the percentage of D-SNP enrollees in plans offering SDOH benefits increased from 9% to 46%, whereas the percentage fluctuated between 23% and 39% for LTSS benefits. There was an increase in non-D-SNP enrollees with LTSS (from 9% to 22%) and SDOH (from 4% to 20%) benefits from 2020 to 2023, which decreased in 2024. In 2024, the most offered LTSS benefit was in-home support services, and the most offered SDOH benefit was food and produce. The percentage of enrollees with these benefits varied across counties in 2024. In multivariable linear regression models, among D-SNPs, enrollment in plans offering any SDOH benefits was higher in counties with greater MA penetration (coefficient, 5.0 percentage points [pp] per 10-pp change; 95% CI, 2.1-7.9 pp), in urban counties (coefficient, 7.2 pp vs rural counties; 95% CI, 3.8-10.6 pp), in counties with greater enrollment in fully integrated D-SNPs (coefficient, 3.0 pp per 10-pp change; 95% CI, 2.2-3.9 pp), and in counties in states with approved Medicaid home- and community-based services waivers for individuals 65 years or older or those with disabilities (coefficient, 10.8 pp; 95% CI, 4.0-17.6 pp). Enrollment in D-SNPs offering LTSS benefits was also higher in counties with greater MA penetration (coefficient, 5.9 pp per 10-pp change; 95% CI, 2.4-9.5 pp), urban vs rural counties (coefficient, 4.6 pp; 95% CI, 1.1-8.1 pp), and counties with greater enrollment in fully integrated D-SNPs (coefficient, 3.0 pp per 10-pp change; 95% CI, 2.1-3.9 pp) in addition to counties with greater social vulnerability scores (coefficient, 1.4 pp per 10-pp change; 95% CI, 0.3-2.5 pp). Conclusions and Relevance: In this cross-sectional study of MA plans and enrollees, an increase in enrollment was most consistent in D-SNPs offering SDOH benefits compared with LTSS benefits and in D-SNPs compared with non-D-SNPs. Geographic variation in enrollment patterns highlights potential gaps in access to LTSS and SDOH benefits for rural MA beneficiaries and dual-eligible enrollees living in counties with lower enrollment in fully integrated D-SNPs and states with more limited Medicaid home- and community-based services coverage.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 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 ».