County-Level Enrollment in Medicare Advantage Plans Offering Expanded Supplemental Benefits
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".