The Impacts of Supplemental Benefits on Medicare Advantage Plan Composition

ABSTRACT

Objectives: In 2019 and 2020, Medicare Advantage (MA) plans received historic flexibility to begin to address members’ nonmedical and social needs through a set of primarily health-related benefits (PHRBs) and Special Supplemental Benefits for the Chronically Ill (SSBCIs). We aimed to evaluate the impact of adoption on the number and composition of new MA plan enrollees.

Study Design: A difference-in-differences design of retrospective Medicare enrollment data linked to publicly available plan and county-level data.

Methods: We linked individual-level Medicare enrollment data to publicly available, plan-level MA benefit, crosswalk, and penetration files from 2016 to 2020. We compared the number of new enrollees and the proportion of new enrollees who were Black, Hispanic, younger than 65 years, partially and fully Medicare and Medicaid dual eligible, and disabled in plans that adopted a PHRB or SSBCI vs a set of matched control plans that did not.

Results: In fully adjusted models, PHRB adoption was associated with a 2.2% decrease in the proportion of fully dual-eligible new members (95% CI, –4.0% to –0.5%). SSBCI adoption was associated with a 2.3% decrease in the proportion of new members younger than 65 years (95% CI, –3.6% to –0.9%). After accounting for multiple comparisons, these results were no longer statistically significant.

Conclusion: We determined that supplemental benefit adoption was not associated with demographic shifts in MA plan enrollment.

Am J Manag Care. 2024;30(7):e210-e216. https://doi.org/10.37765/ajmc.2024.89583

Takeaway Points

We evaluated the impact of adopting expanded Medicare Advantage (MA) supplemental benefits on plan composition as measured by the number of new enrollees and the proportion of new enrollees who were Black, Hispanic, younger than 65 years, partially and fully Medicare and Medicaid dual eligible, and disabled.

• Adoption was not associated with demographic shifts in MA plan enrollment.
• After accounting for multiple comparisons, there was no significant relationship between primarily health-related benefits or Special Supplemental Benefits for the Chronically Ill adoption and a change in the number and composition of new MA enrollees.
• These results were largely consistent across multiple sensitivity analyses.

Medicare Advantage (MA), the capitated and privately run segment of the Medicare program, has grown rapidly, currently enrolling more than half of Medicare enrollees.^1,2 Originally, MA was required to cover the same benefits as traditional Medicare. Over time, CMS and Congress enabled MA plans to offer additional benefits to address enrollees’ needs. CMS initially allowed plans to cover select “primarily health-related” benefits, including vision, dental, hearing, and fitness memberships.³ In 2019, CMS expanded the definition of primarily health-related benefits (PHRBs) to include a wider array of nonmedical services, allowing plans to manage enrollees’ illnesses or injuries and reduce avoidable health care utilization (eAppendix Figure 1 [eAppendix available at ajmc.com]). Beginning in 2019, MA plans could offer services such as caregiver support and in-home support services.⁴ Congress passed the Creating High-Quality Results and Outcomes Necessary to Improve Chronic (CHRONIC) Care Act to give MA plans greater flexibility to address members’ social needs through the Special Supplemental Benefits for the Chronically Ill (SSBCIs). As of 2020, plans could tailor social needs benefits for members with complex illnesses. Benefits included nonmedical transportation, food and produce, and structural home modifications.⁵

Studies show that initial uptake of the expanded supplemental benefits was limited.^4-6 In 2019, fewer than 15% of MA plans adopted a PHRB in the first year; the most common were caregiver support (eg, respite care, counseling, training)​ and in-home services (eg, personal care and homemaker services).^4,7,8 In 2020, fewer than 5% of MA plans adopted an SSBCI; the most common were nonmedical transportation, fresh food (eg, produce), and pest control.⁵ In qualitative studies, MA plan representatives reported they lacked evidence on whether the new benefits would influence their Medicare market share or change the composition of their member panel.^6,9,10

More than 70% of new MA plan enrollees switched between MA plans or from traditional Medicare; a minority of new plan enrollees were new to the Medicare program.¹¹ There is limited research on the drivers of plan selection. Studies have found that higher county-level MA penetration, changes in costs, physician networks, supplemental benefits, and health status drive older adults’ decisions to switch plans.^12-15 Previously, MA plans strategically adopted supplemental benefits (eg, fitness memberships) to attract and retain healthier members.¹⁶ However, as MA has grown, enrollment has increased among individuals managing medical and social complexities such as multiple comorbidities, activities of daily living limitations, dual eligibility, and living in areas of higher neighborhood disadvantage.^17-20 Plans may use the new supplemental benefits to address current members’ needs while attracting more complex members.

This study explored the association between supplemental benefit adoption and the number and composition of new MA plan enrollees. We hypothesized that plans that created new PHRBs or SSBCIs would attract more medically and socially complex members. Using a difference-in-differences (DID) design, we measured the association between PHRB and SSBCI adoption and the number and composition of new MA plan enrollees.

METHODS

Data and Study Population

We linked MA plan benefit data, which detailed PHRB and SSBCI adoption and plan-level covariate data, to monthly enrollment files, which provided county-level plan enrollment and the contract start date, using plan and contract identifiers. We linked plans across years using MA crosswalk files. We linked to individual-level enrollment and demographic data from the 100% Medicare Beneficiary Summary File, which provided data on members’ ages, Medicare and Medicaid dual-eligibility status, and original reason for entitlement. Race and/or ethnicity were measured using the Research Triangle Institute (RTI) Race Code, which incorporates surnames to improve identification of Hispanic enrollees. Then we used the member’s 5-digit Federal Information Processing Standards codes to link to county-level data on MA penetration. Additional information is available in eAppendix Table 1.

Our study population included new enrollees in MA plans that offered the PHRBs or SSBCIs and new enrollees in comparator plans that did not offer the benefits. New enrollees were beneficiaries who joined or changed their plan during the study period. Because current members are often likely to remain in a plan, new entrants would likely drive compositional changes.¹² We excluded enrollees in plans that changed the maximum out-of-pocket spending, deductible, or premium by more than $10 throughout the study period to isolate the impact of these benefits from other changes in plan characteristics.¹⁶ We limited our sample to plans offered throughout the study period. We included health maintenance organization (HMO) and preferred provider organization (PPO) plans but removed other plan types. Additional information is available in eAppendix Tables 2 and 3.

The primary explanatory variable was a binary variable for whether a plan adopted 1 or more of the PHRBs in 2019 or SSBCIs in 2020. The PHRBs included adult day care, home-based palliative care, in-home support, caregiver support, and nonopioid pain management benefits.⁴ To identify the PHRBs, we isolated new nonmedical benefits offered in 2019 and not in prior years (2015-2018). For the PHRBs, the prepolicy period was 2017-2018 and the postpolicy period was 2019. The SSBCIs included nonmedical transportation, pest control, air quality control, service dog support, meals, food and produce, social needs benefits, services supporting self-direction, and structural home modifications.⁵​ For the SSBCIs, the prepolicy period was 2018 to 2019 and the postpolicy period was 2020. In the primary SSBCI analysis, we removed plans that had also adopted a PHRB in 2019 or 2020 to isolate the impact of SSBCI adoption. For additional information, see eAppendix Tables 4 and 5.

In MA, medically and socially complex members are more likely to be members of racial/ethnic minority groups, be dual eligible, or receive Medicare due to disability.^21-23 Therefore, the outcome of interest was the total number of new enrollees and the proportion of new enrollees who were younger than 65 years, Black, Hispanic, partially dual eligible, fully dual eligible, and originally entitled to Medicare due to disability. Enrollees were identified as either fully or partially dual eligible based on the month of enrollment in their MA plan. We excluded individuals without data on county residence.

Analysis

We used a DID design to study the association between PHRB adoption in 2019 or SSBCI adoption in 2020 and the number and composition of MA plan enrollees who were new to their plan in separate models. The underlying assumption for the DID design was that trends in the number and composition of new enrollees in plans that adopted the new supplemental benefits and in control plans would have remained consistent; it did not require baseline similarity between adopters and nonadopters. The assumption required that the number and composition of new enrollees in MA plans that adopted the benefits would have trended the same as the nonadopters if the treatment plans had not adopted these benefits. We tested the parallel trends assumption statistically and graphically (eAppendix Sections A and B, eAppendix Figures 2-5, and eAppendix Table 6).

We matched plans that adopted a PHRB in 2019 or SSBCI in 2020 to a set of control plans offered in the same county that did not adopt either each year. As PHRB and SSBCI adoption varied across counties, we restricted the control plans to plans that operated in the same counties as treatment plans to best reflect a member’s choice set.²⁴ We excluded plan-counties where the plan had less than 50 enrollees in the county. We controlled for plan characteristics that were time varying and associated with plan choice, including plan type, Special Needs Plan (SNP) status, star ratings, plan size, county-level plan enrollment, and MA penetration.^25,26 We adjusted for contract start year (eg, prior to 2006, 2006-2013, or 2014-2019) due to significant changes to the MA program: the 2006 Medicare Modernization Act and the 2013 Medicare Advantage Organization Quality Improvement Program under the Affordable Care Act. When measuring the change in partial or full dual eligibility, we excluded Dual Eligible SNPs.

We compared plan characteristics in 2019 and 2020 using χ² tests. Our primary analysis was 2 multivariate linear regression models accounting for the continuous outcome of the number and composition of new MA plan enrollees and PHRB adoption in 2019 or SSBCI adoption in 2020. Each regression included an indicator variable for supplemental benefit adoption, an indicator for prepolicy vs post policy, and an interaction term between the supplemental benefit adoption indicator and postpolicy variables, which represents the mean difference in the number and composition of new MA plan enrollees between plans that did and did not adopt a PHRB in 2019 or SSBCI in 2020. Our analysis included plan-, county-, and year-level fixed effects to account for differences between plans and counties over time. We included 6 sensitivity tests. First, we applied a Bonferroni correction to account for multiple comparisons; P < .007 was a familywise value of 0.05.^27-30 Second, we restricted our analysis to new plan entrants who were also newly eligible for Medicare. Third, we compared plans that did and did not adopt PHRBs or SSBCIs using an inverse probability weighted (IPW) sample to balance adopters and nonadopters on observable characteristics.^31-33 Fourth, we controlled for plans that changed premium, deductible, or maximum out-of-pocket spending rather than excluding them. Fifth, we restricted the sample to members with a chronic condition using MA nursing home and home health assessments and hospitalization encounter data (more details in eAppendix Tables 1 and 7). Finally, we analyzed the impact of plans that adopted both a PHRB and SSBCI in 2020 that had not previously adopted a PHRB. This study received approval from the Brown University Institutional Review Board. All analyses were conducted using Stata 18 (StataCorp LLC).

RESULTS

The PHRB study sample consisted of 835 plans (16.9%) eligible for inclusion representing 7369 plan-counties and enrolling 7,291,309 individuals. A total of 157 plans (18.8%) that enrolled 2,026,571 individuals adopted a PHRB (eAppendix Table 2). Plan-counties that adopted a PHRB were more likely to be SNPs (54.8% vs 21.8%) and operate in counties with lower MA penetration (36.8% vs 40.3%) (Table 1). At baseline, plans that adopted a PHRB had a higher number of new members (223 vs 183) and a higher proportion of new Black members (23.3% vs 14.0%), members younger than 65 years (31.7% vs 17.0%), members with disability (45.9% vs 25.9%), partially dual-eligible members (15.6% vs 4.7%), and fully dual-eligible members (11.1% vs 4.0%) (Table 1).

For the SSBCI analysis, the study sample consisted of 182 (3.3%) plans eligible for inclusion representing 1,388,426 enrollees. Twelve plans (13.3%) with 151,512 enrollees adopted an SSBCI (eAppendix Table 3). Plan-counties that adopted an SSBCI were more likely to be HMOs (100% vs 55.7%) and SNPs (43.8% vs 24.4%) (Table 1). At baseline, plan-counties where the plan adopted a SSBCI had a higher proportion of new members who were partially dual eligible (20.0% vs 3.0%) (Table 1).

Table 2 presents the unadjusted and adjusted DID estimates of the effect of PHRB and SSBCI adoption on the number and composition of new MA plan enrollees. In fully adjusted models, PHRB adoption is associated with an average decrease of 2.2% in the proportion of new members who were fully dual eligible (95% CI, –4.0% to –0.5%) (Table 2 and Figure 1). In fully adjusted models, SSBCI adoption is associated with a 2.3% reduction in the proportion of new members who are younger than 65 years (95% CI, –4.4% to –0.3%) (Table 2 and Figure 2). After applying a Bonferroni correction to account for type I error (P < .07), supplemental benefit adoption was not associated with a significant change in the number and composition of new MA plan enrollees (Table 2).

In the sensitivity analysis of enrollees new to Medicare, for PHRB adoption, the adjusted results were nonsignificant (eAppendix Table 8). SSBCI adoption was associated with a 2.6% decrease in the proportion of new enrollees who were Black, accounting for multiple comparisons (eAppendix Table 8). In IPW models, PHRB adoption is associated with a 1.5% decrease in the proportion of new members who were fully dual enrolled accounting for multiple comparisons (95% CI, –2.4% to –0.5%); SSBCI adoption was not associated with significant changes (eAppendix Table 9). Controlling for and not excluding plans that changed premiums, maximum out-of-pocket spending, or deductibles, we found that PHRB adoption was associated with a 31-person decrease in new members (95% CI, –51 to –11) accounting for multiple comparisons (eAppendix Table 10). For individuals with chronic conditions, PHRB or SSBCI benefit adoption was not associated with significant changes after accounting for multiple comparisons (eAppendix Tables 7 and 11). There was no significant association between benefit adoption and composition shifts for plans that adopted both benefits (eAppendix Table 12).

DISCUSSION

We found that supplemental benefit adoption was not associated with changes in the number and composition of new MA plan enrollees. After accounting for multiple comparisons, there was no significant relationship between PHRB or SSBCI adoption and a change in the number and composition of new MA enrollees.

Prior to 2019, MA enrollment growth was driven by Black, Hispanic, and dual-eligible enrollees; we found that benefit adoption did not drive increases in the proportion of these enrollees.¹⁸ One explanation is that the overall growth trajectory was large enough across plans to overshadow smaller shifts between plans. Alternatively, at baseline, Black and dual-eligible enrollees were more likely to be enrolled in a plan that subsequently adopted the benefits; therefore, these plans may have had less potential for growth and may have adopted the benefits to retain existing members. Future studies could isolate plans that had more opportunity for growth within specific compositional groups to identify the added impact of the supplemental benefits.

Another explanation is that enrollees were unaware of the new benefits, or if aware, their decision-making was driven by other plan characteristics (eg, plans with more preferable provider networks).^12,34 Plans face a marketing challenge with the new benefits. Because PHRBs are more limited in scope and SSBCIs are offered only to chronically ill members, plans may not want to highlight benefits for which most potential members may not be eligible. If individuals enroll in a plan based on benefits they are not eligible for, this could negatively impact plan satisfaction and continuity.¹⁰ Plans may have been hesitant to market these new offerings to avoid incentivizing enrollment among ineligible individuals. For older adults who compare plans regularly using platforms such as the Medicare Compare website, some information on the availability of nonmedical benefits exists. For example, enrollees can assess whether a plan offers a transportation, in-home support, or home safety modification benefit.³⁵ However, information and/or eligibility criteria about other nonmedical benefits are not available to enrollees without reviewing extensive plan benefit or marketing materials.³⁵ Although there is some literature on what drives older adults’ plan preferences, it is limited to certain geographies, and further research should be conducted on the impact of the new benefits.^12,36

It is too early to determine whether the lack of impact of benefit adoption on the number and composition of new MA plan enrollees is a positive or negative trend. Originally, plan leadership cited hesitancy to adopt the supplemental benefits, given the lack of data on how adoption would impact their member panel.^6,9 Our findings suggest that plans may not risk attracting more complex members by adopting the expanded benefits. Adoption of the expanded supplemental benefits has dramatically increased. In 2023, 22% of HMO or PPO plans offered a PHRB and 26% offered an SSBCI. As MA costs grow, policy makers have grown skeptical of the role of supplemental benefits in MA’s value proposition. President Joseph R. Biden’s 2023 budget proposal highlighted this by extending the 85% medical loss ratio to any supplemental benefit investment to ensure benefits were not selectively designed to attract enrollment without leading to large uptake.³⁷ Because MA dominates more than half of Medicare, plans may no longer rely on attracting healthy members as a growth strategy. Instead, they manage increasingly complex members. At baseline, plans that adopted the new supplemental benefits had a higher proportion of new members who were dual eligible or eligible for Medicare originally due to disability—groups that disproportionately switch plans or disenroll from MA.^13-15,38 Therefore, plan leaders may look to supplemental benefits to retain existing members. Once data are available, researchers should examine the impact of supplemental benefit usage on member disenrollment, plan satisfaction, and health care utilization. Finally, qualitative research could allow policy makers and plan representatives to understand which benefits drive plan preference and enrollee experience.

Limitations

Our study had several limitations. We could only account for whether a benefit was adopted at a plan level and not whether it was available and marketed within a specific county. CMS reinterpreted the uniformity clause, which allowed plans to target the supplemental benefits to individuals with specific chronic conditions.³⁹ Therefore, our results represent a conservative estimate because this contributes to a form of measurement error that could bias our results toward the null. We included a sensitivity analysis and found our results remained nonsignificant when we restricted our sample to individuals with a chronic condition. RTI’s race and ethnicity data may not capture an enrollee’s self-identified race/ethnicity; however, it is the most accurate and widely used source available.^18,40,41 There is likely overlap among race/ethnicity, age, dual-eligibility status, and disability; individuals with intersectional identities may face unique challenges or have specific priorities when selecting a plan. However, given the importance of each factor in potentially driving compositional shifts, we did not assess this.¹⁸ We accounted for important changes in plan composition (eg, maximum out-of-pocket spending, deductible, or premium) but may not have fully isolated the effect of supplemental benefit adoption. In the SSBCI analysis, small sample sizes may have limited our ability to detect potentially meaningful differences. Although these estimates were not statistically significant, the CIs mostly lie below the null, signaling a trend toward a decrease in the proportion of Black, Hispanic, younger than 65 years, disabled, and partially dual-eligible new members, which warrants further research. Finally, for dual-eligible individuals, we were not able to identify the availability of overlapping benefits offered by MA plans and Medicaid, which may help explain enrollment patterns among fully dual-eligible enrollees.⁴²

CONCLUSIONS

We find that supplemental benefit adoption was not associated with demographic shifts in MA plan enrollment. These results were largely consistent across multiple sensitivity analyses. This information will be useful to plans as they consider whether their membership may change because of their decisions to invest in these supplemental benefits.

Acknowledgments

This work was supported by Data Use Agreement CMS DUA RSCH-2022-58370.

Author Affiliations: Department of Health Services, Policy, and Practice (ELT, DJM, ANT, KST) and Center for Gerontology and Health Care Research (DJM, ANT, KST), Brown University School of Public Health, Providence, RI; Division of Research, Kaiser Permanente Northern California (ELT), Oakland, CA; Providence Veterans Affairs Medical Center (ANT), Providence, RI; Department of Family and Community Medicine, University of California, San Francisco (LMG), San Francisco, CA; Center for Equity in Aging, School of Nursing, Johns Hopkins University (KST), Baltimore, MD.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ELT, DJM, ANT, LMG, KST); acquisition of data (ELT); analysis and interpretation of data (ELT, DJM, ANT, LMG, KST); drafting of the manuscript (ELT); critical revision of the manuscript for important intellectual content (ELT, DJM, ANT, LMG, KST); statistical analysis (ELT); administrative, technical, or logistic support (ELT); and supervision (DJM, ANT, LMG, KST).

Address Correspondence to: Emma L. Tucher, PhD, Division of Research, Kaiser Permanente Northern California, 4480 Hacienda Dr, Pleasanton, CA 94588. Email: emma.l.tucher@kp.org.

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