First-Time Medicare Advantage Enrollees Demonstrate Increasing Demographic and Clinical Diversity

ABSTRACT

Objectives: To examine demographic and clinical characteristics of first-time Medicare Advantage (MA) enrollees between 2012 and 2022 and to better understand changes in this population as it has grown.

Study Design: We used enrollment files and pharmacy claims from 2012 to 2022 to examine data from 5,075,279 MA beneficiaries with no prior enrollment in a large national health plan.

Methods: We descriptively examined annual rates of clinical severity (measured using the RxRisk-V Comorbidity Index) and patient demographics (race, age, sex, enrollment, and geographic characteristics). We used multivariate linear regression analyses to assess mean annual changes in clinical severity, controlling for demographic, geographic, and enrollment characteristics.

Results: Results showed a rise in the share of dually eligible beneficiaries (from 13.0% to 23.0%), low-income subsidy recipients (from 19.4% to 27.0%), Special Needs Plan enrollees (from 2.6% to 14.8%), and Black beneficiaries (from 12.7% to 18.3%) from 2012 to 2022. MA enrollees who became Medicare eligible due to disability comprised 31.7% of the sample. We observed a small but statistically significant mean annual increase in the RxRisk-V Comorbidity Index score (0.021; 95% CI, 0.020-0.022; P < .0001), translating to a mean increase of 2 diagnoses per 100 beneficiaries each year.

Conclusions: In the past decade, the first-time MA enrollee population has become more demographically diverse and slightly more clinically complex. As MA enrollment continues to grow, policy makers and MA insurers should consider these trends in Medicare plan features, as enrollees may require additional social support. Designing Medicare benefits that align with the changing composition of the MA population is critical for addressing this population’s health needs.

Am J Manag Care. 2026;32(Spec. No. 3):SP156-SP162. https://doi.org/10.37765/ajmc.2026.89906

Medicare Advantage (MA) enrollment as a share of the eligible Medicare population surged from 19% in 2007 to 54% in 2024, and MA is projected to account for 60% of the eligible population by the end of the decade.¹ This growth is partly driven by policy changes that prompted private health insurers to offer more robust and affordable plan choices. MA insurers receive rebates for providing supplemental benefits (eg, dental coverage, gym memberships, debit cards for over-the-counter medical supplies not covered by traditional Medicare [TM]), enabling them to offer generous plans with reduced cost sharing, lower premiums, and capped out-of-pocket spending. Further, MA plans provide all-inclusive coverage for medical care and prescription drugs, rather than a separate Part D plan, offering simplicity and convenience.^2,3

Despite rising MA enrollment, surprisingly little is known about how the population of those who choose to enroll in MA has changed demographically and clinically. Studies from the mid-2000s found that MA beneficiaries were somewhat healthier and less costly than the average Medicare-eligible enrollee, but this may no longer reflect the current composition of the population.^4-7 Newer research has shown that MA enrollees are increasingly non-White, are more commonly dually eligible for Medicare and Medicaid, and have more social risk factors (eg, low education and income levels).^8-10 However, few studies examine changes such as medical need (eg, qualifying for Medicare due to disability) or enrollment type (eg, in a Special Needs Plan [SNP]). Further, recent findings on clinical complexity in MA are mixed. Some results indicate that MA beneficiaries remain lower risk than the average Medicare enrollee,^11-14 whereas others suggest that chronically ill individuals are increasingly enrolling in MA^15,16 and remain clinically comparable to the broader Medicare population.^17,18

Varying findings on clinical complexity may stem from analyses that include all beneficiaries rather than only first-time enrollees. This approach can pose challenges in disentangling plan effects from clinical changes over time, as coverage benefits may affect health care utilization and health status. Further, such analyses may capture differential coding intensity practices or the notion that MA’s payment structure could incentivize MA insurers to more thoroughly document diagnoses.^19-22 Examining MA beneficiaries at the start of their enrollment captures clinical risk at baseline, painting a clearer picture of true changes within this population and mitigating potential concerns on the impacts of MA enrollment on health status and coding intensity.⁹

Understanding today’s MA population is a critical policy issue. As MA expands, understanding the demographic and clinical trends of this population can inform whether benefit design promotes enrollees’ health needs. In this study, we examine changes in clinical severity and patient demographics among first-time MA beneficiaries from 2012 to 2022. No studies to date have examined MA enrollee characteristics over a full decade, particularly among those with no previous enrollment history, who may best capture the evolving MA population.

METHODS

We used pharmacy claims and enrollment data from a large national health plan between 2012 and 2022. Our sample included 5,075,279 MA beneficiaries with no previous enrollment history and 6 months of continuous enrollment. As the data were from 1 health plan, we could not identify enrollees previously insured by a different MA plan (ie, who may not be new to the MA program). We limited the study to beneficiaries during the first 6 months of enrollment to capture the population during Medicare plan selection, rather than over time, as clinical severity may be affected by changes in coding practices and plan selection. Further, our main outcome, clinical severity (measured using the RxRisk-V Comorbidity Index)^23,24 required a 6-month look-back period.

First, we descriptively examined the annual distribution of clinical severity and patient demographic, enrollment, and geographic characteristics. Then we used multivariate linear regressions to assess mean annual changes in clinical severity, controlling for patient demographic, enrollment, and geographic characteristics. The model used a continuous year variable to measure time. Demographic characteristics included age, sex, and race. Enrollment characteristics included plan type, SNP enrollment, dual-eligibility status, disability as the original reason for Medicare eligibility, and receipt of a low-income subsidy (LIS). We used county-level fixed effects to account for geographic differences (eg, variation in physician practice patterns and public policies).

The RxRisk-V comorbidity score, which determined our outcome, consists of 43 categories of comorbidity and is used to determine an individual’s current comorbidities based on prescription drugs dispensed. It was initially developed to predict health care costs and was later adapted to predict mortality in outpatient populations.^23-26 Evidence shows that the RxRisk-V comorbidity score is less sensitive to variations in coding intensity than other methods (eg, Elixhauser or Charlson comorbidity indices), which require diagnostic information in their construction.²²

Supplementary analyses examining the mean annual change in clinical risk used the Elixhauser and Charlson indices to test for robustness. We tested the appropriateness of the linear model compared with a quadratic specification. Because it is plausible that some variables were correlated (eg, dual eligibility and SNP enrollment), we also tested for collinearity using variation inflation factors (VIFs) (eAppendix [available at ajmc.com]).

RESULTS

Results showed increased demographic and socioeconomic diversity among first-time MA enrollees at a large national health plan. In our sample, the mean age was 66.6 years, 25.4% of beneficiaries were LIS recipients, 19.0% were dually eligible, 17.1% were Black, 9.3% were enrolled in a SNP, and 31.7% became eligible for Medicare through disability (Table, panel A).

Between 2012 and 2022, the number of first-time MA enrollees grew by 84.9% (from 326,770 to 604,187). There were higher shares of dually eligible beneficiaries (from 13.0% to 23.0%), SNP enrollees (from 2.6% to 14.8%), LIS recipients (from 19.4% to 27.0%), and Black beneficiaries (from 12.7% to 18.3%). MA enrollees who became eligible for Medicare due to disability comprised nearly one-third of the sample annually, although the proportion decreased slightly between 2012 and 2022 (from 33.0% to 27.9%) (Figure, panel A).

TheTable, panel B, shows multivariate linear regression results for the mean annual increase in the RxRisk-V comorbidity score between 2012 and 2022. We observed a small but statistically significant mean annual increase in the RxRisk-V comorbidity score (0.022; 95% CI, 0.021-0.023; P < .0001) among first-time enrollees, after controlling for demographic, enrollment, and geographic characteristics.

We compared the mean annual RxRisk-V comorbidity score and IQR in 2012 vs 2022. The mean RxRisk-V comorbidity score increased slightly, from 3.3 to 3.4, between 2012 and 2022. Further, we observed greater variation in the RxRisk-V comorbidity score over time, as evidenced by a widening IQR (2012: 1-5; 2022: 0-6), suggesting increased clinical diversity (Figure, panel B).

In the eAppendix, we also repeated regression analyses using 2 claims-based indices of clinical risk. Results are robust to the type of risk score selected; however, our main result using the RxRisk-V comorbidity score was slightly lower in magnitude. Supplementary analyses suggested no evidence of collinearity between covariates, with all VIFs under 2.02. Our main findings were comparable in linear and quadratic model specifications.

DISCUSSION

Results show that a large MA insurer is serving an increasingly diverse population in terms of race, income, and medical need, concurrent with a slight rise in clinical risk and variation—translating to a mean increase of 2 diagnoses per 100 beneficiaries each year. This estimate is conservative, as it is limited to first-time MA enrollees, who may be younger and healthier than the average Medicare beneficiary.

Demographically, our findings align with external literature showing that new MA enrollees are increasingly non-White and socioeconomically disadvantaged.^8-10 Previous studies have found that greater racial and socioeconomic diversity in MA, represented by a rise in non-White and dually eligible beneficiaries, is accompanied by a proportionate decrease in TM.²⁷ Although our study does not directly compare characteristics of MA and TM enrollees, it is plausible that the demographic trends captured in this analysis are concentrated in MA and not represented across the full Medicare-eligible population. Nonetheless, it would be valuable for future studies to explore these differences.

Previous work is mixed with respect to recent trends in clinical severity within MA.^11-18 The current study seeks to illustrate true changes in clinical complexity in MA by limiting the sample to first-time enrollees. Using this novel approach, we capture clinical risk at the time of enrollment—before plan effects and coding intensity practices can contribute to health care utilization and diagnoses—characterizing clinical and demographic shifts among first-time MA beneficiaries over a full decade.

The current study provides insight into MA enrollment patterns as the program has grown, as well as plan features that may attract certain populations. Most notably, we highlight that nearly one-third of first-time MA enrollees qualify for Medicare based on disability. Beneficiaries with disabilities represented 12% of the general Medicare population in 2022, indicating that this MA plan likely enrolls a relatively high share of this population.²⁸ However, we could only observe those who originally qualified for Medicare based on disability, rather than those who currently qualify, so this difference is not an apples-to-apples comparison. Nonetheless, Medicare enrollees with disabilities are less likely to be satisfied with their coverage and more likely to experience issues related to cost and access compared with those who qualify for Medicare based on age. Therefore, a tendency for these individuals to select MA may indicate that benefits unique to MA, including SNPs and lower cost sharing, are particularly attractive to these individuals. This is further supported by growing SNP enrollment, which increased more than 5-fold over the study period. These benefits can strengthen the Medicare program for individuals with high medical needs, including younger adults with disabilities.²⁸

This work is also among the first to evaluate clinical risk among MA enrollees using the RxRisk-V comorbidity score, a pharmacy claims-based measure. In contrast, earlier studies have calculated clinical risk using medical claims-based diagnoses (eg, the CMS Hierarchical Condition Category risk adjustment model).^11-14 As demonstrated in earlier research using a drug-based risk score to study the MA population, this approach is less sensitive to changes in coding intensity practices, as prescription drug utilization is not subject to the pressures that incentivize greater reporting of diagnosis codes in medical claims.²² Consistent with this finding, we expect that the RxRisk-V comorbidity score is more likely to reflect true clinical risk in the MA population.

Demographic and clinical trends among first-time MA enrollees suggest that MA plan features may support the growing complexity of this population. Future research should focus on the practical significance of rising diversity in MA to inform optimal policy decisions to meet the needs of this population.

Limitations

Our study has limitations. It did not compare MA with TM beneficiaries, making it challenging to determine whether population trends are reflective of MA or the general Medicare-eligible population. Nonetheless, existing literature observing declining demographic diversity in TM²⁷ suggests that takeaways on first-time MA enrollees could remain unchanged even with this comparison. Further, because our study did not directly compare MA with TM, we could not examine switching behavior in MA vs TM.²⁹

Next, data were limited to administrative claims from one insurer and may not represent the full MA population; however, this insurer covers 18% of MA beneficiaries.³⁰ Although we aimed to generate reliable estimates of clinical risk by focusing on first-time enrollees, there remain concerns of differential coding among MA insurers over time. Our analyses identified only beneficiaries new to a single MA plan, rather than the MA program as a whole, which may amplify these concerns. Further, our analyses excluded those who rapidly disenroll from MA. Comparing rapid disenrollees with full-year and multiyear MA enrollees in future studies would provide a more complete understanding of trends in the MA population.

Finally, we did not directly explore how demographic and clinical trends in MA impact Medicare payment and benefit design. Future analyses examining the relationship between MA population characteristics and related policies would provide additional insights for policy makers.

CONCLUSIONS

In the past decade, first-time MA enrollees have become more demographically diverse and slightly more clinically complex. As MA enrollment continues to grow, policy makers and MA insurers should consider these trends in Medicare plan features, as enrollees may require more social support. Designing Medicare benefits that align with MA enrollees’ changing composition is critical for promoting this population’s health needs.

Author Affiliations: Humana Healthcare Research (DB, AS, EB, GS), Louisville, KY; Humana Inc (RH, CM, KG), Louisville, KY.

Source of Funding: None.

Author Disclosures: Dr Bozzi, Dr Sutherland, Dr Boudreau, and Ms Sylwestrzak are employed by Humana Healthcare Research and hold equity/stock in Humana. Dr Heyborne, Ms Monks, and Dr Goodrich are employed by Humana, and Ms Monks and Dr Goodrich hold equity/stock in Humana.

Authorship Information: Concept and design (DB, EB, GS, RH, CM, KG); acquisition of data (AS); analysis and interpretation of data (DB, AS, EB, GS, RH, KG); drafting of the manuscript (DB, AS, RH, CM); critical revision of the manuscript for important intellectual content (DB, AS, EB, GS, RH, CM, KG); statistical analysis (AS); obtaining funding (GS); and supervision (EB, GS, RH, KG).

Send Correspondence to: Debra Bozzi, PhD, Humana Healthcare Research, 500 W Main St, Louisville, KY 40202. Email: dbozzi@humana.com.

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