Prior Authorization Requirements in Medicare Advantage and County Social Vulnerability

ABSTRACT

Objective: To assess whether Medicare Advantage (MA) beneficiaries in socially vulnerable counties face more restrictive prior authorization (PA) requirements.

Study Design: Cross-sectional analysis of 2945 US counties in 2022.

Methods: The primary outcome was the mean percentage of Medicare-covered service categories requiring PA across all MA plans in a county, adjusted for service mix and plan enrollment and derived from the CMS MA benefit, landscape, and enrollment files. Secondary outcomes included county-level PA rates for selected service categories, the number of plans with more vs less restrictive PA policies, and mean monthly premiums. The exposure was the county-level Social Vulnerability Index (SVI) subdomains (socioeconomic status, household characteristics, racial/ethnic minority status, housing and transportation), categorized into deciles. Unadjusted linear regression models were used, weighted by county MA enrollment.

Results: Counties in the highest vs lowest SVI decile in the socioeconomic domain had overall PA rates that were 7.2 percentage points (PP) higher (P < .001), with the largest gap for psychiatric services (16.9 PP; P < .001). Conversely, mean monthly premiums declined across socioeconomic deciles, from $29 in the first decile to $8 in the 10th (P < .001). Total plan counts were stable across deciles, but the composition differed: The most vulnerable counties offered 5 fewer plans with less restrictive PA policies and 4 more plans with more restrictive policies (both P < .001).

Conclusions: MA beneficiaries in socially vulnerable counties face more restrictive PA requirements, especially for psychiatric services. These disparities may compound existing structural barriers and warrant consideration in future MA policy reforms.

Takeaway Points

Prior authorization (PA) is widely used by Medicare Advantage (MA) plans to manage costs. This study found that MA plans in more socially vulnerable counties imposed PA on a higher share of Medicare-covered services, raising concerns about equitable plan design.

• MA plans in socially vulnerable counties impose more restrictive PA policies.
• These counties offer fewer plans with less restrictive PA policies and more plans with more restrictive policies.
• PA for psychiatric services shows the strongest association with social vulnerability.
• Enrollees in high-vulnerability counties tended to have lower premiums, suggesting trade-offs that beneficiaries may face when selecting plans.

As of 2024, 54% of Medicare beneficiaries were enrolled in Medicare Advantage (MA).¹ MA is managed care provided by private insurers receiving a monthly capitated payment from Medicare for enrollees. Private insurers offer MA plans through contracts with CMS, and each contract can include multiple plans within a local market that spans a single county or a broader region. MA has grown in popularity for various reasons including offering extra benefits (eg, vision, hearing, dental), low premiums, annual out-of-pocket cost caps, and aggressive marketing.² At the same time, MA plans use various utilization management tools to control costs, most notably restrictive provider networks and prior authorizations (PAs).³

PA is the process through which a provider obtains approval from an insurer before rendering certain medical services. MA plans use PA more frequently than other insurance programs (eg, traditional Medicare or Medicaid).^4,5 Nearly all (99%) MA plans require PA for some services, especially relatively expensive services such as hospital and skilled nursing facility stays.⁶ Although PA is designed to reduce unnecessary services and waste, it might create barriers to access, delay necessary care, and increase unmet care needs.^7-10 Notably, PA use varies substantially across MA insurers and plans.^11,12 If individuals in different markets are exposed to and subsequently enroll in plans with varying PA requirements, PA might disproportionally impact certain groups, potentially exacerbating health disparities.

The Social Vulnerability Index (SVI), developed by the CDC, is designed to help identify communities that are susceptible to adverse events such as infectious disease outbreaks or natural disasters. It incorporates a wide range of social determinants of health, including income, education, employment, population density, housing, and racial and ethnic compositions.¹³ Growing evidence shows that social vulnerability is associated with worse health outcomes, including disability, comorbidity, and mortality,^14-16 as well as increased health care costs and use.¹⁷ Social vulnerability is also linked to individuals’ access to medical care, treatment adherence, and consistent engagement with the health care system.¹⁸ For instance, individuals in communities with lower education and income levels may lack health literacy or access to transportation, limiting their ability to obtain timely care.¹⁶

For MA beneficiaries living in more socially vulnerable counties, additional structural barriers may arise depending on the types of health plans available to them. Evidence has shown that racial and ethnic minority groups are more likely to enroll in MA plans with lower Star Ratings,¹⁹ driven in part by limited access to high-quality plans in their local markets.²⁰ In addition, counties with higher social vulnerability tend to have fewer highly rated MA plans.^21,22 Although these findings speak to disparities in plan quality, it is also plausible that MA plans enter socioeconomically disadvantaged markets with more restrictive PA requirements to control cost, given the relatively high health care spending among beneficiaries in these areas.¹⁷ However, to the best of our knowledge, no study has examined whether socially vulnerable counties are more likely to be served by MA plans with more restrictive PA policies.

This study aimed to assess the relationship between county-level social vulnerability and the restrictiveness of PA policies in MA plans. Using multiple publicly available national data sets, we examined whether higher social vulnerability is associated with higher PA rates for Medicare-covered services among MA plans available in each county. Findings will contribute to understanding how MA plan design may reinforce or mitigate disparities in access to care.

METHODS

Data and Sample

The primary data source was CMS MA plan benefits data, submitted annually by plans during the bidding process and detailing PA requirements across service categories. We defined counties as local markets and aggregated MA benefits data at the county level using the CMS MA landscape file, which lists all plans available in each county, along with each plan’s monthly premiums and parent organization. These data were then linked to the CDC’s SVI and the MA enrollment file. All data were from 2022; benefits data were from the third quarter.

We excluded counties with incomplete information, resulting in 2945 counties across all 50 US states and the District of Columbia. Consistent with prior studies,^12,22 we excluded MA plans limited to specific populations or subject to different payment regulations, including Special Needs Plans, Medicare-Medicaid plans, Program of All-Inclusive Care for the Elderly plans, employer-sponsored plans, Part B–only plans, and cost plans.

PA Rates

The main outcome was the mean PA rate for in-network Medicare-covered services across all MA plans in each county. We first calculated the percentage of service categories requiring PA for each plan using a set of binary indicators from the plan benefits data, where 1 indicated PA was required and 0 otherwise. We included 34 Medicare-covered service categories, excluding supplemental benefits to ensure consistency in the denominator across plans. Each service category was weighted by the corresponding Medicare fee-for-service (FFS) spending from CMS’ Provider Summary by Type of Services files, giving greater weights to more costly or commonly used services (eg, inpatient hospital) relative to lower-cost or rarely used services (eg, digital rectal exam). We assumed that the relative distribution of spending across service categories between FFS and MA is comparable, making FFS spending a valid proxy for weighting. The list of service categories, relevant procedure codes used to define each category, corresponding FFS spending, and final weights is provided in eAppendix Table 1 (eAppendix available at ajmc.com). We then averaged PA rates across all plans in each county, weighted by plan enrollment, to capture the average restrictiveness of plans.

We further examined county-level PA rates by service category, defined as the percentage of plans requiring PA for that service within a county, weighted by plan enrollment. Because there are 34 distinct service categories, we followed Neprash et al and selected 4 sentinel categories based on cost (inpatient acute hospital services), access concerns (psychiatric services), rapid growth in PA use (diagnostic procedures, tests, and laboratory services), and high prevalence of PA requirements (Part B drugs).¹²

Finally, we examined the distribution of MA plans in each county, including the total number of plans, and the number with less or more restrictive PA policies. Plans were classified as having less or more restrictive PA policies if their service mix– and enrollment-adjusted PA rates fell into the first national quintile (requiring PA for 0%-80% of services, with a mean of 42%) or fifth national quintile (requiring PA for 99%-100% of services, with a mean of 99%), respectively.

Monthly Premiums

We calculated the mean monthly consolidated premiums (Parts C and D) across all MA plans for each county, adjusted by plan enrollment.

SVI

Our primary exposure was the county-level SVI by subdomains (ie, socioeconomic status, household characteristics, racial and ethnic minority status, and housing and transportation) (eAppendix Table 2), measured using national percentile rankings and grouped into deciles, with the first representing the least vulnerable and the 10th the most.

Statistical Analysis

We first described mean PA rates and monthly premiums by SVI deciles, grouped into low (deciles 1-3), moderate (deciles 4-7), and high (deciles 8-10) vulnerability. Differences across groups were assessed using F tests. Analyses were weighted by county MA enrollment so that larger counties contributed proportionally more. To visualize geographic variation and potential spatial correlation, we overlaid county-level SVI scores and mean PA rates on a national map. For brevity, we present results based on the socioeconomic status SVI domain; findings for the other domains were consistent.

Next, we examined the association of county-level SVI deciles with mean PA rates using linear regression models, weighted by county MA enrollment. Predicted values from the linear models were presented. We also conducted several secondary analyses. First, we repeated the analysis by service category to assess whether the association varied across services. Second, we examined the association of SVI with mean monthly premiums to explore potential trade-offs faced by beneficiaries. Third, we examined the association between SVI and plan availability (overall and by the restrictiveness of PA policies) to evaluate whether it was driven by specific types of plans. Finally, to determine whether these patterns reflect variations in PA policies within individual MA carriers, we examined the association between SVI and mean PA rates at the carrier level, weighted by the number of enrollees per carrier.

Sensitivity Analyses

We conducted 5 sensitivity analyses. First, we used the PA rate without adjustment for service mix, applying equal weights across the 34 service categories, because any PA requirement can introduce delays or deter care regardless of a service’s fiscal importance. Second, we adjusted our regression models by county-level characteristics from the Area Health Resources Files not captured by SVI but likely associated with variations in MA markets, such as total population, number of active doctors of medicine, hospital beds, skilled nursing facility beds, and rurality. Third, we excluded counties in the lowest or highest 1% of population size (n = 58) to examine whether our findings were driven by very small or large counties. Fourth, we stratified the sample by rurality to assess whether the association differed across metropolitan and nonmetropolitan areas. Fifth, to account for market competition, we stratified the sample by counties with 3 or fewer vs more than 3 insurance carriers.

RESULTS

Sample Characteristics

Compared with less socioeconomically vulnerable counties (deciles 1-3), highly vulnerable counties (deciles 8-10) had MA plans with higher mean PA rates, adjusted for service mix and plan enrollment (92% vs 87%; P < .001) (Table). Across service categories, differences were smaller for diagnostic procedures/tests/laboratory services (92% vs 90%; P = .036) and larger for psychiatric services (89% vs 77%; P < .001). Consistently, high-SVI markets had fewer permissive plans (6 vs 9; P < .001) and more restrictive plans (13 vs 7; P < .001). In contrast, mean monthly premiums were lower in high-SVI counties ($9 vs $29; P < .001). A bivariate map showed that counties characterized by both high vulnerability and high PA rates were concentrated in Southern states (eAppendix Figure 1).

PA Rates

The mean PA rates increased most notably across the socioeconomic SVI domain, from 86.3% in the first decile to 93.5% in the 10th decile (Figure 1), a difference of 7.2 percentage points (PP) (P < .001) (eAppendix Table 3). The household characteristics domain showed a smaller difference of 5.5 PP (P < .001). Estimates for other domains did not show consistent patterns.

Service Categories

Consistent with the main findings, associations were generally strongest for the socioeconomic domain across service categories, except for diagnostic procedures/tests/laboratory services, where the household characteristics domain showed the largest increase in PA rates (81.9%-96.4% from the first to 10th decile; P < .001) (eAppendix Figures 2-5). For psychiatric services, considerable differences were observed in both the socioeconomic domain (16.9 PP; 75.4%-92.3%; P < .001) and the household characteristics domain (17.3 PP; 73.1%-90.4%; P < .001). For acute hospital services and Part B drugs, which already have high PA use, increases in PA rates across socioeconomic SVI deciles were smaller (5.1 PP and 4.5 PP, respectively; both P < .001).

Monthly Premiums

Premiums showed a pattern opposite to PA rates. In the socioeconomic domain, mean monthly premiums declined from $29 in the first decile to $8 in the 10th (P < .001) (Figure 2 and eAppendix Table 4). Similar declines were observed in the household characteristics and racial/ethnic minority domains, although the magnitudes were smaller ($19 and $9 lower, respectively; both P < .001).

Plan Availability

For brevity, we present findings only for the socioeconomic domain. The total number of MA plans was stable across SVI deciles (Figure 3 and eAppendix Table 5). However, the composition by PA restrictiveness differed. Compared with the least vulnerable counties, the most vulnerable counties offered 4.9 fewer plans with more permissive PA policies (P < .001) and 3.5 more plans with more restrictive policies (P < .001).

Distributions by Insurer

We observed substantial variation in PA policies across insurers. UnitedHealthcare, the largest by market share, applied the most restrictive policies, with 99% of service categories requiring PA across socioeconomic SVI deciles, whereas Kaiser Permanente applied a relatively lower PA rate (77%) (eAppendix Table 6). Overall, the positive association between SVI and PA rates persisted at the insurer level (Figure 4). Notably, this association appears to be driven by smaller insurers.

Sensitivity Analyses

Findings were robust to multiple sensitivity analyses: (1) using PA rates unadjusted for service mix, (2) adjusting for county characteristics, (3) excluding counties with very small or large populations, (4) stratifying by rurality, and (5) stratifying by 3 or fewer vs more than 3 insurance carriers in the county (eAppendix Figures 6-12).

DISCUSSION

This cross-sectional national study provides descriptive evidence that MA beneficiaries in more socially vulnerable counties face more restrictive PA requirements, particularly for psychiatric services. These patterns reflect differences in plan availability and insurer-level policies, with fewer permissive plans, more restrictive plans, and generally stricter PA policies among insurers in high-SVI markets. The associations were concentrated in the socioeconomic domain, indicating that beneficiaries in counties with lower income and educational attainment may be disproportionately exposed to restrictive PA policies. We also observed that enrollees in high-SVI counties tended to have lower premiums, suggesting trade-offs that beneficiaries may face when choosing plans.²³

The results highlight important and previously understudied disparities for MA beneficiaries. Higher SVI has been associated with reduced access to care; greater prevalence of chronic conditions such as cancer, cardiovascular disease, and Alzheimer disease; and increased health care spending driven by higher rates of postoperative complications, emergency department visits, and readmissions.^16-18,24-26 This study demonstrates that, among MA beneficiaries, restrictive PA policies may compound existing disparities. In more vulnerable counties, both beneficiaries and providers may face greater administrative burdens in filing PA requests and navigating denials and appeals, while beneficiaries may also be at greater risk of delayed or deferred care—each of which may contribute to poorer outcomes and greater downstream service use.^27,28

Of the 4 service categories examined in detail, psychiatric services showed the largest gap in mean PA rates across SVI deciles. Psychiatric services also experienced rapid growth in PA use between 2009 and 2019.¹² Prior studies have documented substantial disparities in access to mental health services, care quality, and out-of-pocket costs among MA enrollees with mental health conditions, both within MA and compared with traditional Medicare.^29-32 Notably, psychiatrist networks in MA plans are much narrower than those in Medicaid managed care and Affordable Care Act Health Insurance Marketplaces.³³ This study suggests that, coupled with these barriers, variation in PA policies may further aggravate disparities in access to and use of psychiatric services for MA beneficiaries in more vulnerable counties.

These findings contribute to the limited body of literature on geographic variation in MA plans’ use of PA and its potential role in exacerbating disparities. Existing studies on PA have focused on characterizing its overall use^11,34 or assessing its effects on specific services—primarily prescription drugs,^5,35-37 and to a lesser extent, home health,¹⁰ dental care,⁹ imaging,³⁸ and radiology.⁸ One related study documented significant geographic variation in the proportion of MA enrollees subject to PA and found that certain geographic characteristics (ie, poverty rates, racial composition, rurality, and hospital market concentration) were associated with PA exposure, although these associations weakened from 2009 to 2019.¹² Our study expands on this study by using composite measures of social vulnerability that not only capture some of these characteristics, but also incorporate a broader range of social determinants of health—including education, employment, insurance status, housing cost burden, language proficiency, household crowding, and transportation. This approach informs how PA policies vary across counties defined by multidimensional vulnerability and how such variation could affect access for populations facing overlapping structural disadvantages. In addition, our study uses more recent data—from 2022—which is important given the rapid evolution of the MA market over time.

With the increasing use of PA and growing calls for reform in MA,^7,12 it is critical to assess how PA affects socially vulnerable populations. Ongoing regulatory efforts—such as CMS’ proposed rule mandating a 7-day turnaround for standard PA decisions³⁹—may mitigate some adverse events. Our findings suggest CMS should monitor not only overall PA burden but also whether requirements disproportionately affect enrollees in socially vulnerable areas. Policy makers could also consider incorporating social vulnerability into MA payment policy. For example, raising benchmarks in socially vulnerable counties could create stronger financial incentives for plans to participate in these markets and potentially offer more generous benefits, including more permissive PA practices. In parallel, given the importance of plan choice in MA, existing decision-support tools (eg, CMS Plan Finder) and resources (eg, State Health Insurance Assistance Programs) may want to consider incorporating PA requirements as a key plan attribute to help beneficiaries choose optimal coverage. This may be particularly valuable for beneficiaries in socially vulnerable counties, who often have greater care needs but limited health literacy and financial resources.

Limitations

This study has several limitations. First, MA plan benefits data indicate only whether PA is required within broad service categories, without information on its frequency or the specific services subject to PA requirements. Second, the aggregated county-level analysis cannot determine whether PA is beneficial or harmful to individual beneficiaries. Despite criticisms that PA may increase administrative burden and delay necessary care, beneficiaries continue to select plans with these requirements,¹² likely weighing them against other plan attributes.²³ Indeed, we found that counties with higher social vulnerability—where PA requirements are more restrictive—also tended to have lower premiums, suggesting a trade-off between access and cost. Individual-level data are needed to assess enrollment choices and how PA policies actually affect enrollees’ access and use. Third, because our analysis is purely descriptive, we cannot infer causality.

CONCLUSIONS

As MA enrollment continues to rise, it is critical to understand how PA policies shape care delivery, particularly for vulnerable populations. We found that higher social vulnerability was associated with higher PA rates across US counties. These findings suggest that beneficiaries in socioeconomically disadvantaged areas may experience greater administrative burdens, care delays, and poorer outcomes due to more restrictive PA requirements of available plans. Further research is needed to examine how PA policies influence beneficiary plan selection and individual-level outcomes, including care experiences, service use, and health outcomes. n

Author Affiliations: Department of Health Policy and Management, University of Georgia College of Public Health (ES), Athens, GA; Department of Psychiatry, University of Michigan Medical School (LL), Ann Arbor, MI.

Source of Funding: Dr Lei was supported by the National Institute on Aging (R00AG075145).

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 (ES, LL); acquisition of data (ES); analysis and interpretation of data (ES, LL); drafting of the manuscript (ES, LL); critical revision of the manuscript for important intellectual content (ES, LL); and statistical analysis (ES, LL).

Address Correspondence to: Eunhae Shin, PhD, Department of Health Policy and Management, University of Georgia, 100 Foster Rd, Athens, GA 30602. Email: eunhae.shin@uga.edu.

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