Key Points
Question
Do Medicare beneficiaries aged 18 to 64 years with disability entitlement have different rates of enrollment in Medicare Advantage (MA) vs traditional Medicare (TM) compared with other beneficiaries, and how do the 2 programs compare on rates of ambulatory care access and quality for beneficiaries with disabilities?
Findings
In this cohort study of a nationally representative sample of 7201 person-years for Medicare beneficiaries in 2015 through 2018, beneficiaries with disability entitlement were significantly less likely to enroll in MA compared with those without disability entitlement. However, enrollment in MA vs TM was associated with better outcomes on 2 of 3 access measures and 3 of 3 quality measures for beneficiaries with disabilities.
Meaning
Although Medicare beneficiaries with disabilities enrolled in MA at lower rates than other beneficiaries in this study, MA appeared to compare favorably with TM in meeting key ambulatory care access and quality measures for beneficiaries with disabilities.
This cohort study uses data from the Medicare Current Beneficiary Survey to compare differences in enrollment rates, ambulatory care access, and ambulatory care quality among beneficiaries with disabilities who are enrolled in Medicare Advantage vs traditional Medicare.
Abstract
Importance
Medicare beneficiaries with disabilities aged 18 to 64 years face barriers accessing ambulatory care. Past studies comparing Medicare Advantage (MA) with traditional Medicare (TM) have not assessed how well these programs meet the needs of beneficiaries with disabilities.
Objective
To compare differences in enrollment rates, ambulatory care access, and ambulatory care quality for beneficiaries with disabilities in MA vs TM.
Design, Setting, and Participants
This cohort study included a nationally representative, weighted sample of 7201 person-years for beneficiaries aged 18 to 64 years with disability entitlement in the Medicare Current Beneficiary Survey from 2015 through 2018. Differences in program enrollment and in measures of access and quality by program enrollment were compared after adjusting for demographic, insurance, social, health, and area characteristics and after reweighting the sample by propensity to enroll in MA as estimated by observed confounders. Data analyses were conducted between November 1, 2020, and November 11, 2021.
Exposures
Medicare Advantage vs TM program enrollment.
Main Outcomes and Measures
Six patient-reported measures of ambulatory care access (usual source of care, primary care usual source of care, specialist visit) and quality (cholesterol screening, influenza vaccination, colon cancer screening).
Results
The mean (SD) age of the overall study population was 52.1 (11.0) years; 49.5% were female and 50.5% were male; 1.6% were Asian/Pacific Islander; 17.4%, Black; 10.2% Hispanic; 1.4%, Native American; 65.1%, White, and 4.2%, multiracial. Among all beneficiaries living in the community, individuals with disability entitlement were less likely to enroll in MA than other beneficiaries (34.8% vs 41.2%). The final sample of beneficiaries with disabilities included 2444 person-years in MA and 4757 person-years in TM. Beneficiaries with disabilities in MA vs TM were more likely to be of a minority race or ethnicity (35.7% vs 27.6%) and less likely to be enrolled in private insurance (11.9% vs 25.0%). Comparing MA with TM among beneficiaries with disabilities, those in MA had significantly better rates of access to a usual source of care (90.2% vs 84.9%; adjusted propensity-weighted marginal difference [APWMD], 2.9%; 95% CI, 0.2%-5.7%), access to specialist visits (53.2% vs 44.8%; APWMD, 5.5%; 95% CI, 0.6%-10.5%), cholesterol screenings (91.1% vs 86.4%; APWMD, 3.8%; 95% CI, 0.9%-6.7%), influenza vaccinations (61.4% vs 51.5%; APWMD, 10.4%; 95% CI, 5.3%-15.5%), and colon cancer screenings (68.4% vs 54.6%; APWMD, 10.3%; 95% CI, 4.8%-15.8%).
Conclusions and Relevance
In this cohort study, Medicare beneficiaries with disabilities were enrolled in MA at significantly lower rates than those without disabilities. However, MA was associated with significantly better ambulatory care access and quality for these beneficiaries on 5 of 6 measures compared with TM.
Introduction
Medicare beneficiaries with disabilities aged 18 to 64 years comprise 15% of the Medicare population and experience considerable disparities in access to care compared with beneficiaries aged 65 years or older.1,2 However, access to care for this vulnerable population has been understudied. Medicare beneficiaries with disabilities may enroll in the traditional Medicare (TM) program administered by the Centers for Medicare & Medicaid Services (CMS) or in private Medicare Advantage (MA) plans.
Medicare beneficiaries with disabilities report greater difficulty accessing care and are less likely to receive needed preventive care than are other beneficiaries.3,4,5,6,7,8 Traditional Medicare beneficiaries with disabilities aged 21 to 64 years are 33% and 49% less likely to have access to primary care clinicians (PCCs) and specialists, respectively, than are beneficiaries without disabilities.2 Lack of access to high-quality ambulatory care is associated with negative clinical outcomes.2,9,10,11
Comparisons of MA with TM in the general population indicate that MA beneficiaries have lower use of outpatient visits12,13,14 yet receive similar or better preventive care.12,15,16,17,18,19 In prior years, sicker beneficiaries were more likely to select into TM vs MA,20,21,22 although in recent years, this may no longer be the case as younger beneficiaries with disabilities enroll in MA in higher numbers.23,24 The consequences of MA vs TM for risk selection, access to care, and quality of care for beneficiaries with disabilities are unknown.
Beneficiaries with disabilities may also enroll in MA special needs plans (SNPs) designed to coordinate and integrate care for dual Medicaid enrollees or beneficiaries with chronic and disabling conditions.25,26 However, it is unclear whether SNPs consistently provide high-quality, integrated, cost-efficient care as intended.26,27,28 To our knowledge, only 1 study has assessed SNPs among beneficiaries aged 18 to 64 years, and it found that dual beneficiaries who enrolled in SNPs vs other plans had higher hospitalization rates.29
The present study focused on 2 primary questions: (1) Do beneficiaries with disabilities enroll in MA at different rates than beneficiaries without disabilities? (2) How do rates of ambulatory care access and quality for beneficiaries with disabilities compare in MA vs TM? In secondary exploratory analyses, we expanded these questions to compare beneficiaries in MA SNPs vs non-SNPs vs TM.
Methods
This cohort study was deemed exempt by the Saint Louis University Institutional Review Board, and informed consent was waived for the use of deidentified data. The study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies.30
Data and Study Population
A cohort study was conducted using MA and TM beneficiaries in the Medicare Current Beneficiary Survey (MCBS) for the years 2015 through 2018. The MCBS is an annual, nationally representative sample of the Medicare population with a rotating cohort design.31
To calculate annual MA enrollment rates, a sample was created of all Medicare beneficiaries with and without current disability entitlement and at least 1 month of Part A and B enrollment living in the community. To compare MA with TM among beneficiaries with disabilities, the sample was limited to beneficiaries aged 18 to 64 years with current disability entitlement, at least 12 months of exclusive Part A and B enrollment in MA or TM, completion of the MCBS, and residence in a US zip code. The MCBS cross-sectional survey weights were used to compute nationally representative estimates. These weights account for the selection probability of each person sampled and include adjustments for stratified sampling design and survey nonresponse.31
Exposure and Outcome Variables
The primary exposure variable, MA vs TM enrollment, was identified using Medicare administrative data linked to the MCBS. The secondary exposure variable, SNP vs non-SNP enrollment, was identified for the 89% of MA beneficiaries who had MA plan and contract identifiers in the MCBS that could be linked to published SNP data.32 The outcome variables were 6 measures of ambulatory care access and quality self-reported by beneficiaries. Some questions were asked in the following year in reference to the previous year, and respondents who dropped out of the MCBS at the end of the previous year were excluded.
Three measures of ambulatory care access were assessed. (1) Access to a usual source of care (USOC) was identified as beneficiaries who self-reported that they went to a particular place for medical care and identified the place as a doctor’s office, clinic, health center, or home. (2) Access to a USOC that was a PCC was identified as beneficiaries who reported the specialty of their USOC clinician to be family practice, general practice, geriatrics, gynecology, internal medicine, osteopathy, or physician assistant. (3) Access to a specialist visit in the past year was identified as beneficiaries who reported seeing a specialist apart from a PCC.
Three preventive care services received in the ambulatory setting were assessed as quality measures: (1) blood cholesterol checks in the past year for beneficiaries with diabetes, ischemic heart disease, or heart failure, indicating they were at high risk for cardiovascular complications33; (2) annual influenza vaccinations for all beneficiaries34; and (3) colon cancer screening for beneficiaries aged 45 years or older who self-reported not having colon cancer and who received within the past 5 years either a colonoscopy or sigmoidoscopy in the doctor’s office or a fecal occult blood test at home or in the doctor’s office.35
Beneficiary Characteristics
Age and sex were assessed using administrative records, and veteran status was self-reported. Beneficiaries self-reported their own race and ethnicity in the MCBS. The National Institute on Minority Health and Health Disparities racial and ethnic framework36 was used to identify beneficiaries as belonging to a racial or ethnic minority group if they reported a race or ethnicity of Asian/Pacific Islander, Black, Hispanic, or Native American. We identified beneficiaries who reported they were White or multiracial and not Hispanic as being of other race and ethnicity. We assessed race and ethnicity because racial and ethnic minority beneficiaries experience disparities in access to care in both MA and TM.19
We used administrative records to identify whether beneficiaries had other forms of health insurance that may influence their access to and quality of care apart from MA vs TM enrollment, including Medicaid, private insurance, and Part D (drug) insurance.
Social risk factors were assessed using patient-reported measures of material capital (annual income and federal poverty status), human capital (highest level of education attained), and social support (living alone).
Health behaviors and status were assessed using patient-reported measures of tobacco use, alcohol abuse (at least 4 drinks most days), and obesity (based on height and weight). Poor self-rated health was identified for those who rated their health in the past year as fair or poor. Functional impairment was measured as counts (ranging from 0 to 6 functional limitations) of self-reported difficulty with activities of daily living (ADLs) and instrumental ADLs (IADLs).
Four common chronic conditions in Medicare populations—diabetes, heart failure, ischemic heart disease, and asthma/chronic obstructive pulmonary disease—were identified if beneficiaries reported ever having those conditions. Mental health conditions were identified for those who reported any psychiatric illness, including depression. Finally, the presence of an intellectual and/or developmental disability (IDD) was identified for those who reported (or their proxy respondent reported) an IDD.
Local area variables were assessed as (1) MA market penetration (ie, uptake) rate in beneficiaries’ counties of residence, identified using CMS data37; and (2) rural vs urban residence, identified by linking beneficiaries’ residential zip codes to rural-urban commuting area codes.38
Propensity Score Adjustment
The sample was reweighted by propensity to enroll in MA as estimated by observed confounders. This adjustment was done to change the distribution of confounders in both MA and TM beneficiaries so that they were the same as the distribution in the national Medicare population with disability entitlement (eMethods and eTables 1-2 in the Supplement).
Statistical Analysis
Data analyses were conducted between November 1, 2020, and November 9, 2021. Unadjusted rates of enrollment in MA, and in SNPs vs non-SNPs, among beneficiaries with vs without disabilities were compared and then adjusted for county-level MA market penetration and state fixed effects.
Descriptive statistics (with and without propensity weight adjustment) were computed on beneficiaries’ characteristics, and the Wald test was used to compare differences by beneficiaries’ MA vs TM enrollment status.
Unadjusted rates of the 6 outcomes were computed for beneficiaries with disabilities enrolled in MA vs TM, as well as for those in MA SNPs vs MA non-SNPs vs TM.
Multivariable logistic regression models with and without propensity weight adjustment were estimated at the person-year level to assess the association between MA (vs TM) enrollment and the 6 outcomes. Models were adjusted for the beneficiary characteristics listed above as well as for county-level MA market penetration rates and state fixed effects. All models included year fixed effects to control for secular trend and were adjusted for the complex survey design of the MCBS and clustered standard errors on beneficiaries to adjust for intraperson correlation over time. Results were reported as marginal differences, which can be interpreted as percentage point differences in the outcomes associated with the exposure variables. The propensity-weighted results can be interpreted as the average national treatment effect of MA vs TM on the 6 outcomes among beneficiaries with disabilities.
In secondary exploratory analyses, the above multivariable regression models were re-estimated with enrollment in MA SNPs vs TM and in MA non-SNPs vs TM as the exposures. This analysis was done to assess whether there was a different pattern of associations with each of the 6 outcomes for MA beneficiaries in SNPs vs non-SNPs.
In sensitivity analyses, the primary analytic models were re-estimated after (1) using fixed effects for the Dartmouth hospital referral regions of beneficiaries’ residences instead of states, (2) relaxing continuous enrollment criteria and including beneficiaries with less than 12 months of enrollment, and (3) interacting counts of ADL and IADL limitations with MA enrollment to test for heterogeneous treatment associations by level of functional impairment.
The threshold for statistical significance was P < .05 with 2-sided tests. Analyses were performed using SAS, version 9.4 (SAS Institute Inc) and Stata, version 16 (StataCorp LLC).
Results
Description of Study Population and MA Enrollment Rates
The mean (SD) age of the overall study population was 52.1 (11.0) years; 49.5% were female and 50.5% were male; 1.6% were Asian/Pacific Islander; 17.4%, Black; 10.2% Hispanic; 1.4%, Native American; 65.1%, White, and 4.2%, multiracial. Of 52 587 (unweighted) person-years in the community with at least 1 month of Part A and Part B enrollment, 21 144 (40.2%) had exclusive enrollment in MA and 28 984 (55.1%) had exclusive enrollment in TM (eFigure in the Supplement). Of those exclusively enrolled in MA and TM, there were 2713 and 5206 person-years with disability entitlement, respectively. After excluding those who lacked 12-month enrollment or had missing data, the final sample consisted of 7201 person-years, including 2444 person-years in MA (weighted 9 583 292) and 4757 person-years in TM (weighted 15 412 888). Beneficiaries excluded from the study had less enrollment and were more likely to die (eTable 3 in the Supplement); however, the mortality rate was not statistically different for MA vs TM beneficiaries (eTable 4 in the Supplement).
Beneficiaries with disabilities were less likely to enroll in MA than those without disabilities (34.8% vs 41.2%; adjusted difference [AD], −6.7%; 95% CI, –4.3% to –9.1%) (Table 1). Among MA beneficiaries, individuals with vs without disabilities were more likely to enroll in SNPs (28.1% vs 8.7%; AD, 13.0%; 95% CI, 10.0% to 16.1%).
Table 1. Weighted Percentages of Medicare Beneficiaries With Exclusive Annual Enrollment in Medicare Advantage (MA) by Disability Entitlement, 2015-2018.
| Variable | Disability entitlement | No disability entitlement | % (95% CI) | |
|---|---|---|---|---|
| Unadjusted difference | Market-adjusted differencea | |||
| Total unweighted patient-years, No.b | 8716 | 43 871 | NA | NA |
| Exclusive MA enrollment among all beneficiaries, % | 34.8 | 41.2 | –6.4 (–9.0 to –3.8) | –6.7 (–9.1 to –4.3) |
| SNP enrollment among MA beneficiariesc | ||||
| SNP (as % of MA beneficiaries)d | 28.1 | 8.7 | 19.4 (15.3 to 23.4) | 13.0 (10.0 to 16.1) |
| Non-SNP (as % of MA beneficiaries) | 71.9 | 91.3 | NA | NA |
Abbreviations: NA, not applicable; SNP, special needs plan.
We adjusted for county-level MA market penetration rate (proportion of Medicare beneficiaries in the beneficiary's county enrolled in MA) and state fixed effects to adjust for state policy differences and state differences in supply of medical services, clinician practice intensity, and coding intensity. We included year fixed effects to adjust for secular trend.
Medicare beneficiaries living in the community with at least 1 month of enrollment in Part A and B benefits. Unweighted sample sizes are reported. Estimates from the 2015-2018 Medicare Current Beneficiary Survey were weighted to be nationally representative using cross-sectional weights accounting for the overall annual selection probability of each person sampled and including adjustments for the stratified sampling design, survey nonresponse, and coverage error.
Sampling MA beneficiaries with an available MA plan and contract identification numbers in the Medicare Current Beneficiary Survey linked to public data on MA special needs plans published annually by the Centers for Medicare & Medicaid Services at the plan and contract level (n = 2368 of 2732 for beneficiaries with disability entitlement and n = 16 408 of 18 412 for beneficiaries without disability entitlement).
SNPs for chronic and disabling conditions, dually enrolled Medicare-Medicaid beneficiaries, and beneficiaries institutionalized in long-term care.
Beneficiaries with disabilities in MA were more likely to belong to a racial or ethnic minority group (35.7% vs 27.6%), less likely to be enrolled in private insurance (11.9% vs 25.0%), more likely to be enrolled in Part D (97.8% vs 80.3%), and less likely to live in poverty (37.7% vs 43.7%) than beneficiaries with disabilities in TM (Table 2). Beneficiaries with disabilities in MA were more likely to have diabetes (40.5% vs 34.4%) and ischemic heart disease (18.8% vs 14.2%), less likely to have an IDD (13.6% vs 16.5%), less likely to live in rural areas (17.1% vs 28.9%), and more likely to live in counties with higher MA penetration rates (37.7% vs 29.3%) than those in TM.
Table 2. Characteristics of Community-Dwelling Medicare Beneficiaries With Disability Entitlement Enrolled in Medicare Advantage (MA) vs Traditional Medicare (TM) Insurance, Aged 18-64 Years, 2015-2018.
| Characteristic | Unadjusted weighted % | P valuea | Propensity-adjusted weighted % | P valuea | ||
|---|---|---|---|---|---|---|
| MA | TM | MA | TM | |||
| Total number of patient years, unweighted, No. | 2444 | 4757 | NA | 2444 | 4757 | NA |
| Demographic characteristics | ||||||
| Age, mean (SD), y | 54.0 (9.2) | 50.8 (11.9) | <.001 | 52.0 (10.0) | 51.9 (10.3) | .88 |
| Sex | ||||||
| Male | 48.2 | 51.9 | .09 | 52.7 | 50.0 | .38 |
| Female | 51.8 | 48.1 | 47.3 | 50.0 | ||
| Minority race and ethnicity | 35.7 | 27.6 | <.001 | 30.6 | 30.9 | .23 |
| Asian/Pacific Islander | 1.0 | 2.0 | 0.8 | 1.9 | ||
| Black | 19.3 | 16.2 | 18.5 | 18.1 | ||
| Hispanic | 14.2 | 7.8 | 10.0 | 9.5 | ||
| Native American | 1.1 | 1.6 | 1.3 | 1.5 | ||
| White | 59.9 | 68.3 | 65.6 | 65.3 | ||
| Multiracial | 4.4 | 4.0 | 3.9 | 3.8 | ||
| Veteran (served in US armed forces) | 6.2 | 8.7 | .05 | 7.0 | 7.5 | .75 |
| Other health insurance | ||||||
| Medicaid (dually enrolled) | 51.7 | 55.4 | .13 | 53.2 | 55.7 | .43 |
| Private (including medical, drug, vision, and dental) | 11.9 | 25.0 | <.001 | 22.2 | 21.3 | .79 |
| Medicare Part D (stand-alone or with Part C) | 97.8 | 80.3 | <.001 | 86.0 | 87.0 | .78 |
| Social risk factors | ||||||
| Annual income in thousands, mean (SD) | 24.6 (35.6) | 25.7 (32.6) | .45 | 25.1 (32.1) | 25.6 (29.8) | .78 |
| Poverty (≤100% of federal poverty level) | 37.7 | 43.7 | .003 | 39.7 | 42.1 | .36 |
| Education | ||||||
| No high school or college education | 21.5 | 22.2 | .59 | 21.2 | 22.6 | .46 |
| High school/some college education | 69.0 | 69.4 | 68.9 | 69.5 | ||
| College/graduate school education | 9.5 | 8.3 | 10.0 | 7.9 | ||
| Lives alone | 30.2 | 27.2 | .12 | 30.8 | 29.0 | .43 |
| Health behaviors and status | ||||||
| Current smoker | 32.1 | 33.3 | .57 | 34.9 | 32.5 | .32 |
| Alcohol abuse (≥4 alcoholic drinks most days) | 20.9 | 18.9 | .26 | 21.3 | 20.2 | .75 |
| Obesity (BMI ≥30) | 45.4 | 46.5 | .62 | 47.2 | 46.9 | .94 |
| Poor self-rated health | 56.5 | 56.3 | .91 | 53.9 | 56.7 | .27 |
| Activities of daily living with difficulty/cannot do (0-6), mean (SD) | 1.4 (1.7) | 1.4 (1.9) | .50 | 1.4 (1.7) | 1.4 (1.7) | .93 |
| Instrumental activities of daily living with difficulty/cannot do (0-6), mean (SD) | 1.7 (1.6) | 1.8 (1.9) | .02 | 1.7 (1.7) | 1.8 (1.7) | .16 |
| Health conditions | ||||||
| Diabetes | 40.5 | 34.4 | .004 | 34.2 | 36.6 | .41 |
| Heart failure | 9.9 | 8.7 | .37 | 8.4 | 9.0 | .64 |
| Ischemic heart disease | 18.8 | 14.2 | .003 | 16.1 | 15.4 | .74 |
| Chronic obstructive pulmonary disease/asthma | 33.8 | 31.7 | .27 | 31.6 | 33.1 | .55 |
| Mental health conditionb | 63.8 | 65.7 | .34 | 62.5 | 65.0 | .32 |
| Intellectual and/or developmental disability | 13.6 | 16.5 | .02 | 15.1 | 14.7 | .82 |
| Local area characteristics | ||||||
| Rural | 17.1 | 28.9 | <.001 | 22.2 | 24.1 | .56 |
| Urban | 82.9 | 71.1 | 77.8 | 75.9 | ||
| MA market penetration rate | 37.7 | 29.3 | <.001 | 33.4 | 32.8 | .49 |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared).
P value on the Wald test of significance, equivalent to the F statistic for continuous variables and the χ2 statistic for categorical variables.
Self-reported any psychiatric illness, including depression.
Covariates were balanced after propensity-weighting the sample, with no remaining significant differences on any beneficiary characteristics (Table 2).
Medicare Advantage beneficiaries with disabilities in SNPs vs non-SNPs were more likely to belong to racial or ethnic minority groups, be dually enrolled in Medicaid, live in poverty, lack a high school education, smoke, have diabetes, and have an IDD (eTable 5 in the Supplement).
Association of MA vs TM Enrollment With Access and Quality Outcomes
Comparing MA vs TM among beneficiaries with disabilities, those in MA displayed significantly better rates of access to a USOC (90.2% vs 84.9%; adjusted propensity-weighted marginal difference [APWMD], 2.9%; 95% CI, 0.2%-5.7%), access to specialist visits (53.2% vs 44.8%; APWMD, 5.5%; 95% CI, 0.6%-10.5%), cholesterol screenings (91.1% vs 86.4%; APWMD, 3.8%; 95% CI, 0.9%-6.7%), influenza vaccinations (61.4% vs 51.5%; APWMD, 10.4%; 95% CI, 5.3%-15.5%), and colon cancer screenings (68.4% vs 54.6%; APWMD, 10.3%; 95% CI, 4.8%-15.8%) (Table 3). Marginal differences of MA vs TM were statistically similar for propensity-weighted and non-propensity-weighted results.
Table 3. Association of Medicare Advantage (MA) vs Traditional Medicare (TM) With Ambulatory Care Access and Quality for Beneficiaries With Disability Entitlement, 2015-2018.
| Variable | Unadjusted results | Adjusted marginal difference of MA vs TM, (95% CI) | |||
|---|---|---|---|---|---|
| MA | TM | Absolute difference (95% CI) | Regression resultsa | Propensity-weighted regression resultsb | |
| Accessc | |||||
| Usual source of care, % | 90.2 | 84.9 | 5.3 (3.2 to 7.4) | 3.7 (1.5 to 5.9) | 2.9 (0.2 to 5.7) |
| Usual source of care is PCC, % | 77.4 | 70.1 | 7.2 (3.5 to 11.0) | 5.0 (1.1 to 9.0) | 3.0 (– 0.8 to 6.8) |
| Specialist visit, % | 53.2 | 44.8 | 8.3 (4.3 to 12.3) | 4.9 (0.7 to 9.0) | 5.5 (0.6 to 10.5) |
| Quality | |||||
| Annual cholesterol screen, %d | 91.1 | 86.4 | 4.7 (1.7 to 7.8) | 3.5 (0.8 to 6.2) | 3.8 (0.9 to 6.7) |
| Annual flu shot, %e | 61.4 | 51.5 | 9.9 (6.0 to 13.8) | 10.1 (5.3 to 14.8) | 10.4 (5.3 to 15.5) |
| Colon cancer screening, %f | 68.4 | 54.6 | 13.8 (9.3 to 18.3) | 11.5 (6.4 to 16.5) | 10.3 (4.8 to 15.8) |
Abbreviation: PCC, primary care clinician.
We estimated multivariable logistic regression models for each outcome that also adjusted for the characteristics listed in Table 2 (with race and ethnicity collapsed into minority vs other). We added fixed effects for the states that beneficiaries resided in to control for state policy differences and state differences in supply of medical services, clinician practice intensity, and coding intensity. We included year fixed effects to control for secular trend and adjusted our P values for the complex survey design of the Medicare Current Beneficiary Survey and intra-person correlation over time. We used Stata's Margins command to report our results as the marginal difference of MA vs TM for the dependent variables by modeling the response in the dependent variables to the exposure variable at the population means.
We estimated the same multivariable logistic regression models as in a, but this time reweighting the sample using the propensity score weights described previously to change the distribution of observed confounders in both the treated (MA) and untreated (TM) beneficiaries so that they are the same as the distribution in the entire sample. These estimates should be interpreted as what we would expect to see if every Medicare beneficiary in our nationally representative sample enrolled in MA vs what we would expect to see if nobody enrolled in MA (ie, the average treatment effects).
Unweighted sample n = 6525. Met baseline study inclusion and responded to Medicare Current Beneficiary Survey questions for outcome variables.
Unweighted sample n = 2715. Met baseline study inclusion and exclusion criteria and self-reported having diabetes, ischemic heart disease, or heart failure and responded to Medicare Current Beneficiary Survey questions for outcome variable.
Unweighted sample n = 6462. Met baseline study inclusion and exclusion criteria and responded to Medicare Current Beneficiary Survey question for outcome variable.
Fecal occult blood test at home or physician’s office or colonoscopy or sigmoidoscopy within past 5 years, excluding patients who self-reported having colon cancer or were younger than 45 years. Unweighted sample n = 3233 for patients who met above criteria as well as baseline study inclusion and exclusion criteria and responded to Medicare Current Beneficiary Survey questions for outcome variable.
Association of MA SNP and Non-SNP vs TM Enrollment With Access and Quality Outcomes
Comparing MA SNPs with TM, beneficiaries in MA SNPs displayed significantly better rates of influenza vaccinations (AD, 7.9%; 95% CI, 0.2%-15.6%), and colon cancer screenings (AD, 13.1%; 95% CI, 5.6%-20.5%) (Table 4). Comparing MA non-SNPs vs TM, those in MA non-SNPs displayed significantly better rates of access to a USOC (AD, 5.5%; 95% CI, 3.3%-7.7%), access to a primary-care USOC (AD, 9.3%; 95% CI, 4.5%-14.0%), access to specialist visits (AD, 5.9%; 95% CI, 1.3%-10.5%), cholesterol screenings (AD, 2.8%; 95% CI, 0.2%-5.4%), influenza vaccinations (AD, 11.3%; 95% CI, 5.0%-17.6%), and colon cancer screenings (AD, 11.4%; 95% CI, 5.5%-17.4%).
Table 4. Association of Medicare Advantage Special Needs Plans and Non–Special Needs Plans vs Traditional Medicare With Ambulatory Care Access and Quality for Beneficiaries With Disability Entitlement, 2015-2018.
| Variable | Unadjusted results, weighted % | Adjusted marginal difference (95% CI)a | ||||
|---|---|---|---|---|---|---|
| Special needs plan | Non–special needs plan | Traditional Medicare | P valueb | Special needs plan | Non–special needs plan | |
| Accessc | ||||||
| Usual source of care | 86.7 | 92.3 | 84.9 | <.001 | 1.1 (–2.7 to 4.9) | 5.5 (3.3 to 7.7) |
| Usual source of care is PCC | 69.4 | 81.6 | 70.1 | <.001 | –0.7 (–5.9 to 4.4) | 9.3 (4.5 to 14.0) |
| Specialist visit | 41.8 | 57.7 | 44.8 | <.001 | 0.0 (–7.8 to 7.8) | 5.9 (1.3 to 10.5) |
| Quality | ||||||
| Annual cholesterol screend | 88.3 | 91.7 | 87.1 | .08 | 1.3 (–2.3 to 4.9) | 2.8 (0.2 to 5.4) |
| Annual flu shote | 57.2 | 63.5 | 51.1 | <.001 | 7.9 (0.2 to 15.6) | 11.3 (5.0 to 17.6) |
| Colon cancer screeningf | 66.9 | 69.3 | 54.4 | <.001 | 13.1 (5.6 to 20.5) | 11.4 (5.5 to 17.4) |
Abbreviation: PCC, primary care clinician.
We estimated multivariable logistic regression models for each outcome that also adjusted for the characteristics listed in Table 2 (with race and ethnicity collapsed into minority vs other). We added fixed effects for the states that beneficiaries resided in to control for state policy differences and state differences in supply of medical services, clinician practice intensity, and coding intensity. We included year fixed effects to control for secular trend and adjusted our P values for the complex survey design of the Medicare Current Beneficiary Survey and intra-person correlation over time. We used Stata's Margins command to report our results as the marginal difference of Medicare Advantage vs traditional Medicare for the dependent variables by modeling the response in the dependent variables to the exposure variable at the population means.
On a χ2 test for difference in proportions across Medicare Advantage Special Needs Plan vs Medicare Advantage non–special needs plan vs traditional Medicare.
Unweighted sample n = 6257. Met baseline study inclusion and responded to Medicare Current Beneficiary Survey questions for outcome variables.
Unweighted sample n = 2388. Met baseline study inclusion and exclusion criteria and self-reported having diabetes, ischemic heart disease, or heart failure and responded to Medicare Current Beneficiary Survey questions for outcome variable.
Unweighted sample n = 6019. Met baseline study inclusion and exclusion criteria and responded to Medicare Current Beneficiary Survey question for outcome variable.
Fecal occult blood test at home or physician’s office or colonoscopy or sigmoidoscopy within past 5 years, excluding patients who self-reported having colon cancer or were younger than 45 years. Unweighted sample n = 2840 for patients who met above criteria as well as baseline study inclusion and exclusion criteria and responded to Medicare Current Beneficiary Survey questions for outcome variable.
In sensitivity analyses, results were statistically similar under alternative specifications (eTables 6-7 in the Supplement). No heterogenous treatment effects by ADL and IADL limitations were found on 11 of 12 tests (eTable 8 in the Supplement).
Discussion
In this nationally representative cohort study, Medicare beneficiaries with disabilities were enrolled in MA at significantly lower rates than their peers without disabilities. In addition, although there were some significant differences in beneficiary characteristics between MA and TM, we found no systematic evidence that MA served a more favorable risk profile of beneficiaries with disabilities than TM. Beneficiaries with disabilities who enrolled in MA did display better access to ambulatory care and appeared to receive better quality of care than their similar peers in TM. These results were statistically similar across unadjusted, adjusted, and propensity-weighted estimates (although adjusted and propensity-weighted point estimates were smaller), including adjustment for other insurance benefits, as well as state and market differences. Although the beneficial association of MA vs TM extended to both MA SNP and non-SNP beneficiaries on 2 of 3 quality outcomes, only MA non-SNP beneficiaries displayed significantly better rates than TM beneficiaries on all 6 access and quality outcomes.
These findings suggest that MA compares favorably with TM in meeting key preventive and ambulatory care needs of beneficiaries with disabilities. These findings also raise questions as to why MA enrollment rates are lower among beneficiaries with disabilities. Prior studies have found favorable selection into MA; sicker beneficiaries have higher rates of disenrollment from MA and switching to TM than their peers,20,21,39 although this may no longer be the case in recent years.23 Thus, it is possible that MA plans select against beneficiaries with disabilities. However, the present findings were robust to adjustment for MA county penetration and state and market differences, implying that lower MA enrollment rates among beneficiaries with disabilities are unlikely to reflect strategic decisions by MA plans to avoid areas with large populations of people with disabilities. Beneficiaries with disabilities may be less familiar with MA plan offerings than are elderly beneficiaries who receive regular MA marketing from sources they trust, such as the American Association of Retired Persons. More research is needed to understand why beneficiaries with disabilities are less likely to enroll in MA plans.
This study expands on prior findings in the general Medicare population that beneficiaries in MA appear to receive better preventive care for processes such as cancer screening and flu shots compared with the subpopulation of TM beneficiaries with disabilities who are younger than 65 years.15,16,18,19,40 This finding may be due to greater use and tracking of Healthcare Effectiveness Data and Information Set quality measures in MA plans that affect MA performance ratings.16 However, in contrast to prior studies of the general population finding lower use of outpatient visits in MA than TM,12,13 this study found that beneficiaries with disabilities in MA were more likely to have access to usual care and specialists in the ambulatory setting. This finding is important because beneficiaries with disabilities in TM are less likely to have access to PCCs and specialists on an annual basis than their peers without disabilities.2 By early 2020, 77% of MA beneficiaries also belonged to plans with telehealth benefits.41 It is possible that greater flexibility by MA plans in paying for home and telemedicine visits contribute to improved access for beneficiaries with disabilities.
The most surprising finding of this study was that the beneficial association of MA enrollment with access to care was concentrated among beneficiaries not enrolled in SNPs. The Medicare Payment Advisory Commission recently reported that only 18% of SNPs for dual beneficiaries have meaningful integration with Medicaid.26 Most beneficiaries with disabilities enrolled in SNPs in the present study were dual beneficiaries. More research is needed to understand whether MA SNPs are meeting the needs of beneficiaries with disabilities.
Limitations
This study has several limitations. First, Medicare beneficiaries aged 18 to 64 years with disabilities were identified based on their current disability entitlement status in the Medicare program. This population did not include beneficiaries aged 65 or older who originally qualified for Medicare because of disability; nor did it include those aged 18 to 64 years with disabilities who have not qualified for Medicare.
Second, this was an observational study. The comparisons between MA and TM may be influenced by unobserved variables associated with plan or program enrollment. In comparative studies of MA vs TM, the confounding issues of primary concern include administrative coding bias due to diagnosis upcoding in MA,42 spillover effects of MA onto TM practice patterns within local areas,43 strategic MA plan entry and exit in local markets,41 beneficiary selection into MA vs TM based on health status, availability of other supplemental health insurance, and other factors.44 In this study, rich patient-reported data supplemented by administrative data were used to control for administrative coding bias (via patient-reported health instead of coded diagnoses), spillover effects (via adjustment for county MA penetration rates), regional differences (via state and hospital market fixed effects), and other beneficiary health insurance, including Medicaid, private insurance, and Part D, that may influence MA vs TM enrollment decisions. Although this study controlled for the most likely confounders, potential confounding due to other unobserved factors cannot be ruled out, and therefore the study estimates do not imply causality.
Conclusions
In this nationally representative cohort study of Medicare beneficiaries covering the period 2015 through 2018, beneficiaries aged 18 to 64 years with disabilities were significantly less likely to enroll in MA than other beneficiaries. However, MA enrollment was significantly associated with better outcomes for ambulatory care access and quality on 5 of 6 measures among beneficiaries with disabilities.
eMethods. Propensity Score Weight Adjustment
eTable 1. List of Covariates Used in Estimation of Propensity Score Adjustment
eTable 2. Hosmer-Lemeshow Goodness of Fit Test for Propensity Score
eFigure. Sample Selection Flowchart
eTable 3. Comparing Medicare Beneficiaries with Disabilities Included Versus Excluded From Study Sample, Ages 18-64, 2015-2018
eTable 4. Characteristics of Community-Dwelling Medicare Beneficiaries with Disability Entitlement Enrolled in Medicare Advantage Versus Traditional Medicare Insurance, Ages 18-64, 2015-2018. After Relaxing Continuous Enrollment Inclusion Criteria
eTable 5. Characteristics of Community-Dwelling Medicare Advantage Beneficiaries with Disability Entitlement by Special Needs Plan vs Non Special Needs Plan Enrollment, Ages 18-64, 2015-2018
eTable 6. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. With Dartmouth Hospital Referral Region Fixed Effects
eTable 7. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. After Relaxing Continuous Enrollment Inclusion Criteria
eTable 8. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. Testing for Heterogeneous Treatment Effects by Level of Functional Impairment
References
- 1.Medicare Payment Advisory Commission . Health Care Spending and the Medicare Program: A Data Book. 2019. Accessed June 16, 2020. https://www.medpac.gov/docs/default-source/data-book/jun19_databook_entirereport_sec.pdf?sfvrsn=0
- 2.Johnston KJ, Wen H, Joynt Maddox KE, Pollack HA. Ambulatory care access and emergency department use for Medicare beneficiaries with and without disabilities. Health Aff (Millwood). 2021;40(6):910-919. doi: 10.1377/hlthaff.2020.01891 [DOI] [PubMed] [Google Scholar]
- 3.Kennedy J, Gimm G, Blodgett E. Return to work: a critical aspect of care coordination for younger dual eligibles. Disabil Health J. 2013;6(2):95-99. doi: 10.1016/j.dhjo.2013.01.003 [DOI] [PubMed] [Google Scholar]
- 4.Bernal OA, McClintock HF, Kurichi JE, et al. Patient satisfaction and perceived quality of care among younger Medicare beneficiaries according to activity limitation stages. Arch Phys Med Rehabil. 2019;100(2):289-299. doi: 10.1016/j.apmr.2018.09.114 [DOI] [PubMed] [Google Scholar]
- 5.Cubanski J, Neuman P. Medicare doesn’t work as well for younger, disabled beneficiaries as it does for older enrollees. Health Aff (Millwood). 2010;29(9):1725-1733. doi: 10.1377/hlthaff.2009.0962 [DOI] [PubMed] [Google Scholar]
- 6.Iezzoni LI, Davis RB, Soukup J, O’Day B. Quality dimensions that most concern people with physical and sensory disabilities. Arch Intern Med. 2003;163(17):2085-2092. doi: 10.1001/archinte.163.17.2085 [DOI] [PubMed] [Google Scholar]
- 7.Iezzoni LI. Quality of care for Medicare beneficiaries with disabilities under the age of 65 years. Expert Rev Pharmacoecon Outcomes Res. 2006;6(3):261-273. doi: 10.1586/14737167.6.3.261 [DOI] [PubMed] [Google Scholar]
- 8.Na L, Streim JE, Pezzin LE, et al. Disparities in receipt of recommended care among younger versus older medicare beneficiaries: a cohort study. BMC Health Serv Res. 2017;17(1):241. doi: 10.1186/s12913-017-2168-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Johnston KJ, Hockenberry JM. Are two heads better than one or do too many cooks spoil the broth? the trade-off between physician division of labor and patient continuity of care for older adults with complex chronic conditions. Health Serv Res. 2016;51(6):2176-2205. doi: 10.1111/1475-6773.12600 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Johnston KJ, Wen H, Joynt Maddox KE. Lack of access to specialists associated with mortality and preventable hospitalizations of rural Medicare beneficiaries. Health Aff (Millwood). 2019;38(12):1993-2002. doi: 10.1377/hlthaff.2019.00838 [DOI] [PubMed] [Google Scholar]
- 11.Romaire MA, Haber SG, Wensky SG, McCall N. Primary care and specialty providers: an assessment of continuity of care, utilization, and expenditures. Med Care. 2014;52(12):1042-1049. doi: 10.1097/MLR.0000000000000246 [DOI] [PubMed] [Google Scholar]
- 12.Park S, Larson EB, Fishman P, White L, Coe NB. Differences in health care utilization, process of diabetes care, care satisfaction, and health status in patients with diabetes in Medicare Advantage versus traditional Medicare. Med Care. 2020;58(11):1004-1012. doi: 10.1097/MLR.0000000000001390 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Landon BE, Zaslavsky AM, Saunders RC, Pawlson LG, Newhouse JP, Ayanian JZ. Analysis of Medicare Advantage HMOs compared with traditional Medicare shows lower use of many services during 2003-09. Health Aff (Millwood). 2012;31(12):2609-2617. doi: 10.1377/hlthaff.2012.0179 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Park S, White L, Fishman P, Larson EB, Coe NB. Health care utilization, care satisfaction, and health status for Medicare Advantage and traditional Medicare beneficiaries With and without Alzheimer disease and related dementias. JAMA Netw Open. 2020;3(3):e201809. doi: 10.1001/jamanetworkopen.2020.1809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hung A, Stuart B, Harris I. The effect of Medicare Advantage enrollment on mammographic screening. Am J Manag Care. 2016;22(2):e53-e59. [PubMed] [Google Scholar]
- 16.Timbie JW, Bogart A, Damberg CL, et al. Medicare Advantage and fee-for-service performance on clinical quality and patient experience measures: comparisons from three large states. Health Serv Res. 2017;52(6):2038-2060. doi: 10.1111/1475-6773.12787 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Figueroa JF, Blumenthal DM, Feyman Y, et al. Differences in management of coronary artery disease in patients with Medicare Advantage vs traditional fee-for-service Medicare among cardiology practices. JAMA Cardiol. 2019;4(3):265-271. doi: 10.1001/jamacardio.2019.0007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ayanian JZ, Landon BE, Zaslavsky AM, Saunders RC, Pawlson LG, Newhouse JP. Medicare beneficiaries more likely to receive appropriate ambulatory services in HMOs than in traditional medicare. Health Aff (Millwood). 2013;32(7):1228-1235. doi: 10.1377/hlthaff.2012.0773 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Johnston KJ, Hammond G, Meyers DJ, Joynt Maddox KE. Association of race and ethnicity and Medicare program type with ambulatory care access and quality measures. JAMA. 2021;326(7):628-636. doi: 10.1001/jama.2021.10413 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Meyers DJ, Belanger E, Joyce N, McHugh J, Rahman M, Mor V. Analysis of drivers of disenrollment and plan switching among Medicare Advantage beneficiaries. JAMA Intern Med. 2019;179(4):524-532. doi: 10.1001/jamainternmed.2018.7639 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Rahman M, Keohane L, Trivedi AN, Mor V. High-cost patients had substantial rates of leaving Medicare Advantage And joining traditional Medicare. Health Aff (Millwood). 2015;34(10):1675-1681. doi: 10.1377/hlthaff.2015.0272 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Curto V, Einav L, Finkelstein A, Levin J, Bhattacharya J. Health care spending and utilization in public and private Medicare. Am Econ J Appl Econ. 2019;11(2):302-332. doi: 10.1257/app.20170295 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jacobson G, Cicchiello A, Sutton J, Shah A. Medicare Advantage vs. traditional Medicare: how do beneficiaries’ characteristics and experiences differ? The Commonwealth Fund. 2021. Accessed November 8, 2021. https://www.commonwealthfund.org/publications/issue-briefs/2021/oct/medicare-advantage-vs-traditional-medicare-beneficiaries-differ
- 24.Teigland C, Pulungan Z, Shah T, Schneider E, Bishop S. As it grows, Medicare Advantage is enrolling more low-income and medically complex beneficiaries. Commonwealth Foundation. 2020. Accessed November 9, 2021. https://www.commonwealthfund.org/publications/issue-briefs/2020/may/medicare-advantage-enrolling-low-income-medically-complex
- 25.Centers for Medicare and Medicaid Services . Special Needs Plans. Accessed November 6, 2021. https://www.cms.gov/Medicare/Health-Plans/SpecialNeedsPlans
- 26.Medicare Payment Advisory Comission. Promoting Integration in Dual-Eligible Special Needs Plans. 2019. Accessed November 6, 2021. https://www.medpac.gov/docs/default-source/reports/jun19_ch12_medpac_reporttocongress_sec.pdf?sfvrsn=0
- 27.Elmaleh-Sachs A, Schneider EC. Strange bedfellows: coordinating Medicare and Medicaid to achieve cost-effective care for patients with the greatest health needs. J Gen Intern Med. 2020;35(12):3671-3674. doi: 10.1007/s11606-020-05914-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Grabowski DC. Special needs plans and the coordination of benefits and services for dual eligibles. Health Aff (Millwood). 2009;28(1):136-146. doi: 10.1377/hlthaff.28.1.136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Keohane LM, Zhou Z, Stevenson DG. Aligning Medicaid and Medicare Advantage managed care plans for dual-eligible beneficiaries. Med Care Res Rev. Published online June 2, 2021. doi: 10.1177/10775587211018938 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Vandenbroucke JP, von Elm E, Altman DG, et al. ; STROBE Initiative . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297. doi: 10.1371/journal.pmed.0040297 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Centers for Medicare and Medicaid Services . Medicare Current Beneficiary Survey Methodology Report. 2018. Accessed November 10, 2021. https://www.cms.gov/research-statistics-data-and-systemsresearchmcbscodebooks/2018-mcbs-methodology-report
- 32.Centers for Medicare and Medicaid Services . Special Needs Plan Data. Last modified November 1, 2021. Accessed November 6, 2021. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/Special-Needs-Plan-SNP-Data
- 33.Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139(25). doi: 10.1161/CIR.0000000000000625 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Centers for Disease Control and Prevention . Who needs a flu vaccine and when. 2021. Accessed June 7, 2021. https://www.cdc.gov/flu/prevent/vaccinations.htm
- 35.Davidson KW, Barry MJ, Mangione CM, et al. ; US Preventive Services Task Force . Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi: 10.1001/jama.2021.6238 [DOI] [PubMed] [Google Scholar]
- 36.Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D. The National Institute on Minority Health and Health Disparities research framework. Am J Public Health. 2019;109(S1):S16-S20. doi: 10.2105/AJPH.2018.304883 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Centers for Medicare and Medicaid Services . MA State/County Penetration. Accessed December 31, 2020. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-County-Penetration
- 38.U.S. Department of Agriculture . Rural-Urban Commuting Area Codes. Last modified December 1, 2021. Accessed March 2, 2021. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx
- 39.Goldberg EM, Trivedi AN, Mor V, Jung HY, Rahman M. Favorable risk selection in Medicare Advantage: trends in mortality and plan exits among nursing home beneficiaries. Med Care Res Rev. 2017;74(6):736-749. doi: 10.1177/1077558716662565 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Agarwal R, Connolly J, Gupta S, Navathe AS. Comparing Medicare Advantage and traditional Medicare: a systematic review. Health Aff (Millwood). 2021;40(6):937-944. doi: 10.1377/hlthaff.2020.02149 [DOI] [PubMed] [Google Scholar]
- 41.Kaiser Family Foundation . A dozen facts about Medicare Advantage in 2020. 2020. Accessed October 27, 2020. https://www.kff.org/medicare/issue-brief/a-dozen-facts-about-medicare-advantage-in-2020/#:~:text=1.,doubled%20over%20the%20past%20decade&text=In%202020%2C%20more%20than%20one,time%20since%20the%20early%202000s
- 42.Kronick R, Welch WP. Measuring coding intensity in the Medicare Advantage program. Medicare Medicaid Res Rev. 2014;4(2):mmrr2014.004.02.a06. doi: 10.5600/mmrr.004.02.sa06 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Feyman Y, Pizer SD, Frakt AB. The persistence of Medicare Advantage spillovers in the post-Affordable Care Act era. Health Econ. Published online November 21, 2020. doi: 10.1002/hec.4199 [DOI] [PubMed] [Google Scholar]
- 44.Meyers DJ, Trivedi AN, Mor V. Limited Medigap consumer protections are associated with higher reenrollment in Medicare Advantage plans. Health Aff (Millwood). 2019;38(5):782-787. doi: 10.1377/hlthaff.2018.05428 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Propensity Score Weight Adjustment
eTable 1. List of Covariates Used in Estimation of Propensity Score Adjustment
eTable 2. Hosmer-Lemeshow Goodness of Fit Test for Propensity Score
eFigure. Sample Selection Flowchart
eTable 3. Comparing Medicare Beneficiaries with Disabilities Included Versus Excluded From Study Sample, Ages 18-64, 2015-2018
eTable 4. Characteristics of Community-Dwelling Medicare Beneficiaries with Disability Entitlement Enrolled in Medicare Advantage Versus Traditional Medicare Insurance, Ages 18-64, 2015-2018. After Relaxing Continuous Enrollment Inclusion Criteria
eTable 5. Characteristics of Community-Dwelling Medicare Advantage Beneficiaries with Disability Entitlement by Special Needs Plan vs Non Special Needs Plan Enrollment, Ages 18-64, 2015-2018
eTable 6. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. With Dartmouth Hospital Referral Region Fixed Effects
eTable 7. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. After Relaxing Continuous Enrollment Inclusion Criteria
eTable 8. Association of Medicare Advantage vs. Traditional Medicare with Ambulatory Care Access and Quality for Beneficiaries with Disability Entitlement, 2015-2018. Testing for Heterogeneous Treatment Effects by Level of Functional Impairment