Work Description

Title: Racial and Ethnic Disparities in Satisfaction with Healthcare Access and Affordability Data Set Open Access Deposited

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Methodology
Description
  • We analyzed satisfaction with care, out-of-pocket costs, and specialist access among community-dwelling Medicare Current Beneficiary Survey respondents, 2015–2019, in the 50 states and Washington, DC. For each measure, we constructed a binary indicator indicating very satisfied (vs. very dissatisfied to satisfied).

  • We used logistic regression to model outcomes as a function of Medicare Advantage - MA (vs. Traditional Medicare - TM) enrollment, respondent-reported race/ethnicity, and interactions of MA with race/ethnicity. Race/ethnicity was categorized as non-Hispanic Black, Hispanic, and non-Hispanic White. We adjusted for age, sex, education, income, tobacco use, chronic conditions, functional limitations, disability, and geographic factors. Racial/ethnic disparities reflect effects of structural factors that systematically disadvantage members of minoritized racial/ethnic groups. Because structural racism contributes to disparities in socioeconomic status (including income and education), we verified that our estimates did not change appreciably when we did not adjust for socioeconomic factors.

  • Analyses were weighted by a composite of survey weights and propensity score weights to balance MA and TM populations within racial/ethnic groups. Separate analyses were conducted for beneficiaries with vs. without dual eligibility for full Medicaid. We used SAS to process the data.
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Contact information
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Funding agency
  • National Institutes of Health (NIH)
ORSP grant number
  • R01AG076437
Keyword
Citations to related material
  • Roberts ET, Ruggiero DA, Stefanesu A, Patel S, Hames AG, Tipirneni R. Racial and Ethnic Disparities in Satisfaction with Healthcare Access and Affordability in Medicare Advantage vs. Traditional Medicare. Journal of general internal medicine. 2024 September;39(12):2368-2371. PubMed PMID: 38926325; PubMed Central PMCID: PMC11347532; DOI: 10.1007/s11606-024- 08892-7.
Resource type
Last modified
  • 04/25/2025
Published
  • 04/25/2025
DOI
  • https://doi.org/10.7302/jrpq-sv90
License
To Cite this Work:
Roberts, E., Ruggiero, D., Stefanesu, A., Patel, S., Hames, A., Tipirneni, R. (2025). Racial and Ethnic Disparities in Satisfaction with Healthcare Access and Affordability Data Set [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/jrpq-sv90

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Files (Count: 5; Size: 164 KB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
Readme.txt 2025-04-25 2025-04-25 1.85 KB Open Access
1_AHRF_setup_for_phys_per_1000.sas 2025-04-21 2025-04-21 1.69 KB Open Access
2_mcbs_import_2016_2019_final.sas 2025-04-21 2025-04-21 28.6 KB Open Access
3_Cost_and_access_models_final.sas 2025-04-21 2025-04-21 4.3 KB Open Access
aspe_fed_pov.sas7bdat 2025-04-21 2025-04-21 128 KB Open Access

Code documentation for “Racial and Ethnic Disparities in Satisfaction with Healthcare Access and Affordability in Medicare Advantage (MA) vs. Traditional Medicare (TM)”

Last Updated 04-18-2025 by Dominic Ruggiero, University of Pennsylvania
([email protected])

This series of programs is designed to produce analyses satisfaction and access to care among Medicare beneficiaries. The code here makes use of the following input files:

1. 2016-2019 MCBS Survey Modules: DEMO, HISUMRY, WEIGHTS, RXMED, CHRNCOND, SATWCARE, HITLINE, HHCHAR, MAPLANQX, ACCESSCR, NICOALCO, NAGIDIS, (available from http://cms.gov/data-research/research/medicare-current-beneficiary-survey).

2. 2021-2022 AHRF (Area Health Resources Files) File (available from https://data.hrsa.gov/data/download)

3. ASPE Federal Poverty Guidelines (outlined below)

The files included here are intended to be used in addition to those listed above, and are as follows:

File Number File Name Functions
1 1_AHRF_setup_for_phys_per_1000.sas Calculate county-year level count of physicians per 1000 county residents

2 2_MCBS_import_2016_2019_final.sas Read in raw datasets from MCBS survey modules
Import external files for area covariates and poverty level
Process and recategorize survey variables for final analytic variables
Create propensity score weights to standardize across MA and TM
Calculate weighted and unweighted descriptive statistics

3 3_Cost_and_access_models_final.sas Specify covariates and matrices for running logistic regression models and calculating disparities +differences in disparities
Execute regression and disparity estimates for each of the desired binary outcomes

4 Aspe_fed_pov.sas7bdat Contains federal poverty level data, with values for Continental US, Alaska, and Hawaii by household size and year.

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