Evidence map›Paper›PMID 37356821›Full record

ArticleHealth services research2023

Assessing the validity of race and ethnicity coding in administrative Medicare data for reporting outcomes among Medicare advantage beneficiaries from 2015 to 2017.

Andrew W Huang, David J Meyers

Open access · bronzeAbstract read
In one paragraph

Article in Health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
18.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 33 citations in OpenAlex.

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  20. Fractures by race and ethnicity in a diverse sample of postmenopausal women: a current evaluation among Hispanic and Asian origin groups.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Andrew W HuangDepartment of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.ORCID 0000-0001-7552-7091
David J MeyersDepartment of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.ORCID 0000-0002-4081-1751
Brown University · US

Funding

Understanding the Relationship between Medicare Advantage Provider Network Diversity on Health DisparitiesR21MD016147 · NIMHD · BROWN UNIVERSITY · PI MEYERS, DAVID JOSEPH · 2022 to 2023
$429k
NIMHD NIH HHS R21 MD016147
6 · The paper itself

Abstract

objectiveTo assess the validity of race/ethnicity coding in Medicare data and whether misclassification errors lead to biased outcome reporting by race/ethnicity among Medicare Advantage beneficiaries. DATA SOURCES AND STUDY

settingIn this national study of Medicare Advantage beneficiaries, we analyzed individual-level data from the Health Outcomes Survey (HOS) and the Consumer Assessment of Healthcare Providers and Systems (CAHPS), race/ethnicity codes from the Medicare Master Beneficiary Summary File (MBSF), and outcomes from the Medicare Provider Analysis and Review (MedPAR) files from 2015 to 2017. STUDY

designWe used self-reported beneficiary race/ethnicity to validate the Medicare Enrollment Database (EDB) and Research Triangle Institute (RTI) race/ethnicity codes. We measured the sensitivity, specificity, and positive and negative predictive values of the Medicare EDB and RTI codes compared to self-report. For outcomes, we compared annualized hospital admission, 30-day, and 90-day readmission rates. DATA COLLECTION/EXTRACTION

methodsData for Medicare Advantage beneficiaries who completed either the HOS or CAHPS survey were linked to MBSF and MedPAR files. Validity was assessed for both self-reported multiracial and single-race beneficiaries. PRINCIPAL

findingsFor beneficiaries enrolled in Medicare Advantage, the EDB and RTI race/ethnicity codes have high validity for identifying non-Hispanic White or Black beneficiaries, but lower sensitivity for beneficiaries self-reported Hispanic any race (EDB: 28.3%, RTI: 85.9%) or non-Hispanic Asian American or Native Hawaiian Pacific Islander (EDB: 56.1%, RTI: 72.1%). Only 8.7% of beneficiaries self-reported non-Hispanic American Indian Alaska Native are correctly identified by either Medicare code, resulting in underreported annualized hospitalization rates (EDB: 31.5%, RTI: 31.6% vs. self-report: 34.6%). We find variation in 30-day readmission rates for Hispanic beneficiaries across race categories, which is not measured by Medicare race/ethnicity coding.

conclusionsCurrent Medicare race/ethnicity codes misclassify and bias outcomes for non-Hispanic AIAN beneficiaries, who are more likely to select multiple racial identities. Revisions to race/ethnicity categories are needed to better represent multiracial/ethnic identities among Medicare Advantage beneficiaries.

Indexed as

EthnicityMedicare Part CRacial GroupsAgedHumansUnited Statesethnicityhealth inequitiesMedicareminority healthracial groupsvalidation study

Identifiers

PMID37356821
PMCPMC10480088
OpenAlexW4381948547

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.