Imputing Race and Ethnicity

Lines, Lisa; Humphrey, Jamie L.; Barch, Daniel H.

doi:10.1097/mlr.0000000000001717

Cited by 5 publications

(10 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, CMS should regularly audit the classification performance of their race/ethnicity imputation algorithms and implement processes to validate against self‐reported race/ethnicity. While the performance of the RTI imputation algorithm may have been sufficient when first developed, the current process of using imputation combined with administrative EDB data may no longer be sufficient 14,31 . Recent advances in race/ethnicity imputation algorithms, including the Medicare Bayesian Improved Surname Geocoding (MBISG), combine Medicare administrative records with additional data elements to improve classification performance, but a race/ethnicity code derived from this algorithm is not included in datasets available to researchers, and early versions of the algorithm also performed poorly for identifying AIAN and multiracial beneficiaries, which underscores the need for continuous monitoring, validation, and improvement over time 32–34 …”

Section: Discussionmentioning

confidence: 99%

“…While the performance of the RTI imputation algorithm may have been sufficient when first developed, the current process of using imputation combined with administrative EDB data may no longer be sufficient. 14,31 Recent advances in race/ ethnicity imputation algorithms, including the Medicare Bayesian Improved Surname Geocoding (MBISG), combine Medicare administrative records with additional data elements to improve classification performance, but a race/ethnicity code derived from this algorithm is not included in datasets available to researchers, and early versions of the algorithm also performed poorly for identifying AIAN and multiracial beneficiaries, which underscores the need for continuous monitoring, validation, and improvement over time. [32][33][34] When we compare beneficiaries who are correctly or incorrectly identified by Medicare race/ethnicity codes, we find no differences in age, sex, or reasons for Medicare enrollment, but we do observe differences in geographic location and Medicare-Medicaid dual eligibility (eTable 7, eTable 8).…”

Section: Implications For Health Services Research and Policymentioning

confidence: 99%

“…If the RTI race code disproportionately misclassifies beneficiaries from certain race and ethnicity groups, there is risk of additional algorithmic biases being introduced when reporting any outcomes of interest stratified by predicted race/ethnicity. 14,15 Instead, the use of self-reported race and ethnicity should be considered the gold-standard for identifying beneficiary race and ethnicity. 16,17 Recent studies have assessed the validity of EDB and RTI race/ ethnicity coding in Medicare data using 2015 home health 18 and 2018-2020 nursing home 19 assessments; however, the populations studied are specific to beneficiaries receiving postacute care, and it is unclear how consistently beneficiary race and ethnicity is collected during assessment visits compared to self-report.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, any misclassification of race and ethnicity by the RTI imputation algorithm could be representative of error due to both response bias and prediction model bias. If the RTI race code disproportionately misclassifies beneficiaries from certain race and ethnicity groups, there is risk of additional algorithmic biases being introduced when reporting any outcomes of interest stratified by predicted race/ethnicity 14,15 . Instead, the use of self‐reported race and ethnicity should be considered the gold‐standard for identifying beneficiary race and ethnicity 16,17…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Huang

Meyers

2023

Health Services Research

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Implications For Health Services Research and Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Huang

Meyers

2023

Health Services Research

View full text Add to dashboard Cite

show abstract

“…We also compared disenrollment over time by race and ethnicity using the RTI International race code available in the MBSF. 18 The RTI International code is based on race and ethnicity information taken from Social Security information and updated based on an algorithm to improve the reporting of race and ethnicity and more accurately capture Hispanic ethnicity. The RTI International variable includes categories for American Indian/Alaska Native, Asian, Black, Hispanic, White, and other, with other including beneficiaries who do not fall into the included categories.…”

Section: Covariatesmentioning

confidence: 99%

Trends in Cumulative Disenrollment in the Medicare Advantage Program, 2011-2020

Meyers,

Ryan,

Trivedi

2023

JAMA Health Forum

View full text Add to dashboard Cite

ImportanceThe Medicare Advantage (MA) program is rapidly growing. While previous work has found that beneficiaries with substantial health needs disenroll from plans at higher rates, the long-term frequency of disenrollment is not well understood.ObjectiveTo compare cumulative disenrollment trends in the MA program by beneficiary and plan characteristics.Design, Setting, and ParticipantsThis retrospective, serial cross-sectional study included beneficiaries with any MA enrollment from January 1, 2011, to December 31, 2020. Data analysis took place from September 2022 to March 2023.ExposuresBeneficiary characteristics, including race and ethnicity, length of Medicare enrollment, dual eligibility, and comorbidity burden, and contract characteristics, including vertical integration status, premium, and MA star rating.Main Outcomes and MeasuresThe main outcome was disenrollment from an MA contract within 5 years. Rates of cumulative disenrollment by beneficiary and contract characteristics were compared. Pearson correlation coefficients were calculated to assess the correlation between a contract’s 1-year disenrollment and the contract’s disenrollment over a longer period.ResultsThe sample included 82 377 917 beneficiaries (524 442 225 beneficiary-year observations; 56.7% female; mean [SD] age, 71.9 [10.3] years). After 1 year, 13.2% of nondually enrolled and 15.9% of dually enrolled beneficiaries had left their contract, increasing to 48.3% and 53.4%, respectively, after 5 years. Black enrollees disenrolled at the highest rates among race and ethnicity categories, with 14.8% disenrolling after 1 year and 52.6% disenrolling after 5 years. Contracts had a median disenrollment rate of 9.8% (IQR, 4.5%-19.0%) after 1 year and 56.1% (IQR, 23.1%-79.0%) after 5 years. Contracts rated 5 stars had substantially lower 5-year disenrollment rates (23.0% after 5 years compared with 41.2% for 4- to 4.5-star contracts and 67.2% for 3- to 3.5-star contracts). Disenrollment from a contract after 1 year was not well correlated with disenrollment after 5 years (r, 0.46).Conclusions and RelevanceThis cross-sectional study found substantial cumulative rates of disenrollment from MA plans within 5 years between 2011 and 2020, with wide variation in 5-year disenrollment by contract. The findings suggest that evaluating long-term disenrollment rates in MA performance measures may capture different outcomes than single-year disenrollment alone.

show abstract

Racialized Inequities in Knee Arthroplasty Receipt After Osteoarthritis Diagnosis in the US Military Health System

Sowa,

Patzkowski,

Ismawan

et al. 2024

Arthritis Care & Research

View full text Add to dashboard Cite

ObjectiveTo evaluate institutional inequities in the US Military Health System in knee arthroplasty receipt within three years of knee osteoarthritis diagnosis when accounting for other treatments received (e.g., physical therapy, medications).MethodsIn this retrospective observational cohort study, medical record data of patients (N =29,734) who received a primary osteoarthritis diagnosis in the US Military Health System between January 2016 to January 2020 were analyzed. Data included receipt of physical therapy 1‐year pre‐diagnosis and up to 3‐years post‐diagnosis, pre‐diagnosis opioid and non‐opioid prescription receipt, health‐related factors associated with levels of racism, and the primary outcome, knee arthroplasty receipt within 3‐years post‐diagnosis.ResultsIn a generalized additive model with time‐varying covariates, Asian and Pacific Islander (Incidence Rate Ratio (IRR) 0.58; 95% CI 0.45, 0.74), Black (IRR 0.52; 95%CI 0.46, 0.59), and Latinx (IRR 0.66; 95%CI 0.52, 0.85) patients experienced racialized inequities in knee arthroplasty receipt, relative to white patients (all p < 0.001).ConclusionsIn the present sample, Asian and Pacific Islander, Black, and Latinx patients were significantly less likely to receive a knee arthroplasty, relative to white patients. Taken together, system‐level resources are needed to identify and address mechanisms underlying institutional inequities in knee arthroplasty receipt, such as factors related to systemic and structural, institutional, and personally‐mediated racism.

show abstract

Imputing Race and Ethnicity

Cited by 5 publications

References 32 publications

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

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

Trends in Cumulative Disenrollment in the Medicare Advantage Program, 2011-2020

Racialized Inequities in Knee Arthroplasty Receipt After Osteoarthritis Diagnosis in the US Military Health System

Contact Info

Product

Resources

About