Inequities in Patient Access to Care Among Asian American, Native Hawaiian, and Pacific Islander Adults in Medicaid.

BACKGROUND: Despite decades of advocacy for disaggregated data collection and reporting for Asian American, Native Hawaiian, and Pacific Islander (AA and NHPI) people, significant gaps remain in our ability to understand AA and NHPI individuals' access to care. We assess inequities in access to care measures between non-Hispanic White and AA and NHPI adult Medicaid enrollees. METHODS: We used the 2014-15 Nationwide Adult Medicaid Consumer Assessment of Healthcare Providers and Systems, the first-and-only nationally representative sample of Medicaid enrollees. Our main outcomes were access to needed care, access to a personal doctor, timely access to a checkup, and timely access to specialty care. Using multivariable linear probability models, we assessed the relationship between racial/ethnic group and our outcomes, both in the aggregate and disaggregated into ten racial/ethnic groups, and adjusted for enrollee-level sociodemographic characteristics, health status, and state-level Medicaid expansion status. RESULTS: In aggregate, AA and NHPI enrollees reported worse access to care than White enrollees on all four metrics (p < 0.001). The magnitude of disparities varied across the ten AA and NHPI ethnic groups. Disparities relative to White enrollees were particularly large in magnitude, roughly 1.5 to 2 times greater, for Chinese, Korean, and Vietnamese enrollees than for the aggregated AA and NHPI group. CONCLUSIONS: Despite comparable insurance coverage, there were inequities in multiple access to care metrics between non-Hispanic White and AA and NHPI Medicaid enrollees. Collection of disaggregated health data on AA and NHPI patients reveals important variation in access to care by ethnic group.

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

OBJECTIVE: To 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 SETTING: In 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 DESIGN: We 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 METHODS: Data 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 FINDINGS: For 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. CONCLUSIONS: Current 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.

Infections, Hospitalizations, and Deaths Among US Nursing Home Residents With vs Without a SARS-CoV-2 Vaccine Booster.

Importance: A SARS-CoV-2 vaccine booster dose has been recommended for all nursing home residents. However, data on the effectiveness of an mRNA vaccine booster in preventing infection, hospitalization, and death in this vulnerable population are lacking. Objective: To evaluate the association between receipt of a SARS-CoV-2 mRNA vaccine booster and prevention of infection, hospitalization, or death among nursing home residents. Design, Setting, and Participants: This cohort study emulated sequentially nested target trials for vaccination using data from 2 large multistate US nursing home systems: Genesis HealthCare, a community nursing home operator (system 1) and Veterans Health Administration community living centers (VHA CLCs; system 2). The cohort included long-term (≥100 days) nursing home residents (10 949 residents from 202 community nursing homes and 4321 residents from 128 VHA CLCs) who completed a 2-dose series of an mRNA vaccine (either BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) and were eligible for a booster dose between September 22 and November 30, 2021. Residents were followed up until March 8, 2022. Exposures: Receipt of a third mRNA vaccine dose, defined as a booster dose (boosted group), or nonreceipt of a booster dose (unboosted group) on an eligible target trial date. If participants in the unboosted group received a booster dose on a later target trial date, they were included in the booster group for that target trial; thus, participants could be included in both the boosted and unboosted groups. Main Outcomes and Measures: Test-confirmed SARS-CoV-2 infection, hospitalization, or death was followed up to 12 weeks after booster vaccination. The primary measure of estimated vaccine effectiveness was the ratio of cumulative incidences in the boosted group vs the unboosted group at week 12, adjusted with inverse probability weights for treatment and censoring. Results: System 1 included 202 community nursing homes; among 8332 boosted residents (5325 [63.9%] female; 6685 [80.2%] White) vs 10 886 unboosted residents (6865 [63.1%] female; 8651 [79.5%] White), the median age was 78 (IQR, 68-87) years vs 78 (IQR, 68-86) years. System 2 included 128 VHA CLCs; among 3289 boosted residents (3157 [96.0%] male; 1950 [59.3%] White) vs 4317 unboosted residents (4151 [96.2%] male; 2434 [56.4%] White), the median age was 74 (IQR, 70-80) vs 74 (IQR, 69-80) years. Booster vaccination was associated with reductions in SARS-CoV-2 infections of 37.7% (95% CI, 25.4%-44.2%) in system 1 and 57.7% (95% CI, 43.5%-67.8%) in system 2. For hospitalization, reductions of 74.4% (95% CI, 44.6%-86.2%) in system 1 and 64.1% (95% CI, 41.3%-76.0%) in system 2 were observed. Estimated vaccine effectiveness for death associated with SARS-CoV-2 was 87.9% (95% CI, 75.9%-93.9%) in system 1; however, although a reduction in death was observed in system 2 (46.6%; 95% CI, -34.6% to 94.8%), this reduction was not statistically significant. A total of 45 SARS-CoV-2-associated deaths occurred in system 1 and 18 deaths occurred in system 2. For the combined end point of SARS-CoV-2-associated hospitalization or death, boosted residents in system 1 had an 80.3% (95% CI, 65.7%-88.5%) reduction, and boosted residents in system 2 had a 63.8% (95% CI, 41.4%-76.1%) reduction. Conclusions and Relevance: In this study, during a period in which both the Delta and Omicron variants were circulating, SARS-CoV-2 booster vaccination was associated with significant reductions in SARS-CoV-2 infections, hospitalizations, and the combined end point of hospitalization or death among residents of 2 US nursing home systems. These findings suggest that administration of vaccine boosters to nursing home residents may have an important role in preventing COVID-19-associated morbidity and mortality.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review.

BACKGROUND: With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending. METHODS: We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual's health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field. DISCUSSION: Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.