Objective To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services’ (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. Data Sources/Study Setting Data from 284 627 respondents to the 2014 Medicare CAHPS survey. Study Design We compared performance (cross‐validated Pearson correlation of estimates and self‐reported race/ethnicity) for several alternative models predicting self‐reported race/ethnicity in cross‐sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. Data Collection/Extraction Methods We linked survey‐reported race/ethnicity to CMS administrative and US census data. Principal Findings MBISG 2.0 removed 25‐39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self‐reported race/ethnicity for these groups. Conclusions MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS’ public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.

A recently published study by the present authors (Aharoni et al., 2013) reported evidence that functional changes in the anterior cingulate cortex (ACC) within a sample of 96 criminal offenders who were engaged in a Go/No-Go impulse control task significantly predicted their rearrest following release from prison. In an extended analysis, we use discrimination and calibration techniques to test the accuracy of these predictions relative to more traditional models and their ability to generalize to new observations in both full and reduced models. Modest to strong discrimination and calibration accuracy were found, providing additional support for the utility of neurobiological measures in predicting rearrest.

Results were consistent with the hypothesis that patients in worse health weigh care coordination more heavily in global physician assessments than patients in better health. Emphasis on improving care coordination, especially for patients in poorer health, may improve patients' overall assessments of their providers. The study provides further evidence for the importance of care coordination experiences in the era of patient-centered care.

Since 2006, Medicare beneficiaries have been able to obtain prescription drug coverage through standalone prescription drug plans or their Medicare Advantage (MA) health plan, options exercised in 2015 by 72 percent of beneficiaries. Using data from community-dwelling Medicare beneficiaries older than age sixty-four in 700 plans surveyed from 2007 to 2014, we compared beneficiaries' assessments of Medicare prescription drug coverage when provided by standalone plans or integrated into an MA plan. Beneficiaries in standalone plans consistently reported less positive experiences with prescription drug plans (ease of getting medications, getting coverage information, and getting cost information) than their MA counterparts. Because MA plans are responsible for overall health care costs, they might have more integrated systems and greater incentives than standalone prescription drug plans to provide enrollees medications and information effectively, including, since 2010, quality bonus payments to these MA plans under provisions of the Affordable Care Act.

Background: Each year, about 10% of Medicare Advantage (MA) enrollees voluntarily switch to another MA contract, while another 2% voluntarily switch from MA to fee-for-service Medicare. Voluntary disenrollment from MA plans is related to beneficiaries’ negative experiences with their plan, disrupts the continuity of care, and conflicts with goals to reduce Medicare costs. Little is known about racial/ethnic disparities in voluntary disenrollment from MA plans. Objective: The objective of this study was to investigate differences in rates of voluntary disenrollment from MA plans by race/ethnicity. Subjects: A total of 116,770,319 beneficiaries enrolled in 736 MA plans in 2015. Methods: Differences in rates of disenrollment across racial/ethnic groups [Asian or Pacific Islander (API), Black, Hispanic, and White] were summarized using 4 types of logistic regression models: adjusted and unadjusted models estimating overall differences and adjusted and unadjusted models estimating within-plan differences. Unadjusted overall models included only racial/ethnic group probabilities as predictors. Adjusted overall models added age, sex, dual eligibility, disability, and state of residence as control variables. Between-plan differences were estimated by subtracting within-plan differences from overall differences. Results: Adjusted rates of disenrollment were significantly (P<0.001) higher for Hispanic (+1.2 percentage points), Black (+1.2 percentage points), and API beneficiaries (+2.4 percentage points) than for Whites. Within states, all 3 racial/ethnic minority groups tended to be concentrated in higher disenrollment plans. Within plans, API beneficiaries voluntarily disenrolled considerably more often than otherwise similar White beneficiaries. Conclusion: These findings suggest the need to address cost, information, and other factors that may create barriers to racial/ethnic minority beneficiaries’ enrollment in plans with lower overall disenrollment rates.