Faculty self-reported experience with racial and ethnic discrimination in academic medicine.

BACKGROUND: Despite the need to recruit and retain minority faculty in academic medicine, little is known about the experiences of minority faculty, in particular their self-reported experience of racial and ethnic discrimination at their institutions. OBJECTIVE: To determine the frequency of self-reported experience of racial/ethnic discrimination among faculty of U.S. medical schools, as well as associations with outcomes, such as career satisfaction, academic rank, and number of peer-reviewed publications. DESIGN: A 177-item self-administered mailed survey of U.S. medical school faculty. SETTING: Twenty-four randomly selected medical schools in the contiguous United States. PARTICIPANTS: A random sample of 1,979 full-time faculty, stratified by medical school, specialty, graduation cohort, and gender. MEASUREMENTS: Frequency of self-reported experiences of racial/ethnic bias and discrimination. RESULTS: The response rate was 60%. Of 1,833 faculty eligible, 82% were non-Hispanic white, 10% underrepresented minority (URM), and 8% non-underrepresented minority (NURM). URM and NURM faculty were substantially more likely than majority faculty to perceive racial/ethnic bias in their academic environment (odds ratio [OR], 5.4; P <.01 and OR, 2.6; P <.01, respectively). Nearly half (48%) of URM and 26% of NURM reported experiencing racial/ethnic discrimination by a superior or colleague. Faculty with such reported experiences had lower career satisfaction scores than other faculty (P <.01). However, they received comparable salaries, published comparable numbers of papers, and were similarly likely to have attained senior rank (full or associate professor). CONCLUSIONS: Many minority faculty report experiencing racial/ethnic bias in academic medicine and have lower career satisfaction than other faculty. Despite this, minority faculty who reported experiencing racial/ethnic discrimination achieved academic productivity similar to that of other faculty.

Using claims data to examine mortality trends following hospitalization for heart attack in Medicare.

OBJECTIVE: To see if changes in the demographics and illness burden of Medicare patients hospitalized for acute myocardial infarction (AMI) from 1995 through 1999 can explain an observed rise (from 32 percent to 34 percent) in one-year mortality over that period. DATA SOURCES: Utilization data from the Centers for Medicare and Medicaid Services (CMS) fee-for-service claims (MedPAR, Outpatient, and Carrier Standard Analytic Files); patient demographics and date of death from CMS Denominator and Vital Status files. For over 1.5 million AMI discharges in 1995-1999 we retain diagnoses from one year prior, and during, the case-defining admission. STUDY DESIGN: We fit logistic regression models to predict one-year mortality for the 1995 cases and apply them to 1996-1999 files. The CORE model uses age, sex, and original reason for Medicare entitlement to predict mortality. Three other models use the CORE variables plus morbidity indicators from well-known morbidity classification methods (Charlson, DCG, and AHRQ's CCS). Regressions were used as is--without pruning to eliminate clinical or statistical anomalies. Each model references the same diagnoses--those recorded during the pre- and index admission periods. We compare each model's ability to predict mortality and use each to calculate risk-adjusted mortality in 1996-1999. PRINCIPAL FINDINGS: The comprehensive morbidity classifications (DCG and CCS) led to more accurate predictions than the Charlson, which dominated the CORE model (validated C-statistics: 0.81, 0.82, 0.74, and 0.66, respectively). Using the CORE model for risk adjustment reduced, but did not eliminate, the mortality increase. In contrast, adjustment using any of the morbidity models produced essentially flat graphs. CONCLUSIONS: Prediction models based on claims-derived demographics and morbidity profiles can be extremely accurate. While one-year post-AMI mortality in Medicare may not be worsening, outcomes appear not to have continued to improve as they had in the prior decade. Rich morbidity information is available in claims data, especially when longitudinally tracked across multiple settings of care, and is important in setting performance targets and evaluating trends.