Impact of Race and Socioeconomics on Outcomes After Hospitalization Appears Systemic
Patients admitted for acute MI, heart failure, or pneumonia have different outcomes depending on race and income, but hospitals are not to blame.
Patients hospitalized for acute MI, heart failure, or pneumonia have different outcomes depending on their race and neighborhood income, but an analysis suggests the cause of those differences is not rooted at the hospital level.
“What we found is that the differences that exist among the hospitals are fairly consistent,” said senior author Harlan M. Krumholz, MD (Yale New Haven Hospital, CT). “Hospitals that tend to perform well for whites tend to perform well for blacks. Hospitals that perform well for high income performed the same for low income. So, these differences we observed seemed to be systemic in nature. They are part of our health system and our society, but they are not sequestered to specific hospitals.”
According to Krumholz, whatever is driving the differences appears to be the same everywhere, which came as a surprise because the assumption was that the hospitals taking care of disproportionate numbers of black and low-income patients would be the poorest performers across the board.
“It may reflect issues in our society that are somehow linking these factors—race and socioeconomic status—but these are nonclinical factors,” he told TCTMD. “In a perfect world, they shouldn’t be influencing how patients do, but they are.”
Black Patients Have Lower Mortality, Higher Readmission
For the study, published online September 7, 2018, in JAMA Network Open, Krumholz along with first author Nicholas S. Downing, MD (Brigham and Women’s Hospital, Boston, MA), looked at within-hospital and between-hospital differences in outcomes of 144,417 patients hospitalized for acute MI, 507,799 patients hospitalized for heart failure, and 335,659 patients hospitalized for pneumonia. Hospitals were categorized and analyzed according to region (Northeast, Midwest, South, or West), teaching status (nonteaching or teaching hospital), and number of beds (< 300, 300 to < 600, and > 600). The majority of US hospitals (between 74% and 91%) were not diverse enough in their patient populations to be included in the study. Those included were more likely to be large and to be teaching hospitals.
Compared with white patients, risk-standardized 30-day mortality rates among black patients were lower for acute MI and pneumonia, although the difference was statistically significant only for heart failure (P < 0.001). However, readmissions rates for all three conditions were significantly higher among black patients compared with white patients (P < 0.001 for all comparisons).
Overall, mean within-hospital differences in risk-standardized mortality and readmission for black versus white patients were small (from -4.7% to 4.3%). Despite some variations between hospitals in risk-standardized mortality rates, the proportion of black patients admitted to each hospital was not associated with the hospitals’ overall risk-standardized mortality rate, nor was the proportion of patients from lower-income neighborhoods. Neighborhood income had no statistically significant impact on readmission rates for any of the three conditions.
In sensitivity analyses, the researchers reported that within-hospital differences were “extremely consistent” with those in the original analysis, and that there was no evidence of meaningful between-hospital differences.
“A persistent question is whether factors extrinsic to the hospital are responsible for observed differences in the outcomes of patients by income,” the researchers write. “However, our study does not support the community explanation, namely, that extrinsic factors drive differences in outcomes, because we did not observe significant differences in outcomes between patients with different neighborhood income groups who were treated at the same hospital.” An alternative explanation, they add, is there may be patterns of hospital and postdischarge care that are similar across the healthcare system, perhaps reflecting institutional biases that exist throughout the country.
Digging for Answers
“We’ve got to begin to try to understand what it is that’s driving these differences, because it’s not a certain part of the country or a certain site, it’s more general than that,” Krumholz noted. “There are implicit biases in the way that people are treated, not just in terms of prescriptions or procedures, but the way we listen to people, the way we respond to people, and what we do in response to what we hear.”
Krumholz added that research into structural racism supports the idea that certain inherent biases in how people are treated can become “the norm,” even if it is not intentional.
“I think this study shows we have to dig down and find out what exactly is happening differently for these people,” he said. “This was a claims-based analysis, but I think we’re going to have to roll up our sleeves . . . and figure out what is it that happens in the hospital and in recovery that is different and how it is affecting outcomes.”
To TCTMD, Krumholz said one piece of the puzzle may be social determinants of health—those factors beyond the healthcare system that somehow influence outcomes in a way that has yet to be appreciated.
“Studies like this should stimulate us to ask what is really going on,” he added. “It’s a general phenomenon that has yet eluded explanation.”
Downing NS, Wang C, Gupta A, et al. Association of racial and socioeconomic disparities with outcomes among patients hospitalized with acute myocardial infarction, heart failure, and pneumonia: an analysis of within- and between-hospital variation. JAMA Network Open. 2018;1(5):e182044.
- Krumholz reports being a recipient of a research agreement from Johnson & Johnson and Medtronic, through Yale University, to develop methods of clinical trial data sharing; receiving research support through a grant from the US Food and Drug Administration (FDA) and Medtronic to develop methods for postmarket surveillance of medical devices; receiving grants from Medtronic, Johnson & Johnson, and the FDA; receiving contracts, through Yale, from the Centers for Medicare & Medicaid Services to develop performance measures that are publicly reported; receiving personal fees from UnitedHealthcare; receiving personal fees from IBMWatson Health; receiving personal fees from Element Science; receiving personal fees from Aetna; being the founder and owner of Hugo, a personal health information platform; serving as chair for a cardiac scientific advisory board for UnitedHealth; serving as a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; and serving as a participant/participant representative of the IBMWatson Health Life Sciences Advisory Board.