Study Underscores Role of Social Determinants in Readmission Rates

NYAPRS Note: An article in this morning’s Crain’s Health Pulse reported that hospitals that have been financially penalized for avoidable readmission’ are seeking to place more responsibility on social factors that they say are more predictive of those readmissions than the quality of their care. They are pointing to the results of a recent study (see below) that concluded that “socioeconomic factors, such as race, income, and payer status, also showed strong statistical significance in predicting readmissions.” Based on this finding, the hospital groups would like to see CMS revise the way it determines hospital readmission penalties.

Crain’s goes on to report that “in January, CMS announced a program that would provide funding to address the social determinants of health. The Accountable Health Communities initiative will fund 44 consortia of health care, higher education and governmental organizations, at $1 million to $4.5 million.”

The impact of the social determinants on population health will be a prominent topic at NYAPRS’ April 21-2 Annual Executive Seminar, “Beyond Survival to Success,” both in the opening keynote by Dr. Ron Manderscheid and the follow up plenary that will feature the Center for Social Innovation’s Jeff Olivet and Lenox Hill Hospital’s Dr. Ruth Shim. Reduced rates at the Albany Hilton will lend this Friday so register today! For program and registration details, please go to


Patient Factors Predictive of Hospital Readmissions Within 30 Days

Kroch, Eugene; Duan, Michael; Martin, John; Bankowitz, Richard A.

Journal For Healthcare Quality: March/April 2016 - Volume 38 - Issue 2 - p 106–115

Background: Under the Affordable Care Act, the Congress has mandated that the Centers for Medicare and Medicaid Services reduce payments to hospitals subject to their Inpatient Prospective Payment System that exhibits excess readmissions. Using hospital-coded discharge abstracts, we constructed a readmission measure that accounts for cross-hospital variation that enables hospitals to monitor their entire inpatient populations and evaluate their readmission rates relative to national benchmarks.

Methods: Multivariate logistic regressions are applied to determine which patient factors increase the odds of a readmission within 30 days and by how much. This study uses deidentified discharge abstract data from a database of approximately 15 million inpatient discharges representing 611 acute care hospitals from Premier healthcare alliance over a 2-year period (2008q4–2010q3). The hospitals are geographically diverse and represent large urban academic centers and small rural community hospitals.

Results: This study demonstrates that meaningful risk-adjusted readmission rates can be tracked in a dynamic database. The clinical conditions responsible for the index admission were the strongest predictive factor of readmissions, but factors such as age and accompanying comorbid conditions were also important. Socioeconomic factors, such as race, income, and payer status, also showed strong statistical significance in predicting readmissions.

Conclusions: Payment models that are based on stratified comparisons might result in a more equitable payment system while at the same time providing transparency regarding disparities based on these factors. No model, yet available, discriminates potentially modifiable readmissions from those not subject to intervention highlighting the fact that the optimum readmission rate for any given condition is yet to be identified