Neighborhood socioeconomic status and 30-day readmission amoung heart failure patients


Dr. Randi Foraker, College of Public Health, Epidemiology (no longer at OSU)
Rank at time of award: Assistant Professor 


The objectives of the proposed project are to 1) Determine the effect of neighborhood socioeconomic  status (nSES) on 30-day readmission among heart failure (HF) patients, and 2) Identify additional patient-level characteristics in the context of nSES which predict 30-day readmission among HF patients.Our central hvpothesis is that patients with an index admission for HF who are living in areas of lower nSES are at a higher risk of being readmitted within 30 days compared to patients living in areas of higher nSES, even with consideration of patient-level characteristics.The rationale  for the proposed research is that if low nSES does increase readmission risk, strategies can be developed to improve the care of HF patients living in underserved neighborhoods, resulting in fewer readmissions. Our long-term goal is to decrease the burden of readmission on both HF patients and hospitals.


Ending Synopsis

Penalties for exceeding a risk-standardized threshold in 30-day readmissions have prompted hospitals to investigate predictors of readmission in conditions such as heart failure. However, different modeling approaches have produced discrepant results, in magnitude and sometimes in direction, even when the same predictors of readmission are incorporated. Given these discrepancies, our goal was to compare modeling approaches in order to provide guidance to investigators and policy-makers in interpreting previous results and conducting future studies. We considered five non-administrative censoring scenarios under four settings of readmission times, and simulated exponential readmission times and censoring times for each case. We fit three models to the generated data: two logistic regression models with different exclusion criteria, and one Cox proportional hazards regression model. The Cox model produced stable estimates of the hazard ratios that appropriately reflected the differences in readmission between levels of exposure, whereas the logistic models produced highly variable estimates of the odds ratios driven by type and degree of censoring as well as by choice of exclusion criteria. We therefore recommend the use of Cox proportional hazards regression in future studies of readmission to avoid substantial issues using logistic regression in these settings.