Table 3.
Impact of Patient Characteristics on Difference in Probability of Readmission between Participants Admitted to Hospitals with Higher vs. Lower Readmission Ratesa
| Model | Description | Probability of Readmissionb (%) | Difference in Probability of Readmissionb (%) (95% CI) |
Reduction in Difference from Previous Modelc (%) (95% CI) |
|
|---|---|---|---|---|---|
| Admitting Hospital in Lowest HWRR Quintile |
Admitting Hospital in Highest HWRR Quintile |
||||
| 1 | Unadjustedd | 14.53 | 20.39 | 5.86 (2.61, 9.21) | - |
| 2 | Variables used by CMS to adjust readmission ratese |
15.04 | 19.45 | 4.41 (1.19, 7.54) | −1.45 (−2.63, −0.48) |
| 3 | Model 2 + additional claims data on eligibility categories and diagnosesf |
15.74 | 19.24 | 3.50 (0.31, 6.67) | −0.91 (−1.78, −0.04) |
| 4 | Model 3 + additional clinical and social characteristics from the HRS g |
16.06 | 18.36 | 2.29 (−0.77, 5.31) | −1.21 (−2.07, −0.21) |
Abbreviations: Hospital Wide Readmission Rate (HWRR) measure, Health and Retirement Study (HRS), Center for Medicare and Medicaid Services (CMS), 95% confidence interval (95% CI).
From logistic regression estimates, we simulated probabilities of readmission and differences in readmission probabilities (see eMethods in Supplement for details). For each of the 4 models, we took 10,000 draws of model coefficients, assuming a multivariate normal distribution. For each draw of coefficients, we obtained the model prediction for each observation, alternately setting the highest and lowest HWRR quintile indicator to 1. Then for each draw, we calculated the mean predicted probability of readmission across observations under each of the two scenarios (HWRR quintile = highest vs. lowest). We calculated the absolute difference between these mean predicted probabilities under the two scenarios for each draw and then took the mean of these probabilities and absolute differences across draws and report these means in the table, along with 95% CIs derived from the 2.5th and 97.5th percentiles of the distribution across draws.
The average reduction and 95% confidence interval are estimated comparing each model to the one in the row above using bootstrap methods.
Model 1 adjusted for year fixed effects alone.
Model 2 includes age, sex, discharge diagnosis and 31 additional condition indicators included in the publicly reported HWRR measure.14
Model 3 includes all variables in model 2 as well as indicators for Medicaid eligibility, disability as the original reason for Medicare enrollment, end-stage renal disease, HCC score, and 26 CCW condition indicators.18
Model 4 includes all variables in model 3 as well as 24 social and clinical characteristics from the HRS (variables listed in Tables 1 and 2 that were not already present in model 3) and pre-specified interaction terms (see eMethods in Supplement for details).