Repeated hospital admissions constitute a large proportion of healthcare expenses, but are incurred by a small minority of chronically ill patients. Rising healthcare expenditures and the link between readmissions and quality of care make readmission rates a high priority for clinicians as well as insurance payers. Though hospital readmissions have many components, one of the relationships which is still inconclusive is that between socioeconomic status and hospital readmission rates.
Investigating the conditions which have a substantial impact on the rate of hospital readmissions, heart failure stands out as it is the leading cause of death in the United States. This condition disproportionately affects older patients due to the progressive nature of the disease. Assessing differences in sociodemographic characteristics between two groups of heart failure patients, readmitted versus non-readmitted, to determine the factors which are most influential in predicting a readmission was the aim of this study.
A case control study design was used to examine the relationship between indicators of socioeconomic status and the likelihood of a hospital readmission in Medicare patients with heart failure. Hospital administrative data from Michigan hospital inpatient databases was linked to data from the U.S. Census Bureau’s American Community Survey.
Two different statistical models were utilized: a binomial model and a multivariate linear regression model. Each of these models presented different variables and allowed for comparison between the models to evaluate fit and relevance to study population. Binomial model was chosen as the best fit, due to violation of normality assumptions in the multivariate linear model. By linking indicators of socioeconomic status to rates of heart failure readmissions, this study was able to determine which socioeconomic factors most strongly correlated with the outcome of interest. Assessing the variables included in the final models, it can suggest the target areas in order to most effectively reduce heart failure related hospital readmissions. The final models which were created are listed below:
Linear - Heart Failure Readmissions = 1.245 + (Wh_Cancer.age.45.64. x -3.85e-3) + (Age_65_84 x -3.78e-3) + (Average.Life.Expectancy x -1.12e-2) + (Asian x 1.93e-2) + (Prim_Care_Phys_Rate x -3.43e-4) + (Uninsured x 1.63e-6) + (Pneumo_Vax x -5.38e-4) + (HepA_Rpt x -1.72e-3).
Binomial - Heart Failure Readmissions = 2.09 + (Average.Life.Expectancy x -4.46e-2) + (Asian x 5.90e-2) + (Prim_Care_Phys_Rate x -7.31e-4) + (Disabled_Medicare x 2.98e-6) + (Hispanic x -1.03e-2).
There were three variables which were seen in both final models - Asian ethnicity, average life expectancy, and rate of visits to a primary care provider. Some of the factors which point to increased likelihood of readmission are factors which the patient cannot control, however there are some that are controllable, which will be the target for focused interventions as a result of the study
