Income-Dependent Hospital Admissions Statistical Analysis

Nathan Hall, Siana Aspy, Tim Altmansberger

The discrepancies between medical facilities in low-income and high-income American communities can be striking. Health insurance is a majorly controversial political issue in the modern age, largely because of the very high price of medical care; This exorbitant price has a tendency to affect those who can least afford it, the same people that are unable to pay for insurance. Preventative care will ward off higher costs in the future, but it can be seen as an unnecessary expense by those who are in financial turmoil. Intuitively, then, a sample of people with lower income would be less likely to seek medical attention for a given problem until it becomes serious. It logically follows that, for a given hospital in a low-income area, the severity of cases would be, on average, higher than that found at a hospital in a high-income area.
In order to test the idea that low-income hospitals would see cases of a higher severity for a given ailment, we propose a hypothesis test on data for multiple admissions at a variety of hospitals. The number of times a patient is readmitted should correlate to an inability to pay for preventative care – maintaining overall health and well-being can get expensive! The null hypothesis in this study would take the following form:

H0 = Low-income hospitals display a similar readmission rate per patient than higher income hospitals for any given type of illness.

Accordingly, the Alternative Hypothesis would be as follows:

Ha= Low-income hospitals will display a significantly higher readmission rate per patient than higher income hospitals for any given type of illness.

(The terms “low-income hospitals” and “high-income hospitals” will, for the duration of the project, refer to hospitals with a low-income patient base and high-income patient base respectively. This may also be determined by the area in which the hospital is located if no patient salary information is available)

Sampling error may result from the types of cases being treated at the hospital in question. For instance, a cancer patient would be expected to undergo more return visits (thus producing a higher readmission rate) than a stomach virus patient. Thus, a hospital with a more state-of-the-art cancer facility would generally be more likely to have a higher readmission rate due to a flaw in sampling. This can occur for any hospital with a field of specialization, which may skew the data drastically. To avoid the possibility of sampling error, readmission rates will be grouped by type of ailment. The data will be segregated into categories (such as oncology, gastrointestinology, etc.) for a more representative comparison.

The data obtained on readmissions at different hospitals can be analyzed using various statistical techniques. Confidence intervals with varying significance levels can be constructed based on the mean readmission rate at high-income hospitals. If the mean readmission rate from low-income hospitals falls outside the confidence interval, the null may be rejected in favor of the alternative. In addition, because a larger percentage of Medicare and Medicaid funding goes to people with lower income, a linear regression could be used to find what correlation (if any) exists between Medicare/Medicaid spending and readmission rates to hospitals. Using these forms of statistical analysis, it can be determined whether or not there is a link between patient income and readmission rates to hospitals.