Risk Factor Analysis of Hospital Readmissions at St. Petersburg General Hospital.

Background: Decreased readmission rates are largely seen as an indicator of effective care and improved resource management. The case management team at St. Petersburg General Hospital in St. Petersburg, Florida identified chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and sepsis as 3 of the leading diagnoses on index admission that later led to 30-day readmissions. By examining patients with these 3 diagnoses on index admission, we decided to investigate potential readmission risk factors including patient age, sex, race, body mass index (BMI), length of stay during the index admission, insurance type during index admission, discharge placement after index admission, coronary artery disease, heart failure, and type 2 diabetes. Methods: We conducted a retrospective study with data from 4180 patients at St. Petersburg General Hospital from 2016 through 2019 with index admission diagnoses of COPD exacerbation, pneumonia, and sepsis. A univariate analysis was conducted on patient sex, race, BMI, length of stay during the index admission, health insurance type during the index admission, discharge placement after the index admission, presence of coronary artery disease, presence of heart failure, and presence of type 2 diabetes. Subsequently, a bivariate analysis was run on these variables in relation to 30-day readmissions. Then a multivariable analysis was completed using binary logistic regression and pairwise analysis to determine the significance between variables within the categories of discharge disposition and insurance type. Results: Of the 4180 patients included in this study, 926 (22.2%) were readmitted within 30 days of discharge. In the bivariate analysis race, BMI, mean length of stay during the index admission, coronary artery disease, heart failure, and type 2 diabetes were not significantly associated with readmission. The bivariate analysis revealed that patients discharged to skilled nursing facilities had the highest readmission rates (28%), followed by home care (26%) (P = .001). Medicaid patients (24%) and Medicare patients (23%) demonstrated higher read-mission rates than those with private insurance (17%) (P = .001). Readmitted patients were slightly younger (62.14 vs. 63.69 years; P = .02) in the bivariate analysis. However, in the multi-variable analysis, only patients with type 2 diabetes and patients with non-private insurance were associated with increased readmission rates. Pairwise analysis of the variables within insurance and discharge disposition categories demonstrates decreased readmission for individuals with Private/Other when compared to other insurance subtypes and decreased readmission for Other when compared to discharge disposition subtypes. Conclusion: Our data demonstrate that hospital readmissions are associated with a diagnosis of type 2 diabetes and having a non-private insurance status. Our findings lead us to suggest further investigation into changes in hospital policies and procedures for these groups that will aim to decrease readmission rates in the future.

Mechanistic Models of Conflict between Ant Colonies and Their Consequences for Territory Scaling.

Territory size in social insects depends on the rules by which border conflicts are resolved. We present three mechanistic mathematical models of conflict, inspired by the behavior of the pavement ant Tetramorium immigrans, to predict the advantage of larger colonies in pairwise contests and the resulting scaling of territory size with worker force. The models track the number of ants in the nest traveling to and from the boundary or engaged at the boundary. Ants at the boundary base their recruitment response on the relative numbers of ants from the two colonies. With two colonies, our central result is that the larger colony gains a territory disproportionately larger than the ratio of worker forces would indicate. This disproportionate territory control determines the scaling relation of territory size with worker force in a population. In two dimensions, if territory size were proportional to worker force, the slope of the scaling relation between log territory size and log worker force would be 1.0. With disproportionate territories, this slope is larger and can be explicitly approximated in terms of model parameters, and it is steepest when colonies are packed close to each other, when ants run quickly, or when colonies are small. A steeper slope exaggerates the advantage of larger colonies, creating a positive feedback that could amplify the inequality of the worker force distribution.