Identifying predictors of frequency of 1-year readmission in adult patients with diabetes using count data regression models
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Saw Swee Hock School of Public Health, National University of Singapore, Singapore
Respiratory and Critical Care Medicine, National University Hospital, Singapore
Medical Affairs - Clinical Governance, National University Hospital, Singapore
Publication date: 2023-04-27
Popul. Med. 2023;5(Supplement):A1840
Background: Diabetes mellitus is the third most common chronic condition associated with frequent hospital readmission. Predictors of the number of readmissions within one year among patients with diabetes are less often studied compared with those of 30-day readmission. Objective: This study Aims to identify predictors of number of readmissions within one year amongst adult patients with diabetes and compare different count regression models with respect to model fit. Research methods: Data from 2008-2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Inpatients aged ≥18 years at the time of index admission with a hospital stay >24 hours who survived until discharge were included. The zero-inflated negative binomial (ZINB) model was fitted and compared with three other count models (Poisson, zero-inflated Poisson and negative binomial) in terms of predicted probabilities, misclassification proportions and model fit. Results: Adjusted for other variables in the model, the odds ratio for expected number of readmissions was 1.42 (95% confidence interval [CI] 1.07 to 1.90) for peripheral vascular disease, 1.60 (95% CI 1.34 to 1.92) for renal disease and 2.37 (95% CI 1.67 to 3.35) for Singapore residency. Other predictors included number of emergency visits, number of drugs and age, with length of stay fitted as a zero-inflated component. Model comparisons suggested that ZINB provides better prediction than the other three count models. Conclusions: The ZINB model outperformed other count regression models but should be validated in prospective studies before clinical adoption.