City Research Online

Abstract

In-hospital mortality and length of stay are fundamental metrics for evaluating healthcare quality, patient outcomes and resource utilization. While length of stay reflects hospital efficiency and capacity management, mortality provides insights into patient safety and the effectiveness of clinical interventions. These outcomes are interdependent, and demographic, clinical and laboratory factors simultaneously influence both hospitalization duration and mortality. To address this, a copula additive distributional regression framework is employed, enabling the joint modeling of these hospital metrics as functions of covariate effects. Application to COVID-19 data demonstrates that key predictors, including age, oxygenation and inflammation markers, modulate the dependence between mortality and hospitalization duration. The joint modeling approach provides a probabilistic, patient-level characterization of the interplay between these indicators, supporting risk stratification, resource planning and actionable
clinical decision-making.

Publication Type:	Article
Additional Information:	This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of the Royal Statistical Society, Series A following peer review. The version of record will be available online at: academic.oup.com/jrsssa
Publisher Keywords:	additive predictor, copula regression, COVID-19, discharge, hospital length of stay, mortality, joint distribution, patient-level risk
Subjects:	H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QR Microbiology > QR180 Immunology R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments:	Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

Export

Downloads