Multimorbidity Clusters and Secondary Bacterial Infections among Hospitalised COVID-19 Patients in Victoria, Australia: Insights from Population-Wide Linked Data
1
1 Barwon South West Public Health Unit, Barwon Health, Geelong, Australia
2
3 Centre for Innovation in Infectious Disease and Immunology Research, Deakin University, Geelong, Australia
3
2 Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Australia
Popul. Med. 2026;8(Supplement Supplement 1):A634
ABSTRACT
INTRODUCTION:
Limited evidence exists on how multimorbidity combinations influence the risk of secondary bacterial infections (SBI). This study identified multimorbidity clusters among patients hospitalised with COVID-19 in Victoria (2020–2023) examined their association, and assessed their impact on hospital outcomes, including ICU admission, hospital and ICU length of stay (LOS), and mortality to support antimicrobial prescription assessments.
METHODS:
We used population-wide linked hospital data and applied cluster analysis to ICD-10 coded chronic conditions to identify multimorbidity clusters. Risks of SBI were compared across clusters versus patients with one or no chronic conditions. We used multivariate logistic regression, negative binomial regression, Kaplan–Meier curves, and Cox proportional hazards models to analyse associations with SBI and hospital admission outcomes.
RESULTS:
Among 179,688 COVID-19 hospital admissions, three multimorbidity clusters were identified: neuropsychiatric, cardiorespiratory, and neoplastic. Each cluster was associated with significantly higher odds of SBI: neuropsychiatric (Odds Ratio (OR) 2.74, 95% CI 2.59–2.89), cardiorespiratory (OR 3.87, 95% CI 3.63–4.13), and neoplastic (OR 1.98, 95% CI 1.84–2.14; all p<0.001). For patients with SBI, cardiorespiratory multimorbidity had the highest increase in hospital LOS (Incident Rate Ratio (IRR) 1.94, 95% CI 1.82–2.05) and ICU LOS (IRR 2.75, 95% CI 2.36–3.20). Mortality was significantly elevated across all clusters and was highest in the cardiorespiratory group.
CONCLUSIONS:
Multimorbidity significantly increased the risk of SBI and worsens clinical outcomes in hospitalised COVID-19 patients. Integrating multimorbidity profiles into clinical decision-making may enhance antimicrobial stewardship by identifying patients most likely to benefit from antibiotic therapy.
Limited evidence exists on how multimorbidity combinations influence the risk of secondary bacterial infections (SBI). This study identified multimorbidity clusters among patients hospitalised with COVID-19 in Victoria (2020–2023) examined their association, and assessed their impact on hospital outcomes, including ICU admission, hospital and ICU length of stay (LOS), and mortality to support antimicrobial prescription assessments.
METHODS:
We used population-wide linked hospital data and applied cluster analysis to ICD-10 coded chronic conditions to identify multimorbidity clusters. Risks of SBI were compared across clusters versus patients with one or no chronic conditions. We used multivariate logistic regression, negative binomial regression, Kaplan–Meier curves, and Cox proportional hazards models to analyse associations with SBI and hospital admission outcomes.
RESULTS:
Among 179,688 COVID-19 hospital admissions, three multimorbidity clusters were identified: neuropsychiatric, cardiorespiratory, and neoplastic. Each cluster was associated with significantly higher odds of SBI: neuropsychiatric (Odds Ratio (OR) 2.74, 95% CI 2.59–2.89), cardiorespiratory (OR 3.87, 95% CI 3.63–4.13), and neoplastic (OR 1.98, 95% CI 1.84–2.14; all p<0.001). For patients with SBI, cardiorespiratory multimorbidity had the highest increase in hospital LOS (Incident Rate Ratio (IRR) 1.94, 95% CI 1.82–2.05) and ICU LOS (IRR 2.75, 95% CI 2.36–3.20). Mortality was significantly elevated across all clusters and was highest in the cardiorespiratory group.
CONCLUSIONS:
Multimorbidity significantly increased the risk of SBI and worsens clinical outcomes in hospitalised COVID-19 patients. Integrating multimorbidity profiles into clinical decision-making may enhance antimicrobial stewardship by identifying patients most likely to benefit from antibiotic therapy.
| eISSN: | 2654-1459 |
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