Unveiling the hidden effect of multi-morbidities on the severity of Covid-19: a latent class analysis approach
Abstract Background Epidemiological studies showed that Covid-19 patients with underlying diseases had higher rates of severe Covid-19. Previous studies focused on the presence of a single chronic disease but this study investigated the prevalence and patterns of multi-morbidities in patients with C...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-025-22523-8 |
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| Summary: | Abstract Background Epidemiological studies showed that Covid-19 patients with underlying diseases had higher rates of severe Covid-19. Previous studies focused on the presence of a single chronic disease but this study investigated the prevalence and patterns of multi-morbidities in patients with Covid-19 and its relationship with the severity of Covid-19. Methods This retrospective study focused on patients age 30 years and older with positive polymerase chain reaction (PCR) results in 24 hospitals of Mashhad in northeastern Iran from 20-3-2020 to 21-1-2022. The number of studied confirmed patients was 318,502. The underlying diseases were identified according to the International Classification of Diseases, and the severity of Covid-19, including death, need for ventilation, and need for treatment in the intensive care unit (ICU). The pattern of multi-morbidities in these confirmed cases was investigated using latent class analysis (LCA), and the relationship between this pattern and the severity of Covid-19 was determined by multivariate logistic regression. Results The most common coexisting diseases were hypertension in 30,100 patients (9.5%), metabolic disorders in 23,798 (7.5%) and hyperlipidemia in 22,454 (7%). Different comorbidities were grouped into three classes by the LCA model. Class 1 was patients without multi-morbidities, or 83% people., Class 2, which included 9% patients, was patients with hypertension, diabetes, respiratory diseases, and mental behavioral disorders (HRMD class). Class 3, which included patients with metabolic diseases, for whom the probability of developing hypertension, hyperlipidemia, diabetes, and metabolic disorders was high, included 7% patients. The results of multivariate logistic regression showed that having HRMD and metabolic diseases compared to no multi-morbidity adjusted for some risk factors increased the odds of developing severe Covid-19 by 81% and 55%, respectively. Conclusions The classes identified in this study provided a clear view of different groups of Covid-19 patients with certain multi-morbidities and underscore the importance of considering these patterns, rather than individual comorbidities, in risk assessment and management of COVID-19 patients. This approach will guide clinical decision-making and resource allocation in the ongoing management of the COVID-19 pandemic. |
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| ISSN: | 1471-2458 |