Automated Author ProfileCho, Jin Gun
Cho, Jin Gun
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Objectives: Internationally, socioeconomic disadvantage is related to severe outcomes of COVID-19. We investigated the impact of socioeconomic disadvantage on infection rates, hospitalisation, and in-hospital outcomes for COVID-19 with standardised medical care. Design: Retrospective cross-sectional study. Setting: SARS-CoV-2 PCR-confirmed patients, ≥18 years old, admitted to a major public hospital between January 2020 and December 2021. Main outcome measurements: Severe COVID-19 outcomes were defined by a composite outcome of in-hospital death or other critical complications. A generalised linear regression model of demographic features, co-existing conditions, and socioeconomic status [Socioeconomic Index for Area (SEIFA)] was used to determine the risks of the composite outcome. Results: Of 797,343 individuals aged ≥18 in the health district, 50,906 (6.4%) were PCR-positive, and 1,962 were hospitalised. Compared with the whole health district population, infected individuals were younger (median [interquartile range] age 35 [25-48] years vs 42 [31-58] years) and from areas with the greatest socioeconomic disadvantage (34.4% vs 20%; both p<0.0001). Hospitalised patients were older, with more females compared to the PCR-positive group (46 years [33-61], 53.5%, respectively; p<0.001), and 51.2% were from postcodes with greatest socioeconomic disadvantage (p<0.0001). The composite outcome occurred in 11.5%, with an in-hospital mortality of 3.8%. Higher risk of the composite outcome was observed in males (OR 1.72, 95% CI [1.26-2.42], p <0.001), patients aged ≥ 65 years (OR 6.96, [3.3-14.6], p <0.001), those with ≥4comorbidities (OR 2.67, [1.54-4.63], p <0.001), and unvaccinated patients (OR 1.57, [1.05-2.38], p < 0.05). The risk of composite outcome did not increase with socioeconomic disadvantage (OR 0.97, [0.68, 1.42], p = 0.64). Conclusion: In the absence of capacity restraints, socioeconomic disadvantage was not associated with severe in-hospital outcomes in a well-resourced care environment despite the increased rates of infection and hospitalisation. This highlights the impact of universally accessible, standardised, protocolised, high-quality in-hospital care in reducing the risk of adverse in-hospital outcomes in socioeconomically disadvantaged patients.
Authors
- Fahimeh, Faqihi ;
- Perri, Rita ;
- Chien, Jimmy ;
- Cho, Jin Gun ;
- Milne, Stephen ;
- Bag, Shopna ;
- Gilroy, Nicole ;
- Wheatley, John ;
- Kairaitis, Kristina