Monitoring the burden of COVID-19 and impact of hospital transfer policies on Australian aged-care residents in residential aged-care facilities in 2020
Bibliographic record
Abstract
BACKGROUND: Residential aged-care facilities in Australia emerged as the high-risk setting the COVID-19 outbreaks due to community transmission. The vulnerable aged-care residents of these facilities suffered due to low hospital transfers and high mortality and morbidity rates. This study aimed to monitor and report the burden of COVID-19 in residential aged-care facilities across Australia and the impact of hospital transfer policies on resident hospitalisation during the first year of the pandemic. METHODS: We conducted a retrospective cohort study by collecting data from weekly aged-care outbreak reports published by open sources and official government sources between 1st March and 20th November 2020. A comprehensive line list of outbreaks was created using open-source data. The line list included the name of the facility, location, COVID-19 cases among residents, & staff, resident hospitalisations, mode of transmission, number of resident deaths, and state policies involving resident hospitalisation. We also searched the websites of these facilities to collect data on their COVID-19 policies for the residents, staff, and visitors. Statistical analyses were performed on the data obtained. RESULTS: 126 aged-care COVID-19 outbreaks were identified in Australia during the study period. The incidence rate of COVID-19 infections among aged-care residents in Australia was (1118.5 per 100,000 resident population) which is 10 times higher than the general population (107.6 per 100,000 population). The hospitalisation rate for aged-care residents in Australia was 0.93 per 100,000 population. The hospitalisation rate of aged-care residents in Victoria was 3.14 per 100,000 population despite having the highest COVID-19 cases. Excluding South Australia, all states followed ad-hoc case-by-case hospital transfer policies for aged-care residents. CONCLUSION: This study documented a higher risk of COVID-19 infection for aged-care residents and workers but found low hospitalisation rates among residents across Australia. The hospitalisation rates in Victoria were higher than the national average but low when considering the COVID-19 infection rates in the state. The hospitalisation rates could have been impacted due to the state hospital transfer policies at that time. Immediate transfer of infected residents to hospitals may improve their survival and reduce the risk of infection to the other residents, as healthcare settings have more advanced infection control measures and are well-equipped with trained staff and resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".