Stratified sampling design and loss to follow-up in survival models: evaluation of efficiency and bias

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.

COVID-19 vaccination coverage and vaccine hesitancy among Australians with disability and long-term health conditions

ISSUE ADDRESSED

COVID-19 vaccination is the cornerstone of managing Australia's COVID-19 pandemic and the success of the vaccination program depends on high vaccination coverage. This paper examined differences in COVID-19 vaccination coverage and vaccine hesitancy for people with disability, long-term health conditions, and carers - subgroups that were prioritised in Australia's vaccination program.

METHODS

Using data from 2400 Australians who participated in two waves of the Taking the Pulse of the Nation survey in April and May 2021, we described vaccination coverage and hesitancy among people with disability, severe mental health conditions, severe long-term health conditions, frequent need for assistance with everyday activities, and carers.

RESULTS

Vaccination coverage was estimated to be 8.2% in the population overall and was similar for people with disability, those with frequent need for assistance, and carers. It was higher for people with severe long-term health conditions (13.4%) and lower for people with severe mental health conditions (4.3%). Vaccine hesitancy was high overall (35.6%) and was similarly high across the priority groups, with only small differences for people with disability, severe long-term health conditions and frequent need for assistance.

CONCLUSIONS

This study highlights a lack of difference in vaccination coverage for people with disability, long-term health conditions, and carers compared to the general population. So what? Sub-optimal vaccination coverage for people in the priority groups leaves many people at significant risk of serious disease or death if exposed to COVID-19, particularly in light of easing of disease-control restrictions across Australia and the emergence of new variants.