A helpful research study published by Hippisley-Cox and others in the BMJ 17th September 2021 helps to analyse this.
They identified a range of important clinical risk factors for severe COVID-19 outcomes in people in the UK, 14 days or more after COVID-19 vaccination when some immunity is expected to have developed.
They used national linked data steps from general practice, national immunisation and SARS-CoV-2 testing, death registry, and hospital episode data from a population representative sample of more than 6.9 million adults.
Risk ratios were highest for people with:
- Down’s syndrome
- Kidney transplantation
- Sickle cell disease
- Care home residency
- Recent bone marrow transplantation
- Solid organ transplantation
- Parkinson’s disease
- Neurological conditions
- Liver cirrhosis.
They developed and evaluated novel clinical risk prediction models to estimate the absolute risk of COVID-19 related hospital admission and mortality in the general population of vaccinated people as well as in a subset of people with positive SARS-CoV-2 test results. The risk models showed high levels of discrimination and good calibration.
For many of the predictors included they found that individual characteristics such as age, obesity, pre-existing medical conditions, and socioeconomic disadvantage was known to affect immune competence and, at least for certain diseases, affect the response to some vaccines or to immunosuppressive drugs.
The associations with Down’s syndrome in all the models are likely to reflect increased susceptibility to infection and genetic predisposition. Compared with the white ethnic group, the Pakistani and Indian groups had up to two fold increased hazards of COVID-19 death and hospital admission after vaccination.
These ethnic disparities in COVID-19 outcomes could represent residual differential exposure linked to behaviour, lifestyle, household size and occupation more than differential susceptibility mechanisms – although the authors acknowledge that being vaccinated could change behaviour and exposure in some groups more than others.
These algorithms can help to determine future policy and needs within communities for stretched resources during this COVID pandemic.
Dr Paul Ettlinger
BM, DRCOG, FRCGP, FRIPH, DOccMed