Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake


30 Second Summary

There is a decreasing uptake of COVID and flu vaccinations among healthcare workers. Identifying patterns by staff uptake, gender, ethnicity, deprivation, and other factors can help. Using geographic and boosted tree modelling, along with regression analysis, can capture these patterns and provide useful data for stakeholders to address the issue.

Geographic Modelling
Machine Learning
Vaccination
COVID-19
Workforce
Author
Affiliation

Yasmin Ibrahim

NHS England

There is low uptake of Healthcare Workers for COVID and Flu, which is in many Trusts decreasing with each season.

Trusts and regions, as well as NHS England policy teams would benefit from identifying patterns among healthcare workers: staff uptake, gender, ethnicity, deprivation, frontline status, age group, eligibility (in another cohort other than healthcare worker status).

Using a mixed-method approach. Geographic modelling of healthcare worker uptake by Trust, ICB and region, and boosted tree modelling of the data to capture patterns in the data and reduces bias and variance. Regression modelling would also be useful to produce data tables to present to stakeholders (e.g. odds ratios for logistic regression).