Trends and clinical characteristics of COVID-19 vaccine recipients: a federated analysis of 57.9 million patients' primary care records in situ using OpenSAFELY
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Abstract
On 8 December 2020 NHS England administered the first COVID-19 vaccination.
Aim
To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout.
Design and setting
With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY.
Method
Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described.
Results
A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose.
Conclusion
The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.
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SciScore for 10.1101/2021.01.25.21250356: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data management and analysis was performed using the OpenSAFELY software libraries and Jupyter notebooks, both implemented using Python3. Python3suggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The key strengths of this study are the scale, detail and completeness of the underlying raw EHR data. The OpenSAFELY-TPP platform runs analyses across the full dataset of all raw, pseudonymised, single-event-level clinical events for all 23.4 …
SciScore for 10.1101/2021.01.25.21250356: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data management and analysis was performed using the OpenSAFELY software libraries and Jupyter notebooks, both implemented using Python3. Python3suggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The key strengths of this study are the scale, detail and completeness of the underlying raw EHR data. The OpenSAFELY-TPP platform runs analyses across the full dataset of all raw, pseudonymised, single-event-level clinical events for all 23.4 million patients at all 2,545 GP practices in England using TPP software; this includes data on all tests, treatments, diagnostic events, and other salient clinical and demographic information. OpenSAFELY-TPP also provides data in near-real time, providing unprecedented opportunities for audit and feedback to rapidly identify and resolve concerns around health service activity and clinical outcomes: the delay from entry of a clinical event into the EHR to it appearing in the OpenSAFELY-TPP platform varies from two to nine days. This is substantially faster than any other source of comprehensive GP data; and is additionally linked to other sources of data including hospitalisations data from SUS, ITU data from ICNARC, and COVID test data from SGSS, and death data from ONS, supporting timely monitoring of vaccine coverage, safety, and effectiveness as well as other covid-related analyses [14]. We recognise some limitations to our analysis. Our population, though extremely large, may not be fully representative: there is some geographic clustering in choice of electronic health record system, and only 17% of general practices in London use TPP software. Solely for our analysis of geographic variation we include on...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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