Suchergebnisse

Objectives Due to the availability of data and the speed of delivery required, NHS number (Health ID) was used during the COVID-19 pandemic to join different health datasets to census 2021. This research sought to assess the quality of linkage done by NHS number, between COVID-19 vaccinations data and census 2021, by comparing to the quality that could be achieved with deterministic linkage using Personal Identifiable Information (PII). Specifically, we wanted to assure against concern that unlinked records may have been misclassified as unvaccinated due to inaccurate recording of NHS number at time of vaccination.
Approach We used deterministic (rules-based) linkage to identify extra links that were not made on NHS number. We also investigated how many links were incongruent between NHS number linkage and PII deterministic linkage.
Results We were able to link 98.5% of census records to vaccines data, with a precision of 98.94% and a recall of 99.71% This identified 1,833,626 additional links made using PII linkage. Furthermore, analysis of the linked data was re-run with the additional links.
Conclusion We were able to assure that the NHS number linkage used to produce COVID-19 vaccinations statistics, was valid. Whilst 1,833,626 more links were made, the findings resulting from the linked data were unchanged.
Implications Whilst this research demonstrates the utility of NHS number-based linkage, acquisition and use of PII on health datasets can increase the link rate and plays a vital role in estimating linkage error.

ObjectivesQuality assessment is an integral part of linkage, often involving clerical (manual) review of records. Excel spreadsheets are commonly used to review record pairs, which can be difficult to read across the screen. We decided to create an adaptable piece of software that improved the user experience.
ApproachOur approach was to utilise tkinter, a pythonic graphical user interface (GUI) package. Tkinter is easy to use and has simple syntax, making it a good choice for open-source code. The software takes in wide format datafiles and displays record pairs one at a time in an easy-to-compare format, allowing a user to select whether the items are a 'match' or a 'non-match'. By adapting parameters in a configuration file, a user can very easily adapt the CROW to the variables available in each dataset. We have also developed capability for highlighting differences between record pairs.
ResultsFeedback from users indicates that as a result of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency, allowing projects to complete earlier than planned. On one key Labour Market Survey based linkage, the CROW led to an estimated 25% time saving. The ability to highlight the differences in records increases the accuracy and efficiency of decision making. We were successful in building adaptable software, that can be transferred to different datasets. It has been used in a wide range of projects across the ONS including key Covid-19 and Census 2021 linkage projects. The open-sourced nature of this software means it is freely available for use.
ConclusionIn conclusion, CROW is an open-sourced piece of software for clerical review that is easy to use and adapt; it makes the experience of clerical review more pleasant as well as saving resources. It is a useful tool that enables quality assurance to be broadly implemented across the linkage domain.

ObjectivesClerical review is an integral part of linkage and is fundamental to quality assurance. CROW is a low dependency, adaptable, clerical matching tool that has been deployed across 40 linkage projects. Due to limitations of the interface, with the initial version (including the limited ability to view large clusters); this project sought to re-develop the tool as a flask application.
ApproachOur approach was to transform the existing CROW desktop application into a Flask (web based) application. Flask was chosen because it is a flexible web framework that enabled the development of a more dynamic interface, as well as better integration with the infrastructure of secure environments. We also engaged with accessibility experts to ensure the inclusivity of our product. Once the development was completed, testing was carried out by a range of users and stakeholders, to identify further issues and bugs for resolution.
ResultsThe successful redevelopment of CROW into a Flask application improved functionality, usability, parametrisation, and accessibility of the tool. Feedback from users indicates that because of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency and leads to better decision making.
ConclusionThe successful redevelopment of CROW is a significant improvement to the clerical review process for data linkage projects.
ImplicationsCROW is open-source and low dependency so could be adapted to work in most environments. Therefore, it could be adapted and used by the wider data linkage community.