The ASSIGN algorithm matches Unique Property Reference Numbers with the addresses in health records, enabling de-identified household analyses for research into the wider determinants of health.
ASSIGN (AddreSS MatchInG to Unique Property Reference Numbers) makes it possible to use de-identified residential addresses – and therefore property or household characteristics – as part of health research at scale.
The algorithm assigns Unique Property Reference Numbers (UPRNs) to the free text addresses in patient health records. This gives each address a unique identifier that can be used to link the records of patients who share an address, and link health information to other datasets such as property information or local authority records. Most importantly, the patient records and UPRNs are de-identified which keeps addresses and patient identities hidden.
UPRNs are routinely allocated to every property and managed in an Ordnance Survey database. ASSIGN mirrors human pattern recognition to compare addresses in patient health records with the Ordnance Survey database, one element at a time. The algorithm has been proven to correctly match 98.6% of patient addresses at 38,000 records per minute, making bulk address-matching with UPRNs scalable and fast, using a rigorously tested and standardised method.
ASSIGN is open source and free of charge.
Assigning UPRNs to the addresses in health records enables two key things: linking people who share a household at a point in time to understand variations in household health, and linking to other data sources, such as property information and local authority records, to study other wider determinants of health.
CEG has worked with the NHS in North East London to assign UPRNs in real time to every patient address in GP health records. We are using the de-identified data, sometimes linked with other datasets, to investigate the health impacts of household overcrowding and household clustering of people affected by multiple long-term conditions.
Please contact Professor Carol Dezateux c.dezateux@qmul.ac.uk to discuss potential collaboration with CEG on research using ASSIGN.
ASSIGN was developed by CEG and Endeavour Health Charitable Trust, with support from Barts Charity (MGU0419) and HDRUK. Dr Gill Harper is supported by a UKRI Ernest Rutherford Fellowship.