Linking people to places is powerful – it can help us understand how health is impacted by social and environmental factors, like the characteristics of a household, green space or air pollution. But patient addresses are entered into NHS records as free text so the same address can be written in different ways, making data linkage difficult.
What we are doing
We have developed an address-matching algorithm, in partnership with David Stables of Endeavour Health, that allocates a Unique Property Reference Number (UPRN) to patient records. Every property in the UK already has a UPRN - they are allocated by local authorities and made nationally available by Ordnance Survey, giving every address a standardised format that enables pseudonymised linkage to other sources of data.
The algorithm, known as ASSIGN (AddreSS MatchInG to Unique Property Reference Numbers), compares addresses in the NHS record with Ordnance Survey’s ‘Address Base Premium’ UPRN database, one element at a time, and decides whether there is a match. It mirrors human pattern recognition, so it allows for certain character swaps, spelling mistakes and abbreviations. It also includes patients’ past addresses, making it possible to study the effects of circumstances over time. After rigorous testing and adjustments, ASSIGN correctly matches 98.6% of patient addresses at 38,000 records per minute.
We are using ASSIGN to map neighbourhoods that have high concentrations of people living with multiple long-term conditions, and to show areas of measles susceptibility as part of our drive to prevent future outbreaks. Using the Discovery Data Service, which receives new primary care data from across London every day, it will be possible to map these situations in near real-time and provide valuable intelligence to local authorities.
We’re also exploring the many other ways ASSIGN could support better public health. Our researchers are investigating whether overcrowded or multi-generational households are at greater risk from Covid-19, how the health of other household members impacts childhood obesity, and how ASSIGN could enable GPs to identify patients living in care homes more easily so they can provide more effective care. ASSIGN is open source and freely available, creating opportunities for other researchers to link data in this way to inform policy decisions and improve population health across the country.
Research lead: Dr Gill Harper, UKRI Rutherford postdoctoral research fellow, REAL Child Health.