Ahmed, Z

Boomla, K

Dezateux, C

Fry, R

Harper, G

Robson, J

Stables, D

International Journal for Population Data Science

English

Queen Mary Research Online

International Journal of Population Data Science (2019) 4:3:119International Journal ofPopulation Data ScienceJournal Website: www.ijpds.orgAllocating Unique Property Reference Numbers (UPRNs) to general practitioner-recorded patient addresses using a deterministic address-matching algorithm:evaluation of representativeness and bias in an ethnically-diverse inner city pop-ulationHarper, G1*, Stables, D2, Boomla, K1, Robson, J1, Fry, R3, Ahmed, Z1, and Dezateux, C11Queen Mary University of London2Endeavour Health3Swansea UniversityBackgroundPseudonymised UPRNs based on patient addresses can beused to link environmental information to electronic healthrecords (EHRs), however the representativeness and potentialdemographic or health-related biases in linkage using existingaddress-matching algorithms have not been evaluated usingpatient addresses.Main aimTo evaluate representativeness and bias in assigning UPRNsusing an address-matching algorithm based on general prac-titioner (GP)-recorded patient addresses for a geographically-defined multi-ethnic inner city population.MethodsWe evaluated the Discovery Programme deterministic address-matching algorithm, comprising 213 rules applied, in rank or-der of minimising false positives, to the GP-recorded addressof 879,286 (48% female) patients currently registered with allGP practices in four boroughs in inner east London.We used logistic regression to estimate the adjusted odds(aOR) of an address not being linked to a UPRN by: ageband (reference group: <1 year), sex (female), ethnic group(White British), Index of Multiple Deprivation (IMD) quintile(most deprived), number of long-term conditions (none); andtiming of GP registration (most recent quartile). We evalu-ated the linkage and algorithm error rates in an independentvalidated NHS address dataset using best practice linkage re-porting standards.Results99% of patients had a UPRN assigned. Men(aOR;95%CI:0.87;0.8,0.91), and patients aged 15-19(0.51;0.39,0.68), 20-24 (0.67;0.51,0.89), or ≥90 years(0.35;0.83,0.91), of Chinese ethnic background (95% CI; 0.50;0.45,0.56), or living in the least deprived IMD quintile (0.24;0.20,0.30) were less likely, and those with a GP-registrationpreceding mid-2016 (p-value 0.00) more likely, to have aUPRN assigned. The sensitivity, specificity, positive and neg-ative predictive-values and F-measure of the algorithm were,respectively: 0.993, 0.019, 0.914, 0.204, and 0.9516.ConclusionWe have demonstrated, for the first time, a high GP-addressUPRN match rate and quantified error rates and biases forusers. Further work is needed to investigate addresses in pa-tients with more complex address histories.∗Corresponding Author:Email Address: g.harper@qmul.ac.uk (G Harper)https://doi.org/10.23889/ijpds.v4i3.1282November 2019 c© The Authors. Open Access under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)

Allocating Unique Property Reference Numbers (UPRNs) to general practitioner-recorded patient addresses using a deterministic address-matching algorithm: evaluation of representativeness and bias in an ethnically-diverse inner city population

https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/66586/2/Dezateux_Allocating%20Unique%20Property%20Reference%20Numbers_Published_2019.pdf

Allocating Unique Property Reference Numbers (UPRNs) to general practitioner-recorded patient addresses using a deterministic address-matching algorithm: evaluation of representativeness and bias in an ethnically-diverse inner city population

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