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IntroductionLinking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers.
ObjectivesTo describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses.
MethodsBest practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables.
ResultsWe found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population.
The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations.
ConclusionsASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

INTRODUCTION: Linking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers. OBJECTIVES: To describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses. METHODS: Best practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables. RESULTS: We found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population. The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations. CONCLUSIONS: ASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other ...

"April 1995." ; Shipping list no.: 95-0143-P. ; Distributed to some depository libraries in microfiche. ; Mode of access: Internet.

"November 1992." ; Shipping list no.: 93-0194-P. ; Distributed to some depository libraries in microfiche. ; At head of title: 102d Congress, 2d session. Committee print. ; Mode of access: Internet.

Objectives. We examined approaches to reduce sodium content of food served in settings operated or funded by the government of the County of Los Angeles, California.

IntroductionHouseholds are increasingly studied in population health research as an important context for understanding health and social behaviours and outcomes. Identifying household units of analysis in routinely collected data rather than traditional surveys requires innovative and standardised tools, which do not currently exist.
ObjectivesTo design a utility that identifies households at a point in time from pseudonymised Unique Property Reference Numbers (UPRNs) known as Residential Anonymised Linkage Fields (RALFs) assigned to general practitioner (GP) patient addresses in electronic health records (EHRs) in north east London (NEL).
MethodsRule-based logic was developed to identify households based on GP registration, address date, and RALF validity. The logic was tested on a use case on the household clustering of childhood weight status, and bias in success of identifying households was examined in the use case cohort and in a full population cohort.
Results92.1% of the use case cohort was assigned a household. The most frequent dominant reason (55.3%) for a household not assigned was that a person had no valid household RALFs available across their patient registration address records. Other reasons are having none or multiple valid household RALFs, or not being alive at the event date.
In the use case, children not assigned to a household were more likely to attend schools in City & Hackney and living in the third most deprived quintile of lower super output areas.
88.9% of the population cohort was assigned a household. Patients not assigned to a household were more likely to be aged 18 to 45 years, living in City & Hackney, and living in the second quintile of most deprived lower super output areas.
ConclusionsWe have developed a method for deriving households from primary care EHRs that can be implemented quickly and in real-time, providing timely data to support population health research on households.

BackgroundRacial/ethnic minority groups have higher risks for disease resulting from obesity.Community contextThe University of California, Los Angeles, and the Los Angeles County Department of Public Health partnered with community organizations to disseminate culturally targeted physical activity and nutrition-based interventions in worksites.MethodsWe conducted community dialogues with people from 59 government and nonprofit health and social service agencies to develop wellness strategies for implementation in worksites. Strategies included structured group exercise breaks and serving healthy refreshments at organizational functions. During the first 2 years, we subcontracted with 6 community-based organizations (primary partners) who disseminated these wellness strategies to 29 organizations within their own professional networks (secondary worksites) through peer modeling and social support. We analyzed data from the first 2 years of the project to evaluate our dissemination approach.OutcomePrimary partners had difficulty recruiting organizations in their professional network as secondary partners to adopt wellness strategies. Within their own organizations, primary partners reported significant increases in implementation in 2 of the 6 core organizational strategies for promoting physical activity and healthy eating. Twelve secondary worksites that completed organizational assessments on 2 occasions reported significant increases in implementation in 4 of the 6 core organizational strategies.InterpretationDissemination of organizational wellness strategies by trained community organizations through their existing networks (train-the-trainer) was only marginally successful. Therefore, we discontinued this dissemination approach and focused on recruiting leaders of organizational networks.