Determining households from patient addresses and unique property reference numbers in general practitioner electronic health records.

Introduction: Households 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.

Objectives: To 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).

Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating unique property reference numbers to general practitioner-recorded patient addresses.

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.

Automated electronic reminders to facilitate primary cardiovascular disease prevention: randomised controlled trial.

Background: Primary care databases contain cardiovascular disease risk factor data, but practical tools are required to improve identification of at-risk patients.

Aim: To test the effects of a system of electronic reminders (the 'e-Nudge') on cardiovascular events and the adequacy of data for cardiovascular risk estimation.

Design Of Study: Randomised controlled trial.

Identifying individuals for primary cardiovascular disease prevention in UK general practice: priorities and resource implications.

Targeted cardiovascular disease prevention relies on risk-factor information held in primary care records. A risk algorithm, the 'e-Nudge', was applied to data from a population of >or=50-year-olds in 19 West Midlands practices, to identify those individuals at risk of cardiovascular disease. Altogether, 5.

Identifying undiagnosed diabetes: cross-sectional survey of 3.6 million patients' electronic records.

Background: Around 1% of the UK population has diabetes that is either undiagnosed or unrecorded on practice disease registers.

Aim: To estimate the number of people in UK primary care databases with biochemical evidence of undiagnosed diabetes. To develop simple practice-based search techniques to support early recognition of diabetes.