Simon has explained what we wanted to do with QOF in the previous post.

Looking at the prevalence data from the QOF files, we see the GP Code, the Indicator Group (the health condition this relates to), the number of patients on that GP’s register that have that condition, the list type and list size.

These last two fields are important – not all conditions are measured across all ages: in the example below, Chronic Kidney Disease is only counted for people over 18.

gp_code	Indicator_group	register	patient_list_type	list_size
AGP001	AF	100	TOTAL	1000
AGP001	AST	300	TOTAL	1000
AGP001	CKD	125	18OV	800

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Data Unlocked are working on a project with Inside Outcomes and Birmingham City University to estimate the rate of diagnosis of over twenty health conditions in England using openly available datasets. In this blog post Simon Whitehouse explains how we have made our estimations.

Earlier this year we spoke with Darren Wright of Inside Outcomes about the possibility of estimating the diagnosis of health conditions in England by area, something we were surprised wasn’t already available.

The National Health Service (NHS) do produce statistics, called Quality Outcome Framework (QOF) measures, that break down the prevalence of diagnosis of twenty five different health conditions from Atrial Fibrillation to Stroke, by GP practice.

They also produce statistics about the number of patients registered in GPs practices, broken down by the Lower Layer Super Output Area (LSOA) they live in.Continue Reading