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About the data


How to use Healthier Lives

We’ve created a video tutorial explaining how to get the best out of Healthier Lives. This video shows you how to use the online tool and what the website can tell you about health care in your area.

Interpreting the maps

Key findings

There is a 75% difference in detection rates of people with high blood pressure

There are wide variations in control of risk factors for high blood pressure

On the whole, blood pressure management could be much better  – on average 30% of adults under 80 years are not achieving control to ≤140/90

There is an unwarranted variation in provision of risk assessments for people diagnosed with hypertension

Across England there is a 100% variation in the proportion of people with hypertension and high CVD risk who are treated with a statin

Controlling blood pressure is done well for those with heart disease and stroke

Blood pressure control is poor in those with diabetes but is improving in some areas; achievement is poor for hypertension control in those with CKD and there is unwarranted variation – 35-40% are missing the target

Next steps

What to do if your area or GP practice is below average – questions to ask

What to do if your area or GP practice is above average

Wider work to support you

Public Health England has convened the Blood Pressure System Leadership Board with representatives across national and local government, the health system, voluntary sector and academia.

This group has published in November 2014 Tackling high blood pressure: from evidence into action. This plan is intended to support partners at all levels to focus on the work that will have the biggest impact tackling this condition.

In parallel with our plan, PHE is providing support to local areas including a resource hub of case studies, data and guidance and new economic modelling.

Data information

The majority of the data presented in Healthier Lives: High Blood Pressure has been drawn from the Quality Outcomes Framework (QOF), which is based upon general practice records. The QOF data is for the financial year 2013/14. We have not used the more usually published achievement scores, but the intervention rates which look at practices’ performance on all relevant patients without any exception.

This is supplemented with other data sources for individual indicators, as detailed in the call-out boxes:

The tool will be updated as new data becomes available, at least annually.

More information about individual indicators and definitions can be found in the call-out boxes which appear when an area on the map is clicked.

This data is not new, it has been available in the public domain but this is the first time it’s been published in this form making information easy to access, view and compare.

This data can tell us about:

The data covers the whole of England and are broken down by:

 Why does Healthier Lives present QOF data without taking account of exceptions ?

There are two valid methods to calculate QOF indicators. We have chosen to show the proportion of patients receiving the intervention because:

From a public health perspective we are more interested in the actual proportion of patients receiving the intervention, i.e. the proportion of all patients with this condition who were treated. The HSCIC (QOF FAQs, p.11) states "Percentage of patients receiving the intervention, gives a more accurate indication of the rate of the provision of interventions as the denominator for this measure covers all patients to whom the indicator applies, regardless of exception status."

We consider this to be the better comparable indicator because, while there are very good reasons why a patient might not be treated (such as terminal illness), a generous interpretation of exception rules can also be used to improve practice performance.

The tool is intended to highlight variation and encourage conversation about the causes of variation. We are not suggesting that every practice should, or can, achieve a 100% intervention rate for every indicator - clearly there are patients it would not be desirable to be included - however it is clear that there is unwarranted variation in exception rates and the data is not available for us to make adjustments.

Triangulation with other sources of primary care data such as the National Diabetes Audit (NDA) support this approach. For those QOF indicators which match NDA indicators, a higher degree of correlation was found with intervention rates than with achievement scores, so intervention rates seem a better measure of true performance.