Knowledge of the frequency of disease is an important prerequisite for understanding population health, case finding, commissioning and planning services, and understanding variation in health and care. The most commonly used measure of disease frequency is *prevalence* which is an estimate of the number of cases of a given disease or risk factor in the population at a point in time (*point prevalence*) or over a given time period (*period prevalence*).

This is the first iteration of a data tool which:

      • consolidates data from across Fingertips into a single dataset
      • over time is intended to be a single point of access for all disease prevalence estimates from across Fingertips and other tools. 
      • provides access to the latest modelled prevalence estimates developed in conjunction with Imperial College

      We have organised the prevalence data in this site into 5 domains:

        • Modelled estimates. This domain contains updated prevalence estimates (see below)
        • Estimates of mental health and disability prevalence
        • Estimates for long term conditions
        • Estimates of risk factor prevalence
        • Prevalence data from the Quality and Outcomes Framework

      New estimates

      Which diseases?

      PHE working with Imperial College, commissioned a series of updated prevalence estimates for:

      • COPD
      • Coronary heart disease
      • Stroke
      • Peripheral arterial disease
      • Heart failure
      • Hypertension - diagnosed and undiagnosed
      • Depression

      We have retained previous similar estimates for reference and comparison.

      What geographical scale?

      The data for these is available for:

      • Lower tier local authorities
      • General practices

      In future iterations of the dataset we hope to provide aggregations to higher level geographies inlcuding CCGs, STP footprints and  upper tier local authorities.

      How do these estimates differ from previous versions and how have they been calculated?

      There are differences in case definitions, methods, data sources, predictor variables, and age banding for the new estimates which mean they are not strictly comaparable with the 2011 modelled estimates,

      A summary of key methods and results of the latest prevalence modelling data is available here.

      Full technical details, input data and code is available on request.

      The technical documents describing how the latest modelled estimates were generated are available below:

      Other sources of disease prevalence estimates.

      Estimates from the diabetes prevalence model are available here.

      The 2015 GBD visualisation tool now publishes the prevalence estimates used to calculate Disability Adjusted Life Years. In some cases they differ from the measured or estimated values contained in this tool. There are a number of reasons for this:

      • Different data sources
      • Requirement for between country comparablity in estimating disease prevalence
      • Different modelling methodology


      Recent updates

      December 2017

      • QOF-based prevalences updated for CCGs and GPs

      April 2017

      • first iteration