Mapping English GP prescribing data: a tool for monitoring health-service inequalities

Abstract

ObjectiveThe aim of this paper was to show that easily interpretable maps of local and national prescribing data, available from open sources, can be used to demonstrate meaningful variations in prescribing performance.DesignThe prescription dispensing data from the National Health Service (NHS) Information Centre for the medications metformin hydrochloride and methylphenidate were compared with reported incidence data for the conditions, diabetes and attention deficit hyperactivity disorder, respectively. The incidence data were obtained from the open source general practitioner (GP) Quality and Outcomes Framework. These data were mapped using the Ordnance Survey CodePoint Open data and the data tables stored in a PostGIS spatial database. Continuous maps of spending per person in England were then computed by using a smoothing algorithm and areas whose local spending is substantially (at least fourfold) and significantly (p<0.05) higher than the national average are then highlighted on the maps.SettingNHS data with analysis of primary care prescribing.PopulationEngland, UK.ResultsThe spatial mapping demonstrates that several areas in England have substantially and significantly higher spending per person on metformin and methyphenidate. North Kent and the Wirral have substantially and significantly higher spending per child on methyphenidate.ConclusionsIt is possible, using open source data, to use statistical methods to distinguish chance fluctuations in prescribing from genuine differences in prescribing rates. The results can be interactively mapped at a fine spatial resolution down to individual GP practices in England. This process could be automated and reported in real time. This can inform decision-making and could enable earlier detection of emergent phenomena.

Organisation of services for managing ADHD

“…In other continents, including much of Asia, Africa and South America rates of recognition remain very low (Hinshaw et al 2011). Not only is there considerable variation between different regions and countries, but similar variability in practice is also seen within countries (Rowlingson et al 2013;Visser et al 2013Visser et al , 2014; Australian Commission on Safety and Quality in Health Care, 2015). Attempts to explain the source of these within country variations in care have been largely unsuccessful (Wright et al 2015).…”

In Response

“…Each dot on the map is the location of an NHS general practice, colour‐coded according to its prescribing rate per registered patient for the month of October 2011. The underlying data are freely available at http://bit.ly/2lgdaHQ; a detailed description is in Rowlingson et al 1 …”

Statistics: a Data Science for the 21st Century

“…Since December 2011, comprehensive data on National Health Service prescribing throughout England has been made freely available, by general practice and calendar month. Rowlingson et al (2013) combined these data with (also freely available) data from the 'General practitioner quality and outcomes framework' (Department of Health, 2003) and Ordnance Survey Code-Point open data (https://www.ordnancesurvey.co.uk/business-and-government /products/code-point-open.html), and used these data sets to construct maps of the countrywide variation in prescribing rates for particular conditions. Using a simple kernel smoothing method to identify extreme local variations they found, among other things, striking variations in prescribing rates for methylphenidate (Ritalin), which is the recommended medication for treatment of attention deficit hyperactivity disorder (see National Institute for Health and Clinical Excellence (2009)).…”