Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering: A Comparison of Prescribing Behaviours Between Practice Types

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Abstract

Traditionally General Practitioner (GP) practices have been labelled as being in Rural, Urban or Semi-Rural areas with no statistical method of identifying which practices fall into each category. The main aim of this study is to investigate whether location and other characteristics can provide a tautology to identify different types of GP practice and compare the prescribing behaviours associated with the different practice types. To achieve this monthly open source prescription data were analysed by practice considering location, practice size, population density and deprivation rankings. One year’s data was subjected to k-means clustering with the results showing that only two different types of GP practice can be classified that are dependent on location characteristics in Northern Ireland. Traditional labels did not describe the two classifications fully and new classifications of Metropolitan and Non-Metropolitan were used. Whilst prescribing patterns were generally similar, it was found that Metropolitan practices generally had higher prescribing rates than Non-Metropolitan practices. Examining prescribing behaviours in accordance with British National Formulary (BNF) categories (known as chapters) showed that Chapter 4 (Central Nervous System) was responsible for most of the difference in prescribing levels. Within Chapter 4 higher prescribing levels were attributable to Analgesic and Antidepressant prescribing. The clusters were finally examined regarding the level of deprivation experienced in the area in which the practice was located. This showed that the Metropolitan cluster, having higher prescription rates, also had a higher proportion of practices located in highly deprived areas making deprivation a contributing factor.
Original languageEnglish
Article number18289
Pages (from-to)1-15
Number of pages15
JournalScientific Reports
Volume11
Issue number1
Early online date14 Sep 2021
DOIs
Publication statusE-pub ahead of print - 14 Sep 2021

Keywords

  • Geolocation
  • k-means clustering
  • Health
  • General Practice
  • Open Data
  • Prescriptions

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