Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering

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 pr...

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Autores principales:	Frederick G. Booth, Raymond R Bond, Maurice D Mulvenna, Brian Cleland, Kieran McGlade, Debbie Rankin, Jonathan Wallace, Michaela Black
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/20331afe7e2e4301bb50d29667775a3d
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spelling	oai:doaj.org-article:20331afe7e2e4301bb50d29667775a3d2021-12-02T17:25:43ZDiscovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering10.1038/s41598-021-97716-32045-2322https://doaj.org/article/20331afe7e2e4301bb50d29667775a3d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97716-3https://doaj.org/toc/2045-2322Abstract 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.Frederick G. BoothRaymond R BondMaurice D MulvennaBrian ClelandKieran McGladeDebbie RankinJonathan WallaceMichaela BlackNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frederick G. Booth Raymond R Bond Maurice D Mulvenna Brian Cleland Kieran McGlade Debbie Rankin Jonathan Wallace Michaela Black Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
description	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.
format	article
author	Frederick G. Booth Raymond R Bond Maurice D Mulvenna Brian Cleland Kieran McGlade Debbie Rankin Jonathan Wallace Michaela Black
author_facet	Frederick G. Booth Raymond R Bond Maurice D Mulvenna Brian Cleland Kieran McGlade Debbie Rankin Jonathan Wallace Michaela Black
author_sort	Frederick G. Booth
title	Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
title_short	Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
title_full	Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
title_fullStr	Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
title_full_unstemmed	Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering
title_sort	discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of k-means clustering
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/20331afe7e2e4301bb50d29667775a3d
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Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering

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