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Linking community pharmacy dispensing data to prescribing data of general practitioners

BACKGROUND: Databases are frequently used for pharmacoepidemiological research. However, most of these databases consist either of prescribing, dispensing or administrative data and therefore lack insight in the interaction between the several health professionals around the patient. METHODS: To det...

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Autores principales: Florentinus, Stefan R, Souverein, Patrick C, Griens, Fabiënne AMG, Groenewegen, Peter P, Leufkens, Hubert GM, Heerdink, Eibert R
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1450270/
https://www.ncbi.nlm.nih.gov/pubmed/16584557
http://dx.doi.org/10.1186/1472-6947-6-18
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author Florentinus, Stefan R
Souverein, Patrick C
Griens, Fabiënne AMG
Groenewegen, Peter P
Leufkens, Hubert GM
Heerdink, Eibert R
author_facet Florentinus, Stefan R
Souverein, Patrick C
Griens, Fabiënne AMG
Groenewegen, Peter P
Leufkens, Hubert GM
Heerdink, Eibert R
author_sort Florentinus, Stefan R
collection PubMed
description BACKGROUND: Databases are frequently used for pharmacoepidemiological research. However, most of these databases consist either of prescribing, dispensing or administrative data and therefore lack insight in the interaction between the several health professionals around the patient. METHODS: To determine the success rate of linking records from the dispensing database of the Foundation for Pharmaceutical Statistics to the prescribing database of the second Dutch national survey of general practice, conducted by NIVEL (Netherlands Institute for Health Services Research), a deterministic record linkage approach was used with patient and prescription characteristics as matching variables between the two databases. RESULTS: The catchment area included 123 community pharmacies, 90 GP practices and approximately 170,000 unique patients. Overall 110,102 (64.8%) unique patients were linked using the matching variables patient's gender, year of birth, the 4-digit part of the postal code, date of dispensing/prescribing and ATC-code. The final database contains of the 110,102 both prescribing data from 83 GP practices and dispensing data of 112 community pharmacies. CONCLUSION: This study shows that linkage of dispensing to prescribing data is feasible with a combination of patient characteristics, such as gender, year of birth and postal code, and prescription characteristics like prescription date and ATC-code. We obtained a linkage proportion of 64.8% resulting in complete prescribing and dispensing history of 110,102 patients. This offers an opportunity to gain insight in the mechanisms and factors influencing drug utilisation in general practice.
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spelling pubmed-14502702006-04-29 Linking community pharmacy dispensing data to prescribing data of general practitioners Florentinus, Stefan R Souverein, Patrick C Griens, Fabiënne AMG Groenewegen, Peter P Leufkens, Hubert GM Heerdink, Eibert R BMC Med Inform Decis Mak Research Article BACKGROUND: Databases are frequently used for pharmacoepidemiological research. However, most of these databases consist either of prescribing, dispensing or administrative data and therefore lack insight in the interaction between the several health professionals around the patient. METHODS: To determine the success rate of linking records from the dispensing database of the Foundation for Pharmaceutical Statistics to the prescribing database of the second Dutch national survey of general practice, conducted by NIVEL (Netherlands Institute for Health Services Research), a deterministic record linkage approach was used with patient and prescription characteristics as matching variables between the two databases. RESULTS: The catchment area included 123 community pharmacies, 90 GP practices and approximately 170,000 unique patients. Overall 110,102 (64.8%) unique patients were linked using the matching variables patient's gender, year of birth, the 4-digit part of the postal code, date of dispensing/prescribing and ATC-code. The final database contains of the 110,102 both prescribing data from 83 GP practices and dispensing data of 112 community pharmacies. CONCLUSION: This study shows that linkage of dispensing to prescribing data is feasible with a combination of patient characteristics, such as gender, year of birth and postal code, and prescription characteristics like prescription date and ATC-code. We obtained a linkage proportion of 64.8% resulting in complete prescribing and dispensing history of 110,102 patients. This offers an opportunity to gain insight in the mechanisms and factors influencing drug utilisation in general practice. BioMed Central 2006-04-03 /pmc/articles/PMC1450270/ /pubmed/16584557 http://dx.doi.org/10.1186/1472-6947-6-18 Text en Copyright © 2006 Florentinus et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Florentinus, Stefan R
Souverein, Patrick C
Griens, Fabiënne AMG
Groenewegen, Peter P
Leufkens, Hubert GM
Heerdink, Eibert R
Linking community pharmacy dispensing data to prescribing data of general practitioners
title Linking community pharmacy dispensing data to prescribing data of general practitioners
title_full Linking community pharmacy dispensing data to prescribing data of general practitioners
title_fullStr Linking community pharmacy dispensing data to prescribing data of general practitioners
title_full_unstemmed Linking community pharmacy dispensing data to prescribing data of general practitioners
title_short Linking community pharmacy dispensing data to prescribing data of general practitioners
title_sort linking community pharmacy dispensing data to prescribing data of general practitioners
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1450270/
https://www.ncbi.nlm.nih.gov/pubmed/16584557
http://dx.doi.org/10.1186/1472-6947-6-18
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