OBJECTIVE: To compare different indicators for assessing the quality of drug prescribing and establish their agreement in identifying doctors who may not adhere to treatment guidelines. DATA SOURCES/STUDY SETTING: Data from 181 general practitioners (GPs) from The Netherlands. The case of asthma is used as an example because, in this area, different quality indicators exist whose validity is questioned. The study is part of the European Drug Education Project. STUDY DESIGN: Spearman rank correlations were assessed among the GPs' scores on self-report instruments, aggregated prescribing indicators, and individualized prescribing indicators. Kappa values were calculated as agreement measures for identifying low adherence to the guidelines. DATA COLLECTION: Prescribing data from GPs were collected through pharmacies, public health insurance companies, or computerized GP databases. Two self-report instruments were mailed to the GPs. The GPs first received a questionnaire assessing their competence regarding the treatment of asthma patients. Three months later they received a series of 16 written asthma cases asking for their intended treatment for each case. PRINCIPAL FINDINGS: Correlations between scores based on self-report instruments and indicators based on actual prescribing data were mostly nonsignificant and varied between 0 and 0.21. GPs identified as not adhering to the guidelines by the prescribing indicators often had high scores on the self-report instruments. Correlations between 0.20 and 0.55 were observed among indicators based on aggregated prescribing data and those based on individualized data. The agreement for identifying low adherence was small, with kappa values ranging from 0.19 to 0.30. CONCLUSIONS: Indicators based on self-report instruments seem to overestimate guideline adherence. Indicators assessing prescribing quality at an aggregated level give clearly different results, as compared to indicators evaluating prescribing data on an individual patient level. Caution is needed when using such prescribing indicators to identify low adherence to guidelines. Further validation studies using a gold standard comparison are needed to define the best possible indicator.
OBJECTIVE: To compare different indicators for assessing the quality of drug prescribing and establish their agreement in identifying doctors who may not adhere to treatment guidelines. DATA SOURCES/STUDY SETTING: Data from 181 general practitioners (GPs) from The Netherlands. The case of asthma is used as an example because, in this area, different quality indicators exist whose validity is questioned. The study is part of the European Drug Education Project. STUDY DESIGN: Spearman rank correlations were assessed among the GPs' scores on self-report instruments, aggregated prescribing indicators, and individualized prescribing indicators. Kappa values were calculated as agreement measures for identifying low adherence to the guidelines. DATA COLLECTION: Prescribing data from GPs were collected through pharmacies, public health insurance companies, or computerized GP databases. Two self-report instruments were mailed to the GPs. The GPs first received a questionnaire assessing their competence regarding the treatment of asthmapatients. Three months later they received a series of 16 written asthma cases asking for their intended treatment for each case. PRINCIPAL FINDINGS: Correlations between scores based on self-report instruments and indicators based on actual prescribing data were mostly nonsignificant and varied between 0 and 0.21. GPs identified as not adhering to the guidelines by the prescribing indicators often had high scores on the self-report instruments. Correlations between 0.20 and 0.55 were observed among indicators based on aggregated prescribing data and those based on individualized data. The agreement for identifying low adherence was small, with kappa values ranging from 0.19 to 0.30. CONCLUSIONS: Indicators based on self-report instruments seem to overestimate guideline adherence. Indicators assessing prescribing quality at an aggregated level give clearly different results, as compared to indicators evaluating prescribing data on an individual patient level. Caution is needed when using such prescribing indicators to identify low adherence to guidelines. Further validation studies using a gold standard comparison are needed to define the best possible indicator.
Authors: C C Veninga; P Lagerløv; R Wahlström; M Muskova; P Denig; J Berkhof; M M Kochen; F M Haaijer-Ruskamp Journal: Am J Respir Crit Care Med Date: 1999-10 Impact factor: 21.405
Authors: A Granados; E Jonsson; H D Banta; L Bero; A Bonair; C Cochet; N Freemantle; R Grilli; J Grimshaw; E Harvey; R Levi; D Marshall; A Oxman; L Pasart; V Räisänen; E Rius; J A Espinas Journal: Int J Technol Assess Health Care Date: 1997 Impact factor: 2.188
Authors: Lisa G Pont; Petra Denig; Thys van der Molen; Willem Jan van der Veen; Flora M Haaijer-Ruskamp Journal: Eur J Clin Pharmacol Date: 2003-11-18 Impact factor: 2.953