Identifying potentially inappropriate prescribing in entire populations: coding the STOPP-START and Beers criteria for use with large, routinely collected population health datasets

<h2>ABSTRACT</h2><h3>Background and Objective</h3> Adverse medication events significantly contribute to emergency department visits, unplanned hospitalizations, and in-hospital morbidity and mortality, particularly in older adults. Identifying potentially inappropriate prescribing is essential to improve safety, care quality, and medication management. The objective of this study was to identify and codify the STOPP-START and Beers criteria for large, population-level health databases to detect potentially inappropriate prescribing at the population level. <h3>Methods</h3> A subset of the 2014 STOPP/START and 2015 Beers criteria applicable to health administrative data were codified using diagnostic (ICD; International Classification of Diseases) and medication (DIN; Drug Identification Number) codes using provincial health administrative databases in Ontario, Canada, which comprise individual-level, linked information on medication dispensation, physician services use, emergency room visits, hospitalizations, mortality, and sociodemographic data. <h3>Results</h3> Overall, 103 of 177 (58.2%) criteria were codable (76.5% of 81 STOPP, 23.5% of 34 START, and 53.2% of 62 Beers). Some criteria could not be coded because the population health data used was missing information necessary to the operationalization these criteria. This included laboratory values (renal function) and diagnostic or clinical information (such as blood pressure, body mass index) that could not be indirectly derived from the population health data. <h3>Conclusion</h3> Applying a large subset of codified and well-established clinical criteria to health administrative data offers a promising and potentially cost-effective approach to detect potentially inappropriate prescribing at the population level. The present study contributes the required coding which constitutes the core of this approach.

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Journal
Journal of Clinical Epidemiology
Published
2025-10-24
DOI
https://doi.org/10.1016/j.jclinepi.2025.112019
Primary Topic
Pharmaceutical Practices and Patient Outcomes
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article
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article

Identifying potentially inappropriate prescribing in entire populations: coding the STOPP-START and Beers criteria for use with large, routinely collected population health datasets

Barbara Farrell, Tim Ramsay, Cristín A. Ryan, Caitríona Cahir et al.
Journal of Clinical Epidemiology
Pharmaceutical Practices and Patient Outcomes
article

Identifying potentially inappropriate prescribing in entire populations: coding the STOPP-START and Beers criteria for use with large, routinely collected population health datasets

Barbara Farrell, Tim Ramsay, Cristín A. Ryan, Caitríona Cahir, Steven Hawken, Roland Halil, Douglas G. Manuel, Lise M. Bjerre, Kasim E. Abdulaziz, Christina Catley, Ulrika Gillespie, Kednapa Thavorn, Glenys Smith
article en

Abstract

<h2>ABSTRACT</h2><h3>Background and Objective</h3> Adverse medication events significantly contribute to emergency department visits, unplanned hospitalizations, and in-hospital morbidity and mortality, particularly in older adults. Identifying potentially inappropriate prescribing is essential to improve safety, care quality, and medication management. The objective of this study was to identify and codify the STOPP-START and Beers criteria for large, population-level health databases to detect potentially inappropriate prescribing at the population level. <h3>Methods</h3> A subset of the 2014 STOPP/START and 2015 Beers criteria applicable to health administrative data were codified using diagnostic (ICD; International Classification of Diseases) and medication (DIN; Drug Identification Number) codes using provincial health administrative databases in Ontario, Canada, which comprise individual-level, linked information on medication dispensation, physician services use, emergency room visits, hospitalizations, mortality, and sociodemographic data. <h3>Results</h3> Overall, 103 of 177 (58.2%) criteria were codable (76.5% of 81 STOPP, 23.5% of 34 START, and 53.2% of 62 Beers). Some criteria could not be coded because the population health data used was missing information necessary to the operationalization these criteria. This included laboratory values (renal function) and diagnostic or clinical information (such as blood pressure, body mass index) that could not be indirectly derived from the population health data. <h3>Conclusion</h3> Applying a large subset of codified and well-established clinical criteria to health administrative data offers a promising and potentially cost-effective approach to detect potentially inappropriate prescribing at the population level. The present study contributes the required coding which constitutes the core of this approach.

Journal of Clinical EpidemiologyVol. 189
Uppsala University (SE), University of Ottawa (CA), Trinity College Dublin (IE), Children's Hospital of Eastern Ontario (CA), Ottawa Hospital (CA), Bruyère (CA), Institut du Savoir Montfort (CA), Ottawa Hospital Research Institute (CA)
Openalex Percentile: Top 63%
Pharmaceutical Practices and Patient Outcomes
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