The quantification and epistemology of medicines use and polypharmacy tested in an observational study
- 1Department of Medicine University of Otago Christchurch New Zealand
- 2Department of Clinical Pharmacology Health New Zealand – Te Whatu Ora Waitaha|Canterbury Christchurch New Zealand
Abstract
Aim: Quantify the prevalence and incidence of medicines use per patient hospital admission and illustrate the importance of definitions in quantifying medicines use.
Method: Data from all public hospitals in Canterbury, New Zealand, between 01/07/2022 and 01/07/2023 were extracted, totalling 53 580 hospital admissions of 39 703 adult patients who survived to discharge. Patients' medicines use from hospital admission to discharge was quantified and linked to outcomes data.
Results: The average number of medicines at discharge was 3.7 (standard deviation [SD] 3.4) long term, 1.2 (SD 1.2) short course and 4.3 (SD 2.4) as needed. The prevalence of polypharmacy at discharge was 36% (19 511/53 580) for long‐term medicines and 84% (45 214/53 580) for all medicines. Per admission, an average of 1.0 (SD 1.9) long‐term medicines were started, 0.5 (SD 1.1) were stopped and 0.3 (SD 0.7) had dose changes. Per admission, there were 11.5 (SD 6.0) different medicines prescribed, 14.4 (SD 9.9) prescriptions and 26 (IQR 11 to 60) doses of medicines administered. For patients with five or more long‐term medicines at discharge, the adjusted odds of mortality were decreased (adjusted odds ratio [aOR] 0.73, 95% CI 0.67 to 0.79), and the adjusted odds of hospital readmission or mortality and adverse drug reaction occurrence were increased (aOR 1.09, 95% CI 1.03 to 1.15, and aOR 1.33, 95% CI 1.23 to 1.44).
Conclusion: Standard definitions are needed to validly quantify medicines use and compare use between health care settings. Where longitudinal data exist, changes in medicines use can be measured, rather than inferred from cross‐sectional studies.
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BibTeX
@article{pairman2026quantification,
title = {{The quantification and epistemology of medicines use and polypharmacy tested in an observational study}},
author = {Lorna Pairman and Paul Chin and Matthew Doogue},
journal = {British Journal of Clinical Pharmacology},
year = {2026},
month = jul,
doi = {10.1002/bcp.70684},
url = {https://doi.org/10.1002/bcp.70684},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - The quantification and epistemology of medicines use and polypharmacy tested in an observational study
AU - Lorna Pairman
AU - Paul Chin
AU - Matthew Doogue
PY - 2026
DA - 2026/07/07
JO - British Journal of Clinical Pharmacology
DO - 10.1002/bcp.70684
UR - https://doi.org/10.1002/bcp.70684
AB - Aim: Quantify the prevalence and incidence of medicines use per patient hospital admission and illustrate the importance of definitions in quantifying medicines use. Method: Data from all public hospitals in Canterbury, New Zealand, between 01/07/2022 and 01/07/2023 were extracted, totalling 53 580 hospital admissions of 39 703 adult patients who survived to discharge. Patients' medicines use from hospital admission to discharge was quantified and linked to outcomes data. Results: The average number of medicines at discharge was 3.7 (standard deviation [SD] 3.4) long term, 1.2 (SD 1.2) short course and 4.3 (SD 2.4) as needed. The prevalence of polypharmacy at discharge was 36% (19 511/53 580) for long‐term medicines and 84% (45 214/53 580) for all medicines. Per admission, an average of 1.0 (SD 1.9) long‐term medicines were started, 0.5 (SD 1.1) were stopped and 0.3 (SD 0.7) had dose changes. Per admission, there were 11.5 (SD 6.0) different medicines prescribed, 14.4 (SD 9.9) prescriptions and 26 (IQR 11 to 60) doses of medicines administered. For patients with five or more long‐term medicines at discharge, the adjusted odds of mortality were decreased (adjusted odds ratio [aOR] 0.73, 95% CI 0.67 to 0.79), and the adjusted odds of hospital readmission or mortality and adverse drug reaction occurrence were increased (aOR 1.09, 95% CI 1.03 to 1.15, and aOR 1.33, 95% CI 1.23 to 1.44). Conclusion: Standard definitions are needed to validly quantify medicines use and compare use between health care settings. Where longitudinal data exist, changes in medicines use can be measured, rather than inferred from cross‐sectional studies.
SN - 0306-5251
SN - 1365-2125
ER -