The quantification and epistemology of medicines use and polypharmacy tested in an observational study

The quantification and epistemology of medicines use and polypharmacy tested in an observational study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 February 2026 V1 Latest version Share on The quantification and epistemology of medicines use and polypharmacy tested in an observational study Authors : Lorna Pairman 0000-0002-2436-4277 , Paul Chin 0000-0002-5470-5191 , and Matthew Doogue [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177182651.12555991/v1 128 views 96 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Aim : To 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 the health district of 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 (SD 3.4) long-term, 1.2 (SD 1.2) short-course, and 4.3 (SD 2.4) PRN. 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 and adverse drug reaction occurrence were increased (aOR 1.22, 95% CI 1.16 to 1.29, and aOR 1.34, 95% CI 1.23 to 1.45). 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. Introduction There are an increasing number of people living longer with more conditions (multimorbidity) and taking more medicines (polypharmacy) 1-3 both of which are associated with adverse outcomes. 4-6 Several definitions of polypharmacy are used in literature, 7 limiting comparisons between studies. 1,8,9 The most common definition of polypharmacy ‘use of five or more medicines daily’ is used in about 46% of articles. 9 These medicines may include all medicines at a moment in time, all medicines over a defined period, regular long-term medicines only, or another subset. Furthermore, studies of polypharmacy are often limited to elderly patients, single services, or other exclusion criteria. 10-14 Both hospitalisation and community care visits provide an opportunity for doctors to review and change a patient’s medicines. In community care, dispensing data is commonly used to examine prevalence of medicines use with cross sectional analysis. In contrast, hospital medicines data is a continuous record of medicines use during a patient’s admission and can be analysed cross sectionally or longitudinally, 5,15,16 although most published hospital-based studies are cross-sectional. 13,14 The continuous record of medicines use in hospital provides a robust basis to refine methodology and definitions of medicines use. These definitions and methodology can then be applied to longitudinal studies in other hospitals and settings. To inform clinical practice and medicines policy we need to consistently describe medicines use over time, whether this be over a hospital admission, or over a patient’s lifetime in the community. In this study there were two primary aims: 1) quantify medicines use and changes in medicines use per hospital admission, and 2) highlight definitional challenges and opportunities in quantifying prevalent and incident medicines use. Additionally, we compared overall medicines use in hospital with subsequent patient outcomes. Setting and participants The study was conducted in the public hospitals in Canterbury New Zealand which serve 600,000 people. There is one large acute tertiary hospital, one primary psychiatric hospital, one primary rehabilitation and elective surgical hospital, and small community hospitals. There are 1300 public hospital beds with shared clinical records and approximately 250,000 inpatient admissions annually. We included adult patients ≥16 years admitted to district hospitals between 00:00 01/07/2022 to 23:59 30/06/2023 who survived to discharge. Transfers between hospitals within the health region constituted a single admission, provided the same medication chart was used at both hospitals. Emergency department presentations and ‘day cases’ were excluded. Data Source Prescribing data from MedChart TM , the district electronic prescribing and administration system, was used for this study. Almost all inpatient prescriptions are prescribed in MedChart TM , with infusions remaining on paper charts. Approximately 1,000,000 inpatient prescriptions are ordered annually in the local district through MedChart TM . Data from electronic systems at our local hospital are extracted and uploaded to a regional data warehouse. In this study, inpatient and prescribing data were accessed from the data warehouse using Structured Query Language (SQL) in Azure Data Studio (version 1.42.0). Inpatient admission data included patient age, gender, ethnicity, deprivation (NZ Deprivation Score 2013), Charlson Comorbidity Index, date of admission, length of inpatient stay, and service on discharge. These variables were identified ‘at discharge’. Patient outcome data included readmission to hospital within 30-days, adverse drug reaction occurrence during hospital admission, and mortality within 6-months. Prescribing data for prescriptions created during the patients’ admission included the generic medicine name, dose amount and frequency, route and form, and whether the medicine was administered. Definitions Definitions for key variables related to medicines use, with examples, are in Table 1. As this is a per medicine per patient admission analysis, the first regular prescription for each patient medicine was used to identify medicine route and form. For changes in dose (amount or frequency), a comparison between the earliest and the latest prescription for a medicine was used. Further detail is provided in the Supplementary Appendix 1. Medicines were divided into three use categories: long-term regular medicines, short-course regular medicines, and intermittent as needed (PRN, pro re nata ) medicines, based on the frequency of administration, route of administration, and the New Zealand Formulary (NZF) class. Each medicine was counted once regardless of number of prescriptions, and if both regular and PRN, it was classified as a regular medicine. Medicines were also classified as either ‘systemic’ (such as paracetamol oral) or ‘non-systemic’ (such as chloramphenicol eye drops) to facilitate sub-analysis considering the potential systemic impact of medicines for a patient. Further detail is in Supplementary Appendix 2. Definitions of length of inpatient stay, Charlson Comorbidity Index, admission hours, and service, as well as further information regarding definitions of total daily dose and number of medicines is available in Supplementary Appendix 3. In this study polypharmacy was defined as five or more medicines at a specific point in time. We have calculated polypharmacy prevalence including both all medicines, as a comparator to other studies with this definition, as well as only long-term medicines, to validly reflect medication burden and compare to community-based studies. The patient outcomes were: the 30-day readmission rate to hospital, the 6-month mortality rate post discharge, and the occurrence of an adverse-drug-reaction during hospital admission. The 30-day readmission rate was calculated by identifying patients with a new hospital admission within 30 days of their original discharge date, excluding patients who died prior to 30-days post-discharge. Only readmissions to Canterbury district hospitals were identified, and admissions for elective procedures and transfers within internal facilities were not included. The 6-month mortality rate was calculated by identifying patients who died within 6 months of their hospital discharge date and had a date of death recorded in the electronic hospital record. Occurrence of an adverse drug reaction during the admission was defined using ICD-10 codes. Further detail is documented in Supplementary Appendix 4. Statistical Analysis Means and proportions were used to describe the characteristics of patients, medicines, and services. Proportions, parametric, and non-parametric descriptive analyses were used to quantify medicines use, polypharmacy, changes to medicines use, and medicines exposure. Further detail is included in Supplementary Appendix 5. Unadjusted and adjusted odds ratios were calculated using univariable and multivariable logistic regression to determine the relationship between number of long-term medicines at discharge and patient outcomes. The multivariable model was adjusted for age, gender, ethnicity, Charlson Comorbidity Index (non-age adjusted), acute or elective admission indicator, admitted in or out of hours, discharge specialty, and length of inpatient stay. These variables were selected based on clinical relevance. Removal of individual variables did not meaningfully improve the model Akaike information criterion. Statistical analysis was conducted using R (version 4.4.0). This study was approved by the institutional research office as a locally approved project HD23/032. and assessed as exempt from review by the Health and Disability Ethics Committees (HDEC) NZ. Informed consent did not need to be obtained. The study was conducted in accordance with the STROBE guidelines for observational studies, 17 and the CODE-HER guidelines for studies using electronic health record data. 18 See Supplementary Table 1 and 2, and Supplementary Figure 1 for further detail. Results Patient Population There were 53,580 admissions of 39,703 patients in the year 1 July 2022 to 30 June 2023 (Figure 1). Most patients were female (57%), NZ European/Pākehā (77%) admitted out of hours (71%), and equal proportions were admitted to medical and surgical services (41% each). Characteristics of patients by inpatient admission can be found in Table 2. Further detail regarding discharge services is provided in Supplementary Figure 2. Medicine Use On average, patients were on 8.4 (SD 4.5) medicines at admission, and 9.3 (SD 4.7) at discharge (Figure 2 and Supplementary Figure 3). Of the 488,162 medicines at discharge, 40.2% (196,080) were ‘long-term’, 13.0% (63,674) were ‘short-course’, and 46.8% (228,408) were ‘PRN’ (Figure 3 and Table 3). Of the 196,080 long-term medicines, 93.9% (184,142) were ‘systemic’ and 6.1% (11,938) were ‘non-systemic’. The most common medicines on admission, on discharge, started, stopped, and with a dose change by medicine type are described in Supplementary Appendix 6. The distribution of medicines use by age is shown in Figure 2. The number of medicines used was greatest in patients in their mid-sixties. Overall use increased with age yet decreased in patients older than 90 years. When classified in age bands, adult patient <65 years had an average of 2.5 (SD 2.9) long-term medicines at discharge, elderly 65-79 years had 5.2 (SD 3.4) long-term medicines at discharge, and very elderly patients ≥80 years had 5.5 (SD 3.1) long-term medicines at discharge. Including all medicines, adult patient <65 years had an average of 8.2 (SD 4.6) medicines at discharge, elderly 65-79 years had 10.8 (SD 4.6) medicines at discharge, and very elderly patients ≥80 years had 10.3 (SD 4.3) medicines at discharge. Patients discharged under a medical service had on average 9.2 (SD 4.8) medicines on their chart at discharge, of which 4.8 (SD 3.4) were long-term medicines, whereas patients discharged under a surgical service had 10.2 (SD 4.3) medicines on their chart at discharge, of which 3.1 (SD 3.0) were long-term medicines. Polypharmacy There were five or more long-term medicines prescribed at discharge in 36.4% (19,511) of inpatient admissions (Table 3). For all medicines (including short-course and PRN medicines) 84.4% (45,214‬) of inpatient admissions had five or more medicines at discharge. By age, five or more long-term medicines were prescribed at discharge to 20.0% (6,048) of adults under 65 years, 55.1% (7,495) of elderly 65-79 years, and 60.9% (5,968) of very elderly ≥80 years. For all medicines, five or more were prescribed at discharge to 78.4% (23,658) of adults under 65 years, 92.2% (12,541) of elderly 65-79 years, and 91.9% (9,015) of very elderly ≥80 years. For patients discharged from a medical service, 49.9% (11,004) had five or more long-term medicines and 83.5% (18,401) had five or more medicines of any type. For patients discharged from surgical services, 28.2% (6,155) had five or more long-term medicines, and 93.5% (20,418) had five or more medicines of any type. For medicines use by service see Supplementary Table 3. Patient Outcomes Five or more long-term medicines at discharge was associated with increased readmission within 30-days (adjusted OR 1.23, 95% CI 1.16-1.30), decreased mortality within 6-months (adjusted OR 0.73, 95% CI 0.68-0.79), and increased adverse drug reaction occurrence (adjusted OR 1.33, 95% CI 1.23-1.44). For further detail on patient outcomes by medicines use see Supplementary Tables 4 and 5. For all three patient outcomes there was a dose-relationship with number of long-term medicines at discharge (Figure 3). Changes to Medicines Use During a hospital admission an average of 1.1 (SD 1.9) long-term medicines were started, 0.5 (SD 1.1) were stopped, and 0.3 (SD 0.7) had dose changes. For all medicines, an average of 3.0 (SD 4.2) medicines were started, 2.2 (SD 2.8) were stopped, and 0.7 (SD 1.3) had dose changes. For average number of medicines started, stopped, and with dose changes see Supplementary Table 6. Long-term medicines constituted 34.2% (54,874) of all medicines started, 22.3% (28,132) of all medicines stopped, and 36.2% (4,003) of all dose changes (Table 4). Medicines started were most commonly for long-term (34.2%, 54,874) or PRN (34.4%, 55,214) use, whereas short-course accounted for the largest proportion of medicines stopped (74,633, 59.3%) or with a dose change (4,234, 38.3%). Of all admissions, 40.3% (21,609) had one or more long-term medicines started, 24.8% (13,328) had one or more long-term medicines stopped, and 22.4% (11,982) had a dose change of one or more long-term medicines (Table 4). By age, long-term medicines were started, stopped and had dose changes respectively in 32.5%, 16.7% and 16.3% of adult patients aged <65 years, 48.8%, 32.1%, and 28.4% of 65-79-year-old patients, 52.5%, 39.7% and 32.4% of ≥80-year-old patients. Medicines Exposure The average number of long-term medicines prescribed at any time per admission was 4.2 (SD 3.8) and for all medicines was 11.5 (SD 6.0). There was an average of 14.4 (SD 9.9) prescriptions, and a median of 26 (IQR 11-60) doses administered per admission. The average number of systemic long-term medicines was 4.0 (SD 3.6). Discussion Use of hospital medicines data allowed for precise quantification of medicines use and changes in medicines use over time. The average number of medicines on a patient’s medication chart at discharge from our district hospitals was nine: four long-term, four PRN, and one short-course medicine. The number of long-term medicines increased with age and then plateaued around 80 years. 12 In hospital an average of 1.0 long-term medicines were started, 0.5 were stopped, and 0.3 had a dose change per admission. For all medicines (long-term, short-course, and PRN) an average of 3.0 medicines were started, 2.2 were stopped, and 0.7 had dose changes per admission. During hospital admission, an average of 14 prescriptions were written, and a median of 25 doses were administered. Elderly had approximately twice as many long-term medicines on their chart at discharge than adults <65 years. Patients aged long-term medicines at discharge respectively. In contrast, elderly had only 25% more than adults when including short-course and PRN medicines: for patients aged <65 years, 65-79 years, and ≥80 years the number of all medicines on the chart at discharge was 8.2, 10.8, and 10.3 respectively. This is a characteristic of hospital medicines data whereby PRN medicines are commonly prescribed for symptomatic use. Quantifying the number of long-term medicines prescribed at discharge provides a better comparison to community-based patient care than quantifying all medicines (including PRN and short-course). A patient’s medicines change over time. During hospital admission changes are concentrated into a short period and recorded in the electronic prescribing and administration system. In our study, during the average admission 1.0 long-term medicines were started, 0.5 were stopped, and 0.3 dose changes were made. For total medicines, including PRN and short-course, there was an average of 3.0 medicines started, 2.2 stopped, and 0.7 with dose changes, reflecting the high use of PRN medicines in hospitals. To measure changes in medicines use, longitudinal analyses are needed to capture the initiation, cessation, and dose changes of each medicine, rather than comparing the difference between two cross sectional points in time. There is limited published data, but our findings were similar to the 4.4 medicines changed per admission reported by Viktil et al. 2012. 19 Other inpatient studies comparing medicines use at admission and discharge cite an average net change in number of medicines between 0.5 and 2.9. 12,19-25 Few studies characterise ‘changes’ in medicines, and fewer still delineate long-term medicines. 10-12,26 Cross-sectional studies can describe medicines use using a common denominator such as per patient, per hospital admission, or per appointment. Longitudinal studies can describe changes in medicines use over various time periods: a clinic visit, a hospital admission, or a lifetime. Our study has quantified medicines use at a cross-section in time (discharge from hospital), and the changes in medicines use over a longitudinal period (the hospital admission). For both cross sectional and longitudinal studies, common denominators should be used to facilitate valid comparisons. Polypharmacy To our knowledge, this is the first study quantifying use of long-term medicines in adults using hospital prescribing data not restricted to one service, age group, or comorbidity. ‘Number of medicines’ is often dichotomised as less than five or five or more (polypharmacy). In this study the rate of polypharmacy at discharge was 43% for long term medicines and 84% for all medicines. A systematic review of hospital polypharmacy by Delara et. al found a prevalence of 52%. 27 The studies summarised in the systematic review were in specific patient groups including: hip fractures, falls, schizophrenia, diabetes, transient ischemic attacks, spinal cord trauma, and elective surgery. 27 It was not clear whether these prevalence estimates included short term and PRN use. Some studies recommend a qualitative dimension (appropriateness) to polypharmacy. 14 In our view, appropriateness of use, while related, is a separate question to answer. 28,29 Patient outcomes In our study odds of 6-month mortality were lower for those on more medicines after adjusting for Charlson Comorbidity Index and length of inpatient stay, amongst other variables. In contrast, several studies, including a systematic review in 2017, have described a positive dose response relationship between polypharmacy and mortality. 30-32 Some authors suggest ‘number of medicines’ acts as a surrogate marker for number of comorbidities 33-35 whilst two studies have shown a persisting relationship between polypharmacy and mortality, even after accounting for number of comorbidities. 36,37 In our study, the change in direction of the dose response relationship between number of medicines and mortality following model adjustment suggests Charlson Comorbidity Index and length of inpatient stay are not independent. Complex interactions between disease (multimorbidity), treatment (number of medicines), and other variables influence a patient’s outcome. Hence, a complex model is needed to prevent oversimplification of the relationship and inaccurate quantification of risk. Strengths and limitations A strength of this study was the use of a large, internally validated, inpatient and prescribing dataset. The study included all patients admitted to hospital in our local health district over a one-year period, rather than a sample population. We have included all adult and elderly patients, and all services. Medicines use was calculated using time specific data for starting, stopping, and changing each medicine as outlined in the methods. In the absence of existing standards, definitions of long-term, short-course, PRN, systemic, and non-systemic medicines were internally developed, and we hope these can be improved on and standardised to support valid quantification of medicines use across settings. A limitation of this study was use of a single New Zealand health district, and therefore findings may not be generalisable to dissimilar health systems. Data on medicines use was limited to hospital prescribing and administration data, and community data was not sourced. We inferred long term medicine use using frequency of administration and class of medicine. As mentioned above, definitions were developed for the purposes of this study: these need to be validated in other datasets and standard definitions agreed for meaningful comparisons to be undertaken. Conclusion Hospital prescribing and administration systems provide precise medicines data, well suited to develop and test definitions to quantify medicines use. In this study, patients were typically discharged on nine medicines, of which four were for long-term use. An average of three medicines (one long-term, two short-course or PRN) were started per admission. The results are valid for the hospital setting but are not representative of medicines use in the community, as agreed definitions are needed to quantify the prevalence of medicines use and the incidence of changes in other settings. This will help facilitate comparisons between studies. However, the methodology used in this study to quantify prevalence and incidence using hospital data could be extrapolated other time periods and settings. Abbreviations: • ePA: electronic prescribing and administration • SQL: Structured Query Language • ICD-10: 10th revision of the International Statistical Classification of Diseases and Related Health Problems • PRN: pro re nata (as needed) Acknowledgements: We thank Olivia Clendon and Richard McNeill of the Department of Clinical Pharmacology, Te Whatu Ora – Waitaha Canterbury, and Milan Sundermann of the Department of Medicine, University of Otago for their support. We also thank Scott Maxwell, Holly Wang, and Monica Smith, along with the rest of the Business Intelligence and Data Analytics department at Te Whatu Ora – Waitaha Canterbury for their assistance in data extraction and validation. Conflict of interest statement: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare: all authors had financial support from either the University of Otago or Health New Zealand for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Funding information: LP was funded by a University of Otago doctoral scholarship, whilst MD and PC are employees at the University of Otago. The funder had no role in study design, data collection, analysis, or interpretation, report writing, or in the decision to submit the article for publication. The researchers confirm that they are independent from the funder of the study. Data availability statement: Due to the nature of the research, supporting data is not available in accordance with New Zealand privacy laws. References 1. Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, Grymowicz M. The World Health Organization (WHO) approach to healthy ageing. Maturitas 2020;139:6-11. doi: 10.1016/j.maturitas.2020.05.018 2. Hill A, Davidson N. Time for action on medicine mistakes: Health and Disability Commissioner, 2019. 3. Mair A, Fernandez-Llimos F, Alonso A, et al. Polypharmacy management by 2030: a patient safety challenge. 2nd ed. Coimbra (PT): SIMPATHY Consortium; 2017. 4. Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. 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Brockhattingen KK, Anru PL, Masud T, Petrovic M, Ryg J. Association between number of medications and mortality in geriatric inpatients: a Danish nationwide register-based cohort study. Eur Geriatr Med 2020;11(6):1063-71. doi: 10.1007/s41999-020-00390-3 Tables Table 1 Definitions Prescription A ‘regular’ medicine prescribed to an admitted patient that was administered at least once, or a medicine prescribed ‘PRN’ and that was available for patient administration for at least one hour. This definition excluded prescriptions in MedChart TM that were stopped or changed before affecting patient care. Felodipine 5 mg oral once daily prescribed at 16:30 and administered at 08:00. Medicine A ‘medicine’ was a unique product existing as a prescription at least once on the patient’s drug chart. Combination products were defined as a separate medicine to the constituent products. The medicine ‘felodipine’ would encompass the prescriptions ‘felodipine 10 mg oral once daily’ prescribed at 16:30, administered at 08:00, stopped, then prescribed at 10:00 as ‘felodipine 10 mg oral once daily’ and administered the following day at 08:00. Long-term A medicine with a prescribed route plausibly available in the community, being associated with NZF class for chronic rather than symptomatic use and being prescribed with ‘regular’ frequency. Felodipine prescribed for oral administration once daily. It is in NZF class 2.6 (nitrates, calcium-channel blockers, and other antianginal drugs). Short-course A medicine with an NZF class for typically symptomatic or non-chronic use, being prescribed with a route typically not available in the community or being prescribed with ‘stat’ frequency. Amoxicillin 500 mg three times daily for five days in NZF class 5.1 (Antibacterial drugs). Rituximab 500 mg/50 mL IV infusion stat as it is prescribed for IV route and is a stat medication. PRN A ‘PRN’ medicine was a medicine prescribed with ‘PRN’ frequency. Salbutamol 200 micrograms (2 x 100 microgram inhalations) for use PRN. Systemic A medicine with a prescribed route resulting in primarily systemic absorption and systemic effects. Paracetamol 1000mg oral four times daily. Non-systemic A medicine with a prescribed route resulting in primarily non-systemic absorption and non-systemic effects. Clotrimazole 1% (10 mg/g) cream for application 2-3 times daily. Prescribed on admission The earliest prescription for a medicine created in the period of time between a patient’s admission, and the next 08:00 occurrence. For a patient admitted at 16:00 and prescribed felodipine 5 mg oral once daily at 16:30, the medicine ‘felodipine’ would be considered prescribed ‘on admission’. Continued at discharge The latest prescription for a medicine remaining live on the patient’s chart at the discharge datetime. It was assumed to be continued in the community. Felodipine 5 mg oral once daily stopped on 09/08/2024 11:34:00, and the patient discharged on 09/08/2024 11:34:00. Started medicine Medicines with a prescription started during the admission, whereby no prescription for the same product was ‘prescribed on admission’. Started medicines could be continued or stopped prior to discharge. Paracetamol 1000mg oral four times daily started on the third day of the admission, not previously being prescribed during the admission. Stopped medicine Medicines with a prescription stopped or completed during the admission, whereby no prescription for the same product was live on the chart at the discharge datetime. Stopped medicines could be prescribed on admission or started during admission. Clotrimazole 1% (10 mg/g) cream for application 2-3 times daily stopped five days before discharge, and not prescribed again prior to discharge. Total daily dose a The amount of a given medicine prescribed for a 24-hour period, defined by the medicine dose, frequency of administration, and commenced datetime. Paracetamol 1000mg oral four times daily would have a total daily dose of 4000mg. Change in dose A change in total daily dose between the first and last prescription for a medicine, providing the medicine was continued at discharge (not stopped). Felodipine 5 mg oral once daily on admission, changed to felodipine 10 mg oral once daily on day three, and then continued at discharge on day five. Number of medicines - all b A four-group categorical variable defined by the number of unique medicines prescribed at a specific point in time: no medicines, one to four medicines, five to nine medicines, and 10 or more medicines. The latter two categories have been combined to define ‘polypharmacy’, and calculate rates of polypharmacy prevalence, comparable to other studies. A patient on felodipine 5 mg oral once daily, paracetamol 500mg oral four times daily, paracetamol 500mg oral PRN, and clotrimazole 1% (10 mg/g) cream for application 2-3 times daily would be on three medicines (category ‘one to four medicines’). Number of long-term medicines b A four-group categorical variable defined by the number of unique long-term medicines prescribed at a specific point in time. Taking a medicine continuously long-term is different to a medicine used short-term or as needed. We wanted to compare the prevalence of polypharmacy using two different definitions. For both calculations of polypharmacy prevalence and multivariable analysis, five or more long-term medicines has been used as the definition of ‘polypharmacy’. A patient on felodipine 5 mg oral once daily, paracetamol 500mg oral four times daily, paracetamol 500mg oral PRN, and clotrimazole 1% (10 mg/g) cream for application 2-3 times daily would be on one long-term medicine (category ‘one to four long-term medicines’). a. Use of these variables ensured multiple prescriptions for the same product with different administration frequency and commencement datetime were accounted for. Further information is available in Supplementary Appendix 3. b. The four-group categorisation reflects existing definitions used in literature and allows the rate of polypharmacy at Christchurch Hospital to be compared to other hospitals. Further information is available in Supplementary Appendix 3. Table 2 Patient characteristics per inpatient admission Age in years mean (SD) 56.3 (22.9) Gender a (n (%)) Female Male Other 30,308 (56.6%) 23,202 (43.3%) 70 (0.1%) Ethnicity (n (%)) NZ European/Pākehā Māori Pacifica Other 41,019 (76.6%) 5,905 (11.0%) 1,721 (3.2%) 4,935 (9.2%) Charlson Comorbidity Index b (n (%)) 0 1-2 3-4 5+ 18,421 (34.4%) 10,721 (20.0%) 13,765 (25.7%) 10,673 (19.9%) Length of Inpatient Stay (n (%)) ≤1 day 2-3 days 4-9 days ≥10 days 12,590 (23.5%) 19,075 (35.6%) 15,206 (28.4%) 6,709 (12.5%) Admission Time (n (%)) In hours Out of hours 15,411 (28.8%) 38,169 (71.2%) Service at Discharge (n (%)) Medical Surgical Maternity/obstetrics Disabilities and older persons Mental health 22,041 (41.1%) 21,841 (40.8%) 5,820 (10.9%) 2,326 (4.3%) 1,552 (2.9%) Number of Medicines at Discharge mean (SD) Total Systemic Long-term Short course PRN Non-systemic 9.3 (4.7) 8.6 (4.2) 3.5 (3.2) 1.1 (1.1) 3.9 (2.3) 0.7 (1.1) a. Patient reported gender was available in our data source. Other gender includes those with ‘gender diverse’ and ‘unknown’ recorded. Sex disaggregated data was not available. b. Age-adjusted Charlson Comorbidity Index Table 3 Medicines at admission and discharge, subcategorised by long-term medicines, short-course medicines, PRN medicines, and patient age. Characteristic Adult 10 At discharge 0 1-4 5-9 >10 10,598 (35.1%) 14,966 (49.6%) 3,996 (13.2%) 613 (2.0%) 8,794 (29.1%) 15,331 (50.8%) 5,069 (16.8%) 979 (3.2%) 1,344 (9.9%) 6,109 (44.9%) 5,193 (38.2%) 956 (7.0%) 893 (6.6%) 5,214 (38.3%) 5,930 (43.6%) 1,565 (11.5%) 636 (6.5%) 4,024 (41.0%) 4,527 (46.2%) 618 (6.3%) 436 (4.4%) 3,401 (34.7%) 4,977 (50.8%) 991 (10.1%) 12,578 (23.5%) 25,099 (46.8%) 13,716 (25.6%) 2,187 (4.1%) 10,123 (18.9%) 23,946 (44.7%) 15,976 (29.8%) 3,535 (6.6%) Short-course medicines At admission 0 1-4 5-9 >10 At discharge 0 1-4 5-9 >10 9,143 (30.3%) 18,340 (60.8%) 2,676 (8.9%) 14 (0.0%) 11,397 (37.8%) 18,437 (61.1%) 338 (1.1%) 1 (0.0%) 3,791 (27.9%) 8,491 (62.4%) 1,314 (9.7%) 6 (0.0%) 3,993 (29.4%) 9,320 (68.5%) 287 (2.1%) 2 (0.0%) 2,989 (30.5%) 6,443 (65.7%) 373 (3.8%) 0 (0.0%) 3,141 (32.0%) 6,535 (66.6%) 129 (1.3%) 0 (0.0%) 15,923 (29.7%) 33,274 (62.1%) 4,363 (8.1%) 20 (0.0%) 18,531 (34.6%) 34,292 (64%) 754 (1.4%) 3 (0.0%) PRN medicines At admission 0 1-4 5-9 >10 At discharge 0 1-4 5-9 >10 2,578 (8.5%) 14,651 (48.6%) 12,698 (42.1%) 246 (0.8%) 1,712 (5.7%) 12,330 (40.9%) 15,567 (51.6%) 564 (1.9%) 1,226 (9.0%) 7,806 (57.4%) 4,494 (33.0%) 76 (0.6%) 615 (4.5%) 6,828 (50.2%) 5,977 (43.9%) 182 (1.3%) 1,062 (10.8%) 6,737 (68.7%) 1,976 (20.2%) 30 (0.3%) 529 (5.4%) 6,338 (64.6%) 2,871 (29.3%) 67 (0.7%) 4,866 (9.1%) 29,194 (54.5%) 19,168 (35.8%) 352 (0.7%) 2,856 (5.3%) 25,496 (47.6%) 24,415 (45.6%) 813 (1.5%) Total medicines At admission 0 1-4 5-9 >10 At discharge 0 1-4 5-9 >10 911 (3.0%) 7,147 (23.7%) 12,066 (40.0%) 10,049 (33.3%) 1,125 (3.7%) 5,390 (17.9%) 12,825 (42.5%) 10,833 (35.9%) 119 (0.9%) 1,605 (11.8%) 5,222 (38.4%) 6,656 (48.9%) 142 (1.0%) 919 (6.8%) 4,376 (32.2%) 8,165 (60.0%) 59 (0.6%) 1,178 (12.0%) 4,326 (44.1%) 4,242 (43.3%) 76 (0.8%) 714 (7.3%) 3,559 (36.3%) 5,456 (55.6%) 1,089 (2.0%) 9,930 (18.5%) 21,614 (40.3%) 20,947 (39.1%) 1,343 (2.5%) 7,023 (13.1%) 20,760 (38.7%) 24,454 (45.6%) Table 4 Medicines changes per inpatient admission, subcategorised by long-term medicines, short-course medicines, PRN medicines, and patient age. Characteristic Adult <65 years n=30,173 Elderly 65-79 years n=13,602 Elderly ≥80 years n=9,805 Total n=53,580 Long-term medicines Started 0 1-2 3-4 5+ Stopped 0 1-2 3-4 5+ Dose changed 0 1-2 3-4 5+ 20,351 (67.4%) 7,018 (23.3%) 1,764 (5.8%) 1,040 (3.4%) 25,111 (83.2%) 4,314 (14.3%) 553 (1.8%) 195 (0.6%) 25,228 (83.6%) 4,536 (15.0%) 365 (1.2%) 44 (0.1%) 6,962 (51.2%) 3,890 (28.6%) 1,513 (11.1%) 1,237 (9.1%) 9,224 (67.8%) 3,416 (25.1%) 645 (4.7%) 317 (2.3%) 9,740 (71.6%) 3,378 (24.8%) 432 (3.2%) 52 (0.4%) 4,658 (47.5%) 2,973 (30.3%) 1,227 (12.5%) 947 (9.7%) 5,917 (60.3%) 2,885 (29.4%) 683 (7.0%) 320 (3.3%) 6,630 (67.6%) 2,803 (28.6%) 333 (3.4%) 39 (0.4%) 31,971 (59.7%) 13,881 (25.9%) 4,504 (8.4%) 3,224 (6.0%) 40,252 (75.1%) 10,615 (19.8%) 1,881 (3.5%) 832 (1.6%) 41,598 (77.6%) 10,717 (20.0%) 1,130 (2.1%) 135 (0.3%) Short-course medicines Started 0 1-2 3-4 5+ Stopped 0 1-2 3-4 5+ Dose changed 0 1-2 3-4 5+ 19,069 (63.2%) 7,152 (23.7%) 2,707 (9.0%) 1,245 (4.1%) 13,076 (43.3%) 9,970 (33.0%) 5,389 (17.9%) 1,738 (5.8%) 26,452 (87.7%) 3,671 (12.2%) 50 (0.2%) 0 (0.0%) 7,758 (57.0%) 3,903 (28.7%) 1,200 (8.8%) 741 (5.4%) 5,965 (43.9%) 4,627 (34.0%) 1,979 (14.5%) 1,031 (7.6%) 11,215 (82.5%) 2,346 (17.2%) 40 (0.3%) 1 (0.0%) 5,346 (54.5%) 3,052 (31.1%) 926 (9.4%) 481 (4.9%) 4,629 (47.2%) 3,527 (36.0%) 1,155 (11.8%) 494 (5.0%) 7,732 (78.9%) 2,032 (20.7%) 41 (0.4%) 0 (0.0%) 32,173 (60.0%) 14,107 (26.3%) 4,833 (9.0%) 2,467 (4.6%) 23,670 (44.2%) 18,124 (33.8%) 8,523 (15.9%) 3,263 (6.1%) 45,399 (84.7%) 8,049 (15.0%) 131 (0.2%) 1 (0.0%) PRN medicines Started 0 1-2 3-4 5+ Stopped 0 1-2 3-4 5+ Dose changed 0 1-2 3-4 5+ 18,814 (62.4%) 7,122 (23.6%) 2,479 (8.2%) 1,758 (5.8%) 23,353 (77.4%) 5,762 (19.1%) 841 (2.8%) 217 (0.7%) 24,629 (81.6%) 5,424 (18.0%) 119 (0.4%) 1 (0.0%) 7,865 (57.8%) 3,507 (25.8%) 1,426 (10.5%) 804 (5.9%) 10,978 (80.7%) 2,128 (15.6%) 375 (2.8%) 121 (0.9%) 11,729 (86.2%) 1,838 (13.5%) 35 (0.3%) 0 (0.0%) 5,471 (55.8%) 2,659 (27.1%) 1,103 (11.2%) 572 (5.8%) 7,720 (78.7%) 1,582 (16.1%) 331 (3.4%) 172 (1.8%) 8,576 (87.5%) 1,209 (12.3%) 20 (0.2%) 0 (0.0%) 32,150 (60.0%) 13,288 (24.8%) 5,008 (9.3%) 3,134 (5.8%) 42,051 (78.5%) 9,472 (17.7%) 1,547 (2.9%) 510 (1.0%) 44,934 (83.9%) 8,471 (15.8%) 174 (0.3%) 1 (0.0%) Total medicines Started 0 1-2 3-4 5+ Stopped 0 1-2 3-4 5+ Dose changed 0 1-2 3-4 5+ 13,072 (43.3%) 7,007 (23.2%) 3,912 (13.0%) 6,182 (20.5%) 10,474 (34.7%) 9,582 (31.8%) 6,029 (20.0%) 4,088 (13.5%) 19,112 (63.3%) 9,507 (31.5%) 1,328 (4.4%) 226 (0.7%) 4,462 (32.8%) 3,343 (24.6%) 2,000 (14.7%) 3,797 (27.9%) 4,246 (31.2%) 4,625 (34.0%) 2,361 (17.4%) 2,370 (17.4%) 7,555 (55.5%) 4,829 (35.5%) 970 (7.1%) 248 (1.8%) 2,889 (29.5%) 2,510 (25.6%) 1,462 (14.9%) 2,944 (30.0%) 3,108 (31.7%) 3,500 (35.7%) 1,477 (15.1%) 1,720 (17.5%) 5,034 (51.3%) 3,758 (38.3%) 834 (8.5%) 179 (1.8%) 20,423 (38.1%) 12,860 (24.0%) 7,374 (13.8%) 12,923 (24.1%) 17,828 (33.3%) 17,707 (33.0%) 9,867 (18.4%) 8,178 (15.3%) 31,701 (59.2%) 18,094 (33.8%) 3,132 (5.8%) 653 (1.2%) Figure legends Figure 1 Flowchart of inpatient inclusion and exclusion Figure 2 Number of medicines and long-term medicines prescribed at discharge by patient age. Figure 3 Forrest plot with adjusted odds of patient outcomes Information & Authors Information Version history V1 Version 1 23 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Lorna Pairman 0000-0002-2436-4277 University of Otago Christchurch Department of Medicine View all articles by this author Paul Chin 0000-0002-5470-5191 University of Otago Christchurch Department of Medicine View all articles by this author Matthew Doogue [email protected] University of Otago Christchurch Department of Medicine View all articles by this author Metrics & Citations Metrics Article Usage 128 views 96 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lorna Pairman, Paul Chin, Matthew Doogue. The quantification and epistemology of medicines use and polypharmacy tested in an observational study. Authorea . 23 February 2026. 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