Abstract
Study design
Retrospective cohort study.
Objectives
To examine the prevalence of polypharmacy for individuals with nontraumatic spinal cord dysfunction (NTSCD) following inpatient rehabilitation and to determine associated risk factors.
Setting
Ontario, Canada.
Methods
Administrative data housed at ICES, Toronto, Ontario were used. Between 2004 and 2015, we investigated prescription medications dispensed over a 1-year period for persons following an NTSCD-related inpatient rehabilitation admission. Descriptive and analytical statistics were conducted. Using a robust Poisson multivariable regression model, relative risks related to polypharmacy (ten or more drug classes) were calculated. Main independent variables were sex, age, income quintile, and continuity of care with outpatient physician visits.
Results
We identified 3468 persons with NTSCD during the observation window. The mean number of drug classes taken post-inpatient rehabilitation was 11.7 (SD = 6.0), with 4.0 different prescribers (SD = 2.5) and 1.8 unique pharmacies (SD = 1.0). Significant predictors for post-discharge polypharmacy were: being female, lower income, higher comorbidities prior to admission, lower Functional Independence Measure at discharge, previous number of medication classes dispensed in year prior to admission, and lower continuity of care with outpatient physician visits. The most common drugs dispensed post-inpatient rehabilitation were antihypertensives (70.0%), laxatives (61.6%), opioids (59.5%), and antibiotics (57.8%).
Conclusion
Similar to previous research with traumatic spinal cord injury, our results indicate that polypharmacy is prevalent among persons with NTSCD. Additional research examining medication therapy management for NTSCD is suggested.
Introduction
Spinal cord injury/dysfunction is often a devastating life-long condition that results in significant impairments, morbidity, and lower life expectancies [1]. Most individuals with spinal cord damage experience episodic secondary health complications (e.g., urinary tract infections, spasticity, constipation, respiratory infections) and multiple chronic conditions (e.g., chronic pain, diabetes, heart disease, fatigue, osteoporosis, depression) [2, 3]. Pharmacotherapy is often provided for these complications and multimorbidity rendering persons with spinal cord injury/dysfunction at risk for polypharmacy [4].
In the general population, polypharmacy (e.g., often defined as five or more drugs) can sometimes be problematic because it may increase the risk of drug therapy problems, potentially inappropriate prescribing, challenges with adherence, and negative health outcomes [5]. Although limited and mostly focused on traumatic spinal cord injury (TSCI), previous studies have identified a high prevalence of polypharmacy among persons with spinal cord injury/dysfunction [4]. For example, a large cohort by Kitzman and colleagues used administrative health data in the United States to examine the prevalence of polypharmacy and drug therapy problems in 4800 hospitals over three years among patients with and without injury/dysfunction [6]. Slightly more than half of the cohort (56%; n = 7399 of a total 13,160) was on ≥5 concomitant prescription medications. Moreover, 23% of the cohort was on ≥10 prescription medications as compared with 7% for the age- and sex-matched control group. Polypharmacy was identified as an important factor contributing to drug-related problems, as persons with injury/dysfunction who were taking five or more medications were 3.7 times more likely to have a drug-related problem compared with comparison control group. While Kitzman and colleagues used population-based data with large, generalizable sample size, the analysis was not stratified by type of injury/dysfunction (i.e., nontraumatic, traumatic), and did not examine the factors associated with polypharmacy.
A recent scoping review conducted by Cadel and colleagues identified possible negative clinical outcomes associated with polypharmacy, such as drug therapy problems (e.g., intoxication by negative drug interactions), and bowel complications (e.g., diarrhea, constipation) [4]. Factors related to polypharmacy included older age, higher level of injury, and greater severity of injury. Cadel and colleagues concluded more research is needed in this area; particularly with nontraumatic spinal cord dysfunction (NTSCD), with minimal studies specifying the mechanism of injury or examining polypharmacy by injury [4]. Given the age-related etiologies associated with NTSCD such as cancer and degeneration of the spinal column [7], it is important to further investigate the prevalence of polypharmacy within this group. To address this gap, the current study aimed to describe the prevalence of polypharmacy among persons with NTSCD using administrative health data and examine factors that impact the likelihood of polypharmacy in this population.
Methods
Using administrative health data housed at ICES, Toronto, Ontario (www.ices.on.ca), we conducted a retrospective cohort study. ICES is a nonprofit research institute funded by the Ontario Ministry of Health and Long-Term Care. As a prescribed entity under privacy legislation in Ontario, ICES is authorized to collect and use healthcare data for the purposes of health system evaluation and improvement. The datasets contain records of all publicly funded healthcare encounters within the province of Ontario, Canada. As Canada’s most populous province, Ontario represents ~40% of the Canadian population with over 14 million residents. Ontario has a universal health system under the Canada Health Act that funds all medically necessary care by physicians, hospitals, inpatient rehabilitation, and some other beneficiary care. A comprehensive list of drugs is publicly funded through the Ontario Drug Benefit (ODB) program for individuals of 65 years and older and individuals <65 years who receive financial social assistance or have catastrophic drug costs.
These datasets were linked using encoded identifiers and analyzed at ICES. The National Rehabilitation Reporting System (NRS) provided information individuals who received inpatient rehabilitation care (e.g., admission and discharge dates, transfer information, and diagnostic codes). The Discharge Abstract Database (DAD) provided hospital admission and discharge information (e.g., dates, transfer information, the most responsible diagnosis and up to 24 secondary diagnostic codes [based on International Classification of Disease, Tenth Revision Canada, ICD-10-CA codes]). The National Ambulatory Care Reporting System (NACRS) provided information related to emergency department visits, and same day surgeries (e.g., diagnostic codes). The Ontario Health Insurance Plan (OHIP) database provided information on outpatient physician visits (e.g., physician specialty, service date and location, and diagnostic codes). The ODB database provided records of drugs dispensed in community pharmacies. In Canada, every drug product has a different identifier (drug identification number). The Ontario Registered Persons database provided basic demographic information (e.g., sex, age, date of birth, residential postal code), and vital statistics information (e.g., death date).
Study population
Individuals who were admitted to inpatient rehabilitation with a NTSCD between April 1, 2004 and March 31, 2015 were included. Individuals were excluded if they died before the end of their index episode of care, if they were not living in the community for at least 275 days in the year after their index episode of care [8], or if they were not ODB eligible in the year after their index episode of care (Fig. 1). These last two exclusions were done to ensure that adequate data were available in the ODB database.
Study variables
Main outcome: polypharmacy
Our primary outcome was the prevalence of polypharmacy within the NTSCD population during a 1-year period after their index episode of care. An episode of care began with the initial inpatient rehabilitation stay and continued through transfer (if any) to another inpatient rehabilitation, complex continuing care, long-term care, or home. Individuals could be at home for up to 14 days between periods of inpatient stays and still be considered to be in the same episode of care, to account for temporary discharges home (e.g., waiting for a bed in rehabilitation or complex care). Episodes of care ended when more than 14 days elapsed without institutional care.
The observation window for drug claims began after the episode of care and lasted for a 1-year period. We chose to begin follow-up for polypharmacy after the index episode of care due to the high readmission rates with this population [9, 10] and the inability for the administrative databases to capture drugs dispensed while in hospital. Based on our previous work with TSCI, polypharmacy was defined using a threshold of ten or more drug classes [11]. We used cumulative polypharmacy, which is an established approach for pharmacoepidemiology research [12]. All unique drug classes received were summed over the observation period. Drug classes were defined as drugs with similar therapeutic or pharmacologic use, using the third level of the World Health Organization’s Anatomical Therapeutic Chemical drug classification (ATC) system [13].
Independent variables of interest
Our main independent variables were sex, age, continuity of care, and income quintile. Based on Cadel and colleagues review [4], we also included the following variables in the model: morbidity, functional status, length of stay in rehabilitation, and the number of drug classes dispensed in the year prior to inpatient rehabilitation admission.
Continuity of care was defined as the proportion of physician visits (office, home, phone) to the provider frequented most often in the post-discharge observation year divided by all physician visits [14]. High continuity was defined as at least 75% of visits with the same physician and low continuity was defined as <50% of visits made to the same physician. Continuity of care was not calculated if an individual had fewer than three visits in the observation period.
We used postal code and census information to determine neighborhood income quintile for each individual. We also used established methods to calculate prevalence of the following chronic diseases prior to inpatient rehabilitation admission: asthma, congestive heart failure, chronic obstructive pulmonary disease, hypertension, diabetes, rheumatoid arthritis, and dementia [15,16,17,18,19,20,21,22]. ICES databases were used for determination of chronic diseases, mostly using the DAD, NACRS, OHIP, and ODB [15,16,17,18,19,20,21,22]. Morbidity burden was determined using the Johns Hopkins ACG® System Ver. 10. For the 2 years prior to the inpatient rehabilitation admission, the number of Aggregated Diagnosis Groups (ADGs) from hospitalization, emergency department, and physician office visits were calculated. A higher number of ADGs indicated greater morbidity [23]. Functional status was determined at discharge by the Functional Independence Measure (FIM), with higher scores indicating more independence [24].
Statistical analysis
We used means (standard deviations), medians (interquartile ranges), and proportions to describe demographic and clinical characteristics. Differences between groups were tested using chi squared tests for categorical characteristics, t-tests for means, Wilcoxon–Mann–Whitney tests for medians, and the Cochran–Armitage test for ordinal characteristics. We used the exact McNemar’s test for differences in drugs dispensed before and after inpatient rehabilitation. A test was considered statistically significant at an alpha level of 0.05. Factors were examined independently in univariate Poisson regression models with polypharmacy (10+ different drug classes dispensed) as a binary outcome. Statistically significant factors were included in the final Poisson multivariable regression model with robust standard errors [25]. Relative risks (RRs) and 95% confidence limits were calculated. SAS version 9.4 (SAS Institute Inc., Cary, NC, USA; www.sas.com) was used to analyze the data.
Results
Overall, there were 3468 individuals who had an admission to inpatient rehabilitation for NTSCD between April 1, 2004 and March 31, 2015 (Fig. 1) and who met our inclusion/exclusion criteria. The median age (interquartile range) at admission was 70 (62–77), with almost even sex distribution (49.7% female; Table 1). The median length of stay for inpatient rehabilitation was 30 days (IQR = 16–52), and the mean FIM score at discharge was 106.2 (standard deviation [SD] = 16.1). The majority of persons resided in an urban setting (92.6%). Healthcare utilization in the year prior to inpatient rehabilitation was relatively high, with an average of 7.7 (SD = 7.1) specialist visits, 9.6 (SD = 8.5) family physician visits, and 12.4 (SD = 39.0) home care visits. The mean number of preadmission ADGs was 10.1 (SD = 4.0), with 20.3% of persons having high comorbidity. The majority of persons had a previous diagnosis of arthritis (79.8%), while 68.9% had hypertension, 30.6% had diabetes, 27.2% had coronary syndrome, 24.5% had cancer, and 23.1% had mood/anxiety disorders (see Table 1).
Post-injury healthcare utilization and prescription drugs dispensed
The mean number of drug classes taken following inpatient rehabilitation was 11.7 (SD = 6; median 11 (IQR = 7–16); see Table 2), with 62.7% on ten or more unique drug classes and 89% on five or more. There was an average of 4.0 different prescribers (SD = 2.5) and 1.8 unique pharmacies (SD = 1.0) used over the observation window. Continuity of care was relatively low as 47.8% of the cohort had <50% of their outpatient visits with the same physician after inpatient rehabilitation. Overall, there were significant increases in the majority of drug classes dispensed following inpatient rehabilitation (Table 3). The most commonly dispensed drug classes during this period were antihypertensives (70.0%), laxatives (61.6%), opioids (59.5%), antibiotics (57.8%), cholesterol lowering medications (46.6%), and proton pump inhibitors (44.5%).
Predictors of the likelihood of polypharmacy
A number of variables were significantly associated with the risk of polypharmacy (10+ different drug classes dispensed) following inpatient rehabilitation (Fig. 2). Notable significant risk factors included being female (RR = 1.06; 95% confidence interval [CI] 1.02–1.11), low continuity of care (RR = 1.07; 95% CI 1.02–1.12), low income (RR = 1.09; 95% CI 1.03–1.16), ≤102 FIM score (RR = 1.30; 95% CI 1.21–1.41), 103–111 FIM score (RR = 1.19; 95% CI 1.04–1.29), and 112–117 FIM score (RR = 1.14; 95% CI 1.06–1.24). Higher number of drug classes used prior to injury (RR = 1.86; 95% CI 1.75–1.98) was also significantly associated with an increase in risk of polypharmacy after inpatient rehabilitation. Morbidity (RR = 1.01; 95% CI 1.01–1.02 per ADG) and length of stay in inpatient rehabilitation (RR = 1.00; 95% CI 1.00–1.00 per day) were statistically significant but the RRs were small. Age was not statistically significant.
Reference categories are indicated in brackets: female (male), low income (high income), moderate income (high income), low continuity of care (high continuity of care), ≤102 FIM score (117+ FIM score), 103–111 FIM score (117+ FIM score), and 112–117 FIM score (117+ FIM score). ADG Aggregated Diagnosis Groups (measure of morbidity), FIM Functional Independence Measure, LOS length of stay.
Discussion
The present study found the prevalence of polypharmacy to be high among persons with NTSCD, with more than half of the cohort on ten or more different drug classes. However, prior to inpatient rehabilitation, persons with NTSCD had significant preexisting morbidity and prescription drug claims. The most common pre-inpatient rehabilitation comorbidities included those related to cardiovascular, cancer, and mental health. The most common prescribed drugs following inpatient rehabilitation were related to cardiovascular disease, bowel care, pain, and infections. Notable risk factors associated with polypharmacy following inpatient rehabilitation among persons with NTSCD were related to sex, income, continuity of care, previous drug claims, morbidity, and function.
Importantly, our findings reinforce the impact of medical complexity among persons with NTSCD. In comparison with the general older adult Canadian population and older adults with TSCI, persons with NTSCD in our cohort had significantly higher rates of polypharmacy. Our findings indicated that 68% of persons with NTSCD >66 years of age were on ten or more different drug classes, and 94% were on five or more different drug classes. Comparatively, the Canadian Institute for Health Information has reported 26.5% of older adults (65 years and older) are on ten or more prescribed drugs, while 66% are on five or more drugs [26]. Similarly, persons with NTSCD have higher prevalence of polypharmacy compared with TSCI, as we previously identified 56% of older adults with TSCI being prescribed ten or more drugs [11].
The higher rates of polypharmacy among persons with NTSCD are important for clinical and research considerations. Clinically, these findings suggest the importance for overall awareness of polypharmacy among this population. Understanding subpopulations who may be more at risk for polypharmacy and associated potential harm are important. Adverse drug events increase with polypharmacy and the potential for drug to drug interactions, side effects, compromised medication adherence, morbidity, and mortality [5]. We identified persons most at risk are women, those with multimorbidity, decreased functional independence, lower income, and lower continuity of care. Hand and colleagues also identified women, those experiencing polypharmacy and morbidity at increased risk for drug therapy problems for all types of injury/dysfunction [27]. Medication reviews and deprescribing opportunities (e.g., tapering opioids) may be warranted for further clinical and research considerations.
Similar to previous research on TSCI [11], continuity of care was an important predictor of polypharmacy, that is, the higher continuity, the less risk for polypharmacy. Persons with NTSCD in our cohort had a mean of four different prescribers and visited 1.8 different pharmacies in the observation window. Given the medical complexity and prevalence of polypharmacy, it is important for informational continuity which may be enhanced by sharing electronic medical records and by establishing a continued relationship with providers, such as a usual pharmacy [28].
These findings reinforce the importance for conversations around medication self-management and medication-taking behavior among persons with NTSCD during their inpatient rehabilitation. For example, educational components may include improving health literacy, such as understanding what medications are prescribed for, how to take them, side effects to watch out for and who to follow up with should there be any additional concerns [28]. Moreover, previous research has shown medication-taking behavior can be compromised if there are mental health concerns [29]. Approximately 31% of our cohort had either a mood disorder or another mental illness diagnosis prior to their inpatient rehabilitation stay. Clinicians should be sensitive to challenges with taking medications as recommended among persons with NTSCD who also have concomitant mental health concerns.
Strengths and limitations
There are a few limitations of this study. In using administrative health data, we were unable to identify persons who are on private plans or those who pay out-of-pocket. In addition, we were not able to capture prescriptions that were prescribed but never dispensed or were dispensed in an inpatient setting. Thus, our findings represent a conservative estimate of polypharmacy in this population. We mitigated this by starting the observation window after the initial inpatient episode of care to minimize the unaccounted inpatient prescriptions. While we identified the first inpatient rehabilitation stay for individuals during our observation window, we do not know the time of diagnosis for a NTSCD. While those <65 who are receiving drug coverage due to social assistance or catastrophic drug coverage may be uniquely different than those over 65 who are receiving coverage due to age, we did not find any differences in findings with sensitivity analyses. We chose to use cumulative polypharmacy to be consistent with how others report polypharmacy to which we were comparing our data (e.g., the Canadian Institute for Health Information), rather than simultaneous or continuous polypharmacy. There is no gold standard on the best method for capturing polypharmacy. Finally, we were not able to capture over-the-counter medications or natural health products or identify the indications for the medications. There are several strengths to this study, including the use of population-level data to examine prevalence of polypharmacy. We used the ATC system for classifying medications, which is an internationally adopted system such that our results will be more easily compared with future research in this area. We were also able to capture almost all prescription medications dispensed to persons over the age of 65, using the ODB database.
Future directions
Future research examining medication therapy management for NTSCD would be warranted. The present study identified high rates of polypharmacy and associated risk factors; however, more research is needed to understand prescription drug use and the types of drugs used over time. We identified for example more than half of the cohort is prescribed opioids, and future research would be useful to monitor trends in opioid use over time following inpatient rehabilitation. Future research examining the impact of morbidity and function on polypharmacy would be useful. Further, qualitative research is warranted to explore perceptions of medications, factors related to adherence and self-management, and more specifically, how persons with NTSCD are integrating medications into their everyday life.
Summary
There is limited research to date examining polypharmacy among persons with NTSCD. This study identified a high prevalence of polypharmacy. Risk factors associated with polypharmacy were female sex, low income, high morbidity, lower functional status, and low continuity of care.
Data availability
The dataset from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the dataset publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www.ices.on.ca/DAS.
References
Savic G, DeVivo MJ, Frankel HL, Jamous MA, Soni BM, Charlifue S. Long-term survival after traumatic spinal cord injury: a 70-year British study. Spinal Cord. 2017;55:651–8.
Rivers CS, Fallah N, Noonan VK, Whitehurst DG, Schwartz CE, Finkelstein JA, et al. Health conditions: effect on function, health-related quality of life, and life satisfaction after traumatic spinal cord injury. A prospective observational registry cohort study. Arch Phys Med Rehabil. 2018;99:443–51.
Adriaansen JJ, Ruijs LE, van Koppenhagen CF, van Asbeck FW, Snoek GJ, van Kuppevelt D, et al. Secondary health conditions and quality of life in persons living with spinal cord injury for at least ten years. J Rehabil Med. 2016;48:853–60.
Cadel L, C. Everall A, Hitzig SL, Packer TL, Patel T, Lofters A et al. Spinal cord injury and polypharmacy: a scoping review. Disabil Rehabil. 2019: 1–13. https://doi.org/10.1080/09638288.2019.1610085.
Duerden M, Avery T, Payne R. Polypharmacy and medicines optimizations: making it safe and sound. 11–13 Cavendish Square London W1G 0AN: The King’s Fund; 2013. http://www.kingsfund.org.uk/sites/files/kf/field/field_publication_file/polypharmacy-and-medicines-optimisation-kingsfund-nov13.pdf. Accessed 16 Jun 2016.
Kitzman P, Cecil D, Kolpek JH. The risks of polypharmacy following spinal cord injury. J Spinal Cord Med. 2017;40:147–53.
New PW, Cripps RA, Lee BBonne. Global maps of non-traumatic spinal cord injury epidemiology: towards a living data repository. Spinal Cord. 2014;52:97–109.
Morgan SG, Weymann D, Pratt B, Smolina K, Gladstone EJ, Raymond C, et al. Sex differences in the risk of receiving potentially inappropriate prescriptions among older adults. Age Ageing. 2016;45:535–42.
Jaglal SB, Munce SE, Guilcher SJ, Couris CM, Fung K, Craven BC, et al. Health system factors associated with rehospitalizations after traumatic spinal cord injury: a population-based study. Spinal Cord. 2009;47:604–9.
Guilcher SJT, Munce SEP, Couris CM, Fung K, Craven BC, Verrier M, et al. Health care utilization in non-traumatic and traumatic spinal cord injury: a population-based study. Spinal Cord. 2010;48:45–50.
Guilcher SJT, Hogan ME, Calzavara A, Hitzig SL, Patel T, Packer T, et al. Prescription drug claims following a traumatic spinal cord injury for older adults: a retrospective population-based study in Ontario, Canada. Spinal Cord. 2018;56:1059–68.
Fincke BG, Snyder K, Cantillon C, Gaehde S, Standring P, Fiore L, et al. Three complementary definitions of polypharmacy: methods, application and comparison of findings in a large prescription database. Pharmacoepidemiol Drug Saf. 2005;14:121–8.
World Health Organization Collaborating Centre for Drug Statistics Methodology. Anatomical therapeutic chemical code classification index with defined daily doses. http://www.whocc.no/atcddd/.
Manitoba Centre for Health Policy. Continuity of care (Ambulatory): glossary definition. 2011. http://mchp-appserv.cpe.umanitoba.ca/viewDefinition.php?definitionID=102475.
Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying patients with physician-diagnosed asthma in health administrative databases. Can Respir J. 2009;16:183–8.
Schultz SE, Rothwell DM, Chen Z, Tu K. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can. 2013;33:160–6.
Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying individuals with physcian diagnosed COPD in health administrative databases. COPD. 2009;6:388–94.
Tu K, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1:e18–26.
Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care. 2002;25:512–6.
Widdifield J, Bombardier C, Bernatsky S, Paterson JM, Green D, Young J, et al. An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance. BMC Musculoskelet Disord. 2014;15:216.
Jaakkimainen RL, Bronskill SE, Tierney MC, Herrmann N, Green D, Young J, et al. Identification of physician-diagnosed alzheimerʼs disease and related dementias in population-based administrative data: a validation study using family physiciansʼ electronic medical records. J Alzheimers Dis. 2016;54:337–49.
Koné Pefoyo AJ, Bronskill SE, Gruneir A, Calzavara A, Thavorn K, Petrosyan Y, et al. The increasing burden and complexity of multimorbidity. BMC Public Health. 2015;15:415.
Austin PC, van Walraven C, Wodchis WP, Newman A, Anderson GM. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Med. Care. 2011;49:932–9.
Granger CV, Hamilton BB, Linacre JM, Heinemann AW, Wright BD. Performance profiles of the functional independence measure. Am J Phys Med Rehabil. 1993;72:84–9.
Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–6.
The Canadian Institute for Health Information. Drug use among seniors on public drug programs in Canada, 2016. 2018. https://secure.cihi.ca/free_products/drug-use-among-seniors-2016-en-web.pdf.
Hand BN, Krause JS, Simpson KN. Polypharmacy and adverse drug events among propensity score matched privately insured persons with and without spinal cord injury. Spinal Cord. 2018;56:591–7.
Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc. 2011;86:304–14.
Grenard JL, et al. Depression and medication adherence in the treatment of chronic diseases in the United States: a meta-analysis. J Gen Intern Med. 2011;26:1175–82.
Acknowledgements
This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information. However, the analyses, conclusions, opinions, and statements expressed herein are those of the author, and not necessarily those of the Canadian Institute for Health Information. The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources; no endorsement is intended or should be inferred.
Funding
This project was funded by a Connaught New Investigator Award (University of Toronto), and the Craig H. Neilsen Psychosocial Research Pilot grant (PSR2-17, grant #441259). SJTG is supported by a Canadian Institutes of Health Research Embedded Clinician Scientist Salary Award on Transitions in Care working with Ontario Health (Quality; formerly Health Quality Ontario). AKL is supported by a Canadian Institutes of Health Research New Investigator Award, as a Clinician Scientist at the University of Toronto Department of Family and Community Medicine, and as the Chair in Implementation Science at the Peter Gilgan Centre for Women’s Cancers at Women’s College Hospital in partnership with the Canadian Cancer Society.
Author information
Authors and Affiliations
Contributions
SJTG conceptualized the study. SJTG, SLH, TP, TaP, and AKL obtained acquisition of study funding and designing the study. SJTG, M-EH, DMC, and AJC, prepared, coordinated, and guided the data analyses and interpretations. DMC and AJC analyzed the data. All authors assisted with overall interpretation and contextualization. SJTG, M-EH and QG assisted with the first draft of the manuscript. All authors critically reviewed and approved manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics approval
The use of data in this project was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board. However, we received Research Ethics Board approval from the University of Toronto (#34063).
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Guilcher, S.J.T., Hogan, ME., McCormack, D. et al. Prescription medications dispensed following a nontraumatic spinal cord dysfunction: a retrospective population-based study in Ontario, Canada. Spinal Cord 59, 132–140 (2021). https://doi.org/10.1038/s41393-020-0511-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41393-020-0511-x
This article is cited by
-
Examining the impact of COVID-19 on health care utilization among persons with chronic spinal cord injury/dysfunction: a population study
Spinal Cord (2023)
-
The Relationship of Continuity of Care, Polypharmacy and Medication Appropriateness: A Systematic Review of Observational Studies
Drugs & Aging (2023)
-
Prevalence of prescribed opioid claims among persons with nontraumatic spinal cord dysfunction in Ontario, Canada: a population-based retrospective cohort study
Spinal Cord (2021)