POLYPHARMACY AMONG PERSONS RESIDING IN LONG-TERM CARE
FACILITIES BEFORE AND DURING THE COVID-19 PANDEMIC IN CANADA: A
RETROSPECTIVE COHORT ANALYSIS
by
Anam Liaqat
M.Phil. in Pharmacology., Riphah International University, 2019
M.Sc., University of Northern British Columbia, 2023

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
HEALTH SCIENCES

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
December 2023
© Anam Liaqat, 2023

Abstract
According to the World Health Organization, one in nine persons are designated older
adults, or 60 years of age or older, and the percentage of the global population over 60 years will
nearly double from 12% to 22% between 2015 and 2050 (World Health Organization [WHO,
2022]). Individuals living in long-term care facilities (LTCFs) are often frail, and present with
co-morbidities that demand the administration of multiple drugs referred to as
“polypharmacy”. Polypharmacy poses a significant concern for individuals in LTCFs due to
several factors. The metabolic changes and reduced drug clearance associated with aging make
older individuals more susceptible to adverse drug reactions and drug-drug interactions, resulting
from the utilization of multiple medications (Dagli et al., 2014). Furthermore, the COVID-19
pandemic has brought attention to the significant vulnerability of individuals receiving care in
LTCFs, attributable to both the potential for inappropriate polypharmacy and the direct risk of
COVID-19 and its potential complications. There have been limited studies examining the
prevalence and factors associated with polypharmacy among persons living in LTCFs, especially
in the context of the COVID-19 pandemic in Canada. This study intends to fill this information
gap by using secondary data to determine the prevalence and risk factors of polypharmacy
among newly admitted Canadians in LTCFs before and during the COVID-19 pandemic.
Identifying risk factors may lead to more effective and tailored therapy or even prevention of
inappropriate prescribing especially in the context of the COVID-19 pandemic.
Keywords: Polypharmacy, Risk Factors, Nursing Homes, Medication, Older Adults

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Table of Contents
Abstract ..................................................................................................................................... ii
Table of Contents ...................................................................................................................... iii
List of Tables .......................................................................................................................... viii
List of Figures ........................................................................................................................... ix
Acknowledgement ...................................................................................................................... i
Introduction ................................................................................................................................1
Definition of Polypharmacy ....................................................................................................3
Prevalence of Polypharmacy ...................................................................................................3
Polypharmacy and Multi-morbidity .........................................................................................4
Polypharmacy and Potentially Inappropriate Medications (PIMs) ............................................5
Harms Associated with Polypharmacy.....................................................................................7
Polypharmacy and COVID-19 Pandemic ................................................................................8
Literature Review ...................................................................................................................... 11
Aim ....................................................................................................................................... 11
Methods ................................................................................................................................ 11
Research Question ............................................................................................................. 11
Search Strategy ................................................................................................................. 11
Studies Selection ................................................................................................................ 12
Data Extraction and Synthesis ........................................................................................... 12
Results .................................................................................................................................. 13

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Characteristic of Study Participants ....................................................................................... 15
Polypharmacy ....................................................................................................................... 17
Factors Associated with Polypharmacy ................................................................................. 18
Falls .................................................................................................................................. 24
Drug-Drug Interactions and Adverse Drug Events ............................................................ 25
Hospitalization Risk .......................................................................................................... 28
Co-morbidities, Disease Symptoms, and Age ..................................................................... 29
Cognitive Function ............................................................................................................ 35
Mortality ........................................................................................................................... 36
Discussion ................................................................................................................................. 37
Strengths and Limitations ...................................................................................................... 41
Implications for Practice and Future Recommendations......................................................... 42
Conclusions .............................................................................................................................. 44
Chapter 2: Analyzing Factors Associated with Polypharmacy; Aims and Methods .................... 46
Aim ....................................................................................................................................... 46
Research Questions ............................................................................................................... 46
Data Collection ..................................................................................................................... 46
Data Cleaning........................................................................................................................ 47
Definition of Polypharmacy .................................................................................................. 49
Measurements ....................................................................................................................... 49

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Sociodemographic Factors ................................................................................................ 50
Clinical Scales................................................................................................................... 50
Clinical Assessment Protocols (CAPs) ............................................................................... 52
Mental Health Conditions. ................................................................................................. 56
Data Quality .......................................................................................................................... 57
Ethical Consideration ............................................................................................................ 57
Data Analysis ........................................................................................................................ 57
Chapter 3: Results .................................................................................................................... 58
Bivariate Analysis ................................................................................................................. 58
Sociodemographic Factors ................................................................................................ 58
Medication Dispensing ...................................................................................................... 59
Mental Health Conditions.................................................................................................. 65
Clinical Scales................................................................................................................... 66
Results of Multivariate Analysis ............................................................................................ 72
Sociodemographic Factors ................................................................................................ 72
Mental Health Conditions.................................................................................................. 75
Clinical Scales................................................................................................................... 76
Adjusted Analysis of All Variables ..................................................................................... 79
Chapter 4: Discussion ............................................................................................................... 81
Implications of Findings ........................................................................................................ 89

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Strengths & Limitations ........................................................................................................ 92
Future Research Directions.................................................................................................... 95
Conclusions .............................................................................................................................. 97
References ................................................................................................................................ 99
Appendix One ......................................................................................................................... 114
Different Keywords Selected for All Databases ................................................................... 114
Search Criteria from CINAHL Database using MeSH Terms and Key Words ..................... 115
Search Criteria from PubMed Database using MeSH Terms and Key Words ....................... 116
Search Criteria from Web of Science Database using MeSH Terms and Key Words............ 117
Search Criteria from APA Psych info Database using MeSH Terms and Key Words ........... 118
Appendix Two ........................................................................................................................ 119
Characteristics of Participants and Studies, N = 38 Articles (2012-2023) ............................. 119
Appendix Three ...................................................................................................................... 129
Prevalence of Polypharmacy in Different Age Groups in 10 Studies from a Total of N = 38
Articles (2012 – 2023). ........................................................................................................ 129
Appendix Four ........................................................................................................................ 130
Prevalent Drugs Classes Among All Residents According to Anatomical Therapeutic
Classification System in 18 Studies from a Total of N = 38 Articles (2012 – 2023) ............. 130
Appendix Five ........................................................................................................................ 131
Descriptive Analysis of Sample Population N = 53,550 ....................................................... 131
Appendix Six .......................................................................................................................... 136

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Research Ethical Board Letter ............................................................................................. 136

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List of Tables
Table 1: Prevalent Co-morbidities in All Residents in N = 38 Articles (2012-2023) .................. 16
Table 2: Factors Associated with Polypharmacy in N = 38 Articles (2012 - 2023) ..................... 19
Table 3.1a: Bivariate Analysis for Sociodemographic Characteristics in Relation to
Polypharmacy Among Individuals in Pre-pandemic N = 30,544 (January 1st – December 1st,
2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021) ......................... 64
Table 3.1b: Mental Health Conditions and Clinical Scales Associated with Polypharmacy
Among Individuals in Pre-Pandemic N = 30544 (January, 1st – December, 1st 2019) and
Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021) ......................................... 68
Table 3.2a: Association Between Sociodemographic Factors and Polypharmacy for Multiple
Logistic Regression Models, N = 52,356 ................................................................................... 74
Table 3.2b: The Association Between Mental Health Conditions and Polypharmacy for Multiple
Logistic Regression Model, N = 53,526 .................................................................................... 76
Table 3.2c: The Association Between Clinical Scales and Polypharmacy for Multiple Logistic
Regression Model, N = 53,539 .................................................................................................. 78
Table 3.2d: The Association Between All Significant Factors and Polypharmacy for Multiple
Logistic Regression Model, N = 52,346 .................................................................................... 80

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List of Figures
Figure 1: Flow Diagram for Searches Under PRISMA 2020 New systematic Reviews guidelines,
Including Database and Other Source Searches Conducted on April 29, 2023. .......................... 14
Figure 3.1: Prevalent Medical Conditions Among Individuals in Pre-pandemic N = 30,544
(January 1st, 2019 – December 1st, 2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 –
March 1st, 2021) ....................................................................................................................... 59
Figure 3.2: Medications Dispensing Patterns in LTCFs Individuals in Pre-pandemic N = 30,544
(January,1st – December 1st, 2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 –
March 1st, 2021) ....................................................................................................................... 60
Figure 3.3: Pie Diagram for Different Categories of Polypharmacy ........................................... 61
Figure 3.4 a: Forest Plot for Pre-Pandemic Cohort to Determine the Association Between CAPs
Triggers and Polypharmacy ....................................................................................................... 71
Figure 3.4 b: Forest Plot for Pandemic Cohort to Determine the Association Between CAPs
Triggers and Polypharmacy ....................................................................................................... 71

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Acknowledgement
All praise and glory to the Almighty Creator Allah Subhanahu wa Ta'ala, who is the most
beneficent and merciful, and who has given me the ability to observe, mind to think, and the
courage to work more. I stepped into the field of medical sciences as a pharmacist and earned my
bachelor's degree in Doctor of Pharmacy from Pakistan. Because of my strong interest in patient
care and clinical research, I opted to pursue a career as a hospital and clinical pharmacist and
researcher, and I gained work experience at the Armed Forces Institute of Cardiology, as well as
the Isolation and Infectious Treatment Center in Pakistan, and also completed the M.Phil. degree
in Pharmacology alongside. Furthermore, I decided to pursue another master’s degree in health
sciences from Canada to gain international experience and enhance my research skills, and the
University of Northern British Columbia is the perfect place to do so.
First and foremost, I would like to express my heartfelt gratitude to my Late father
Muhammad Liaqat Ali, and my mother Rehana Liaqat for believing in me to achieve my goals.
Secondly, I would like to acknowledge my advisor, Dr. Shannon Freeman, for her unwavering
support and direction throughout this project, and her persistence, inspiration, zeal, and vast
knowledge. In addition, I am grateful to my committee members Dr. Waqar Haque, Dr. Tammy
Klassen-Ross, and Dr. Piper Jackson for providing me with expert research advice as well as
appreciation and feedback on my research work. Last but not least, I'd like to thank Dr. Ranjana
Bird, for her invaluable guidance in addressing and improving my weaknesses.

Introduction
According to the World Health Organization (WHO), the world's population is aging, and
the percentage of the global population over 60 years will nearly increase from 12% to 22%
between 2015 and 2050 (World Health Organization [WHO], 2022). The aging process is a
dynamic and intricate phenomenon that is marked by an increased susceptibility to the onset of
various chronic diseases. This often necessitates the concurrent use of multiple medications for
treatment and prophylaxis and puts additional strain on healthcare systems (Dwyer et al., 2010).
According to Canadian Institute for Health Information (CIHI), nearly two-thirds (65.7%) of
older adults received prescriptions for at least five different drug classes, more than one-quarter
(26.5%) received prescriptions for 10 or more different drug classes, and 8.4% received
prescriptions for 15 or more different drug classes (Canadian Institute for Health Information
[CIHI], 2018).
Long-term care facilities (LTCFs) play a significant role in delivering continuous care to
individuals with complex care needs, including nursing care, personal assistance, and various
therapeutic and supportive services. Long-term care is defined as services that offer 24-hour
specialized supervision and assistance in a safe and supportive environment for individuals who
cannot be cared for in their own houses or an assisted living place (Ministry of Health, Province
of British Columbia). The LTCFs are also known as nursing homes, continuing care facilities,
and residential care homes and can be public or private (CIHI Statistics Canada, 2016).
Individuals living in LTCFs may experience various medical conditions, including cognitive
impairment, physical disabilities, and chronic diseases, necessitating the use of multiple
medications (Bjork et al., 2016). Moreover, individuals living in LTCFs face an increased

1

susceptibility to drug-related issues, attributable to factors such as frailty, compromised mental
health, and limited physical mobility (Iniesta-Navalón et al., 2019; Mallet et al., 2007).
The first case of coronavirus disease (COVID-19) with apparent pneumonia emerged in
Wuhan, China, in December 2019, caused by the severe acute respiratory syndrome-virus
(SARS-CoV-2). Subsequently, on March 11, 2020, WHO declared the outbreak of COVID-19 a
global pandemic (World Health Organization [WHO], 2020). COVID-19 is highly contagious
and can lead to fatal co-morbidities, with acute respiratory distress syndrome being a prominent
concern. In 75% of patients, it manifests as bilateral pneumonia, serving as a clear indicator for
intensive care unit admissions and increased risk of fatality (Ruan et al., 2020; Singhal, 2020).
Individuals 65 years of age and older are particularly at risk from COVID-19 infections due to
frailty, co-morbidities, and reduction in inherent capacity which weakens their resistance to fight
disease (Center for Disease Control and Prevention [CDC], 2020). According to the WHO, 95%
of COVID-19 deaths involved individuals over the age of 60 years, and more than 50% of all
deaths involved individuals over the age of 80 years. In addition, eight out of 10 deaths involved
at least one co-morbidity, particularly cardiovascular diseases, hypertension, diabetes, and a
variety of other chronic conditions (Fischer et al., 2020; Koff and Williams, 2020).
The COVID-19 pandemic in Canada was particularly hard on LTCFs. Furthermore, there
were approximately 44,000 COVID-19 cases and 9,200 fatalities in senior homes including
LTCFs by the middle of December (during the second wave, which lasted from September 2020
to February 2021) (Public Health Agency of Canada [PHAC], 2020). Early March 2021 report
showed that senior homes including LTCFs continued to be the primary locations for outbreakrelated cases and casualties, accounting for about 7% of all cases and more than 50% of all
deaths (PHAC, 2021).

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Definition of Polypharmacy
The definition of polypharmacy in the literature lacks clear consensus. According to a
systematic review of 110 studies on polypharmacy, the commonly acknowledged standard cutoff for clinically significant polypharmacy is the use of five or more medications, whereas the
use of 10 or more drugs is frequently described as excessive polypharmacy and is seen as a sign
of "high risk" prescribing (Gnjidic et al., 2012; Masnoon et al., 2017; Morin et al., 2018).
According to WHO polypharmacy is defined as the administration of multiple drugs at the same
time or the administration of a large number of drugs (World Health Organization [WHO],
2004).
Prevalence of Polypharmacy
Polypharmacy is common in LTCFs, which poses significant difficulties for residents,
clinicians, and aged care workers (Payne & Avery, 2011). Up to 91%, 74%, and 65% of
individuals in LTCFs worldwide have been found to use five, nine, or 10 drugs, respectively.
The frequency of polypharmacy varies between LTCFs and geographical areas (Beloosesky et
al., 2013; Jokanovic et al., 2015). Studies have indicated that 60% of individuals over 65 years
receive prescriptions for three or more drugs, with approximately 39% receiving prescriptions
for more than five medicines (Kantor et al., 2015; Scott et al., 2015). Using prescription claims
data, a large cross-sectional Canadian study of 589 LTCFs found that diuretics (68.2%), protonpump inhibitors (54.8%), angiotensin-converting enzyme inhibitors (51.7%), and beta-blockers
(43.2%) were the most commonly prescribed medication classes among residents who received
nine or more medications (Bronskill et al., 2012). Also, high rates of polypharmacy were found
in the European SHELTER study, which examined medication use among nursing home
residents in seven European Union (EU) nations and one non-EU (Israel). The rates were 49.7%

3

for five to nine drugs and 24.3% for 10 drugs. The prevalence of polypharmacy was found to be
varied between countries and was directly correlated with comorbidities, depression, and chronic
pain. Particularly, the prevalence of excessive polypharmacy was lowest in Italy (8.8%),
followed by Israel (13.9%), Germany (15.7%), England (22.7%), the Netherlands (24.4%), the
Czech Republic (25.5%), France (30.2%), and highest in Finland (56.7 %) (Onder et al., 2012).
Polypharmacy and Multi-morbidity
The term "multi-morbidity" refers to the coexistence of two or more chronic diseases,
which may or may not be co-related, and represents a significant challenge for the health systems
(Violan et al., 2014). Individuals living in LTCFs often experience multi-morbid conditions,
leading to various nonspecific symptoms and geriatric syndromes (Gordon et al., 2014). Multimorbidity is strongly associated with age, affecting up to 95% of the population with ages 65
years and older, and more common in females across all age groups (Rankin et al., 2018).
According to the Canadian Longitudinal Study on Aging, the prevalence of multi-morbidity was
found to increase with age, increasing from 29.7% in the 45-49 years age group to 52% in the
60-64 years age group (Sakib et al., 2019). Philip et al. (2021) also found that age is closely
related to the prevalence of chronic diseases, and Canadians with age groups 45-54 years, 55-64
years, 65-74 years, and 75 years and older all had an average of 2.1 conditions, 2.9 conditions,
3.8 conditions, and 4.8 conditions, respectively. The prevalence of multi-morbidity is also
expected to rise over the next few decades, manifesting at younger ages, which is due to
improved survival among adults with chronic conditions (Canizares et al., 2018; Lebenbaum et
al., 2018).
Multi-morbidity is associated with an increased risk of hospitalization, physical decline,
depression, premature death, and polypharmacy (Masnoon et al., 2017). Polypharmacy (five or

4

more drugs) and hyper-polypharmacy (10 or more drugs) estimates in individuals 65 years and
older have significantly increased over the past 20 years as a result of multi-morbidity (Guthrie
et al., 2015; Charlesworth et al., 2015), with most recent international estimates falling between
50-66% and 23-27%, respectively (CIHI, 2016; Giovannini et al., 2018; Ellenbogen et al., 2020).
According to a cross-sectional study in Ontario LTCFs, the percentage of individuals with ages
85 years and older increased from 45.1% to 53.8% over 15 years. Additionally, parallel increases
in the percentages of individuals with nine or more prescription drugs and seven or more chronic
diseases (from 44.9% to 64.2%) have occurred through this time, with hypertension,
osteoarthritis, and dementia being the most common conditions reported between 2000 and 2015
(Ryan et al, 2020).
Effectively addressing multi-morbidity through pharmacological interventions poses a
challenge for healthcare professionals, especially in the care of older and frail individuals. The
complexity arises from the need for a focused approach that takes into account the
interconnected nature of multiple health conditions (Johnston et al., 2019). This results in
complex medication regimens that put these individuals at risk for inappropriate use, treatment
burden, non-adherence, and adverse drug events (Bailey et al., 2020; Thorell et al., 2019;
Wastesson et al., 2018).
Polypharmacy and Potentially Inappropriate Medications (PIMs)
Inappropriate medication prescribing is a worldwide issue. While polypharmacy appears
to be necessary for managing multi-morbidity and attempting to target the pathogenesis of a wide
range of disorders, inappropriate prescription of multiple medications can be unsafe (World
Health Organization [WHO], 2019). A substantial amount of research, including systematic
reviews, identified polypharmacy as the main factor for determining the use of potentially

5

inappropriate drugs (overprescribing, miss-prescribing, and under-prescribing) (Morin et al.,
2016; Storms et al., 2017).
The term potentially inappropriate medications (PIMs) is frequently used to describe
medications with a higher risk for adverse drug events. Inappropriate polypharmacy occurs when
prescriptions of multiple medications fail to produce the desired therapeutic effect and increase
the risk of adverse effects (Soler et al., 2019). PIMs have been linked to increased risk of drug
interactions, medication non-adherence, functional decline, unnecessary health outcomes, and
increased the risk of morbidity and mortality in older adults (Davies et al., 2019; Galli et al.,
2016). Studies have revealed a high prevalence of PIMs use in nursing home settings with 23.7%
- 70% of residents being prescribed at least one PIMs. Furthermore, 25.1% to 37.8% of residents
experienced potential drug-drug interactions, of which 72% were of moderate or major severity
(Chen et al., 2012; Iniesta-Navalón et al., 2019; Ryan et al., 2013). In addition, PIMs use among
seniors is extremely common in Canada. According to CIHI, nearly 50% of older adults (49.4%)
had at least one prescription on the Beers Criteria of PIMs, while 8.1% of seniors who were
chronic users of two or more medicines had prescriptions for multiple drugs on the PIMs Beers
Criteria list, accounting for approximately 18.0% of PIMs use in LTCFs (Canadian Institute for
Health Information [CIHI], 2018). Furthermore, it was found that 29% of individuals were taking
at least one drug that was never considered appropriate for them (Holmes et al., 2008). To
prevent inappropriate polypharmacy guidelines have been created globally, and the Beers and the
Screening Tool of Older Individuals' Potentially Inappropriate Prescriptions STOPP criteria are
the two most frequently used quality standards (Campanelli et al., 2012; Hamilton et al., 2011).
Inappropriate polypharmacy not only endangers the safety of individuals by resulting in
hospitalizations and poor adherence to medications but also places a financial burden on the

6

healthcare system (Dookeeram et al., 2017; Rodriguese et al., 2016). In a Japanese cohort study,
it was found that individuals on polypharmacy were taking one or more PIMs and exhibited a
higher risk of hospitalization and more outpatient visit days, leading to a 33% rise in healthcare
costs in comparison to individuals on polypharmacy with no PIMs (Akazawa et al., 2010).
Nyborg and colleagues discovered that the concurrent use of three or more psychotropic drugs,
antihypertensive, and the regular use of hypnotics were PIMs among Norwegian nursing home
residents aged 70 years (Nyborg et al., 2017), whereas Bor and colleagues discovered that PIMs,
such as pantoprazole and trimetazidine were significant risk factors for falls among individuals
in LTCFs (Bor et al., 2017). In Finland, Juola and colleagues found a sequential correlation
between the use of multiple potentially harmful medications and poorer health-related quality of
life (Juola et al., 2016).
Harms Associated with Polypharmacy
Polypharmacy has been identified as a safety concern, and the risk of an adverse event
increases exponentially with the use of five or more medications (Koper et al., 2013).
Polypharmacy is associated with adverse drug reactions (ADRs), drug-drug interactions (DDIs),
poor adherence to therapy, geriatric syndromes, and hospitalizations in LTCFs (Lalic et al.,
2016). Polypharmacy has also been linked to an increased risk of negative health outcomes such
as frailty, disability, and falls (Izza, et al., 2020). Additionally, ADRs by polypharmacy can
significantly affect morbidity and mortality and have a substantial financial effect on the
healthcare system (Díez et al., 2022). Comorbidity and age-related physiological changes such as
a decrease in the capacity of Phase I reaction enzymes (CYP450) can influence medication
disposition, and metabolism and may explain increased susceptibility to drug-drug interactions
and ADRs in older adults (Drenth-Van et al., 2019; Tornio et al., 2019). All of these effects may

7

be worsened by pre-existing diseases and lead to treatment infectiveness in this population
(Nechba et al., 2015; Teramura-Grönblad et al., 2016). According to Doan and colleagues,
potential CYP450-mediated DDIs are present in 80% of hospitalized individuals with
polypharmacy, and their frequency increases with the number of medications prescribed (Doan
et al., 2013). Additionally, polypharmacy may cause anxiety or excitability, trouble sleeping,
discomfort, confusion, dizziness, tremors weakness, hallucinations, and dizziness, showing a
detrimental effect on the quality of life (Davies and O’mahony, 2015; Laurie et al., 2020).
ADRs are more common in LTCFs, with individuals using nine or more medications
being twice as likely to experience an ADR compared to those taking fewer than nine
medications. Identifying ADRs is challenging as symptoms overlap with those commonly
present in older adults, complicating the selection of appropriate treatments and potentially
leading to further inappropriate prescriptions (Nyborg et al., 2017; Soler & Barreto, 2019).
Furthermore, clinical manifestations of several ADRs are proven to be brought on by a number
of particular medicines. For instance, when an individual is prescribed two or more medications
that increase the risk of falling, their likelihood of experiencing falls will also increase (Schiek et
al., 2019). Hence, the management of polypharmacy can be challenging, particularly in older
individuals, requiring significant time and complexity, and presenting a substantial dilemma
(Molokhia & Majeed, 2017; Sinha et al., 2021).
Polypharmacy and COVID-19 Pandemic
The COVID-19 pandemic has had a devastating impact on the standard of care and life
expectancy of older adults with complicated chronic diseases (Ouslander et al., 2020). Although
the overall COVID-19 mortality rate in Canada was lower than the rates in other Organization
for Economic Co-operation and Development (OECD) nations, it had the highest percentage of

8

deaths in LTCFs. Compared to an average of 38% in other OECD nations, LTCFs residents
made up 81% of all reported COVID-19 deaths in Canada during the first wave of the pandemic
(March-August 2020) (Canadian Institute of Health Research [CIHI], 2020).
Many older adults who dwell in LTCFs with advanced age and complex chronic
conditions have significant infection rates and unfavorable health outcomes during the pandemic.
Increasing multi-morbidity, particularly cardio-metabolic multi-morbidity, and polypharmacy
were found to be associated with an increased risk of developing COVID-19 infection
(McQueenie et al., 2020). The risk of developing a severe COVID-19 infection that can lead to
hospitalization, admission to an intensive care unit, or death remained highest in poly-medicated
older individuals with pre-existing multi-morbidities. For instance, 66% of the 1.7 billion
individuals with underlying illnesses who were at an increased risk of developing severe
COVID-19 infection were those aged 70 years and older (Arons et al., 2020; Clark et al., 2020;
White et al., 2020). SARS-CoV-2 has been widely transmitted in LTCFs due to structural and
operational considerations, and 50% to 80% of COVID-19-related early deaths occurred among
individuals receiving community care in LTCFs in economically developed countries (ComasHerrera et al., 2020).
Achieving optimal drug use in the population of older adults is difficult and complex
under normal circumstances. This issue worsened when COVID-19 disproportionately affected
LTCFs, where individuals may have received less timely and appropriate clinical care, including
medication management, as a result of the decline in the number of available clinicians and other
healthcare professionals, and disruptions in communication and services during the
pandemic (Campitelli et al., 2021; Nagham et al., 2021). Exposure to multiple medications may
serve as an indicator of disease severity, but it also amplifies the risk of adverse events, rendering

9

older adults more susceptible to complications, particularly in the context of COVID-19. In a
systematic review, polypharmacy was linked to a higher risk of unfavorable clinical outcomes
among individuals diagnosed with COVID-19. Drug classes such as antipsychotics, non-tricyclic
antidepressants, opioid analgesics, and medications for peptic ulcer, and gastroesophageal reflux
disease were associated with adverse outcomes among these individuals (Iloanusi et al., 2021). In
a population cohort study in Quebec, the number of drugs and the risk of death and
hospitalization in confirmed cases of COVID-19 in the population older than 65 years of age was
evaluated. While controlling for age and chronic diseases, polypharmacy was found to be
significantly linked to an increased risk of COVID-19-related hospitalizations and mortality in
this group of older adults (Sirois et al., 2022). Given that polypharmacy has been demonstrated
to result in less ideal treatment outcomes for a number of diseases, the simultaneous threat of
COVID-19 infection and polypharmacy in LTCFs residents with pre-existing multi-morbidities
is particularly concerning.

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Literature Review
Aim
This review aims to identify the factors associated with polypharmacy among individuals
residing in LTCFs.
Methods
Using Askey and O'Malley's (2005) framework, a scoping review strategy was conducted
in five steps. (1) Determining the research question (2) Finding relevant studies (3) Study
selection (4) Data charting, and (5) Compiling, analyzing, and reporting the results. This strategy
assists in locating any gaps in the research topic, reviewing the relevant studies, and gaining the
knowledge necessary to fill the gaps in the literature (Askey & O'Malley's, 2005).
Research Question
The research question investigated was what are the factors associated with
polypharmacy among individuals residing in residential long-term care settings?
Search Strategy
To find relevant papers written in English and published in the last 10 years, we searched
PubMed, Web of Science, APA-PsycInfo, and Cumulative Index to Nursing and Allied Health
Literature (CINAHL) databases. Search terms included Mesh terms and keywords related to
polypharmacy (“polypharmacy” OR multiple medicine* OR multiple medication* OR multiple
drug*), factors (“Risk factors” OR Associated factor* OR Co-morbidit* OR Factor* OR
Risk*OR Protective factor* ) and long-term care (“Long-term care” OR “Residential facilities”
OR aged care facilit*, OR Nursing home*OR long-term care home* OR long-term care facilit*
nursing homes) were combined and searched in all databases (See Appendix 1 for details of the
full search strategy).

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Studies Selection
Zotero (Zotero, 2023) was used to import potentially relevant articles, and duplicate items
were identified and removed by running a duplicate scan. The titles and abstracts were screened
and reviewed for inclusion and exclusion into the review based on the eligibility criteria. Finally,
the full-text copies of published studies were retrieved and independently evaluated for potential
inclusion. Prisma flow chart of systematic reviews, which incorporates databases and other
source searches, were used to guide the selection of studies as shown in Figure 1 (Page et al.,
2021). The following inclusion criteria were met by research studies: (1) polypharmacy was
stated clearly (2) factors associated with polypharmacy were discussed in the text or figures or
tables (3) studies were conducted in LTCFs. Studies that were conducted in hospital based
LTCFs were excluded, also if they focused only on potentially inappropriate medications,
deprescribing, and specific medication classes were excluded as well. Duplicate studies, reviews
that did not meet the eligibility criteria for inclusion, abstracts only, editorial letters, video
recordings, book chapters, dissertation, and protocols were excluded.
Data Extraction and Synthesis
An Excel spreadsheet (Microsoft Corporation, 2022) was used to develop a standardized
data extraction form and piloted for review. The data retrieved from the selected studies included
information about study designs, aims and objectives, publication year, settings, and methods;
population characteristics such as sample size, mean age, gender, and prevalent co-morbidities;
medical information relevant to polypharmacy such as mean number of drugs per resident,
prevalence, most frequently prescribed drugs, or classes; findings and results. The medication
cut-offs used to define polypharmacy, the period of time used to evaluate polypharmacy, and the
terminology used to define polypharmacy were obtained from individual studies. Factors

12

associated with polypharmacy were identified in studies that performed regression analysis and
also from one qualitative survey analysis.
Results
Total N = 38 studies satisfied the inclusion criteria including cross-sectional (n = 22),
retrospective (n = 9), prospective (n = 4), survey-based qualitative analysis (n = 1), and
longitudinal studies (n = 2). Primary research studies were conducted in Japan (n = 1),
Switzerland (n = 1), Netherlands (n =1), Spain (n = 5), United Kingdom (n = 3), Cyprus (n = 1),
Germany (n = 3), Denmark (n = 1), Mexico (n = 1), Australia (n = 6), Malaysia (n = 2), Sweden
(n = 2), Europe and Israel (n = 1), Hungary (n = 1), Canada (n = 2), Sweden(n = 1), Hong Kong
(n = 1), central Israel (n = 1). Four SHELTER studies included in this review were conducted in
eight different countries such as the Czech Republic, Germany, Israel, Italy, England, Finland,
France, and the Netherlands.

13

Note. The figure was created using PRISMA guidelines 2020 provided by Page et al., (2021)

14

Figure 1: Flow Diagram for Searches Under PRISMA 2020 New systematic Reviews guidelines, Including Database and Other Source Searches Conducted on
April 29, 2023.

Characteristic of Study Participants
Participants in studies were often 65 years or older (n = 18) or all (n = 9). The mean age
of participants ranged from 67.6 (SD = 3.8) to 87.5 (SD = 6.2) years, with females accounting for
56% to 78% of the population (n = 33). Eleven studies focused on stay lengths ranging from one
month to one year (See Appendix 2 for the population and characteristics of studies included in
this review). Appendix Three shows the prevalence of polypharmacy in various age groups. The
prevalent pathologies among residents according to the International Classification of Diseases
(ICD-10) were diabetes (n = 14), hypertension (n = 8), heart failure (n = 11), and dementia (n
=12) as shown in Table 1.

15

36.1
71.0
88.0

Zazzara et al., 2023

Visser et al., 2023

Oya et al., 2022
Díez et al., 2022
Villani et al., 2021

58.6
63.2

Dörks et al., 2016
Hallgren et al., 2016
Lilac et al., 2016
Herson, et al., 2015

60.5
43.6

78.0

59.2

55.6

55.5
58.4

55.5

HTN
(%)

2.1
3.8

32.0
32.0

34.0

Bone
Fractures
(%)

44.2

16.0

21.8

23.8

23.9

Depression
(%)

19.6
21.7

22.7

22.8
19.1

33.0

22.0
22.0
29.6

19.4
22.9

28.8
22.5

21.6

Diabetes
(%)

Note. HTN = Hypertension, CVA = Cardiovascular Accident, IHD = Ischemic Heart Disease

Leung et al., 2013
Davide et al., 2013
Graziano et al., 2012

57.0

McCracken et al., 2017

44.1

53.0
53.0
23.2

67.2

Cadenas et al., 2021
Velázquez-Alva et al.,
2020
Izza et al., 2020
Iniesta-Navalón et al.,
2019
Morin et al., 2018
Vetrano et al., 2018
Jokanovic et al., 2017

19.3

Dementia
(%)

Studies

Table 1: Prevalent Co-morbidities in All Residents in N = 38 Articles (2012-2023)

36.0

36.0

29.7
5.0

Arthritis
(%)

24.2
22.1

22.1
22.1

22.0
22.0

15.8
14.9

22.0

15.0

22.1

Stroke (%)

24.6
26.3

13.6

28.0
28.0
33.7

14.2

25.7

IHD
(%)

14.6
17.7

16.7

17.7

24.0

17.0
17.0

20.4

17.4

3.0

Heart
Failure
(%)
17.7

9.2
10.9

20.4

10.0
10.0

10.7

10.9

Cancer
(%)

16

Polypharmacy
Overall, 34 studies defined polypharmacy using a cutoff point and the definition of
polypharmacy in terms of the number of medications used varied among studies, and the most
commonly used definition being employed was the use of five or more drugs (n = 12) (Anliker et
al., 2023; Astrop et al., 2020; Beloosesky et al., 2013; Díaz et al., 2020; Gutiérrez-Valencia et al.,
2018; Hasan et al., 2020; Izza et al., 2020; Morin et al., 2018; Nozomu et al., 2022; Visser et al.,
2023; Wastesson et al., 2019; Zazzara et al., 2023), five to nine medicines (n = 8) (Cadenas,
2021; Davide et al., 2013; Diez et al., 2022; Dorks et al., 2016; Fassmer et al., 2019; Vetrano et
al., 2018; Villani et al., 2021) and 9 or more drugs (n = 8) (Herson et al., 2015; Jokanovic et al.,
2017; Jeon et al., 2019; Lilac et al., 2016; McCracken et al., 2017; Moore et al., 2014; Susan et
al., 2012). In addition, the time duration of polypharmacy evaluation was given as the use of
concurrent medications (n = 12), regular medications (n = 8), medications taken for at least one
month (n = 6), medications taken for at least three months (n =1), use of chronic medications (n
= 2), prescriptions dispensed in 28 days (n = 1), drugs ordered in three days (n =1), and
prescription dispensed in a year (n = 1). 16 studies specified the inclusion criteria for
polypharmacy definitions and did not include supplements (n =7), over the counter medication (n
= 4), complementary medicine (n = 2), as needed (n = 2), topical (n =1), anti-infective (n = 1),
and medical equipment’s (n = 2). However, supplements were included in n = 2, over the counter
in n = 4, complimentary medicines in n = 1, as needed in n = 1, and topical preparations in n = 3
studies. The mean number of medications among residents ranged from 3.54 (SD = 1.96) to 8.7
(SD = 3.9).
Overall, 12 studies defined excessive polypharmacy as the use of 10 or more
medications. According to the Anatomical Therapeutic Classification system (World Health

17

Organization, [WHO] ), the most common medication classes used by residents were peptic
ulcer and gastroesophageal reflux disease drugs, acetaminophen, proton-pump inhibitors,
antipsychotics, anticoagulants, anti-depressant, anti-hypertensive including angiotensin
converting enzyme inhibitors and angiotensin receptor blockers, benzodiazepines including
hypnotic sedative anxiolytics, anti-epileptic drugs, lipid-lowering agents including statins, antidementia drugs, beta blockers, and calcium channel blockers ( Appendix Four).
Factors Associated with Polypharmacy
Table 2 presents studies that investigated factors independently associated with
polypharmacy following regression analysis.

18

Fassmer et al., 2020

Villani et al., 2021

Cristina Ionescu et al.,
2021
Lexow et al, 2021

Cadenas et al., 2021

Clare et al., 2021

Oya et al., 2022

Anliker et al., 2023
Díez R et al., 2022

Zazzara et al., 2023
Visser et al., 2023

Studies

Inverse
Inverse
Positive

Inverse
Inverse
Positive

OR = 2.23 (1.11 - 4.51)

Contact with GP in past 2 Positive
months
Age ≥85 years
Being demented
Suspected Adverse
events
Frailty
Frailty
Out-of-hours medical
care contact

OR = 0.88 (0.50 - 1.53)

Inverse

Age (85–94 years)

OR = 0.72 (0.59 - 0.87)
OR = 0.75 (0.60 - 0.94)
RR = 2.03 (1.59 - 2.59)

OR = 0.43 (0.20 - 0.90)
OR = 0.38 (0.17 - 0.82)
OR = 2.01 (1.39 – 2.71)

OR = 1.09 (1.00 - 1.20)
OR = 43.02 (11.4 -162.00)
OR = 134.0 (12.01 -1495.5)
OR = 5.01 (1.96, 12.81)
OR = 13.5 (1.62 - 112.54)
OR = 0.13 (0.04 - 0.45)
OR = 1.33, (1.00 - 1.77)
OR = 2.41 (1.30 - 4.50)
OR = 0.73 (0.64 - 0.84)
OR = 1.86 (1.04 - 3.34)
OR = 1.75 (1.01 - 3.02)
OR = 2.0 (1.04 - 3.85)

HR = 1.61 (1.09 - 2.40)
OR = 1.09 (1.10 - 1.20)

Results (95% CI)

Positive
Positive
Positive
Positive
Positive
Inverse
Positive
Positive
Inverse
Positive
Positive
Positive

Positive
Positive

Association

Risk of mortality
COVID-19 related
mortality
Poor oral health status
High anticholinergic risk
5–7 PIMs
≥2 severe/moderate DDIs
≥2 drug duplicities
6–10 pathologies
Initial falls
Injuries after falls
Dementia
Age (70–74 years)
Age (80–84 years)
Hospitalization

Factors Associated with
Polypharmacy

Table 2: Factors Associated with Polypharmacy in N = 38 Articles (2012 - 2023)

≥5
≥10
≥10

0 - 11

≥10

≥10

≥5

≥10

≥5

≥5
≥5

≥ 10
≥5

Number
of Drugs

19

Care homes with 75%
high- ACFI
Risk of fall
Decline in CPS score
Drug-drug interactions
Frailty
Decline in CPS score
Frailty
Hypertension
Depression
Ischemic heart disease
Diabetes without endorgan damage

Jeon et al., 2019

Marhanis et al., 2019

Davide et al., 2019
Iniesta-Navalón et al.,
2019

Morin et al., 2018
Vetrano DL et al., 2018
Gutiérrez-Valencia et
al., 2018

Jokanovic et al., 2017

Izza et al., 2020
Velázquez-Alva., 2020

Hasan et al., 2020
Astrop et al., 2020

Kapoor et al., 2020
Díaz et al., 2020

Acute hospital
admissions
Adverse reaction
Fall risk in past 12
months
Frailty
Age ≥85
Number of DDIs ≥1 per
resident in the FDA
database.
Number of DDIs ≥1 per
resident Danish database
Falls
Malnutrition

Positive
Positive
Positive
Positive

Inverse
Inverse
Inverse

Positive
Positive

Positive

Positive

OR = 1.51 (1.12 - 2.05)
OR = 1.96 (1.43 - 2.68)
OR = 2.09 (1.49 - 2.94)
OR = 2.14 (1.50 - 3.07)

OR = 0.74 (0.23 - 2.43)
OR = 0.13 (0.01 - 0.24)
OR = 0.54 (0.21 - 1.42)

b = 0.10 (0.01, 0.20)
OR = 9.6 (4.8 - 19.1)

OR = 1.13 (1.02 - 1.24)

OR = 1.48 (1.39 - 1.57)

OR = 1.06 (1.03 - 1.09)
OR = 1.83 (1.27 - 2.63)

r = 0.40

Positive
Positive
Positive

OR = 6.07 (1.71 - 21.56)
r = - 0.15
r = 0.55

HR = 1.53 (1.07 - 2.18)
OR = 1.29 (1.04 - 1.60)

RR = 1.94 (1.60 - 2.35)

Positive
Inverse
Positive

Positive
Positive

Positive

≥9

≥5
≥5
≥5

5-9
≥ 10

Total

≥9

≥5
≥3

≥5
≥5

≥6
≥5

20

Lalic et al., 2016

Hallgren et al., 2016

Dörks et al., 2016

Bor et al., 2017
McCracken et al., 2017

Time to first
hospitalization
Number of
hospitalizations

Female sex
Age
Body Mass Index >25
kg/m2
Hypertension
Diabetes
Hospitalization risk

Chronic pulmonary
disease
A higher median CCI
Dementia
Analgesics and
antipyretics
Drugs for constipation
Antidepressants
Antithrombotic agents
Drugs for peptic ulcer
Opioids
High-ceiling diuretics
Lipid-modifying agents –
plain
Beta-blocking agents
Fall risk
Overtreated diabetes
Over treated
hypertension
Moderate renal failure
Severe renal failure
OR = 1.68 (1.04 - 2.71)
OR = 2.84 (1.42 - 5.68)

Positive
Positive

HR = 1.84 (1.21 - 2.79)
IRR = 1.51 (1.09 - 2.10)

Positive
Positive

OR = 2.10 (1.44 - 3.06)
OR = 2.36 (1.44 - 3.8)
HR = 1.20 (1.03 - 1.40)

OR = 3.27 (2.29 - 4.69)
OR = 1.71 (1.31 - 2.81)
RR = 4.0 (0.97 - 16.41)
RR = 1.77 (1.07 - 2.96)

Positive
Positive
Positive
Positive

Positive
Positive
Positive

OR = 2.23 (1.63 - 3.08)
OR = 2.92 (2.12 - 4.04)
OR = 3.40 (2.46 - 4.73)
OR = 4.68 (3.35 - 6.60)
OR = 2.83 (2.04 - 3.94)
OR = 3.95 (2.81 - 5.58)
OR = 3.37 (2.37 - 4.80)

Positive
Positive
Positive
Positive
Positive
Positive
Positive

OR = 1.14 (0.74 - 1.75)
OR = 0.96 (0.94 - 0.98)
OR = 1.86 (1.25 - 2.76)

OR = 1.2 (1.08 - 1.33)
OR = 0.39 (0.27, 0.54)
OR = 2.57 (1.80 - 3.72)

Positive
Inverse
Positive

Positive
Inverse
Positive

OR = 1.76 ([1.21 - 2.55)

Positive

≥9

≥ 6.8

≥5

≥4
≥9

21

OR = 2.66 (1.46 - 4.84)
OR = 2.84 (1.36 - 5.85)
OR = 1.20 (1.43 - 3.39)
OR = 3.12 (1.99 - 4.89)
OR = 2.57 (1.31 - 5.07)
OR = 2.56 (1.36, 5.85)
OR = 0.74 (0.59 - 0.93)
OR = 0.35 (0.19 - 0.53)
OR = 0.27 (0.12 - 0.60)

Positive
Positive
Positive
Positive
Positive
Positive
Positive
Inverse
Inverse
Inverse

Diabetes mellitus
Parkinson's disease
Gastrointestinal
symptoms
Pain
Dyspnea
Recent hospitalization
Age
Age (65 - 74 years)
Feeding tube status

Susan et al., 2012

Beloosesky et al., 2013

Davide L. Vetrano et
al., 2013

Positive
Positive
Positive

CCI 2 or more
Anxious behaviors
Depression

OR = 1.3 (1.20 - 1.40)
OR = 1.4 (1.30 - 1.40)

OR = 2.3 (2.10 - 2.40)

OR = 3.68 (2.01 - 6.74)

b = .091

Positive

Ischemic heart disease

OR = 3.22 (1.51 - 6.86)
OR = 3.13 (1.10 - 8.85)
OR = 0.34 (0.17 - 0.67)
b = .081

Positive
Positive
Inverse
Positive

Leung et al., 2013

Moore et al.,2014

OR = 5.10 (2.21 - 11.8)

IRR = 1.19 (1.16 - 1.23)

Positive
Positive

IRR = 1.15 (1.06 - 1.24)

Positive

M. Herson et al., 2015

IRR = 1.39 (1.24 - 1.56)
HR = 1.17 (1.06 - 1.29)

Positive
Positive

Hospital stay per year
10-unit increase in MRCI
and time to first
hospitalization
MRCI and number of
hospitalizations
MRCI and hospital days
per person-year
Chronic pulmonary
disease
Diabetes
Heart Failure
Dementia
Hospitalization
Frequency
Emergency visits

≥9
≥ 10

≥5
≥5
≥9

≥ 10

≥ 10

≥3

≥9

22

OR = 1.7 (1.60 - 1.80)
OR = 1.81 (1.38 - 2.37)
OR = 2.31 (1.80 - 2.97)
OR = 2.29 (1.61- 3.27)
OR = 1.73 (1.35 - 2.21)
OR = 0.85 (0.74, 0.96)
OR = 0.64 (0.47 - 0.88)
OR = 0.39 (0.26 - 0.57)

Positive
Positive
Positive
Positive
Positive
Inverse
Inverse
Inverse

≥ 10

Note, CCI = Charlson comorbidities index, CPS = Cognitive performance scale, DBI = drug burden index, DDIs = Drug-drug interactions, BMI = Body mass
index, MRCI = Medication complex regimen index, OR = Odds ratios, RR = Relative risk, IRR = Incidence rate ratio, HR = Hazard ratio, ACFI = Aged care
funding instrument

Graziano et al., 2012

Prescriptions from
multiple physicians
Depression
Pain
Dyspnea
Gastrointestinal
symptoms
Age
Cognitive impairment
(mild/moderate)
Cognitive impairment
(severe)

23

Falls
Studies that examined the association between polypharmacy and fall risks found a
positive association. Oya and colleagues conducted a one-year retrospective cohort study in four
Japanese LTCFs to identify the incidence of drug-related falls with and without injury among
549 individuals with a mean age of 87 years. They identified 645 falls, including 146 injurious
falls and 16 severe injurious falls requiring inpatient care, and found the association of fall risk
and injuries after falls with the use of five or more drugs. Furthermore, medication use affected
over three-quarters of all falls, and the use of psychiatric medicines accounted for more than 80%
of all falls among individuals living in LTCFs (Oya et al., 2022). Another prospective cohort
study conducted by Izza and colleagues involved 1,655 individuals from 84 LTCFs in the UK
with a mean age of 85 (SD = 8.9) years and was followed over three months to evaluate the
occurrence of falls. Overall, 31% fell in three months, with 67.9% being females. The use of five
or more regularly prescribed drugs, as well as the use of one regular psychiatric medication were
found to be independent risk factors for falling in these individuals. In addition, the risk of falls
was higher among individuals taking antidepressants and benzodiazepines, but not
antipsychotics (Izza et al., 2020). Diaz and colleagues conducted a nationwide study to analyze
the occurrence of falls among 2,849 individuals aged 70 years and above who had been living in
residential settings for at least a year in 113 Spanish LTCFs. In the last 12-month period, the
incidence of fallers was 45.3%, with a proportion of recurring fallers of 51.7%. Falls were found
to be positively associated with severe or moderate cognitive impairment and the use of four or
more medications in these individuals (Díaz et al., 2020). Marhanis and colleagues investigated
the prevalence of medicines that cause falls and the risk of falling in a cross-sectional study. In
this study, 212 elderly individuals in 27 Malaysian LTCFs were recruited and classified as frail,

24

pre-frail, or robust. Frailty was shown to be strongly related to the risk of falling with 68.4% of
individuals taking at least one orthostatic medicine or fall-risk-causing drug, with antipsychotics
and use of calcium channel blockers being the most prevalent fall-risk-increasing drugs
(Marhanis et al. 2019). Finally, Bor and colleagues conducted retrospective epidemiological
research in Hungary and included 197 LTCFs individuals (55 fallers and 142 non-fallers). Fall
risk factors were found to be associated with being above the age of 80 years, taking four or
more chronic drugs, and independent use of pantoprazole, vinpocetine, and trimetazidine. In
addition, male fallers were taking a high number of chronic medications with a mean of 12.4
drugs (SD = 4.0), and the use of PIMs having fall risk was prevalent in 70.9% of fallers (Bor et al
2017).
Drug-Drug Interactions and Adverse Drug Events
Different studies examined the association between polypharmacy and the risk of drugdrug interactions and adverse drug events. Diez and colleagues conducted a cross-sectional study
in Spanish LTCFs among 222 individuals aged 65 years or above to see the association between
polypharmacy or the use of five or more drugs with drug-related events. Drug-drug interactions
were prevalent in 81.8% of individuals with polypharmacy and 24.7% experienced severe to
moderate interactions. PIMs were prevalent in 96.8% of individuals, with 53.6% taking five or
more PIMs. Drugs acting on the nervous system were associated with 97.2% of severe to
moderate drug-drug interactions, especially with benzodiazepine class including opioids. Among
benzodiazepines lorazepam, trazodone, mirtazapine, citalopram, and alprazolam most frequently
contributed to these drug-drug interactions. In addition, 38.5% of adverse reactions were also
contributed by nervous system drugs and analysis revealed that four to five or six to ten
pathologies raise the risk of potential adverse reactions by 29.5 and 12.7 times, respectively.

25

Nearly half of the drug-drug interactions (47.6%) were involved in central nervous system
depression and one-fifth (18.2%) in QT prolongation. Benzodiazepine duplicity was found in
34.4% of individuals which increased the likelihood of pharmacological interactions leading to
increased risk of sedation, falls and fractures, or mental confusion (Díez R et al., 2022). Lexow
and colleagues performed a cross-sectional observational analysis in three LTCFs in Germany
and identified 424 adverse events, and a median of four (1-14) per patient in 99% of individuals
(103 of 104). Individuals with gastrointestinal, psychiatric, skin, and subcutaneous tissue
diseases as well as renal and urinary disorders frequently had 20.8%, 13%, 13%, and 11.8% of
the detected adverse events, respectively. Additionally, they found that the median number of
three drugs (0-11) that had actual adverse events were reported in the summary of product
characteristics (Lexow et al, 2021). Astrop and colleagues evaluated drug-drug interactions and
ADRs in 25 Danish LTCFs among 100 individuals aged 65 years and above who were taking
five or more drugs concurrently (Astrop et al., 2020). The most common comorbid illness was
cardiovascular disease, while the commonly used drugs were for gastrointestinal and
metabolism-related diseases. The age group 75 - 84 years taking five or more drugs showed the
highest number of drug-drug interactions 7.5 (4 - 13.75) and a positive correlation was found
between old age 85 years or above and the number of drugs with drug-drug interactions in this
study. According to the Food and Drug Administration (FDA) database, the maximum drug
interactions were moderate in 78.6% of individuals while according to the Danish database,
68.6% of interactions were of mild severity (Astrop et al., 2020). The prevalent drug-drug
interactions reported by the Danish and FDA were due to a combination of furosemide and
metoprolol in 13 cases, with clinical effects of hyperglycemia and hypertriglyceridemia of
moderate severity among individuals (Astrop et al., 2020). In another study, Kapoor and

26

colleagues investigated the association between all adverse events and preventable adverse
events developing in the 45 days following discharge back to LTCFs and demographic variables,
hospital length of stay, Charlson Comorbidity Index (CCI), and Activities of daily living (ADL)
dependency with a number of regularly scheduled drugs in prospective cohort analysis of 32
LTCFs from six New England states (Kapoor et al., 2020). From these LTCFs, all individuals
who were hospitalized and returned to the same facility from March 1, 2016, to December 31,
2017, were analyzed. They reported that 22.7% of individuals were taking zero to nine drugs,
26.4% were on 18 or more drugs, and 23.9% were on 14 -17 drugs. The use of 18 or more
regularly scheduled drugs was positively associated with an adverse drug event. Also, a positive
association between hospital length of stay of more than nine days, CCI of four or more, and 19
or higher levels of functional ADL dependency with the use of 18 or more regularly scheduled
medications was identified (Kapoor et al., 2020). A study conducted by Iniesta-Navalón, and
colleagues based on an observational, multicenter, cross-sectional study design in a sample of
275 individuals aged 65 years and above from 10 LTCFs in Spain to determine the drug-drug
interactions due to polypharmacy. A total of 210 potential drug-drug interactions were present
among individuals taking 10 or more drugs, of which 120 were clinically relevant (57.1%),
affecting 25.8% of elderly individuals. Diuretics, bronchodilators, anti-thrombotic, cardiac
medications, calcium salts, renin-angiotensin inhibitors, antihypertensives, proton pump
inhibitors, and psycholeptics accounted for 70.2% of the drug-drug interactions (Iniesta-Navalón
et al., 2019). Individuals who used 10 or more medications per day, those who took antiinflammatory drugs, and those who suffered from multiple pathologies were all found with
clinically significant drug-drug interactions (Iniesta-Navalón et al., 2019).

27

Hospitalization Risk
Studies have shown positive associations between polypharmacy and the risk of
hospitalization. Cadenas and colleagues found that a higher rate of hospitalization in the previous
12 months was directly associated with the usage of five or more medicines by 326 individuals in
LTCFs in Spain, having a mean age of 86.8 years (SD = 7.5). A direct positive correlation
between 10 or more medicines and hospitalization over the previous year as well as contact with
a general physician within the previous two months was also observed. Additionally, individuals
taking five or more drugs showed increased usage of cardiovascular medicines (Cadenas et al.,
2021). Fassmer and colleagues evaluated the use of out-of-hours medical treatment, visits to
emergency departments, and acute hospital admissions among German LTCFs individuals. In a
retrospective cohort analysis, claims data from 1665 individuals with an average age of 80.5
years was used to examine the incidence rates of numerous services. Polypharmacy or the use of
10 or more drugs, a higher dependency on care, and the male sex all appeared to play a role in
predicting acute health care services (Fassmer et al., 2020). Hallgren and colleagues assessed the
factors associated with hospitalization risk among 429 LTCFs individuals aged 65 to 101 years
from 11 LTCFs in Sweden, in a prospective study with three years of follow-up. They found that
45.7% of individuals were hospitalized at least once during the three-year follow-up period and
25.4% during the first six months of the study. The findings of the study revealed that
undernutrition, fall history, use of a higher number of drugs (≥ 6.8), and cardiovascular illness,
were positively associated with the increased risk of hospitalization (Hallgren et al., 2016). Lilac
and colleagues investigated the association between polypharmacy or the use of nine or more
regular medicines and medication regimen complexity (MRCI) with time to the first
hospitalization, the number of hospitalizations, and the number of hospital days, over 12 months

28

and included 383 individuals from six Australian LTCFs. They found a positive association
between polypharmacy and time to first hospitalization, number of hospitalizations, and hospital
days per person-year. Similarly, in adjusted analyses, a 10-unit increase in MRCI was positively
linked with the time to first hospitalization, the number of hospitalizations, and hospital days per
person-year (lilac et al., 2016). Leung and colleagues looked at the frequency of emergency and
hospitalization use among individuals with Alzheimer’s disease at admission and after a year in a
single long-term care facility in Hong Kong. They enrolled 169 newly admitted individuals with
Alzheimer's disease and found that 15.98% required hospitalization at admission, and 11.24%
required emergency services during the last 90 days of admission assessment. The frequency of
hospitalization was significantly correlated with polypharmacy or the use of three or more drugs
as well as psychotropic drug use. The frequency of hospitalization and utilization of emergency
services were also significantly positively correlated during a one-year follow-up with cognitive
impairment and polypharmacy (Leung et al 2013).
Co-morbidities, Disease Symptoms, and Age
Studies examining the association between polypharmacy and diseases found a positive
association; however, contradictory results were observed due to age. A cross-sectional study
was conducted by Cristina and colleagues among 199 individuals, with a mean age of 84.3 years
(SD = 6.7) from eight LTCFs in Cyprus, of which 67.8% had dementia and 35.6% were entirely
dependent (non-mobile). They found the use of 10 or more drugs occurred in 15.5% of the
individuals and was inversely associated with age 85 years or more and being demented. The
mean CCI score was 5.54 (SD = 1.3) and was positively associated with age 85 years or above
and the use of 10 or more drugs (Cristina Ionescu et al., 2021). Clare and colleagues performed a
cross-sectional analysis of all medicines dispensed to 147 nursing care individuals in Scotland. In

29

total, 32.3% of individuals were using 10 or more drugs, and these were prevalent among
individuals aged 70-74 years and 80-84 years. The use of 10 or more drugs was inversely linked
to individuals with dementia. Across all adverse drug categories, PIMs of two medicines were
seen, with the largest prevalence seen in drugs causing constipation (35.8%), sedation (27.7%),
and renal damage (18.0%) (Clare et al., 2021). The Velázquez-Alva study examined the
association between nutritional status, depressive symptoms, and the number of prescription
medications among individuals residing in LTCFs in Mexico City. A total of 262 participants
were recruited for a cross-sectional study with mean a age of 83.1 years (SD = 8.6). Nearly 2/3 of
individuals (59.9%) in the study were at risk of being malnourished while 21.1% of the
individuals were found to be malnourished. Regarding depression, 11.4% of individuals
demonstrated severe depression, whereas 27.9% displayed mild depression. Individuals who
took three or more prescription medications daily were five times more likely to be at risk for
malnutrition or to be malnourished, and it was found that low nutritional status was linked to
depression itself (Velázquez-Alva., 2020). In a cross-sectional study conducted in six LTCFs in
British Columbia, Rita and colleagues assessed the overtreatment of diabetes and hypertension
and its association with polypharmacy or the use of nine or more drugs in 214 individuals of
which 48% were frail who received more than nine prescription medications. Over-treated
diabetes was defined as the prescription of at least one hypoglycemic drug and hemoglobin A1C
(HbA1c) less than 7.5%, while over-treated hypertension was defined as the prescription of one
blood pressure controlling drug when systolic pressure is less than 128 mm Hg. The individuals
with polypharmacy were mostly diagnosed with hypertension and heart failure and were less
likely with dementia. It was found that individuals in LTCFs appear to have a high rate of
overtreated diabetes and hypertension, and polypharmacy was linked to more aggressive

30

treatment of these risk factors (Rita et al., 2019). Another cross-sectional analysis by Jokanovic
and colleagues in Australia was performed to determine the prevalent medications and
comorbidities among individuals with nine or more drug use across 27 LTCFs. They found that
the use of nine or more drugs was positively associated with hypertension, depression, ischemic
heart disease, diabetes without end-organ damage, chronic pulmonary disease, and a higher
median CCI score among these individuals. While there was an inverse association between
dementia and the use of nine or more drugs, no association was identified in cases of
osteoarthritis, incontinence, cerebrovascular illness, or falls with polypharmacy. Moreover,
analgesics and antipyretics, medications for constipation, antidepressants, antithrombotic agents,
drugs for peptic ulcer and gastric reflux disease, opioids, high-ceiling diuretics, lipid-modifying
agents, and beta-blocking agents were associated with the use of nine or more drugs. However,
no association was observed between the usage of antipsychotics and polypharmacy (Jokanovic
et al., 2017). Herson and colleagues conducted a cross-sectional analysis among individuals in
six LTCFs in South Australia. They included 383 study participants, with a mean age of 87.5
(SD = 6.2) years and a median of 13.0 (1- 30) for regular and as-needed drugs. The average
MRCI was 43.5 (4 – 113). High regimen complexity was associated with diabetes, congestive
heart failure, and chronic pulmonary illnesses, whereas independence in ADLs and dementia
diagnosis were inversely associated with high MRCI (Herson et al., 2015). Furthermore,
Beloosesky and colleagues in a cross-sectional multicenter analysis investigated the association
between the use of five or more drugs and age, length of stay, and comorbidities among 993
individuals, 75.5% of whom were fully dependent and 24.5% of whom were mobile demented
and required institutional care, and with a mean age of 65 years. An inverse association was
found between the use of five or more medicines and length of stay greater than two years and

31

older age groups. The usage of more than five medicines was found to be inversely associated
with the age group of 65-74, 75-84, and 85 years or more, while being in the younger age group
65-74 years, was the risk factor for polypharmacy or use of five or more drugs. The mean CCI
score was 2.45 (SD = 1.59) and no association between age and CCI score was found in these
individuals. Individuals taking more than five medicines had a higher CCI score than those
without polypharmacy: 2.99 (SD = 1.69) vs 2.04 (SD =1.38). The most commonly used drugs
used among them were drugs for gastrointestinal, neurological, and cardiovascular problems
(Beloosesky et al., 2013). In a cross-sectional analysis of 1,449 LTCFs individuals with
advanced cognitive impairment participating in a study gathering data on individuals admitted to
57 LTCFs in eight countries known as Services and Health for Elderly in Long-Term Care
project, Vetrano and colleagues assessed the prevalence and factors related to polypharmacy and
found that the use of 10 or more drugs was directly associated with ischemic heart disease,
diabetes mellitus, Parkinson's disease, Alzheimer's disease, and hospitalization risk. Furthermore,
the mean age of 84 years (SD = 9.0), ADLs, and the presence of a geriatrician on the LTCFs staff
were found to have an inverse association with the usage of 10 or more medicines (Vetrano et
al., 2013). In addition, Graziano and colleagues performed a cross-sectional analysis on 4,023
individuals admitted to 57 LTCFs in eight different countries. Of 24.3% of individuals taking 10
or more drugs was found to be strongly correlated with the existence of chronic diseases, as well
as depression, pain, dyspnea, and gastrointestinal problems. A mean age of 83.3 years (SD =
8.8), ADLs disability, and cognitive impairment were found to have an inverse association with
the use of 10 or more drugs among these individuals (Graziano et al., 2012). Apart from this
Susan and colleagues conducted a cross-sectional study that included 64,394 individuals aged 66
years and older from 589 LTC facilities in Ontario, Canada. The use of nine or more drugs or

32

polypharmacy was found in 15.5% of individuals, which was strongly associated with the
number of comorbidities, a CCI score of two or higher, and a length of stay of 90 days or less.
Furthermore, individuals with polypharmacy had higher rates of cardiac, endocrine, and
pulmonary disorders than those who used fewer drugs. Also, the prevalence of depression and
anxiety was greater among individuals using nine or more drugs, and prescriptions from various
doctors were also linked to higher odds of polypharmacy. The proportion of younger older
individuals aged 66-74 years was higher among polypharmacy recipients compared to 66-74
years getting fewer than nine medications. Among individuals who received nine or more drug
therapies concurrently, diuretics (68.2%), proton pump inhibitors (54.8%), and angiotensinconverting enzyme inhibitors (51.7%) were the most frequently recommended drug therapies.
Additionally, individuals with polypharmacy were more likely to take psychoactive drugs
including antipsychotics (37.5%), benzodiazepine derivatives (41.5%), and selective serotonin
reuptake inhibitors (41.1%) (Susan et al., 2012).
Frailty
Studies investigating the association between polypharmacy and frailty have found mixed
results. Villani and colleagues conducted a cross-sectional study based on data from the Services
and Health for Elderly in Long-Term Care project to evaluate the association between frailty and
polypharmacy and drug prescription patterns. 4,121 LTCFs individuals from Europe and Israel
were included in the study, with 46.6% being frail with a mean age of 84.6 years (SD = 9.2). The
use of five or more medicines, as well as the use of 10 or more medications, was found to be
associated with a lower likelihood of frailty. In addition, polypharmacy (five or more drugs) and
hyper polypharmacy (10 or more drugs) were significantly lower in frail individuals compared to
non-frail individuals from old to older ages (80-85 years and greater than 85 years). Furthermore,

33

the use of symptomatic drugs such as laxatives, paracetamol, and opioids was more prevalent
among frail individuals. Age, female sex, a history of falls, and delirium were all associated with
a higher risk of being frail, and a history of stroke was also associated with a twofold increase in
the likelihood of being frail, after adjusting factors for frailty. The likelihood of frailty was also
associated with mild to severe cognitive impairment, but not with a dementia diagnosis (Villani
et al., 2021). Hasan and colleagues investigated the association between frailty, anticholinergic,
and sedative burden among 151 individuals aged 60 years or more and had at least one long-term
medical condition, in a cross-sectional analysis involving 11 aged care facilities in Malaysia. The
use of five or more drugs was found to be positively associated with frailty and anticholinergic
and sedative burden in a substantial number of cognitively intact older adults (Hasan et al.,
2020). Gutiérrez-Valencia and colleagues investigated the negative association between frailty,
use of five or more drugs, and under-prescription in a cross-sectional analysis including 110
subjects of 65 years or older and living in two LTCFs in Spain of which 60.9% were frail. The
participants' mean age was 86.3 years (SD = 7.3), and 73.6% of them took five or more chronic
medications. The use of five or more drugs was found to be inversely associated with frailty. In
addition, the frail population had a statistically significant greater prevalence of underprescription than the non-frail population (87.5% vs 50.0%) (Gutiérrez-Valencia et al., 2018).
Renal Failure
A positive association was found between polypharmacy and renal failure in one article.
The association between the use of five or more drugs, particularly in renal failure patients was
investigated by Dörks and colleagues in a cross-sectional study involving 21 German LTCFs and
685 individuals. 53.3% of individuals were taking five to nine drugs, while 16.4% were taking
10 or more drugs. Severe or moderate renal failure was prevalent in 63.6% of individuals and a

34

positive association was seen between the use of five to nine drugs with moderate to severe renal
failure. Diuretics and psycholeptics were the two commonly prescribed drugs in them.
Additionally, the use of five to nine drugs as well as a body mass index of 25.0 kg/m2 were
found to be positively associated with diabetes, hypertension, and other health-related conditions
(Dörks et al., 2016).
Cognitive Function
Studies examining the association between polypharmacy and cognition found a negative
association. In a study by Davide and colleagues, LTCFs individuals from Europe and Israel
were examined for their one-year changes in physical and cognitive function. A sample size of
3,234 individuals with an average age of 83.4 years was taken. In total, 50% of individuals used
five to nine drugs, and 24% used 10 or more. With increasing polypharmacy status, the
prevalence of ischemic heart disease, heart failure, stroke, Parkinson's disease, dementia,
diabetes, cancer, and symptoms like pain, dyspnea, and depressive symptoms increased. The
higher cognitive performance scale showed a larger decline in cognitive function in individuals
using five to nine drugs and those using 10 drugs compared to individuals using less than five
drugs. However, ADLs did not significantly change with polypharmacy status (Davide et al.,
2018). Moore and colleagues collected data for 380 individuals over three months retrospectively
from client databases at four Victorian Public Sector Residential Aged Care Services in
Australia. The results of the logistic regression models revealed that individuals with a higher
body mass index were 6% less likely to fall, while high levels of cognitive impairment increased
the risk of falling by 8%. Also using a gait aid while ambulant doubled the risk of falling in
individuals compared to individuals who were not ambulant, and a higher cognitive impairment

35

was associated with a 6% reduction in the likelihood of polypharmacy or the use of nine or more
drugs (Moore et al., 2014).
Two studies conducted Nationwide in Sweden longitudinally investigated polypharmacy
including LTCFs individuals. In the study by Morin and colleagues, the proportion of time spent
with polypharmacy increases with age 75 years or older, as well as with multidose drug
dispensing over regular prescriptions. The proportion of LTCFs individuals with an amount of
time spent with polypharmacy was higher than the proportion of community dwellers (90.7% vs
79.1%) (Morin et al., 2018). Furthermore, Wastesson and colleagues found that factors
associated with chronic polypharmacy included older age, female sex, being in an institution,
chronic multimorbidity, and multidose dispensing (Wastesson et al., 2019).
A survey-based analysis conducted by Jokanovic and colleagues in Australia involving
health care professionals found that the top five factors contributing to polypharmacy among
aged care facilities included changes in resident mix, rising numbers of prescribers, avoidance
by one prescriber to discontinue medications given by a different prescriber, adherence to
clinical guidelines, increasing reliance on locum not aware with residence medical history and
more management of pain pharmacologically (Jokanovic et al., 2016).
Mortality
In a retrospective analysis, Visser and colleagues investigated the association between the
number of drugs used and the 30-day COVID-related death rate among Netherlands nursing
home residents with mostly dementia who were diagnosed with COVID-19. 384 individuals
from 15 nursing homes, with a median age of 84 years (65% female), were included in the study.
While accounting for age, sex, CCI, BMI, and vaccination status, the researchers used
multivariable logistic regression analyses to assess the effect of polypharmacy on COVID-

36

related mortality. According to the study, taking more prescription drugs was linked to a higher
30-day COVID-related mortality risk, particularly among nursing home residents who were not
vaccinated (Visser et al., 2023). Additionally, longitudinal cohort research conducted by Zazzara
and colleagues using information from the Services and Health for Elderly in Long Term Care
(SHELTER) study evaluated the increased risk of death among nursing home residents who were
taking five to nine drugs (polypharmacy) or ten drugs or more (hyper polypharmacy), as well as
whether this risk is affected by the presence of frailty or disability. In total, 4023 nursing home
residents from 50 European and 7 Israeli facilities were evaluated. The individuals were assessed
by the researchers using the interRAI - LongTerm Care assessment tool. The study found that
among residents of nursing homes, frailty, and disability were the greatest predictors of death.
While among non-frail participants, multimorbidity and hyper-polypharmacy (taking 10 or more
medications) were positively associated with a higher risk of death (Zazzara et al., 2023).
Discussion
Overall, this review suggests that polypharmacy is common among individuals living in
LTCFs. However, the prevalence varies among individuals depending on the definition used and
in different age groups. This review also identified different definitions of polypharmacy used in
the literature with a cutoff of 5 or more drugs being the most popular. Several factors were
shown to be associated with polypharmacy including a higher CCI score, co-morbidities,
hospitalization risk, drug-drug interactions, adverse drug events, risk of falls, depression, and the
number of prescribers. Factors inversely associated with polypharmacy included cognitive
impairment, dementia, frailty, and activity of daily life disabilities.
In 2 studies, polypharmacy was found to be positively associated with mortality risk.
Visser et al. (2023) found that nursing home residents with COVID-19 disease who took five or

37

more drugs had a higher risk of COVID-19 related mortality within 30 days emphasizing the
importance of reducing the number of medications prescribed and improving vaccination rates in
this vulnerable population. While Zazzara et al. (2023) found a positive association between the
use of 10 or more drugs and a high risk of mortality among nursing home residents suggesting
that identification of individuals who are non-frail and have multimorbidity or hyper
polypharmacy may help in tailoring their medication use through deprescription, which could
improve their outcomes.
Polypharmacy was found to be positively associated with fall risks in five studies (Bor et
al., 2017; Clare et al., 2021; Díaz et al., 2020; Marhanis et al., 2019; Nozomu et al., 2022). Since
dementia is one of the common disorders among LTCFs individuals which is also evident from
our results, more than half of all falls were impacted by the use of psychotropic medications to
treat the behavioral and psychological symptoms of dementia in long-term care homes. It is,
therefore, crucial to accurately assess the need for psychiatric medications in these individuals
and to monitor the fall risk to prevent injuries due to falls in older adults (Jester et al., 2021).
Old age was found to be inversely associated with a lower prevalence of polypharmacy in
5 studies (Astrop et al., 2020; Beloosesky et al., 2013; Cadenas et al., 2021; Graziano et al.,
2012; Vetrano et al., 2013) and positively associated in 2 studies (Clare et al., 2021; Dörks et al.,
2016). A range of factors may be postulated for these mixed findings. The decreased prevalence
of polypharmacy in older age or individuals who are 85 years or older is likely due to the dearth
of information regarding the benefits of chronic therapy in older individuals with a shorter life
expectancy. When starting a new therapeutic regimen, the time required for therapeutic efficacy
is evaluated against life expectancy. There has been increasing focus on the discontinuation of
unnecessary medications and the fact that frailty has also been linked to an increased risk of

38

adverse drug events (Cullinan et al., 2016) which may result in a lower number of medications
used in the oldest individuals (Ouellet et al., 2018; Wastesson et al., 2017). However, comorbidity burden in older ages and adherence to specific clinical guidelines could explain this
positive association. The common comorbid conditions in older adults living in LTCFs that have
been documented included cardiovascular diseases, hypertension, diabetes, musculoskeletal,
mental, gastrointestinal, and behavioral problems (Moore Et al., 2014) which supports our
findings that polypharmacy is linked to these pathologies and a high CCI score. Because clinical
practice guidelines suggest combination treatment for these conditions, therefore, prescriber’s
adherence to clinical guidelines frequently results in complex prescriptions and infrequently
offers suggestions tailored specifically to older individuals with multimorbidity, resulting in
polypharmacy (Tinetti et al., 2019). While complicated regimens could be unavoidable, it's not
obvious how much they reflect the attitude of practitioners toward achieving tight therapeutic
control (Maxwell et al., 2016). Furthermore, the prevalence of pain among individuals in
residential long-term care was reported to be as high as 80%, with significant variation between
institutions (Lukas et al., 2013), and greater management of pain mong LTCFs individuals was
found to be associated with polypharmacy which is consistent with our findings.
The occurrence of various adverse drug reactions and drug-drug interactions is one of the
polypharmacy’s key challenges and could be a strong predictor for an increase in hospitalization
risks and emergency department visits as evident from the results of this study. The increase in
the number of PIM use among LTCFs individuals was shown to be a major determinant in
potential adverse reactions and drug-drug interactions, while Diez and colleagues reported the
highest prevalence of PIM use in 96.8% of individuals (Diez et al., 2021), proton pump
inhibitors are found to be the most commonly overprescribed medications worldwide, presenting

39

30-50% of inappropriate prescriptions with the prevalence of 79.7% among LTCFs facility
individuals (Chubert et al., 2018; Lanas et al., 2016; Schnoll-Sussman et al., 2020). Furthermore,
drug duplication due to benzodiazepines and overdoses of citalopram, escitalopram, and other
antidepressants are found to be associated with notable prolongation of QT in individuals of all
ages (Crépeau-Gendron et al., 2019). The findings emphasize the significance of undertaking a
clinical review of each medicine to confirm the suitability of therapy and avoid pharmaceutical
overprescribing in older adults. Combining organized medication reviews with adverse event
assessments aids in the identification and evaluation of drug-related problems. When treating
older adults with coexisting cognitive impairment, it is important to avoid drugs that may impair
cognition or cause delirium and behavioral symptoms, and the criteria given by the American
Geriatrics Society's Beers criteria or the STOPP/START criteria (O’Mahony et al., 2015) is
recommended. In addition, given the potential clinical importance of herbal drug interactions
(Babos et al., 2021), it is important to include so-called poly-herbal medication in future studies
to address the potential polypharmacy concerns in older adults.
This review also found a significant positive association between polypharmacy and
depression. LTCFs have a high prevalence of depressive illnesses due to biological and
psychosocial factors such as loneliness and lack of social connection. Biological reasons include
depression due to medical conditions linked to vascular abnormalities in the brain known as latelife vascular depression. Individuals in LTCFs exhibit elevated levels of neuroticism and are also
more susceptible to depression, and as a result, they require more medication to treat their
symptoms (Sachs et al., 2014; Virtanen et al., 2017).
The findings in this review also revealed a complicated two-way link between
polypharmacy and malnutrition. Drugs may affect one's taste buds, hyposalivation, and appetite,

40

all of which would lead to a reduction in the amount of food consumed. The development of
comorbidities, on the other hand, is linked to poor nutritional status and will consequently
necessitate the use of medication for therapy (Kok et al., 2022). Due to this reciprocal link,
doctors need to exercise caution when prescription medications to older adults.
Studies examining the association between dementia, cognitive impairment, and frailty
with polypharmacy showed an inverse association. This may be explained by LTCFs individuals'
specific characteristics, such as short life expectancy and high prevalence of functional and
cognitive loss (Jerez-Roig et al., 2017), and frailty management tends to be focused on specific
therapies to adjust the therapeutic goals, which could play a role in the decision of less
prescribing. The Inverse association of cognitive impairment with polypharmacy could be related
to our findings of a higher decline on the Cognitive Performance Scale in individuals with
polypharmacy where harmful effects of anti-anticholinergics on cognitive function are
documented (Satabdi et al., 2020). Furthermore, the inverse association between dementia with
complex drug regimen is suggestive of doctors deprescribing to individuals with dementia and
demonstrate a switch in treatment approach from curative to palliative. Long-term preventive
treatment may not be as beneficial for these individuals as short-term control of symptoms
(Sawan et al., 2021).
Strengths and Limitations
A thorough literature search was conducted in this literature review guided by Scopus
review methodology to find relevant articles to include in this review, to determine the range of
factors that are associated with polypharmacy including prescriber-related factors. Another
strength of this study is that it covered all definitions of polypharmacy as well as the time to
define polypharmacy and the inclusion and exclusion of over the counter and supplements. This

41

review also investigated the prevalence of polypharmacy in various age groups. However,
quality check of the studies included in this review was not performed because the main focus
was to gather the information from all available literature. Furthermore, this review did not
include any study on factors associated with polypharmacy in the context of the COVID-19
pandemic in Canada due to the limitations of the currently available evidence and the fact that no
studies have been conducted as of yet, to the best of our knowledge, in the setting of residential
long-term care institutions.
Implications for Practice and Future Recommendations.
The study underlines the necessity of reviewing routine drug dispensing for long-term
care individuals to reduce the likelihood of drug-related issues, identify diseases that do not
require therapies, and differentiate between inappropriate and appropriate polypharmacy. A
comprehensive medication review together with adverse events assessment by an
interdisciplinary team of doctors, nurses, and pharmacists, may help to reduce drug-drug
interactions, inappropriate medications, and adverse drug reactions in LTCFs individuals.
Physicians should routinely monitor the cognitive functioning, polypharmacy, and psychiatric
medication use of long-term care individuals who have various pathologies. For appropriate
prescribing, it is also important to consider the benefit-to-risk ratio of psychotropic and fall risk
medications in frail individuals. To assist caregivers in keeping track of their individuals' health
and determining the need for additional medical evaluation, all LTCFs should provide a concise
medication checklist on the common medications that have an elevated risk of causing falls and
other harm in individuals. Dosage adjustments should be undertaken with caution, and the use of
contraindicated medications should be avoided. Computerized decision-assisting tools and
electronic monitoring of adverse reactions should be taken into account when determining the

42

appropriateness of medications for long- term care individuals. Furthermore, LTCFs care should
limit individuals' access to outside clinics and treatment should be provided by a small number of
specifically designated clinicians.
The adoption of consumer-driven service delivery models by healthcare professionals and
involving planned care for individuals’ clinical outcomes may reduce polypharmacy related
issues. An important strategy that could help to optimize pharmacological therapy is to assess
potentially improper prescribing in older individuals as per the STOPP/START and BEERS
criteria (O'Mahony et al., 2015). A more careful and evidence-based strategy is required when
treating any acute condition, especially in the context of COVID-19 disease, keeping the burden
of pills in mind. International recommendations advise non-pharmacological music or
psychological therapies for dementia patients as a first-line treatment approach and switching to
pharmacological therapy when the former does not work.
More interventional and longitudinal investigations are required to reduce the number of
medical facilities and to ascertain how polypharmacy can change over time respectively, to guide
strategies for reduction of polypharmacy in LTCFs individuals. Future studies should pay more
attention to potential additive effects, and there is a need to further investigate the causes of
greater utilization of acute care services. In addition, further research examining the
appropriateness of drugs, potential combinations, adverse medication interactions, and the
clinical importance of herbal medicine interactions involving a substantial number of nursing
facilities should also be considered. Observational studies on polypharmacy as a time-varying
exposure can be conducted to see whether longer exposure to polypharmacy raises the risk of
harmful effects or whether these risks are dangerous even with brief exposure. Individuals,
prescribers, and facility factors that may have contributed to the variation in polypharmacy

43

would also be noteworthy. Future studies should examine the prevalence of polypharmacy in the
COVID-19 pandemic and the factors associated with it, in addition to how the variety of risk
factors and early symptoms may help clinicians’ decisions about treatment strategies. Further
investigations should evaluate the benefit-to-risk ratio while prescribing and create a
comprehensive understanding and detection of cases of inappropriate polypharmacy among older
adults.
Studies are needed to determine how the severity of various conditions affects
polypharmacy's chronicity and the fact that polypharmacy is not only linked to an increased risk
of overprescribing inappropriate medications but has also been linked to an increased risk of
under prescribing of possibly beneficial medications. Furthermore, investigations to clarify the
role of drugs in dementia or the impact of medicine cessation in individuals with end-stage
dementia are required as dementia is a chronic disease with changing goals of care at each stage.
It is necessary to conduct more research to see whether the regimen simplification process would
be aided by including a tool, such as the drug regimen complexity, in routine medication
examination. Future research should examine the long-term effects of individual drug classes on
the emergence of polypharmacy and the harms related to the frequent prescribing of particular
drug classes in individuals with polypharmacy.
Conclusions
Individuals in LTCFs have a high prevalence of polypharmacy which varies depending
on the definitions employed. Polypharmacy was associated with several factors. Comorbidities,
high CCI index, drug-drug interactions, adverse drug events, falls, hospitalization risks, and
prescriber-related factors all showed significant positive associations with polypharmacy. Age,
cognitive impairment, Activity of daily life disability, and dementia were all negatively

44

correlated with polypharmacy. Future longitudinal and interventional research on risk factors
associated with polypharmacy may guide to inform strategies to reduce polypharmacy.

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Chapter 2: Analyzing Factors Associated with Polypharmacy; Aims and Methods
Aim
The purpose of this study is to explore the risk factors associated with polypharmacy
among individuals newly admitted in LTCFs before and during COVID-19 pandemic in Canada.
Research Questions
Research Question 1
Which sociodemographic factors, clinical scales, mental health conditions, and specific
clinical assessment protocols (CAPs) are associated with a greater risk of polypharmacy among
individuals in LTCFs in both the pandemic and pre-pandemic cohorts, and are the findings
statistically significant between individuals with no polypharmacy and polypharmacy in both
cohorts?
Research Question 2
What is the prevalence of polypharmacy, as well as antipsychotic, antidepressant,
antianxiety, and hypnotic medications among individuals newly admitted in LTCFs in prepandemic and pandemic cohorts?
Data Collection
Data Source
The Continuous Care Reporting System (CCRS) database was used to assess record-level
data for individuals in residential LTCFs in Canada from 2019 to 2021. The CCRS database
contains demographic, clinical, functional, and resource utilization information on individuals
receiving continuing care services in hospitals or long-term care homes in Canada and was used
to collect information about data variables of individuals receiving continuous care services in
long-term care homes in Canada. Data is primarily collected using the Resident Assessment
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Instrument Minimum Data Set (RAI-MDS 2.0), a clinical assessment instrument developed by
http://www.interrai.org/.
The RAI-MDS 2.0 is a comprehensive assessment that is used to assess the preferences,
needs, and strengths of individuals who live and receive care in residential and hospital-based
continuing care facilities. It is a method for monitoring important changes in each resident's
health status upon admission, quarterly, and annually. It also offers a picture of the services they
receive. The non-profit international research network interRAI created this standardized clinical
assessment to enhance care for individuals with complicated medical issues. The RAI MDS 2.0
information is submitted to CCRS together with administrative, demographic, and resource use
data and made available in Canada for care planning and monitoring, quality improvement, and
resource allocation (Hutchinson et al., 2010).
Data Set
Data of newly admitted individuals who had their initial evaluations completed at the
time of admission was collected. The individuals were divided into two groups. Individuals
admitted to LTCFs during the fiscal year January 1 st, 2019 - December 1st, 2019, were classified
into the pre-pandemic cohort, whereas those admitted during the fiscal year March 1st, 2020, to
March 1st, 2021, were classified in the pandemic cohort.
Data Cleaning
The first step in preparing the dataset for analysis was data cleaning. The total dataset
received in CSV format contained 107,352 records including the initial admission assessment
and first follow up assessment, which was then imported into SPSS. The variables that were not
relevant to the analysis were removed from the SPSS dataset by constructing syntax for
removing variables. Furthermore, cases involving tracheostomy and ventilator use were excluded

47

using the SPSS case selection method (107 cases of tracheostomy and 89 cases of ventilator use)
due to a different clinical context, medication patterns, and treatment goals in these cases than
the study target population and focus. Specifically, a total of 56 cases with missing
polypharmacy information were removed from the dataset. Following data cleaning steps, the
dataset underwent a structural transformation from a longitudinal to a wide format in SPSS. This
restructuring was done to separate and organize the data into two distinct assessment types,
which were the initial assessment and the first follow-up assessment, to facilitate the subsequent
analysis. The final dataset comprised 53,550 records in the initial assessment and 53,550 records
in the first follow-up assessment. Finally, the analysis focused on examining the associations
between sociodemographic factors, clinical scales, mental health conditions, and CAPs during
the initial assessment at baseline with polypharmacy observed in the first follow-up assessment
(completed with 92 days following initial assessment).
String Variables Recoding
For the purpose of analysis, certain variables underwent re-coding to establish more
meaningful and interpretable categories. The variable "Number of Medicines" was redefined into
two categories: "polypharmacy" (representing individuals taking five or more medicines) and
"no polypharmacy" (encompassing those taking 0 to 4 medicines). Similarly, the Body Mass
Index (BMI) Group variable was transformed into categorical groups: 0 denoting a healthy
weight (used as the reference category), 1 for underweight, 2 for overweight (excluding obesity),
3 for Class I obesity, 4 for Class II obesity, and 5 for Class III obesity; later, these were further
reclassified into broader BMI categories of healthy weight, underweight, overweight, and
obesity. The "Age Group" initially underwent categorization into age ranges: 0 for individuals
younger than 65 years (used as the reference category),1 for 65-69 years, 2 = 70-74 years, 3 =

48

75-79 years, 4 = 80-84 years, 5 = 85-89 years, 6 = 90-94 years, 7 = 95 and above, subsequently,
these were reclassified into "less than 65 years old," "young-old (65-74 years)," "middle-old (7584 years)," and "oldest-old (85 years and above)" groups. The "DRS group" string variable was
recoded as follows: 0 for no depression symptoms (used as the reference category), 1 for some
depression symptoms (1-2), and 3 for possible depressive disorder (3-14). The "ABS group" was
recoded into ABS categories: 0 for no aggressive behavior (used as the reference category), 1 for
moderate aggressive behavior (1-2), 2 for severe aggressive behavior (3-5), and 3 for very severe
aggressive behavior (6 or more instances). Similarly, the "CPS group" was recoded into
"CPS_category": 0 for relatively intact cognitive functioning (0-1) (used as the reference
category), 1 for mild to moderate impairment (2-3), and 2 for severe impairment (4-6). Lastly,
the "Pain group" was recoded into pain categories: 0 for no pain (used as the reference category),
1 for less than daily pain, 2 for daily pain without severity, and 3 for severe pain. Crosstabulation was performed on all recoded variables to confirm the accuracy of the recoding
process and ensure that the new categories aligned with the intended definitions.
Definition of Polypharmacy
This study used the most common definition of polypharmacy, which is the use of five or
more drugs (Masnoon et al., 2017), and includes all over-the-counter and prescription
medications given within the last seven days via any route (oral, IV, injection, patch, etc.), as
well as topical treatments such as ointments, creams, eyedrops, vitamins, and suppositories in
addition to any medications the resident takes on their own.
Measurements
The variables were assessed across four domains and considered as associated factors:
sociodemographic factors, clinical scales, mental health conditions, and CAPs.

49

Sociodemographic Factors
The sociodemographic and lifestyle factors included age groups (in years), sex (male or
female), daily use of tobacco and weekly alcohol use dichotomized as yes or no, and BMI
(kg/m2).
Clinical Scales
InterRAI LTCF outcome scales are standardized algorithms detailed in the Canadian
Institute of Health Information (CIHI) reference guide, which categorizes an individual in
standardized clinical domains and includes information on the outcome scale description, items
used for calculation, and an example of an individual with a specific score. InterRAI verified the
outcome scales against available gold standard measurements when developing the scales. The
assessment during admission into LTCFs provides baseline scales for an individual which can be
monitored quarterly to evaluate the changes over time to provide comprehensive information to
clinicians, policymakers, managers, and researchers to enhance quality of life and care.
(Canadian Institute for Health Information [CIHI], 2021). The following clinical scales were

included in this study: Aggressive Behavior Scale (ABS), Activities of Daily Living Scale
(ADL), Depression Rating Scale (DRS), Cognitive Performance Scale (CPS), and Pain Scale
(PS) (Status and Outcome Scales | interRAI, n.d).
The ABS is used as a tool to determine the severity of aggressive behavior in an
individual based on the frequency of verbal and physical abuse, as well as behavior that was
disruptive in social settings and care-resistance. Scores on the scale range from 0 to 12, with
higher values indicating higher levels of aggressive behavior, and classified individuals with no
aggressive behavior (0), moderate aggressive behavior (1-2), severe aggressive behavior (3-5),
and with very severe aggressive behavior (6-12) (Perlman & Hirdes., 2008).

50

The scores on CPS range from 0 to 6 and evaluated the cognitive performance status of
an individual as relatively intact (0-1), mild to moderate cognitive impairment (2-3), and
very severe cognitive impairment (4-6). The scores on cognitive performance are calculated
using four items including two cognitive (short-term memory and decision-making), one
communicative (ability to make oneself understood), and one ADL factor such as eating (Morris
et al., 1994).
The PS determines the severity of pain and ranges from 0 to 3 based on frequency and
intensity of pain, with 0 indicating no pain, 1 suggesting less than daily pain, 2 representing
daily but not severe pain, and 3 indicating severe daily pain (Fries et al., 2001).
The DRS screens the depression level in individuals which ranges from 0 to 12 based on
seven items such as making negative statements, being persistently angry with oneself or others,
expressing unrealistic fears, repetitive health complaints, persistent anxious complaints/concerns,
saddened/ worried expression on the face, and tearfulness and crying. The scores on the scale
categorized an individual’s depression severity as having no depression symptoms (0), having
some depressive symptoms (1-2), and having possible depressive symptoms (3-14) (Burrows et
al., 2000).
The ADL term refers to essential skills required to independently take care of oneself,
such as moving, eating, and bathing initially described by Sidney Katz (Katz, 1983). The ADL
Self Performance Hierarchy Scale depicts disablement by categorizing ADL performance levels
into distinct degrees of loss, such as early loss for personal hygiene, middle loss for toileting and
mobility, and late loss for eating. The scores on the scale ranged from 0 to 6, and higher levels
imply a higher decline in ADL performance indicating a progressive decline. The scale uses 4
items including personal hygiene, toilet use, locomotion, and eating to code ADL performance as

51

follows: Independent (0), At least supervision in one ADL (1), Limited assistance in one or more
of the 4 ADL (2); At least extensive assistance in personal hygiene or toilet use (3); Maximum
assistance in eating or locomotion (4); Dependent in eating or locomotion (5); and
complete dependence in four ADL items (6) (Morris, 1999).
Clinical Assessment Protocols (CAPs)
CAPs employ MDS-coded items to generate person-centered assessment areas that help
in complete care or service planning. Through standard protocols, CAPs assist in continuity of
care planning and encourage collaborative decision-making by identifying potential areas for
referral to specialized services for individuals who could benefit from further evaluations of
particular problems because their risk of decline is higher than expected, their potential for
improvement has increased, or their symptoms could be reduced if the problem were addressed
(Clinical Assessment Protocols (CAPs) | interRAI, 2021.) CAPs can be triggered at distinct
levels, such as triggered to facilitate improvement or detect risk levels or triggered to prevent
decline or identify risk levels (Canadian Institute for Health Information (CIHI), 2008; Fries et
al., 2007). The CAPs included and defined in this study are clinical issues CAPs such as Falls
CAP; Pain CAP; Cardio-Pulmonary Conditions CAP; Under Nutrition CAP; Dehydration CAP;
Urinary incontinence CAP; Functional Performance CAPs including ADL; Cognition CAPs
including Cognitive loss (interRAI Clinical Assessment Protocols (CAPs) for Use with
Community and Long-Term Care Assessment Instruments, (Standard English Edition), 9.1.2 |
interRAI Catalog, n.d.).
Falls Clinical Assessment Protocols
A fall is defined as a change in position that is unintentional and causes an individual to
land on a lower level, such as the floor, ground, or seat. The Falls CAP identifies underlying fall

52

risks, alters those risks, and identifies common pathways between falls, incontinence, and further
decline. The prevention of falls is part of an overall objective to increase physical activity and
improve quality of life. The Fall CAP triggers identify two categories of individuals for
specialized follow-up. 1) Fall CAP triggered into the high-risk group for future falls based on the
history of multiple falls, and 2) Fall CAP triggered into the medium risk category for future falls
based on the history of a single fall. 3) Not triggered due to no history of falls (Canadian Institute
for Health Information (CIHI), n.d.) and coded as (0 = Not Triggered, 1 = Triggered into the
medium risk of future falls group, 2 = Triggered into the high risk of future falls group).
Cardiopulmonary Conditions Clinical Assessment Protocols
The InterRAI cardio-respiratory conditions CAPs in LTCFs include specific triggers that
allow healthcare professionals to begin a comprehensive assessment and design a care plan for
individuals with cardiovascular or respiratory problems. The Cardio-pulmonary CAP triggers are
based on clinical symptoms suggesting the presence of a cardio-respiratory condition or an
increased risk of developing one. The cardiopulmonary CAP Triggers determine whether the
symptom is present and include individuals who present with any of the following symptoms
such as chest pain, Shortness of breath, irregular pulse, dizziness, systolic blood pressure greater
than 200 or less than 100 mmHg, respiratory rate greater than 20 breath per minute, Heart rate
greater than 100 beats per minute or less than 50 per minute, oxygen saturation less than 94 %.
Individuals in not triggered group Included those who do not display any of the symptoms listed
above and coded as 0 = Not Triggered, 1 = Triggered.
Undernutrition Clinical Assessment Protocols
Undernutrition is defined by the BMI (person weight to height ratio) and ranges from less
than 18 BMI for mild undernutrition, 16 to 18 BMI for moderate undernutrition, and to less than

53

16 BMI for severe undernutrition. Undernutrition CAPs address the root causes, medical
conditions, or medicines that contribute to and increase the risk of undernutrition, to implement a
reasonable treatment plan to ensure adequate caloric intake. The overall goal is to improve
quality of life by preventing further weight loss and negative effects of undernutrition. The
undernutrition CAPs have three levels based on BMI, and weight and height are the parameters
when calculating this trigger. Individuals are divided into two categories based on the triggers 1);
high-risk triggered and include those who have a baseline BMI score below 19 and are usually
considered underweight. Half of the individuals in this group regularly left 25% of their food
uneaten and are more likely to lose weight in the future than others 2); Triggered- Medium risk
and include those with BMI of 19 to 21, and one out of every five residents of LTCFs will have a
BMI of 18 or less in next assessment. At most meals, four out of 10 individuals typically leave
25% of the food uneaten. The CAP for undernutrition is coded as 0 = Not Triggered, 1 =
Triggered - medium risk, and 2 = Triggered - high risk, in this study.
Dehydration Clinical Assessment Protocols
When the body loses more water than is being consumed (via breathing, sweating,
defecating), it is said to be dehydrated. Dehydration CAPs overall goal is to identify and treat the
main causes of dehydration, to rehydrate the patient, to establish a suitable approach to lab
testing and monitoring to ensure recovery and the maintenance of a proper fluid balance, to
prevent associated complications like hypotension, falls, delirium, and constipation, and to offer
comfort to those whose treatment is primarily supportive. The interRAI assessment instrument's
two elements, such as dehydration and insufficient fluid intake, which both contribute to more
serious clinical relevance, serve as the basis for these CAPs. There are two trigger levels: high
and low, and those who are in the high-level category show one or more overt signs of

54

dehydration. A physician needs to examine them promptly based on their clinical course. Those
in the low-level group might be able to manage if their hydration intake is increased and they are
closely monitored, even if they still need close clinical monitoring. Individuals in the triggered
high-level category were first identified as dehydrated or receiving insufficient hydration.
Second, they have been evaluated as having one or more of the following reasons for
dehydration complications: diarrhea, vomiting, delirium, fever, dizziness, syncope, constipation,
and weight loss. Triggered low level category individuals have been classified as dehydrated or
receiving insufficient fluid but do not have any of the accompanying conditions listed in the
triggered high-level group. The coding for undernutrition CAPs trigger is given as 0 = Not
Triggered, 1 = Triggered - low level, 2 = Triggered - high level.
Urinary Incontinence Clinical Assessment Protocols
Urinary incontinence is defined as the inability to control urine as required in a socially
appropriate manner. The overall goal of care is to recognize urinary incontinence and establish
the cause, to expedite improvement in bladder function in those who could improve by
instituting appropriate diagnostic and therapeutic interventions, and second to prevent worsening
of bladder function who respond to treatment.
The categories of individuals triggered to require specialized follow-up are.
Triggered to facilitate improvement in bladder function and included individuals with all
of these characteristics: 1) experiencing recurrent incontinence episodes with no urine output
(even less than weekly); 2) possess at least a basic level of cognitive capabilities (i.e.,
independence or modified independence in cognitive ability for daily decision-making; 3) are not
entirely dependent or receiving substantial assistance in movement; 4) and fulfill one or both of
the acute triggering conditions listed below; 1) There is no regular schedule for toileting; or 2)

55

one or more of the following conditions, such as hip fracture, recent decline in ADLs, use of an
indwelling catheter, pneumonia, or diarrhea, may indicate a more recent onset of incontinence or
that it may improve.
Triggered to prevent decline- a higher rate of decline is expected. The individuals of this
group exhibit all of these characteristics: 1) less than weekly recurrent incontinence episodes or
no incontinence episodes; 2) independence to mild cognitive impairment for daily decisionmaking; 3) and failure to meet the aforementioned acute criteria under improvement trigger (the
schedule toileting program and fluctuating status).
Not Triggered- Continent: Include individuals who are continent during assessment time.
Not Triggered-Poor Decision Making: Individuals in this category were evaluated as
having severe cognitive impairments or no discernible consciousness for daily decision-making
at the time of assessment. The CAPs for urinary incontinence trigger are coded as 0 = Not
Triggered; 1 = Not Triggered - continent at baseline; 2 = Triggered to prevent decline; 3 =
Triggered to facilitate improvement.
Mental Health Conditions.
Factors related to mental health conditions included, a history of mental illness,
Depression, Anxiety Disorder, Mania, Schizophrenia, Cerebral Palsy, Parkinson's Disorder,
Seizure Disorders, and Multiple Sclerosis coded as 0, 1 (No or yes). The disease diagnosis and
other health conditions occurring frequently in LTCFs are classified and coded according to
ICD-10-CA (International Statistical Classification of Diseases and Related Health Problems,
10th Revision, Canada) (Canadian Institute for Health Information [CIHI], 2022; Codes and
Classifications for Clinical Health Data, n.d).

56

Data Quality
CIHI maintains rigorous data quality standards and ensures the accuracy and reliability
of data about Canadian healthcare for informed decision-making. (Data and Information Quality,
n.d.)
Ethical Consideration
The study did not require ethical approval from the University of Northern BC Research
Ethics Board since the analysis was based on the use of de-identified secondary data.
Data Analysis
IBM SPSS Statistics Version 28.0 for Windows was used for data analysis (IBM
Corporation, 2021). The descriptive statistics of categorical variables were expressed in terms of
frequencies and percentages. The chi-square test of independence was used in both pre-pandemic
and pandemic groups to examine the association between categorical independent factors with
two levels and dependent variable polypharmacy with two levels (yes, no). Binary logistic
regression was used to determine the association between independent factors with more than
two levels and the categorical dependent variable of polypharmacy (yes, no). The unadjusted odd
ratios (ORs) and 95% confidence intervals (CIs) were determined, and a p value less than 0.05
was considered statistically significant. Multiple regression analysis was done to compute the
adjusted ORs.

57

Chapter 3: Results
The results chapter highlighted the associations between sociodemographic factors,
clinical scales, mental health conditions, and CAPs at baseline during the initial assessment of
newly admitted individuals in LTCFs with polypharmacy at the time of first follow-up
assessment. The results of unadjusted analysis which is defined as a bivariate relationship
between an independent and dependent variable that does not control for confounders (Voils et
al., 2011), revealed the associations of various factors with polypharmacy in both the prepandemic and pandemic cohorts. These associations were further examined using adjusted
analysis which is defined as analysis that accounts for the influence of cofounding variables to
reveal true relationships between primary independent variables with the outcome variable in the
study (Voils et al., 2011). The results chapter also highlights the prevalence of polypharmacy and
psychotropic medication classes (antidepressants, antipsychotics, anti-anxiety, and hypnotics)
among newly admitted individuals in LTCFs in pre-pandemic and pandemic cohorts during their
first follow-up assessment.
Bivariate Analysis
Sociodemographic Factors
A total of 53,550 new admissions into LTCFs were included in the dataset, with 57.0% (n
= 20,544) in pre-pandemic, and 43.0% (n = 23,006) in the pandemic cohort. In terms of sex, a
total of 61.3% (n = 32,829) were females, whereas 38.7% (n = 20,710) were males. The total
number of admissions was highest for individuals in the oldest-old category, 50.1% (n =26,826)
(51.2% pre-pandemic vs 48.6% pandemic). The prevalent medical conditions among individuals
were hypertension (61.2% vs 62.1%), dementia (50.4% vs 50.2%) arthritis (35.4% vs 32.8%),

58

depression (25.4% vs 24.8%), and diabetes mellitus (25.2% vs 25.7%) in pre-pandemic and
pandemic cohorts, respectively (Figure 3.1).

Medical conditions

Figure 3.1: Prevalent Medical Conditions Among Individuals in Pre-pandemic N = 30,544 (January 1st, 2019 –
December 1st, 2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021)
HYPERTENSION
DEMENTIA
ARTHRITIS
DEPRESSION
DIABETES
GASTRO INTESTINAL DISEASE
OSTEOPOROSIS
HYPOTHYROIDISM
CEREBORVASCULAR ACCIDENTS
EMPHYSEMA
ANXIETY DISORDER
HEART FAILURE
ARTERIO HEART DISEASE
ANEMIA
CONSTIPATION
RENAL FAILURE
CANCER
PARKINSON
ARRYTHMIA
PERIPHERAL VASCULAR DISEASE
ASTHMA
SEIZURE DISORDER
DIARRHEA
ANTIBIOTIC RESISTANT INFECTION
SCHIZOPHERENIA
HYPOTENSION
MANIA

0

25

50

75

100

Percentages
pandemic (%)

pre-pandemic (%)

Medication Dispensing
The medication dispensing depicted that polypharmacy was observed in 89.8% (N =
48,065) of individuals, with 89.5% (n = 27,323) in the pre-pandemic and 90.2% (n = 20,747) in
the pandemic cohort. In total, 58.50% (n = 31,346) were taking more than nine drugs, with
59

57.5% (n = 17,570) taking more than nine in the pre-pandemic, and 59.9% (n = 13,776) taking
more than nine drugs in the pandemic cohort. Notably, antipsychotic dispensing increased
significantly among individuals in the pandemic cohort, rising from 27.3% in the pre-pandemic
to 32.4% in the pandemic. Antidepressant dispensing, which was the most commonly dispensed
medication in both cohorts, increased slightly in the pandemic to 54.4% from 52.3% in prepandemic, while hypnotic dispensing increased to 8.6% in pandemic from 7.5% in the prepandemic. Antianxiety dispensing remained relatively stable across both cohorts, with rates of
8.8% in the pre-pandemic and 9.2% in the pandemic (Figure 3.2).
Figure 3.2: Medications Dispensing Patterns in LTCFs Individuals in Pre-pandemic N = 30,544 (January,1st –
December 1st, 2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021)
100

PRE-PANDEMIC

Percentage

75

PANDEMIC

50

***
25

0

POLYPHARMACY

ANTI-DEPRESSANTS ANTI-PSYCHOTICS

ANTI-ANXIETY

HYPNOTICS

Psychotropic Medications
Note: *** represents statistical significance p < 0.001
In the pre-pandemic and pandemic cohorts, 85.1% vs 86.3% of individuals always had
polypharmacy, 7.8% vs 7.5% of individuals never had polypharmacy, 4.3% vs 4.1% of
individuals newly developed polypharmacy, and 2.8% vs 2.3% individuals had reduced
polypharmacy, respectively (Figure 3.3).
60

Figure 3.3: Pie Diagram for Different Categories of Polypharmacy

Newly Developed
Polypharmacy
(4.0%)

Reduced Polypharmacy
(3.0%)

Never Had
Polypharmacy (8.0 %)
Always Had
Polypharmacy
(85.0%)

Table 3.1a presents results for unadjusted analysis for sociodemographic variables in both
cohorts. The oldest old age category showed a higher prevalence in both the pre-pandemic
(51.2%) and pandemic (48.6%) cohorts, with the majority being female (62.2% in pre-pandemic
and 60.1% in pandemic), and unmarried individuals constituted the most prevalent group, with a
prevalence of 60.1% in the pandemic and 56.9% in the pre-pandemic cohort. However, there was
no significant difference in marital status between individuals between the pre-pandemic and
pandemic cohorts.
Geographical distributions showed that most assessments were completed in Ontario
(57.4%), which decreased in the pandemic (51.5%) compared to the pre-pandemic (61.9%) .
However, the proportion of assessments in British Columbia increased by 6.7% during the
pandemic (26.7%) than in pre-pandemic (20.6%). Alberta also revealed a higher proportion of
61

assessments during the pandemic (16.4%) compared to the pre-pandemic (13.0%) (Appendix
five).
In terms of age distributions, admissions for young-old individuals (65 -74 years) were
slightly higher (13.9%) in the pandemic than pre-pandemic cohort (12.3%), while admissions for
the oldest-old category (85 and above) were slightly lower (48.6%) in the pandemic compared
to pre-pandemic cohort (51.2%). Middle-aged admissions (75- 84 years) remained constant in
both pandemic and pre-pandemic cohorts (30.4% vs. 31.0%). In addition, individuals in youngold, middle-old, and oldest-old categories were shown to have a slightly higher likelihood of
polypharmacy compared to those under 65 in the pre-pandemic, but this association was nonsignificant in the pandemic cohort. Furthermore, females sex accounted for 60.1% of admissions
during the pandemic, slightly less than in pre-pandemic (62.2%). Admissions for males increased
slightly in the pandemic (39.9%) as compared to pre-pandemic (37.7%). In both cohorts, males
had a slightly highly likelihood of polypharmacy than females.
The prevalence of tobacco use prior to admission was similar in the pandemic (3.8%) and
pre-pandemic cohorts (4.2%), while alcohol use decreased slightly from 6.2% in the prepandemic cohort to 5.4% in the pandemic cohort. In both cohorts, the association between
tobacco and alcohol use and polypharmacy was not statistically significant.
Finally, BMI trends showed that admissions for underweight patients slightly increased
in the pandemic, rising to 11.8% than 10% in the pre-pandemic. The percentage of overweight
(26.6% vs. 28.7%) and obese (19.7% vs. 17.7%), however, slightly decreased in the pandemic
compared to pre-pandemic. Unadjusted analysis revealed that underweight individuals had lower
odds of polypharmacy in pre-pandemic (OR = 0.79, p < 0.001) and pandemic (0.85, p < 0.001)
cohorts, whereas overweight individuals had a slightly higher likelihood of polypharmacy in pre-

62

pandemic (OR = 1.28, p < 0.001) and pandemic (OR = 1.26, p < 0.001) cohorts and obese
individuals had a moderately higher risk of polypharmacy in pre-pandemic (OR = 2.24, p <
0.001) and pandemic (OR = 2.16, p < 0.001) cohorts compared to the healthy weight .

63

Male

Female

76.6 (2,467)

7 (225)

No

Yes

Weekly Alcohol Use

4.4 (141)

20.7 (5,660)

10.9 (351)

Yes

29 (7,911)

26.6 (858)

6.1 (1,661)

79.7 (2,1788)

4.2 (1,136)

85.5 (23,355)

9.5 (2,594)

14.3 (459)

84 (2,706)

39.4 (10,759)

46.5 (1,497)

62 (16,947)

51.1 (13,975)

51.8 (1,669)

64 (2,062)

30.6 (8,369)

28.4 (916)

37.9 (10,369)

12.4 (3,375)

12 (388)

36 (1,158)

5.9 (1,604)

0.84 (0.72 - 0.97)

Reference

0.93 (0.78 - 1.12)

Reference

2.24 (1.99 - 2.53)

1.28 (1.17 - 1.40)

0.79 (0.7 - 0.88)

Reference

1.09 (1.01 - 1.18)

Reference

1.29 (1.12, 1.49)

1.41 (1.22 - 1.64)

1.34 (1.13 - 1.59)

Reference

Pre-pandemic
Unadjusted Odds
Polypharmacy Ratios (95% CI)
89.5% (n
=27,323)

7.7 (248)

No

Daily Use of Tobacco

Body Mass Index
Healthy
weight (18.524.9 kg/m2)
Under weight
(< 18.5
kg/m2)
Overweight
(25-29.9
kg/m2)
Obesity
(>29.9 kg/m2)

Sex

Less than 65
Young-old
(65-74 years)
Middle-old
(75-84 years)
Oldest-old
(85 and more)

Age Groups

No
Polypharmacy
10.5% (n =
3221)

>0.05

0.45

<0.001

<0.001

<0.001

0.03

<0.001

<0.001

<0.001

p - value

4.5 (103)

78.5 (1,777)

3.6 (81)

83.4 (1,888)

9.9 (225)

24.6 (557)

15.5 (352)

46.6 (1056)

62.5 (1415)

37.5 (849)

50.9 (1153)

29.4 (665)

13.2 (299)

6.5 (147)

9.8% (n = 2,264)

No
polypharmacy

5.5 (1,147)

78.5 (16,274)

3.8 (795)

83.6 (17,335)

18.5 (3,840)

26.8 (5,556)

11.3 (2,353)

40.2 (8,339)

59.8 (12,405)

40.2 (8,334)

48.4 (10,029)

31.1 (6,461)

13.9 (2,891)

6.6 (1361)

1.22 (0.99 - 1.50)

Reference

1.07 (0.85 - 1.35)

Reference

2.16 (1.86 - 2.51)

1.26 (1.13 - 1.41)

0.85 (0.74 - 0.96)

Reference

1.12 (1.02 - 1.23)

Reference

0.94 (0.78 - 1.13)

1.05 (0.87 - 1.27)

1.04 (0.85 - 1.29)

Reference

Pandemic
Unadjusted Odds
Polypharmacy
Ratios (95% CI)
90.2% (n =
20,742)

64

0.06

0.58

<0.001

<0.001

0.01

0.01

0.5

0.62

0.68

p - value

Table 3.1a: Bivariate Analysis for Sociodemographic Characteristics in Relation to Polypharmacy Among Individuals in Pre-pandemic N = 30,544 (January 1st
– December 1st, 2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021)

Mental Health Conditions
Mental health conditions and clinical scales associated with polypharmacy are presented
in Table 1b. There was a statistically significant difference between the pre-pandemic and
pandemic cohorts in terms of mental illness and cerebrovascular accidents. The proportions of
individuals with Parkinson's Disease, Seizure Disorders, Anxiety, Cerebral Palsy, Dementia,
Multiple Sclerosis, Depression, Mania, and Schizophrenia remained relatively stable, with no
statistically significant differences (Table 3.1b).
Dementia, one of the most common mental health conditions in both the pre-pandemic
(49.8%) and pandemic (50.4%) cohorts had nearly equal prevalence. History of Mental Illness
showed a small increase from 10.9% in pre-pandemic to 11.4% in the pandemic cohort.
Similarly, Cerebrovascular Accidents exhibited a slight increase from 17.9% in pre-pandemic to
18.6% in the pandemic cohort. In both cohorts, individuals diagnosed with Cerebrovascular
Accidents (OR = 2.03 pre-pandemic; OR = 2.04 pandemic, p < 0.001), Depression, (OR = 2.21
pre-pandemic; OR = 2.27 pandemic p < 0.001), Parkinson’s disease (OR = 2.28 pre-pandemic vs
2.61 pandemic, p < 0.001), and Mania (OR = 3.02 pre-pandemic vs 2.75 pandemic, p < 0.001)
were associated with a moderately higher likelihood of polypharmacy, while individuals with
Seizure Disorder (OR = 1.82 pre-pandemic vs OR = 1.74 pandemic, p < 0.001) and Anxiety
Disorder (OR = 1.76 pre-pandemic vs OR = 1.88 pandemic, p < 0.001) had a small higher risk of
polypharmacy as compared to individuals without these conditions. Conversely, the association
between Multiple Sclerosis and polypharmacy was not significant in the pre-pandemic cohort,
but slightly lower in the pandemic cohort (OR = 0.68, p = 0.04). The associations between
polypharmacy and Cerebral Palsy and Schizophrenia were not significant in both cohorts. There

65

was no significant difference between these associations between pre-pandemic and pandemic
cohorts.
Clinical Scales
The Activities of Daily Living (ADL) Hierarchy scale results revealed significant
changes between the pre-pandemic and pandemic cohorts. In the pre-pandemic cohort, 31.3% of
individuals required extensive assistance in one or more ADLs, which decreased to 24.4% in the
pandemic cohort. Conversely, the number of individuals who were maximally dependent on one
or more ADLs increased from 17% in pre-pandemic to 31.9% during the pandemic. In addition,
individuals who required limited assistance and extensive assistance in one or more ADLs
showed a slightly higher likelihood of polypharmacy in both cohorts. While individuals who
needed maximum assistance in one or more ADLs, as well as those categorized as totally
dependent, exhibited a significantly higher likelihood of polypharmacy in both cohorts compared
to individuals who were independent in ADL.
In terms of PS, the results were comparable in both cohorts, with 64.2% of individuals
reporting less than daily pain in the pre-pandemic and 63.9% in the pandemic cohort, while 26%
experienced daily but not severe pain in the pre-pandemic compared to 26.2% in the pandemic
cohort. Individuals experiencing less than daily pain had a moderately higher likelihood of
polypharmacy in both cohorts. The daily but not severe pain category also showed a higher risk
of polypharmacy, with an even greater risk observed in individuals with severe pain compared to
the no pain category during both the pre-pandemic and pandemic cohorts.
According to scores on CPS, individuals with moderate cognitive impairment represented
59.4% of the pre-pandemic and 57.7% in the pandemic cohort, while those with severe cognitive
decline accounted for 20.3% pre-pandemic and 21.4% in the pandemic cohort. Notably,

66

individuals with mild to moderate cognitive decline had a lower likelihood of polypharmacy in
both cohorts (OR = 0.6 pre-pandemic vs OR = 0.6 pandemic, p < 0.001). Similarly, individuals
with severe cognitive decline exhibited significantly reduced odds of polypharmacy during both
pre-pandemic (OR = 0.49, p < 0.001) and pandemic cohorts (OR = 0.53, p < 0.001) compared to
those with intact cognitive function.
Among individuals with some depressive disorders, 28.1% were in pre-pandemic and
28.4% were in the pandemic cohort. For those with possible depressive disorders, a slight
increase from 18.7% in pre-pandemic to 19.9% during the pandemic was seen. Individuals with a
possible depressive disorder had a slightly higher risk of polypharmacy in both cohorts (OR =
1.21 pre-pandemic vs OR = 1.58 pandemic, p < 0.001).
ABS revealed that in the pandemic cohort, 22.2% of individuals exhibited moderate
aggressive behavior, which was similar to 22.7% in the pre-pandemic cohort. Furthermore,
individuals displaying moderate aggressive behavior and severe aggressive in the pre-pandemic
cohort had a slightly lower likelihood of polypharmacy compared to individuals with no
aggressive behavior. However, the association between ABS and polypharmacy was
insignificant in the pandemic cohort.
There were no significant differences in the associations between sociodemographic
variables such as age, sex, and marital status, with polypharmacy among newly admitted
individuals in both the pre-pandemic and pandemic cohorts. Similarly, mental health conditions,
such as Depression, Cerebrovascular Accidents, Mania, Seizure Disorders, Anxiety, and
Parkinson's disease, maintained stable associations with polypharmacy in both pre-pandemic and
pandemic cohorts. Robustness in these associations with polypharmacy was further evident in
the analysis of clinical scales, including ADL Hierarchy, PS, and CPS, as well as CAPs triggers.

67

92.9 (25,393)
7.1 (1,930)
96.1 (26,246)
3.9 (1,077)
85.2 (23,289)
14.8 (4,034)
99.7 (27,236)
0.3 (87)
81.2 (22,199)
18.8 (5,124)
98.9 (27,035)
1.1 (288)
73.3 (20,028)
26.7 (7,295)
98 (26,768)
2 (555)
97.6 (26,660)
2.4 (663)

96.8 (3,117)
3.2 (104)
97.8 (3,150)
2.2 (71)
91.1 (2,933)
8.9 (288)
99.5 (3,205)
0.5 (16)
89.8 (2,892)
10.2 (329)
99 (3,190)
1 (31)
85.9 (2,766)
14.1 (455)
99.3 (3,199)
0.7 (22)
98 (3,155)
2 (66)

56.9 (1,834)
24.3 (782)

Clinical Scales
Aggressive Behavior Scale
No
Moderate

61.7 (16,855)
22.5 (6,138)

88.9 (24,288)
11 (3,015)

90.7 (2,921)
9.3 (300)

Reference
0.85 (0.78 - 0.93)

Reference
1.19 (0.92 - 1.54)

Reference
3.02 (1.97 - 4.62)

Reference
2.21 (2.00 - 2.45)

Reference
1.1 (0.76 - 1.59)

Reference
2.03 (1.8 - 2.28)

Reference
0.64 (0.38 - 1.09)

Reference
1.76 (1.56 – 2.00)

Reference
1.82 (1.43 - 2.32)

Reference
2.28 (1.86 - 2.78)

Reference
1.21 (1.07 - 1.37)

Pre-pandemic (N = 30,544)
Unadjusted Odds
Polypharmacy
Ratios 95% CI
89.5% (n
=27,323)

Mental Health Illness
No
Yes
Parkinson
No
Yes
Seizure Disorder
No
Yes
Anxiety
No
Yes
Cerebral Palsy
No
Yes
Cerebrovascular Accident
No
Yes
Multiple Sclerosis
No
Yes
Depression
No
Yes
Mania
No
Yes
Schizophrenia
No
Yes

No
Polypharmacy
10.5% (n =
3,221)

<0.001

0.18

<0.001

<0.001

0.63

<0.001

0.10

<0.001

<0.001

<0.001

0.003

p – value

59.6 (1,350)
23.7 (536)

98.1 (2221)
1.9 (43)

99.2 (2245)
0.8 (19)

86.6 (1,960)
13.4 (304)

98.5 (2,230)
1.5 (34)

89.4 (2,024)
10.6 (240)

99.6 (2,256)
0.4 (8)

92 (2,083)
8 (181)

97.4 (2,206)
2.6 (58)

97.3 (2,202)
2.7 (62)

89.7 (2,030)
10.3 (233)

No
polypharmacy
9.8% (n =
2,264)

61.5 (12,762)
22 (4,562)

97.5 (20,216)
2.5 (526)

97.7 (20,271)
2.3 (471)

74 (15,339)
26 (5,403)

99 (20,529)
1 (213)

80.5 (16,701)
19.5 (4,041)

99.6 (20,662)
0.4 (80)

86 (17,832)
14 (2,910)

95.6 (19,837)
4.4 (905)

93.2 (19,322)
6.8 (1,420)

88.4 (18,344)
11.5 (2,395)

0.05

0.06

<0.001

<0.001

0.04

<0.001

0.81

<0.001

<0.001

<0.001

0.08

p - value

68

Reference
0.9 (0.81 - 1.00)

Reference
1.34 (0.98 - 1.84)

Reference
2.75 (1.73 - 4.35)

Reference
2.27 (2.01 - 2.57)

Reference
0.68 (0.47 - 0.98)

Reference
2.04 (1.78 - 2.34)

Reference
1.09 (0.53 - 2.26)

Reference
1.88 (1.61 - 2.2)

Reference
1.74 (1.33 - 2.27)

Reference
2.61 (2.02 - 3.38)

Reference
1.14 (0.99 - 1.31)

Pandemic (N= 23,006)
Unadjusted Odds
Polypharmacy
Ratios 95% CI
90.2% (n =
20,742)

Table 3.1b: Mental Health Conditions and Clinical Scales Associated with Polypharmacy Among Individuals in Pre-Pandemic N = 30544 (January, 1st – December, 1st
2019) and Pandemic Cohorts N = 23,006 (March 1st, 2020 – March 1st, 2021)

3.5 (957)

6.1 (196)
11.2 (360)
19.1 (616)
31.8 (1,024)
14.8 (476)
12.5 (402)
4.6 (147)

Supervision

Limited assistance

Extensive assistance

Maximal assistance

Dependent

Total dependence

3.4 (923)

17.5 (4,795)

23.1 (6,313)

31.2 (8,529)

15.2 (4,153)

6 (1,653)

1.4 (395)

9.1 (2,480)

3.7 (118)
0.3 (10)

27.2 (7,427)

62.3 (17,021)

80 (2,578)
16 (515)

18.9 (5,170)

16.5 (532)

28.1 (7,677)

53 (14,476)

55.8 (1,798)
27.7 (891)

21.1 (5,767)
59.3 (16,212)
19.6 (5,344)

11.7 (3,187)
4.2 (1,138)

14 (452)
59.5 (1,918)
26.4 (851)

14.1 (455)
4.7 (150)

Severe
Activities of Daily Living Scale
Independent

Less than daily pain
Daily pain but not
severe

Possible depressive
disorder
Pain Scale
No pain

Some depressive
symptoms

Cognition Performance Scale
Intact
Mild/moderate
Severe
Depression Rating Scale
No depression
symptoms

Severe
Very severe

1.29 (1.02 - 1.62)

2.44 (2.03 - 2.94)

2.72 (2.27 - 3.25)

1.71 (1.44 - 2.02)

1.38 (1.16 - 1.65)

0.94 (0.78 - 1.14)

Reference

3.18 (2.63 - 3.85)
5.98 (3.19 - 11.22)

2.18 (1.98 - 2.41)

Reference

1.21 (1.09 - 1.34)

1.07 (0.98 - 1.17)

Reference

Reference
0.66 (0.6 - 0.74)
0.49 (0.44 - 0.55)

0.76 (0.68 - 0.85)
0.83 (0.69 - 0.99)

0.03

<0.001

<0.001

<0.001

<0.001

0.53

<0.001

<0.001

<0.001

<0.001

0.12

<0.001
<0.001

<0.001
0.03

5 (114)

21.8 (494)

11.4 (259)

25.4 (574)

18.7 (423)

11.1 (251)

6.6 (149)

0.3 (6)

3.8 (85)

16.5 (374)

79.5 (1,799)

14.4 (327)

28.9 (654)

56.7 (1,283)

13.8 (313)
61.1 (1,383)
25.1 (568)

12.6 (285)
4.1 (93)

4.9 (1,008)

33.0 (6,839)

17.0 (3,528)

24.3 (5,038)

12.6 (2,605)

5.1 (1,067)

3.2 (657)

1.4 (295)

9.2 (1901)

27.2 (5,649)

62.2 (12897)

20.5 (4,260)

28.3 (5,871)

51.2 (10,611)

21.7 (4,491)
57.3 (11,892)
21 (4,359)

11.8 (2,455)
4.6 (957)

2.01, (1.54 - 2.61)

3.14 (2.57 - 3.83)

3.09 (2.48 - 3.84)

1.99 (1.63 - 2.43)

1.4 (1.14 - 1.72)

0.96 (0.77 - 1.21)

69

Reference

3.12 (2.5 - 3.9)
6.86 (3.05 - 15.41)

2.11 (1.88 - 2.37)

Reference

1.58, (1.39 - 1.79)

1.09, (0.98 - 1.20)

Reference

Reference
0.6 (0.53 - 0.68)
0.53 (0.46 - 0.62)

0.91 (0.8 - 1.04)
1.09 (0.87 - 1.36)

<0.001

<0.001

<0.001

<0.001

<0.001

0.75

<0.001

<0.001

<0.001

<0.001

0.11

<0.001
<0.001

0.18
0.45

Clinical Assessment Protocols (CAPs)
There was a statistically significant difference between pre-pandemic and pandemic
cohorts in terms of urinary incontinence CAPs, undernutrition CAPs, and cardiorespiratory
condition CAPs, while insignificant in case of dehydration CAPs, pain CAPs, and falls CAPs.
The proportion of individuals with fall CAPs triggered into the medium risk group was
the same in both pre-pandemic and pandemic cohorts, i.e., 13.0% versus 13.6%, respectively.
Individuals with Cardiopulmonary CAPs trigger decreased slightly during the pandemic to 6.6%
from 7.3% in pre-pandemic. In the case of the undernutrition CAPs, the percentage of individuals
in the medium-risk group CAPs trigger category during the pandemic increased marginally from
11.5% to 12.2%. A more notable increase, from 11.7% in pre-pandemic to 13.3% in pandemic,
was seen in individuals with triggers into the high risk for undernutrition CAPs. The proportion
of individuals between pre-pandemic and pandemic cohorts for urinary incontinence CAPs was
identical, with 54.4% and 54.9% of patients triggered to prevent decline, respectively. However,
a drop from 12.4% in pre-pandemic to 10.7% in the pandemic for urinary incontinence CAPs
triggered to facilitate improvement was seen. Analysis of CAPs triggers for various health
conditions showed that individuals in the medium risk fall group exhibited a moderately higher
likelihood of polypharmacy. Those with dehydration CAPs triggers at low-level and urinary
incontinence CAPs triggers (focused on prevention and improvement) demonstrated a slightly
higher likelihood of polypharmacy. Similarly, individuals with CAPs triggers for
Cardiorespiratory issues had a moderately higher likelihood of polypharmacy in the prepandemic cohort and a significantly higher likelihood during the pandemic. Conversely,
individuals with undernutrition CAPs triggers in both medium-risk (1) and high-risk (2) groups
had a lower likelihood of polypharmacy in both cohorts. The association between polypharmacy

70

and bowel condition CAPs triggers was not significant. There was no significant difference
between these associations between pre-pandemic and pandemic cohorts. Figures 3.4a and 3.4b
show the association between various CAPs triggers in relation to polypharmacy.
Figure 3.4 a: Forest Plot for Pre-Pandemic Cohort to Determine the Association Between CAPs Triggers and
Polypharmacy

Urinary Incontinence (facilitate improvement)
Urinary Incontinence (prevent decline)
Dehydration (low level)

Under nurtrition (high risk)
Under nurtrition (medium risk)
Cardiorespiratory
Falls (high risk)
Falls (medium risk)
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Figure 3.4 b: Forest Plot for Pandemic Cohort to Determine the Association Between CAP Triggers and
Polypharmacy

Urinary Incontinence (facilitate improvement)
Urinary Incontinence (prevent decline)
Dehydration (low level)
Under nurtrition (high risk)
Under nurtrition (medium risk)
Cardiorespiratory

Falls (high risk)
Falls (medium risk)
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

71

There were no significant differences in the polypharmacy associations between
sociodemographic variables such as male sex and BMI with polypharmacy among newly
admitted individuals in both the pre-pandemic and pandemic cohorts. Similarly, individuals
diagnosed with depression, cerebrovascular accidents, mania, seizure disorders, anxiety, and
Parkinson's disease all showed a similar pattern in their associations with polypharmacy in both
pre-pandemic and pandemic cohorts. Comparable odds ratios were also found in the analysis of
clinical scales such as ADL Hierarchy, PS, and CPS, as well as CAPs triggers such as urinary
incontinence, cardiorespiratory conditions, falls, undernutrition, and dehydration between prepandemic and pandemic cohorts. This similar pattern suggests that the factors influencing
polypharmacy within LTCFs may be greatly influenced by factors other than pandemic.
Results of Multivariate Analysis
Sociodemographic Factors
Table 3.2a and Models A and B present the association between sociodemographic
factors and polypharmacy for the multivariate logistic regression model without and with the
adjustment of COVID-19 pandemic respectively. Sociodemographic factors that showed a
significant association with polypharmacy at the bivariate level, including age, sex, and BMI,
were included in multiple logistic regression analyses. The model's findings shown in Table 1
Model 1A revealed that individuals in all age groups over age 65 years had a slightly higher
likelihood of polypharmacy in reference to those aged under 65 years. Further, the model
indicated that obesity was a significant contributor to polypharmacy, with individuals with
higher weights (overweight and obese) displaying a greater likelihood of polypharmacy in
reference to individuals of a healthy weight. Conversely, underweight individuals exhibited a
slightly reduced likelihood of polypharmacy in reference to individuals of a healthy weight.

72

Males were found to have a slightly higher risk of polypharmacy in reference to females. The
model was then adjusted with the pandemic time, as shown in Table 1 Model 1B, and although
the pandemic itself showed only a very slight increased likelihood of polypharmacy, associations
between sociodemographic variables and polypharmacy remained consistent. The C-statistic
(Area Under the Curve, AUC) for both models were 0.58 (p <0.001), suggesting a moderate
level of predictive accuracy in model performance.

73

Table 3.2a: Association Between Sociodemographic Factors and Polypharmacy for Multiple Logistic Regression
Models, N = 52,356
MODEL A
Adjusted Odds Ratios
(95% CI)

MODEL B
p-value

Adjusted Odds Ratios
(95% CI)

p-value

Age
< 65 years
Young-old (65-74 years)
Middle-old (75-84 years)

Reference
1.22 (1.07 - 1.39)
1.32 (1.17 - 1.49)

0.004
<0.001

Reference
1.22 (1.07 -1.39)
1.33 (1.18 -1.49)

0.004
<0.001

Oldest-old (85 and more)

1.3 (1.15 - 1.45)

<0.001

1.3 (1.16 -1.46)

<0.001

Sex
Female
Male

Reference
1.1 (1.04 - 1.17)

0.002

Reference
1.1 (1.03 -1.17)

0.002

Reference
0.83 (0.76 - 0.9)
1.27 (1.19 - 1.36)

<0.001
<0.001

Reference
0.82 (0.76 - 0.9)
1.27 (1.19 - 1.36)

<0.001
<0.001

2.25 (2.05 - 2.47)

<0.001

2.26 (2.05 - 2.48)

<0.001

Reference
1.1 (1.04 - 1.17)

0.001

0.58 (0.57 - 0.59)

<0.001

Body Mass Index
Healthy weight (18.5-24.9 kg/m2)
Under weight (< 18.5 kg/m2)
2

Overweight (25-29.9 kg/m )
Obesity (>29.9 kg/m2)
Covid-Cohort
Pre-pandemic
Pandemic
AUC

0.58 (0.57 - 0.59)

<0.001

74

Mental Health Conditions
Table 3.2b and Model A/B present the association between mental health conditions and
polypharmacy for the multiple logistic regression model. A history of mental illness and MS did
not show a statistically significant association with polypharmacy. However, mental health
conditions including Depression, Cerebrovascular Accident, Parkinson Disease, and Mania were
found to be significantly associated with polypharmacy. Individuals diagnosed with these
conditions were found to have a higher likelihood of polypharmacy (p < 0.001). Furthermore,
individuals with Anxiety and Seizure Disorders showed a moderately higher likelihood of
polypharmacy (p < 0.001). When the analyses were adjusted for age categories, sex, and the
pandemic, these associations remained significant. In addition, all three age categories showed a
moderately higher likelihood of polypharmacy, while males and the pandemic time showed a
slightly higher likelihood of polypharmacy compared to females and the pre-pandemic time,
respectively. The C-statistic (Area Under the Curve, AUC) for both models was 0.62 (p <0.001),
suggesting a moderate level of predictive accuracy in model performance.

75

Table 3.2b: The Association Between Mental Health Conditions and Polypharmacy for Multiple Logistic Regression
Model, N = 53,526
MODEL A

MODEL B

Adjusted Odds
Ratios (95% CI)

p-value

p-value

0.24

Adjusted odds
Ratios (95% CI)
1 (0.9 - 1.1)

Mental health history

0.94 (0.86 - 1.04)

Parkinson disease

2.38 (2.03 - 2.79)

<0.001

2.33 (1.98 - 2.73)

<0.001

Seizure disorder
Anxiety disorder

1.6 (1.33 - 1.92)
1.44 (1.3 - 1.6)

<0.001
<0.001

1.73 (1.44 - 2.08)

<0.001

Cerebrovascular accidents

2.01 (1.84 - 2.21)

<0.001

1.47 (1.33 - 1.63)
2.02 (1.84 - 2.21)

<0.001
<0.001

Multiple Sclerosis
Depression

0.88 (0.68 - 1.14)
2.02 (1.86 - 2.19)

0.33
<0.001

1.05 (0.8 - 1.37)
2.05 (1.89 - 2.23)

0.74
<0.001

Mania

2.69 (1.96 - 3.69)

<0.001

2.85 (2.07 - 3.92)

<0.001

0.94

Age
< 65 years
Young-old (65-74 years)

-

Reference
1.24 (1.09 - 1.42)
0.002

Middle-old (75-84 years)

-

1.41 (1.25 - 1.6)

<0.001

Oldest-old (85 and more)

-

1.45 (1.28 - 1.64)

<0.001

-

Reference
1.12 (1.06 - 1.19)
<0.001

-

Reference
1.08 (1.02 - 1.15)
0.006

Sex
Female
Male
Covid-Cohort
Pre-pandemic
Pandemic
AUC

0.62 (0.61- 0.62)

<0.001

0.63 (0.62 – 0.63)

< 0.001

Clinical Scales
Table 3.2c and Model A/B present the association between clinical scales and polypharmacy. The
findings from scores on the ADL scale indicated that individuals who required extensive assistance with
personal hygiene or toileting, maximal assistance with eating or locomotion, displayed dependence in
eating or locomotion, or exhibited total dependence in all ADL were at significantly more likely to
experience polypharmacy. Further, individuals diagnosed with depression disorder had a moderately
higher likelihood of polypharmacy. Similarly, individuals reporting daily but not severe pain and those
with daily severe pain were significantly more likely to experience polypharmacy compared to those
reporting no pain. Conversely, individuals with severe and very severe aggressive behavior exhibited a
76

slightly lower likelihood of polypharmacy than those with no aggressive behavior. Additionally,
individuals with severe cognitive impairments showed a significantly lower likelihood of being on
polypharmacy. These associations persisted even after adjusting for age categories, sex, and pandemic
except individuals with very severe aggressive behavior with a slightly lower likelihood of polypharmacy.
While age categories and male sex were associated with a slightly higher likelihood of polypharmacy, the
pandemic time did not exhibit any significant association with polypharmacy. The C-statistic (Area Under
the Curve, AUC) for both models was 0.66 (p < 0.001), suggesting a moderate level of predictive
accuracy in model performance.

77

Table 3.2c: The Association Between Clinical Scales and Polypharmacy for Multiple Logistic Regression Model, N
= 53,539

Activities of
Daily Living
Scale

Depression Rating
Scale

Pain Scale

MODEL A
Adjusted Odds
p-value
Ratios (95% CI)
Reference

MODEL B
Adjusted Odds
p-value
Ratios (95% CI)
Reference

Supervision
Limited assistance
Extensive assistance
Maximal assistance
Dependence
Total dependence

1.06 (0.92 - 1.23)
1.56 (1.36 - 1.78)
2.15 (1.89 - 2.45)
3.28 (2.85 - 3.78)
3.12 (2.72 - 3.59)
2.29 (1.91 - 2.75)

1.06 (0.91 - 1.23)
1.57 (1.37 - 1.8)
2.16 (1.9 - 2.47)
3.34 (2.89 - 3.85)
3.18 (2.76 - 3.65)
2.35 (1.95 - 2.83)

No depression

Reference

Some depression
symptoms
Possible depressive
disorder

1.1 (1.03 - 1.18)

0.006

1.11 (1.04 - 1.19)

0.002

1.35 (1.23 - 1.47)

<0.001

1.39 (1.27 - 1.53)

<0.001

<0.001
<0.001
<0.001

Independence

0.43
<0.001
<0.001
<0.001
<0.001
<0.001

0.44
<0.001
<0.001
<0.001
<0.001
<0.001

Reference

No pain
Less than daily pain
Daily, but not severe
Severe daily pain

Reference
1.88 (1.74 - 2.03)
2.62 (2.26 - 3.03)
4.49 (2.73 - 7.4)

<0.001
<0.001
<0.001

Reference
1.9 (1.76 - 2.05)
2.65 (2.29 - 3.07)
4.57 (2.77 - 7.52)

Aggressive
Behavior Scale

Moderate
Severe
Very severe

0.88 (0.82 - 0.95)
0.81 (0.73 - 0.89)
0.87 (0.75 - 1.01)

0.001
<0.001
0.08

0.87 (0.81 - 0.93)
0.78 (0.71 - 0.86)
0.84 (0.72 - 0.98)

<0.001
<0.001
0.026

Cognitive
Performance
Scale

Intact
Mild/moderate impairment
Severe impairment

Reference
0.66 (0.6 - 0.72)
0.47 (0.43 - 0.53)

<0.001
<0.001

Reference
0.65 (0.6 - 0.71)
0.47 (0.42 - 0.52)

<0.001
<0.001

Age

< 65 years
Young-old (65-74 years)
Middle-old (75-84 years)
Oldest-old (85 and more)

-

Reference
1.3 (1.14 - 1.49)
1.41(1.25 - 1.59)
1.21 (1.08 - 1.36)

<0.001
<0.001
0.001

Female
Male
Pre pandemic
Pandemic

-

Reference
1.22 (1.15-1.29)
Reference
1.02 (0.97 - 1.09)

Sex
Covid-Period

AUC

0.66 (0.65 -0.67)

<0.001

0.66 (0.65 – 0.67)

<0.001
0.44
<0.001

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Adjusted Analysis of All Variables
The Mega Model 4 as shown in Table 3.2d was then adjusted for all variables that
demonstrated a significant association with polypharmacy at the bi-variate level. Analysis of
clinical scales for the level of assistance needed in ADL revealed that individuals requiring
limited assistance in one or more ADLs had a slightly higher likelihood of polypharmacy.
Similarly, those needing extensive assistance in ADL were at moderately higher likelihood of
polypharmacy while those requiring maximal assistance or those who were totally dependent in
all ADL were all significantly more likely to be associated with polypharmacy as compared to
individuals who were dependent in all ADL. In addition, as compared to individuals with no
depression those individuals with some depression symptoms and possible depressive disorder
were both associated with slightly higher likelihood of polypharmacy. The pain scale revealed
that those who experienced daily, but not severe pain were moderately higher and those with
severe daily pain were a significantly higher likelihood of polypharmacy than those who did not
experience pain. In terms of cognitive impairment, those with mild/moderate impairment and
severe impairment had lower odds of polypharmacy than individuals with intact cognition. Age,
sex, and BMI also revealed important associations. Being in the middle-old or oldest-old age
categories, being male, and being overweight were all associated with slightly higher association
with polypharmacy while obese were at moderately higher risk of polypharmacy. Finally,
individuals with Parkinson's disease notably had moderately higher odds of polypharmacy.
Interestingly pandemic time showed insignificant association with polypharmacy as compared to
the pre-pandemic. The C-statistic (Area Under the Curve, AUC) for the model was 0.68 (p
<0.001), suggesting a higher level of predictive accuracy compared to models 3.2a, 3.2b, and
3.2c in model performance.

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Table 3.2d: The Association Between All Significant Factors and Polypharmacy for Multiple Logistic Regression
Model, N = 52,346

Activities of Daily Living Scale

Depression Rating Scale

Pain Scale

Aggressive Behavior Scale

Cognitive Performance Scale

Age

Model 4
Supervision
Limited assistance
Extensive assistance
Maximal assistance
Dependence
Total dependence
No
Some depression symptoms
Possible depressive disorder
No pain
Less than daily pain
Daily, but not severe
Severe daily pain
No
Moderate
Severe
Very severe
Intact
Mild/moderate impairment
Severe impairment
< 65 years
Young-old (65-74 years)
Middle-old (75-84 years)
Oldest-old (85 and more)

Sex

Female
Male

Body Mass Index

Healthy weight (18.5-24.9 kg/m2)
Under weight (< 18.5 kg/m2)
Overweight (25-29.9 kg/m2)
Obesity (>29.9 kg/m2)
Pre-pandemic
Pandemic period

Covid-Period

Parkinson Disease
AUC

Yes

Adjusted Odds
Ratios (95% CI)
1.08 (0.93,1.26)
1.59 (1.38,1.83)
2.12 (1.86,2.42)
3.3 (2.85,3.81)
3.16 (2.74,3.65)

p-value

2.47 (2.05,2.99)
Reference
1.12 (1.04 - 1.2)

<0.001

1.38 (1.26 - 1.52)
Reference
1.87 (1.73 - 2.02)
2.64 (2.27 - 3.06)
4.4 (2.66 - 7.25)
Reference
0.88 (0.82 - 0.95)
0.8 (0.73 - 0.88)
0.9 (0.77 - 1.05)
Reference
0.68 (0.62 - 0.74)

<0.001

0.5 (0.45 - 0.55)
Reference
1.25 (1.09 - 1.43)
1.4 (1.24 - 1.58)

<0.001

1.31 (1.16 - 1.48)
Reference

<0.001

1.16 (1.09 - 1.23)
Reference
0.75 (0.68 - 0.82)
1.29 (1.20 - 1.38)
2.03 (1.84 - 2.24)
Reference

<0.001

1.05 (0.99 - 1.11)
2.19 (1.86 - 2.57)

0.11
<0.001
<0.001

1.082
<0.001
<0.001
<0.001
<0.001

0.002

<0.001
<0.01
<0.001
0.001
<0.001
0.17
<.001

0.002
<0.001

<0.001
<0.001
<0.001

0.68 (0.67 - 0.70)

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Chapter 4: Discussion
Polypharmacy receives significant attention in the scientific community
worldwide because of its potential impact on patient safety, healthcare costs, and overall health
outcomes. The existing body of literature provides a thorough understanding of the numerous
factors associated with polypharmacy. Recognizing the breadth of research in this area is
critical, to unravel the complexities of polypharmacy. The present study uncovered significant
associations between polypharmacy at the time of the first follow-up assessment with a range of
variables including sociodemographic factors, clinical scales, mental health conditions, and
CAPs measured at baseline during the initial assessment, among newly admitted individuals in
LTCFs in both pre-pandemic and pandemic cohorts.
Despite the unprecedented challenges posed by the pandemic, the study reveals
remarkably consistent patterns in associations between polypharmacy and factors investigated in
this study in both pre-pandemic and pandemic cohorts. The associations between risk factors
found in this study and polypharmacy were not significantly altered by pandemic-related
challenges. The findings emphasize the importance of recognizing and understanding the nature
of these underlying associations for effective healthcare strategies, both in normal circumstances
and during exceptional events such as a pandemic
Furthermore, the high prevalence of polypharmacy during the first follow-up assessment
in both the pre-pandemic and pandemic cohorts indicates that polypharmacy is a common
concern among individuals in LTCFs across Canada. These findings closely matched previous
research findings, which reported high rates of polypharmacy among LTCF residents worldwide,
in which individuals in LTCFs were frequently taking five, nine, or even ten medications, with
prevalence rates ranging from 91%, 74%, and 65%, respectively (Jokanovic et al., 2015). The

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high prevalence of polypharmacy in LTCFs emphasizes the importance of conducting
comprehensive medication regimen reviews involving multidisciplinary teams of physicians,
pharmacists, and nursing staff. Such reviews can subsequently evaluate the potential risks
associated with a higher number of medications.
In addition, the prevalence of antidepressants among newly admitted individuals at the
time of the first follow-up assessment remained remarkably high among newly admitted
individuals in both pre-pandemic and pandemic cohorts. Antidepressants were prescribed to a
total of 53.2% of individuals in both cohorts, with only a 2% increase observed in the pandemic
cohort. Previous research findings demonstrated increased antidepressant use during the
pandemic attributed to the worsening of emotional and behavioral disorders as a result of
COVID-19 restrictions (Ferro Uriguen et al., 2022). The notable prevalence of antidepressants in
this study underscores the significant risk of depression among individuals in LTCFs and
emphasizes the importance of addressing the mental health challenges experienced by
individuals in LTCFs, regardless of the challenges posed by the pandemic. Urgent attention is
needed to address factors contributing to depression in LTCFs, including issues such as social
isolation, lack of activities, lack of emotional support, and challenges posed by the COVID-19
pandemic (Simard et al., 2020).
Antipsychotic medications emerged as the second most frequently prescribed
psychotropic drugs, with a significant 5.1% increase in the prevalence of antipsychotic
medications dispensed among newly admitted individuals in LTCFs in the pandemic cohort
during their first follow-up assessment. The LTCFs may have struggled to manage the mental
health needs of newly admitted individuals during the pandemic, leading to an increase in
antipsychotic prescriptions (Ali et al, 2022). Additionally, newly admitted individuals during the

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pandemic time may have experienced increased anxiety and distress, and those diagnosed with
COVID-19 exhibited increased levels of agitation, which may have prompted LTCFs to
prescribe more antipsychotics (Ali et al, 2022). Challenges such as understaffing and insufficient
training in addressing distress and adhering to antipsychotic prescribing guidelines may have
also contributed to this trend (Anderson et al., 2021). These findings suggest areas of further
investigation and imply a greater need for targeted interventions to address complex
psychological challenges arising from the pandemic-related restrictions due to the risk of virus
spread. The high prevalence of psychotropic medications in LTCFs is concerning due to the risk
of dependence, increased fall risk, cognitive impairment, and the potential overlooking of
behavioral and environmental interventions for sleep issues (Johnell, 2017)
In both pre-pandemic and pandemic cohorts, hypnotic and anti-anxiety medications
showed the lowest total prevalence rates, at 8% and 9%, respectively. The greater frequency of
antidepressants in LTCFs, which are frequently used to treat anxiety and insomnia, is probably
what accounts for this low trend. Because antidepressants address a wider range of mental health
issues and help to prevent the potential dependence and side effects associated with hypnotics,
contributing to eliminating the need for additional medications intended only to manage anxiety
or sleep-related issues.
The study investigated associations between sociodemographic factors and polypharmacy
by analyzing age categories, sex, BMI, tobacco, and alcohol use in pre-pandemic and pandemic
cohorts. A significant association between all age categories; the young-old, middle-old, and
oldest-old, and polypharmacy in the pre-pandemic cohort was found. These findings align with
previous research by Clare and their colleagues in which individuals 65 years old were at higher
risk of polypharmacy due to the presence of multiple chronic conditions necessitating complex

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medication regimens (Clare et al., 2021). The higher prevalence of conditions such as
hypertension, dementia, arthritis, and depression in this study likely contributed to
polypharmacy, as these complex conditions often require the use of multiple medications for
effective management. After accounting for the pandemic time in the adjusted analysis, the
associations between young old, middle-old, and oldest-old age groups and polypharmacy
remained significant . Moreover, a higher risk of polypharmacy was associated with the oldestold age category in this study contradicts findings by Jokanovic et al. (2017), in which an inverse
association with polypharmacy was found among individuals aged 85 years and older (Jokanovic
et al., 2017). This is also supported by other studies indicating reduced therapeutic efficacy of
drugs at the end of life and an increased risk of adverse drug reactions in older age populations
contributing to a lower risk of polypharmacy (Astrop et al., 2020; Cristina et al., 2021). The
findings suggest the need for careful medication monitoring and more investigations are required
to uncover the associations between different age groups and polypharmacy among individuals
in LTCFs. In addition, males were at slightly higher risk of polypharmacy than females, in both
cohorts. One explanation could be due to sex-specific physiological differences affecting drug
metabolic rates and responses to medications which can influence the types and number of
medications required (Soldin & Mattison, 2009). These findings align with the findings by Dörks
et al. (2016) but contradict the findings by Jokanovic et al. (2017), suggesting that males were at
a reduced risk of polypharmacy, and thus the association between sex and the risk of
polypharmacy is inconclusive. Further investigations are required to determine whether
healthcare practices and patient preferences may contribute to the observed sex differences in
medication use. Future research should aim to underscore the inherent biological and genetic

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differences between males and females for the development of tailored and effective treatments
that optimize health outcomes while minimizing potential adverse effects.
The study also found a higher and similar risk of polypharmacy in obese individuals in
both pre-pandemic and pandemic cohorts. This aligns with findings indicating a higher
prevalence of pain, hypertension, diabetes, peripheral vascular disease, and psychiatric disorders
among obese individuals in LTCFs, contributing to increased medication use (De Souto Barreto
et al., 2015). While this association is acknowledged in the literature, a more thorough
investigation is needed to assess how lifestyle interventions or preventive measures can reduce
the risks of polypharmacy in obese individuals. Obese individuals frequently have comorbidities
like diabetes, hypertension, heart disease, and osteoarthritis and as a result, high polypharmacy
risk should be managed by adopting lifestyle interventions to avoid medication-related
complications, drug interactions, and poor health outcomes (Barreto et al., 2015).
The lack of association between alcohol or tobacco use and polypharmacy in this study
suggests that there may be complex interactions involving factors like physical and mental
health, genetics, chronic diseases, and substance use patterns that play a more significant role in
an individual's medication regimen, overshadowing the potential impact of alcohol and tobacco
use and calls for further investigations.
The analysis of various clinical scales and their association with polypharmacy suggests
significant associations in pre-pandemic and pandemic cohorts with no substantial differences
observed between them. Individuals with different scores on the ADL hierarchy scale, PS, and
DRS all exhibited a higher risk of polypharmacy. These findings emphasize the importance of
considering these factors by healthcare professionals when making prescription decisions.
Greater ADL dependency is linked to a higher risk of polypharmacy in this study could be

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explained by low physical function as a risk factor for various chronic diseases (Katsimpris et al.,
2019), suggesting taking an individual's functional level into account during prescribing.
Similarly, as the scores on PS increase, the risk of polypharmacy also increases, which is also
supported by previous studies (Davide L. Vetrano et al., 2013; Graziano et al., 2012). This
suggests that alternative non-pharmacological pain management approaches should be explored
in LTCFs to mitigate the risk of polypharmacy. Furthermore, in both pre-pandemic and
pandemic cohorts, severe depression was associated with a higher risk of polypharmacy with no
substantial differences observed between the two cohorts. This aligns with prior research
findings showing a moderate risk of polypharmacy in depression disorder (Graziano et al., 2012;
Jokanovic et al., 2017; Susan et al., 2012) likely linked to management of other medical
conditions along with management of psychological distress in LTCFs and suggests careful
considerations in antidepressants prescribing. The study also found a lower risk of polypharmacy
among individuals with different scores on the CPS and ABS in both cohorts. Individuals with
mild to moderate cognitive impairment exhibited moderately lower risk, while those with severe
cognitive impairment exhibited significantly lower risk of polypharmacy, with no substantial
differences in pre-pandemic and pandemic cohorts. This finding is consistent with previous
research findings in which severe cognitive impairment was found to be associated with a lower
risk of polypharmacy, explained by the need to avoid medications that could further impair
cognition or induce delirium and behavioral symptoms in patients with existing cognitive
impairment (Graziano et al., 2012; Moore et al., 2014). However, the findings contradict the
investigations (Davide et al., 2019 & Vetrano et al., 2018), where lower scores on CPS were
linked with a higher risk of polypharmacy, likely due to complexities in the regimen for the
management of comorbidities, specific symptoms, age, and functional status. These inconclusive

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findings necessitate more investigations to determine whether severe cognitive impairment
affects medication adherence and the risk-to-benefit ratio of drug therapies to provide a more
definitive understanding of these associations. The findings demonstrating lower and similar
risks of polypharmacy in pre-pandemic and pandemic cohorts among individuals with different
scores on ABS might be attributed to concerns about exacerbating aggressive behavior due to
polypharmacy, as many medications have the potential to promote violence (Anderson & Bokor,
2012). This underscores the necessity for additional research to investigate factors contributing
to these associations, especially due to the lack of available studies on this specific association.
These findings remained consistent in adjusted analysis after controlling for factors such as age,
sex, and the pandemic.
A significant association between different mental health conditions and polypharmacy
was also found in this study. Specifically, individuals diagnosed with Parkinson's, Seizure
Disorder, Cerebrovascular Accident, and Mania were found to be at a higher risk of
polypharmacy, while no substantial difference in associations was observed across two cohorts.
However, the risk of polypharmacy was not significant in individuals diagnosed with
Schizophrenia and Multiple Sclerosis in both cohorts. The findings remained consistent even
after accounting for age, sex, and pandemic. Previous research demonstrated a higher risk of
polypharmacy with Parkinson's disease (Leung et al., 2013) while further research is needed to
determine the association between these mental health conditions and polypharmacy. The
findings underscore the complex interplay between mental and physical well-being,
demonstrating that individuals with mental health illnesses frequently have physical health
challenges (Sampogna et al., 2022), which may necessitate the use of various medications, and

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highlight the significance of integrated care in treating both mental and physical health
conditions.
The analysis of CAPs triggers for various health conditions reveals significant
associations with polypharmacy with no substantial differences observed between both prepandemic and pandemic cohorts. The association between fall CAPs triggers and a higher risk of
polypharmacy underscores the importance of regularly reviewing and limit ing medications to
prevent fall-related hospitalizations (Zaninotto et al., 2020), and to consider potential
confounding factors that may contribute to falls. Furthermore, a higher risk of polypharmacy in
individuals with dehydration CAPs triggers (low-level) uncovers the multifaced nature of
dehydration and its potential interaction with conditions such as renal senescence, multiple
diseases, and polypharmacy all of which contribute to increased risk of fluid loss (Alsanie et al.,
2022). These findings underscore the need to address dehydration issues as part of prescribing
medications. The urinary incontinence CAPs triggers (for prevention and improvement) suggest
an association between polypharmacy, delirium, and urinary incontinence, which can lead to
complex medical issues and increased medication use (Hogan, 1997). This finding suggests
conducting thorough medication reviews to address these multifaceted concerns (Hogan, 1997).
Specific medications that may be contributing to incontinence were not accounted for in this
study, thus limiting the ability to suggest targeted interventions, and calls for future research.
Similarly, the cardiorespiratory CAPs findings emphasized the complexities of cardiovascular
pharmacotherapy in older individuals, with age-related pharmacokinetics and dynamic changes
affecting medication efficacy, necessitating individualized approaches to optimize regimens
(Tamargo et al., 2022). However, specific cardiovascular drugs and their interactions were not
accounted for in this study, limiting insights into medication management strategies for

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individuals with cardiovascular issues. A more in-depth investigation is needed to determine the
appropriate medication management strategies for individuals with cardiovascular CAPs triggers
and polypharmacy.
The inverse association observed in undernutrition CAPs triggers (medium risk and high
risk) may be attributed to individuals with undernutrition receiving preferential treatment
focused on dietary changes and nutritional interventions over medications since the intervention
aims to improve nutritional status while reducing the need for multiple medications (Croggan et
al., 2009). However, specific nutritional interventions and their impact on medication usage have
yet to be thoroughly explored, emphasizing the need for further research to investigate these
associations comprehensively.
Finally, the study's overall rigor was maintained through detailed data information,
precise statistical analysis, ethical considerations, and a solid overview of the existing literature.
The findings of the study provide important insights into the complex associations between
polypharmacy and various factors, which can be used to inform clinical practice and future
research in the field. Furthermore, the associations displayed no noteworthy differences between
the various factors studied in both pre-pandemic and pandemic cohorts. Future research should
prioritize studies that reveal variations in these associations between the two cohorts, delving
deeper into the specific factors contributing to polypharmacy trends. This exploration is
important for gaining a comprehensive understanding of how the pandemic may have influenced
medication usage, allowing for more targeted interventions and improved healthcare strategies.
Implications of Findings
The study underscores the persistent issue of polypharmacy in LTCFs, in both prepandemic and pandemic cohorts irrespective of pandemic influence. To address this concern,

89

comprehensive medication reviews including multidisciplinary teams are urgently needed to
reduce the harms associated with multiple drugs, and to enhance the health outcomes of
individuals in LTCFs. Furthermore, the high prevalence of antidepressants suggests the need to
address the underlying reasons for depression, such as social isolation and a lack of activity.
Non-pharmacological interventions such as emotional support, should be prioritized in LTCFs.
In addition, given the complex psychological challenges that may have occurred during the
COVID-19 pandemic as a result of pandemic-related restrictions, an increase in the use of
antipsychotic drugs during the pandemic emphasizes enhanced staff training and education on
behavioral management techniques, enhanced psychosocial support services, and active
involvement of family and caregivers can contribute to a holistic care approach.
The associations between sociodemographic factors, including age, sex, obesity, and
polypharmacy underscore that healthcare providers should be careful when prescribing
medications to individuals in LTCFs who fall within the young-old, middle-old, and oldest-old
age categories, tailoring treatment plans to their specific needs and to avoid potential risks
associated with polypharmacy in these age groups. Policymakers should consider initiatives that
promote regular medication reviews, foster interprofessional partnerships and prioritize nonpharmacological therapies to manage conditions that can derive significant benefits from such
approaches and develop guidelines to simplify medication regimes for individuals with multiple
medical conditions.
The significant associations between mental health conditions, including Depression,
Mania, and Anxiety, polypharmacy suggests that healthcare providers should prioritize evidencebased psychotherapeutic interventions, behavioral therapies, and non-pharmacological
approaches as primary modalities for managing mental health conditions at the time of initial

90

assessment to minimize reliance on multiple medications during follow up assessments, thereby
reducing the potential for adverse effects and drug interactions. Regular assessments and
personalized treatment plans that consider the unique needs of individuals with mental health
conditions are crucial to promoting optimal mental well-being while avoiding unnecessary
pharmacological interventions. Policymakers should consider integrating mental health-specific
guidelines into LTCF protocols to ensure the provision of mental health care aligns with best
practices to optimize overall mental health outcomes in these settings.
The increased risk of polypharmacy associated with higher scores on the PS, ADL scale,
and DRS highlights the challenges of managing individuals with complex health needs.
Healthcare providers must carefully evaluate and implement suitable pain management
strategies, and consider the adverse effects of polypharmacy, such as an increased likelihood of
falls and worsened cognitive decline in individuals already experiencing cognitive impairment. It
is essential for healthcare professionals and policymakers to work together in formulating
guidelines to address the complex clinical needs found through these outcome scales to enhance
the overall well-being of individuals.
The higher risk of polypharmacy among individuals with CAPs triggers for falls,
cardiorespiratory conditions, urinary incontinence, and dehydration suggests that healthcare
providers and policymakers should reassess the appropriateness of existing CAPs and consider a
more comprehensive approach to address the root causes of these health issues. The implications
suggest a need for a shift towards non-pharmacological interventions, preventive measures, and
targeted care plans tailored to the individual needs of individuals in LTCFs. It emphasizes the
importance of refining CAPs to prioritize holistic, person-centered care, minimizing the risk of

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polypharmacy while effectively managing the health concerns identified in the initial
assessments.
The lack of significant differences in these associations between the pre-pandemic and
pandemic cohorts implies that the observed trends are not attributable to the influence of the
pandemic. This underscores the persistent nature of the identified risk factors, suggesting that
they exist independently of external events such as the pandemic. Efforts for mitigating
polypharmacy should be continuous and should not be solely based on specific external
circumstances and healthcare professionals should adopt sustained and proactive approaches in
addressing the underlying risk factors associated with polypharmacy at the time of initial
assessments. This recognition underscores the need for ongoing, comprehensive interventions to
effectively manage and reduce polypharmacy, irrespective of external contextual changes.
Strengths & Limitations
Investigating and understanding the risk factors associated with polypharmacy in LTCFs, which
are often frail and more vulnerable to the harms associated with polypharmacy than those living
in the community, can improve risk stratification, evidence-based decision-making, and clinical
outcomes among individuals in LTCFs, potentially in the context of the COVID-19 pandemic.
One of the study's main strengths is the large and diverse sample of the CIHI dataset, which
allows for a comprehensive understanding of the LTCF population. Furthermore, large datasets,
being representative of broader populations, increase statistical power and improve the ability to
generalize findings to other LTCFs. One limitation of using large datasets is the risk of
identifying statistically significant associations that are not clinically significant or relevant. To
address this limitation, the study focuses on odds ratios in the findings to demonstrate the
magnitude of the associations and their practical importance. Another notable strength of this

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study lies in its unique contribution to the existing literature. While a substantial body of
literature addresses polypharmacy prevalence within the context of LTCFs, limited research is
available on risk factors associated with polypharmacy specifically in the context of the COVID19 pandemic in LTCFs in Canada. To the best of our knowledge, this is the first study to
investigate factors associated with polypharmacy in LTCFs across Canada both before
and during the COVID-19 pandemic. Furthermore, this is the only study to compare
polypharmacy and psychotropic medication prevalence before and during the COVID-19
pandemic, adding a unique dimension to the existing body of knowledge.
In the context of the COVID-19 pandemic, the existing body of research has primarily
focused on assessing the relationship between polypharmacy and the risk of mortality. In a
retrospective analysis, Visser and colleagues found that higher prescription drugs were
associated with an increased 30-day COVID-related mortality risk, particularly in nonvaccinated individuals (Visser et al., 2023). Additionally, a longitudinal study involving 4023
nursing home residents across Europe revealed that among non-frail participants, hyperpolypharmacy (10 or more medications) was associated with a higher risk of death (Zazzara et
al., 2023). As mortality data was not available in the current study, this limitation should be
addressed in future research.
One of the limitations of this study is its retrospective nature, due to which it was
difficult to determine a causality relationship or monitor changes in polypharmacy trends over
time. In addition, due to the lack of information available regarding the drug classes used by
individuals of LTCFs, the study has not taken specific medication regimens and their potential
interactions into account. Because this study focused on LTCFs, therefore findings are not
applicable to other healthcare settings or demographics. Another limitation of this study is that

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alternative definitions of polypharmacy, such as the use of five to nine drugs or nine or more
drugs were not employed which could have provided a deeper understanding of factors
associated with different medication patterns in the LTCFs and should be investigated in future
research. The decision to define polypharmacy as the concurrent use of five or more medications
in this study is rooted in its wide utilization in clinical practice and research. Clinically, this
threshold signifies the point where individuals encounter complexity in managing medication
regimens, increasing the risk of adverse drug reactions and interactions. Furthermore, this
definition holds practical relevance in healthcare settings, representing the juncture at which
clinicians typically enhance monitoring due to the escalation in medical regimen complexities
involving five or more drugs. Modeling nine or more medications which is generally defined as
hyper polypharmacy introduces a more stringent criterion, potentially excluding individuals with
moderately complex medication regimens. By focusing on five or more medications, the study
aimed to strike a balance between inclusivity and statistical robustness. However, exploring
alternative definitions of polypharmacy in future studies is essential to achieve a more
comprehensive understanding of polypharmacy's spectrum, allowing for a deeper exploration of
the clinical significance of different polypharmacy categories and providing insights for more
targeted interventions and personalized healthcare strategies.
Furthermore, a moderate 5.1% increase in the dispensing of antipsychotic medications
was seen during the pandemic, which could be explained by changes in the mental health needs
of LTCF individuals during the pandemic linked to a higher prevalence of antipsychotic use
(Yan et al., 2023). However, this study did not delve into the specific factors contributing to the
increase in antipsychotic use in LTCFs in the pandemic cohort as it extends beyond the scope of

94

this research. This limitation highlights an avenue for future research exploration to determine
the specific factors leading to an increase in antipsychotic use in LTCFs during the pandemic.
Future Research Directions
Future longitudinal studies are needed to determine the polypharmacy trend over time in
LTCFs and to investigate the longitudinal impact of polypharmacy on the health outcomes of
LTCF individuals in Canada. Investigations should be conducted on the effectiveness of
interventions aimed at reducing polypharmacy in long-term care settings, such as medication
reviews, deprescribing strategies, and the implementation of non-pharmacological interventions
to manage health conditions.
Future research should include a comprehensive analysis of drug classes used by the
individuals in LTCFs, with particular attention to antipsychotics. An investigation into the
factors contributing to the significant increase in the dispensing of antipsychotics during the
pandemic as observed in this study, is required, alongside with an evaluation of the potential
impacts of antipsychotic medications on the mental health of individuals residing in LTCFs.
Additionally, studies examining the effects of changes in medication patterns on patient
outcomes are essential. Research is needed to determine potential drug-drug interactions, adverse
effects, and causality relationships.
To better understand why males may be at higher risk more study is needed to explore the
factors underlying the sex discrepancy in polypharmacy, such as pharmacokinetic and
pharmacodynamic differences, and to develop sex-specific interventions if necessary. Future
research directions should consider adopting other definitions of polypharmacy, such as the use
of five to nine drugs and the use of nine or more drugs, to investigate their associations with
various factors within the LTCFs. This exploration will contribute to a more comprehensive

95

understanding of medication patterns and their implications for individuals in LTCFs, allowing
for targeted interventions and improved healthcare practices.
Individuals with specific diagnoses, such as Parkinson's Disease, Seizure Disorders,
Cerebrovascular Accidents, Depression, Mania, and Anxiety Disorders, could benefit from
interventions tailored to their needs. Future research into the efficacy and feasibility of such
targeted approaches is critical for progressing personalized care in LTCFs.
The findings that individuals with moderate to severe aggressive behaviors, as well as
moderate to severe cognitive impairments, had a lower risk of polypharmacy warrants further
research. The underlying causes of this phenomenon should be investigated to explore
its implications for medication management in this subset of LTCFs individuals. Furthermore,
future research should focus on investigating non-pharmacological interventions for individuals
who have higher scores on PS, ADL scale, and DRS. It is critical to investigate nonpharmacological approaches and interdisciplinary strategies to manage these factors to reduce
reliance on multiple medications.
Research on the role of technology, such as decision support systems and electronic
health records in lowering the risks of polypharmacy in LTCFs is warranted. Optimizing
medication management practices in LTCFs requires investigations into how technology can
support individualized prescribing, medication reconciliation, and real-time monitoring.
Future research direction should also focus on associations between different CAPs and
polypharmacy. It is critical to investigate the outcomes and effectiveness of CAPs for falls,
cardiorespiratory conditions, urinary incontinence, and dehydration in reducing polypharmacy.
Future research should look into whether these protocols contribute to more informed prescribing
practices and better overall patient outcomes.

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It is critical to investigate the impact of pandemic-related disruptions on polypharmacy,
such as changes in healthcare delivery understaffing and resource availability. Future research
should prioritize studies that reveal variations in associations between the two-time frames to
better understand the impact of the pandemic on polypharmacy trends. A comprehensive
understanding of whether the changes made during the pandemic will influence medication
management practices in LTCFs is critical for developing strategies for future healthcare
challenges. Moreover, investigations should be conducted to examine the possible long-term
consequences of the pandemic on individual general well-being and delivery of healthcare in
LTCFs, considering the changing landscape of healthcare post-pandemic. This involves looking
into the adoption of telehealth, modifications to care protocols, and incorporation of new
healthcare practices that arose during the pandemic.
Conclusions
This study describes various sociodemographic factors, clinical scales, mental health
conditions, and CAPs were associated with a higher risk of polypharmacy among individuals in
LTCFs in both pre-pandemic and pandemic cohort. In LTCFs, the overall differences in
prevalence of polypharmacy between the pre-pandemic and pandemic cohorts were minimal.
Among psychotropic medications, antidepressants were the most frequently dispensed, showing
a slight increase in prevalence among individuals in the pandemic cohort. Antipsychotic
medications were the second most prevalent and the prevalence increased significantly to 5.1%
in the pandemic cohort. A smaller but higher risk of polypharmacy was observed among
individuals in the young-old, middle-old, and oldest-old age categories, as well as among males,
while a moderately higher risk of polypharmacy was observed among obese individuals. Several
mental health conditions were associated with increased risk polypharmacy. Individuals with

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moderate and severe aggressive behaviors, as well as those with moderate and severe cognitive
impairments, exhibited a lower risk of polypharmacy. Conversely, the risk of polypharmacy was
higher among individuals with higher scores on the PS, ADL scale, and DRS. In addition, a
higher risk of polypharmacy in individuals with CAPs for falls, cardiorespiratory conditions,
urinary incontinence, and dehydration was observed. Importantly, there were no significant
differences, and a similar pattern was observed between the pre-pandemic and pandemic cohorts.
These findings shed light on the complexities of polypharmacy in LTCFs and emphasize
the importance of a patient-centered, comprehensive approach while managing medications, with
a focus on limiting the risks of polypharmacy while optimizing the health outcomes of
individuals in LTCFs. This study adds to the existing literature as a national study which
describes a broad range of factors associated with polypharmacy in LTCFs across Canada, in
pre-pandemic and COVID-19 pandemic cohorts and which describes medication utilization. This
analysis provides an understanding of the impact of the pandemic on medication patterns,
especially in the realm of psychotropic medications, which holds implications for both clinical
practice and policy development. By addressing this gap in the literature, this study not only
contributes empirical evidence to the field but also serves as a foundational resource for
informing future research endeavors and healthcare strategies in the context of LTCFs.

98

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Appendix One
Different Keywords Selected for All Databases
RISK FACTORS

POLYPHARMACY

LONG-TERM CARE

Associated factors

Multiple medicines

Long-term care facilities

Co morbidities

Multiple drugs

Aged-care facilities

Risk

Multiple medications

Nursing homes

Factors

Long term care homes

Protective factors
Predictive factors

114

Search Criteria from CINAHL Database using MeSH Terms and Key Words
CINAHL DATABASE
1
2

(MH "Polypharmacy”) OR AB multiple medicines OR AB
multiple medications OR AB multiple drugs
(MH "Risk Factors+") OR
(MH "Comorbidity") OR AB risks OR AB factors OR AB
associated factors AB protective factors OR predictive factors

3

(MH "Long Term Care") OR (MH "Residential Facilities+")
OR (MH "Nursing Homes") OR AB long-term care facilit* OR
AB aged care facilit* OR AB long-term care home*

4

#1 AND #2 AND #3

Number of Retrieved
Articles
10,661

1,348,266
59,196
219 (150 last 10 years,
English)

115

Search Criteria from PubMed Database using MeSH Terms and Key Words
PubMed Database

Retrieved Articles

1

"Polypharmacy"[MeSH Terms] OR "polypharmacy"[All Fields]
OR "multiple medicine*"[Title/Abstract] OR "multiple
drug*"[Title/Abstract] OR "multiple
medication*"[Title/Abstract]

24,671

2

"Risk factors"[MeSH Terms] OR ("risk"[All Fields] AND
"factors"[All Fields]) OR "risk factors"[All Fields] OR
"factor*"[Title/Abstract] OR "risk*"[Title/Abstract] OR
"associated factor*"[Title/Abstract] OR "protective
factor*"[Title/Abstract] OR "predictive factor*"[Title/Abstract]
OR "co morbidit*"[Title/Abstract]

6,158,831

3

104,732
"Long term care"[MeSH Terms] OR "long term care"[All
Fields] OR "residential facilities"[MeSH Terms] OR "residential
facilities"[All Fields] OR "long term care
home*"[Title/Abstract] OR "nursing home*"[Title/Abstract] OR
"aged care facilit*"[Title/Abstract] OR "long term care
facilit*"[Title/Abstract]

4

#1 AND #2 AND #3

518
306, last 10 years and
English

116

Search Criteria from Web of Science Database using MeSH Terms and Key Words

1

Web of Science Database

Retrieved Articles

TI= (polypharmacy OR multiple medications OR
multiple medicines OR multiple drugs) OR AB=
(polypharmacy OR multiple medicat* OR multiple
medici* OR multiple drug*) OR TS= (polypharmacy
OR multiple medicat* OR multiple medici* OR
multiple drug*)

217,137

2

3,731,375
TI= (risk factors OR associated risks OR risks OR
factors OR protective factors OR predictive factors)
OR AB= (risk factors OR risk NEAR2 factor* OR
associated risk* OR predictive factor* OR protective
factor*) OR TS = (risk factors OR risks NEAR2
factor* OR associated risk* OR protective factor* OR
Predictive factor*)

3

21,346
TI=(long-term care OR residential facilities OR long
term care facilities OR nursing homes OR long term
care homes OR aged care facilities ) AND TS=(longterm care OR residential facilit* OR nursing home*
OR long term care home* OR long term care facilit*
OR aged care facilit*) AND AB = (long-term care OR
residential facilit* OR nursing home* OR long term
care home* OR long term care facilit* OR aged care
facilit*)

4

#1 AND #2 AND #3

241
181 last 10 years,
English

117

Search Criteria from APA Psych info Database using MeSH Terms and Key Words
APA Psych info Database

Retrieved Articles

1

(MH "Polypharmacy") OR AB Multiple drugs OR AB Multiple
medications OR AB Multiple medicines

5246

2

(MH "Risk Factors") OR (MH " (Protective factors") OR AB
Associated factors OR AB Factors OR AB Risks OR AB
predictive factors.

1,049,021

3

MH "Long Term Care" OR MH "Nursing Home Residents" OR
MH "Nursing Homes” OR AB long-term care facilit* OR AB
aged care facilit* OR AB long term care home*

18,315

4

#1 AND #2 AND #3

52
30 last 10 years, English

118

Visser, A. G. R., Winkens, B.,
Schols, J. M. G. A., Janknegt, R.,
& Spaetgens, B. (2023). The
impact of polypharmacy on 30-day
COVID-related mortality in
nursing home residents: a
multicenter retrospective cohort
study. European geriatric
medicine, 14(1), 51–57.
https://doi.org/10.1007/s41999022-00723-4

Oya N, Ayani N, Kuwahara A,
Kitaoka R, Omichi C, Sakuma M,
Morimoto T, Narumoto J. Over
Half of Falls Were Associated with
Psychotropic Medication Use in
Four Nursing Homes in Japan: A
Retrospective Cohort Study. Int J
Environ Res Public Health. 2022
Mar 7;19(5):3123

3.

4.

2.

Anliker, N., Molinero-Mourelle,
P., Weijers, M. et al. Dental status
and its correlation with
polypharmacy and multimorbidity
in a Swiss nursing home
population: a cross-sectional
study. Clin Oral Invest (2023).
https://doi.org/10.1007/s00784023-04906-6
Zazzara, M. B., Villani, E. R.,
Palmer, K., Fialova, D.,
Corsonello, A., Soraci, L., Fusco,
D., Cipriani, M. C., Denkinger, M.,
Onder, G., & Liperoti, R. (2023).
Frailty modifies the effect of
polypharmacy and multimorbidity
on the risk of death among nursing
home residents: Results from the
SHELTER study. Frontiers in
medicine, 10, 1091246.
https://doi.org/10.3389/fmed.2023.
1091246

Reference

1.

Reference ID

To identify the
incidence of
drug-related
falls with and
without injury

To evaluate
how
polypharmacy
affects 30-day
COVID-related
mortality in
residents of
nursing homes
presented with
COVID-19.

To evaluate the
association of
polypharmacy
and hyper
polypharmacy
with increase in
risk of death

To examine the
association
between oral
health
condition, and
polypharmacy
and/or
multimorbidity

2022

2023

2023

2023

retrospective

Retrospectiv
e

Longitudinal
cohort based
on
secondary
analysis

Cross
sectional

Article Information
Aims
and Publication
Study
Objectives
Date
Design

Characteristics of Participants and Studies, N = 38 Articles (2012-2023)

1 August
2016 to
31 July
2017 (one
year)

March
2020December
2021

20092011

Study
duration

Japan

Netherlan
ds

50
European
NH
facilities

Switzerla
nd

Country

4 nursing
homes

Nursing
homes (10
in Czech
Republic,
9 in
England,
4 in
Finland, 4
in France,
9 in
Germany,
10 in
Italy, and
4 in the
Netherlan
ds) and
from 7
facilities
in Israel.
15 nursing
homes

3 Nursing
homes

Settings

≥5 medications

≥5 medications

Polypharmacy
(5-9
medications),
hyper
polypharmacy
(10 or more
drugs)

N/A

Concurrent use

Concurrent use

46%

N/A

Polypharmacy
in 42.80 %,
hyper
polypharmacy
in 52.90 %.
27.90% (0 - 4
drugs)

Polypharmacy Information
Polypharmacy
Inclusion criteria Prevalence of
definition
for
Polypharmacy
polypharmacy
in residents
definition
≥5 medications
Concurrent use
92. 00%

Appendix Two

All residents
except with short
term stay

COVID-19
positive residents

Randomly
selected older
adults residing in
participating NHs
at the beginning
of the study and
those admitted in
the 3-month
enrolment period
following the
initiation of the
study

individuals with
dental and
medical records
over 65 years old
with diagnosed
polypharmacy
and/or
multimorbidity

Characteristics

459

384

4156

180

Number of
residents

87 years
(SD: 6.9

84 years
(SD = 8)

83.6 (SD =
9.4)

85.5 ± 7.4

median (IQR) 4
(2–6)

6 (SD = 3)

N/A

12.1 ± 5.6.

Population demographics
Mean/
Number of
Median
drugs per
age
resident

75%

65%

73.20%

75. 00%

gender/
female

resident care
records

Medical
records

secondary
analysis based
on data from
the Services
and Health for
Elderly in
Long Term
care
(SHELTER)
study

Medical
records

Data
Collection

119

Díez R, Cadenas R, Susperregui J,
Sahagún AM, Fernández N, García
JJ, Sierra M, López C. DrugRelated Problems and
Polypharmacy in Nursing Home
Residents: A Cross-Sectional
Study. Int J Environ Res Public
Health. 2022 Apr 4;19(7):4313

Excessive polypharmacy and
potentially inappropriate
prescribing in 147 care homes: a
cross-sectional study
Clare MacRae, David AG
Henderson, Stewart W Mercer,
Jenni Burton, Nicosha De Souza,
Paula Grill, Charis Marwick, Bruce
Guthrie BJGP Open 2021; 5 (6):
BJGPO.2021.

Cadenas R, Diez MJ, Fernández N,
García JJ, Sahagún AM, Sierra M,
López C, Susperregui J, Díez R.
Prevalence and Associated Factors
of Polypharmacy in Nursing Home
Residents: A Cross-Sectional
Study. Int J Environ Res Public
Health. 2021 Feb 19;18(4):2037.

Cristina Ionescu, Aleksandar
Jovanović Rates, Variability and
Associated Factors of
Polypharmacy in Nursing Homes
in Cyprus. Aging Medicine and
Healthcare 2021;12(4):125-130

Villani, E.R., Vetrano, D.L.,
Liperoti, R. et al. Relationship
between frailty and drug use
among nursing homes residents:
results from the SHELTER study.
Aging Clin Exp Res 33, 2839–
2847 (2021).
https://doi.org/10.1007/s40520021-01797-z

5.

6.

7.

8.

9.

To investigate
the association
of frailty with
polypharmacy

To assess the
consumption of
drugs and to
establish if
polypharmacy
is related to the
most common
comorbid
diseases.
To assess the
rates of
polypharmacy
and the factors
related to it

To assess the
pattern of
polypharmacy
in a nursing
home in Leon,
one of Spain’s
most ageing
regions, and its
relationship
with different
drug-related
problems
To describe
excessive
polypharmacy
and potentially
inappropriate
prescribing
predisposing
care home
residents to
adverse events

2021

2021

2021

2021

2022

crosssectional

crosssectional

crosssectional

crosssectional

crosssectional

2009 2011

May
2019August
2019(4
months)

January to
June 2019

N/A

February
to July
2021

Czech
RepublicE
ngland,
Finland,
France,
Germany,
Italy,
Netherlan
ds, and
Israel.

Cyprus

Spain

UK

Spain

50
European
facilities
and 7
facilities
in Israel

8 nursing
homes in
the region
of
Limassol

Spanish
Nursing
home
located in
the
province
of Leon

147 care
homes

A nursing
home in
Leon, one
of Spain’s
most
ageing
regions

Nonpolypharmacy
(0-5 drugs),
polypharmacy
(6-9 drugs) and
excessive
polypharmacy
(≥10 drugs).
polypharmacy 59 medications,
and hyper
polypharmacy as
use of 10 or
more drugs

nonpolypharmacy
(0–4 medicines),
polypharmacy 59 drugs an
excessive
polypharmacy
(≥10 Drugs)

Excessive
polypharmacy
≥10 distinct drug
classes
determined by
subsections of
the British
National
Formulary

nonpolypharmacy
(0–4 medicines),
polypharmacy
(5–9 medicines),
and excessive
polypharmacy
(at least 10
medicines)

concurrent use of
medications

Current'
prescribing
(prescription in
the 56 days before
data collection)
and Repeat
prescriptions
issued in 28-day
cycles.
Prescriptions for
medical
appliances
were excluded
treatment at least 1
month prior to
data collection.
complementary,
over the counter
medications and
nutritional
supplements were
excluded.
prescribed
medication given
for at least 1
month prior to
data collection

Prescription drugs
administered to
residents for at
least 1 month.
Over the counter
medications,
nutritional
supplements, and
herbal medicines
were not
considered.

polypharmacy
6-9 drugs,
39.7% residents
and excessive
polypharmacy
≥10 drugs
15.5%
polypharmacy
in 49.5% and
excessive in
25.40%
residents

polypharmacy
in 54.9% of
residents and
excessive in
22.1% of
residents

32.3%
(excessive
polypharmacy)

78.8%

data of All NH
participants from
SHELTER study
conducted

age ≥65 and being
institutionalized
for at least one
month

residents
institutionalized
for at least 1
month in nursing
home with age
≥65 years

residents aged
≥60 years

Residents aged
≥65 years or older
taking chronic
medications for at
least one month

4121

199

326

4468

222

mean age
84.6 ± 9.2
years

Mean age
was 84.32
(SD 6.7)
years, range
65-97 years

The mean
age was
86.8 ± 7.5
years
(range 67–
107 years),

Mean age
was 84.9
years [SD]
8.1

The mean
age was
85.5 ± 7.8
years
(range 65–
107,
median 86),

mean SD 7.0
(3.6)

mean
6.06 (SD 2.91)
with a range of
0 to 14

mean 7.02 ±
3.31 (range: 0–
17).

mean of 7.8
(interquartile
range [IQR]
3.5–12.1) and
median of 8
(IQR 3–13)

median of 7
(range = 0 to
17)

73.10%

70.35%

63.80%

71.50%

67.1%.

Inter RAILTCF

medical files

medical charts

NHS health
coverage
linkage
database

management
software and
medical charts

120

Fassmer AM, Hoffmann F. Acute
health care services use among
nursing home residents in
Germany: a comparative analysis
of out-of-hours medical care,
emergency department visits and
acute hospital admissions. Aging
Clin Exp Res. 2020
Jul;32(7):1359-1368. Doi:
10.1007/s40520-019-01306-3.
Epub 2019 Aug 19. PMID:
31428997.

Kapoor A, Field T, Handler S,
Fisher K, Saphirak C, Crawford S,
Fouayzi H, Johnson F, Spenard A,
Zhang N, Gurwitz JH.
Characteristics of Long-Term Care
Residents That Predict Adverse
Events after Hospitalization. J Am
Geriatr Soc. 2020
Nov;68(11):2551-2557. Doi:
10.1111/jgs.16770. Epub 2020
Aug 20. PMID: 32816317.
Astorp J, Gjela M, Jensen P, Bak
RD, Gazerani P. Patterns and
characteristics of polypharmacy
among elderly residents in Danish
nursing homes. Future Sci OA.
2020 May 29;6(8).

11.

12.

13.

Lexow M, Wernecke K, Schmid
GL, Sultzer R, Bertsche T, Schiek
S. Considering additive effects of
polypharmacy: Analysis of adverse
events in geriatric patients in longterm care facilities. Wien Klin
Wochenschr. 2021 Aug;133(1516):

10.

To identify
patterns and
characteristics
of
polypharmacy
by investigating
the drug-drug
interactions and
potential
adverse effects.

Analysis of
resident
characteristics
for association
with adverse
events
transitioning
back from the
hospital to their
LTC facility.

Investigating
German NH
residents' use of
out-of-hours
medical care,
visits to
emergency
departments
and acute
hospital
admissions.

To identify
adverse events
occurring in
LTC facility
residents and to
assess plausible
concomitant
drug causes.

2020

2020

2020

2021

cross
sectional

Prospective

retrospective

crosssectional

7–27
November
2019

March 1,
2016, to
December
31, 2017.

1 January
2014- 31
December
2015

10 months

Denmark

New
England

Germany:

Germany

Twentyfive
nursing
homes

A total of
32 nursing
homes
from six
New
England
states.

This
cohort
study was
a
componen
t of the
HOMER
N project
which
explores,
in depth,
health
care of
NH
residents

three
LTC
facilities
in
Germany.

five or more

N/A

polypharmacy 59, excessive
polypharmacy 10
or more

use of medicines
≥3

concurrent use of
prescription-based
medications

regularly schedule
medications

the number of
different drugs
prescribed during
the quarter of
nursing home
admission

all drugs
administered to
the patient during
the acquisition
period

polypharmacy
in 68% residents
and excessive in
32%

22.7% were on
0-9 medications.
26.4% were on
18+
medications,
23.9% were on
14–17
medicines
26.4% were on
18+ medications

47.0% with 5-9
and 30.6% with
10 or more

N/A

Residents were 65
years or older,
concurrently
taking five or
more medications
for longer period
of time

Cohort members
(categorized into
four groups: ≤ 69,
70−79, 80−89
and ≥ 90 years),
were
institutionalized
for the first time
between 1
January 2014 and
31 December
2015 and were
required to have a
preceding
insurance period
of at least 1 year
All residents who
had resided in the
facility for
100 days or
longer.

age ≥65 years,
long-term/chronic
medicines ≥3 and
multimorbidity
with ≥3
comorbidities at
the time of
recruitment, more
than 8 weeks stay
in the LTC
facility, and a life
expectancy of
more than
6 months

100

555

1665

104

The mean
(± standard
deviation)
age
82.9 ± 7.7 y
ears

N/A

mean age
80.5 year,

median 86
(range: 66–
101) years
old

(mean ± SD:
8.5 ± 2.6)

N/A

N/A

median
(Q25/Q75; min–
max)
8
(6/10; 2–18)

68%

63.40%

66.30%

72.10%

The requested
information
was either
collected from
the nursing
homes in
person or
received by Email. data
management
was performed
using Google
sheet.

nursing home
records and
physician
review

claims data of
insured
individuals

interviews and
chart review.

121

Izza, M.A.D., Lunt, E., Gordon,
A.L. et al. Polypharmacy,
benzodiazepines, and
antidepressants, but not
antipsychotics, are associated with
increased falls risk in UK care
home residents: a prospective
multi-centre study. Eur Geriatr
Med 11, 1043–1050 (2020).
https://doi.org/10.1007/s41999020-00376-1

Wastesson JW, Morin L, Laroche
ML, Johnell K. How Chronic Is
Polypharmacy in Old Age? A
Longitudinal Nationwide Cohort
Study. J Am Geriatr Soc. 2019
Mar;67(3):455-462

17.

18.

16.

15.

Díaz, L.B.; Casuso-Holgado, M.J.;
Labajos-Manzanares, M.T.; BarónLópez, F.J.; Pinero-Pinto, E.;
Romero-Galisteo, R.P.; MorenoMorales, N. Analysis of Fall Risk
Factors in an Aging Population
Living in Long-Term Care
Institutions in SPAIN: A
Retrospective Cohort Study. Int. J.
Environ. Res. Public
Health 2020, 17, 7234.
https://doi.org/10.3390/ijerph1719
7234
Velázquez-Alva MC, IrigoyenCamacho ME, Cabrer-Rosales MF,
Lazarevich I, Arrieta-Cruz I,
Gutiérrez-Juárez R, ZepedaZepeda MA. Prevalence of
Malnutrition and Depression in
Older Adults Living in Nursing
Homes in Mexico City. Nutrients.
2020 Aug 13;12(8):2429. Doi:
10.3390/nu12082429. PMID:
32823579; PMCID: PMC7468927.
Drug burden index, polypharmacy,
and patient health outcomes in
cognitively intact older residents of
aged care facilities in Malaysia. S.
S. Hasan PhD, S. H. Chang
BPharm, K. Thiruchelvam
BPharm, MSc, D. W. K. Chong
BPharm, MSc, Z. U. Babar PhD

14.

To evaluate the
chronicity of
polypharmacy
among older
adults and to
identify factors
associated with

To examine the
association of
the drug burden
index and
polypharmacy
with patient
health outcomes
among
cognitively
intact older
residents in
aged care
facilities
To explore the
link between
polypharmacy,
psychotropic
medications,
and falls risk

The association
between
nutritional
status,
depressive
symptoms, and
the number of
prescription
drugs taken by
older adults.

To assess the
prevalence of
falls among
residents and to
identify risk
factors for fall.

2019

2020

2020

2020

2020

Longitudinal

prospective

crosssectional

crosssectional

retrospective
cohort

37 months
(Oct
2010Dec2013)

18 months
between
August
2016 and
February
2018.

July–
Septembe
r 2017

May to
October
2018.

past 12
months

Nationwid
e, Sweden.

UK

Malaysia

Mexico

Spain

Living
arrangeme
nt was
given as
“living in
institution

84 UK
long term
care
homes

Eleven
RACFs in
Kuala
Lumpur

Three
nursing
homes in
Mexico

113 longterm care
institution
s
distributed
across the
different
provinces
of Spain.

Use of five or
more drugs

≥5 Drugs

5 or more

3 or more
medications

5 or more drugs.

Eye drops and
nasal sprays were
included, Overthe-counter
medications were
included. taken as
needed
were included.
Medical
equipment and
dressings, topical
medications,
vaccinations, and
dietary
supplements were
excluded
drugs taken in one
month. over-thecounter drugs
were excluded

medication taken
at single time.
complementary
medications,
health
supplements and
medications
prescribed on an
as needed basis
were excluded

use of
prescription drugs,
and current
antidepressant use
(within the past 30
days).

5 or more
medications daily

Overall, 82%
were
continuously
exposed to
polypharmacy
for 6 months or
longer, and 74%

61.90%

27.15%

73.70%

85.4%.

(Aged 65 years
or older) who
were exposed to
five or more drugs
in October
2010.

N/A

Participants were
eligible to take
part in the study if
they had been
permanent
residents in the
facility for ≥6
months, were
aged ≥60 year

65 years and
older, staying for
more than one
year in the
nursing home,

Persons over 70
years old who
were living in a
residential setting
for at least 1 year

711432

1655

200

262

2849

. Mean age
at baseline
was 77 (SD
= 7.8)
years,

mean age
85(SD 8.9)

74.50± 84.0
mean age

83.1 ± 8.6
years

85.21 (SD
6.6) years
old,

mean SD 8.0
(3.1)

median 6 (IQR
3–9)

3.54 ±1.96

mean 5.1 ± 3.9

Mean, SD) 8.07
(3.6)

59%

67.91%

49.10%

66.40%

68.30%

The Swedish
Prescribed
Drug Register
was linked to
the National
Patient
Register, the

Medicine
Administration
Record
charts

medical
records

medical
records

institutional
databases from
SAR quavitae
Company

122

Iniesta-Navalón C, GascónCánovas JJ, Gama ZADS,
Sánchez-Ruiz JF, GutiérrezEstrada EA, De-la-Cruz-Sánchez
E, Harrington-Fernández O.
Potential, and clinically relevant
drug-drug interactions among
elderly from nursing homes: a
multicentre study in Murcia, Spain.
Cien Saude Colet. 2019 May
30;24(5):1895-1902. Doi:
10.1590/141381232018245.16032017. PMID:
31166522.

Jeon YH, Casey AN, Vo K, Rogers
K, Poole B, Fethney J.
Associations between clinical
indicators of quality and aged-care
residents' needs and consumer and
staff satisfaction: the first
Australian study. Aust Health Rev.
2019 Apr;43(2):133-141. Doi:
10.1071/AH17213. PMID:
29335089.

Marhanis Salihah Omar, Lee
Shiow-Ling, The use of Fall RiskIncreasing Drugs among Older
People in Nursing Home, Age and
Ageing, Volume 48, Issue
Supplement_4, December 2019,
Pages iv34iv39,
https://doi.org/10.1093/ageing/afz1
64.138

19.

20.

21.

To ascertain
Australian
multistate
prevalence and
incidence of
five commonly
collected
clinical
indicators such
as falls,
physical
restraints,
pressure injury,
unplanned
weight loss and
polypharmacy
of aged-care
home quality
and to measure
associations
between these
clinical
indicators and
levels of care
needs and
consumer and
staff
satisfaction
To determine
the prevalence
of drugs
causing falls
and the fall risk
status among
older people
residing in
nursing homes

To determine
the prevalence
of potential and
clinically
relevant DrugDrugInteractions
institutionalized
older adults and
to identify the
pertinent factors
associated.

chronic
polypharmacy.

2019

2019

2019

crosssectional

retrospective
analysis

crosssectional

March
2018 November
2018

1 January
2014 – 1
May 2016

last
quarter of
2010.

Malaysia

Australia

Spain

27 long
term care
facilities
in
Malaysia

426 agedcare
homes

10 nursing
homes of
Murcia
(Spain)

number of
medications

N/A

Polypharmacy (9
or more
medications)

Ten or more
drugs

N/A

including
prescription and
over the counter
drugs

medications taken
for at least 3
months

N/A

(Median 42.2%,
IQR 27.3–
58.3%)

for 12 months or
longer. The
proportion of
individuals who
remained
exposed until
the end of the
study was 55%.
20.70%

aged 65 years
and above,
staying at a longterm care facility
for at least three
months

N/A

The study
assesses people
over 65 years

212

N/A

275

age group
70-74
(23.1%)
and 75-79
years
(n=47%,
22.2%)
accounting
for the most

N/A

average
81.6 ± 7.7

Total mean rank
114.42

N/A

6.6±3.8 drugs
per person

60.80%

N/A

61.50%

researcherassisted
questionnaire

Using an
existing
database from
a private
benchmarking
company

clinical record

National
Cause of
Death
Register, and
the Social
Services
Register

123

Morin L, Johnell K, Laroche ML,
Fastbom J, Wastesson JW. The
epidemiology of polypharmacy in
older adults: register-based
prospective cohort study. Clin
Epidemiol. 2018 Mar 12; 10:289298.

Bor A, Matuz M, Csatordai M,
Szalai G, Bálint A, Benkő R, Soós
G, Doró P. Medication use and risk
of falls among nursing home
residents: a retrospective cohort
study. Int J Clin Pharm. 2017
Apr;39(2):408-415. Doi:
10.1007/s11096-017-0426-6. Epub
2017 Feb 10. PMID: 28188510.

24.

25.

23.

Gutiérrez-Valencia, M., Izquierdo,
M., Lacalle-Fabo, E. et al.
Relationship between frailty,
polypharmacy, and under
prescription in older adults living
in nursing homes. Eur J Clin
Pharmacol 74, 961–970 (2018).
https://doi.org/10.1007/s00228018-2452-2
Vetrano DL, Villani ER, Grande
G, Giovannini S, Cipriani MC,
Manes-Gravina E, Bernabei R,
Onder G. Association of
Polypharmacy With 1-Year
Trajectories of Cognitive and
Physical Function in Nursing
Home Residents: Results from a
Multicenter European Study. J Am
Med Dir Assoc. 2018
Aug;19(8):710-713. Doi:
10.1016/j.jamda.2018.04.008.
Epub 2018 May 31. PMID:
29861194.

22.

To measure the
prevalence of
polypharmacy
at baseline, to
measure the
incidence rate
of
polypharmacy
over time, and
finally to
investigate the
factors
independently
associated with
both prevalent
and incident
polypharmacy.
To investigate
the possible
predictors of
geriatric falls
annualized over
a 5-year-long
period, as well
as to evaluate
the medication
use of nursing
home residents.

To examine the
possible
association
between
medication
underprescription,
polypharmacy,
and frailty
To test the
association
between
polypharmacy
and 1-year
change in
physical and
cognitive
function among
nursing home
residents.

2017

2018

2018

2018

retrospective

prospective

Longitudinal

crosssectional

20102015

November
1, 2010December
20, 2013.

2009 and
2011.

N/A

Hungary

Sweden.

Europe
and Israel

Spain

Nursing
home in
Szeged

using
data from
the
Swedish
National
Board of
Health
and
Welfare’s
Social
Services
Register.

NH in
Europe (n
= 50) and
Israel (n =
7).

two
nursing
homes

four or more

polypharmacy
was defined as
the concurrent
use of ≥5
medications and
“excessive
polypharmacy”
as the concurrent
use of ≥10
medications

Polypharmacy
was defined as
the concurrent
use of 5 to 9
drugs and
excessive
polypharmacy as
the use of ≥10
drugs

polypharmacy
was defined as ≥
5 medications

use of chronic
medications

concomitant use
of medications
excluding antiinfective for
systemic use

concurrent use of
medications
including topical
treatments

chronic
medications

89.3% of
residents.

The prevalence
of
polypharmacy
was 44.0% and
excessive
polypharmacy
was 11.7%.

50% with
polypharmacy
and 24% with
excessive
polypharmacy

73.60%

Every patient who
was the resident
of the investigated
nursing home for
at least 12 months
was included into
the study.

Individuals aged
≥65 years at
baseline
(November 1,
2010) were
included

All the NH
residents admitted
to the participant
facilities before
the beginning of
the study were
included.

older than 65
years

197

1,742,336

3234

110

Mean age
SD 81.2 ±
8.9

Mean age
was 75
years (SD,
7.8),

mean 83.4
SD 9.2

86.3 years
(SD 7.3)

percentage
of residents

mean SD 8.32 ±
3.88

mean 4.6 (SD,
4.0)

N/A

N/A

76.20%

56%

74%

71.80%

medical
record

data from the
Services and
Health for
Elderly in
Long Term
care
(SHELTER)
study. Drug
data were
taken from
order sheets
and drug
administration
records and
reported in the
InterRAILTCF.
Total
Population
Register and
record-linkage
at the
individual
level between
multiple
registers with
national
coverage in
Sweden

a questionnaire

124

Jokanovic N, Jamsen KM, Tan
ECK, Dooley MJ, Kirkpatrick CM,
Bell JS. Prevalence and Variability
in Medications Contributing to
Polypharmacy in Long-Term Care
Facilities. Drugs Real World
Outcomes. 2017 Dec;4(4):235-245.
doi: 10.1007/s40801-017-0121-x

McCracken R, McCormack J,
McGregor MJ, Wong ST, Garrison
S. Associations between
polypharmacy and treatment
intensity for hypertension and
diabetes: a cross-sectional study of
nursing home patients in British
Columbia, Canada. BMJ Open.
2017 Aug 11;7(8): e017430. Doi:
10.1136/bmjopen-2017-017430.
PMID: 28801438; PMCID:
PMC5724061.

Dörks M, Herget-Rosenthal S,
Schmiemann G, Hoffmann F.
Polypharmacy and Renal Failure in
Nursing Home Residents: Results
of the Inappropriate Medication in
Patients with Renal Insufficiency
in Nursing Homes (IMREN)
Study. Drugs Aging. 2016
Jan;33(1):45-51. Doi:
10.1007/s40266-015-0333-2.
PMID: 26659732.

26.

27.

28.

To investigate
which
medications
were more
prevalent
among residents
with
polypharmacy
and to
determine the
variability in
prescribing of
these
medications
across LTCFs
Describe
nursing home
polypharmacy
prevalence in
the context of
prescribing for
diabetes and
hypertension
and determine
possible
associations
between lower
surrogate
markers for
treated
hypertension
and diabetes
(overtreatment)
and
polypharmacy.
To assess
polypharmacy
in residents
with renal
failure
2016

2017

2017

cross
sectional

cross
sectional

cross
sectional

October
2014April
2015

July–
November
2014

May 2015

Germany

Canada

Australia

21 nursing
homes in
northwest
ern
Germany
(Bremen
and
Lower
Saxony).

6 nursing
homes in
British
Columbia,
Canada

27 LTCfs

, polypharmacy
(concurrent use
of 5–9 drugs)
and excessive
polypharmacy
(concurrent use
of C10 drugs)

Polypharmacy
was defined as
≥9 regular
medications.

Polypharmacy
was defined as
nine or more
regular
medications.

scheduled
medication
prescribed
regularly,
excluding as
needed
medications. overthe-counter drugs
were included,
while
extemporaneous
products,
homeopathic
drugs and dietary
supplements were
not taken into
account

taking regular
medications.
dietary
supplements and
topical
preparations were
excluded. Onceonly, telephone
orders, nurseinitiated
medications, asneeded and shortterm medications
were also
excluded.
regular
medications
including vitamins
and supplements.

Polypharmacy
(5–9 drugs) was
found in 53.3 %
residents and
excessive in
16.4 %

48% residents

Polypharmacy
(nine or more
regular
medications)
was observed in
272 (36.0%)
residents.

All residents for
whom at
least one serum
creatinine value
and information
about sex
were available, so
the eCLCR could
be calculated.

All patients were
very frail

All

685

214

754

The mean
age was
83.3 ± 10.6
years
(interquartil
e range
[IQR] 79–
91 years).

mean
84±10

The median
age of
residents
with and
without
polypharma
cy was 85
and
86 years,
respectively
.

mean SD 6.3 ±
3.3 long-term
medications
(IQR 4–9) per
resident

mean 8.7
(SD ±3.9)

mean ± standard
deviation of
7.46 ± 3.35
(range 0–19)

75.20%

69.70%

69.90%

using a
questionnaire
and medical
records

paper chart
and local
health
authority
database

electronic
medication
charts and
medical
records

125

Jokanovic N, Tan EC, Dooley MJ,
Kirkpatrick CM, Elliott RA, Bell
JS. Why is polypharmacy
increasing in aged care facilities?
The views of Australian health
care professionals. J Eval Clin
Pract. 2016 Oct;22(5):677-82.

Lalic S, Sluggett JK, Ilomäki J,
Wimmer BC, Tan EC, Robson L,
Emery T, Bell JS. Polypharmacy
and Medication Regimen
Complexity as Risk Factors for
Hospitalization Among Residents
of Long-Term Care Facilities: A
Prospective Cohort Study. J Am
Med Dir Assoc. 2016 Nov
1;17(11):

M. Herson, J.S. Bell, E.C.K. Tan,
T. Emery, L. Robson, B.C.
Wimmer, Factors associated with
medication regimen complexity in
residents of long-term care
facilities. European Geriatric
Medicine, Volume 6, Issue 6,
2015, Pages 561-564, ISSN 18787649,
https://doi.org/10.1016/j.eurger.20
15.10.003.
Moore KJ, Doyle CJ, Dunning TL,
Hague AT, Lloyd LA, Bourke J,
Gill SD. Public sector residential
aged care: identifying novel
associations between quality
indicators and other demographic
and health-related factors. Aust
Health Rev. 2014 Jun;38(3):32531. Doi: 10.1071/AH13184.
Erratum in: Aust Health Rev. 2015
Feb;39(1):120. PMID: 24807681.

30.

31.

32.

33.

Hallgren J, Ernsth Bravell M,
Mölstad S, Östgren CJ, Midlöv P,
Dahl Aslan AK. Factors associated
with increased hospitalisation risk
among nursing home residents in
Sweden: a prospective study with a
three-year follow-up. Int J Older
People Nurs. 2016 Jun;11(2):130-9

29.

To explore
associations
among quality
indicators with
other
demographic
and healthrelated factors.

To investigate
the association
between
polypharmacy
and medication
regimen
complexity with
time to first
hospitalization,
number of
hospitalizations,
and number of
hospital days
over a 12month period.
The aim of this
study was to
investigate
factors
associated with
medication
regimen
complexity in
residents of
LTCFs.

To identify and
prioritize
factors
contributing to
the increasing
prevalence of
polypharmacy

To evaluate
physical and
psychological
factors
associated with
hospitalization
risk

2014

2015

2016

2016

2016

retrospective

cross
sectional

prospective

survey based

Prospective

over 3
months

April August
2014

April
2014August
2014

NGT
survey
was
conducted
at
November
2014

2008–
2010

Australia

Australia

Australia

Australia

Sweden

Victorian
Public
Sector
Residentia
l Aged
Care
Services

6 LTCFs

6
residential
aged care
facilities
in
Adelaide
and
regional
South
Australia

aged care
facilities

11 nursing
homes in
three
different
municipali
ties in
Sweden

≥9 drugs

Polypharmacy w
as defined as the
use of nine or
more regular
medications.

Polypharmacy
was defined as
the use of 9 or
more regular
medications

polypharmacy
was defined as
the use of nine or
more regular
medicines.

N/A/ number of
drugs

at any one time

regular and asneeded medication
use were included.
Minerals and
vitamins,
complementary
and alternative
medications were
included

regular
medications
including over the
counter,
dermatologic
products, vitamin,
and herbal
supplements were
included

regular medicines.

N/A

N/A

63.4%

63%

N/A

N/A

data of residents
with full 3 months
from their
admission date
except residents
with stay <3
months

permanent
residents aged 65
years or older

Health care
professionals with
experience in
aged care clinical
practice, research
or health policy
were purposively
sampled from
metropolitan and
regional Victoria
and South
Australia.
permanent
residents with
aged 65 years or
older

All Swedish
nursing home
residents, ages
65– 101 years

380

383

383

17 health
care
professional
s

429

80.9 ± 11.0

mean age
87.5 (SD:
6.2) years

The median
age
was 88
years
(interquartil
e range 8492

N/A

The mean
age of the
sample was
85 (range
65–101)
years

N/A

The median
number is 13.0
(range: 1–30).

median number
10 IQR (7-13)

N/A

6.8 (3.1) mean
SD

58%

77.50%

78%

Seventeen
health care
professionals
,

70.90%

databases

medication
and medical
record

administration
chart and
electronic
medical
records

commercial
tool
SurveyMonke
y

data from the
Study of
Health and
Drugs in
Elderly living
in institution.

126

Beloosesky Y, Nenaydenko O,
Gross Nevo RF, Adunsky A,
Weiss A. Rates, variability, and
associated factors of polypharmacy
in nursing home patients. Clin
Interv Aging. 2013; 8:1585-90.

Davide L. Vetrano, Matteo Tosato,
Giuseppe Colloca, Eva Topinkova
et al, Polypharmacy in nursing
home residents with severe
cognitive impairment: Results
from the SHELTER Study,
Alzheimer's & Dementia, Volume
9, Issue 5, 2013,
Pages 587-593, ISSN 1552-5260,
https://doi.org/10.1016/j.jalz.2012.
09.009
Susan E. Bronskill, Sudeep S. Gill,
J. Michael Paterson, Chaim M.
Bell, Geoffrey M. Anderson, Paula
A. Rochon. Exploring Variation in
Rates of Polypharmacy Across
Long Term Care Homes, Journal
of the American Medical Directors
Association, Volume 13, Issue 3,
2012, Pages 309.e15-309.e21

35.

36.

37.

Leung AY, Kwan CW, Chi I.
Residents with Alzheimer's disease
in long-term care facilities in Hong
Kong: patterns of hospitalization
and emergency room use. Aging
Ment Health. 2013;17(8):959-65.
Doi:
10.1080/13607863.2013.768211.
Epub 2013 Feb 12. PMID:
23402396.

34.

To quantify the
extent to which
nine or more
drug therapies
were
concurrently
dispensed to
Ontario LTC
residents, to
identify resident
and LTC home
characteristics
associated with
polypharmacy,
to explore the
variation in
polypharmacy
across LTC
homes, and to
consider the
relationship bet

This study
examined the
frequency and
predictors of
hospitalization
and emergency
room use
among residents
with
Alzheimer's
disease at
admission and
after 1 year in a
long-term care
facility.
To determine
the rate and
variability of
polypharmacy
in nursing home
residents and
investigate its
relationship to
age, sex,
functional
status, length of
stay, and
comorbidities.
To assess
prevalence and
factors related
to
polypharmacy
with advanced
cognitive
impairment.

2012

2013

2013

2013

cross
sectional

cross
sectional

cross
sectional

Secondary
analysis of
Longitudinal
Study on
Long-Term
Care Facility
Residents.

Retrospectiv
e

The
census
date from
the LOC
served as
the
reference
date for
study; the
fall of
2005,

2009 to
2011

Nov
2011feb2012.

2004–
2010

Canada

Czech
RepublicE
ngland,
Finland,
France,
Germany,
Israel,
Italy, the
Netherlan
ds.

central
Israel

Hong
Kong

589
LTCFs in
Ontario

57 nursing
homes

six NHs
(four forprofit and
two notfor-profit
long-term
care
institution
s).

10
residential
long-term
care
facilities

Polypharmacy w
as defined as
taking nine or
more distinct
drug therapies at
the time of the
census.

no
polypharmacy
(0-4 drugs),
polypharmacy
(5-9 drugs), and
excessive
polypharmacy
(≥10 drugs).

Two types of
polypharmacy
were determined:
polypharmacy
defined as >5
drugs/day and
polypharmacy
defined as >7
drugs/day

The number of
medications
taken by each
participant
during the
previous 7 days.
Polypharmacy is
given as >3
drugs

all drug therapies
dispensed in 2005
were identified.
Over the counter
were excluded

Only chronic
medications given
orally, by
inhalation, or eye
drops, given for at
least 1 month prior
to data collection,
were recorded.
complementary,
over the counter,
and nutritional
supplements were
not recorded
drugs ordered and
assumed in the 3
days prior to the
assessment were
recorded topical
treatments were
included

medications taken
during the
previous 7 days.

15.5% of
residents

50.7% with
polypharmacy
and excessive
polypharmacy
in 16.9%.
residents

Mean rates of
polypharmacy
>5 drugs and
polypharmacy
>7 drugs were
42.6% and
18.6%,
respectively.

N/A

residents aged 66
years and older
residing in LTC
homes in the fall
of 2005.

All participants
willing to
participate

residents aged
≥65 who had been
institutionalized
for at least 1
month

All residents with
Alzheimer's
disease who were
newly admitted
between 2005 and
2010 was
included in the
analysis

64,394

1449

993

169

Mean 82.6
SD 6.9 for
individuals
dispensed
with ≥9
drugs and
mean age
84.8 SD 7.5
of
individual
dispensed
with <9
drugs

Mean age
84.2 ± 9
years

The mean
age was
85.04±7.55
(65–108)
years.

The mean
age was
82.74 (SD
= 8.07).

N/A

the mean
number of drugs
used was
6.2 ± 3.2.

Mean number
of chronic drugs
per resident was
5.14±2.60

M (SD) 5.44
(3.17) VS 5.78
(3.55) (at
admission and
after 1 year)

N/A

75.00%

71.200%

72%

Data was
assembled
from four
large, linked
health care
administrative
databases
housed at the
Institute for
Clinical
Evaluative
Sciences

using the
InterRAI
LTCF.

medical files

This secondary
analysis used
data collected
with the
Chinese
version of the
Residential
Assessment
Instrument
Minimum
Data Set 2.0 at
admission and
12-month
intervals

127

38.

Graziano Onder, Rosa Liperoti,
Daniela Fialova, Eva Topinkova,
Matteo Tosato, Paola Danese,
Pietro Folino Gallo, Iain Carpenter,
Harriet Finne-Soveri, Jacob
Gindin, Roberto Bernabei,
Francesco Landi, for the
SHELTER Project, Polypharmacy
in Nursing Home in Europe:
Results from the SHELTER Study,
The Journals of Gerontology:
Series A, Volume 67A, Issue 6,
June 2012, Pages 698–704,
https://doi.org/10.1093/gerona/glr2
33

This study
assesses
prevalence and
patients’
characteristics
related to
polypharmacy

ween
polypharmacy
and other
published
indicators of
prescribing
quality.

2012

cross
sectional

2009 to
2011

7
European
Union
(EU)
countries
(Czech
RepublicE
ngland,
Finland,
France,
Germany,
Italy, and
The
Netherlan
ds) and 1
non-EU
country
(Israel)

57
facilities

nonpolypharmacy
(0-4 drugs),
polypharmacy
(5-9 drugs) and
excessive
polypharmacy (≥
10 drugs).

concurrent use of
medications.
polypharmacy
in 49.7%
residents and
excessive in
24.3% resident

All participants
willing to
participate

4023

83.5
(SD 9.3)
years

mean number of
drugs
used was 7.0
(median 7.0, SD
3.6)

73.20%

The
SHELTER
study is aimed
at validating
the interRAI
strument for
long-term care
facilities
(interRAI
LTCF),

128

Appendix Three
Prevalence of Polypharmacy in Different Age Groups in 10 Studies from a Total of N = 38 Articles (2012 –
2023).
Studies

Age Groups (years)

Clare et al., 2021

60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90 - 94
≥ 95

Cadenas et al.,
2021

Cristina et al.,
2021

Astorp etal., 2020

Villani et al., 2021

Wastesson etal.,
2019

Morin et al., 2018

Dörks et al., 2016

Polypharmacy Prevalence (%)
≥ 5- 9 Drugs
≥10 Drugs
2.2
1.3
3.3
3.7
6.1
7
10.5
11.3
19.2
20.9
25.3
27.2
23.4
19.9
9.9
8.7

65 - 74
75 - 84
85 - 94
≥ 95

6.2
26.6
52
15.2

5.6
29.6
56.3
8.5

65 - 74
75 - 84
≥ 85
65 - 74
75 - 84
85 - 94
≥ 95
< 65
65 - 69
70 - 74
75 - 79
80 - 84
≥ 85

5.1
14.3
25.7
16.2
33.8
42.6
7.40
38.7
42.1
52.7
50.5
45.9
50

1.7
9.2
6.88
9.4
65.6
21.9
3.1
20.4
23.5
19.8
25.1
24.9
21.2

65 - 74
75 - 84
85 - 94
≥ 95
65 - 74
75 - 84
85 - 94
≥ 95
< 70
70 – 79
80 – 89
≥ 95

42.3
38.4
18.2
1.1
32.8
530
65.5
67
58.6
56.1
52.4
51

15.7
14.6
18.4
14.6

65 – 74
75 – 84
> 85
66 -74
75 – 84
85+

58.3
44
39
53.7
27.5
41

13.4
45.3
41.3

Beloosesky etal.,
2013

Susan et al., 2012

129

Anti-psychotics

Visser et al.,
2023
25.0%
Anliker et
al., 2023
Oye et al.,
2022
72.0%
Clare et al.,
2021
Cadenas et
al., 2021
17.9%
Cristina et
al., 2021
Lexow et al.,
2021
Iniesta-Navalón et al.,
2019
Villani et al.,
2021
25.7%
Marhanis et
al., 2019
52.0%
Bor et al.,
2017
Jovenk et al.,
2017
29.0%
McCracken
et al., 2017
21.0%
Lalic et al.,
2016
Davide et al.,
2013
35.6%
Beloosesky
et al., 2013
Susan et al.,
2012
37.5%
Graziano et
al., 2012
26.4%

Anti-depressants

35.6%

31.8%

63.6%

3.9%

83.0%

34.4%

4.8%

30.0%

10.7%

8.1%

62.1%

94.5%

66.9%

23.0%

51.7%

31.0%

17.4%

39.0%

20.8%

22.3%

69.1%

39.2%

12.6%

54.9%

18.3%

Angiotensin
converting enzymes
inhibitors

56.2%

55.0%

11.6%

10.30%

26.4%

Anti-dementia

39.0%

78.3%

37.6%

36.6%

34.4%

4.0%

21.1%

47.2%

26.9%

1.8%

45.0%

Acetyl salicylic acid

22.0%

44.2%

29.0%

Anti-hypertensive
62.0%

Anti-thrombotic
60.0%

Anti-ulcers
40.9%

37.2%

53.5%

71.7%

17.9%

86.1%

Anti-platelets
37.7%

40.6%

34.5%

32.2%

Anti-diabetics
9.3%

21.4%

7.6%

20.0%

9.0%

31.7%

Benzodiazepines
36.0%

41.5%

46.9%

35.3%

40.0%

34.8%

29.6%

25.0%

Beta-blockers
22.6%

43.2%

29.3%

17.3%

38.0%

39.7%

12.7%

21.9%

33.6%

Calcium-channel
blockers
16.8%

38.8%

21.1%

13.9%

34.0%

34.3%

19.0%

Diuretics
35.5%

68.2%

26.9%

26.5%

9.0%

55.9%

10.3%

34.5%

50.9%

41.2%

57.4%

20.6%

Osmotic laxatives
41.8%

49.4%

73.6%

61.8%

40.4%

36.3%

32.5%

50.0%

10.0%

13.3%

26.0%

Opioids

Prevalent Drugs Classes Among All Residents According to Anatomical Therapeutic Classification System in 18 Studies from a Total of N = 38 Articles (2012 – 2023)

Appendix Four

Psychotropics
41.1%

38.6%

10.0%

90.3%

38.7%

43.4%

14.3%

49.6%

54.0%

Proton pump
inhibitors
54.8%

48.0%

40.0%

78.3%

44.2%

35.8%

44.0%

14.8%

36.9%

22.2%

10.0%

25.0%

41.9%

14.4%

33.2%

23.9%

27.3%

Statins

130

Body Mass Index
Healthy weight
Under weight
Overweight
Obesity
Urban Rural Status
Urban
Rural
Tobacco Use
No
Yes
Alcohol Use
No

Sociodemographic Status
Age Groups
Less than 65
Young-old
Middle-old
Oldest-old
Sex
Female
Male

40.1 (12,256)
10.0 (3,053)
28.7 (8,769)
19.7 (6,011)
81.1 (24,771)
14.0 (4,270)
85.3 (26,061)
4.2 (1,277)
79.4 (24,255)

80.3 (43,007)
14.1 (7,550)
84.6 (45,284)
4.0 (2,153)
79 (42,306)

62.2 (19,009)
37.7 (11,527)

61.3 (32,829)
38.7 (20,710)

40.4 (21,651)
10.8 (5,758)
27.8 (14,882)
18.8 (10,076)

6.1 (1,852)
12.3 (3,763)
30.4 (9,285)
51.2 (15,644)

57 (30,544)

Pre-pandemic

6.3 (3,360)
13 (6,953)
30.6 (16,411)
50.1 (26,826)

100 (53,550)

Total

Descriptive Analysis of Sample Population N = 53,550

Appendix Five

78.5 (18,051)

83.6 (19,223)
3.8 (876)

79.3 (18,236)
14.3 (3,280)

40.8 (9,395)
11.8 (2,705)
26.6 (6,113)
17.7 (4,065)

60.1 (13,820)
39.9 (9,183)

6.6 (1,508)
13.9 (3,190)
31 (7,126)
48.6 (11,182)

43 (23,006)

Pandemic

131

Married
Not married

Alberta
British Columbia
Ontario
Others

English/French
Others

Medication Status
Polypharmacy
No polypharmacy
Polypharmacy
Antipsychotic Dispensing
Not received
Received
Antianxiety Dispensing
Not received
Received
Antidepressants Dispensing
Not received
Received
Hypnotic Dispensing
Not received
Received
Mental Health Conditions

Marital Status

Provinces

Language

Yes

10.5 (3,221)
89.5 (27,323)
72.7 (22204)
27.3 (8340)
91.2 (27,855)
8.8 (2,689)
47.7 (14,574)
52.3 (15,970)
92.5 (28,258)
7.5 (2,286)

70.5 (37765)
29.5 (15785)
91 (48,735)
9 (4,815)
46.8 (25,057)
53.2 (28,493)
92 (49,278)
8 (4,272)

26.3 (8,023)
56.9 (17,373)

13 (3,976)
20.6 (6,288)
61.9 (18,919)
4.5 (1,361)

86.4 (26,396)
13.6 (4,148)

6.2 (1,886)

10.2 (5,485)
89.8 (48,065)

25.6 (13,692)
55.6 (29,778)

14.5 (7,740)
23.2 (12,433)
57.4 (30,763)
4.9 (2,614)

86.9 (46,560)
13.1 (6,990)

5.9 (3,136)

91.4 (21,020)
8.6 (1,986)

45.6 (10,483)
54.4 (12,523)

90.8 (20,880)
9.2 (2,126)

67.6 (15,561)
32.4 (7,445)

9.8 (2,264)
90.2 (20,742)

24.6 (5,669)
53.9 (12,405)

16.4 (3,764)
26.7 (6,145)
51.5 (11,844)
5.4 (1,253)

87.6 (20,164)
12.4 (2,842)

5.4 (1,250)

132

Mental Illness History
No
Yes
Parkinson Disease
No
Yes
Seizure Disorder
No
Yes
Anxiety Disorder
No
Yes
Cerebral palsy
No
Yes
Cerebrovascular Accidents
No
Yes
Dementia
No
Yes
Multiple Sclerosis
No
Yes
Depression
No
Yes
Mania
No
Yes
89.1 (27,209)
10.9 (3,315)
93.3 (28,510)
6.7 (2,034)
96.2 (29,396)
3.8 (1,148)
85.8 (26,222)
14.2 (4,322)
99.7 (30,441)
0.3 (103)
82.1 (25,091)
17.9 (5,453)
49.6 (15,144)
50.4 (15,400)
99 (30,225)
1.0 (319)
74.6 (22,794)
25.4 (7,750)
98.1 (29,967)
1.9 (577)

88.9 (47,583)
11.1 (5,943)
93.4 (50,034)
6.6 (3,516)
96.1 (51,439)
3.9 (2,111)
86.2 (46,137)
13.8 (7,413)
99.6 (53,359)
0.4 (191)
81.8 (43,816)
18.2 (9,734)
49.9 (26,701)
50.1 (26,849)
98.9 (52,984)
1.1 (566)
74.9 (40,093)
25.1 (13,457)
98 (52,483)
2 (1,067)

97.9 (22,516)
2.1 (490)

75.2 (17,299)
24.8 (5,707)

98.9 (22,759)
1.1 (247)

50.2 (11,557)
49.8 (11,449)

81.4 (18,725)
18.6 (4,281)

99.6 (22,918)
0.4 (88)

86.6 (19,915)
13.4 (3,091)

95.8 (22,043)
4.2 (963)

93.6 (21,524)
6.4 (1,482)

88.6 (20,374)
11.4 (2,628)

133

No
Yes

Clinical Scales
Activity of Daily Living Hierarchy Scale
Independent
Supervision
Limited assistance
Extensive assistance
Maximal assistance
Dependent
Total dependence
Aggressive Behavior Scale
No aggressive behavior
Moderate aggressive
behavior
Severe aggressive behavior
Very severe aggressive
behavior
Cognitive Performance Scale
Relatively intact
Mild/Moderate
Severe
Depression Rating Scale
No depression symptoms
Some depressive symptoms
Possible depressive disorder
Pain Scale
No pain
Less than daily pain

Schizophrenia

3.8 (1,153)
6.6 (2,013)
15.6 (4,769)
31.3 (9,553)
22.2 (6,789)
17 (5,197)
3.5 (1,070)
61.2 (18689)
22.7 (6920)
11.9 (3642)
4.2 (1288)
20.4 (6,219)
59.4 (18,130)
20.3 (6,195)
53.3 (16,274)
28.1 (8,568)
18.7 (5,702)
64.2 (19,599)
26 (7,942)

61.3 (32,801)
22.4 (1,2018)
11.9 (6,382)
4.4 (2,338)
20.6 (11,023)
58.6 (31,405)
20.8 (11,122)
52.6 (28,168)
28.2 (15,093)
19.2 (10,289)
64 (34,295)
26.1 (13,965)

97.6 (29,815)
2.4 (729)

3.7 (1,959)
6.2 (3,331)
14.6 (7,797)
28.3 (15,165)
19.7 (10,576)
23.4 (12,530)
4.1 (2,192)

97.6 (52,252)
2.4 (1,298)

63.9 (14,696)
26.2 (6,023)

51.7 (11,894)
28.4 (6,525)
19.9 (4,587)

20.9 (4,804)
57.7 (13,275)
21.4 (4,927)

4.6 (1,050)

22.2 (5,098)
11.9 (2,740)

61.3 (14,112)

3.5 (806)
5.7 (1,318)
13.2 (3,028)
24.4 (5,612)
16.5 (3,787)
31.9 (7,333)
4.9 (1,122)

97.5 (22,437)
2.5 (569)

134

Daily pain but not severe
Severe
Clinical Assessment Protocols (CAPs)
Falls CAPs
Not triggered
Triggered into the medium
risk of future falls group
Triggered into the high risk
of future falls group
Cardiorespiratory CAPs
Not Triggered
Triggered
Under nutrition CAPs
Not triggered
Triggered - medium risk
Triggered - high risk
Dehydration CAPs
Not triggered
Triggered - low level
Triggered - high level
Urinary Incontinence CAPs
Not triggered
Not Triggered - continent at
baseline
Triggered to prevent decline
Triggered to facilitate
improvement

8.5 (2,598)
1.3 (405)

78 (23,827)
13 (3,979)
9 (2,738)
92.7 (28,317)
7.3 (2,227)
76.7 (23,441)
11.5 (3,524)
11.7 (3,579)
95.1 (29,046)
2.8 (869)
2.1 (629)
11.6 (35,30)
21.7 (6,634)
54.4 (16,607)
12.4 (3,773)

8.6 (4,584)
1.3 (706)

77.7 (41,613)
13.3 (7,116)
9 (4,821)
93 (49,801)
7 (3,749)
75.8 (40,597)
11.8 (6,320)
12.4 (6,633)
95.1 (50,904)
2.8 (1,526)
2.1 (1,120)
11.9 (6,394)
21.8 (11,680)
54.6 (29,238)
11.6 (6,238)

10.7 (2,465)

21.9 (5,046)
54.9 (12,631)

12.4 (2,864)

95 (21,858)
2.9 (657)
2.1 (491)

74.6 (17,156)
12.2 (2,796)
13.3 (3,054)

93.4 (21,484)
6.6 (1,522)

9.1 (2,083)

13.6 (3,137)

77.3 (17,786)

8.6 (1,986)
1.3 (301)

135

Research Ethical Board Letter

Appendix Six

136