Potential Fall Risk Increasing Drugs and Falls in Community-dwelling Older Adults: A Prospective Cohort Study
Conditions: Fall, Fall Injury, Aging, Aged, Medication
Sponsor: Aalborg University Hospital

 
 
1 
 
FRIDs and fall risk among older adults - protocol 
 
 
Title:  
Potential Fall-Risk-Increasing Drugs and falls in community-dwelling older adults: A prospective 
cohort study  
 
Registration 
Clinicaltrials.gov Identifier: NCT05169476 
 
Document date 
12-23-2021 
 

 
 
2 
 
Funding 
This research will be funded by The Department of Geriatrics, Aalborg University Hospital, Den-
mark, The Department of Clinical Medicine, Aalborg University, Denmark, The municipality of 
Hjørring, Denmark and by external funding. The external funding sources will have no role in the 
design of this study, during its execution, analyses, interpretation of data or decision to submit re-
sults.  
Authors 
Corresponding author: 
Silas Zacharias Clemmensen, MS 
Department of Geriatrics, Aalborg University Hospital, Denmark  
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark 
E-mail: silas.clemmensen@rn.dk  
 
Jens Eg Nørgaard, MSc 
Department of Geriatrics, Aalborg University Hospital, Denmark 
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark 
E-mail: jens.noergaard@rn.dk  
 
Gustav Valentin Gade, MD, PhD 
Department of Geriatrics, Aalborg University Hospital, Denmark 
Department of Clinical Medicine, Aalborg University, Denmark. 
E-mail: gustav.soerensen@rn.dk  
Martin Grønbech Jørgensen, MSc, PhD, Assistant Professor 
Department of Geriatrics, Aalborg University Hospital, Denmark.  
E-mail: mgj@rn.dk 
 
 

 
 
3 
 
Nadja Albertsen, MD  
Department of Geriatric Medicine, Aalborg University Hospital, Aalborg, Denmark 
Arctic Health Research Centre, Aalborg University Hospital, Aalborg, Denmark 
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark 
n.albertsen@rn.dk  
 
Stig Andersen, MD, PhD, Professor. 
Department of Geriatrics, Aalborg University Hospital, Denmark and 
Arctic Health Research Centre, Aalborg University Hospital, Aalborg, Denmark 
Department of Clinical Medicine, Aalborg University, Denmark. 
E-mail: lasa@rn.dk  
 
Sponsor contact information 
Study sponsor:  
Department of Geriatrics, Aalborg University Hospital, Denmark.  
Contact name:  
Stig Andersen, MD, PhD, Professor. E-mail: lasa@rn.dk  
Address:  
Geriatrisk afdeling, Gl. Rød bygn. 6, 2. Etage 
Aalborg Universitetshospital, Syd, Hobrovej 18-22, 9000 Aalborg 
 
 
 
 
 
 

 
 
4 
 
Introduction  
Falls in community-dwelling older adults are a major public health issue with an annual incidence 
of approximately 30% among adults aged ≥65 years increasing to 32-42% among those aged ≥75 
years (1). In addition, the number of fallers is expected to increase in the future since the proportion 
of older people over 60 years is increasing worldwide (2). In Europe, the most frequent reason for 
older adults to contact an emergency department is due to falls - accounting for 50% of the total 
contacts (3). Even though most of the falls result in minor injuries, an increasing proportion leads to 
fractures or hospitalization. Furthermore, consequences of falls include increased morbidity and 
mortality along with loss of independence, which may lead to early institutionalization (1,4,5). Fi-
nally, the increasing number of fallers also leads to increased financial burden on the health care 
system (6).  
Although some falls have a single obvious reason, most falls have a multifactorial aetiology such as 
previous falls, advanced age, cognitive impairment, environmental risks, hazardous activities, along 
with presence of chronic disease, muscle weakness, gait and balance disorders, and medication use 
(1,4,7–13). Recently, The European Geriatric Medicine Society (EuGMS) Task and Finish Group 
published a consensus paper classifying 14 medication classes as fall-risk-increasing drug (FRID) 
(14). Most of them were psychotropics, but several new possible FRIDs were identified. However, 
the group did not reach consensus on 17 medication classes as potential FRIDs (antihypertensives, 
cardiac glycosides, antiarrhythmics, gastrointestinal agents, central nervous system agents, chemo-
therapeutics, hypoglycaemics, fluoroquinolones, corticosteroids, and nonsteroidal anti-inflamma-
tory drugs). This was substantiated by the studies presented for the panellists being heterogenous 
due to various settings, different cohorts of older adults as well as unclear descriptions of the medi-
cation and falls ascertainment methods. Hence, the evidence was insufficient to attain consensus.  
This is consistent with the current gap in the literature with only a few studies investigating fall risk 
related to these types of drugs (15–17). Moreover, the group advocated for a higher research quality 
in primary studies due to issues with precise definitions of target medications and falls as well as 
lack of controlling for confounders (14,18). Therefore, to achieve a better understanding of these 17 
medication classes as potential FRIDs, more studies taking these issues into account are warranted.  
 
 

 
 
5 
 
Primary objective: 
To examine the association between use of the potential FRIDs and falls rate with a 1-year follow-
up in a cohort of community-dwelling older adults, aged 75 years or more.    
Study methods  
Study design and sample size:  
This prospective cohort study builds on previous data from the PREFALL study conducted in coop-
eration with Hjørring Municipality, Denmark (19). A pre-registered protocol on the PREFALL 
study was accepted at Clinicaltrials.gov (NCT03608709) on 01/08/2018 (20). The study recruited 
participants from 14/06/2018 to 18/07/2019 and had a 1-year follow-up period. The sample size was 
derived without a recommended power calculation; hence, the sample size builds on feasible re-
cruitment within the 13 months inclusion period granted by the municipality due to economical and 
administrative reasons.   
Timing of final analysis:  
All study data were accessible from the PREFALL study before the completion of this protocol. 
Therefore, a detailed statistical analysis plan for this study was prepared before any outcome analy-
sis was conducted to provide transparency on the intended and pre-planned analysis. This is pre-
specified later in the protocol.   
Study population and setting 
The participants were community-dwelling aged 75 or more. Participants were excluded if they had 
1) a dementia diagnosis; 2) acute illness defined by the presence of a participant-reported experi-
ence of illness within 7 days before inclusion affecting their ability to participate in social activities 
outside their homes; 3) inability to understand Danish; 4) Unable to stand unassisted for one mi-
nute. Support was defined by any assistive devices or help from another person. In Denmark, mu-
nicipalities develop and initiate prophylactic and health-promoting initiatives for senior citizens 
(22). Thus, the participants were recruited through preventive home visits (PHV), senior activity 
centres (SAC), along with senior clubs and associations in Hjørring Municipality, Northern Jutland, 
Denmark. For PHVs, all the community-dwellers aged 75+ years who accepted the invitation to 
PHV were assessed for eligibility and included consecutively. For SACs, senior clubs, and senior 
associations, participants were conveniently recruited by oral study presentations since a 

 
 
6 
 
consecutive sampling method was not possible due to varying attendees each day. Lastly, recruit-
ment videos were transmitted by the local televisions channel along with ads in newspapers and so-
cial media. For a more detailed description of the study setting and recruitment, the investigators 
refer to the PREFALL study (19). 
Follow-up: 
Follow-up time will be defined as the time from inclusion until death, withdrawal, or end of the 
study at the 18/07/2020. A summary of the participants’ loss to follow-up will be presented with 
reasons and baseline characteristics to describe the adequacy of follow-up.  
Baseline characteristics: 
Data collection was performed at baseline and based on a multifactorial falls risk test battery includ-
ing various physical tests (static balance under dual-task conditions, grip- and lower limb strength, 
reaction time of lower limbs and gait speed); self-report questionnaire on sociodemographic charac-
teristics, frailty, nutrition, disability, fear of falling, health-related quality of life, depressive symp-
toms, several physical symptoms, cognitive tests; and reporting of comorbidities (23). The partici-
pants’ comorbidities were obtained from the participants’ hospital records and the diagnoses were 
indexed according to the International Classification of Diseases 10th revision (ICD-10). For a more 
detailed description of physical test and self-report questionnaire and their rationale, the investiga-
tors refer to the PREFALL study (19).  
Baseline medication:  
To determine the participants’ medications at inclusion, the investigators consulted the national 
Shared Medication Record (SMR). The SMR is a national registry of current medication use and 
pharmacy records of prescriptions to each citizen in Denmark. Any physician involved in the pa-
tients’ clinical course has access to the SMR data and all details of each prescription and the date of 
the latest medication reconciliation are provided (24). For each participant all medication registered 
in SMR at baseline was documented and indexed according to the WHO-recommended anatomical 
therapeutic chemical (ATC) medication classification system. The potential FRIDs which will be 
investigated in this study are: Beta-blockers (C07A); angiotensin-converting enzyme (ACE) inhibi-
tors (C09A); calcium channel blockers (C08); cardiac glycosides (C01A); antiarrhythmic drugs 
(C01B); laxatives (A06A); proton pump inhibitors (A02BC); antiparkinson drugs (N04); acetylcho-
linesterase inhibitors (N06DA); beta blocker eye drops (S01ED); corticosteroid (H02A); oral 

 
 
7 
 
hypoglycaemic drugs (A10B); vitamin D (A11CC05); nonsteroidal anti-inflammatory (NSAID) 
drugs (M01A); fluoroquinolones (J01MA); and antineoplastic agents (L01).  
Confounding covariates: 
Many different risk factors and predictors for falls in older adults have been described previously 
and classified into different domains: sociodemographic, medical conditions, psychological, balance 
and mobility, and medication use (4,25,26). All these risk factors are possible confounders to the 
potential FRIDS; hence, the following a priori list specifies the confounding variables to be adjusted 
for in the multivariate regression model:    
• Age (5,8). 
• Sex (4). 
• History of previous falls (19,27,28).  
• The comorbidity related to the potential FRID to adjust for confounding by indication (18). 
• Fear of falling using Short Falls Efficacy Scale-International 7-item (4,27–30). 
• Gait speed measured with a 4-meter walk test (31–33). 
• Cognitive impairment estimated by the Orientation-Memory-Concentration test performed 
over the telephone (4,27,33,34).  
• Frailty using the Tilburg Frailty Indicator (35).  
• Physical and instrumental disability in activities of daily living using the Vulnerable Elders 
Survey 13 (4,32,33,36).  
• Depressive symptoms using Geriatric Depression Scale 4 item, a depression diagnosis or 
prescription of any antidepressants (4,16,27,28).  
• Use of benzodiazepines, benzodiazepines derivatives or non-benzodiazepines (Z-drugs) 
(16,37).  
• Use of opioids (15,38).  
Statistical analysis 
Outcome: 
The primary outcome is fall rate per person-year. Fall rate will be calculated as the total number of 
falls divided by length of follow-up in years. A fall was defined as “an unexpected event in which 
the participants come to rest on the ground, floor, or lower lever” (39). Falls were recorded 

 
 
8 
 
prospectively with daily fall calendars with a monthly reporting and validated by a phone call if a 
fall was registered. If the calendar was not received within 14 days after deadline, the participant 
was contacted to retrieve the missing data.  
Statistical methods: 
A flowchart will be given to illustrate the flow of participants through the study including number 
of participants with and without falls. The participants’ baseline characteristics and follow-up time 
will be summarised using descriptive statistics. Continuous variables will be summarized using me-
dians with interquartile range, and categorical variables will be presented using counts and percent-
age proportions. Furthermore, the number of participants with missing data for the outcome, base-
line characteristics, and confounding covariates will be presented. Then, missing data will be han-
dled with multiple imputation methods. Finally, a univariate and multivariate Poisson regression 
model will be used to specify the association (expressed as incidence rate ratio, IRR) between the 
potential FRID and the rate of falls. For each potential FRID, a multivariate Poisson regression 
model will be performed adjusting for the medical indication of each drug and all other confounders 
from the a priori list. However, the Poisson regression model will only be performed for the poten-
tial FRID if more than 5 participants have a prescription registered at baseline. P-values lower than 
0.05 will be considered as statistically significant. All statistical analyses will be carried out using 
R, version 4.0.2, (R Foundation).  
Dissemination and reporting  
The investigators want to publish the study in peer-reviewed journals in English. The reporting of 
the study will follow the Strengthening the Reporting of Observational studies in Epidemiology 
(STROBE) statement.   
 
 
 
  
 
 

 
 
9 
 
References: 
1.  
Masud T, Robert OM. Epidemiology of falls. 2001;3–7.  
2.  
Organization WH. WHO Global report on Falls Prevention in Older Age. Community 
Health. 2007.  
3.  
EuroSafe. Injuries in the European Union, Summary on Injury Statistics 2012-2014. 
Amsterdam; 2016.  
4.  
Deandrea S, Lucenteforte E, Bravi F, Foschi R, La Vecchia C, Negri E. Risk factors for falls 
in community-dwelling older people: A systematic review and meta-analysis. Epidemiology. 
2010 Sep;21(5):658–68.  
5.  
Rubenstein LZ. Falls in older people: Epidemiology, risk factors and strategies for 
prevention. Age Ageing. 2006;35(SUPPL.2):37–41.  
6.  
Scuffham P, Chaplin S, Legood R. Incidence and costs of unintentional falls in older people 
in the United Kingdom. J Epidemiol Community Health. 2003;57(9):740–4.  
7.  
Ambrose AF, Paul G, Hausdorff JM. Risk factors for falls among older adults: A review of 
the literature. Maturitas [Internet]. 2013;75(1):51–61. Available from: 
http://dx.doi.org/10.1016/j.maturitas.2013.02.009 
8.  
Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in 
the community. Vol. 319, The New England Journal of Medicine. 1988. p. 1701–6.  
9.  
Leipzig RM, Cumming RG, Tinetti ME. Drugs and falls in older people: a systematic review 
and meta-analysis: II. Cardiac and analgesic drugs. J Am Geriatr Soc. 1999;47:40–50.  
10.  
Leipzig RM, Cumming RG, Tinetti ME. Drugs and falls in older people: a systematic review 
and meta-analysis: I. Psychotropic drugs. J Am Geriatr Soc. 1999;47:40–50.  
11.  
Cumming RG. Epidemiology of medication-related falls and fractures in the elderly. Drugs 
Aging. 1998 Jan;12(1):43–53.  
12.  
Hartikainen S, Lönnroos E, Louhivuori K. Medication as a risk factor for falls: critical 
systematic review. J Gerontol A Biol Sci Med Sci. 2007 Oct;62(10):1172–81.  
13.  
Woolcott JC, Richardson KJ, Wiens MO, Patel B, Marin J, Khan KM, et al. Meta-analysis of 
the impact of 9 medication classes on falls in elderly persons. Arch Intern Med. 2009 
Nov;169(21):1952–60.  
14.  
Seppala LJ, Petrovic M, Ryg J, Bahat G, Topinkova E, Szczerbińska K, et al. STOPPFall 
(Screening Tool of Older Persons Prescriptions in older adults with high fall risk): a Delphi 
study by the EuGMS Task and Finish Group on Fall-Risk-Increasing Drugs. Age Ageing. 
2021;50(4):1189–99.  
15.  
Seppala LJ, van de Glind EMM, Daams JG, Ploegmakers KJ, de Vries M, Wermelink 
AMAT, et al. Fall-Risk-Increasing Drugs: A Systematic Review and Meta-analysis: III. 
Others. J Am Med Dir Assoc. 2018 Apr;19(4):372.e1-372.e8.  
16.  
Seppala LJ, Wermelink AMAT, de Vries M, Ploegmakers KJ, van de Glind EMM, Daams 
JG, et al. Fall-Risk-Increasing Drugs: A Systematic Review and Meta-Analysis: II.  

 
 
10 
 
Psychotropics. J Am Med Dir Assoc. 2018 Apr;19(4):371.e11-371.e17.  
17.  
de Vries M, Seppala LJ, Daams JG, van de Glind EMM, Masud T, van der Velde N. Fall-
Risk-Increasing Drugs: A Systematic Review and Meta-Analysis: I. Cardiovascular  Drugs. J 
Am Med Dir Assoc. 2018 Apr;19(4):371.e1-371.e9.  
18.  
Seppala LJ, van der Velde N, Masud T, Blain H, Petrovic M, van der Cammen TJ, et al. 
EuGMS Task and Finish group on Fall-Risk-Increasing Drugs (FRIDs): Position on  
Knowledge Dissemination, Management, and Future Research. Drugs Aging. 2019 
Apr;36(4):299–307.  
19.  
Gade GV, Jørgensen MG, Ryg J, Masud T, Jakobsen LH, Andersen S. Development of a 
multivariable prognostic PREdiction model for 1-year risk of FALLing in a cohort of 
community-dwelling older adults aged 75 years and above (PREFALL). BMC Geriatr. 2021 
Jun;21(1):402.  
20.  
Gade GV, Jørgensen MG, Ryg J, Masud T, Andersen S. Development of a Multivariable 
Prognostic PREdiction Model for 1-year Risk of FALLing in Community-dwelling Older 
Adults in a Non-clinical Setting (PROTOCOL) [Internet]. [cited 2021 Oct 5]. Available 
from: https://clinicaltrials.gov/ct2/show/NCT03608709 
21.  
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The 
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) 
statement: guidelines for reporting observational studies. Lancet [Internet]. 2007 Oct 
20;370(9596):1453–7. Available from: https://doi.org/10.1016/S0140-6736(07)61602-X 
22.  
Sundhedsstyrelsen. Forebyggelse på ældreområdet - håndbog til kommunerne [Internet]. 
2015 [cited 2021 Oct 5]. Available from: https://www.sst.dk/-
/media/Udgivelser/2015/Forebyggelse-på-ældreområdet---håndbog-til-
kommunerne/Forebyggelse-på-ældreområdet,-d-,-Håndbog-til-
kommunerne.ashx?la=da&hash=74B7EC7B72EA05AD8BE434F866BFA586AC36BF9B 
23.  
Sørensen G V., Jørgensen MG, Ryg J, Masud T, Andersen S. Development, feasibility, 
acceptability, and adjustment of a portable, multifactorial falls risk test battery for 
community-dwelling older adults. Cogent Med [Internet]. 2019;6(1):1674099. Available 
from: https://doi.org/10.1080/2331205X.2019.1674099 
24.  
Munck LK, Hansen KR, Grethe Mølbak A, Balle H, Kongsgren S. The use of shared 
medication record as part of medication reconciliation at hospital admission is feasible. Dan 
Med J. 2014;61(5):1–5.  
25.  
Jehu DA, Davis JC, Falck RS, Bennett KJ, Tai D, Souza MF, et al. Risk factors for recurrent 
falls in older adults: A systematic review with meta-analysis. Maturitas [Internet]. 2021 
Feb;144(November 2020):23–8. Available from: 
https://doi.org/10.1016/j.maturitas.2020.10.021 
26.  
Lord SR, Menz HB, Sherrington C. Falls in Older People. In: Osteoporosis in Clinical 
Practice: A Practical Guide for Diagnosis and Treatment [Internet]. London: Springer 
London; 2004. p. 93–9. Available from: https://doi.org/10.1007/978-0-85729-402-9_12 
27.  
Pluijm SMF, Smit JH, Tromp EAM, Stel VS, Deeg DJH, Bouter LM, et al. A risk profile for 
identifying community-dwelling elderly with a high risk of recurrent falling: Results of a 3-
year prospective study. Osteoporos Int. 2006;17(3):417–25.  

 
 
11 
 
28.  
Delbaere K, Close JCT, Heim J, Sachdev PS, Brodaty H, Slavin MJ, et al. A multifactorial 
approach to understanding fall risk in older people. J Am Geriatr Soc. 2010;58(9):1679–85.  
29.  
Rossat A, Fantino B, Nitenberg C, Annweiler C, Poujol L, Herrmann FR, et al. Risk factors 
for falling in community-dwelling older adults: which of them are  associated with the 
recurrence of falls? J Nutr Health Aging. 2010 Nov;14(9):787–91.  
30.  
Kempen GIJM, Yardley L, Van Haastregt JCM, Zijlstra GAR, Beyer N, Hauer K, et al. The 
Short FES-I: A shortened version of the falls efficacy scale-international to assess fear of 
falling. Age Ageing. 2008;37(1):45–50.  
31.  
Quach L, Galica AM, Jones RN, Procter-Gray E, Manor B, Hannan MT, et al. The nonlinear 
relationship between gait speed and falls: the Maintenance of Balance,  Independent Living, 
Intellect, and Zest in the Elderly of Boston Study. J Am Geriatr Soc. 2011 Jun;59(6):1069–
73.  
32.  
Stenhagen M, Nordell E, Elmståhl S. Falls in elderly people: a multifactorial analysis of risk 
markers using data from  the Swedish general population study “Good ageing in Skåne”. 
Aging Clin Exp Res. 2013 Apr;25(1):59–67.  
33.  
Stenhagen M, Ekström H, Nordell E, Elmståhl S. Falls in the general elderly population: a 3- 
and 6- year prospective study of risk  factors using data from the longitudinal population 
study “Good ageing in Skane”. BMC Geriatr. 2013 Aug;13:81.  
34.  
Dellasega CA, Lacko L, Singer H, Salerno F. Telephone screening of older adults using the 
Orientation-Memory-Concentration test. Geriatr Nurs. 2001;22(5):253–7.  
35.  
Andreasen J, Sørensen EE, Gobbens RJJ, Lund H, Aadahl M. Danish version of the Tilburg 
Frailty Indicator--translation, cross-cultural  adaption and validity pretest by cognitive 
interviewing. Arch Gerontol Geriatr. 2014;59(1):32–8.  
36.  
Saliba D, Elliott M, Rubenstein LZ, Solomon DH, Young RT, Kamberg CJ, et al. The 
Vulnerable Elders Survey: a tool for identifying vulnerable older people in the  community. J 
Am Geriatr Soc. 2001 Dec;49(12):1691–9.  
37.  
Treves N, Perlman A, Kolenberg Geron L, Asaly A, Matok I. Z-drugs and risk for falls and 
fractures in older adults-a systematic review and  meta-analysis. Age Ageing. 2018 
Mar;47(2):201–8.  
38.  
Yoshikawa A, Ramirez G, Smith ML, Foster M, Nabil AK, Jani SN, et al. Opioid Use and 
the Risk of Falls, Fall Injuries and Fractures among Older Adults: A  Systematic Review and 
Meta-Analysis. J Gerontol A Biol Sci Med Sci. 2020 Sep;75(10):1989–95.  
39.  
Lamb SE, Jørstad-Stein EC, Hauer K, Becker C. Development of a common outcome data 
set for fall injury prevention trials: The Prevention of Falls Network Europe consensus. J Am 
Geriatr Soc. 2005;53(9):1618–22.