An estimated 20–30% of community-dwelling Canadian adults aged
65 years or older experience one or more falls each year. Fall-related injuries
are a leading cause of hospitalization and can lead to functional independence.
Many fall prevention interventions, often based on modifiable risk factors, have
been studied. Apart from the magnitude of the benefits and harms from different
interventions, the preferences of older adults for different interventions as
well as the relative importance they place on the different potential outcomes
may influence recommendations by guideline panels. These reviews on benefits and
harms of interventions, and on patient values and preferences, will inform the
Canadian Task Force on Preventive Health Care to develop recommendations on fall
prevention for primary care providers.
Methods
To review the benefits and harms of fall prevention interventions,
we will update a previous systematic review of randomized controlled trials with
adaptations to modify the classification of interventions and narrow the scope
to community-dwelling older adults and primary-care relevant interventions. Four
databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials,
Ageline), reference lists, trial registries, and relevant websites will be
searched, using limits for randomized trials and date (2016 onwards). We will
classify interventions according to the Prevention of Falls Network Europe
(ProFANE) Group’s taxonomy. Outcomes include fallers, falls, injurious falls,
fractures, hip fractures, institutionalization, health-related quality of life,
functional status, and intervention-related adverse effects. For studies not
included in the previous review, screening, study selection, data extraction on
outcomes, and risk of bias assessments will be independently undertaken by two
reviewers with consensus used for final decisions. Where quantitative analysis
is suitable, network or pairwise meta-analysis will be conducted using a
frequentist approach in Stata. Assessment of the transitivity and coherence of
the network meta-analyses will be undertaken. For the reviews on patient
preferences and outcome valuation (relative importance of outcomes), we will
perform de novo reviews with searches in three databases (MEDLINE, PsycInfo, and
CINAHL) and reference lists for cross-sectional, longitudinal quantitative, or
qualitative studies published from 2000. Selection, data extraction, and risk of
bias assessments suitable for each study design will be performed in duplicate.
The analysis will be guided by a narrative synthesis approach, which may include
meta-analysis for health-state utilities. We will use the CINeMa approach to a
rate the certainty of the evidence for outcomes on intervention effects analyzed
using network meta-analysis and the GRADE approach for all other
outcomes.
Discussion
We will describe the flow of literature and characteristics of all
studies and present results of all analyses and summary of finding tables. We
will compare our findings to others and discuss the limitations of the reviews
and the available literature.
An estimated 20–30% of older Canadian adults (> 65 years old)
living in the community experience one or more falls each year [1]. In older adults, fall-related injuries
(limiting normal activities) occur more frequently in females than in males
(e.g., 67 vs. 46 per 1000 in 2010) and increase with age—rates for those ≥
85 years of age are about double for those 65 to 74 years of age [1].
Approximately 10% of falls among older adults result in a fracture
[2, 3]. The majority of cost, morbidity, and
mortality generated by fall-related injuries are attributed to peripheral
fractures, especially at the hip, rather than vertebral fractures or injuries to
the soft tissues or organs [1,
4]. Less serious injuries, such
as bruising, lacerations, and sprains, can still lead to pain, reduced function,
and substantial costs to the individual and healthcare system [5]. In Canada, the leading causes of
fall-related injuries in older adults were walking (16% on snow or ice and 45%
on other surfaces) and accidents going up or downstairs (13% of injuries)
[1]. The 2009/10 Canadian
Community Health Survey found that the most common fall-related injury in older
adults was a fracture (35%) followed by sprains/strains (30%) [1]. Emergency room visits following a fall
are not limited to those experiencing a fracture; the survey also found that of
older adults who reported seeking treatment related to a fall, most (67%) went
to an emergency room. Injuries resulting from a fall are a leading cause of
hospitalizations among Canadian adults aged > 65 years, and the length of
hospitalization from fall-related injuries is on average 9 days longer (e.g., 21
vs. 12 days) than for all causes of hospitalization [1]. Based on data from Canadian acute care
hospitals, 17% of older adults who were hospitalized for a fall were moved from
living in the community to a continuing care facility [6]. Many older people also experience
psychological difficulties after falls, including a fear of falling or loss of
confidence, which may contribute to further falling [7]. Falls have been associated with reduced
quality of life [8] and can lead to
loss of function in activities of daily living (ADLs), thereby impacting
independence and overall health outcomes [9].
Risk factors and screening for risk
Falls often are a result of a complex combination of risk factors,
interacting to cause an inability to maintain or regain one’s balance
[1]. Risk factors may be
classified as (i) biological, related to disease(s), and the natural aging
process (e.g., balance and gait deficiency, acute or chronic health conditions,
cognitive impairment, low vision); (ii) behavioral, such as the use of
unsuitable/poorly maintained assistive devices including footwear and clothing,
fear of falling, the use of certain medications (e.g., psychotropic, sedatives,
hypnotics), and vitamin D intake to improve the function of the skeletal muscle
[10, 11]); (iii) social and economic (e.g.,
social isolation, poverty, poor access to healthcare); and (iv) environmental
from factors in the community (e.g., building entrances, lack of handrails),
living environment (e.g., type of furniture, home clutter), and/or related to
weather and climate (e.g., icy surfaces) [1].
Screening patients for their risk for falls has been considered,
with the aim of determining who may benefit the most from further assessment
and/or an intervention to prevent falls. Many different methods for screening
have been developed or considered, relying on single or multiple-item history
questions, self-report measures/questionnaires (e.g., Falls Efficacy Scale
[12]), or performance-based
measures (e.g., 30 Second Chair Stand [13], Berg Balance Scale [14], and Timed Up and Go [15]). However, no single screening method has demonstrated to
be simple and highly accurate for predicting risk, intervention effects may not
be modified by fall risk status, and we are not aware of any trials or
systematic reviews on screening effectiveness. A 2018 systematic review of 26
assessment tools for falls risk in older adults found that no single tool showed
sufficiently high predictive validity (e.g., sensitivities and area-under-the
curves ranged from 63 to 76% and 0.76 to 0.81, respectively, for those used in
community-dwelling populations) to differentiate between people at high versus
low risk of falls [16]. The authors
concluded that clinicians should consider using at least two assessment tools
together to better evaluate their patients’ risk. Likewise, when evaluating the
predictive ability of single and combined use of medical history questions,
authors of another review concluded that no single question emerged as a
powerful predictive tool, but that querying several factors together (i.e., fall
history, difficulty with ADLs, the use of an ambulatory device, concern about
falling, and use of psychoactive medication) could be useful (e.g., using a
calculation of cumulative post-test probability to indicate that a patient’s
risk for falling would change from 30% without screening to ≥ 60%) [17]. The time burden to adopt a multiple
tool-based or question strategy may lead to unacceptably low uptake by primary
care providers due to competing demands that occur in the care of older
patients, often with multiple comorbidities to manage during a clinical
encounter. Further, several reviews of fall prevention intervention studies have
found no significant difference in effects for exercise, multiple component, or
multifactorial interventions based on whether or not above-average or high-fall
risk was used for study inclusion [18,19,20].
Fall prevention interventions
Many interventions for preventing falls have been studied. These
are often based on known, modifiable risk factors for falling. Most fall
prevention interventions can be classified according to the internationally
accepted taxonomy developed by the Prevention of Falls Network Europe (ProFANE)
Group [21]. A major feature of the
taxonomy is the distinction between different categories and combinations of
interventions. Interventions may comprise single-component interventions, involving one or a combination
of two or more interventions (e.g., gait and balance training with
strength/resistance exercises) from the same category (e.g., exercise), or
interventions where more than one intervention from different categories are
offered to everyone (multiple component
interventions) or tailored based on an individual’s risk
assessment (multifactorial interventions).
Single and multiple component interventions may include some form of fall risk
assessment or be directed towards those with one or more particular risk
factor(s), but they do not tailor the intervention components to each
individual’s risk as do multifactorial interventions. Some of the interventions
within the taxonomy are more applicable than others to the general population of
community-dwelling older adults and the practice of primary care (i.e.,
first-contact, accessible, continued, comprehensive and coordinated care
[22]). These may be provided
directly by primary care providers (e.g., vitamin D supplementation, nurse-led
education on falls risk and prevention), by an inter-professional team of
providers (e.g., exercise and cognitive-behavioral therapy), through referral to
an allied health care provider (e.g., environmental/home hazard assessment), or
in the community (e.g., patient-initiated attendance at Tai Chi classes). Other
interventions, such as management of urinary incontinence or cataract surgery,
target populations having a specific diagnosis or condition and, when given
alone, are not considered to address the primary aim of fall prevention.
Interventions may differ depending on the population (e.g., general vs.
increased risk based on recruitment from emergency departments vs. frail) and
delivery setting (e.g., community vs. nursing homes vs. hospitals). They may
also focus on the primary prevention of a fall or secondary prevention of
subsequent falls. Some interventions may be better considered “add-on”
strategies, that are likely insufficient on their own to prevent falls, but
rather used to enhance the uptake or implementation of a main intervention
(e.g., patient appointment reminders, clinician training in exercise therapy, or
fall risk assessment).
Relevance of values and preferences
Health-care decision-making is influenced by the health effects of
interventions as well as people’s values and preferences [23]. While acceptance rates across various
types of fall prevention interventions, on average, appear quite high (e.g.,
approximately 70% of older adults agree to participate in studies regardless of
eligibility [24]), there is
evidence on stated preferences for different types and/or formats of
interventions (e.g., [25,26,27]) that could inform decisions about which interventions to
recommend in general and to specific populations. Moreover, preferences for or
against an intervention are viewed as a consequence of the relative importance
people place on the expected or experienced health outcomes it incurs
[28]. When considering multiple
different types of interventions where the impact on different outcomes may vary
between types, the relative importance placed by patients on the different
potential outcomes may influence which interventions are considered more or less
effective.
Aims and rationale for reviews
The findings of three systematic reviews will be used by the
Canadian Task Force on Preventive Health Care—supplemented by input from patient
and organizational stakeholder consultations and by other sources of information
on feasibility, acceptability, costs/resources, and equity―to make
recommendations for primary care providers on fall prevention interventions. The
following key questions (KQs) will be answered.
KQ1: What are the benefits and harms of interventions
compared with usual care to prevent falls in community-dwelling
adults aged 65 and older?
KQ2: How do community-dwelling adults aged 65 and older
weigh the potential benefits and harms of interventions to prevent
falls?
KQ3: What are the preferences of community-dwelling
adults aged 65 and older regarding different interventions
demonstrated to prevent falls?
A comprehensive search for systematic reviews related to our KQs
published between 2014 and 2019 found that while many systematic reviews (>
80) exist, most have focused on specific types of interventions (e.g., exercise)
and/or specific populations (e.g., cognitively impaired). Although some recent
reviews could be considered as closely meeting our aim for KQ1 on benefits and
harms of various interventions [18,
29,30,31], no
review matches this review’s scope fully in terms of their population,
interventions, comparators, outcomes, timing, and setting (i.e., PICOTS). To
avoid duplication of effort and build on others’ work, we will rely on a review
with the broadest scope/PICOTS [29]
for identification and data extraction of studies related to KQ1 on benefits and
harms. We will update the literature base and make adaptations where suitable to
meet the narrower scope determined by the Task Force (e.g., exclude
interventions delivered exclusively in hospital or nursing home settings). We
did not identify an existing review fully answering KQ2 on the relative
importance of outcomes or KQ3 on preferences for different interventions,
respectively, and will therefore conduct de novo reviews for these two
questions.
Methods
The Evidence Review and Synthesis Centre at the University of Alberta’s
Alberta Research Centre for Health Evidence will complete the reviews (JP, DK-L, BV,
SR, LH). The reviews will be developed, conducted, and prepared according to the
Task Force methods [32], using methods
guided by the Cochrane Handbook [33]
and Grading of Recommendations Assessment, Development and Evaluation (GRADE)
working group [23, 34, 35]. This protocol follows reporting standards [36]. The review for KQ1 on the benefits and
harms of fall prevention interventions will be conducted in collaboration with the
authors of the review which is being adapted (ACT, SES, SMT) [29]. The protocol was reviewed by peer–reviewers
and organizational stakeholders (n = 9). This
final version of the protocol has been approved by the entire Task Force.
A working group of Task Force members (JJR, HC, EL, AEM, BDT, BJW, AT)
and content experts (CD, ME, JH-L, JM) was formed for development of KQs and PICOTS.
Task Force members chose and rated outcomes in terms of their importance for
creating a recommendation, according to methods of GRADE [35]. Outcome ratings were finalized after input
from an outcome rating exercise and focus groups conducted with a sample of older
adults in Canada, by an independent group, led by SES, with expertise in knowledge
translation from St. Michael’s Hospital in Toronto, Ontario. Eight outcomes were
considered critical for decision making (i.e., rated 7 or above on a scale of 1–9)
by the Task Force: number of fallers, number of falls, number of injurious falls,
number of fractures, number of hip fractures, residential
status/institutionalization, health-related quality of life, and functional status.
Intervention-related adverse effects (AEs; any or serious) were rated as important
(i.e., rated 4–6) and included.
The Science Team of the Global Health and Guidelines Division at the
Public Health Agency of Canada (PHAC) (LAT, BM, ERH) provided assistance and input
on Task Force methodological considerations during the development of the
protocol.
Eligibility criteria
Tables 1, 3, and 4
outline each KQ’s study eligibility criteria (i.e., PICOTS). Table 2 is specific to the components of fall
prevention interventions, and study comparators, of interest.
The main population of interest for all KQs is adults aged 65 or
older living in the community, that is, at home or in independent
living/retirement facilities where no or minimal assistance is provided. We will
include studies only recruiting people who have never fallen as well as those
that include people who have a history of falls. For KQ2, when looking at the
valuation/importance of the outcomes of fractures and transfer to residential
status/institutionalization, we will also include studies of populations newly
admitted to residential care/nursing homes. We will exclude studies with
recruitment based exclusively on one or more specific medical diagnoses (e.g.,
stroke, Parkinson’s disease), because these populations are expected to require
fall prevention interventions and management/usual care that are substantially
different from those applicable to the general population of community-dwelling
adults. For KQ2 and 3, studies may include family members or caregivers who
participate on behalf of people with cognitive impairment or otherwise unable to
understand the study procedures.
For KQ1 on benefits and harms, we will include studies with at
least one eligible single intervention as described in Table 2, of those chosen by the Task Force working
group to reflect interventions having a primary aim to prevent falls in a broad
population of community-dwelling older adults, and delivered in, or referable
from, a primary care setting. We will exclude interventions that are solely used
for screening or assessment, or as “add ons” to improve the uptake or
implementation of interventions targeted at preventing falls but not proposed to
reduce falls themselves. Participants can be recruited in hospitals, but the
intervention must be primarily delivered outpatient in primary care or the
community.
The main KQ1 comparator is usual care (UC), which is considered the
medical and health care received by the target population within primary care
that does not include any specific intervention to reduce falls. We will also
include studies with a control having a non/minimally active intervention such
as a pamphlet on falls risk or social engagement activities. We will seek
clinical input in cases where there is uncertainty about whether the UC (as
described by authors) is applicable to the general population of interest; if
not applicable (e.g., comprehensive geriatric assessment is provided to all
patients), the study will be excluded. Although the main interest of the Task
Force is the effects of interventions versus UC, rather than the relative
effects between different types of interventions, for critical outcomes, we will
include head-to-head trials of different interventions and conduct network
meta-analysis (NMA) to maximize the amount of data used and to generate
estimates of the effects versus UC for those interventions that have not been
(or have been minimally) studied in direct comparison with UC [37,38,39]. Studies that only compare different
interventions that are both defined within one single intervention of our
taxonomy (Table 2; e.g., different doses
of vitamin D or intensities of strength training) will be excluded. Final
inclusion of head-to-head comparisons will be based on the intervention (node)
configurations in the final NMAs (see Data Synthesis for Key Question 1). For
outcomes for which we do not undertake NMA, we will define the interventions as
per the nodes used in the NMAs and only include studies using comparisons with
UC or non-/minimally active interventions.
We will include randomized controlled trials (RCTs), of any design,
with at least 3 months of follow-up after randomization to adequately capture
the potential effects on the outcomes. Apart from English language reports, we
will include those reported in French, as the Task Force considers reports
published in both official languages in Canada (English and French). Literature
suggests that language restrictions in systematic reviews on conventional
medicine topics do not appear to bias results from meta-analyses [40, 41]. No restrictions will be placed on publication status,
date, country, or risk of bias.
For KQs 2 and 3, the effects of the interventions are not of
interest but rather the valuation/relative importance of the critical outcomes
(KQ2) and the preferences of older adults for different interventions or
intervention attributes (KQ3). The eligibility criteria for the studies in KQ2
align with those described by the GRADE working group [23, 28, 34]. For
KQ2, we will prefer studies comparing two or more of the relevant outcomes
(e.g., falls versus fractures) and/or with a comparison with a healthy
population; studies without these will be considered if evidence is lacking on
the importance of one or more outcomes. KQ3 will be conducted after KQ1, because
we will only examine the preferences between different interventions, or between
different attributes of interventions, that are shown to be effective by the KQ1
analysis. The attributes of interest will also be decided after completion of
KQ1, but prior to study selection for KQ3. We will use a hierarchy of study
designs for KQs 2 and 3, in order to prioritize the most informative study
designs for each KQ (see Tables 3 and
4). Qualitative studies would be
very informative if the KQs were exploring reasoning (e.g., beliefs, barriers,
expectations) behind the preferences, but the most relevant evidence on
preferences as specified for these KQs is quantitative in nature. Studies
reported in English or French will be sought. No restrictions will be placed on
publication status, country, or risk of bias. We will limit inclusion to studies
published on or after 2000 because it is expected that people’s preferences
change over time and because we expect a large proportion (> 90%) of studies
on fall prevention interventions to be conducted after this date [29].
For KQ1 on benefits and harms, we will locate full texts of all
studies included in the previous review [29]. Further, a librarian will update this review’s
peer-reviewed searches (Additional file 1 contains the search for MEDLINE) from January 1, 2016, in
Ovid MEDLINE (1946-), Ovid Embase (1996-), Wiley Cochrane Central Register of
Controlled Trials (inception-), and Ageline. The search contains Medical Subject
Heading terms and key words combining the concepts of falls/fallers, adults, and
randomized controlled trials. Reference lists of all new trials and recent (2018
onwards) systematic reviews will be hand-searched by one reviewer. We will also
search the World Health Organization Clinical Trials Search Portal (http://apps.who.int/trialsearch/), which searches multiple trial registries, and ask our clinical
experts to provide us with a list of four to five organizational websites to
search for conference abstracts and/or reports of research (2018 onwards). Where
studies are only reported in conference abstracts or trial registries, first
authors will be contacted by email (with two reminders over 1 month) to obtain
full study reports and/or additional study or outcome data. If not received,
these studies will be excluded with the reason documented.
A search for patient values and preferences (covering both KQs 2
and 3) has been developed combining Medical Subject Heading terms and key words
for falls, fractures, and transition to residential care with those for patient
preferences, quality of life, various preference–based instrument/methodology
terms (e.g., EQ-5D, conjoint analysis), decision making, attitudes, and
acceptability (Additional file 1).
This search has been peer-reviewed by another librarian using the PRESS 2015
checklist [42]. For this KQ, we
will search Ovid MEDLINE (1946-), Ovid PsycInfo (1987-), and CINAHL via
EBSCOhost (1937-) databases and hand-search reference lists of included studies
and of relevant systematic reviews.
We will export the results of database searches to an EndNote
Library (version X7, Clarivate Analytics, Philadelphia, US, 2018) for
record-keeping and will remove duplicates. We will document our supplementary
search process, for any study not originating from the database searches, and
enter these studies into EndNote individually. We will update electronic
database searches for all KQs approximately 4 to 5 months prior to publication
of the Task Force guideline. Results of new studies will be reported and, if
considered to potentially impact conclusions and feasible, the relevant analyses
will be re-run.
Selection of studies
Records retrieved from the database searches will be uploaded to
DistillerSR (Evidence Partners Inc., Ottawa, Canada) for screening. For all
citations retrieved from the database searches, two reviewers will independently
screen all titles and abstracts using broad inclusion criteria. Full texts of
any citation from the search considered potentially relevant by either reviewer
will be retrieved. One exception is for the study designs in KQ2 on values and
preferences that are lowest in our hierarchy (i.e., surveys, qualitative
studies), where the full texts will only be reviewed if the other designs offer
very low certainty evidence and we proceed to these designs. Two reviewers will
independently review all full texts (including the studies from the previous
review [29]) against a structured
eligibility form, and a consensus process will be used for any full text not
included by both reviewers. If necessary, a third reviewer with methods or
clinical expertise and/or author contact will be used to arbitrate decisions.
The screening and full-text forms will be pilot-tested with a sample of at least
100 abstracts and 20 full texts, respectively, until the agreement is high (>
95%). Screening studies located from reference lists, trial registries, and
websites will be conducted by one experienced reviewer, with two reviewers
reviewing full texts. Some exclusions are expected to occur after the final
groupings/nodes of interventions is conducted (see below), should the study have
no comparison between two different groups used for analysis. We will document
the flow of records through the selection process, with reasons provided for all
full-text exclusions, and present these in a PRISMA flow diagram [43] and appended excluded studies
list.
Data extraction
We will rely on data extraction from the previous review team
[29], as able and suitable.
Because we are modifying the coding of interventions and adding an outcome of
functional status, some data will be required to be extracted anew from the
studies included in this review. For this data and for all data from new
studies, one reviewer will extract data and another will verify all data for
accuracy and completeness. We will adapt the data extraction form and related
instructions used by the other review team, as necessary, and provide training
for all reviewers involved in extraction. The data extraction form will be
piloted with a sample of at least 10 studies, until agreement on all elements is
high (> 95%).
Sufficient data will be collected to allow examination of the
homogeneity and similarity assumptions for meta-analysis, and for assessment of
the risk of bias, as described in the sections below. The main data items
include the study characteristics (i.e., year and country of conduct, sample
size enrolled, setting of recruitment [hospital vs. other], trial design);
intervention(s) components (coded via Table 2), duration (total duration in weeks), dose (number of
sessions/hours), assessment and delivery personnel (e.g., primary care provider
or team vs. other); description of UC or other control (see Table 2); participant characteristics (sex, age,
proportion with previous falls); and outcome tools, ascertainment, and result
data (with sample size) at longest follow-up. Although not a focus for the
analysis, studies with individuals or populations that may require equity (e.g.,
Indigenous peoples, newcomers to Canada, low income) [44] considerations by the Task Force will be
noted and the applicability of the interventions to these populations will be
assessed.
Table 1 contains our
outcome definitions. Falls will often be defined as “an unexpected event in
which the participant comes to rest on the ground, floor, or lower level”
[45] although we will not
exclude studies not using this or another definition. Fall-related injuries can
be defined in various ways, focusing on symptoms (e.g., limiting one’s normal
activities, with or without fracture) and/or resource use (e.g., requiring
attendance at the emergency department) [46]. To this end, if a study reports on various related
fall-injury outomes, one will be extracted per study using a hierarchy based on
assumed severity: falls leading to hospitalization, falls requiring emergency
department visit, falls requiring physician visit, or any injurious fall. Of
note, the previous review team allowed for data on falls to be included for
their outcome of fallers, if the number of fallers was not reported and the
number of falls was smaller than the study population. We are keeping these
separate because rates of falls may be more sensitive to change than the
proportion of fallers [20], and
other reviews have found a difference in the effects between falls and number of
fallers from falls prevention interventions [18,19,20]. For the outcomes of injurious falls,
fractures, and hip fractures, we will rely on the number of people having one or
more event but will include data on the number of events when necessary and
assume that a participant would only have one event during follow-up. For the
falls outcome, we will use raw data on incidence rates (number of falls per
person-year) in each group where available; otherwise, we will calculate
incidence rates or use the reported rate ratio (RaR). For the other outcomes, we
will extract the crude data on the number of people with the event and the
sample size, unless only the risk ratio (RR) or odds ratio (OR) between study
groups is reported. If studies report both adjusted and unadjusted ratios, we
will use the unadjusted estimate unless the adjustment is for clustering. We
will convert RRs to ORs for analysis.
We will record outcome data using an intention-to-treat approach,
where possible; if not possible, for instance when only relative effects/ratios
between groups are reported instead of raw counts and intention-to-treat not
used, we will rely on results from last-observed-carry-forward or, if necessary,
per protocol/completer approaches.
When two or more interventions in a three- or four-arm trial are
classified as having the same intervention as per our classification (e.g.,
different intensities of a strength training intervention), we will combine the
results from the two interventions [33], to avoid loss of information.
For continuous outcomes measures, we will extract (by arm) the mean
baseline and endpoint or change scores, standard deviations (SDs) or other
measures of variability, and the number analyzed. If necessary, we will
approximate means from medians. If SDs are not given, they will be computed or,
if necessary estimated using established imputation methods [33]. When computing SDs for change from
baseline values, we will assume a correlation of 0.5, unless other information
is present in the study that allows us to compute it more precisely
[47]. We will use available
software (i.e., Plot Digitizer, http://plotdigitizer.sourceforge.net/) to estimate effects from figures if no numerical values are
provided.
We will use an intraclass correlation coefficient (ICC) of 0.01
[48], to adjust findings in
cluster-design RCTs that have not done this. We will not adjust studies that
randomize by household, considering the likelihood of the clustering effect to
be very small [19]. If cross-over
trials are included, we will limit the data extraction to the first period of
the study, because of the potential for carry-over effects from the nature of
fall prevention interventions, and treat the trial as if it used a
parallel-group design; the possible unit-of-analysis error introduced is
recognized to provide a conservative estimate of the trial effects [33].
For KQs 2 and 3 on patient values and preferences, we will collect
data on the population (as per KQ1) as well as exposure to any of the related
outcomes and/or to fall prevention interventions. We will extract details about
any instrument used, including development and composition of scenarios of
health states, choice tasks including definitions of all attributes, or survey
questions. Any details provided to participants about the potential benefits and
harms of fall prevention interventions will be extracted. Where studies provide
results (e.g., health utility values) for more than one type of falls (e.g.,
people falling once, twice, and more) or fracture outcome (e.g., wrist, tibia,
distal femur), we will extract the findings as a range. If including qualitative
studies, any relevant section of the results section will be pasted into a
Microsoft Excel spreadsheet for further analysis.
We will contact study authors of newly identified studies by email,
with 2 reminders over 1 month, if important study data or reporting appear to be
missing or are unclear. When there are multiple publications of the same study,
we will consider the earliest full publication of the primary outcome data to be
the primary data source, while all others will be considered as secondary
sources/associated publications. We will extract data from the primary source
first, adding in data from the secondary source(s).
Within-study risk of bias assessments
For KQ1, to align with the previous review conduct [29], we will use the Cochrane Effective
Practice and Organisation of Care (EPOC) Group’s risk-of-bias tool [49]. Results by domain for all studies will
be reported, although we will also code trials as being at low, moderate, or
high risk of bias.
For KQ2, we will use the tool for preference-based studies as per
GRADE guidance, which includes questions related to the choice/selection of
representative participants; appropriate administration and choice of
instrument; analysis and presentation of methods and results;
instrument-described health state presentation, of all relevant outcomes and
valid with respect to health state; patient understanding; and subgroup analysis
to explore heterogeneity [23].
Critical appraisal tools from the Critical Appraisal Skills Programme
[50] and the Centre for
Evidence-Based Management [51] will
be used for qualitative and cross-sectional/survey studies, respectively, in KQs
2 and 3.
For the trials included in the previous review [29], we will rely on the prior assessments
by this team. For all other studies, two reviewers will independently assess the
studies using the previous team’s reviewer instructions and come to a consensus
on the final scores for each question using a third reviewer where necessary.
Each risk of bias tool will be piloted with a sample of at least five studies,
using multiple rounds until agreement on all elements is high. These assessments
will be incorporated into our assessment of the risk of bias across studies when
assessing the certainty of the evidence for each outcome (see below).
Preliminary grouping of intervention components (nodes)
Because there will be the possibility of many different
combinations of interventions based on their components, we will form meaningful
groups (“nodes” when referring to the NMA) before analysis. After the review
team codes all study arms based on their intervention components (Table
2) and other key dimensions (e.g.,
recruitment setting, delivery personnel), but before any analysis, they will
chart the data and consult with the Task Force and clinical experts to create
and clarify decision rules for grouping interventions in a meaningful way. The
primary consideration will be whether the interventions are considered a single
component, multiple components, or multifactorial. Some single-component
interventions, differing by single interventions but within the same
intervention category in Table 2, may be
grouped together (e.g., lighting and flooring). Groupings of different
multicomponent and multifactorial interventions may focus on the number of
studies to some extent, for example, home hazard assessment and modification
combined with exercise may involve different types of exercise if few studies
examine each type. Groupings will also focus on factors thought to relate to
implementation, such as feasibility, acceptability, access, preferences of
patients and providers, and/or modify effects. If requested by the Task Force,
we will conduct one or more meta-regressions or stratified analyses using the
pair-wise comparisons with UC to see where intervention effects may be modified
based on a priori intervention covariates of interest including the inclusion of
exercise (in multiple component interventions), dose, intensity, setting, and
delivery provider. This would also potentially help prevent heterogeneity in the
network meta-analysis. After this process, preliminary networks will be created
and the synthesis started. In some cases, the final network configuration may be
revised based on the assessment of the NMA, as described below.
Data synthesis for KQ1 (benefits and harms)
When a meta-analysis is not appropriate, a descriptive summary with
accompanying tables and/or figures to present the data will be performed.
NMAs will be considered for all critical outcomes where indirect
evidence exists for the outcome and connects to the network. This form of
analysis simultaneously evaluates a suite of comparisons. A network of different
comparisons is constructed, with nodes representing the different interventions,
to consider both direct evidence from comparisons of interest (e.g.,
intervention B vs. UC) and indirect evidence from other comparisons where one
intervention is in common, but not all (e.g., effects from intervention A vs. UC
and from intervention A vs. B comparisons will contribute to the estimate of the
“network” effect for intervention B vs. UC). For the important but not critical
outcome of intervention-related AEs, and for comparisons with UC that are not
included in an NMA based on intransitivity or other reasons, pairwise
meta-analyses will be conducted where appropriate.
Pairwise meta-analysis
For pairwise meta-analysis, because of anticipated between-study
heterogeneity, we will employ the DerSimonian Laird random-effects model using
Stata. For dichotomous outcomes, we will report ORs or RaRs with corresponding
95% CIs. For continuous outcomes, we will report a pooled mean difference using
changes scores, when one measurement tool is used. We will use a standardized
mean difference when combining two or more outcome scales measuring similar
constructs based on clinical input. If suitable, we will transform the results
back to the scale most frequently used. If we are not able to use a study’s data
in a meta-analysis (e.g., only p values are reported), we will comment on these
findings and compare them with the results of the meta-analysis. Where SDs have
been imputed or estimated we will perform sensitivity analysis by removing these
studies. When event rates are less than 1%, the Peto OR method will be used,
unless control groups are of unequal sizes, large magnitude of the effect is
observed, or when events become more frequent (5–10%) where the Mantel-Haenszel
method without correction factor will be used [52]. The decision to pool studies will not be based on the
statistical heterogeneity; the I2 statistic will be reported but it is recognized
that the I2 is influenced by the number of studies and
magnitude and direction of effects [52]. Rather, we will rely on interpretations of the clinical
(related to our PICOTS) and methodological differences between studies. When
heterogeneity in effects is seen, we will conduct subgroup or sensitivity
analysis, using the same variables described in the section on assessment of
transitivity in the NMAs. Effect estimates for each outcome will be transformed
to risk differences to allow judgment of the clinical importance [53]. For outcomes having statistically
significant effects, we will calculate the number needed to treat (NNT) and its
95% CI.
Network meta-analysis
We will employ random effects NMA and network meta-regressions in
the most recent version of Stata available at the time of our analysis, using a
frequentist approach that accounts for correlations between effect sizes from
multi-arm studies [54]. The measure
of treatment effect will be an OR with the exception of the rate of falls where
we will report RaRs. The heterogeneity within the same treatment comparison will
be measured with the tau-squared which represents the variance of the random
effects distribution; this variance will be assumed to be common across the
various treatment comparisons although sources of heterogeneity between
different comparisons will be explored by network meta-regressions during the
assessment of intransitivity.
The assumptions underlying NMA are similar to standard pairwise
meta-analysis, but there are additional issues of comparability that need to be
considered to ensure the validity of results [55]. Indirect comparisons are not protected by randomization
and may be confounded by differences between the trials.
Assessment of transitivity
Transitivity means that covariates that act as relative treatment
effect modifiers are similar across different interventions, or adjusted for
using meta-regression, so the effect of all treatments included in the model is
generalizable across all included studies. Our exclusion criteria for certain
populations expected to require different usual care, and for interventions
provided in hospital and home-care settings are thought to prevent substantial
intransitivity.
Across studies grouped by comparison, we will investigate the
distribution of clinical and methodological covariates that, based on findings
from other systematic reviews [19,
20, 29], may be important effect modifiers
related to the population or study design―age (< 80 vs. ≥ 80 years), previous
fallers (100% vs. > 30 ≤ 100% vs. general population risk of ≤ 30%),
recruited at hospitals, countries with the similar healthcare system to Canada
(e.g., high-income, Organisation for Economic Co-operation and Development and
predominantly universal health care [56]), and study design (i.e., follow-up after randomization
[< 12 vs. ≥ 12 months]). We plan to use previous falls rather than
increased/high-risk for falls based on various factors, because this is
consistently shown to have a strong association with risk for falls (e.g., OR
for any fall 2.8 [95% confidence interval 2.4–3.3] and for recurrent fallers 3.5
[95% CI 2.9–4.2]) [57, 58] and there is some evidence to suggest
this risk factor alone may modify treatment effects [29].
We will use graphic methods, including weighting edges (lines
between nodes) in the network plots based on covariates, to examine similarity
between comparisons [38,
55].
Network meta-regressions will also be performed on the NMA to
examine the influence of the aforementioned covariates; the change to the
heterogeneity (tau value) will be tabulated. If one variable is thought to lead
to important statistical heterogeneity, we may verify this with sensitivity
analysis and, if necessary, either split the NMA into subgroups using the
variables or adjust the NMA for the covariate. Otherwise, the results for
relevant comparisons may be rated down for indirectness during the assessment of
certainty (see below).
Assessment of coherence
Incoherence refers to differences between direct and various
indirect effect estimates that contribute to the overall “network” estimate for
each comparison. We will assess incoherence both locally (per comparison) using
the Separate Indirect from Direct Evidence (SIDE, or node-splitting approach
[59]) and globally (all
treatment effects and all possible inconsistency factors are considered
simultaneously) using the design-by-treatment interaction model [54] and comparison of the consistency model
to the inconsistency model. These methods provide p values, and < 0.01 and < 0.10 may be considered to
indicate major and some incoherence [60]. Major global incoherence may result in reconfiguration
of the network or not conducting the NMA; otherwise, the degree of incoherence
will be considered during the assessment of the certainty of effects as
described in that section.
Presentation of results
We will present all final network plots, with the size of the nodes
corresponding to the number of participants randomized to each treatment and the
lines/edges weighted by the number of trials evaluating the comparison. The
summary ORs or RaRs and 95% CIs for all pairwise comparisons will be presented
in a league table (including all direct [where available] and network
estimates). To rank the various treatments for each outcome relative to UC, we
will use surface under the cumulative ranking curves (SUCRA) and present the
SUCRA values in ranking plots; if useful for the working group, we will also
create a heat rank plot to display the SUCRA values for all outcomes analyzed.
SUCRA values account both for the location and the variance
(uncertainty/imprecision) of all relative treatment effects [38]. For each NMA, the overall risk for the
UC group will be calculated using the variance-stabilizing Freeman-Tukey double
arcsine approach. Network estimates for each node compared with usual care will
be transformed to risk differences to allow judgment of the clinical importance
[53]. For outcomes having
statistically significant effects, we will calculate the number needed to treat
(NNT).
Small study effects
For the NMA outcomes, we will consider using comparison-adjusted
funnel plots to assess for small study bias, if clinical input suggests there is
rationale for a particular characteristic to be associated with small study
effects, and assumptions about the direction of small studies can be made (i.e.,
treatments need to be ordered in a meaningful way) [38]. Otherwise, we will conduct a
funnel-plot grouping all interventions versus usual care, and if bias is
evident, we will then assess individual interventions versus UC (if ≥ 10 RCTs)
and assess for this bias as usual for pairwise comparisons.
For outcomes where pairwise meta-analysis is used and when 10 or
more RCTs are in the comparison, we will analyze for small-study effects both
visually using the funnel plot and quantitatively using Egger’s test
[61] (continuous outcomes) or
Harbord’s test [62] (dichotomous
outcomes).
Data synthesis for KQs 2 and 3 (values and preferences)
This analysis will be guided by a narrative synthesis approach
[63]. We will likely rely on
textual descriptions and groupings/clusterings to develop a preliminary
synthesis of the findings. We will explore relationships between the data by
comparing and contrasting study findings while considering study methodology
(e.g., timing of outcome measurement), populations (e.g., age, experience with
the outcome or intervention type), outcome presentations provided to
participants (relevant only for KQ2), comparisons (between outcomes in KQ2 and
between differing intervention attributes in KQ3), and analytical approaches.
Groupings based on key differences between studies will be created; for example,
KQ2 findings from utility and non-utility studies will be separated.
Within-study subgroup analyses will be interpreted. We do not anticipate
performing meta-analysis, although this may be possible for utility values for
some health states/outcomes such as hip fractures if there are two or more
studies using the same measurement method in similar populations. If undertaken,
we will use a random-effects model. Results for health-state utilities will be
separated by utility measurement tool (e.g., EQ-5D, time trade-off) and the main
covariates of interest for subgroup analysis will be age, sex, time since
fracture (≤ 12 months vs. > 12 months), and fracture history [64].
Assessing the certainty of the evidence
We will assess the certainty of evidence for all outcomes, for the
effects of each intervention grouping versus UC. For outcomes analyzed by
pairwise meta-analysis or no meta-analysis, we will follow current GRADE
guidance [23, 34, 65,66,67]. For findings from NMA, we will be
guided by the CINeMa approach and use CINeMA software for some assessments,
which is based on the GRADE framework, although has conceptual and semantic
differences [68]. The assessment
covers six domains: within-study bias, across-studies bias (i.e., publication
and other reporting biases), indirectness, imprecision, heterogeneity (i.e.,
variation between studies within a comparison), and incoherence (i.e., variation
between direct and indirect sources of evidence across comparisons). Findings
during the assessment of transitivity and incoherence of the NMA network will
contribute information to support certainty ratings for indirectness and
incoherence, respectively, as described further below. Similar to GRADE,
judgments for each domain are of no concern, some concern, or major concerns,
and for each outcome are of very low, low, moderate, or high. Some of the
assessments rely on a percentage contribution matrix (see below). Each outcome
starts at high certainty and is rated down for concerns. The six CINeMA domains
are interconnected and should be considered jointly rather than in isolation
[68]; if two concerns are
highly related, we will not rate down twice.
Percentage contribution matrix
Most studies in a network contribute some indirect information to
every estimate of a relative treatment effect. Studies contribute more when
their results are precise (e.g., large studies), when they provide direct
evidence or when the indirect evidence does not involve many “steps.” The
contribution made by each study can be quantified to each relative treatment
effect on a 0 to 100% scale. These quantities can be presented in a percentage
contribution matrix.
Within-study risk of bias
CINeMA combines the studies’ percent contributions with the risk of
bias judgments to evaluate study limitation for each estimate of a relative
treatment effect from a network meta-analysis. More concern about study
limitations exists when there is a larger contribution from studies at high or
moderate risk of bias. With the percentage contributions, weighted average
levels of overall risk of bias are produced. Scores of − 1, 0, and 1 are
considered low, moderate, and high risk of bias.
Across-studies bias
The CINeMA approach provides conditions that would be considered to
provide judgments about “suspected” or “undetected” bias. Suspected bias entails
(i) failure to include unpublished data, (ii) meta-analysis is based on a small
number of positive “early” studies, (iii) the comparison has been funded
primarily by industry-funded trials, or (iv) there is existing evidence of
reporting bias. A judgment of undetected bias arises from (i) inclusion of
unpublished studies with similar findings to those published, (ii) protocols and
clinical trial registries are available for many trials and important
discrepancies are not found, and (iii) the effects from small studies do not
differ from those from large studies [68]. Although our inclusion of gray literature and many
studies, as well as the non-pharmacologic topic, would suggest no suspicion of
bias, we expect [29] a large
portion of the studies to have concerns about selective reporting (e.g., missing
outcomes). Outcomes may be rated down if there is evidence of small-study
effects or if several studies in the review did not report on the outcome
despite inclusion in their protocol and/or when clinical input suggests it
should have measured. This approach is very similar to that used for pair-wise
meta-analysis.
Indirectness
Our inclusion and exclusion criteria are fairly rigid and are
expected to capture studies of high relevance to the Task Force’s main
population, outcomes, and settings of interest. Nevertheless, some comparisons
may have some indirectness. Each study included in the review will be coded
based on its overall relevance to the main PICOTS (low, moderate, high). Similar
to the approach for within-study risk of bias, the findings will then be
combined with the percentage contribution of the studies to each comparison to
provide a value weighted by each comparison. We will also consider information
provided in our assessment of transitivity, when we weighted the edges in the
network plots based on covariates in the associated studies to examine
similarity between comparisons. If the edges for the comparison are of similar
width to those in the majority of comparisons in the network, we will be less
concerned about indirectness.
Imprecision
Imprecision will be assessed in a similar manner as for findings
from pair-wise meta-analysis [69].
Because this review is not focusing on the difference in effects between all of
the different comparisons, determining a range of equivalence for comparing
different interventions (e.g., how much better one needs to be than another)
will not be conducted. We will rate down the evidence for imprecision, if, using
the network estimate, (i) the effect could be considered clinically important
based on the network “point” estimate (e.g., OR ≤ 0.8 for reducing fallers) but
the 95% CI crosses the null or (ii) the estimate is likely too small to be
important (e.g., OR 0.95) but the 95% CI includes values indicating the
possibility of an important effect in either direction. Rating down by two
levels may occur if the effect appears to be of little to no difference but the
95% CI is very wide, indicating possible benefit and harm (e.g., spanning ORs of
both < 0.75 and > 1.25) [69].
Heterogeneity
The concordance between assessments based on confidence intervals,
which do not capture heterogeneity, and prediction intervals, showing where the
true effect of a new study similar to the existing studies is expected to lie,
can be used to assess the importance of heterogeneity. The effect of the
heterogeneity on the conclusions will be considered (see imprecision for general
rules on effect sizes), and if the predictive intervals do not add any concern
over that already assessed for imprecision, we will not rate down for this
domain. Predictive intervals derived from meta-analyses with very few studies
can be unreliable and this will be taken into account.
Incoherence
We will use results from our local (per comparison; using SIDE, or
node-splitting approach [59]) and
global [54] assessments of
incoherence. Both methods provide p values,
and we will consider < 0.01 and < 0.10 to indicate major and some
incoherence [60]. Comparisons that
have > 90% direct evidence will not be rated down. For comparisons that have
only indirect evidence (i.e., local coherence not relevant), we will rate down
due to incoherence one or two levels depending on whether the p value of the design by treatment interaction
model was between 0.01 and 0.10 or less than 0.01, respectively. If there is
> 0% and < 90% direct evidence, we will base the decision on the more
relevant method (e.g., high reliance on node splitting when more direct
evidence). We will also consider the 95% CIs from the direct and indirect
evidence for each comparison; if both are showing the same direction of effect,
but differing magnitudes of beneficial effects, we will have less
concern.
Input from the Task Force will be used when the review team
conducts the certainty assessments for each outcome, for example, when
appraising the applicability/indirectness of the studies in terms of the
population of interest to their recommendation.
Discussion
The review will be published in an open-access journal and reported
using standard checklists for systematic reviews and network meta-analysis
[70, 71]. The results section of the review will include a description
of the flow of literature and characteristics of all studies, results of all
analyses, including planned subgroup and sensitivity analyses as well as the
assessment of the NMAs, and summary of finding tables incorporating assessment of
our confidence in the estimates of effect. In the discussion, we will summarize the
main findings and their implications, compare our findings to others, and discuss
the limitations of the review and the available literature. The results will be used
by the Task Force for developing recommendations about fall prevention in
community-dwelling older adults. It will also serve as a comprehensive review for
clinicians and other decision makers on the effects of interventions and relevant
patient preferences.
Protocol amendments
We will report on any deviations from the protocol within the final
manuscript.
Availability of data and materials
Not applicable.
Abbreviations
ADLs:
Activities of daily living
CI:
Confidence interval
EPOC:
Cochrane Effective Practice and Organisation of Care
GRADE:
Grading of Recommendations Assessment, Development and
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We would like to acknowledge the Canadian Task Force on Preventive Health
Care members who are not in the working group on this topic (Ahmed M. Abou-Setta,
Roland Grad, Donna Reynolds, Guylène Thériault, Michael Kidd, Tina Korownyk, Scott
Klarenbach, John Leblanc, Nav Persaud). We also thank Ms. Tara Landy, MLIS, for
peer-reviewing the search strategies.
Funding
This protocol and the subsequent review will be conducted for the Public
Health Agency of Canada; however, it does not necessarily represent the views of the
Government of Canada. The Staff of the Global Health and Guidelines Division at the
Public Health Agency of Canada (LAT, BM, ERH) provided input during the development
of this protocol and reviewed the protocol, but will not be taking part in the
selection of studies, data extraction, analysis, or interpretation of the findings.
ACT is funded by a Tier 2 Canada Research Chair in Knowledge Synthesis; SES is
funded by a Tier 1 Canada Research Chair in Knowledge Translation, and LH is funded
by a Tier 1 Canada Research Chair in Knowledge Synthesis and
Translation.
Author information
Authors and Affiliations
Alberta Research Centre for Health Evidence, University of Alberta, 11405 87 Avenue, Edmonton, AB, T6G 1C9, Canada
Jennifer Pillay, Diana Keto-Lambert, Sholeh Rahman, Ben Vandermeer & Lisa Hartling
Department of Family Medicine, McMaster University, Hamilton, Canada
John J. Riva & Ainsley E. Moore
Global Health and Guidelines Division, Public Health Agency of Canada, Ottawa, Canada
Laure A. Tessier, Bradley R. Mitchelmore & Elizabeth Rolland-Harris
Occupational Science and Occupational Therapy Department, University of Toronto, Toronto, Canada
Heather Colquhoun
Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada
Eddy Lang
Faculty of Medicine, McGill University, Montreal, Canada
Brett D. Thombs
Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
Brenda J. Wilson
Memorial University of Newfoundland, St. John’s, Canada
Amanda Tzenov
School of Rehabilitation Therapy, Queen’s University, Kingston, Canada
Catherine Donnelly
Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, Canada
Marcel Émond
Department of Medicine, University of Calgary, Calgary, Canada
Jayna Holroyd-Leduc
Schlegel Specialist in Mobility and Falls, Schlegel-UW Research
Institute for Aging, Department of Family Medicine, McMaster University, Hamilton, Canada
Jamie Milligan
Knowledge Translation Program, Li Ka Shing Knowledge Institute, St
Michael’s Hospital, Unity Health Toronto, Toronto, Canada
Andrea C. Tricco, Sharon E. Straus & Sonia M. Thomas
JP drafted the majority of this manuscript. DK-L developed the search
strategy for KQ2. BV provided input for the sections on data extraction and analysis
and reviewed the manuscript. JJR, HC, EL, AEM, BDT, BJW, AT, CD, ME, JH-L, and JM
provided clinical/content input, assisted with developing the inclusion and
exclusion criteria for the review, and provided input to all sections of the
protocol. LAT, BM, and ERH provided methodological input related to guideline
development during the development of the protocol and reviewed the manuscript. LT
drafted some of the background. ACT, SES, and SMT contributed to the development of
the methods and reviewed the manuscript. The authors read and approved the final
version of the manuscript.
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Pillay, J., Riva, J.J., Tessier, L.A. et al. Fall prevention interventions for older
community-dwelling adults: systematic reviews on benefits, harms, and patient values and
preferences.
Syst Rev10, 18 (2021). https://doi.org/10.1186/s13643-020-01572-7