Cost effectiveness of patient education

Discuss Cost effectiveness of patient education for the
prevention of falls in hospital: economic
evaluation from a randomized controlled trial

Abstract
Background: Falls are one of the most frequently occurring adverse events that impact upon the recovery of older
hospital inpatients. Falls can threaten both immediate and longer-term health and independence. There is need to
identify cost-effective means for preventing falls in hospitals. Hospital-based falls prevention interventions tested in
randomized trials have not yet been subjected to economic evaluation.
Methods: Incremental cost-effectiveness analysis was undertaken from the health service provider perspective, over
the period of hospitalization (time horizon) using the Australian Dollar (A$) at 2008 values. Analyses were based on
data from a randomized trial among n = 1,206 acute and rehabilitation inpatients. Decision tree modeling with
three-way sensitivity analyses were conducted using burden of disease estimates developed from trial data and
previous research. The intervention was a multimedia patient education program provided with trained health
professional follow-up shown to reduce falls among cognitively intact hospital patients.
Results: The short-term cost to a health service of one cognitively intact patient being a faller could be as high as
A$14,591 (2008). The education program cost A$526 (2008) to prevent one cognitively intact patient becoming a
faller and A$294 (2008) to prevent one fall based on primary trial data. These estimates were unstable due to high
variability in the hospital costs accrued by individual patients involved in the trial. There was a 52% probability the
complete program was both more effective and less costly (from the health service perspective) than providing
usual care alone. Decision tree modeling sensitivity analyses identified that when provided in real life contexts, the
program would be both more effective in preventing falls among cognitively intact inpatients and cost saving
where the proportion of these patients who would otherwise fall under usual care conditions is at least 4.0%.
Conclusions: This economic evaluation was designed to assist health care providers decide in what circumstances
this intervention should be provided. If the proportion of cognitively intact patients falling on a ward under usual
care conditions is 4% or greater, then provision of the complete program in addition to usual care will likely both
prevent falls and reduce costs for a health service.
Trial registration: Australia and New Zealand Clinical Trials Register: ACTRN12608000015347.
Keywords: Accidental falls, Cost effectiveness, Economic evaluation, Hospital, Prevention

  • Correspondence: steven_mcphail@health.qld.gov.au 8
    Centre for Functioning and Health Research, Metro South Health, Cnr of
    Ipswich Road and Cornwall Street, Buranda, Brisbane, Queensland 4102,
    Australia
    9
    Institute of Health and Biomedical Innovation and School of Public Health
    and Social Work, Queensland University of Technology, Victoria Park Road,
    Kelvin Grove, Brisbane, Queensland 4059, Australia
    Full list of author information is available at the end of the article

© 2013 Haines et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

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Background
Falls are one of the most frequently occurring adverse

events that may impact upon the recovery of older hos-
pital inpatients [1]. Reported rates of falls have varied,

though rates on acute hospital wards have been lower
than those on subacute or rehabilitation wards [2-5].
The consequence of the majority of falls can be considered
relatively minor, with approximately two-thirds resulting

in no injury [6]. However, falls remain a considerable con-
cern for patients and their family as injurious falls can

threaten both the immediate and longer-term health and
independence of the individual. There is need to identify

cost-effective means for preventing falls to guide appro-
priate use of limited resources available to prevent falls in

hospitals [7].
Several randomized controlled trials have previously
been published indicating that falls in hospital can be
prevented [4,8-12]. Many of these programs have been
targeted, multifactorial intervention programs involving

different combinations of individual interventions lea-
ving clinicians and researchers alike puzzling over which

specific interventions should be provided on specific

wards [13]. Intensive patient education has been a cen-
tral component in two large trials of successful multifac-
torial programs for both subacute and acute hospital

settings [8,12]. More recently, a large randomized trial
investigating two forms of patient education in isolation

found that a multimedia patient education program pro-
vided with follow-up from a trained health professional

reduced falls among cognitively intact hospital patients,

and that a less intensive approach of providing multi-
media materials only did not reduce falls [11]. Thus, in-
tensive patient education appears to be an efficacious

means for preventing falls in the hospital setting.

A key consideration in determining whether an inter-
vention should be provided in a given hospital setting is

whether the effects of a program justify the costs of pro-
viding that program [7]. To date, no economic evalua-
tions have been published examining the efficiency of

these programs using data directly arising from these
trials. Only one economic modeling study has been

performed focusing on whether a patient education pro-
gram should be provided to all geriatric rehabilitation

inpatients, no geriatric rehabilitation inpatients, or a
subgroup of geriatric inpatients selected by hospital staff
clinical judgment of being at high risk of falls [14]. This
study found that providing intensive patient education
to all patients would cost a health service A$1,192 to

prevent 3.67 patients from being a faller (that is, expe-
riencing 1 or more falls) during their admission for every

100 patients treated (incremental cost effectiveness = A

$325 per faller prevented), whereas providing this interven-
tion only to patients identified as being at high risk by

their physiotherapist saved A$2,704 and prevented 2.2

patients from becoming fallers for every 100 patients
treated (all costs in Australian Dollars (A$)).
Two assumptions underlying the previous modeling
study were that the intervention would be equally effective
for all subgroups of patients, and that it would reduce the
risk of falls by approximately 30%. These assumptions
were based on results of an exploratory subgroup analysis
[15] conducted on data from a larger randomized trial, [8]
but are now known to not be consistent with the results
of the more recent randomized trial [11]. This earlier

work was limited also to patients being treated in sub-
acute/geriatric rehabilitation wards, and employed only

one cost per faller estimate derived from research in
another country and health system.
The present study seeks to examine the efficiency of
providing the intensive multimedia patient education
program delivered with trained health professional
follow-up to cognitively intact hospital inpatients in
addition to their usual care in comparison to provision

of usual care alone from the health service provider per-
spective over the inpatient care time horizon [11]. Con-
trary to the assumptions employed in the economic

modeling study described above, the patient education
program was found only to be effective for cognitively
intact hospital patients where it reduced the rate of falls
by more than 50% and the proportion of patients who
were fallers by 40%. The present study is the first to
model the cost effectiveness of an intervention for the
prevention of falls in hospitals using both cost and effect
data collected from a randomized controlled trial. This
study also sought to measure the economic burden of an
in-hospital fall and the cost of a person being a faller.
Methods
Design
This study was an economic evaluation (incremental
cost-effectiveness analysis) conducted in parallel with a
multicenter randomized controlled trial conducted from

the health service perspective. The health service per-
spective was chosen as the health service are the deci-
sion makers when determining how to provide care on

their wards in relation to falls prevention.
Participants and setting
Participants in this trial (n = 1,206) were patients over
the age of 60 who were admitted to acute (orthopedic,
respiratory medicine, general medicine) wards and any
patient admitted to subacute (geriatric assessment and

rehabilitation, neurological rehabilitation) wards at Prin-
cess Alexandra Hospital (Brisbane) and Swan Districts

Hospital (Perth), Australia. The Australian health care
system contains a mix of both publicly and privately
funded health services: both of the hospital sites were
public hospital facilities. Health service funding models
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vary from state to state, between acute and subacute
hospital wards and between private and public hospitals.

A detailed description of the demographics of study par-
ticipants has been provided previously [11]. The trial

was registered with the Australia New Zealand Clinical.
Trials Registry (ACTRN12608000015347) on 11 January

  1. The investigation was carried out in compliance
    with the Helsinki Declaration. Ethical clearance was
    provided by the medical research ethics committee of
    the University of Queensland and the human research
    ethics committees of the Princess Alexandra Hospital
    and Swan Districts Hospital. Participants in the trial

provided written informed consent prior to their volun-
tary participation.

Intervention

Two patient education models were tested in the ran-
domized controlled trial; provision of multimedia patient

education materials in addition to usual care (that is,
materials only), and provision of multimedia patient

education materials combined with trained health pro-
fessional follow-up (that is, complete program) in

addition to usual care. These were compared to usual

care alone. The content of the patient education pro-
grams was developed based upon the health-belief

model [16,17]. The materials only group was not differ-
ent to the usual care control in any of the falls outcomes

considered and was not considered further in this eco-
nomic evaluation. A significant group (complete pro-
gram)-by-cognitive status interaction was identified in

the randomized trial where cognitively intact patients
who were allocated to the complete program had a
lower rate of falls (8.72 vs 4.01 falls per 1,000 patient
days, adjusted hazard ratio = 0.43) and a lower odds of

patients who became fallers (30 fallers and 280 non-
fallers in control group vs 20 fallers and 260 non-fallers

in complete program, adjusted odds ratio = 0.51) [11].
Cognitive status was classified according to trial baseline
Short Portable Mental Status Questionnaire outcome
where scores of 8 out of 10 or above were classified as

cognitively intact [18]. Thus, in this economic eva-
luation, we examined the efficiency of the complete pro-
gram versus usual care alone among patients who were

cognitively intact.
The face-to-face education delivered as a part of the
complete program was planned to be delivered across

four sessions, however, the education provider had dis-
cretion to increase or decrease this number as they saw

fit for individual participants. This one-to-one education
often took place at the patient bedside, though patients
were sometimes moved to private areas to have these
discussions. The median (interquartile range) number of

minutes of staff time per patient in providing these ses-
sions was 25 (20, 32) minutes in total. Headphones and

portable digital video disc (DVD) players were used
when interacting with multimedia materials to minimize
contamination of control group participants.
Outcomes
Falls were defined as ‘an event which results in a person
coming to rest inadvertently on the ground or floor or

other lower level’ [19]. Falls data were collated by a re-
search assistant blind to participant group allocation via

three sources: computerized incident reports, hand
searching of individual patient medical notes, and weekly
face-to-face patient interviews. Falls captured through any
of these approaches were included. The blinded research

assistant also collated data on the radiological investiga-
tions, clinical investigations and treatments (medical,

medication and nursing) provided directly as a result of
the fall, length of stay and participant admission diagnosis
from medical records.
Valuation of costs
All costs were calculated in A$ using 2008 as a base-year
value over the period of a participant’s hospitalization.
Costs associated with acute hospitalization (not directly
related to falls) following consent to participate in the
study were valued using the Victorian Weighted Inlier
Equivalent Separation casemix funding system from 2008
to 2009 [20]. At the time of study, this system was the
most advanced activity-based funding system in use in
Australia. It calculates payments made directly to hospitals
for health care services provided by acute hospitals based
primarily upon patient diagnosis related grouping and

length of stay in hospital. Weighted Inlier Equivalent Se-
paration costs were then multiplied by 1.33 as these pay-
ment rates do not cover fixed hospital costs and are

recognized as covering only 75% to 80% of total costs for a

ward stay [21]. Costs associated with inpatient rehabilita-
tion were calculated using local, site specific per diem cost

estimates in use at the time of study (A$805.9 per day in
Western Australia, A$879 per day in Queensland).
‘Costs directly related to falls’ were defined as those
that could be directly attributed to the fall by specific

listing on incident reports or medical records. Costs di-
rectly related to falls were collected during the trial by

research assistants who were blinded to group allocation.
The costs of providing investigations, treatments and
subsequent care for people specifically due to a fall were
valued for this category of costs. Time spent completing

nursing and medical assessments and associated docu-
mentation in medical records (if documented as having

been completed specifically because of the fall and not
as a part of routine ward reviews) were estimated to be
15 minutes per fall for uncomplicated falls, 30 minutes
per fall where injuries were noted, and were valued using
local wage rates (resident medical officer rate [22], level
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1 year 5 nursing officer (equivalent to a nurse with 5
years of experience) pay scale rate [23]) for the relevant
staff. These wage rates were inflated (multiplied by 1.3)

to account for on-costs (for example, sick leave and an-
nual leave entitlements). Time required to provide add-
itional nursing assessments as a result of the fall (for

example, hourly neurological observations for 24 hours)
were estimated by consulting with local hospital staff
and were valued using the level 1 year 5 nursing officer

pay scale. Costs of providing specific radiological investi-
gations were valued using market rates from the private

sector. If a patient on a rehabilitation ward fell and in-
jured themselves resulting in an admission to an acute

ward not included in the study, then the length of stay
and admission diagnosis for that admission was recorded

and valued using the Weighted Inlier Equivalent Sepa-
ration casemix valuation approach as previously de-
scribed. It was considered important to include these

‘additional’ costs that were directly related to falls in this

analysis and in the calculation of the cost per fall esti-
mate as the Weighted Inlier Equivalent Separation

(acute wards) and per diem payment systems are not

sensitive to the additional workload created by falls be-
yond additional length of stay at an individual patient

level. A total cost variable was calculated for each par-
ticipant by summing acute care costs, rehabilitation

costs, and costs directly attributable to falls.
There were no missing data to be accounted for in this
trial as the medical records from which this data were
sourced were available for every case. Data from incident
reports and medical records was supplemented by
weekly and pre-discharge interviews with participants to
capture data regarding each fall that may not have been
recorded otherwise. One participant withdrew from the
trial after consenting to participate, their data was not
included in the analysis due to the revocation of consent
to use their data for this purpose.
Burden of disease

The cost of a fall and of a patient being a faller were es-
timated using two different assumptions regarding costs

not directly related to falls, specifically, whether an in-
crease in the length of hospitalization seen among fallers

was due to the fall(s). Patients who fall may stay longer
in hospital to treat the injuries they sustain as a direct
result of the fall. Thus, the first assumption employed
was that the greater length of stay observed among

fallers, after adjusting for other factors that might con-
tribute to longer length of stay, are entirely due to the

falls observed. Under this assumption, a cohort-style
analysis approach was pursued where regression analyses
were undertaken using total costs (length of stay costs

plus costs directly related to falls) as the dependent va-
riable and faller status (0 = non-faller, 1 = faller: to

estimate cost per faller) or number of falls (to estimate

cost per fall) as independent variables along with con-
founders of age, gender, admission diagnosis grouping,

whether there was admission to a rehabilitation ward,
admission health-related quality of life, and history of
falls in the 6 months prior to hospital admission. Three
regression analysis approaches were used to estimate this

amount: linear regression analysis, linear regression ana-
lysis with removal of outlier data points (more than 3

standard deviations higher than the mean), and robust
regression which first performs an initial screening based
on Cook’s distance >1 to eliminate gross outliers before

calculating starting values and then performs Huber ite-
rations followed by bi-weight iterations in calculating re-
gression coefficients (again to minimize the influence of

outliers) [24]. However, patients who would otherwise
have a longer length of stay in hospital may be more
likely to be observed to fall during an admission as a
consequence of being observed for a longer period of

time. Further, a latent (unobserved) variable may be re-
sponsible for both the increased length of stay and oc-
currence of falls that cannot be adjusted for in the

regression analyses described above. Therefore burden
of disease estimates that excluded costs associated with
greater length of stay in fallers under the assumption
that they do not cause an increase in length of stay were
also calculated. Here the costs directly related to falls
were summed and then divided by the number of fallers
(to calculate the cost per faller) or divided by the total

number of falls (to calculate the incremental cost per in-
cremental fall).

Incremental cost effectiveness using randomized
controlled trial data
The incremental cost-effectiveness analysis examined the
cost per fall prevented and cost per faller prevented of
providing the complete education program as opposed

to usual care among cognitively intact patients in the ran-
domized trial. Usual care in this trial varied from ward-to

-ward and between sites in this trial but consisted of use
of a locally developed falls risk screening tool and generic
interventions (for example, orienting patients to the ward)

for all patients. Multidisciplinary input (for example, me-
dical, nursing, physiotherapy, occupational therapy) was

routinely provided on all wards, although therapists such
as physiotherapists and occupational therapists provided
more intensive input on subacute rehabilitation wards.
Falls risk alert items (for example, arm bands) were used
for those identified as being at high risk. Physical restraint
was not a front-line method for managing patients with
agitation and/or confusion at either site. All patients in

this trial received usual care, those in the control group re-
ceived usual care alone and no subjects in the control

group received the additional education intervention.
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The price of delivery of the education program

consisted of both the start-up costs to commence pro-
viding the education program (training a staff member:

A$440, purchase of 2 × portable DVD players: A$150
each, 8 h of staff member time in training) averaged over
the expected lifespan of the equipment and need to train
another staff member (500 patients), along with ongoing
costs for employment of the health professional who
provided the education program. Labor input was

counted in minutes spent with the patient recorded du-
ring the trial. Labor input and staff time spent in train-
ing was valued ‘Health Professional Level 3 Step 5’

hourly wage rate [25] with an additional 30% loading for
on-costs. This salary level is equivalent to an allied
health professional with 4 years of clinical experience.
The difference in costs between groups was estimated
from the adjusted regression coefficient derived from a
multiple regression model including covariates of age,
gender, admission diagnosis grouping, whether there was

admission to a rehabilitation ward, admission health re-
lated quality of life, and history of falls in the 6 months

prior to hospital admission that was restricted to cogni-
tively intact patients only. The difference in effects was

taken from a regression of the difference in the propor-
tion of patients falling in each group (fallers) and the

total number of falls in each group (falls) adjusted for
admission diagnosis and whether there was admission to
a rehabilitation ward. Bootstrap resampling was then
used to construct 95% confidence intervals around the
incremental cost per fall and cost per faller estimates
[26], while the output of the bootstrap resampling was
used to construct cost-effectiveness acceptability curves
[27] to determine the probability that the intervention
program was both more effective and less costly from
the health service perspective than usual care.
Decision tree modeling and sensitivity analyses
Modeling is the process of representing the real world

with a series of numbers, and mathematical and statis-
tical relationships. Modeling and trial based economic

evaluations are complementary tools when forming pol-
icy advice, as trial based evaluations alone are rarely suf-
ficient to guide policy development [28]. From our

economic evaluation based on trial data, the proportion
of cognitively intact participants who fall during usual
care conditions is set at levels observed during the trial.

However, a policymaker may wish to know how cost ef-
fective the intervention might be if it were delivered to a

slightly different set of wards with a lower proportion of
patients who fall under usual care (perhaps the patient
population are at lower risk of falling or the background
usual care practices are more effective at preventing
falls). Trial based data cannot answer such a question
without conducting another trial on such wards. It also

cannot estimate the impact on cost effectiveness if the
intervention were to be more or less effective as it was
found to be in the trial without further trials [28]. Thus
modeling is required to examine the impact of broader
implementation of a health intervention than what was
undertaken in a trial or trials, and to understand how
variability in contexts might affect the cost effectiveness
of such an implementation.

We used a decision tree analysis model to further in-
vestigate the incremental cost effectiveness of the

complete program compared to usual care alone in
preventing fallers and subject it to three-way sensitivity
analyses among cognitively intact inpatients (Figure 1).

A decision tree model outlines decisions (that is, to pro-
vide an intervention or not), the probability or fraction

of various outcomes (that is, proportion of patients be-
coming fallers), and the valuation of each outcome (that

is, the cost of a patient becoming a faller). The mean
value of a decision is computed analytically by summing
the probability of each outcome with its value [29]. The

following formula was used: Incremental cost effective-
ness (cost per faller prevented) equals:

Costintervention100 þ Costfaller FallersCP100 − Costfaller Fallers
UC100

FallersCP100−FallersUC100 ð Þ

Where Costintervention100 = cost of providing interven-
tion to 100 cognitively intact patients; Costfaller = cost of

a faller; FallersCP100 = number of fallers among 100 cog-
nitively intact patients under complete program condi-
tions; and FallersUC100 = number of fallers among 100

cognitively intact patients under usual care conditions.
We subjected this decision tree model to three-way

sensitivity analyses by varying: (i) the proportion of pa-
tients who were cognitively intact who fall on a ward,

(ii) the cost to a health service of a patient being a faller,

and (iii) the effectiveness of the intervention. The effect-
iveness of the intervention was modeled as producing a

40% reduction in the proportion of patients who became

fallers (taken from trial data), along with more conserva-
tive estimates of 30% and 20% reductions. The propor-
tion of patients who were cognitively intact who became

fallers on a ward was varied between 3% and 20%. In the
main trial, approximately 20% of rehabilitation patients
who were cognitively intact fell, and approximately 5%
of acute hospital patients who were cognitively intact
fell. The cost per faller was modeled using two values
taken from the burden of disease analyses in the present
study. The first was taken from the costs directly related
to falls among cognitively intact patients among all three
groups in the study. The second was from the total costs

attributable to fallers after adjustment for the other con-
founders selected, using the robust regression analysis

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approach (to minimize the influence of outliers and pro-
duce a more conservative estimate) restricted to cogni-
tively intact patients. A third cost per faller included in

these sensitivity analyses was taken from previous re-
search conducted in the hospital setting [30]. This cost

figure was originally expressed as cost per person who

incurred one or more injurious falls while in a US hos-
pital but was converted to cost per faller in 2008 A$

using a process previously employed [14]. The cost per
injurious faller figure from this previous work was
converted to 2008 US$ using the US consumer price
index [31], then converted to A$ by using the exchange
rate as of 1 September 2008 (study mid-point; A$1 = US

$0.8537) [32], then converted to cost per faller by multi-
plying by the proportion of cognitively intact fallers in

the main study who incurred one or more injurious falls
defined as falls resulting in bruising, laceration, fracture,
loss of consciousness, or patient reports of persistent
pain (proportion = 0.49). Threshold sensitivity analyses

were also conducted to identify the point at which a po-
licy decision might change (that is, when the interven-
tion both prevents falls and saves resources).

Results
Burden of disease
Costs that were counted within this study that were

thought to be directly related to a fall (excluding costs as-
sociated with length of stay in hospital) were small in

comparison to the costs associated with length of stay in

hospital (Table 1). For all but one of the subgroups consid-
ered, the amount was A$21 or less per patient or A$93 or

less per faller. The one exception was the ‘complete pro-
gram’ cognitively impaired subgroup, which had a mean of

A$187 per patient. This was largely driven by one patient
who fell and fractured their cervical spine, and was

subsequently transferred to an acute ward outside of the
study data collection wards specifically for the treatment
of this injury. Removal of this participant would have
brought this mean figure back to A$19 for this subgroup.
The cost per fall and cost per faller calculated using
the cohort approach are presented (Table 2). Standard
regression analyses without removal of outliers identified
a cost per fall of A$12,469 and a cost per faller of A
$24,927. Re-examining the data in Table 1 reveals that
there was little difference between the acute care costs

of all patients and fallers, whereas the inpatient rehabili-
tation costs of fallers were substantially higher than for

patients overall. Thus, it appeared that the key driver of
the increase in costs observed using the cohort approach
was an increased length of stay on rehabilitation wards
and not on acute wards. Elimination of outliers 3 standard
deviations higher than the mean led to removal of 29 ‘high
cost’ participants. Regression analyses with these outliers
removed and robust regression analyses both produced

similar cost estimates (Table 2), which were approxi-
mately one-third less than the estimates modeled using

ordinary least squares regression with the outlier va-
riables retained. The cost per faller and cost per fall

among cognitively intact patients were similar to those

for cognitively impaired patients once the outlier pa-
tients were removed from the analyses.

Incremental cost effectiveness using randomized
controlled trial data

The incremental cost effectiveness of the complete pro-
gram relative to usual care was A$526 per faller prevented

and A$294 per fall prevented. There was considerable un-
certainty surrounding the incremental cost-effectiveness

ratios calculated when using primary study data (Figure 2),
which was largely driven by outlier patients who had very

Figure 1 Decision tree structure.
Haines et al. BMC Medicine 2013, 11:135 Page 6 of 12

Table 1 Breakdown of costs related to acute and rehabilitative care, and costs related to falls for all patients, patients who fell, and patients who had an
injurious fall
Cognitive function classification groupinga Control Materials only Complete program
Intact Impaired Intact Impaired Intact Impaired
N 280 101 316 108 310 91
Number of falls 46 35 61 35 25 45
Number of fallers 30 24 32 24 20 24
Number of injurious falls 15 10 25 15 10 22
Number of people who had one or more injurious fall 13 8 17 12 10 16
Mean (SD) acute care costs post consent per patient 8,481
(12,856)
5,140
(8,142)
8,927
(16,776)
6,947
(14,079)
10,774
(18,344)
11,128
(28,570)

Mean (SD) rehabilitation costs post consent per patient 10,964
(19,972)
26,050
(36,776)
15,026
(24,925)
24,892
(31,823)
11,197
(18,906)
21,740
(37,130)
Mean (SD) costs of radiological investigations directly related to falls per patient 4 (27) 7 (59) 2 (33) 6 (43) 0 (0) 11 (54)
Mean (SD) medical costs directly related to falls per patient 2 (10) 5 (17) 2 (10) 4 (15) 1 (4) 6 (20)
Mean (SD) nursing costs directly related to falls per patient 1 (4) 2 (7) 1 (5) 8 (63) 0 (2) 5 (15)
Mean (SD) all costsb directly related to falls per patient (excluding acute care and rehabilitation costs) 8 (47) 15 (85) 7 (54) 21 (96) 1 (7) 187 (1,602)c
Mean (SD) acute care costs post consent among patients who were fallers post consent 8,556
(13,585)
4,176
(8,130)
11,247
(17,369)
3,000
(5,924)
18,751
(41,564)
5,999
(11,329)

Mean (SD) rehabilitation costs post consent among patients who were fallers post consent 33,317
(29,048)
56,406
(55,296)
45,491
(43,073)
44,959
(45,480)
25,489
(21,284)
53,452
(52,861)
Mean (SD) costs of radiological investigations directly related to falls among patients who were fallers post consent 37 (76) 28 (120) 24 (102) 29 (90) 0 (0) 41 (101)
Mean (SD) medical costs directly related to falls among patients who were fallers post consent 21 (23) 21 (30) 20 (25) 20 (28) 12 (13) 23 (33)
Mean (SD) nursing costs directly related to falls among patients who were fallers post consent 7 (10) 10 (13) 9 (13) 38 (132) 4 (8) 18 (27)
Mean (SD) all costsb directly related to falls among patients who were fallers post consent (excluding acute care and
rehabilitation costs)

76 (126) 64 (168) 65 (162) 93 (190) 19 (22) 710 (3,108)c

Mean (SD) acute care costs post consent among patients who had an injurious fall post consent 7,811
(14,313)
4,378
(11,513)
5,908
(10,447)
1,848
(3,491)
24,835
(54,923)
8,034
(13,026)

Mean (SD) rehabilitation costs post consent among patients who had an injurious fall post consent 29,700
(21,118)
52,630
(46,211)
40,758
(30,380)
48,853
(40,312)
24,496
(26,482)
51,871
(48,331)
Mean (SD) costs of radiological investigations directly related to falls among patients who had an injurious fall post consent 62 (102) 73 (207) 33 (136) 58 (123) 0 (0) 62 (119)
Mean (SD) medical costs directly related to falls among patients who had an injurious fall post consent 28 (30) 51 (35) 26 (30) 29 (32) 13 (13) 30 (38)
Mean (SD) nursing costs directly related to falls among patients who had an injurious fall post consent 14 (12) 18 (12) 12 (15) 73 (184) 7 (10) 956 (3,722)
Mean (SD) all costsb directly related to falls among patients who had an injurious fall post consent (excluding acute
care and rehabilitation costs)

126 (171) 156 (275) 94 (214) 172 (248) 23 (24) 1,058
(3,797)c

aBased on Short Portable Mental Status Questionnaire cut off of 7/10 or below is impaired.
bAll costs includes costs of radiological investigations, medical costs, nursing costs, medication costs, on-call payment costs, suture procedure costs, orthoses costs, and other tests costs. cIncludes acute care costs of one patient transferred to an acute ward outside of the study following fracture orbital fossa and C2 vertebra fracture as a result of a fall for treatment of this injury.

Table 2 Cost per faller (in addition to usual care costs) and cost per fall estimates based on adjusted regression coefficients ((standard error),
P value)
regressing faller (dichotomous) or total number of falls (count) on the overall sum of acute care costs, rehabilitation costs and all costs directly related to falls
by subgroup and across all groups
Group OLS regression OLS regression with outliers removed Robust regression
Cost per faller Cost per fall Cost per faller Cost per fall Cost per faller Cost per fall
Control: cognitively intact 17,240 (3,994), P <0.001 8,905 (2,101), P <0.001 18,516 (3,322), P <0.001 9,695 (1,742), P <0.001 21,071 (1,931), P <0.001 9,695 (1,743), P <0.001
Control: cognitively impaired 35,650 (7,307), P <0.001 16,488 (4,041), P <0.001 15,664 (4,481), P <0.001 14,805 (2,907), P <0.001 14,978 (3,932), P <0.001 14,804 (2,908), P <0.001
Materials only: cognitively intact 17,241 (3,994), P <0.001 8,840 (2,040), P <0.001 18,322 (3,763), P <0.001 7,599 (1,480), P <0.001 17,295 (2,310), P <0.001 7,599 (1,480), P <0.001
Materials only: cognitively impaired 35,650 (7,307), P <0.001 6,910 (3,782), P = 0.07 719 (6,071), P = 0.91 4,901 (2,758), P = 0.08 1,566 (6,401), P = 0.81 4,901 (2,758), P = 0.08
Complete program: cognitively intact 14,301 (5,260), P = 0.007 17,571 (3,857), P <0.001 5,976 (4,521), P = 0.19 7,310 (3,503), P = 0.04 6,004 (3,517), P = 0.09 7,311 (3,503), P = 0.04
Complete program: cognitively impaired 26,843 (11,072), P = 0.02 17,178 (4,000), P <0.001 20,797 (5,118), P <0.001 9,305 (2,217), P <0.001 17,156 (3,924), P <0.001 1,468 (1,466), P <0.001
All cognitively intact patients 21,506 (2,632), P <0.001 9,898 (1,326), P <0.001 15,759 (2,158), P <0.001 8,222 (1,055), P <0.001 14,591 (1,431), P <0.001 9,273 (704), P <0.001
All cognitively impaired patients 26,474 (4,686), P <0.001 13,879 (2,088), P <0.001 12,274 (2,924), P <0.001 8,455 (1,055), P <0.001 11,375 (2,534), P <0.001 11,074 (1,094), P <0.001
All patients 24,927 (2,270), P <0.001 12,469 (1,086), P <0.001 14,606 (1,679), P <0.001 8,454 (839), P <0.001 13,522 (1,199), P <0.001 9,629 (561), P <0.001
Coefficients adjusted for age, gender, admission diagnosis grouping, whether there was admission to a rehabilitation ward, admission health related quality of life (assessed by the EuroQol 5 Dimensions (EQ-5D)
instrument), and history of falls in the 6 months prior to hospital admission.
OLS, ordinary least squares.

Haines et al. BMC Medicine 2013, 11:135 Page 8 of 12

long lengths of stay. Acceptability curve analysis indicated
that stakeholders would need to be willing to pay A
$68,108 per faller prevented (in addition to the costs and
savings counted in the present analysis) or A$38,213 per
fall prevented in order to be 95% confident that the
complete program was worthwhile. The probability that
the complete program dominated (that is, was both more
effective and less costly) the usual care condition for the
incremental cost per faller or per fall prevented was 0.52.
Decision tree modeling and sensitivity analyses
Decision tree modeling of the cost effectiveness of this
program and three-way sensitivity analyses used the cost
per faller values of A$58 (cognitively intact inpatients
across all groups; costs directly related to falls), A
$14,591 (cognitively intact inpatients across all groups;
total costs associated with being a faller after adjustment
for confounders using robust regression), and A$2,867

(translated into A$ at 2008 values from previously pub-
lished data [30]).

Sensitivity analyses (Figure 3) indicated that the greatest
uncertainty in the cost-effectiveness estimates calculated
lie in the cost per faller estimates employed. Where the
middle cost per faller estimate of A$2,867 was used along

with the effectiveness of the intervention program esti-
mate taken from the randomized trial (40% reduction), the

complete program appeared to both prevent more falls
and cost less than usual care alone from the health service

perspective as long as at least 4.0% of cognitively intact pa-
tients on a ward were fallers under usual care conditions

during their admission. If more conservative estimates of

intervention effectiveness were used, then a higher pro-
portion of patients falling under usual care conditions was

required (5.3% if the intervention reduced fallers by 30%,
8.0% if the intervention reduced fallers by only 20%). The
intervention program did not demonstrate lower costs
Figure 2 Cost effectiveness of the complete program versus usual care among patients who are cognitively intact with 95%
confidence ellipse for fallers (a) and falls (b) prevented.
Haines et al. BMC Medicine 2013, 11:135 Page 9 of 12
http://www.biomedcentral.com/1741-7015/11/135

than usual care in any scenario modeled when the lower

cost per faller estimate of A$58 was used. The interven-
tion program demonstrated lower costs than usual care in

every scenario modeled when the higher cost per faller es-
timate of A$14,591 was used.

Discussion
The present economic evaluation was designed to assist
health care providers decide in what circumstances
provision of the patient education program should be
provided. If the proportion of cognitively intact patients
falling on a ward under usual care conditions is 4% or
greater, then provision of the complete program in
addition to usual care will likely both prevent falls and
reduce costs for a health service. Three key caveats

should be noted in this recommendation. First, this re-
commendation is sensitive to the different values of the

cost per faller modeled. These estimates were only de-
veloped from the health service perspective and were

limited to the period of inpatient hospitalization. Costs

of care post hospitalization and non-health costs (in-
cluding legal costs) would add to these estimates. Sec-
ond, the effectiveness of the program has been derived

from a randomized trial conducted across two hospitals
in Australia and is relative to the standard of usual care
provided in these settings [11]. It is plausible that usual
care provided in these sites may be different to other

hospitals around the world. Both of these hospitals had
falls prevention interventions in place as a part of usual
care including use of falls risk screening/assessment
tools, provision of falls risk alert signage, nursing falls

care plans and provision of multiple allied health thera-
pies including physiotherapy. However, the principle il-
lustrated in our analyses that the cost effectiveness of an

in-hospital falls prevention intervention depends heavily
on the rate of falls under usual care conditions would
still transfer beyond the Australian context. Third, this
recommendation assumes no further ‘falls risk screening’
or selective targeting of this intervention to those at
higher risk of falls within the targeted population. Previous
research indicates that this may lead to greater economic
efficiency in delivery of falls prevention interventions [14].
The ‘cost per fall’ and ‘cost per faller’ figures developed
using the cohort approach in the present study differed
substantially due to different assumptions regarding the
effect of falls on length of stay. A recent review of cost
per faller estimates from the community and residential
care settings have been found to be in excess of US
$3,766 (2006 dollar value) and up to US$25,955 in some

populations [33] though some concerns with the ap-
proaches used have been raised [34]. This indicates that

the higher value calculated in the present study may be

more accurate, though the authors feel that a value be-
tween these extremes is plausible as it is likely that falls

Figure 3 Decision tree modeling with three-way sensitivity analyses of incremental cost effectiveness per faller prevented.
Haines et al. BMC Medicine 2013, 11:135 Page 10 of 12
http://www.biomedcentral.com/1741-7015/11/135

may cause some increase in length of stay, but not all of
that observed. The value extrapolated from previous
work did sit within this range and thus formed the basis
for the recommendation provided, even though it was
closer to the lower cost per faller estimate than the
higher. This value, through the modeling and sensitivity
analyses, also generated the closest approximation to the

cost-effectiveness ratios seen from the primary random-
ized trial data.

This study was limited in the precision with which it

could construct incremental cost-effectiveness ratios di-
rectly from the primary randomized controlled trial data.

Collecting cost data concurrently with a randomized
trial permits efficiency in data collection, though can
often lead to imprecise estimates as ‘total’ healthcare

cost data are frequently highly skewed, may contain in-
fluential outliers that may be driven by factors other

than falls and trials are rarely powered sufficiently to de-
tect differences in costs between groups that would be

clinically meaningful. This evaluation was also limited in
terms of the perspective taken, being the health service
provider, which ignored costs borne by patients and
family members (including increased informal care) that
might manifest following hospitalization.
The evidence base surrounding the efficacy and now

cost effectiveness of intensive patient education pro-
grams in the hospital setting is growing. Further work is

still required to examine means for successfully incorp-
orating this approach into clinical practice. The present

study has examined the cost effectiveness of this ap-
proach and provided a conservative guide as to the rate

of falls on wards upon which this approach can be
implemented to reduce falls and save resources.
Conclusions
The present economic evaluation was designed to assist
health care providers decide in what circumstances
provision of this falls prevention intervention should be
provided. Conservative modeling from this investigation

indicated that if the proportion of cognitively intact pa-
tients falling on a ward under usual care conditions is

4% or greater, then provision of the complete program
in addition to usual care will likely both prevent falls
and reduce costs for a health service.
Abbreviations
Costfaller: Cost of a faller; Costintervention100: Cost of providing intervention to
100 cognitively intact patients; DVD: Digital video disc; FallersCP100: Number
of fallers among 100 cognitively intact patients under complete program
conditions; FallersUC100: Number of fallers among 100 cognitively intact
patients under usual care conditions.
Competing interests
TPH is Director of Hospital Falls Prevention Solutions Pty Ltd. This is a
research spin-off company that has been used to disseminate education of
health professionals in the education program described in this manuscript.
He has provided expert witness testimony to Minter Ellison Law Firm on the

subject of the prevention of falls in hospitals. He has received payment to
speak at conferences on the subject of the prevention of falls.
Authors’ contributions
TPH contributed to study conception, design, trial management and
undertaking analyses, as well as principal manuscript drafting, appraisal and
editing. A-MH contributed to study conception, design, trial site
management and data collection as well as manuscript appraisal and
editing. KDH, SB, TH and CB contributed to study conception, design and
manuscript appraisal and editing, SMMcP contributed to study conception,
design, trial site management, data collection, analysis review as well as
manuscript drafting, appraisal and editing. All authors read and approved
the final manuscript.
Acknowledgements
This project was funded by a project grant from the National Health and
Medical Research Council (Australia). TPH, SMMcP A-MH and TH receive
salary support through fellowships from the National Health and Medical
Research Council (Australia). The funding body had no role in the study
design; in the collection, analysis, and interpretation of data; in the writing of
the manuscript; or in the decision to submit this manuscript for publication.
Author details
1
Allied Health Research Unit, Southern Health, Corner of Warrigal and
Kingston Roads, Cheltenham, Victoria 3192, Australia. 2
Physiotherapy
Department, School of Primary Health Care, Monash University, McMahons
Road, Frankston, Victoria 3199, Australia. 3

School of Physiotherapy, The
University of Notre Dame Australia, Mouat Street, Fremantle, Western
Australia 6160, Australia. 4

School of Physiotherapy, Curtin University, Kent St,

Bentley, Western Australia 6102, Australia. 5

School of Health and
Rehabilitation Sciences, The University of Queensland, Services Road, St Lucia,
Queensland 4072, Australia. 6

Centre for Research in Evidence-Based Practice,
Bond University, University Drive, Robina, Queensland 4226, Australia. 7
WA
Centre for Health & Ageing, Centre for Medical Research and School of
Medicine & Pharmacology, University of Western Australia, Stirling Highway,
Crawley, Western Australia 6009, Australia. 8

Centre for Functioning and
Health Research, Metro South Health, Cnr of Ipswich Road and Cornwall
Street, Buranda, Brisbane, Queensland 4102, Australia. 9

Institute of Health and
Biomedical Innovation and School of Public Health and Social Work,
Queensland University of Technology, Victoria Park Road, Kelvin Grove,
Brisbane, Queensland 4059, Australia.
Received: 12 November 2012 Accepted: 19 April 2013
Published: 22 May 2013
References

  1. Sari AB, Sheldon TA, Cracknell A, Turnbull A: Sensitivity of routine system
    for reporting patient safety incidents in an NHS hospital: retrospective
    patient case note review. BMJ 2007, 334:79–81.
  2. Cumming RG, Sherrington C, Lord SR, Simpson JM, Vogler C, Cameron ID,
    Naganathan V: Cluster randomised trial of a targeted multifactorial
    intervention to prevent falls among older people in hospital. BMJ 2008,
    336:758–760.
  3. Nyberg L, Gustafson Y: Patient falls in stroke rehabilitation: a challenge to
    rehabilitation strategies. Stroke 1995, 26:838–842.
  4. Dykes PC, Carroll DL, Hurley A, Lipsitz S, Benoit A, Chang F, Meltzer S,
    Tsurikova R, Zuyov L, Middleton B: Fall prevention in acute care hospitals:
    a randomized trial. JAMA 2010, 304:1912–1918.
  5. Haines T, Kuys S, Morrison G, Clarke J, Bew P: Balance impairment not
    predictive of falls in geriatric rehabilitation wards. J Gerontol Med Sci
    2008, 63:523–528.
  6. Hill AM, Hoffmann T, Hill K, Oliver D, Beer C, McPhail S, Brauer S, Haines TP:
    Measuring falls events in acute hospitals – a comparison of three
    reporting methods to identify missing data in the hospital reporting
    system. J Am Geriatr Soc 2010, 58:1347–1352.
  7. Haines TP, Waldron NG: Translation of falls prevention knowledge into
    action in hospitals: what should be translated and how should it be
    done? J Safety Res 2011, 42:431–442.
  8. Haines T, Bennell K, Osborne R, Hill K: Effectiveness of targeted falls
    prevention programme in subacute hospital setting: randomised
    controlled trial. BMJ 2004, 328:676–679.

Haines et al. BMC Medicine 2013, 11:135 Page 11 of 12

  1. Healey F, Monro A, Cockram A, Adams V, Heseltine D: Using targeted risk
    factor reduction to prevent falls in older in-patients: a randomised
    controlled trial. Age Ageing 2004, 33:390–395.
  2. Stenvall M, Olofsson B, Lundstrom M, Englund U, Borssen B, Svensson O,
    Nyberg L, Gustafson Y: A multidisciplinary, multifactorial intervention
    program reduces postoperative falls and injuries after femoral neck
    fracture. Osteoporos Int 2007, 18:167–175.
  3. Haines TP, Hill AM, Hill KD, McPhail S, Oliver D, Brauer S, Hoffmann T,
    Beer C: Patient education to prevent falls among older hospital
    inpatients: a randomized controlled trial. Arch Intern Med 2011,
    171:516–524.
  4. Ang E, Mordiffi SZ, Wong HB: Evaluating the use of a targeted multiple
    intervention strategy in reducing patient falls in an acute care hospital: a
    randomized controlled trial. J Adv Nurs 2011, 67:1984–1992.
  5. Cameron I, Murray G, Gillespie L, Robertson M, Hill K, Cumming R, Kerse N:
    Interventions for preventing falls in older people in nursing care facilities
    and hospitals. Cochrane Database Syst Rev 2010, 1:CD005465.
    doi:005410.001002/14651858.CD14005465.pub14651852.
  6. Haines T, Kuys S, Morrison G, Clarke J, Bew P: Cost-effectiveness analysis of
    screening for risk of in-hospital falls using physiotherapist clinical
    judgement. Med Care 2009, 47:448–456.
  7. Haines TP, Hill KD, Bennell KL, Osborne RH: Patient education to prevent
    falls in subacute care. Clin Rehabil 2006, 20:970–979.
  8. Abraham C, Sheeran P: The health belief model. In Predicting Health
    Behaviour: Research and Practice with Social Cognition
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