Technical Appendix
Decision-analytic model
A Markov model was used to model fall-related outcomes. The model contains four health states
including; low risk (individuals who have never fallen), 2) medium risk (individuals who have previously
fallen but incurred no injury), 3) high risk (previously injured faller) and death (due to a fall or all cause
death). The decision tree imbedded within the Markov model outlines the different outcomes for older
individuals. Within the decision tree the probability of transitioning to another state depends on the
occurrence of various events, such as presenting at the emergency department and being admitted to
hospital. At each decision node, the probability is dependent upon age as well as the risk of falling.
Assumptions made in the model:
 Individuals who fall (fallers) have a fall rate multiplier applied to each fall to represent the fact
that some people fall multiple times in one year

The death rate by age is applied at each chance node to represent death from all causes. Death
due to falls is also included.

Those who fall and are not injured are assumed not to incur any costs

Those who fall (with minor injury) and do not go to emergency incur the cost of seeking other
medical attention

The utility loss due to fear of falling is applied to the medium and high risk fallers at the
beginning of each stage. The disutility associated with fear of falling is applied for one year only.

The perspective of the cost analysis is limited to the costs incurred by the health care system
due to falls

Once an individual progresses to a higher risk state they cannot return to a lower risk state. For
example, once a low risk faller falls and becomes injured, they then transition to being a high
risk faller and remain a high risk faller for the remainder of the analysis.
Model Validation
The Markov model was validated using the Markov traces to compare both fall rates and death rates
with population estimates. For example, based on residential aged care in Australia 2010-11 statistics,
the number of residents dying within 1 year (38%) and within 5 years (82%) is equivalent to our Markov
trace, in which 35% and 85% die within these time frames.
As part of the model validation, a fall rate factor adjustment of 1.2 was added to take into account
individuals who fall multiple times in one year. The fall rate factor was applied every time an individual
falls and all resulting health care costs and utility decrements are adjusted accordingly. The costeffectiveness results are not sensitive to this adjustment.
Cycle length
A cycle length of one year was chosen, as the majority of the effectiveness data was for trials with an
average follow-up of 12 months. Furthermore, the falls statistics (such as fall rate) are usually measured
as yearly rates. A fall rate factor adjustment was used in the model to account for multiple falls in a year.
Effectiveness values
Literature review
A systematic literature review was conducted to identify suitable studies to include in the analysis based
on inclusion and exclusion criteria (see table 2 below). The searches were conducted in a number of
electronic databases, with all searches conducted from 1990 onwards. Results were limited to those
studies published in English, in humans and in study populations with a mean age greater than 65.
Relevant final outcome measures included the number of falls and fallers or the rate of falls and fallers.
Only studies with a sample size greater than or equal to 20 were included. Given that the systematic
review included the same studies as the Cochrane review which was published, the results from the
Cochrane review were used.
Table 2 Probability of Falling
Inclusion criteria
Population mean age ≥ 65 years
Community-dwelling or RACF residents (private homes,
retirement villages, hostels, nursing homes, etc)
including those attending A&E, outpatient clinics and
hospital rehabilitation wards
Relevant final outcome measures (falls and fallers)
New fractures in subpopulations with a prior history of
fractures)
Australia, NZ, Canada, US, UK, Scandinavia, Japan,
Taiwan, Korea and developed European countries
Systematic reviews of RCTs, single RCTs, and pseudoRCTs (Level I, II, III-1 evidence, respectively, see Table
7)
English full text papers
Study data collected after 1990
Exclusion criteria
Non relevant population (mean age < 65 years)
Hospital inpatient population
Non relevant intermediate outcome measures (fear of
falling, falls self-efficacy, fall risk, balance, strength,
mobility, etc)
Non relevant and developing countries
Comparative studies, with and without concurrent
control groups, and case series studies (Level III-2, III-3
and IV evidence, respectively, see Appendix D)
Population-based studies*, pilot studies and method
studies
Papers published in non-English language without an
English abstract
Study data collected prior to 1990
Trial participants, N < 20
Vitamin D supplementations
The effectiveness of Vitamin D supplementation used in the model differed from the Cochrane review
(analysis 4.1). This is because the Cochrane review included a study by Grieger 2009(1) which used a
multi-vitamin containing low dose vitamin D. Figure 1 below demonstrates the results excluding the
Grieger 2009 study from the analysis. The resulting rate ratio is 0.72 (0.55, 0.95), demonstrating that
Vitamin D supplementation significantly reduced the rate of falls compared to no vitamin D
supplementation.
The four trials included in the analysis were all conducted in older people at residential aged care
facilities with mean ages ranging from 83 to 89 years. The intervention used in the costing included
1,000 UI of Vitamin D and 600 mg of calcium based on the supplementation included in the trials.
Bischoff 2003(2) and Broe 2007(3) supplemented patients with 800 UI of Vitamin D, Flicker 2005(4) used
1000 UI of Vitamin D and Law 2006 used 1,100 UI of Vitamin D.
Figure 1. Comparison of Vitamin D supplementation compared to no Vitamin D supplementation.
Medication Review
The effectiveness of Medication Review was based on one study by Zermansky 2006 (5). This study was
conducted in residents of nursing care facilities with a mean age of 85. The intervention was a clinical
medication review by a pharmacist consisting of a review of the GP advice and a consultation with the
individual and residential carers. This recommendation was then forwarded to the residents GP. The
control for this study was usual care. A second study identified in the Cochrane review was excluded
from the analysis (Patterson 2010 (6)). This is because the intervention was specific to psychoactive
medication.
The value used in the analysis was the rate ratio 0.62 (0.53,0.72) demonstrating that a medication
review by a pharmacist significantly reduced the rate of falling compared to usual care. The intervention
used in the costing of the intervention included a RMMR by a pharmacist and a visit to the GP to
correspond with the invention included in Zermansky 2006 (5).
Multifactorial intervention
The Cochrane review presents a number of meta-analyses for multifactorial interventions. Overall a nonsignificant rate of falls was observed compared to usual care. However, a subgroup analysis of
multifactorial interventions have demonstrated a significant benefit (Dyer 2004, Jensen 2002 and
McMurdo 2009) (7),(8),(9). These interventions include an exercise program, a medication review,
modification of environmental hazards, personal care and protection (such as hip protectors), written
materials for educational purposes, and an optical review.
A recent meta-analysis (Vlaeyen 2015) also reported a reduction in falls from multifactorial intervention,
with a reported RR of 0.67 (0.55, 0.82) (10) . This analysis included Dyer 2004(7) and McMurdo 2000(9),
but also included two other studies (Becker 2003(11) and Neyens 2009(12)). The study by Becker 2003
was also added into the meta-analysis, however Neyens 2009 was excluded as it was not as
comprehensive as the studies above and did not include a physical activity component. Figure 2
demonstrates the RR used in the Markov model.
Figure 2. Comparison of Multifactorial interventions compared to usual care
Hip-protectors
The risk ratio used in the model for the effectiveness of hip protectors was sourced from a Cochrane
review (13) and included 13 studies that compared hip protectors versus control (not wearing hip
protectors) in institutional residences. Given that hip protectors reduce the number of fractures as
opposed to the number of falls, the meta-analysis comparing hip protectors and the reduction in the
rate of hip fractures was used. The risk ratio of 0.81 (0.66, 0.99) was applied directly in the model to
reduce the number of hip fractures. This was achieved by adjusting the probability of being hospitalised
after a fall in the model. The costing in the model reflected the cost of the hip protectors only, as it was
assumed the fitting of the hip protectors by the nurses would be covered in the overall nurse wages.
Probability of Falling
The probability of falling each year was based on the statistic that 50% of residential aged care residents
fall each year(14). This value was applied the medium risk group and the age group 75-79. The
probability of falling in the low and high risk groups were extrapolated from the estimates of the
medium risk group and adjusted to take into account that the absolute risk of being a faller if you
fell in the past year was 71%, compared to 32% if you had not fallen in the past year. For the
probabilistic sensitivity analysis a standard error of 0.10 was used to calculate the appropriate alpha and
beta values for the beta distributions. Table 2 summarises the probability values used in the model for
each age group.
Table 2 Probability of Falling
Age group
65-69
70-74
75-79
80-84
85+
Low risk
0.26
0.26
0.32
0.37
0.44
Medium risk
0.36
0.36
0.50
0.57
0.71
High risk
0.57
0.57
0.72
0.82
0.97
Utility values
The starting utility at each cycle was based on an individual’s age at the beginning of that cycle minus
a small decrement (0.072) for a fall in the previous year and a disutility (0.060) for being a resident in
residential aged care. As individual’s moved through the decision tree a utility decrement was
attributed to those who fell, received a minor injury and went the ED or were hospitalised.
Population norms
The population norms were sourced from Norman 2013 (15). These norms were estimated using the
Household, Income and Labour Dynamics in Australia (HILDA) survey. Wave 9 was used in the analysis
and was comprised of 17,630 individuals across 7,234 households. The respondents completed the SF36 and these values were converted into an SF-6D index score using the UK weighted algorithm .
Hospitalisation decrement
Average utility decrements were calculated using the population norms and reference health state
values in Brazier 2002(16). These decrements were then weighted by the number of hip (94%) and
vertebral fractures (6%) based on a population based study of hospitalised fall related injuries (17). This
resulted in a hospitalisation decrement for these fractures of 0.144. Table 3 summarises these
calculations.
Table 3 Utility value for hospitalisation for fractures from a fall
Age
Population
norms
Population norms X
Health state value
hip fractures (0.797)
65-69
0.806
0.642
70-74
0.747
0.595
75-79
0.731
0.583
80-84
0.699
0.557
85+
0.676
0.539
Average decrement
Fracture decrement weighted by injury
admissions (96% hip, 6% vertebral)
Hip fracture
decrement
0.164
0.152
0.148
0.142
0.137
0.149
Population norms
X Health state
value vertebral
fracture (0.909)
0.733
0.679
0.664
0.635
0.614
Vertebral
fracture
decrement
0.073
0.068
0.067
0.064
0.062
0.067
0.144
Emergency attendance decrement
The model assumed that the utility decrement for a wrist fracture would be used as the basis of an
emergency attendance. Using the same methods as for the hospitalisation decrement and using a
health state reference value of 0.977 from Strom 2007(18), the average wrist fracture decrement was
0.014.
Previous fracture decrement
Strom 2007(18) reported multipliers for subsequent years following a fracture. It was reported that a
multiplier of 0.90 and 0.93 could be applied for hip and verterbral fractures respectively. Wrist fractures
only received a disutility in the first year. The disutility for a previous fracture was calculated using the
same methodology as the hospitalisation decrement above. The resulting decrement was calculated to
be 0.072 and was applied in the model the year after a hospitalisation for a hip or vertebral fracture.
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