Eur J Health Econ DOI 10.1007/s10198-014-0605-5

ORIGINAL PAPER

Cost-effectiveness of a multifactorial fracture prevention program for elderly people admitted to nursing homes Dirk Mu¨ller • Lisa Borsi • Claudia Stracke Stephanie Stock • Bjo¨rn Stollenwerk

•

Received: 8 May 2013 / Accepted: 14 April 2014  Springer-Verlag Berlin Heidelberg 2014

Abstract Background Fractures are one of the most costly consequences of falls in elderly patients in nursing homes. Objectives To compare the cost-effectiveness of a ‘multifactorial fracture prevention program’ provided by a multidisciplinary team with ‘no prevention’ in newly admitted nursing home residents. Methods We performed a cost-utility analysis using a Markov-based simulation model to establish the effectiveness of a multifaceted fall prevention program from the perspective of statutory health insurance (SHI) and longterm care insurance (LCI). The rate of falls was used to estimate the clinical and economic consequences resulting from hip and upper limb fractures. Robustness of the results was assessed using deterministic and probabilistic sensitivity analyses. Results Compared to no prevention a multifactorial prevention program for nursing home residents resulted in a cost-effectiveness ratio of €21,353 per quality-adjusted life-year. The total costs for SHI/LCI would result in €1.7 million per year. Results proved to be robust following deterministic and probabilistic sensitivity analyses. Conclusion Multifactorial fracture prevention appears to be cost-effective in preventing fractures in nursing home

D. Mu¨ller (&)  L. Borsi  C. Stracke  S. Stock Cologne Institute for Health Economics and Clinical Epidemiology, The University Hospital of Cologne (Ao¨R), Cologne, Germany e-mail: [email protected] B. Stollenwerk Helmholtz Zentrum Mu¨nchen (GmbH), German Research Center for Environmental Health, Institute of Health Economics and Health Care Management (IGM), Munich, Germany

residents. Since the results were based on the number of falls further research is required to confirm the results. Keywords Fracture prevention  Cost-effectiveness  Elderly people  Nursing home residents JEL Classification

I1  H4

Background Falls are common among the elderly. They are associated with an increased risk of mortality and morbidity and are the most important risk factor in an elderly person suffering a fracture [1]. Fall-related fractures cause high socioeconomic costs, premature mortality and a loss of quality of life [2]. Older people who live in nursing homes or are hospitalized have a notable increased risk of falls. The incidence of falls among elderly living in nursing homes is reported to be about three times as high as elderly who live in the community, equating to rates of 1.5 falls per bed per year [3]. While most fall-related fractures are reported for the hip and upper limb, the largest socioeconomic impact of falls results from hip fracture. In Germany the number of hip fractures and direct treatment costs are expected to increase to 156,000 (osteoporosis-related) fractures and 4 billion euros (€) in the year 2020 [4]. The etiology of falls in older people is complex. Falls might result either from single intrinsic and extrinsic causes such as centrally acting drugs, pain, visual impairment, disability or, as in the majority of cases, they might result from a combination of these and other factors such as inappropriate lighting, wet floors or inappropriate shoes [5]. Nursing home residents with the highest risk of falling are those with a psycho-geriatric or cognitive impairment

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[6]. The more the mobility of these patients decreases the less they are able to recognize, judge and avoid hazards [7]. Strategies to prevent falls include different types of interventions. While fall prevention strategies can be offered alone or with multiple components, not all of them have shown promising evidence of success [5]. Whether a preventive strategy works or not depends on two main aspects. First, the setting where the preventive strategy is provided (e.g., hospital, nursing home or community) plays a role. There is some evidence that the use of hip protectors in care homes prevents hip fractures but there is insufficient evidence for the effectiveness of this and other single interventions in hospitals [8]. Second, available data supports the conclusion that multifactorial interventions in long-term care populations seem more likely to be beneficial than single interventions [5]. The potential components of such multifactorial programs are: a comprehensive structured individual assessment with specific safety recommendations; multidisciplinary programs including general strategies tailored to the setting and strategies tailored specifically to residents; multifaceted interventions including education, environmental adaptation, balance, resistance training, and the provision of hip protectors; and, calcium and/or vitamin D supplementation [5]. The efficacy of multifactorial interventions in nursing homes depends on how many care givers offer the intervention and which outcome is assessed. According to a recent Cochrane review multifactorial interventions were not clearly effective on a single health-professional basis. When provided by a multidisciplinary team, however, they reduced the rate of falls (rate ratio (RR) = 0.60, 95 % confidence interval (CI) 0.51–0.72; four trials, 1,651 participants) and the risk of falling (risk ratio = 0.85, 95 % CI 0.77–0.95; 5 trials, 1,925 participants) [5]. As a result of insufficient study power there is limited evidence for the rare outcome fracture [5]. Therefore, ‘rate of falls’ and ‘risk of falling’ are used as markers (i.e., surrogates) intended to substitute the more patient-relevant outcome ‘fracture’. However, in a subset of studies that reported the number of femoral fractures pooled data from three trials also revealed a significant reduction in the risk of femoral fractures (risk ratio = 0.48, 95 % CI 0.24–0.98) [5]. This indicates a strong correlation between the surrogate markers and the outcome fractures. In general, a surrogate outcome is defined as ‘a laboratory measurement or a physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions or survives’ [9]. Hence, changes in a surrogate endpoint due to therapy are expected to reflect changes in a clinically meaningful endpoint [9]. The usage of a surrogate is considered to be justified if it could be observed more often than the patient-relevant

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outcome, if a strong statistical correlation between the surrogate and the patient-relevant outcome could be shown and if treatment affects both the surrogate and the patientrelevant outcome [10]. Concerning the etiology of bone fractures there are several surrogate endpoints that they have been shown to be associated with. While increased bone turnover markers or decreased bone mineral density, for example, are associated with fracture risk in postmenopausal women, this association is weaker in the frail elderly in whom incident falls are the strongest predictor of fractures [11, 12]. In this group falls account for approximately 87 % of fractures [13, 14]. Based on evidence the number of falls and the number of fractures are highly associated we evaluated the costeffectiveness of a multifactorial program to prevent fractures resulting from falls in German nursing homes compared with no prevention. The modeling-analysis was based on data from the literature and public databases. The analysis was conducted from the perspective of the SHI and LCI funds, which are two parts of the German social security program. While in Germany the SHI covers costs of treatment for fractures, the LCI covers costs of longterm care and preventive measures for nursing home residents.

Methods We performed a cost-effectiveness analysis for a cohort of newly admitted nursing home patients aged 80 years based on a Markov-based simulation. Since the risk of fracture varies over time in nursing home patients we developed a Markov model in TreeAge Pro (TreeAge Software, Williamstown, Massachusetts) with a time horizon of 20 years. The model stops at the age of 100 because for Germany there are no survival data beyond that age [15]. The health benefit was estimated in terms of qualityadjusted life-years (QALYs). To judge the cost-effectiveness of the strategies incremental cost-effectiveness ratios (ICERs) were calculated. Note that this modeling approach was not explicitly based on the incidence of falls. Instead, the more accurately reported fall-related hip fracture incidence was assumed to be reduced accordingly. Overview and model design The model population consists of residents newly admitted to nursing homes, reflecting the fact that fracture rates are highest during the first months after admission [16]. The model consists of six health states: ‘well’, ‘hip fracture’, ‘upper limb fracture without a prior hip fracture’, ‘upper limb fracture with a prior hip fracture’, ‘post-hip-fracture

Cost-effectiveness of a multifactorial fracture prevention program

state’ and ‘death’. The model population consisted of residents with and without prior fractures who started in the ‘well’ state. Because data on incident fractures and efficacy used for the analysis did not provide information about prior fractures we assumed prior fractures to be equally distributed in the study groups. Patients could either stay in this state or move to the states ‘hip fracture’, ‘upper limb fracture without a prior hip fracture’ or ‘death’. Note that falls not resulting in fractures were assumed to incur no costs and loss in health-related quality of life (HR-QOL). A patient with a fracture could move to the post-fracture state (hip), return to the no fracture state (only in case of an upper limb fracture), suffer a re-fracture (hip or upper limb) or die. For patients with upper limb fractures and a prior hip fracture an additional state was modeled because, in contrast to upper limb fractures only, a hip fracture results in a persistent reduction in quality of life. It was assumed that only one fracture could occur within 1 year. A cycle length of 1 year was used for the model except for the first year. By subdividing the fracture states of the first year (in months 1–3, 4–6, 7–9 and 10–12) an initially higher fracture risk which declines over the first year and remains constant from year 2 could be incorporated in the model [16]. The structure of this Markov cohort model is based on a generic checklist which was developed for conducting and reporting economic evaluations of fall prevention strategies [17]. The impact on mortality was modeled for patients with a hip fracture. According to the etiological evidence there is no increase of mortality for upper limb fractures. Post-fracture states were added to reflect a persistent loss of health-related quality-of-life (QoL) (only for hip fractures) and a sustained increase of risk for subsequent fractures (hip and upper limb fractures). Due to a lack of data regarding fractures at other sites (e.g., vertebral fractures and lower limb fractures) a persistent reduction of quality of life from both hip and upper limb fractures or multiple fractures within 1 year were not modeled (Fig. 1). Clinical data Efficacy/effectiveness Based on a geriatric risk assessment nursing home patients in the model were assumed to receive a multifaceted, multidisciplinary program at the beginning of their resident stay. The program was assumed to include education, exercises offered in groups, a hip protector and an assessment of their personal surroundings in the nursing home. According to a subgroup analysis from a recent Cochrane review a combination of these preventive measures has shown a significant reduction in the rate of falls and in the risk of falling [5]. For the model we applied the RR of falls

Well

Upper limb fracture without a prior hip fracture in nursing home

Hip fracture

Upper limb fracture with a prior hip fracture in nursing home

Post-fracture hip

All states

Death

Fig. 1 Overview of the model

(RR 0.60, 95 % CI 0.51–0.72), which was based on the pooled RR of four trials including 1,651 participants, to the rate of fractures. It was assumed that reducing the rate of falls results in a corresponding reduction of fall-related fractures; i.e., the effect of the prevention program is equally distributed to falls and fractures. Adherence was based on the studies included in the meta-analysis and reflects the participation of a wide range of nursing home residents and engagement with the interventions [5, 18]. Based on these studies it was assumed that on average onethird of nursing care residents are likely to be adhering to the interventions with component-specific rates ranging from 11 to 93 % [5, 18]. In the model efficacy of the multifaceted intervention was conservatively assumed to last only for the follow-up of the underlying clinical studies (i.e., 12 months in 3 of 4 studies [5] ). Therefore, from year 2 in the model both the intervention and the group were assumed to have the same fracture risk. Incidence of fractures from falls Fracture rates were obtained from a routine dataset of German residents with and without prior fractures who were newly admitted to nursing homes [16]. Fracture rates of the hip (including femoral neck fractures and

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pertrochanteric fractures) and the upper limb (including humerus, shoulder, forearm, and wrist fractures) were based on 93,424 residents [16] with 8,060 fractures (73 % of these were hip fractures) [16]. Due to the fact that falls account for 87 % of all fractures in older adults the proportion of fractures not resulting from falls was subtracted [13, 14]. The model reflects that fracture rates were highest during the first months after admission to a nursing home and declined thereafter (see Table 4, Appendix). From year 2 incidence rates were assumed to be constant. Re-fracture rates after a prior hip fracture were obtained from published data [19]. Mortality and health-related quality of life Data on mortality was based on patients admitted to a nursing home between 2000 and 2005 in Baden-Wu¨rttemberg, a German federal state with 385,000 citizens C80 years old with 14 % of them being residents [20]. To consider mortality in the first year after a hip fracture time-dependent mortality rates (after 3, 6, and 12 months) were used. While in the first 3 months after a hip fracture mortality of patients increased compared with residents without a hip fracture, the risk of dying was almost equal between months 4 and 6 and reduced during the second half of the year following the fracture [20]. For patients with an upper limb fracture the probability of dying was not increased [20]. To calculate mortality rates from year 2 routine data on the files of long-term care patients insured by the SHI/LCI funds

Table 1 Input data

were used for both fracture sites [21]. Mortality data used for the model reflect both increased mortality due to fractures and background mortality in nursing homes. The utility values of residents without a fracture were based on a survey of 342 residents from 8 different German nursing homes conducted via the generic questionnaire EQ-5D [22]. Loss of utility due to hip or upper limb fracture was based on a systematic review evaluating osteoporosis-related conditions using the EQ-5D questionnaire [23]. The utility value of subjects with a fracture was derived by subtracting this loss of utility from the HRQOL value of nursing home patients. While hip fractures result in a persistent loss of HR-QOL, patients with upper limb fractures were assumed to have a loss of HR-QOL only in the year of the fracture [22] (Table 1). Costs Costs of hip fractures were obtained from a retrospective dataset that included insurants of a sickness fund (Allgemeine Ortskrankenkasse Bavaria) who resided in a nursing home (n = 60,091) [24]. In that analysis, an incidence-based approach was used to estimate the costs of femoral fractures (ICD-10, S72) that occurred between 2006 and 2008. These cost incorporate service use and costs of inpatient care (up to 12 months after the initial hospitalization episode), nursing home care (until death or the end of 2008) and ambulatory care (pharmaceuticals, non-physician providers, and medical supply within 3 months after the initial hospitalization episode).

Variable

Value

Reference

Hip

0.0387 (0.0361–0.0413)

[13, 16]

Upper

0.0118 (0.0104–0.0133)

[13, 16]

Probabilities of fracture in year 1 of nursing home stay

Probabilities of fracture from year 2 of nursing home stay Hip

0.0060 (0.0057–0.0062)

[13, 16]

Upper limb

0.0018 (0.0017–0.0019)

[13, 16]

Hip or upper limb fracture in year 1 after a hip fracture

0.0634 (0.0281–0.0987)

[19]

Hip or upper limb fracture in year 2? after a hip fracture

0.0138 (0.0000–0.0306)

[19]

Relative risk

0.60 (0.51–0.72)

[5]

Mortality In year 1 after a hip fracture

0.4670 (0.4295–0.5085)

[20]

In year 1 of nursing home stay

0.4239 (0.3391–0.5087)

[20]

In year 2? of nursing home stay

0.2764 (0.2211–0.3317)

[20]

No fracture (nursing home)

0.5222 (0.4178–0.6266)

[22]

Hip fracture Upper limb fracture without a prior hip fracture

0.2942 (0.2354–0.3530) 0.4822 (0.3858–0.5786)

[23, 22] [23, 22]

Post hip fracture year

0.3692 (0.2954–0.4430)

[23, 22]

Utilities (QALYs) 95 % CIs are shown in parenthesis. For utilities and mortality within nursing homes we varied mean values by 20 %

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Cost-effectiveness of a multifactorial fracture prevention program Table 2 Cost data Intervention

Costs in € per person

Amount

Total costs in € per person

Reference

Hip protector (‘‘Safehip’’ by Tytex)

69

3

207

[30]

Group education and written information on hip protectors

6

1

6

[31]

Labour costs for education

7

1

7

[32]

Environmental check

21

1

21

[33]

Group exercise (two 30-min sessions per week over 6 months)

7.5

26

195

[34]

Geriatric assessment by general practitioner

13

1

13

[35]

Total

449 (314; 584)

Treatment of fractures

Total costs in € per fracture

Hip fracture year 1

7,186 (5,749; 8,624)

[22]

Hip fracture year 2

335 (268; 402)

[22]

Hip fracture year 3?

155 (124; 186)

[22]

Upper limb fracture Medical treatment

2,740

[Own calculation based on 24]

Physiotherapy

15

18

270

[36]

Ergotherapy

21

18

378

[37]

Total

3,388 (2,710; 4,065)

For treatment of hip and upper limb fractures we varied mean values by 20 %. All costs are given in €2012 and were adjusted for inflation based on the German Consumer Price Index [29]

Additional costs for nursing home and ambulatory care were estimated based on a before/after design. According to that cost analysis costs of treatment for hip fractures in our model were assumed to be incurred for the year of the fracture, the year after the fracture (by modeling transition costs) and, to reflect a persistent increase in the level of care, in the post-fracture state for hip fractures. Costs of upper limb fractures were assumed to account only for the year of the fracture. We estimated the costs of medical treatment for these fractures on an inpatient basis according to surgical and non-surgical procedures from a German public database due to a lack of published data for costs of upper limb fractures [25]. Costs of outpatient physical therapy (i.e., physiotherapy and ergotherapy) were obtained from the catalogue of non-physician care [26, 27]. To calculate the costs of a multifaceted, multidisciplinary program for the intervention group patients were assumed to receive an individual geriatric assessment, education and group exercises, hip protectors and an assessment of their personal area in the nursing home. According to the clinical evidence the costs of a group program including a combination of exercises throughout the period of the intervention were estimated based on the German catalogue of non-physician care (e.g., balance

training, or progressive resistance training), as described in Table 2, and the costs of three hip protectors per patients were added [5]. Costs of environmental rebuilding were not considered in this analysis as they are not covered by the SHI/LCI funds. We conducted a half-cycle correction to reflect the continuous nature of the timing of transitions between health states within a cycle. Costs and benefits were discounted at an annual rate of 3 % [28]. All costs are presented in €2012 values and were adjusted for inflation based on the German Consumer Price Index [29]. Sensitivity analysis We performed both deterministic and probabilistic sensitivity analyses to assess how a simultaneous change of several variables affects the cost-effectiveness ratio. In the deterministic sensitivity analysis we varied parameters considered to be uncertain; i.e., mortality or reduced quality of life after suffering a fracture, incidence of fractures, efficacy of the multifaceted intervention and the discount rate. In addition, extreme scenarios for costs (i.e., high intervention costs combined with low treatment costs and vice versa) were assessed.

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In order to assess how a simultaneous change of several variables affected the cost-effectiveness ratio a probabilistic sensitivity analysis based on a Monte Carlo simulation with 10,000 iterations was conducted. Relying on the model input data listed in Tables 1 and 2 (except for mortality and the discount rate) we assumed cost data to be gamma-distributed whilst variables on a scale between 0 and 1 were assumed to follow a beta-distribution [38]. Information about data used for probabilistic sensitivity analysis is provided in the Appendix (Table 5). Given that the interpretation of negative cost-effectiveness ratios is ambiguous we transformed costeffectiveness ratios into net monetary benefits (NMBs) and cost-effectiveness acceptability curves for different values (€0–€100,000) of a willingness to pay (WTP) [39]. Given that the interpretation of negative cost-effectiveness ratios is ambiguous we transformed cost-effectiveness ratios into NMBs using the following equation [39]: NMB ¼ k  DE  DC where k = maximal willingness to pay, DE = incremental benefit (QALYs), and DC = incremental costs. The decision rule we used was to adopt the screen-and-treat strategies in question if NMB was [0.

Budget impact analysis A budget impact analysis (BIA) with a 1-year time horizon was performed to estimate the annual cost burden on both the SHI and LCI [40]. The incremental costs of a resident who is offered a multifactorial prevention program were multiplied by the number of residents newly admitted to German nursing homes over 1 year and who were insured by the German SHI fund [41]. Total costs were estimated with regard to life expectancy of residents in German nursing homes [42].

Results In the base-case analysis multifactorial fall prevention in nursing home residents resulted in a cost-effectiveness ratio of €21,353 per QALY (Table 3). In the deterministic sensitivity analysis a combined worst case scenario for costs and the RR (i.e., upper bound of the intervention costs, lower bound of the treatment costs, upper bound of RR) more than doubled the ICER, while the opposite scenario decreased it by 85 %. Among

Table 3 Costs per quality-adjusted life year per resident (base case analysis) Strategy

Proportion of subjects with hip fractures (re-fractures)a

Proportion of subjects with upper limb fracturesa

Costs (€) (SE)

Incremental costs in €

QALYs (SE)

Fall prevention

0.089 (0.001)

0.037

510 (18)

1.226 (0.07)

No fall prevention

0.101 (0.002)

0.041

391 (9)

1.220 (0.07) 119

SE standard error, QALY quality-adjusted life years a

Calculated for the models’ time horizon

Fig. 2 Cost-effectiveness acceptability curve probability of being cost-effective

1

0.8

0.6

0.4

0.2

0

willingness to pay

123

Incremental QALYs

ICER (€/QALY)

0.006

21,353

Cost-effectiveness of a multifactorial fracture prevention program

the one-way sensitivity analyses a reduction of QALYs in fracture states and a more favorable RR improved the ICER by more than 30 %, while patients in fracture states assumed to have an increased level of QALY or a less favorable RR almost doubled the ICER. For other variables the results showed higher robustness (see Table 6, Appendix). Based on the results of the BIA the implementation of a multifactorial fall prevention program in nursing homes would result in costs of €2.9 million [CI 2.6–3.2] for the SHI/LCI. The probabilistic sensitivity analysis showed a probability of cost-effectiveness of 40 % for a WTP of €20,000. This probability increases to 64 % at a WTP of €25,000 and achieved 90 % at a WTP of €36,000 (Fig. 2).

Discussion This is the first analysis evaluating the cost-effectiveness of a multifactorial strategy to prevent fractures in nursing home residents. The results of this analysis may suggest that the provision of a multifactorial intervention program to prevent fractures in nursing home residents is cost-effective for the SHI/LCI fund. However, when allocating funds decision makers should be aware that the clinical evidence used for this evaluation was based on the assumption of a strong relationship between the rate of falls and the number of fractures. It has to be noted that even if the results were robust to sensitivity analyses the incremental gain in QALY is relatively small (0.006). This, however, is in line with other well-accepted preventive measures resulting in a small benefit; e.g., mumps vaccine yields a health gain of \0.001 life years [43]. When providing a multifactorial strategy to prevent fractures in nursing home residents some patients benefit a lot from this intervention with an incremental gain in QALYs of 44 % (utility increase of 0.228 for the first year) if a hip fracture is avoided whilst others do not benefit at all. Our analysis has two essential strengths: First, targeting fracture prevention in German nursing homes; the majority of data could be obtained from German sources. Most importantly, data on costs of hip fractures, which is the main cost driver for an evaluation of fractures in nursing homes, as well as data on incidence rates of fractures was based on large datasets from German nursing homes (n = 60,000 for costs and n = 93,000 for incidence rates). Second, the model reflects the increased fracture risk of residents in their initial period of stay in a nursing home. According to the natural course of a patient’s nursing home stay fracture incidences are relatively high in the first

months, decreasing afterwards and remaining constant from year 2. These differences were taken into account and are in accordance with the observation that higher levels of care for sicker patients were associated with a reduced risk of hip fracture both in women and men [20]. In judging the results of our analysis decision makers may express concerns due to the lack of strong clinical evidence for multifactorial fall prevention. We used the rate of falls as a proxy to calculate the number of fractures which might attract a considerable amount of criticism because of the potential overestimation of cost-effectiveness. Due to insufficient power in rare outcome studies clinical trials evaluating efficacy of multifactorial interventions did not use fractures as a primary outcome and in some studies they were not even reported as a secondary outcome [5]. Moreover, for the studies included in the Cochrane review some methodological deficiencies were observed [5]. Allocation concealment and analysis by intention to treat principle could be ensured but neither participants nor assessors were blinded in studies evaluating the rate of falls. In addition, while the studies included in the review had numerous characteristics in common (i.e., exercise and environmental adaptations were included in the intervention in all studies) they differed in some specific components offered to residents. For example, the duration of the interventions ranged from 11 weeks [44] to 12 months [45]. Nevertheless, in our view the ‘rate of falls’ as a proxy for the number of fractures appears to be appropriate as it has been shown to be the strongest predictor of fractures in the frail elderly [11, 12, 46] and to be the main etiological factor in over 90 % of hip fractures [1]. A surrogate endpoint is considered useful if it meets important standards for accuracy, precision and reliability [47]. As for the ‘rate of falls’ epidemiological studies have shown plausibility and a significant association between falls and fractures in the target population [48, 49]. A further prerequisite for the quality of a surrogate endpoint is its response to treatment when compared to the patient-relevant outcome. For the rate of falls clinical trials have demonstrated that the number of falls and injuries (including moderate injuries such as cuts or abrasions) changes consistently in response to fall-prevention, which may explain a substantial proportion of the anti-fracture efficacy [50]. Moreover, in the meta-analysis used for this study efficacy data was reported for three endpoints (i.e., the rate of falls, the number of fallers and, the number of hip fractures). While all of them showed a significant improvement for the intervention group, the number of hip fractures, reported as a secondary outcome, was even most favorable [5].

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There are further inherent limitations of the model which may affect the cost-effectiveness ratio of this analysis. First, our model included fracture states for hip and upper limb fractures. While a combined state for wrist, forearm, humerus and shoulder fractures appears to be acceptable (because these fractures hardly differ in terms of costs and QALYs), we could not include other fractures due to a lack of data. Hence, vertebral fractures, which are commonly suffered by elderly people, and head injuries, which may result in serious medical consequences, could not be considered. Including these injuries as outcomes we would expect a multifactorial intervention to be more cost-effective, in particular because expensive minimal-invasive surgical treatment for vertebral fractures has been used more often in recent times [51]. Moreover, the model does not reflect two or more fractures which might occur during 1 year and does not reflect a persistent reduction of quality of life resulting from multiple fractures. Second, when calculating the costs of treatment for fractures we did not consider the provision of analgesics because data was not available except for hip fractures. Therefore, by avoiding fractures other than hip fractures by multifactorial interventions a more favorable result can be expected. Third, the multifactorial intervention strategy is targeted for the prevention of falls. However, due to a lack of data the model could not consider potential reductions in costs and gains in QALYs that result from a decreased number of these falls without a fracture. Finally, a multifactorial fall prevention program was assumed to be effective only for the first year following admission to a nursing home. This assumption was in line with the usual study period. Although we would expect a more linear decrease of efficacy, there was no data which could confirm this assumption. Although there were some methodological discrepancies, the results of our analysis are similar to those of an evaluation recently published for Germany [52]. In that study a multifactorial fall prevention program based on efficacy data obtained from a prospective, non-randomized and non-blinded study showed an ICER of €7,482 per year free of femoral fracture [52]. In contrast to our study that analysis considered only femur fractures, data on efficacy was based on a subsample derived from claims data and lower intervention costs were assumed (mean costs of around €80 per resident RCTs) [52]. It should be noted that interventions to prevent falls may not always reduce the rate of falls but in some circumstances may increase them, as shown in a cluster-

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randomized controlled trial (RCT) in a residential care home setting [53]. The reason for this paradoxical effect may lie in insufficient resources for the appropriate provision of a fall prevention program. A further explanation could be that trained residents may tend to overestimate their abilities in coordination and balance. However, the inclusion of hip protectors in the multifactorial intervention of our analysis, which were not part of the intervention in the RCT above, may prevent fractures resulting from falls. Since fractures result from various intrinsic and extrinsic factors future research should focus on the complex etiology of falls. The most important risk factors of falls and fall-related injuries among elderly people are prior falls, cognitive impairment, chronic illness, deficits in balance and other factors [13]. A more profound knowledge about the underlying mechanisms of risk factors would enable practitioners to offer residents more individually targeted interventions which could be more beneficial than others. For example, it was found that residents with poorer balance had an increased risk of hip fracture in an intermediate-care environment but a decreased risk in a high-care nursing home environment. The reason for this finding might be that nursing home residents with very poor balance are heavily dependent on nursing staff support for their mobility, while their counterparts have assistance available for daily living activities but are unsupervised for substantial periods [54]. In summary, multifactorial fracture prevention might be cost-effective in preventing fractures in nursing home residents. Until sufficiently-powered RCTs with fractures as a primary endpoint are available the rate of falls is an acceptable marker enabling researchers to evaluate fracture prevention strategies.

Appendix See Tables 4, 5 and 6.

Table 4 Quarterly probabilities of incidence of hip and upper limb fractures in year 1 of the nursing home stay Hip fracture

Upper limb fracture

Month 1–3

0.0136

0.0045

Month 4–6

0.0105

0.0030

Month 7–9

0.0077

0.0021

Month 10–12

0.0075

0.0022

Cost-effectiveness of a multifactorial fracture prevention program Table 5 Information about parameters used for the probabilistic sensitivity analysis Variable

Name

Description

P1

P2

Explanation

Relative risk

d_lnRR

Normal distribution with mean lnRR and std dev se(lnRR)

-0.51

U post-hip fracture

d_u_HF_FJ

beta distr. u_HF_FJ

53.53

91.45

P 1 = a; P 2 = b;

U hip fracture y1

d_u_HF_J1

beta distr u_HF_J1

42.66

102.34

P 1 = a; P 2 = b;

U fracture UL

d_u_OEF_J1

beta distr u_OEF_J1

78.12

83.88

P 1 = a; P 2 = b;

U nursing home

d_u_PH

beta distr u_PH

124.28

113.72

P 1 = a; P 2 = b;

C hip fracture y3?

d_c_HF_FJ

gamma distr c_HF_FJ

482.74

C hip fracture y1 C hip fracture y2

d_c_HF_J1 d_c_HF_J2

gamma distr c_HF_J1 gamma distr c_HF_J2

51,642.65 1,122.24

0.088

P 1 = mean; P 2 = SD;

3.107

P 1 = a; P 2 = k;

7.1866 3.35

P 1 = a; P 2 = k; P 1 = a; P 2 = k;

2,017.72

4.49

P 1 = a; P 2 = k;

22,955.06

6.78

P 1 = a; P 2 = k;

beta distr pT_HF_M3

1,056.70

3,284.30

P 1 = a; P 2 = b;

d_pT_HF_M6

beta distr pT_HF_M6

563.75

3,777.25

P 1 = a; P 2 = b;

d_pT_HF_M7_12

beta distr pT_HF_M7_12

434.93

3,906.07

P 1 = a; P 2 = b;

P hip fracture from y2?

d_pHF_FJ

beta distr pHF_FJ

2,601.78

434,598.50

P 1 = a; P 2 = b;

P UL fracture y2?

d_pOEF_FJ

beta distr pOEF_FJ

771.79

433,790.99

P 1 = a; P 2 = b;

P UL fracture y1

d_pOEF_J1

beta distr pReFH_J1OEF

12.227

638.78

P 1 = a; P 2 = b;

P Re-fracture y1

d_pReF_J1HF

beta distr pReF_J1HF

11.54

170.46

P 1 = a; P 2 = b;

P Re-fracture y2?

d_pReF_J2_5HF

beta distr pReF_J2_5HF

2.50

179.50

P 1 = a; P 2 = b;

C intervention

d_c_Intervention

gamma distr c_Intervention

C UL fracture

d_c_OEF_J1

gamma distr c_OEF_J1

P hip fracture m1–3

d_pT_HF_M3

P hip fracture m4–6 P hip fracture m7–12

U utility, C costs, P probability, UL upper limb, y year

Table 6 Results of the deterministic sensitivity analyses Basecase

Costs: extreme scenario analysis

Costs and RR: extreme scenario analysis

QALYs after fracture

Mortality after hip fracture

Incidence 1st and subsequent fracture

RR

Discount rate

Worst case scenarioa/Best case scenariob

Worst case scenarioc/Best case scenariod

±20 %

95 % CI (;)

95 % CI (;)

95 % CI (;)

0/7 %

Incremental Costs (in €) Incremental QALYs

119

185/53

213/21

119/119

117/120

130/106

92/154

106/132

0.006

0.006/0.006

0.004/0.007

0.003/ 0.008

0.005/0.006

0.005/0.006

0.007/ 0.004

0.006/ 0.005

ICER

21,353

33,228/9,477

53,220/3,124

36,725/ 15,048

23,954/ 19,017

25,583/17,423

13,701/ 38,451

18,620/ 24,559

QALY quality-adjusted life-year, RR relative risk, CI confidence interval, ICER incremental cost-effectiveness ratio Worst case: upper bound of the intervention costs, lower bound of the treatment costs

a

b

Best case: upper bound of the treatment costs, lower bound of the intervention costs

c

Upper bound of the intervention costs, lower bound of the treatment costs, upper bound of RR

d

Lower bound of the intervention costs, upper bound of the treatment costs, lower bound of RR

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